precipitation occurs in closest proximity to the bulk of global cloud formation and its dD values are most influenced by its amount. dD values de- crease as precipitation amount increases (Risi et al., 2008). Additional factors shape the dD values of fatty acids in land-plants as well but their consideration below suggests that our dDn-C26 sequences were unambiguously if qualita- tively reflecting changes in amount of precipitation.
precipitation occurs in closest proximity to the bulk of global cloud formation and its dD values are most influenced by its amount. dD values de- crease as precipitation amount increases (Risi et al., 2008). Additional factors shape the dD values of fatty acids in land-plants as well but their consideration below suggests that our dDn-C26 sequences were unambiguously if qualita- tively reflecting changes in amount of precipitation.
precipitation occurs in closest proximity to the bulk of global cloud formation and its dD values are most influenced by its amount. dD values de- crease as precipitation amount increases (Risi et al., 2008). Additional factors shape the dD values of fatty acids in land-plants as well but their consideration below suggests that our dDn-C26 sequences were unambiguously if qualita- tively reflecting changes in amount of precipitation.
The coral 18O record from this site reflects primarily SST; a linear regression of coral 18O versus SST yields a slope of 0.24 per mil (a€°) 18O per 1A°C, explaining 47% of the SST variance (20).
The availability of instrumental climate data allow the comparison with the coral signal and supports the interpretation of reconstructed past temperature anomalies. The use of air temperatures is justified by the strong long-term correspondence between Bermuda SST (COADS) of a 2 latitude by 2 longitude area and Bermuda air temperature during a 40-year period.
The availability of instrumental climate data allow the comparison with the coral signal and supports the interpretation of reconstructed past temperature anomalies. The use of air temperatures is justified by the strong long-term correspondence between Bermuda SST (COADS) of a 2 latitude by 2 longitude area and Bermuda air temperature during a 40-year period.
This tracer responds to water temperature through a temperature- dependent fractionation [~ -0.2 permil d18O/degree C (19, 20)], as well as to changes in rainfall, due to the influence of isotopically light (more negative d18O) rainfall on surface ocean composition. Because SST and rainfall are intimately linked in tropical areas of strong convective rainfall such as the western equatorial Pacific, the two factors combine to produce an enhanced climatic signal in coral skeletal d18O.
This tracer responds to water temperature through a temperature- dependent fractionation [~ -0.2 permil d18O/degree C (19, 20)], as well as to changes in rainfall, due to the influence of isotopically light (more negative d18O) rainfall on surface ocean composition. Because SST and rainfall are intimately linked in tropical areas of strong convective rainfall such as the western equatorial Pacific, the two factors combine to produce an enhanced climatic signal in coral skeletal d18O.
This coral was not directly calibrated since the top is not modern. They used other calibrations (Porites), and not from Diploria labyrinthiformis. Using more recent Goodkin calibration would be applicable to this coral. "we used calibrations from the Atlantic and Pacific Oceans. For estimating temperatures from coral skeletal d18O we used the gradient of the temperature to N18O equation of Leder et al. (1996) obtained from Florida corals (4.52i¿½C per mil in d18O).
in the Indonesian coral records, coral d18O is more heavily infuenced by rainfall variability
The annual variability of the Bunaken coral d18O refects the seasonal cycle of both salinity and temperature in the surrounding seawater. The characteristics and correlations of the Bunaken Island coral d18O record strongly suggestthat it monitors the air-sea interaction associatedwith Southeast Asian summer monsoon.
ENSO variability is the dominant source of d18O variability in the Palmyra fossil-coral d18O records
ENSO variability is the dominant source of d18O variability in the Palmyra fossil-coral d18O records
ENSO variability is the dominant source of d18O variability in the Palmyra fossil-coral d18O records
ENSO variability is the dominant source of d18O variability in the Palmyra fossil-coral d18O records
ENSO variability is the dominant source of d18O variability in the Palmyra fossil-coral d18O records
ENSO variability is the dominant source of d18O variability in the Palmyra fossil-coral d18O records
ENSO variability is the dominant source of d18O variability in the Palmyra fossil-coral d18O records
ENSO variability is the dominant source of d18O variability in the Palmyra fossil-coral d18O records
ENSO variability is the dominant source of d18O variability in the Palmyra fossil-coral d18O records
ENSO variability is the dominant source of d18O variability in the Palmyra fossil-coral d18O records
ENSO variability is the dominant source of d18O variability in the Palmyra fossil-coral d18O records
ENSO variability is the dominant source of d18O variability in the Palmyra fossil-coral d18O records
oxygen isotope record mainly reflects the seasonal SST cycle of 1a€“2 C at this location
Interannual variability in the MS01 coral d18O for 1910-1997 is clearly dominated by changes in d18Ow because SSTs reconstructed from the coral d18O span 5C [Gagan et al., 1998], while the GOSTA SST range is only 1C (Figure 1). Comparison of local SSTs and rainfall with the coral d18O record shows that d18O increases when SST and rainfall decrease during El Nino events.
Core piM correlates more strongly with precipitation and OLR than COI-5  (Table 1). Note that years of high rainfall coincide with years of large negative coral y18O anomalies in the wet season (and high amplitudes of the annual cycle,
However, over the entire time interval compared, maximum variations of mean summer SST are 1jC, which would account for approximately 0.20x in coral y18O (Juillet- Leclerc and Schmidt, 2001). Over the same period, mean summer coral y18O varies by as much as 0.7x (piM) and 0.4x (COI-5) (Fig. 4); i.e., the maximum SST contribution to the coral isotopic signal is 29% (piM) and 50% (COI-5)
A correlation of seasonal mean rainfall anomalies derived from the NCEP/NCAR reanalysis data set (1948-1995; averaged over 3545E, 20-30S; http://www. cdc.noaa.gov/cdc/data.ncep.reanalysis.derived.html) with coral d18O shows a clear relationship during austral summer (NDJF) significant above the 90% level (Fig. 6). This also holds for a correlation with South African summer rainfall averaged over 25-30E and 15-25S (significant N95%) (Fig. 6). Thus, summer coral d18O is also related to changes in the regional hydrologic (P/E) balance
The d18Ocoral anomaly is significantly correlated with SST anomaly (SSTA) (r = -0.69 and p < 0.01) and SSS anomaly (SSSA) (r = 0.49 and p < 0.01). These results suggest that changes in Guam d18Ocoral are mainly influenced by SST and SSS variations on seasonal and interannual timescales.
The coral captures approximately 60% of the full range of annual SST (4i¿½C = 0.88%).
SSS variability at interanual time scale
The average interannual ?18O variability at Tobago represents a ?SST of ~1.0i¿½C from one year to the next, about 3.5 times the instrumental ?SST value. This disagreement implies that there are significant interannual changes in salinity responsible for the ?18O of the corals from Tobago.
The average interannual d18O variability at Tobago represents a SST of ~1.0C from one year to the next, about 3.5 times the instrumental deltaSST value. This disagreement implies that there are significant interannual changes in salinity responsible for the d18O of the corals from Tobago.
Coral d18O variations are modestly correlated with SST variations over their common period of overlap (1970? 1993), both in terms of the monthly and monthly anomaly time series
both temperature and seawater isotopic composition effect d18O in the Flinders coral record. Δδ18O explains 65% of the variance
displayed by the δ18O signature
IOD  upwelling  cools  the ambient  SST  and  decreases  rainfall,  both  of  which  increase coral d18O.
we show that with the present coral record and relatively short calibration data set, we are unable to derive a robust, statistically significant separation of the salinity and temperature signals within the coral d18O.
Hetzinger 2006 calibration and general d18O to SST slope for corals used (-0.2)
They use calibration from other studies with this coral species, Montastraea faveolata or annularis (the same coral), 0.22 per mil per degree C [Leder et al., 1996]. In Kilbourne 2010, "centenniala€scale variability appears to be driven by temperature".
Correlation and regression assesed with SODA SST, ERSST V2.0, and local air temperature for 1958-1999 for monthly values, extreme values only (max-min mehtod), and annual mean. ERSST extreme values had the correlation, this could be due to subannual dating uncertaincies and linear interpolation to monthly values. Calibration detailed in Hetzinger 2006, 10.1029/2006GC001347
On interannual timescales, Palmyra d18Osw is significantly correlated with ENSO variability (R=-049 for Eastern Pacific Warming, R=-0.19 for Central Pacific Warming), reflecting ENSO related changes in local precipitation and ocean advection. On decadal time-scales, d18Osw likely reflects salinity changes due to larger processes such as PDO.
We develop and test a transfer function constructed using linear regression analysis of SBV coral d18Oanomaly variations and instrumental SSS over the period 1970?2007 CE [Thirumalai et al., 2011]. We performed a calibration-verification exercise (Figure 6) between d18Oanomaly and SSS at SBV to assess the robustness of the transfer function [Quinn and Sampson, 2002]. The slopes in equations (1)?(3) are within error of each other; however, the intercepts are slightly different. Despite this small difference, the similarity of the lines (Figure S2) provides confidence that the transfer function developed in this study can be used to reconstruct past changes in salinity at this locality based on coral d18O variations. We also note that there is a signal in the residuals, which likely reflects a small temperature component in the coral d18O anomaly signal (Figure 6, bottom), most likely due to the small changes in seasonality over this time period (Figure 4c).
interannual d18O variability has a correlation of -0.85 with SST
Modern Line Islands coral d18O records are high- ly correlated to the regional-scale NIi¿½O3.4 index (defined as the average of instrumental SSTs from 5i¿½S to 5i¿½N, and from 120i¿½ to 170i¿½W)
The geochemicalsignature of the corals appears to be influenced by the interdecadal variability of terrestrial runoff associated withrainfall; moreover, these low-frequency variations arelikely initiated by PDO activity. Values of coral(delta)d18O (d18Osw) andd18O are mainly controlled by terrestrial runoff.
The full Butaritari coral Sr/Ca and d18O (Figures 3b and 3c) are well correlated with SST from the 2° x 2° grid cell centered on 4°N, 172°E in ERSSTv3b (R = 0.58 for Sr/Ca and 0.39 for d18O, p < 0.05 on detrended bimonthly data).
Timor d18Osw shows higher SSS during the NW monsoon (September to February). The coral-based SSS record also confirms a delayed response of seawater freshening, which lags the wettest months by several months.
Annual values are most enriched (September) or depleted (March) in between annual SST and SSS maxima and minima, suggesting both contribute to Timor coral d18O
Although empirically determined regression slopes are similar to published SST-d18OCRL fractionation values for P. lutea, in both instances, the percents of explained variance in the regressions are small. This suggests that factors such as SSS are contributing to Palau d18OCRL variability in the pre-instrumental period.
Although empirically determined regression slopes are similar to published SST-d18OCRL fractionation values for P. lutea, in both instances, the per cents of explained variance in the regressions are small. This suggests that factors such as SSS are contributing to Palau d18OCRL variability in the pre-instrumental period.
d18O mirrors interannual and decadal variability in regional SST since 1854 in ERSST, since 1850 in HadSST3 and since 1870 in HadSST1
d18O mirrors interannual and decadal variability in regional SST since 1854 in ERSST, since 1850 in HadSST3 and since 1870 in HadSST2
We found that the correlations with d18O are 0.72 (with total pseudocoral), 0.47 (with SST component), and 0.68 (with SSS component), p < 0.01 for all three (Figure S1), indicating that SSS changes represent a larger fraction of the variance in the coral d18Oanomaly signal than SST changes, as expected from the larger magnitude of interannual SSS variations at this site. Testing several different percent contributions of SSS and SST to create the pseudocoral, we determined that a combination of 35% SST and 65% SSS results in the closest representation to the observed coral d18Oanomaly values. This was calculated by creating a pseudocoral that consisted of percent SST/SSS contributions that ranged from 100/0% to 0/100%, in increments of 5% (i.e., 100/0, 95/5, 90/10a€¦ 5/95, 0/100). The pseudocoral consisting of 35/65% gave the highest correlation with the measured d18O time series, which provides the percent contributions of SST and SSS to the time series. This relationship was determined over the period 1970a€“2007, and is limited by the length of the instrumental SSS data set. We assume stationarity in the proportional contributions of SST and SSS to the coral d18O signal because SSS data needed to evaluate this assumption are lacking in the pre- 1970 period. However, the assumption of stationarity of a proxy-instrumental relationship developed over the instrumental time period affects all proxy-based climate reconstructions that extend beyond the instrumental period. Thus, lacking additional instrument data and/or another independent SST- or SSS-only proxy there is no easy way to reduce the uncertainty of the empirically derived proxy relationship over the calibration-verification interval.
The paper does not assess correlation between d18O and SST due to lack of instrumental SST and upwelling. My assessment of the data shows a 0.36 correlation between coral D18O and coral Sr/Ca suggesting temp rependcany is low. This suggest the coral d18O is primary a salinity signal. note the authors also provide a coral d18Osw data set.
The paper does not assess correlation between d18O and SST due to lack of instrumental SST and upwelling. My assessment of the data shows a 0.36 correlation between coral D18O and coral Sr/Ca suggesting temp rependcany is low. This suggest the coral d18O is primary a salinity signal. note the authors also provide a coral d18Osw data set.
The paper does not assess correlation between d18O and SST due to lack of instrumental SST and upwelling. My assessment of the data shows a 0.36 correlation between coral D18O and coral Sr/Ca suggesting temp rependcany is low. This suggest the coral d18O is primary a salinity signal. note the authors also provide a coral d18Osw data set.
The paper does not assess correlation between d18O and SST due to lack of instrumental SST and upwelling. My assessment of the data shows a 0.36 correlation between coral D18O and coral Sr/Ca suggesting temp rependcany is low. This suggest the coral d18O is primary a salinity signal. note the authors also provide a coral d18Osw data set.
monthly coral d18O data to reconstruct a KC transport record with annual to
interannual resolution for the interval 1953–2004. The field site is located in southern Taiwan on the western
flank of the upstream KC. Increased (reduced) KC transport would generate strong (weak) upwelling,
resulting in relatively high (low) local coral d18O.
Authors calculated the contribution of SST to the δ18Ocoral signal, using a δ18O-SST slope of 0.18‰/°C (Gagan et al., 1998) and a range of published δ18O-SSS slopes (Moore, 1995; Fairbanks et al., 1997; Gorman et al., 2012; Morimoto et al., 2002; Conroy et al., 2017; Table S1 and supporting information text). They estimate that SST has a 29–39% inﬂuence on the δ18Ocoral, supporting that SSS drives the δ18Ocoral signal. Authors therefore used seasonal SSS tie points to develop the age model.
Authors calculated the contribution of SST to the δ18Ocoral signal, using a δ18O-SST slope of 0.18‰/°C (Gagan et al., 1998) and a range of published δ18O-SSS slopes (Moore, 1995; Fairbanks et al., 1997; Gorman et al., 2012; Morimoto et al., 2002; Conroy et al., 2017; Table S1 and supporting information text). They estimate that SST has a 29–39% inﬂuence on the δ18Ocoral, supporting that SSS drives the δ18Ocoral signal. Authors therefore used seasonal SSS tie points to develop the age model.
Salinity variability for the past half century are qualitatively captured in the Palaui Diploastrea δ18Oc, showing a freshening trend consistent with Porites δ18Oc, instrumental SSS, and coral-based records within the IndoPaciﬁc region. However, a robust Diploastrea δ18Oc and SSS calibration at interannual timescales was not achieved as a result of the competing SST and SSS inﬂuences dampening the δ18Oc signal and compounding effects of continuing coral calciﬁcation during different seasons. Diploastrea δ18Oc should be useful in places where there is high salinity range (mean annual salinity >~1.5 psu) and where SST and SSS signals combine to enlarge the δ18Oc amplitude as previously investigated in the central and South Paciﬁc regions.
Salinity variability for the past half century are qualitatively captured in the Palaui Diploastrea δ18Oc, showing a freshening trend consistent with Porites δ18Oc, instrumental SSS, and coral-based records within the IndoPaciﬁc region. However, a robust Diploastrea δ18Oc and SSS calibration at interannual timescales was not achieved as a result of the competing SST and SSS inﬂuences dampening the δ18Oc signal and compounding effects of continuing coral calciﬁcation during different seasons. Diploastrea δ18Oc should be useful in places where there is high salinity range (mean annual salinity >~1.5 psu) and where SST and SSS signals combine to enlarge the δ18Oc amplitude as previously investigated in the central and South Paciﬁc regions.
Linear least squares regressions of four of the tracers (δ18O, δ13O, Ba/Ca, Cd/Ca) against Peruvian SST show highly significant correlations(p<0.0001). Over the annual, biennial, and ENSO (3.8 years) frequency bands, cross‐spectral comparisons show even stronger coherency, with SST frequently accounting for 70–80% of the variance in these tracers.
At Tarawa, results from an ongoing monitoring program confirm that intenst rainfall alters the d18O and salinity of the surface mixed layer.  Shallow-growing corals incorporate this isotopic shift in the d18O of their aragonite skeletons.
salinity linked to rainfall
There is a good visual match between the smoothed coral record and the similarly smoothed Madang rainfall record for the period from the late 1950s onwards (Fig. 3B). Periods of low rainfall coincide with deposition of isotopically heavy skeleton.... Periods of high amplitude of annual cycle tend to coincide in both records, and these periods tend to occur during the El Nifio phase of the Southern Oscillation. These results suggest that, although relatively small in absolute terms, the observed variations in the amplitude of the coral dI80 annual cycle (about 0.l-0.2 permil) may be attributed mainly to variations in rainfall.
Comparison of the d180 with SST and rainfall records indicates a correlation of 0.71 and 0.54, respectively, over the 13-year interval where data overlaps (Fig. 2). Just as in the precipitation and SST comparison, the correlation is much higher at the annual cycle (coherence of 0.95). There is also a slight suggestion of coherence at the El Nino time scale, but the calibration time series is too short to draw any definitive conclusions. Since the 0.71 correlation between SST and d180 explains only half the variance in the SST record, other factors are obviously responsible for the d180 variations.
when precipitation is greater than average, precipitation is inversely correlated with d18O reflecting the input of isotopical depleted precipitation, and vice versa reflectin ghte input of isotopically heavy weaters from the Everglades.
The higher skeletal d18O value reflects the high d180 value of the Bay  water itself (Lloyd, 1964; Swart et al., 1989), which is enriched in d18O as a result of evaporation and the input of 1so enriched freshwater  from the Everglades (Meyers et al., 1993). Salinity values are the highest during the summer, as a result of evaporation, and lower during the winter when the Bay becomes dominated by water from the Gulf of Mexico and the Florida Reef Tract. These changes in salinity should be manifested in the 6180 of the coral, but as the maximum enrichment in the water occurs during the warmest portion of the year (the summer), the temperature and salinity effects tend to counteract each other, thereby reducing the observed range of oxygen isotopic variation seen in the coral.
At  the  Seychelles,  however, one would expect the seasonal temperature variability (of more than 2i¿½C) to dominate the nearly constant d18O of seawater
The relative distributionof rainfall and SST versus time (Figure4) compared with d18O versus core depth over the 1979-1990 period emphasizes a strong similarity between SST and d18O
Calibration between the instrument (local SST and gridded GISST2) and proxy records was evaluated using two regression techniques: standard ordinary least sqaures and reduced major axis.
Oxygen isotope records in banded corals( i¿½!80) are mainly controlled by water composition (i.e., seawater salinity) and sea surface temperature (SST) at the time of accretion
The observed d180 decrease that occurs between 1952 and 1995 (-0.5 permil) equates to a -2.5C warming, an -2 permil decrease in salinity [Fairbanks et al., 1997], or some combination thereof. Instrumental and reconstructed SSTs of a slightly larger region [Reynolds and Smith, 1994; Kaplan et al., 1998] do show an -0.5C increase which can only be partly responsible for the observed trend in the coral records. Moreover, the shift does not appear to be a consequence of a change in the isotopic composition of precipitation in the region [Rozanski et al., 1992].
This relationship between the d180 of the water (~w) and salinity is shown in Eqn. 2 and Fig. 2 (r = +0.71, n = 38, p > 0.05). Salinity = 4.98dw + 25.89 (2) Combining Eqns. (1) and (2), an expression (Eqn. 3) is derived expressing salinity in terms of the ~c and T. Salinity = 4.98delta18Ocoral + 1.18T + 19.62 (3) The confirmation of a positive correlation between salinity and the d180 (ffthe water (dw) seems to discount the notion that significant amounts of freshwater from Shark Slough, which is isotopically enriched in lsO (Meyers et al. 1993), is influencing the salinity of Lignumvitae Basin. If this water were influencing the salinity of Lignumvitae Basin then either an inverse correlation or no correlation at all would be expected between ~w and salinity.
This relationship between the d180 of the water (~w) and salinity is shown in Eqn. 2 and Fig. 2 (r = +0.71, n = 38, p > 0.05). Salinity = 4.98dw + 25.89 (2) Combining Eqns. (1) and (2), an expression (Eqn. 3) is derived expressing salinity in terms of the ~c and T. Salinity = 4.98delta18Ocoral + 1.18T + 19.62 (3) The confirmation of a positive correlation between salinity and the d180 (ffthe water (dw) seems to discount the notion that significant amounts of freshwater from Shark Slough, which is isotopically enriched in lsO (Meyers et al. 1993), is influencing the salinity of Lignumvitae Basin. If this water were influencing the salinity of Lignumvitae Basin then either an inverse correlation or no correlation at all would be expected between ~w and salinity.
It is expected that dD values of botryococcene will decline when precipitation rates increase, possibly providing a metric for the severity of extreme rainfall events that are associated with El Nino events. (All botryococcene dD values were highly correlated with water dD values (R2>0.99). And, Zhang and Sachs 2007, Organic Geochemistry), 38: 582-608.
Condensation temperature influence on isotopic distilation of continental moisture
Condensation temperature influence on isotopic distilation of continental moisture
d-excess is influenced by evaporative boundary conditions at the moisture source (North Pacific)
spatial slope (Jouzel et al., 2003,Magnitude of isotope/temperature scaling for interpretation of central Antarctic ice cores  https://doi.org/10.1029/2002JD002677)
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
spatial slope (Jouzel et al., 2003,Magnitude of isotope/temperature scaling for interpretation of central Antarctic ice cores  https://doi.org/10.1029/2002JD002677)
spatial slope (Jouzel et al., 2003,Magnitude of isotope/temperature scaling for interpretation of central Antarctic ice cores  https://doi.org/10.1029/2002JD002677)
Yao, T., Thompson, L. G., Mosley-Thompson, E., Zhihong, Y., Xingping, Z., and Lin, P.-N.: 1996,Climatological Significance of I´18O in North Tibetan Ice Cores, J. Geophys. Res. 101, 29531a€“29537
Yao, T., Thompson, L. G., Mosley-Thompson, E., Zhihong, Y., Xingping, Z., and Lin, P.-N.: 1996,Climatological Significance of ?18O in North Tibetan Ice Cores, J. Geophys. Res. 101, 29531?29537
spatial slope (Jouzel et al., 2003,Magnitude of isotope/temperature scaling for interpretation of central Antarctic ice cores  https://doi.org/10.1029/2002JD002677)
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
spatial slope (Jouzel et al., 2003,Magnitude of isotope/temperature scaling for interpretation of central Antarctic ice cores  https://doi.org/10.1029/2002JD002677)
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
spatial slope (Jouzel et al., 2003,Magnitude of isotope/temperature scaling for interpretation of central Antarctic ice cores  https://doi.org/10.1029/2002JD002677)
Schlosser 2002: "the observed variations in d18O cannot be explained using the emperature data. Factors other than temperature, such as changes in atmospheric circulation, must be involved."
Stenni et al., 2002 : spatial correlation, A d18O temperature gradient of about 0.6permil/A°C was calculated between the two sites. This value is very close to the data reported by Lorius and Merlivat [1977] for present-day East Antarctic precipitation and by Stenni et al. [2000] for the Northern Victoria Land.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Less (more) depletion of the water isotopes correlates well with anomalously dry (wet) conditions over the Amazon in agreement with the isotopic amount effect [Dansgaard, 1964]., Comparison with general circulation model results and meteorological data suggest that the Andean high altitude records are primarily controlled by precipitation variability over the Amazon basin."
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
spatial slope (Jouzel et al., 2003,Magnitude of isotope/temperature scaling for interpretation of central Antarctic ice cores  https://doi.org/10.1029/2002JD002677)
Yao, T., Thompson, L. G., Mosley-Thompson, E., Zhihong, Y., Xingping, Z., and Lin, P.-N.: 1996,Climatological Significance of ?18O in North Tibetan Ice Cores, J. Geophys. Res. 101, 29531?29537
Yao, T., Thompson, L. G., Mosley-Thompson, E., Zhihong, Y., Xingping, Z., and Lin, P.-N.: 1996,Climatological Significance of I´18O in North Tibetan Ice Cores, J. Geophys. Res. 101, 29531a€“29537
Yao, T., Thompson, L. G., Mosley-Thompson, E., Zhihong, Y., Xingping, Z., and Lin, P.-N.: 1996,Climatological Significance of I´18O in North Tibetan Ice Cores, J. Geophys. Res. 101, 29531a€“29537
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Tian, L., V. Masson-Delmotte, M. Stievenard, T. Yao and J. Jouzel.2001. Tibetan Plateau summer monsoon northward extent revealed by measurements of water stable isotopes.J. Geophys. Res., 106(D22), 28,081?28,088.
spatial slope (Jouzel et al., 2003,Magnitude of isotope/temperature scaling for interpretation of central Antarctic ice cores  https://doi.org/10.1029/2002JD002677)
Tian, L., V. Masson-Delmotte, M. Stievenard, T. Yao and J. Jouzel.2001. Tibetan Plateau summer monsoon northward extent revealed by measurements of water stable isotopes.J. Geophys. Res., 106(D22), 28,081?28,088.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
spatial slope (Jouzel et al., 2003,Magnitude of isotope/temperature scaling for interpretation of central Antarctic ice cores  https://doi.org/10.1029/2002JD002677)
We are using a temporal gradient of 0.5 A± 0.1 a€° per A°C to convert I´18O to temperature, based on the calibration between the I´18O with site temperatures from the European Centre for Medium Range Weather Forecasts (ECMWF) (1980a€“2005)
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
AraguaA´s-AraguaA´s, L., and K. FrA¶hlich (1998), Stable isotope composition of precipitation over southeast Asia, J. Geophys. Res., 103, 28,721a€“28,742.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
spatial slope (Jouzel et al., 2003,Magnitude of isotope/temperature scaling for interpretation of central Antarctic ice cores  https://doi.org/10.1029/2002JD002677)
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
spatial slope from Masson-Delmotte et al., 2008 adapted to the VLG site. "we chose the most local conversion slope from Taylor Dome I´D (4.0a€° per A°C) to provide an estimate for I”T." (Bertler et al., 2011)
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
spatial slope (Jouzel et al., 2003,Magnitude of isotope/temperature scaling for interpretation of central Antarctic ice cores  https://doi.org/10.1029/2002JD002677)
AraguaA´s-AraguaA´s, L., and K. FrA¶hlich (1998), Stable isotope composition of precipitation over southeast Asia, J. Geophys. Res., 103, 28,721a€“28,742.
A d18O temperature gradient of about 0.6permil/A°C was calculated between the two sites. (Stenni et al., 2002)
Temperature anomalies were calculated using a I´Da€“temperature dependence of 6.4a€‰A±a€‰1.3a€°a€‰A°Caˆ’1 (Abram et al. 2011), under the assumption that the modern-day calibration holds over the entire record26, and are given with reference to ada€‰1961a€“1990.
We employ the Taylor Dome I´-borehole temperature calibration of 4A±1.5 a€°I´D perA°C(Steig et al., 1998).
Using ECMWF Interim Re-Analysis (ERA-Interim) data, this study develops a precipitation-weighted I´18O-temperature transfer function of 0.62a€° A°Caˆ’1, which is comparable to other proximal ice cores, such as Taylor, Talos, and Law Domes.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Van Ommen et al., 1997 : seasonal cycle calibration. This comparison yields an isotopea€temperature relation with a gradient (dI´/dT) of 0.44A±0.02a€°/A°C
Steig et al. 2013 suppl: Scaling between I´18O and temperature in West Antarctica can be estimated in a number of ways, all yielding similar results.  The local interannual scaling between WAIS Divide I´18O and Byrd temperature is 0.5a€°/A°C; the ratio of 50-year trends also yields 0.5a€°/A°C (1957-2006, Byrd temperature trend7 = 0.48A°C/decade; WAIS Divide I´18O trend = 0.24a€°/decade).  Regional interannual scaling between the composite I´18O series in Figure 1 and the West-Antarctic average temperature from Steig et al.4 is also 0.5a€°/A°C; the ratio of 50-year trends is 0.6a€°/A°C (1957-2006, West Antarctic temperature trend = 0.19A°C/decade4; composite I´18O trend = 0.12a€°/decade).  Comparison betweenI´18O and the borehole temperature reconstruction from WAIS Divide6 over the last 2000 years also results in similar scaling. See also Kuttel et al, 2012 for more interpretation
Steig et al. 2013 suppl: Scaling between I´18O and temperature in West Antarctica can be estimated in a number of ways, all yielding similar results.  The local interannual scaling between WAIS Divide I´18O and Byrd temperature is 0.5a€°/A°C; the ratio of 50-year trends also yields 0.5a€°/A°C (1957-2006, Byrd temperature trend7 = 0.48A°C/decade; WAIS Divide I´18O trend = 0.24a€°/decade).  Regional interannual scaling between the composite I´18O series in Figure 1 and the West-Antarctic average temperature from Steig et al.4 is also 0.5a€°/A°C; the ratio of 50-year trends is 0.6a€°/A°C (1957-2006, West Antarctic temperature trend = 0.19A°C/decade4; composite I´18O trend = 0.12a€°/decade).  Comparison betweenI´18O and the borehole temperature reconstruction from WAIS Divide6 over the last 2000 years also results in similar scaling. See also Kuttel et al, 2012 for more interpretation
Steig et al. 2013 suppl: Scaling between I´18O and temperature in West Antarctica can be estimated in a number of ways, all yielding similar results.  The local interannual scaling between WAIS Divide I´18O and Byrd temperature is 0.5a€°/A°C; the ratio of 50-year trends also yields 0.5a€°/A°C (1957-2006, Byrd temperature trend7 = 0.48A°C/decade; WAIS Divide I´18O trend = 0.24a€°/decade).  Regional interannual scaling between the composite I´18O series in Figure 1 and the West-Antarctic average temperature from Steig et al.4 is also 0.5a€°/A°C; the ratio of 50-year trends is 0.6a€°/A°C (1957-2006, West Antarctic temperature trend = 0.19A°C/decade4; composite I´18O trend = 0.12a€°/decade).  Comparison betweenI´18O and the borehole temperature reconstruction from WAIS Divide6 over the last 2000 years also results in similar scaling. See also Kuttel et al, 2012 for more interpretation
Steig et al. 2013 suppl: Scaling between I´18O and temperature in West Antarctica can be estimated in a number of ways, all yielding similar results.  The local interannual scaling between WAIS Divide I´18O and Byrd temperature is 0.5a€°/A°C; the ratio of 50-year trends also yields 0.5a€°/A°C (1957-2006, Byrd temperature trend7 = 0.48A°C/decade; WAIS Divide I´18O trend = 0.24a€°/decade).  Regional interannual scaling between the composite I´18O series in Figure 1 and the West-Antarctic average temperature from Steig et al.4 is also 0.5a€°/A°C; the ratio of 50-year trends is 0.6a€°/A°C (1957-2006, West Antarctic temperature trend = 0.19A°C/decade4; composite I´18O trend = 0.12a€°/decade).  Comparison betweenI´18O and the borehole temperature reconstruction from WAIS Divide6 over the last 2000 years also results in similar scaling. See also Kuttel et al, 2012 for more interpretation
Steig et al. 2013 suppl: Scaling between I´18O and temperature in West Antarctica can be estimated in a number of ways, all yielding similar results.  The local interannual scaling between WAIS Divide I´18O and Byrd temperature is 0.5a€°/A°C; the ratio of 50-year trends also yields 0.5a€°/A°C (1957-2006, Byrd temperature trend7 = 0.48A°C/decade; WAIS Divide I´18O trend = 0.24a€°/decade).  Regional interannual scaling between the composite I´18O series in Figure 1 and the West-Antarctic average temperature from Steig et al.4 is also 0.5a€°/A°C; the ratio of 50-year trends is 0.6a€°/A°C (1957-2006, West Antarctic temperature trend = 0.19A°C/decade4; composite I´18O trend = 0.12a€°/decade).  Comparison betweenI´18O and the borehole temperature reconstruction from WAIS Divide6 over the last 2000 years also results in similar scaling. See also Kuttel et al, 2012 for more interpretation
Steig et al. 2013 suppl: Scaling between I´18O and temperature in West Antarctica can be estimated in a number of ways, all yielding similar results.  The local interannual scaling between WAIS Divide I´18O and Byrd temperature is 0.5a€°/A°C; the ratio of 50-year trends also yields 0.5a€°/A°C (1957-2006, Byrd temperature trend7 = 0.48A°C/decade; WAIS Divide I´18O trend = 0.24a€°/decade).  Regional interannual scaling between the composite I´18O series in Figure 1 and the West-Antarctic average temperature from Steig et al.4 is also 0.5a€°/A°C; the ratio of 50-year trends is 0.6a€°/A°C (1957-2006, West Antarctic temperature trend = 0.19A°C/decade4; composite I´18O trend = 0.12a€°/decade).  Comparison betweenI´18O and the borehole temperature reconstruction from WAIS Divide6 over the last 2000 years also results in similar scaling. See also Kuttel et al, 2012 for more interpretation
Steig et al. 2013 suppl: Scaling between I´18O and temperature in West Antarctica can be estimated in a number of ways, all yielding similar results.  The local interannual scaling between WAIS Divide I´18O and Byrd temperature is 0.5a€°/A°C; the ratio of 50-year trends also yields 0.5a€°/A°C (1957-2006, Byrd temperature trend7 = 0.48A°C/decade; WAIS Divide I´18O trend = 0.24a€°/decade).  Regional interannual scaling between the composite I´18O series in Figure 1 and the West-Antarctic average temperature from Steig et al.4 is also 0.5a€°/A°C; the ratio of 50-year trends is 0.6a€°/A°C (1957-2006, West Antarctic temperature trend = 0.19A°C/decade4; composite I´18O trend = 0.12a€°/decade).  Comparison betweenI´18O and the borehole temperature reconstruction from WAIS Divide6 over the last 2000 years also results in similar scaling. See also Kuttel et al, 2012 for more interpretation
Steig et al. 2013 suppl: Scaling between I´18O and temperature in West Antarctica can be estimated in a number of ways, all yielding similar results.  The local interannual scaling between WAIS Divide I´18O and Byrd temperature is 0.5a€°/A°C; the ratio of 50-year trends also yields 0.5a€°/A°C (1957-2006, Byrd temperature trend7 = 0.48A°C/decade; WAIS Divide I´18O trend = 0.24a€°/decade).  Regional interannual scaling between the composite I´18O series in Figure 1 and the West-Antarctic average temperature from Steig et al.4 is also 0.5a€°/A°C; the ratio of 50-year trends is 0.6a€°/A°C (1957-2006, West Antarctic temperature trend = 0.19A°C/decade4; composite I´18O trend = 0.12a€°/decade).  Comparison betweenI´18O and the borehole temperature reconstruction from WAIS Divide6 over the last 2000 years also results in similar scaling. See also Kuttel et al, 2012 for more interpretation
Steig et al. 2013 suppl: Scaling between I´18O and temperature in West Antarctica can be estimated in a number of ways, all yielding similar results.  The local interannual scaling between WAIS Divide I´18O and Byrd temperature is 0.5a€°/A°C; the ratio of 50-year trends also yields 0.5a€°/A°C (1957-2006, Byrd temperature trend7 = 0.48A°C/decade; WAIS Divide I´18O trend = 0.24a€°/decade).  Regional interannual scaling between the composite I´18O series in Figure 1 and the West-Antarctic average temperature from Steig et al.4 is also 0.5a€°/A°C; the ratio of 50-year trends is 0.6a€°/A°C (1957-2006, West Antarctic temperature trend = 0.19A°C/decade4; composite I´18O trend = 0.12a€°/decade).  Comparison betweenI´18O and the borehole temperature reconstruction from WAIS Divide6 over the last 2000 years also results in similar scaling. See also Kuttel et al, 2012 for more interpretation
Steig et al. 2013 suppl: Scaling between I´18O and temperature in West Antarctica can be estimated in a number of ways, all yielding similar results.  The local interannual scaling between WAIS Divide I´18O and Byrd temperature is 0.5a€°/A°C; the ratio of 50-year trends also yields 0.5a€°/A°C (1957-2006, Byrd temperature trend7 = 0.48A°C/decade; WAIS Divide I´18O trend = 0.24a€°/decade).  Regional interannual scaling between the composite I´18O series in Figure 1 and the West-Antarctic average temperature from Steig et al.4 is also 0.5a€°/A°C; the ratio of 50-year trends is 0.6a€°/A°C (1957-2006, West Antarctic temperature trend = 0.19A°C/decade4; composite I´18O trend = 0.12a€°/decade).  Comparison betweenI´18O and the borehole temperature reconstruction from WAIS Divide6 over the last 2000 years also results in similar scaling. See also Kuttel et al, 2012 for more interpretation
Steig et al. 2013 suppl: Scaling between I´18O and temperature in West Antarctica can be estimated in a number of ways, all yielding similar results.  The local interannual scaling between WAIS Divide I´18O and Byrd temperature is 0.5a€°/A°C; the ratio of 50-year trends also yields 0.5a€°/A°C (1957-2006, Byrd temperature trend7 = 0.48A°C/decade; WAIS Divide I´18O trend = 0.24a€°/decade).  Regional interannual scaling between the composite I´18O series in Figure 1 and the West-Antarctic average temperature from Steig et al.4 is also 0.5a€°/A°C; the ratio of 50-year trends is 0.6a€°/A°C (1957-2006, West Antarctic temperature trend = 0.19A°C/decade4; composite I´18O trend = 0.12a€°/decade).  Comparison betweenI´18O and the borehole temperature reconstruction from WAIS Divide6 over the last 2000 years also results in similar scaling. See also Kuttel et al, 2012 for more interpretation
spatial slope (Jouzel et al., 2003,Magnitude of isotope/temperature scaling for interpretation of central Antarctic ice cores  https://doi.org/10.1029/2002JD002677)
spatial slope (Jouzel et al., 2003,Magnitude of isotope/temperature scaling for interpretation of central Antarctic ice cores  https://doi.org/10.1029/2002JD002677)
spatial slope (Jouzel et al., 2003,Magnitude of isotope/temperature scaling for interpretation of central Antarctic ice cores  https://doi.org/10.1029/2002JD002677)
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Steig et al. 2013 suppl: Scaling between I´18O and temperature in West Antarctica can be estimated in a number of ways, all yielding similar results.  The local interannual scaling between WAIS Divide I´18O and Byrd temperature is 0.5a€°/A°C; the ratio of 50-year trends also yields 0.5a€°/A°C (1957-2006, Byrd temperature trend7 = 0.48A°C/decade; WAIS Divide I´18O trend = 0.24a€°/decade).  Regional interannual scaling between the composite I´18O series in Figure 1 and the West-Antarctic average temperature from Steig et al.4 is also 0.5a€°/A°C; the ratio of 50-year trends is 0.6a€°/A°C (1957-2006, West Antarctic temperature trend = 0.19A°C/decade4; composite I´18O trend = 0.12a€°/decade).  Comparison betweenI´18O and the borehole temperature reconstruction from WAIS Divide6 over the last 2000 years also results in similar scaling. See also Kuttel et al, 2012 for more interpretation
Steig et al. 2013 suppl: Scaling between I´18O and temperature in West Antarctica can be estimated in a number of ways, all yielding similar results.  The local interannual scaling between WAIS Divide I´18O and Byrd temperature is 0.5a€°/A°C; the ratio of 50-year trends also yields 0.5a€°/A°C (1957-2006, Byrd temperature trend7 = 0.48A°C/decade; WAIS Divide I´18O trend = 0.24a€°/decade).  Regional interannual scaling between the composite I´18O series in Figure 1 and the West-Antarctic average temperature from Steig et al.4 is also 0.5a€°/A°C; the ratio of 50-year trends is 0.6a€°/A°C (1957-2006, West Antarctic temperature trend = 0.19A°C/decade4; composite I´18O trend = 0.12a€°/decade).  Comparison betweenI´18O and the borehole temperature reconstruction from WAIS Divide6 over the last 2000 years also results in similar scaling. See also Kuttel et al, 2012 for more interpretation
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
The F10 record is significantly correlated (r = 0.44, P = 0.01) with ERA-Interim temperature (t850, 1979a€“2009), and we observe that the pattern of corre- lation with atmospheric pressure and sea surface tempera- tures (SSTs) is similar for both observational I´D and local temperature from reanalysis; however, we do not assume that I´D is a quantitative temperature proxy.
There is no significant relationship between stable isotopes and precipitation (net accumulation) on the Quelccaya ice cap. In other words, the precipitation “amount effect” does not appear to govern the stable isotopic chemistry on this tropical glacier. Because 70 to 80% of the precipitation falling on Quelccaya arrives during the wet season, the δ18O history reflects primarily conditions during austral summer. Decadal averages of precipitation amount and δ18O are not strongly related
Ekaykin et al., 2014. VRS13 is correlated with the local instrumentally measured near-surface air temperature data for the period1958a€“2010. Surprisingly, VRS13 is much more consistent with the temperature of the warm period of the year (Novembera€“January), with a correlation coefficient of 0.57.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Ekaykin et al., 2017. The apparent isotopea€“temperature gradient, obtained as a standard deviation of isotopic values divided by standard deviations of temperature values, is 13.8A±2.5 permil/A°C.
Tian, L., V. Masson-Delmotte, M. Stievenard, T. Yao and J. Jouzel.2001. Tibetan Plateau summer monsoon northward extent revealed by measurements of water stable isotopes.J. Geophys. Res., 106(D22), 28,081?28,088.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Isotope interpreted as responding to SAM variations
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
From the comparison between RICE I´Do and ERAi SAT records, we obtain a temporal slope of 3.37 permil/A°C
Ekaykin et al., 2017. The apparent isotopea€“temperature gradient, obtained as a standard deviation of isotopic values divided by standard deviations of temperature values, is 13.8A±2.5 permil/A°C.
Ekaykin et al., 2017. The apparent isotopea€“temperature gradient, obtained as a standard deviation of isotopic values divided by standard deviations of temperature values, is 13.8A±2.5 permil/A°C.
Ekaykin et al., 2017. The apparent isotopea€“temperature gradient, obtained as a standard deviation of isotopic values divided by standard deviations of temperature values, is 13.8A±2.5 permil/A°C.
Ekaykin et al., 2017. The apparent isotopea€“temperature gradient, obtained as a standard deviation of isotopic values divided by standard deviations of temperature values, is 13.8A±2.5 permil/A°C.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
This annual variation of the isotope content of precipitation usually remains detectable in snow covers (Moser and Stichler, 1975).
This annual variation of the isotope content of precipitation usually remains detectable in snow covers (Moser and Stichler, 1975).
This annual variation of the isotope content of precipitation usually remains detectable in snow covers (Moser and Stichler, 1975).
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
spatial slope (Jouzel et al., 2003,Magnitude of isotope/temperature scaling for interpretation of central Antarctic ice cores  https://doi.org/10.1029/2002JD002677)
Correlation with nearby Halley Station meteorological data 5thompson et al, 1994, fig 4)
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
We have determined a  gradient of 1.16 permil/A°C for this area based on a linear regression with the mean d18O and borehole 10-m temperature. (Isaksson and Karlen, Clim.Changes, 1994)
Comparison with meteorological stations. No quantification attempted. "For the purpose of this paper the d18O is assumed to provide a proxy history for the condensation temperature of Siple precipitation."
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
Yao, T., Thompson, L. G., Mosley-Thompson, E., Zhihong, Y., Xingping, Z., and Lin, P.-N.: 1996,Climatological Significance of ?18O in North Tibetan Ice Cores, J. Geophys. Res. 101, 29531?29537
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
The gradient for this area is 0.73 permil/A°C (R2=0.98), which is very close to the gradient of 0.75 permil/A°C proposed for East Antarctica by Lorius and Merlivat (1977).
The gradient for this area is 0.73 permil/A°C (R2=0.98), which is very close to the gradient of 0.75 permil/A°C proposed for East Antarctica by Lorius and Merlivat (1977).
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
Graf et al., 2002 https://doi.org/10.3189/172756402781816492 DML spatial slope calibration
In winter the d18O also shows the most negative values because the temperatures during snow formation are lowest
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436?468.
Dansgaard W. 1964. Stable isotopes in precipitation. Tellus 16: 436a€“468.
The d18O record from Chichancanab generally supports our E/P inferences based on the S record
Epstein et al., 1953 (GSAB 64:1315)
Although evaporation could potentially cause more 18O enrichment in surface water than in bottom water of a lake, this factor is likely negligible at Farewell Lake, because the lake has a large volume (maximum depth of the south basin: 55 m; surface area: 4 km2) and because no major moisture deficit exists in the region. This argument is supported by the fact that the 18O values (12.2?12.5?) of water samples taken weekly from the lake surface during July 19?September 25, 2000, show no time-dependent trend. 18Oac reflects changes in atmospheric temperature, as abiotic carbonate precipitation occurs in surface water, where the temperature fluctuates in response to atmospheric temperature changes (19). In contrast, because Farewell Lake is deep and has a large water volume, the bottom-water temperature likely remains constant at 4i¿½C, as it was in June 1990 and July 2000, based on field measurements. Therefore, the deviation of 18Oac from contemporaneous 18Obo results from oxygen-isotope frac- tionation induced by the temperature difference between sur- face and bottom waters after accounting for any vital effects.
There are two principal sources of d18Oin to Pyramid Lake: Sierran snowmelt and Lake Tahoe overflow.Evaporation from Lake Tahoe has resulted in a d18O value (5.6m) much larger than that of snowmelt (14.6m). Thus, during droughts when Lake Fig. 19. Plot of TOC, d13C and d18O data from PLB98-2 (solid lines) and the top of PLC97-1 (dashed lines) indicating overlap of cores. 670 L. Benson et al. / Quaternary Science Reviews 21 (2002) 659?682 Tahoe ceases spilling, the d18O value of Truckee River discharge decreases from B11m to B14.6m. Th e input of 14.6m water to Pyramid lake causes its d18O value to first increase as its volume decreases; however, its steady-state value (1.6m) is much smaller than when the lake receives spill from Lake Tahoe.
the water body is affected by a well-developed evaporative isotopic enrichment in spite ofthe rather short residence time (ca 20 days or less) as estimated from catchment size and meteorological data. Surface run-off from the catchment and direct precipitation seems to have no major impact on the isotopic composition oflake water during the ice-free season; groundwater level and related adjustments of lake level and surface/volume ratio of the basin during the ice-free season
Correlation between d18O and Mg/Ca
and our interpretations are based on a simplistic climate-hydrology model in which variations in groundwater generation within the lake catchment produce changes in On a hydrogen-/oxygen-isotope cross-plot (Fig. 6) all groundwater samples as well as lake-water samples from January,March, and April plot close to the global meteoric water line (Craig, 1961), whereas lake-water samples from May to July fall along a local evaporation line with a slope near 5 as expected for water bodies affected by evaporation (Craig and Gordon, 1965). These results suggest that the water body is affected by a well-developed evaporative isotopic enrichment in spite ofthe rather short residence time (ca 20 days or less) as estimated from catchment size and meteorological data. Surface run-off from the catchment and direct precipitation seems to have no major impact on the isotopic composition oflake water during the ice-free season; groundwater level and related adjustments of lake level and surface/volume ratio of the basin during the ice-free season
The d18O, d13C, and Mg percentage in calcite are highly covariant (Fig. 2), a relationship that indicates that Mg and d18O are strongly controlled by the P/E ratio as opposed to other environmental variables (Talbot, 1990).
The diatom blooms that provide the biogenic silica to the sediment occur in the summer months, June?  September, when the lake is ice-free, when temperatures  are highest and when rainfall is heaviest enabling  nutrients to be washed in from the catchment. The  d18Odiatom signal therefore reflects the climatic regime  during the summer months although there will be a  component of winter precipitation released from ice and snow in the catchment during the early summer thaw.; evaporative effects on the isotopic composition of the lake water are minimal.;comparison with  the pollen based temperature reconstruction indicates that the magnitude and direction of the isotope inferred temperature change is unrealistic.;
The TIC d18O derived from downcore sediments effectively parallels the anthropogenic lake-level lowering (Figure 2). The anthropogenically induced drought is recorded in downcore sediments as a 6% increase in TIC d18O. During the El Nin?o years of 1982?83 and 1997?98, Walker Lake (like Pyramid Lake) received well above average moisture. The lake volume increased by 28% from its September 1981 value and the d18OL decreased 3.6% during the 1982?83 event [Benson et al., 1991]. This climate-induced wet event was well recorded in downcore sediments as a 3% decrease in TIC d18O (Figures 2b and 2c)
Variations in carbonate d18O in Aguada X?caamal were principally controlled by changes in the d18O of lake water that, in turn, reflect changes in the lake?s hydrologic budget (evaporation, precipitation, and outflow) and the d18O of incoming precipitation and groundwater
Variations in carbonate d18O in Aguada X?caamal were principally controlled by changes in the d18O of lake water that, in turn, reflect changes in the lake?s hydrologic budget (evaporation, precipitation, and outflow) and the d18O of incoming precipitation and groundwater
Variations in carbonate d18O in Aguada X?caamal were principally controlled by changes in the d18O of lake water that, in turn, reflect changes in the lake?s hydrologic budget (evaporation, precipitation, and outflow) and the d18O of incoming precipitation and groundwater
correlation with Mg/Ca
Modern lake isotope hydrology,  down core changes in other proxies.
Modern lake isotope hydrology, climate calibration, down core changes in other proxies.
range of variability exceeds possible temperature influence; corresponding ostracod species and Mg/Ca ratios
Given the lack of correlation between dDlw and pollen- inferred mean July temperatures (Fig. 6), the downcore dDlw changes likely represent either a change in the ratio of soil water and precipitation utilized by the vegetation and/ or a change in source and pathway of regional precipita- tion. The dDlw data may indicate a heavy reliance of terrestrial plants on cool-season derived soil moisture from 11,000?8000 cal yr BP when conditions were dry. Growing- season precipitation may have been too low to provide sufficient water to the plants. An alternative and not mutually exclusive interpretation is that growing-season precipitation was not enriched in deuterium from 11,000?8000 cal yr BP because the presence of high pressure over the Laurentide ice sheet prevented the northward advection of humid, sub-tropical airmasses that are important for summer precipitation today. Both interpre- tations are consistent with Shuman and Donnelly?s (2006) interpretation that low lake levels prior to ca 8200 cal yr BP resulted from low summer precipitation rates and a high ratio of winter to summer precipitation.
match with pollen based January temperature, C16 n-acids are produced by aquatic plants, and ponds in this region are recharged mainly by winter precipitation
However, two lines of evidence indicate that at Steel Lake, evaporative 18O enrichment related to aridity was the dominant control. First, the d18O values of water samples from the epilimnion and hypolimnion of Steel Lake were 8.7 and 10.7%, respectively, in July 2001 (Figure 1d). The elevated d18O value of the epilimnetic water resulted from evaporative 18O enrichment, suggesting that Steel  Lake is sensitive to evaporation;In particular, the most severe historic drought events in north-central Minnesota, the 1988 drought [Trenberth et al., 1988] and 1930s Dust Bowl [Worster, 1979], are recorded by the heaviest d18O values at Steel Lake
Modern isotope hydrology and downcore d18O d13C covariation.
Rainfall at Addis Ababa has a weighted mean 18O composition of 1.2?, while groundwater values recorded in Ethiopia range from a6.2? to a1.3? (table 2 in Lamb et al., 2000). It seems likely that the oxygen isotope values for the lake surface water (up to 9? VSMOW) result from evaporative enrichment (confirmed by the hydrological balance, Table 1), which is also represented in the high values for the carbonates in the surface sediments (18O 6.2? VPDB).
All the modern lake and groundwater isotope data lie on the Local Meteoric Water Line (LMWL) (IAEA/WMO, 2006) indicating that evaporation is not important. The similarity of the isotope composition of the lake and its different groundwater reservoirs indicate that the lake is likely to be constantly replenished by groundwater input and subterraneous outflow resulting in a short water residence time.
exclusion of other possible processes
Comparison of the oxygen-isotopic record with independent measures of climate suggests that the annual moisture balance is the primary isotopic control, with the seasonal timing of precipitation providing a possible secondary influence. High d18O values are associated with dry climate (e.g., evaporative enrichment of 18O) and/or wet summers relative to winters (higher d18O values of atmospheric precipitation).
comparison with Quelccaya ice core record (see Baker et al. 2009)
The oxygen isotope composition of ostracod carapaces depends primarily on the d18O composition of the water and the tempera- ture at which carbonates precipitate (Craig, 1965; Stuiver, 1970). In tropical, closed-basin lakes within seasonally dry climates, the d18O value of lake water is controlled primarily by the evaporation to precipitation ratio (E/P) of the lake (Fontes and Gonfiantini, 1967; Gasse et al., 1990). During periods of increased (decreased) E/P ratios, the d18O value of lake water will go up (down) as kinetic fractionation processes lead to an increase (decrease) in concen- trations of 18O relative to 16O. Assuming that long-term tempera- ture changes in the tropics are less likely to affect d18O values in the lake than are changes in the E/P ratio (Covich and Stuiver, 1974; Curtis and Hodell, 1993), the d18O value of ostracod carapaces should indicate variations in the E/P ratio of the lake.
The oxygen isotope composition of ostracod carapaces depends primarily on the d18O composition of the water and the tempera- ture at which carbonates precipitate (Craig, 1965; Stuiver, 1970). In tropical, closed-basin lakes within seasonally dry climates, the d18O value of lake water is controlled primarily by the evaporation to precipitation ratio (E/P) of the lake (Fontes and Gonfiantini, 1967; Gasse et al., 1990). During periods of increased (decreased) E/P ratios, the d18O value of lake water will go up (down) as kinetic fractionation processes lead to an increase (decrease) in concen- trations of 18O relative to 16O. Assuming that long-term tempera- ture changes in the tropics are less likely to affect d18O values in the lake than are changes in the E/P ratio (Covich and Stuiver, 1974; Curtis and Hodell, 1993), the d18O value of ostracod carapaces should indicate variations in the E/P ratio of the lake.
Where water loss is mainly through evaporation as is likely for Lake Ashenge, the waters tend to have a variable and elevated oxygen isotope composition relative to regional rainfall (which tends to be negative). Measured d18Ocarb values are always higher than those of ambient precipitation as the lighter isotope 16O is preferentially lost to evaporation (Leng et al.,2006)...The isotope records from Ashenge therefore show large swings in composition as the ratio of precipitation to evaporation (P/E) has changed through time
However, the continuous enrichment of 18O, due to evaporation, during spring and summer results in heavier isotope values at the end of the warm seasons. Consequently, the isotopic oscillations can be interpreted as a reflection of changes in the intensity of the evaporation processes (Li and Ku,1997) that cancel out the opposite effect of the temperature increase (decreasing isotopic fractionation with increasing temperature; McCrea, 1950).
Support from grain size analysis; mineralogy
Short-chain (C16?C18) fatty acids in lake sediments are derived primarily from aquatic sources (Eglinton and Hamilton, 1967; Huang et al., 2002), and the principal control on their ?D is the ?D of lake water (Huang et al., 2002, 2004). ... "The ?D of closed-basin tropical lakes such as Wandakara reflects the hydrologic balance of the lake, with drier conditions causing isotopically enriched lake water due to both the amount effect on precipitation and kinetic fractionation during evaporation from the lake surface, which favors the retention of deuterium relative to hydrogen in the lake water (Craig, 1961). "
Short-chain (C16?C18) fatty acids in lake sediments are derived primarily from aquatic sources (Eglinton and Hamilton, 1967; Huang et al., 2002), and the principal control on their ?D is the ?D of lake water (Huang et al., 2002, 2004). ... "The ?D of closed-basin tropical lakes such as Wandakara reflects the hydrologic balance of the lake, with drier conditions causing isotopically enriched lake water due to both the amount effect on precipitation and kinetic fractionation during evaporation from the lake surface, which favors the retention of deuterium relative to hydrogen in the lake water (Craig, 1961). "
The ?D of long-chain, terrestrially derived fatty acids is controlled by the ?D of the water used by plants, transpiration from leaf surfaces, and kinetic fractionation during biosynthesis (Hou et al., 2008; Sessions et al., 1999).  ... "Thus, although the first-order control on leaf wax ?D in the tropics is the ?D of meteoric waters, interactions between climate and vegetation suggest the potential for complex, non-linear responses of leaf wax ?D to rainfall variability."
Permanent anoxia in the subsurface water confines phytoplankton, dominated by dinoflagellates and diatoms24, to the oxygenated, brackish and deuterium-depleted surface water where their lipid ?D values can be expected to closely co-vary with the surface water ?Dvalues25,26. ?D values of that water are controlled by the amount of rainfall, its isotopic composition and the amount of mixing with the underlying sea water. As rain also increases the density stratification in the lakes, decreasing mixing, surface water ?D values are expected to be significantly lower during wet periods compared with dry periods.  Zhang and Sachs 2007, Organic Geochemistry, 38: 582-608.
Sachs and Sachse 2008, GCA , 72:793-806; "dD values of lipids...were closely correlated with salinity, owing to a decrease in D/H fractionation between lipids and source water as salinity increases. TLE dD values can therefore be used as a salinity indicator in hypersaline evnrionments such as those at Christmas Island".
Shifts of d18Odiatom values are likely not related to changes in diatom taxa or dissolution effects. Modern lake water falls along meteoric water line for the region
The persistent dominance of endogenic carbonate minerals requires a dynamic balance between groundwater inflow, evaporative concentration and groundwater outflow. "Fluid balance"In groundwater-dominated lakes, sediment geochemistry becomes closely linked to changes in lake?groundwater exchange and groundwater recharge at all timescales.
Comparison with lake water, similar trend in both species analysed
These exceptionally low values suggest that factors other than effective moisture must have contributed to the pronounced variations in the Takahula Lake d18O  record. Increased winter precipitation associated with a westerly Aleutian Low position may account for 1 permil of the d18O decrease. Other factors leading to the 18O-depletion during these periods probably  include decreased temperatures, as well as increased lake-ice cover and associated reductions in evaporation.
The d18Onorm record of ostracod valves preserved in Blue Lake reflects a history of evaporative enrichment and fluctuating groundwa- ter recharge.
Comparison between d18O-calcite and instrumental lake levels
The lake was probably hydrologically closed until ?800 cal yr BP, but experienced higher lake levels ?9000e7500 and ?3300e800 cal yr BP and low lake levels during the intervening times
Data from surface waters sampled in the area (Fig. 3B) reveal evaporative enrichment in some lakes consistent with the predicted LEL, whereas data from Lake Spa? ime are strictly constrained to the LMWL, with an average composition identical to that of local groundwater.
(Holmes, 1996; Schwalb, 2003; Holmes et al., 2007).
Recent observations of lake-water d18O, lake level, river discharge, and climate variations, suggest that changes in regional effective moisture (precipitation minus evaporation) are reflected by the lakes hydrologic balance. The observations indicate that the lake is currently 18O-enriched by summer evaporation and that during years of increased precipitation, when groundwater inflow rates to the lake increase, lakewater d18O values decrease.
Therefore, the Challa ?18Odiatom record is tuned to the underlying processes of local precipitation and, especially, seasonal evaporative losses from the lake surface, only modifi ed by small temperature effects.
exclusion of all other possible influencing processes (see Bird et al. 2011)
Leaf wax dD reflects "...variations in the ?D of precipitation (?Dprecip) through time in many systems (Hou et al., 2008; Huang et al., 2004). Observational data and isotope-enabled climate models indicate that ?Dprecip reflects the integrated atmospheric history of a water parcel, including source region, transport history, and convective, evaporative, and distillation processes." / "The Lake Malawi ?Dwax likely reflects the ?D of precipitation and associated changes in precipitation and vapor transport, not bio- synthetic or temperature effects."
The isotopic composition of the Triassic limestone bedrock is characterized by relatively low oxygen isotope values (between -4.0 and -6.0%; average d18Ocalcite = -5.3%)...samples from units 6, 5 and 3 show d18Ocalcite values (-7.1% to -3.2%) similar to the isotopic signal of the bedrock (-4.6% to -5.2%), indicating a dominant clastic origin of the carbonate.
Castor Lake water and sediment oxygen isotope values are primarily influenced by catchment water balance variations resulting from precipitation-evaporation forcing; To establish a meteoric water baseline for the Castor Lake record, we mean centered the independently dated (SI Appendix, Table S1) Lime Lake ?18O data and subtracted these variations from the mean centered Castor Lake ?18O values (Figs. 2 and 3) thereby isolating the isotopic influence of hydrologic forcing on the Castor lake-catchment system (8). The resulting ??18O record represents the cumulative isotopic effects of evaporation
a negative excursion in the d18Ocarb profiles suggests reduced evaporative concentration of lake water. Although it is not straightforward to compare carbonate with water d18O values, d18O values are more similar to groundwater (c. 2&; K. Mills & J.M. Russell, unpubl. data), in contrast to current lake water values indicating net evaporation (values between A¾2 and A¾4&; K. Mills & J.M. Russell, unpubl. data).'
Vegetation and humidity effects are negligble, "Thus, we assume in this study that dDwax represents mean annual dDP." ... "Instrumental measurements of the isotopic composition of East African precipitation are correlated with 850 hPa zonal wind speeds in the central Indian Ocean (5 Ne5 S, 60 Ee90 E), with stronger zonal wind speed resulting in more depleted precipitation (Vuille et al., 2005). Isotope-enabled modeling experiments confirm that shifts in zonal wind speed in the Indian Ocean e such as those that might occur during an IOZM event or during a strong Indian summer monsoon e result in isotopically-depleted rainfall throughout East Africa (Vuille et al., 2005)."
Drought periods (i.e., drier winters and/or a more rain-dominated seasonal precipitation balance) generally correspond with higher calcite d18O values, and vice-versa;although lake-water values are currently observed to change  through the ice-free season from ?16.7? to ?13.5? (n= 10), the late summer values are artificially elevated (inset of Fig. 3.).The lower, early summer values are similar to average weightedprecipitation-?18O, and similar to values inferred from near-surface  calcite-?18O prior to 1953 (see Fig. 3 in Anderson, 2011). It is on this basis that variations in through-flowing Bison Lake water-?18O are interpreted as precipitation-?18O and changes in the ratio of the seasonal precipitation balance between snowfall-dominated ?18O) and rainfall-dominated (higher ?18O).
Drought periods (i.e., drier winters and/or a more rain-dominated seasonal precipitation balance) generally correspond with higher calcite d18O values, and vice-versa;although lake-water values are currently observed to change  through the ice-free season from ?16.7? to ?13.5? (n= 10),  the late summer values are artificially elevated (inset of Fig. 3.).  The lower, early summer values are similar to average weighted  precipitation-?18O, and similar to values inferred from near-surface  calcite-?18O prior to 1953 (see Fig. 3 in Anderson, 2011). It is on  this basis that variations in through-flowing Bison Lake water-?18O are interpreted as precipitation-?18O and changes in the ratio of the seasonal precipitation balance between snowfall-dominated  ?18O) and rainfall-dominated (higher ?18O).
The rates of change of enrichment and depletion of both d18O and d13C (with respect to VPDB) are rapid, indicating large and rapid changes in the lake water due to variations in E/P.
Comparison with salinity reconstruction
Lime Lake d18O values, however, are controlled by the isotopic composition of meteoric water (i.e., rain and snowfall), which is influenced by the origin of atmospheric moisture, air mass trajectory, Rayleigh distillation, and air temperature
We interpret the Renner-Lime Lake ??18O record as a proxy for winter precipitation amounts. Note that the Renner- Lake ??18O dataset was produced by mean centering the two records and subtracting the latter from the former (i.e., the same method used to produce the Castor-Lime Lake ??18O dataset).
The major source of water for plants in the Arctic, where permafrost precludes extensive groundwater systems, is precipitation, either as snowmelt or summer rain (Elberling et al. 2008). Thus, we hypothesize that d2Hwax in the Arctic is likely closely related to d2Hprecip. Leaf wax synthesis occurs during the summer months when plants are neither temper- ature- nor light-limited. The apparent fractionation (e) between leaf waxes and source waters varies with relative humidity (RH) and with potential evapotrans- piration (Sachse et al. 2012). Long-term average growing season (June, July, August) RH at Clyde River is 80% (Environment Canada, 2011). The eleven-month dataset from Silasiutitalik at the head of Clyde Inlet suggests that growing season RH is slightly lower inland, around 75% (Kangiqtugaapik (Clyde River) Weather Station Network, 2011). The n-alkanoic acids synthesized by plants that grow at 70?80% RH have an e of -100 to -120% relative to annual mean precipitation (Hou et al. 2008). The d2Hwax values from Ayr Lake sediments (-240 to -265 %) therefore suggest that source water d2H should range between -140 to -165% (assuming an e of -100%) and -120 to -145% (assuming an e of -120%). These calculated source water d2H values are within the range of measured summer d2Hprecip values for both Pond Inlet and Hall Beach, although the values obtained with the larger e are close to the maximum summer d2Hprecip values for Pond Inlet. We therefore hypothesize that plants in this catchment utilize summer precipitation as their main water source (Fig. 2). Variability in summer d2Hprecip, or utilization of small but varying amounts of winter precipitation, is likely driving variability in d2Hwax at Ayr Lake.
The major source of water for plants in the Arctic, where permafrost precludes extensive groundwater systems, is precipitation, either as snowmelt or summer rain (Elberling et al. 2008). Thus, we hypothesize that d2Hwax in the Arctic is likely closely related to d2Hprecip. Leaf wax synthesis occurs during the summer months when plants are neither temper- ature- nor light-limited. The apparent fractionation (e) between leaf waxes and source waters varies with relative humidity (RH) and with potential evapotrans- piration (Sachse et al. 2012). Long-term average growing season (June, July, August) RH at Clyde River is 80% (Environment Canada, 2011). The eleven-month dataset from Silasiutitalik at the head of Clyde Inlet suggests that growing season RH is slightly lower inland, around 75% (Kangiqtugaapik (Clyde River) Weather Station Network, 2011). The n-alkanoic acids synthesized by plants that grow at 70?80% RH have an e of -100 to -120% relative to annual mean precipitation (Hou et al. 2008). The d2Hwax values from Ayr Lake sediments (-240 to -265 %) therefore suggest that source water d2H should range between -140 to -165% (assuming an e of -100%) and -120 to -145% (assuming an e of -120%). These calculated source water d2H values are within the range of measured summer d2Hprecip values for both Pond Inlet and Hall Beach, although the values obtained with the larger e are close to the maximum summer d2Hprecip values for Pond Inlet. We therefore hypothesize that plants in this catchment utilize summer precipitation as their main water source (Fig. 2). Variability in summer d2Hprecip, or utilization of small but varying amounts of winter precipitation, is likely driving variability in d2Hwax at Ayr Lake.
The major source of water for plants in the Arctic, where permafrost precludes extensive groundwater systems, is precipitation, either as snowmelt or summer rain (Elberling et al. 2008). Thus, we hypothesize that d2Hwax in the Arctic is likely closely related to d2Hprecip. Leaf wax synthesis occurs during the summer months when plants are neither temper- ature- nor light-limited. The apparent fractionation (e) between leaf waxes and source waters varies with relative humidity (RH) and with potential evapotrans- piration (Sachse et al. 2012). Long-term average growing season (June, July, August) RH at Clyde River is 80% (Environment Canada, 2011). The eleven-month dataset from Silasiutitalik at the head of Clyde Inlet suggests that growing season RH is slightly lower inland, around 75% (Kangiqtugaapik (Clyde River) Weather Station Network, 2011). The n-alkanoic acids synthesized by plants that grow at 70?80% RH have an e of -100 to -120% relative to annual mean precipitation (Hou et al. 2008). The d2Hwax values from Ayr Lake sediments (-240 to -265 %) therefore suggest that source water d2H should range between -140 to -165% (assuming an e of -100%) and -120 to -145% (assuming an e of -120%). These calculated source water d2H values are within the range of measured summer d2Hprecip values for both Pond Inlet and Hall Beach, although the values obtained with the larger e are close to the maximum summer d2Hprecip values for Pond Inlet. We therefore hypothesize that plants in this catchment utilize summer precipitation as their main water source (Fig. 2). Variability in summer d2Hprecip, or utilization of small but varying amounts of winter precipitation, is likely driving variability in d2Hwax at Ayr Lake.
The major source of water for plants in the Arctic, where permafrost precludes extensive groundwater systems, is precipitation, either as snowmelt or summer rain (Elberling et al. 2008). Thus, we hypothesize that d2Hwax in the Arctic is likely closely related to d2Hprecip. Leaf wax synthesis occurs during the summer months when plants are neither temper- ature- nor light-limited. The apparent fractionation (e) between leaf waxes and source waters varies with relative humidity (RH) and with potential evapotrans- piration (Sachse et al. 2012). Long-term average growing season (June, July, August) RH at Clyde River is 80% (Environment Canada, 2011). The eleven-month dataset from Silasiutitalik at the head of Clyde Inlet suggests that growing season RH is slightly lower inland, around 75% (Kangiqtugaapik (Clyde River) Weather Station Network, 2011). The n-alkanoic acids synthesized by plants that grow at 70?80% RH have an e of -100 to -120% relative to annual mean precipitation (Hou et al. 2008). The d2Hwax values from Ayr Lake sediments (-240 to -265 %) therefore suggest that source water d2H should range between -140 to -165% (assuming an e of -100%) and -120 to -145% (assuming an e of -120%). These calculated source water d2H values are within the range of measured summer d2Hprecip values for both Pond Inlet and Hall Beach, although the values obtained with the larger e are close to the maximum summer d2Hprecip values for Pond Inlet. We therefore hypothesize that plants in this catchment utilize summer precipitation as their main water source (Fig. 2). Variability in summer d2Hprecip, or utilization of small but varying amounts of winter precipitation, is likely driving variability in d2Hwax at Ayr Lake.
The major source of water for plants in the Arctic, where permafrost precludes extensive groundwater systems, is precipitation, either as snowmelt or summer rain (Elberling et al. 2008). Thus, we hypothesize that d2Hwax in the Arctic is likely closely related to d2Hprecip. Leaf wax synthesis occurs during the summer months when plants are neither temper- ature- nor light-limited. The apparent fractionation (e) between leaf waxes and source waters varies with relative humidity (RH) and with potential evapotrans- piration (Sachse et al. 2012). Long-term average growing season (June, July, August) RH at Clyde River is 80% (Environment Canada, 2011). The eleven-month dataset from Silasiutitalik at the head of Clyde Inlet suggests that growing season RH is slightly lower inland, around 75% (Kangiqtugaapik (Clyde River) Weather Station Network, 2011). The n-alkanoic acids synthesized by plants that grow at 70?80% RH have an e of -100 to -120% relative to annual mean precipitation (Hou et al. 2008). The d2Hwax values from Ayr Lake sediments (-240 to -265 %) therefore suggest that source water d2H should range between -140 to -165% (assuming an e of -100%) and -120 to -145% (assuming an e of -120%). These calculated source water d2H values are within the range of measured summer d2Hprecip values for both Pond Inlet and Hall Beach, although the values obtained with the larger e are close to the maximum summer d2Hprecip values for Pond Inlet. We therefore hypothesize that plants in this catchment utilize summer precipitation as their main water source (Fig. 2). Variability in summer d2Hprecip, or utilization of small but varying amounts of winter precipitation, is likely driving variability in d2Hwax at Ayr Lake.
Subsequent to*3,000 cal yr BP, Quartz Lake became a relatively closed system, with isotopic characteristics of progressively stronger evaporative influence
Winter snow melting during growing season. "dD values of long-chain n-alkanes (dDwax) reflect dD of water used by terrestrial vegetation after modification by biosynthetic processes (Sachse et al., 2012 and references therein). Soil moisture provides the source water for terrestrial plants, while lake water is the source for aquatic macrophytes and algae. dD of water in soils can be modified to some extent from dD of annual precipitation by evaporation. However, it has been shown that dDwax is unaltered by plants following leaf formation and therefore reflects dDwater used during leaf flush (Kahmen et al., 2011; Sachse et al., 2010; Tipple et al., 2013). In the Arctic, the majority of leaf formation takes place in late spring/early summer following the return of appreciable sunlight. Because snowmelt is the predominant water source available to plants at the time of leaf formation, the dDwax is potentially biased toward wintertime dDp (i.e. snowfall months). In Tasiilaq, the difference between precipitation-weighted annual average dDp (?92?) and precipitation- weighted average dDp during months with below zero average temperatures (October-April; ?96?) is negligible (Bowen, 2008; Cappelen et al., 2011), and thus, any changes in the timing of leaf formation during the Holocene, or in the seasonality of precipitation, are unlikely to have had much impact on dDwax."
dD values of long-chain n-alkanes (dDwax) reflect dD of water used by terrestrial vegetation after modification by biosynthetic processes (Sachse et al., 2012 and references therein). Soil mois- ture provides the source water for terrestrial plants, while lake water is the source for aquatic macrophytes and algae. dD of water in soils can be modified to some extent from dD of annual precipitation by evaporation. However, it has been shown that dDwax is unaltered by plants following leaf formation and there- fore reflects dDwater used during leaf flush (Kahmen et al., 2011; Sachse et al., 2010; Tipple et al., 2013). In the Arctic, the majority of leaf formation takes place in late spring/early summer follow- ing the return of appreciable sunlight. Because snowmelt is the predominant water source available to plants at the time of leaf formation, the dDwax is potentially biased toward wintertime dDp (i.e. snowfall months). In Tasiilaq, the difference between precipitation-weighted annual average dDp (?92?) and precipitation- weighted average dDp during months with below zero average temperatures (October-April; ?96?) is negligible (Bowen, 2008; Cappelen et al., 2011), and thus, any changes in the timing of leaf formation during the Holocene, or in the seasonality of precipita- tion, are unlikely to have had much impact on dDwaxd
dD values of long-chain n-alkanes (dDwax) reflect dD of water used by terrestrial vegetation after modification by biosynthetic processes (Sachse et al., 2012 and references therein). Soil moisture provides the source water for terrestrial plants, while lake water is the source for aquatic macrophytes and algae. dD of water in soils can be modified to some extent from dD of annual precipitation by evaporation. However, it has been shown that dDwax is unaltered by plants following leaf formation and therefore reflects dDwater used during leaf flush (Kahmen et al., 2011; Sachse et al., 2010; Tipple et al., 2013). In the Arctic, the majority of leaf formation takes place in late spring/early summer following the return of appreciable sunlight. Because snowmelt is the predominant water source available to plants at the time of leaf formation, the dDwax is potentially biased toward wintertime dDp (i.e. snowfall months). In Tasiilaq, the difference between precipitation-weighted annual average dDp (?92?) and precipitation- weighted average dDp during months with below zero average temperatures (October-April; ?96?) is negligible (Bowen, 2008; Cappelen et al., 2011), and thus, any changes in the timing of leaf formation during the Holocene, or in the seasonality of precipita- tion, are unlikely to have had much impact on dDwaxd
?D values of long-chain n-alkanes (?Dwax) reflect ?D of water used by terrestrial vegetation after modification by biosynthetic processes (Sachse et al., 2012 and references therein). Soil mois- ture provides the source water for terrestrial plants, while lake water is the source for aquatic macrophytes and algae. ?D of water in soils can be modified to some extent from ?D of annual precipitation by evaporation. However, it has been shown that ?Dwax is unaltered by plants following leaf formation and there- fore reflects ?Dwater used during leaf flush (Kahmen et al., 2011; Sachse et al., 2010; Tipple et al., 2013). In the Arctic, the majority of leaf formation takes place in late spring/early summer follow- ing the return of appreciable sunlight. Because snowmelt is the predominant water source available to plants at the time of leaf formation, the ?Dwax is potentially biased toward wintertime ?Dp (i.e. snowfall months). In Tasiilaq, the difference between precip- itation-weighted annual average ?Dp (?92?) and precipitation- weighted average ?Dp during months with below zero average temperatures (October?April; ?96?) is negligible (Bowen, 2008; Cappelen et al., 2011), and thus, any changes in the timing of leaf formation during the Holocene, or in the seasonality of precipita- tion, are unlikely to have had much impact on ?Dwax.
Because plant waxes are produced year-round, we interpret dDwax to record weighted annual dDprecip, which is highly influenced by variations in the Australasian monsoon.
d18O values only
Oxygen isotope values of El Gancho surface water samples are enriched by ~8? relative to local meteoric water (Fig. 3), indicating substantial water loss through both evaporation and nonfractionating outfl ow pathways (i.e., overfl ow and outseepage through the lake bed), and a resulting lake water isotopic sensitivity to changes in hydrologic balance. isotopic and hydrologic modeling supports these assertions, indicating that precipitation [amount] is the principle control on El Gancho lake water !18O values. For example, in response to modeled precipitation changes of i¿½50%, El Gancho exhibited water and ostracod !18O increases of >1.5? and decreases of >1.0?, respectively; largely as a result of hydrologic control of the proportions of water lost through evaporation and overfl ow (Table DR5).
Variation in vegetation type (shrubs and grass) is the main factor influencing the [d2H of leaf waxes in] sediments of Lake Sugan, because of the dD values of Chenopodiceae shrubs are higher than those of grasses. We can still discuss the moisture history using the hydrogen-isotope record, because the vegetation type would be consistent with hydrological condition. [i.e., Chenopodiaceae shrubs tend to be more abundant in dry climates, and Chenopodiaceae shrubs have a smaller apparent fractionation, so dry climates would not only cause greater evaporative enrichment of plant source water, but more leaf waxes with relatively enriched d2H would be produced by Chenopodiaceae shrubs.] In the arid area of western China, vegetation type is controlled by moisture changes (Wu, 2011).  The authors conclude that variations in vegetation type (shrub or grass), which are caused by moisture changes, are the main factor controlling the δD records in the study area.
Variation in vegetation type (shrubs and grass) is the main factor influencing the [d2H of leaf waxes in] sediments of Lake Sugan, because of the dD values of Chenopodiceae shrubs are higher than those of grasses. We can still discuss the moisture history using the hydrogen-isotope record, because the vegetation type would be consistent with hydrological condition. [i.e., Chenopodiaceae shrubs tend to be more abundant in dry climates, and Chenopodiaceae shrubs have a smaller apparent fractionation, so dry climates would not only cause greater evaporative enrichment of plant source water, but more leaf waxes with relatively enriched d2H would be produced by Chenopodiaceae shrubs.] In the arid area of western China, vegetation type is controlled by moisture changes (Wu, 2011).  The authors conclude that variations in vegetation type (shrub or grass), which are caused by moisture changes, are the main factor controlling the δD records in the study area.
All botryococcene dD values were highly correlated with water dD values (R2>0.99). And, Zhang and Sachs 2007, Organic Geochemistry, 38: 582-608.
In closed (endorheic) lakes, lake water dD is driven by the ratio of precipitation to evaporation and the isotopic value of the precipitation which, in the tropics, is strongly negatively correlated with the amount of precipitation on monthly and longer timescales. The combination of these processes amplifies the response of lake water dD to changes in rainfall with lower (higher) dD values occurring during periods of wetter (drier) conditions. Changes in rainfall patterns can therefore be reconstructed from dD values of sedimentary algal lipids, due to the near perfect correlation between water dD and algal lipid dD values.
Downcore variation involved subtle changes in distribution, C22 and C24 covarying and suggesting a similar source, perhaps partially aquatic, with an increase in recent decades perhaps related to indirect fertilization of the lake (Fig. 5a).
SEASONALITY: "First, during water uptake, plants are expected to sample the spring growing season precipitation, at the end of the wet season [October-March], as the temperature warms up and before the dry summer months (Feakins and Sessions, 2010; Kahmen et al., 2011; Tipple et al., 2013). During this time, the plants may record the isotopic signature of just a few storms each year heightening variability; however, sedimentary samples integrating a decade might average the signal from > 20 storms. " PARAMETER: "Paleoclimatic interpretation of the precipitation isotopic history recorded in the Crystal Cave has been suggested to record changes in moisture source primarily and local amount effect secondarily (McCabe-Glynn et al., 2013). ... Our record provides an extension of the Crystal Cave isotopic record, from a more southerly coastal location, but likely also records interdecadal variability in the same processes. ... We suggest that the plant leaf wax record preferentially captures spring precipitation isotopic anomalies integrated over decades, providing a sensitive record of atmospheric circulation and moisture source changes. "
SEASONALITY: "First, during water uptake, plants are expected to sample the spring growing season precipitation, at the end of the wet season [October-March], as the temperature warms up and before the dry summer months (Feakins and Sessions, 2010; Kahmen et al., 2011; Tipple et al., 2013). During this time, the plants may record the isotopic signature of just a few storms each year heightening variability; however, sedimentary samples integrating a decade might average the signal from > 20 storms. " PARAMETER: "Paleoclimatic interpretation of the precipitation isotopic history recorded in the Crystal Cave has been suggested to record changes in moisture source primarily and local amount effect secondarily (McCabe-Glynn et al., 2013). ... Our record provides an extension of the Crystal Cave isotopic record, from a more southerly coastal location, but likely also records interdecadal variability in the same processes. ... We suggest that the plant leaf wax record preferentially captures spring precipitation isotopic anomalies integrated over decades, providing a sensitive record of atmospheric circulation and moisture source changes. "
Downcore variation involved subtle changes in distribution, C22 and C24 covarying and suggesting a similar source, perhaps partially aquatic, with an increase in recent decades perhaps related to indirect fertilization of the lake (Fig. 5a).
Results from both observational and modeling studies suggest that the dominant control on modern interannual Andean ?Dp variability is Rayleigh-type fractionation during rainout along the trajectory of moist air masses across the Amazon Basin up to the Andes (e.g., Rozanski et al., 1993; Hoffmann et al., 2003; Vimeux et al., 2005; Vuille and Werner, 2005). Increased rainout along this trajectory, as well as increased local precipitation in the Andes, results in more depleted ?Dp, consistent with the continentality and amount effects that dominate ?Dp variability throughout most of tropical South America (Vuille et al., 2003).
Results from both observational and modeling studies suggest that the dominant control on modern interannual Andean ?Dp variability is Rayleigh-type fractionation during rainout along the trajectory of moist air masses across the Amazon Basin up to the Andes (e.g., Rozanski et al., 1993; Hoffmann et al., 2003; Vimeux et al., 2005; Vuille and Werner, 2005). Increased rainout along this trajectory, as well as increased local precipitation in the Andes, results in more depleted ?Dp, consistent with the continentality and amount effects that dominate ?Dp variability throughout most of tropical South America (Vuille et al., 2003).
In East Africa, stable isotopes in modern precipitation have been shown to reflect large-scale atmospheric circulation processes (Vuille et al., 2005), and the ?18O of precipitation (and by inference, the ?D of precipitation) on Mt. Kenya has been shown to be a good indicator of moisture balance (Barker et al., 2001). Leaf wax ?D reflects ?Dprecip, after a series of offsets due to soil water evaporation, fractionation of leaf water relative to xylem water, and biosynthesis (Sachse et al., 2012).
δDn-C29 record of the Son Kol sediments can be used to infer information about past summer moisture conditions.
covariation with d13C, comparison with Al content
general isotopic composition of the lake water follows the decreasing solar insolation over ~4200 yrs.
d18O-d13C covariation
d18O-d13C covariation
It is expected that dD values of botryococcene will decline when precipitation rates increase, possibly providing a metric for the severity of extreme rainfall events that are associated with El Nino events. (All botryococcene dD values were highly correlated with water dD values (R2>0.99). And, Zhang and Sachs 2007, Organic Geochemistry), 38: 582-608.
Low frequency correlation with inferred tidal level
Higher isotopic values in the summer compared to the winter (Yao et al., 2013; Bowen and Revenaugh, 2003) suggest that monthly values are indeed driven by temperature. If these seasonal controls also determine interannual variations in the isotopic composition of precipitation then temperature is likely to be a major factor explaining the reconstructed hydrogen isotopic variability. A higher percentage of growing season rainfall (relative to total water used by plants) would result in higher deltaD values.
Higher isotopic values in the summer compared to the winter (Yao et al., 2013; Bowen and Revenaugh, 2003) suggest that monthly values are indeed driven by temperature. If these seasonal controls also determine interannual variations in the isotopic composition of precipitation then temperature is likely to be a major factor explaining the reconstructed hydrogen isotopic variability. A higher percentage of growing season rainfall (relative to total water used by plants) would result in higher deltaD values.
We interpret changes in d18O as indicative of changes in the ratio of evaporation and precipitation (E/P)
Modern lake isotope hydrology, climate calibration, down core changes in other proxies.
In the modern Maya Lowlands, ?Dwax is well correlated with precipitation amount and varies by 60? across an annual precipitation gradient of 2,500 mm (Fig. 2).
In the modern Maya Lowlands, ?Dwax is well correlated with precipitation amount and varies by 60? across an annual precipitation gradient of 2,500 mm (Fig. 2).
In the modern Maya Lowlands, ?Dwax is well correlated with precipitation amount and varies by 60? across an annual precipitation gradient of 2,500 mm (Fig. 2).
In the modern Maya Lowlands, ?Dwax is well correlated with precipitation amount and varies by 60? across an annual precipitation gradient of 2,500 mm (Fig. 2).
In the modern Maya Lowlands, ?Dwax is well correlated with precipitation amount and varies by 60? across an annual precipitation gradient of 2,500 mm (Fig. 2).
In the modern Maya Lowlands, ?Dwax is well correlated with precipitation amount and varies by 60? across an annual precipitation gradient of 2,500 mm (Fig. 2).
Modern lake isotoep hydrology and mineralogy change downcore
Modern lakewater defines a local evaporation line (LEL) distinct from global meteoric water line (GMWL) .
Gat & Levy (1978); Mayr et al. (2007)
As the hydrogen isotopic composition of precipitation in tropical West Africa is controlled mostly by the ?amount effect? (22), ?Dwax values are interpreted here as indicators of changes in wet season precipitation intensity, following corrections for global ice volume and vegetation type (Supplementary Section 2.3 and Fig. 5). Independent support for the ?Dwax record comes from an updated reconstruction of palaeolake-level variations at Lake Bosumtwi (Supplementary Section 2.2; ref. 23).
The average monthly dD of precipitation in the interior of West Asia is correlated with average monthly precipitation at two meteoro- logical stations located 1365 km to the west and 390 km to the east of the Neor peat mire at the same latitude (Fig. 6).
Our interpretation of Lake N3 I´2Hterr as summer temperature is supported in several ways (Figure 3). First, I´2Hterr exhibits similar centennial-scale patterns during the past ~ 300 years as midge-inferred summer temperature anomalies at North Lake, western Greenland (Figure 3c) [Axford et al., 2013]. Coldest conditions in both records correspond with the Little Ice Age and end with warming in recent decades, a pattern also documented in Disko Bugt [Perner et al., 2013]. Second, the centennial-scale variability in Lake N3 I´2Hterr is remarkably similar to the 4 kyr-long record of annual average surface temperature at Greenland Ice Sheet Project 2 (GISP2), Greenland (r2 = 0.43) [Kobashi et al., 2011] (Figure 3b and Figure S5 in the supporting information), suggesting similar con- trols on late Holocene centennial-scale temperature at these sites, desGIte seasonal and elevation differences.
Our interpretation of Lake N3 I´2Hterr as summer temperature is supported in several ways (Figure 3). First, I´2Hterr exhibits similar centennial-scale patterns during the past ~ 300 years as midge-inferred summer temperature anomalies at North Lake, western Greenland (Figure 3c) [Axford et al., 2013]. Coldest conditions in both records correspond with the Little Ice Age and end with warming in recent decades, a pattern also documented in Disko Bugt [Perner et al., 2013]. Second, the centennial-scale variability in Lake N3 I´2Hterr is remarkably similar to the 4 kyr-long record of annual average surface temperature at Greenland Ice Sheet Project 2 (GISP2), Greenland (r2 = 0.43) [Kobashi et al., 2011] (Figure 3b and Figure S5 in the supporting information), suggesting similar con- trols on late Holocene centennial-scale temperature at these sites, desGIte seasonal and elevation differences.
precipitation seasonality
When interpreted in conjunction with other proxy records for moisture and aridity, and by controlling for plant type via detailed pollen stratigraphy and Iuapp data for modern plants, d2Hwax can be used to infer changes in moisture source area and transport history.
The hydrogen isotopic composition (D/H; dD using the delta notation) of leaf wax n-al- kanes reflects the D/H of source water used by a plant after modification by evaporation and biosynthetic fractionation (Kahmen et al., 2013a,b; Sachse et al., 2004, 2012), and therefore is useful for paleohydrologic reconstructions. The movement of water through the hydrologic cycle (Dansgaard, 1964), as well as regional and seasonal climatological conditions (Bowen and Revenaugh, 2003), results in variations of stable isotopes (D/H and 18O/16O) of local precipitation. Algae and macrophytes incorporate hydrogen from lake water while terrestrial plants use hydrogen from soil moisture; both water sources are ultimately derived from local precipitation. Therefore, dD of different n-alkanes can be used to reconstruct the climatic factors controlling the isotopic composi- tion of lake water and precipitation (Balascio et al., 2013; Rach et al., 2014).
The hydrogen isotopic composition (D/H; dD using the delta notation) of leaf wax n-al- kanes reflects the D/H of source water used by a plant after modification by evaporation and biosynthetic fractionation (Kahmen et al., 2013a,b; Sachse et al., 2004, 2012), and therefore is useful for paleohydrologic reconstructions. The movement of water through the hydrologic cycle (Dansgaard, 1964), as well as regional and seasonal climatological conditions (Bowen and Revenaugh, 2003), results in variations of stable isotopes (D/H and 18O/16O) of local precipitation. Algae and macrophytes incorporate hydrogen from lake water while terrestrial plants use hydrogen from soil moisture; both water sources are ultimately derived from local precipitation. Therefore, dD of different n-alkanes can be used to reconstruct the climatic factors controlling the isotopic composi- tion of lake water and precipitation (Balascio et al., 2013; Rach et al., 2014).
The hydrogen isotopic composition (D/H; dD using the delta notation) of leaf wax n-al- kanes reflects the D/H of source water used by a plant after modification by evaporation and biosynthetic fractionation (Kahmen et al., 2013a,b; Sachse et al., 2004, 2012), and therefore is useful for paleohydrologic reconstructions. The movement of water through the hydrologic cycle (Dansgaard, 1964), as well as regional and seasonal climatological conditions (Bowen and Revenaugh, 2003), results in variations of stable isotopes (D/H and 18O/16O) of local precipitation. Algae and macrophytes incorporate hydrogen from lake water while terrestrial plants use hydrogen from soil moisture; both water sources are ultimately derived from local precipitation. Therefore, dD of different n-alkanes can be used to reconstruct the climatic factors controlling the isotopic composi- tion of lake water and precipitation (Balascio et al., 2013; Rach et al., 2014).
The hydrogen isotopic composition (D/H; dD using the delta notation) of leaf wax n-al- kanes reflects the D/H of source water used by a plant after modification by evaporation and biosynthetic fractionation (Kahmen et al., 2013a,b; Sachse et al., 2004, 2012), and therefore is useful for paleohydrologic reconstructions. The movement of water through the hydrologic cycle (Dansgaard, 1964), as well as regional and seasonal climatological conditions (Bowen and Revenaugh, 2003), results in variations of stable isotopes (D/H and 18O/16O) of local precipitation. Algae and macrophytes incorporate hydrogen from lake water while terrestrial plants use hydrogen from soil moisture; both water sources are ultimately derived from local precipitation. Therefore, dD of different n-alkanes can be used to reconstruct the climatic factors controlling the isotopic composi- tion of lake water and precipitation (Balascio et al., 2013; Rach et al., 2014).
we interpret the I´18Ocalcite variations from Lake Nuudsaku as a record of meteoric inputs
Late-Holocene variations in our I´18O record display coherent patterns with regional glacier fluctuations at centennial to millennial scales, suggesting that I´18O minima were related to a combination of low temperatures and enhanced winter snowfall
basis for anything to do with the d18O data in this paper is unclear
6180 values (relative to the PDB standard) in the TIC fraction, in hand-picked carbonate crystals, and in L. ceriotuberosa are shown in Fig. 12b,c. Individual carbonate crystals and the TIC fraction of samples from shallow-lake intervals are isotopically enriched in 180, indicating the importance of evaporative removal of 180-depleted water vapor from the lake relative to the input of 180- depleted streamflow discharge. No 6180 data for ostracodes exist for the shallow-lake intervals since ostracodes were unable to survive the high salinities that characterized these times (Bradbury et al., 1989).
In closed-basin lakes with seasonally dry cliamte, the 18O/16O ratio of lake water is controlled mainly by the ratio of evporatino to precipitation.
This is demonstrated for Lake Punta Laguna by comparing the measured d18O values of lakewater, rainfall, and groundwater. Local groundwater and rainwater have depleted d18O values of 03.91 and 03.92?, respectively, whereas the lakewater is significantly enriched, averaging 0.93? (Table 3)
This is demonstrated for Lake Punta Laguna by comparing the measured d18O values of lakewater, rainfall, and groundwater. Local groundwater and rainwater have depleted d18O values of 03.91 and 03.92?, respectively, whereas the lakewater is significantly enriched, averaging 0.93? (Table 3)
monitoring of lake and inflow waters
In a closed, low-latitude lake basin, the d18O values of the lake water, which are the main control on the d18O values of carbonates, are influenced largely by the balance between inflow (mainly shallow groundwater, in the case of Kajemarum) and evaporation (Talbot 1990). Thus, the d18O record in endogenic carbonate should largely reflect salinity.
Mean lake water was enriched in 18O by evaporation and was 3.6? greater than mean rainwater.
Lake level also important as lake not always closed but Since ,3000 14C yr BP the level of Lake Valencia has been dropping. Lake closed in present day, but last 2k isotoeps realtively fresh suggesting more open system?
Lake level also important as lake not always closed but Since ,3000 14C yr BP the level of Lake Valencia has been dropping. Lake closed in present day, but last 2k isotoeps realtively fresh suggesting more open system?
Lake level also important as lake not always closed but Since ,3000 14C yr BP the level of Lake Valencia has been dropping. Lake closed in present day, but last 2k isotoeps realtively fresh suggesting more open system?
interpreting the data as a record of changing seasonality and/or isotopic composition (and therefore source) of precipitation, are therefore preferred
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Sauer, P. E., Eglinton, T. I., Hayes, J. M., Schimmelmann, A. & Sessions, A. L. Compound-specific D/H ratios of lipid biomarkers from sediments as a proxy for environmental and climatic conditions. Geochim. Cosmochim. Acta 65, 213–222 (2001)
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489-496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
dD values measured in alkenones from marine sediments can be used to reconstruct past changes in freshwater supply to the ocean and hence precipitation and river runoff.
dD values measured in alkenones from marine sediments can be used to reconstruct past changes in freshwater supply to the ocean and hence precipitation and river runoff.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Our results illustrate that changes in atmospheric circulation can trigger changes in precipitation isotopes in a counter-intuitive manner that ultimately impacts surface salinity estimates based on seawater isotopic values.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
Urey, H. C. 1948. Oxygen isotopes in nature and in the laboratory. Science, 108:489?496.
The annual d18Oshell time series each show relatively strong, negative correlations with a broad swath of North Atlantic SSTs during the satellite era
We found that the correlations with d18O are 0.72 (with total pseudocoral), 0.47 (with SST component), and 0.68 (with SSS component), p < 0.01 for all three (Figure S1), indicating that SSS changes represent a larger fraction of the variance in the coral d18Oanomaly signal than SST changes, as expected from the larger magnitude of interannual SSS variations at this site. Testing several different percent contributions of SSS and SST to create the pseudocoral, we determined that a combination of 35% SST and 65% SSS results in the closest representation to the observed coral d18Oanomaly values. This was calculated by creating a pseudocoral that consisted of percent SST/SSS contributions that ranged from 100/0% to 0/100%, in increments of 5% (i.e., 100/0, 95/5, 90/10a€¦ 5/95, 0/100). The pseudocoral consisting of 35/65% gave the highest correlation with the measured d18O time series, which provides the percent contributions of SST and SSS to the time series. This relationship was determined over the period 1970a€“2007, and is limited by the length of the instrumental SSS data set. We assume stationarity in the proportional contributions of SST and SSS to the coral d18O signal because SSS data needed to evaluate this assumption are lacking in the pre- 1970 period. However, the assumption of stationarity of a proxy-instrumental relationship developed over the instrumental time period affects all proxy-based climate reconstructions that extend beyond the instrumental period. Thus, lacking additional instrument data and/or another independent SST- or SSS-only proxy there is no easy way to reduce the uncertainty of the empirically derived proxy relationship over the calibration-verification interval.
precipitation occurs in closest proximity to the bulk of global cloud formation and its dD values are most influenced by its amount. dD values de- crease as precipitation amount increases (Risi et al., 2008). Additional factors shape the dD values of fatty acids in land-plants as well but their consideration below suggests that our dDn-C26 sequences were unambiguously if qualita- tively reflecting changes in amount of precipitation.
precipitation occurs in closest proximity to the bulk of global cloud formation and its dD values are most influenced by its amount. dD values de- crease as precipitation amount increases (Risi et al., 2008). Additional factors shape the dD values of fatty acids in land-plants as well but their consideration below suggests that our dDn-C26 sequences were unambiguously if qualita- tively reflecting changes in amount of precipitation.
precipitation occurs in closest proximity to the bulk of global cloud formation and its dD values are most influenced by its amount. dD values de- crease as precipitation amount increases (Risi et al., 2008). Additional factors shape the dD values of fatty acids in land-plants as well but their consideration below suggests that our dDn-C26 sequences were unambiguously if qualita- tively reflecting changes in amount of precipitation.
precipitation occurs in closest proximity to the bulk of global cloud formation and its dD values are most influenced by its amount. dD values de- crease as precipitation amount increases (Risi et al., 2008). Additional factors shape the dD values of fatty acids in land-plants as well but their consideration below suggests that our dDn-C26 sequences were unambiguously if qualita- tively reflecting changes in amount of precipitation.
precipitation occurs in closest proximity to the bulk of global cloud formation and its dD values are most influenced by its amount. dD values de- crease as precipitation amount increases (Risi et al., 2008). Additional factors shape the dD values of fatty acids in land-plants as well but their consideration below suggests that our dDn-C26 sequences were unambiguously if qualita- tively reflecting changes in amount of precipitation.
precipitation occurs in closest proximity to the bulk of global cloud formation and its dD values are most influenced by its amount. dD values de- crease as precipitation amount increases (Risi et al., 2008). Additional factors shape the dD values of fatty acids in land-plants as well but their consideration below suggests that our dDn-C26 sequences were unambiguously if qualita- tively reflecting changes in amount of precipitation.
precipitation occurs in closest proximity to the bulk of global cloud formation and its dD values are most influenced by its amount. dD values de- crease as precipitation amount increases (Risi et al., 2008). Additional factors shape the dD values of fatty acids in land-plants as well but their consideration below suggests that our dDn-C26 sequences were unambiguously if qualita- tively reflecting changes in amount of precipitation.
precipitation occurs in closest proximity to the bulk of global cloud formation and its dD values are most influenced by its amount. dD values de- crease as precipitation amount increases (Risi et al., 2008). Additional factors shape the dD values of fatty acids in land-plants as well but their consideration below suggests that our dDn-C26 sequences were unambiguously if qualita- tively reflecting changes in amount of precipitation.
The modern analog supporting the association of enhanced rainfall with minimum speleothem I´18O values is the present-day relationship between decreasing rainfall I´18O and increasing annual rainfall amount (Fig. 4).
effects of temperature on d18O in rainfall and alterations of the d18O of soil- and ground water due to evaporation
Difference between the d13C of soil CO2 in areas with C3 and C4 vegetation
To estimate absolute amounts of rainfall integrated from sources to southeast China, we must make assumptions about absolute humidity in tropical source regions. Even assuming a glacial relative humidity as high as today?s and only a modest tropical temper- ature depression (2.5i¿½C), we calculate an ab- solute tropical glacial vapor pressure that is 85% of today?s and 79% of mid-Holocene/ Last Interglacial values (at a mid-Holocene temperature 1i¿½C greater than today and con- stant relative humidity). Coupled with the Rayleigh calculations, the amount of precip- itation integrated between tropical sources and southeast China is today  87% of mid- Holocene/Last Interglacial values and was  65% of mid-Holocene/Last Interglacial val- ues at glacial times corresponding to the heaviest Hulu/Dongge calcite  18O values, indicating that this region was significantly drier during glacial times.
To estimate absolute amounts of rainfall integrated from sources to southeast China, we must make assumptions about absolute humidity in tropical source regions. Even assuming a glacial relative humidity as high as today?s and only a modest tropical temper- ature depression (2.5i¿½C), we calculate an ab- solute tropical glacial vapor pressure that is 85% of today?s and 79% of mid-Holocene/ Last Interglacial values (at a mid-Holocene temperature 1i¿½C greater than today and con- stant relative humidity). Coupled with the Rayleigh calculations, the amount of precip- itation integrated between tropical sources and southeast China is today  87% of mid- Holocene/Last Interglacial values and was  65% of mid-Holocene/Last Interglacial val- ues at glacial times corresponding to the heaviest Hulu/Dongge calcite  18O values, indicating that this region was significantly drier during glacial times.
To estimate absolute amounts of rainfall integrated from sources to southeast China, we must make assumptions about absolute humidity in tropical source regions. Even assuming a glacial relative humidity as high as today?s and only a modest tropical temper- ature depression (2.5i¿½C), we calculate an ab- solute tropical glacial vapor pressure that is 85% of today?s and 79% of mid-Holocene/ Last Interglacial values (at a mid-Holocene temperature 1i¿½C greater than today and con- stant relative humidity). Coupled with the Rayleigh calculations, the amount of precip- itation integrated between tropical sources and southeast China is today  87% of mid- Holocene/Last Interglacial values and was  65% of mid-Holocene/Last Interglacial val- ues at glacial times corresponding to the heaviest Hulu/Dongge calcite  18O values, indicating that this region was significantly drier during glacial times.
Thus, changes in convective intensity within the SASM/SACZ may significantly contribute to and reinforce the effect that the location of this system has on the d18O of precipitation.
Tuning delivers a high correlation between y18O in SPA 12 and D14C (r =0.61). The maxima of d18O coincide with solar minima (Dalton, Maunder, Sporer, Wolf, as well as with minima at around AD 700, 500 and 300).
observed negative correlation between summer monsoon rainfall amount and d18O
Increase of d13C may generally be associated with increase of the proportion of C4 plants in the vegetation or increase of the contribution of bedrock carbonate to the DIC - both are more likely in drier conditions. Stable d18O values suggest that changes in temperature were minor.
Increase of d13C may generally be associated with increase of the proportion of C4 plants in the vegetation or increase of the contribution of bedrock carbonate to the DIC - both are more likely in drier conditions. Stable d18O values suggest that changes in temperature were minor.
Increase of d13C may generally be associated with increase of the proportion of C4 plants in the vegetation or increase of the contribution of bedrock carbonate to the DIC - both are more likely in drier conditions. Stable d18O values suggest that changes in temperature were minor.
Increase of d13C may generally be associated with increase of the proportion of C4 plants in the vegetation or increase of the contribution of bedrock carbonate to the DIC - both are more likely in drier conditions. Stable d18O values suggest that changes in temperature were minor.
Sr/Ca ratios can be used to reconstruct relative rates of epikarstic moisture transport because low moisture levels can cause 1) increased drip water residence times, leading to 2) Ca drawdown via calcite precipitation in the overlying epikarst, which increases Sr/Ca ratios in the residual drip waters and their dependent speleothems
Summer AM precipitation is driven by the thermal contrast between Asia and the tropical Indo-Pacific and therefore should be linked to regional temperature change (16). Based on an ECHO-G climate model simulation (SOM), we modeled Chinese air temperature for the past millennium, with results that were broadly con- sistent with our record (r = ?0.46 and n = 345 data points), particularly with regard to centen- nial and multicentennial time scales and with regard to the distinct double peak in both model output and record during the MWP (Fig. 3A).
changes in the seasonal balance of summer versus winter rainfall
changes in the seasonal balance of summer versus winter rainfall
observed present day correlation between rainfall seasonality and d18O at a nearby station of the Global Network for Isotopes in precipitation
observed present day correlation between rainfall seasonality and d18O at a nearby station of the Global Network for Isotopes in precipitation
During cold climate periods, relatively less winter contribution to the cave drip water will result in relatively higher d18O values and vice versa.  Depleted/enriched oxygen isotopesisotopes are interpreted mainly to indicate relatively more/less winter precipitation and higher/lower summer evaporation reflecting warmer/colder conditions.
There is a presend-day positive relationship between ?18O of precipitation and temperature of +0.35? / i¿½C.,Calculations with dripwater d18O values and calcite d18O values,,1 to 10,lamina counting, high resolution sampling to 100 microns,temperature,,year,,,,,,rainfall amount and source,,,,,,,,,,,,,https://docs.google.com/spreadsheets/d/1WNzh05-BveXAV0WvY6duO7iRR_drkRSgmosDu0BBmyU
It is expected that the summer rainwater I´18O composition in southwestern Mexico is inversely proportional to the rainfall amount (Dansgaard, 1964). This is because southwestern Mexico is influenced by the same wind and climate patterns and moisture sources as Guatemala and Belize (Amador et al., 2006), where the amount effect has been observed to dominate the I´18O composition of rainfall and groundwater.
A low correlation between δ13C and δ18O values (r2=0.133) argues against kinetically driven fractionation during speleothem growth according to traditional Hendy criteria. This suggests that the speleothem most likely grew under isotopic equilibrium conditions. Fluctuations in δ18O values result mainly from changes in δ18O value of the drip water. In low latitude areas, the “amount effect”, i.e. a relationship where the δ18O of meteoric precipitation is negatively correlated with precipitation amount, is the major controlling factor of precipitation δ18O (Dansgaard, 1964; Rozanski et al., 1993) and often overshadows the dependence of δ18O on temperature in Asian monsoon regions (Araguas-Araguas et al., 1998, Wei and Gasse, 1999).
Speleothem d18O gets heavier as temperature increases, therefore this is P_isotope driven
An increase in precipitation may cause lower speleothem d13C directly through its effects on cave hydrology and indirectly through its influence on surface biomass.
The observed strong decrease in the ?18O by about 1.5? between 11 and 10 ka, for example, is rather a change of the source region than a pure temperature  effect. Based on this assumption, during the deglaciation the trajectories more frequently followed southerly routes. Towards the transition into the Holocene the main contribution of precipitation probably originated from more northern regions of the Atlantic, where the surface ocean is generally more depleted in 18O (LeGrande and Schmidt, 2006).
The lamina thickenss negatives during dry periods
Thicker annual bands indicate greater effective annual moisture
The stalagmite ?18O values also contain a temperature imprint: a decrease in LST of ?3?4 i¿½C is responsible for making the stalagmite ?18O values higher by ?0.6?0.8% due to the water?carbonate precipitation temperature
Mean annual air temperatures influence the annual precipitation isotope composition.
Intensity of the South American Summer Monsoon (SASM) excerts the strongest control on precipitation isotopes
Intensity of the South American Summer Monsoon (SASM) excerts the strongest control on precipitation isotopes
Based on classic papers (Dansgaard 1964; Rozanski et al 1993)
Changes in the vegetation related to changes in rainfall amount lead to variation of d13C
Cool weather precipiation is depleted in d18O compared to warm weather precipitation.
Changes in the vegetation related to changes in rainfall amount lead to variation of d13C
Cool weather precipiation is depleted in d18O compared to warm weather precipitation.
Changes in the vegetation related to changes in rainfall amount lead to variation of d13C
The d18Owater salinity relation in the Caribbean is on the order of 0.2 to 0.3%/psu [Ruhlemann et al., 1999; Watanabe et al., 2001]. Oxygen isotope records of these sponges do notreveal reproducible temperature signals. Variations in thed18O of the ambient water and the pH effect on the oxygenisotopic composition of carbonates [Zeebe, 1999] mayobscure temperature signals in thed18O records.
The d18Owater salinity relation in the Caribbean is on the order of 0.2 to 0.3%/psu [Ruhlemann et al., 1999; Watanabe et al., 2001]. Oxygen isotope records of these sponges do notreveal reproducible temperature signals. Variations in thed18O of the ambient water and the pH effect on the oxygenisotopic composition of carbonates [Zeebe, 1999] mayobscure temperature signals in thed18O records.
bohm 2000 10.1016/S0016-7037(99)00408-1
bohm 2000 10.1016/S0016-7037(99)00408-1
Sub-annual cyclic isotopic signatures of several permil represent the annual cycle of local rainfall and relative humidity. Additional data from a plantation tree of known age from ENSO-sensitive northwestern coastal Peru suggests that the 1997–8 ENSO warm phase event was recorded as an 8‰ anomaly in the δ18O of α-cellulose.
Roden et al. 2000 (GCA)
Correlations with 18O of precipitation (GNIP data) showed that source water signals are preserved in d18O tree rings. A possible explanation for the common decadal-to century scale trends, not directly related to mean monthly air temperature or precipitation amount, is variations in moisture source determined by the dominant atmospheric circulation pattern. The NAO index, determinates diminant wind pattern and temperature, and precipitation systems over Europe and influences d18O precipitation in Switzerland.
Correlations with 18O of precipitation (GNIP data) showed that source water signals are preserved in d18O tree rings. A possible explanation for the common decadal-to century scale trends, not directly related to mean monthly air temperature or precipitation amount, is variations in moisture source determined by the dominant atmospheric circulation pattern. The NAO index, determinates diminant wind pattern and temperature, and precipitation systems over Europe and influences d18O precipitation in Switzerland.
After evaluating the relationship between d18O and observed temperature and precipitation it can be concluded that there is some correlation of oxygen isotope values with late winter to early summer temperatures and a clear negative correlation with winter precipitation (November-February).
precipitation d18O is positively correlated with temperature, and tree source water and tree-ring cellulose record precip d18O
precipitation d18O is positively correlated with temperature, and tree source water and tree-ring cellulose record precip d18O
Source water d18O is determined by P_isotopes, and secondary temperature and ET effects.
Source water d18O is determined by P_isotopes, which is strongly influenced by precipitation amount (negative relation).
Source water d18O is determined by P_isotopes, which is strongly influenced by precipitation amount (negative relation).
Source water d18O is determined by P_isotopes, which is strongly influenced by precipitation amount.
Source water d18O is determined by P_isotopes, which is strongly influenced by precipitation amount.
Source water d18O is determined by P_isotopes, which is strongly influenced by precipitation amount (negative relation).
We propose that this correlation is caused by longer duration of the growing season in years of higher rainfall.
We propose that this correlation is caused by longer duration of the growing season in years of higher rainfall.
Roden et al. 2000 (GCA)
Since all the responses of tree-ring δ18O to the climatic factors (precipitation, relative humidity, and temperature) are related to dry–wet conditions, the PDSI is considered to be the most useful single variable to explain the variance of our δ18O chronology.
Calibration with instrumental record
In the monsoon season, high temperature usually indicates less rainfall and low relative humidity, which can make the tree ring cellulose d18O heavier. Low PDSI indicates a dry condition.
In the monsoon season, high temperature usually indicates less rainfall and low relative humidity, which can make the tree ring cellulose d18O heavier. Low PDSI indicates a dry condition.
In the monsoon season, high temperature usually indicates less rainfall and low relative humidity, which can make the tree ring cellulose d18O heavier. Low PDSI indicates a dry condition.
In the monsoon season, high temperature usually indicates less rainfall and low relative humidity, which can make the tree ring cellulose d18O heavier. Low PDSI indicates a dry condition.
In the monsoon season, high temperature usually indicates less rainfall and low relative humidity, which can make the tree ring cellulose d18O heavier. Low PDSI indicates a dry condition.
In the monsoon season, high temperature usually indicates less rainfall and low relative humidity, which can make the tree ring cellulose d18O heavier. Low PDSI indicates a dry condition.
In the monsoon season, high temperature usually indicates less rainfall and low relative humidity, which can make the tree ring cellulose d18O heavier. Low PDSI indicates a dry condition.
In the monsoon season, high temperature usually indicates less rainfall and low relative humidity, which can make the tree ring cellulose d18O heavier. Low PDSI indicates a dry condition.
Positive correlation between cellulose d18O and precipitation d18O; the later is positively influenced by air temperature.
Positive correlation between cellulose d18O and precipitation d18O; precipitation amount influences d18O of precipitation
As recorded in the meteorological data from the monsoon season, higher temperatures usually correlate with lower precipitation, and relative humidity levels, both of which lead to enriched 18O in tree rings. Moreover, as climate parameters are significantly correlated with one another, it is difficult to identify a dominant factor. Although the mechanisms underlying variations in tree- ring d18O can generally be attributed to d18O of source water and evaporative enrichment of 18O leaf water, in pluvial monsoon regions these effects seem to be well represented by the PDSI.
As recorded in the meteorological data from the monsoon season, higher temperatures usually correlate with lower precipitation, and relative humidity levels, both of which lead to enriched 18O in tree rings. Moreover, as climate parameters are significantly correlated with one another, it is difficult to identify a dominant factor. Although the mechanisms underlying variations in tree- ring d18O can generally be attributed to d18O of source water and evaporative enrichment of 18O leaf water, in pluvial monsoon regions these effects seem to be well represented by the PDSI.
As recorded in the meteorological data from the monsoon season, higher temperatures usually correlate with lower precipitation, and relative humidity levels, both of which lead to enriched 18O in tree rings. Moreover, as climate parameters are significantly correlated with one another, it is difficult to identify a dominant factor. Although the mechanisms underlying variations in tree- ring d18O can generally be attributed to d18O of source water and evaporative enrichment of 18O leaf water, in pluvial monsoon regions these effects seem to be well represented by the PDSI.
As recorded in the meteorological data from the monsoon season, higher temperatures usually correlate with lower precipitation, and relative humidity levels, both of which lead to enriched 18O in tree rings. Moreover, as climate parameters are significantly correlated with one another, it is difficult to identify a dominant factor. Although the mechanisms underlying variations in tree- ring d18O can generally be attributed to d18O of source water and evaporative enrichment of 18O leaf water, in pluvial monsoon regions these effects seem to be well represented by the PDSI.
As recorded in the meteorological data from the monsoon season, higher temperatures usually correlate with lower precipitation, and relative humidity levels, both of which lead to enriched 18O in tree rings. Moreover, as climate parameters are significantly correlated with one another, it is difficult to identify a dominant factor. Although the mechanisms underlying variations in tree- ring d18O can generally be attributed to d18O of source water and evaporative enrichment of 18O leaf water, in pluvial monsoon regions these effects seem to be well represented by the PDSI.
As recorded in the meteorological data from the monsoon season, higher temperatures usually correlate with lower precipitation, and relative humidity levels, both of which lead to enriched 18O in tree rings. Moreover, as climate parameters are significantly correlated with one another, it is difficult to identify a dominant factor. Although the mechanisms underlying variations in tree- ring d18O can generally be attributed to d18O of source water and evaporative enrichment of 18O leaf water, in pluvial monsoon regions these effects seem to be well represented by the PDSI.
As recorded in the meteorological data from the monsoon season, higher temperatures usually correlate with lower precipitation, and relative humidity levels, both of which lead to enriched d18O in tree rings?..moreover, as climate parameters are significantly correlated with one another, it is difficult to identify a dominant factor...Although the mechanisms underlying variations in tree ring d18O can generally be attributed to d18O of source water and evaporative enrichment of leaf water d18O, in pluvial monsoon regions these effects seem to be well represented by the PDSI,
Positive correlation between cellulose d18O and precipitation d18O; precipitation amount influences d18O of precipitation
plant waters determined by fog and precipitaiton isotope composition, which is influenced by synoptic conditions. strong correlation with 500 mb pressure anomalies with middle-wood d18O
plant waters determined by fog and precipitaiton isotope composition, which is influenced by synoptic conditions. strong correlation with 500 mb pressure anomalies with middle-wood d18O
plant waters determined by fog and precipitaiton isotope composition, which is influenced by synoptic conditions. strong correlation with 500 mb pressure anomalies with middle-wood d18O
plant water isotope composition determined by fog (July, positive correlation) and precipitaiton (Aug-Sep, negative correlation).
plant water isotope composition determined by fog (July, positive correlation) and precipitaiton (Aug-Sep, negative correlation).
plant water isotope composition determined by fog (July, positive correlation) and precipitaiton (Aug-Sep, negative correlation).
plant waters determined by fog and precipitaiton isotope composition, which is influenced by synoptic conditions. strong correlation with 500 mb pressure anomalies with middle-wood d18O
plant waters determined by fog and precipitaiton isotope composition, which is influenced by synoptic conditions. strong correlation with 500 mb pressure anomalies with middle-wood d18O
plant waters determined by fog and precipitaiton isotope composition, which is influenced by synoptic conditions. strong correlation with 500 mb pressure anomalies with middle-wood d18O
plant water isotope composition determined by fog (July, positive correlation) and precipitaiton (Aug-Sep, negative correlation).
plant water isotope composition determined by fog (July, positive correlation) and precipitaiton (Aug-Sep, negative correlation).
plant water isotope composition determined by fog (July, positive correlation) and precipitaiton (Aug-Sep, negative correlation).
plant waters determined by fog and precipitaiton isotope composition, which is influenced by synoptic conditions. strong correlation with 500 mb pressure anomalies with middle-wood d18O
plant waters determined by fog and precipitaiton isotope composition, which is influenced by synoptic conditions. strong correlation with 500 mb pressure anomalies with middle-wood d18O
plant waters determined by fog and precipitaiton isotope composition, which is influenced by synoptic conditions. strong correlation with 500 mb pressure anomalies with middle-wood d18O
plant waters determined by fog and precipitaiton isotope composition, which is influenced by synoptic conditions. strong correlation with 500 mb pressure anomalies with middle-wood d18O
plant water isotope composition determined by fog (July, positive correlation) and precipitaiton (Aug-Sep, negative correlation).
plant water isotope composition determined by fog (July, positive correlation) and precipitaiton (Aug-Sep, negative correlation).
plant water isotope composition determined by fog (July, positive correlation) and precipitaiton (Aug-Sep, negative correlation).
plant water isotope composition determined by fog (July, positive correlation) and precipitaiton (Aug-Sep, negative correlation).
plant waters are influenced by spring-summer precipitation d18O, which is strongly influenced by minimum spring-summer temperatures
plant water is linked to precipitation, and the isotope composition of precipitation is negatively correlated with P amount
Calibration with instrumental record
March and July air temperature (r = 0.29; p < 0.01) are positively and significantly correlated to the oxygen isotopes for the period from 1929 to 2008. Assuming that the same relation might haver persisted earlier as well.
Warmer spring temperatures result in leaf and soil water enrichment
Comparison with other P_isotope records indicates this tree-ring d18O record is influenced by regional P_isotope trends. However, no climate influence is clearly identified.
Positive correlation between cellulose d18O and precipitation d18O; the later is positively influenced by air temperature.
Each of the individual I´18O series has a significant negative correlation (p < 0.001) with the summer (Junea€“August) EWP
Each of the individual I´18O series has a significant negative correlation (p < 0.001) with the summer (Junea€“August) P_E
Drought conditions lead to enriched soil water and leaf water.
Drought conditions lead to enriched soil water and leaf water.
Positive correlation between cellulose d18O and precipitation d18O; the later is negatively influenced by precipitation amount.
Cellulose d18O is strongly influenced by isotopically enriched leaf waters, which is negatively correlated with RH.
Positive correlation between cellulose d18O and precipitation d18O; the later is positively influenced by air temperature.
Cellulose d18O is strongly influenced by isotopically enriched leaf waters, which is negatively correlated with RH.
Because tree-ring δ18O correlates negatively with precipitation and relative humidity and positively with temperature, moisture-related climatic indices are expected to be suitable parameters to explain the variations in tree-ring oxygen isotopes.
Temperature (positive) and precipitation amount (negative) have a significant influence on the isotopic composition of precipitation, which influences the isotopic composition of cellulose. The southern annular mode is correlated with both climate variables.
In the monsoon season, high temperature usually indicates less rainfall and low relative humidity, which can make the tree ring cellulose d18O heavier. Low PDSI indicates a dry condition.
In the monsoon season, high temperature usually indicates less rainfall and low relative humidity, which can make the tree ring cellulose d18O heavier. Low PDSI indicates a dry condition.
Higher vapor pressure deficit (lower RH) results in leaf water 18O enrichment, which results in higher tree-ring d18O values
Higher vapor pressure deficit (lower RH) results in leaf water 18O enrichment, which results in higher tree-ring d18O values
Higher vapor pressure deficit (lower RH) results in leaf water 18O enrichment, which results in higher tree-ring d18O values
Higher vapor pressure deficit (lower RH) results in leaf water 18O enrichment, which results in higher tree-ring d18O values
Higher vapor pressure deficit (lower RH) results in leaf water 18O enrichment, which results in higher tree-ring d18O values
Higher vapor pressure deficit (lower RH) results in leaf water 18O enrichment, which results in higher tree-ring d18O values