Plant to plant differences in fractionations between dD values of n-alkyl compounds and their precipitation source may introduce the greatest error in interpreting rainfall amount.
Plant to plant differences in fractionations between dD values of n-alkyl compounds and their precipitation source may introduce the greatest error in interpreting rainfall amount.
Plant to plant differences in fractionations between dD values of n-alkyl compounds and their precipitation source may introduce the greatest error in interpreting rainfall amount.
additional variability in coral 18O may arise from vari- ations in the 18O of seawater due to precip- itation, evaporation, or runoff, but at this site these changes are likely to be secondary be- cause local rainfall 18O is not strongly de- pleted from average seawater (21) and salin- ity gradients are small (22). Rainfall and SST influences are likely to be additive in terms of annual effect on coral 18O because warmer years tend to be wetter at this site.
the salinity effect on the isotopic pool of the ambient seawater is considered a negligible background signal and variations in coral d18O can be attributed to be primarily driven by changes in SST.
the salinity effect on the isotopic pool of the ambient seawater is considered a negligible background signal and variations in coral d18O can be attributed to be primarily driven by changes in SST.
SSS linked to rainfall
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.
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: "in the Indonesian coral records, coral d18O is more heavily infuenced by rainfall variability." The characteristics and correlations of the Bunaken Island coral d18O record strongly suggestthat it monitors the air-sea interaction associatedwith Southeast Asian summer monsoon.
In this study we use the G&D-SSS data [Gouriou and Delcroix, 2002] rather than a hindcast model SSS such as that available from NCEP in 1 by 1 degree grid boxes (Behringer et al. [1998]; hereafter referred to as NCEP-SSS) because the former consists of actual salinity measurements.total salinity range over a 20-year period is >3 times larger in G&DSSS than in NCEP-SSS model output (cf., 1.91 versus 0.51). Such large differences in the SSS between these two data sets imply that the SSS data set used for calibrating a measured coral proxy can strongly affect the outcome of the calibration. Three different methods for reconstructing SSSrelated dw variations are compared in this paper.
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.
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).
Core GIM 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,
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 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.
The monthly coral d18O anomaly time series at Rabaul yields no significant correlation with the instrumental SSS record, despite the fact that similar coral records from Fiji [Le Bec et al., 2000] and Vanuatu [Kilbourne et al., 2004] have been demonstrated to reflect SSS variations. It is possible that the monthly coral d18O anomaly record does reflect local SSS variability at Rabaul and the observed low correlation between these two variables results from differences between the ship-track SSS record and the actual SSS variability at Rabaul
IOD  upwelling  cools  the ambient  SST  and  decreases  rainfall,  both  of  which  increase coral d18O.
There are several factors that may prevent the quantification of these relationships, the most likely being the lack of large changes in SSS documented in the instrumental data. Over the length of the Hydrostation S record, salinity (mean annual and winter) has a range of only 0.3 psu, too small to establish a significant relationship for these proxies, given representation error (noise) in the coral and instrumental records.
However, the magnitude of the multidecadal variations in coral d18O is larger than expected based on SST alone. The standard deviation of the smoothed SST index shown in Figure 1B is 0.12i¿½C (based on SST from the HadISSTv.1.1 database; Rayner et al., 2003), while the standard deviation of coral !18O is 0.06?, which would correspond to 0.3 i¿½C, assuming well-established relationships. Hence, ~50% of the amplitude is due to variations in d18Oseawater. The d18Oseawater contribution is estimated by calculating the d18Oresidual (i.e., by subtracting the SST component from measured coral d18O; for details see GSA Data Repository). Figure 1C compares the d18Oresidual with a regional precipitation index computed from weather stations in the southeastern Caribbean, which includes the study site. The d18Oresidual and precipitation are highly correlated at low frequencies, implying that multidecadal d18Oseawater variations are primarily atmosphere-driven (r = ?0.46; 1923?1998; and r = ?0.67 for detrended values).
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.
Annual values are most enriched or depleted 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 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.
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.
The annual range in coral ?18O is larger than expected based on SST alone, using published coral ?18O-SST dependencies of between 0.17??i¿½C?1 and 0.23??i¿½C?1 [Gagan et al., 2012]. Previous studies in the Mentawai Islands chain have shown that ocean-atmosphere climate coupling acts to enhance the IOD-related climate signal captured by coral ?18O over that expected from SST anomalies alone [Abram et al., 2007, 2008]. The sharp 18O enrichments that characterize the winter isotopic maximum in the South Pagai coral similarly support an additional and complementary rainfall modulation of the coral record during the IOD season.
On average, SSS values vary from 33.5 psu in the wet season to 34.4 psu in the dry season, yielding a small seasonal variability of only 0.9 psu or less [Shen et al., 2005b]. The seasonal change in bay water d18O was only 0.2060.05& in 1993 and 1995 [Shen et al., 2005b], accounting for <17% of the seasonal d18Ocoral variation. A negligible correlation (R520.03) between precipitation and d18Ocoral is observed (supporting information Figure S2). Hence, the d18Ocoral data primarily reflect seasonal SST changes of about 58C (IGOSS SST; Figure
Correlation of Tarawa d18O record with local SST on monthly (0.62), seasonal (0.67) and annual (0.78) timescales compared to local rainfall on monthly (0.39 or 0.59), seasonal (0.47 or 0.66), and annual (0.59 or 0.80) timescales.
Very poor signal
inter-annual temperature variations are related to the SO activity, and, although relatively small, these will tend to magnify the SO signal in 
the coral d18O.
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.
Temperature is not significant (r= 0.08)
Furthermore, correspondence with an independent index   of   monsoon   strength demonstrates  that  the  coral  successfully  captures the  large-scale  aspects  of  Asian  monsoon behavior
Effects of evaporative related to circulation are secondary influence on hydrological balance
Correlation between d18O and pollen data
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.
Temperature fractionations between calcite and lake water were considered and are thought to be minor (0.25%/1C).
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).
If changes in vegetation surrounding Lake Wandakara were mainly driven by rainfall, ?13CC30 and ?DC30 should also be positively correlated: during intervals of drought shifts toward C4 ecosystems would occur, recorded by enriched ?DC30 values. However, ?13CC30 and ?DC30 are clearly negatively correlated (Fig. 5). In fact, large positive shifts in ?13CC30 are associated with depleted ?DC30 values, and peak ?13CC30 from AD 1200 to 1440 occurs during an interval of regionally wet climate conditions (Russell et al., 2007, Russell and Johnson, 2007). This suggests that the abrupt shifts toward C4 vegetation were not caused by climate but instead by human activities on the landscape, presumably forest clearance for agriculture (Ssemmanda et al., 2005).
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.
A lowering of lake level by 0.5m would turn the lake into a topographically closed basin. Under such conditions, evaporation becomes the main  process of water loss, and lake-water continues to become 18Oenriched  until d18O of the evaporating vapor equals d18O of the input (Gat and Levy, 1978; Leng and Marshall, 2004). In contrast, a more positive water balance than the present is expected to  shorten the lake?s water-residence time, reducing the influence of evaporation on the lake?s isotope composition (Craig, 1961). Thus  the d18OCc record can be used to infer qualitative changes in  effective moisture.
Lake fed by large rivers, whose d18O correlate with d18Op
Monitoring of the isotopic composition of modern lake water and precipitation shows that, at present, the lake water traces the isotopic composition of precipitation, modified by a slight evaporative enrichment during the open-water season and a minor snowmelt signal.
An adjustment of ?0.2?/i¿½C increases d18Odiatom by 1? from 25 ka to 16 ka and by 0.5? during the late-glacial period and Younger Dryas, values still less than that during the late Holocene (Fig. 3).
Relatively depleted dDwax values at Lake Malawi from ~3.1 ka to present likely reflect the modern hydrological controls on dDprecip, with source changes constituting a more limited role than the amount effect and overall intensity of the hydrological cycle.
In the absence of contamination sources [units 1,2 4a and 4b], the two primary factors controlling d18Ocalcite values are moisture balance (precipitation minus evaporation) and the isotopic composition of lake waters (Griffiths et al. 2002)
discussion in relation to other proxies for upper sediments
Variations in Yellow Lake past lake-water-?18O are interpreted in two ways. When the basin was over-flowing, past lake-water-?18O values are interpreted similarly to  Bison Lake that is in terms of the seasonal balance of precipitation.  When the basin closed, past lake-water-?18O values are interpreted  as secondary effects of summer evaporation: higher lake-water ?18O values reflect more summer evaporation, or less effective moisture  (precipitation?evaporation), whereas lower values reflect less evapo-  ration, or increased effective moisture.
Increased seasonality (hotter and drier summers, cool wet winters) lead to periods of increased E/P, and increased variability in ostracod ?18O.
Bottom line: more snowmelt providing source water to plants causes pool of plant source water to be more 2H-depleted. "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."
Bottom line: more snowmelt providing source water to plants causes pool of plant source water to be more 2H-depleted. "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."
Bottom line: more snowmelt providing source water to plants causes pool of plant source water to be more 2H-depleted. "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."
Bottom line: more snowmelt providing source water to plants causes pool of plant source water to be more 2H-depleted. "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."
Bottom line: more snowmelt providing source water to plants causes pool of plant source water to be more 2H-depleted. "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 cause of the transition to closed-basin conditions may have been geomorphic rather than climatic (but probably less important after 2ka)
see interpretation for independentParameter1
see interpretation for independentParameter1
The effective moisture (ratio of precipitation to evaporation) is considered as the main factor controlling the ?D values of lipid n-alkanes in terrestrial plants (Polissar and Freeman, 2010). In a previous study, the ?D record from a loess profile in the Chinese Loess Plateau displayed a strong correlation to changes in the magnetic susceptibility (MS) over the past 130 ka, and the isoto- pic variation was thought to be strongly affected by aridity (Liu and Huang, 2005). Furthermore, a humidity control experiment in the field verified the correlation between D enrichment and relative humidity (McInerney et al., 2011). A study of lipids from the Santa Barbara Basin from the past 1400 years also revealed the dD of mid-chain acids to be partially correlated with existing data of drought severity (Li et al., 2011).
The effective moisture (ratio of precipitation to evaporation) is considered as the main factor controlling the dD values of lipid n-alkanes in terrestrial plants (Polissar and Freeman, 2010). In a previous study, the dD record from a loess profile in the Chinese Loess Plateau displayed a strong correlation to changes in the magnetic susceptibility (MS) over the past 130 ka, and the isoto- pic variation was thought to be strongly affected by aridity (Liu and Huang, 2005). Furthermore, a humidity control experiment in the field verified the correlation between D enrichment and relative humidity (McInerney et al., 2011). A study of lipids from the Santa Barbara Basin from the past 1400 years also revealed the dD of mid-chain acids to be partially correlated with existing data of drought severity (Li et al., 2011).
changes in rainfall amount are imprinted in the isotopic composition of botryococcenes, with negative (posi- tive) ?D values co-occurring with wetter (drier) periods.
"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."
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."
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."
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).
Air temperature has 2 influences: "air temperature is important for the elevation of the zone of maximum rainfall (ZMR) on Mt. Kenya" and for soil/lake evaporation. "Recent drying may have also been locally enhanced by a recent rise in temperature (via an upward shift in the ZMR and/or increased evaporation of soil water)."
Aragonite precipitation occurred after ad 1580
Periodically low d18O and d13C values from individual subsamples
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 winter snowmelt (relative to total water used by plants) would result in lower 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 winter snowmelt (relative to total water used by plants) would result in lower deltaD values.
This modern variability in dDwax is strongly influenced by soil water evaporation (17), and it is possible that changes in potential evapotranspiration could also impact paleo records.
This modern variability in dDwax is strongly influenced by soil water evaporation (17), and it is possible that changes in potential evapotranspiration could also impact paleo records.
This modern variability in ?Dwax is strongly influenced by soil water evaporation (17), and it is possible that changes in potential evapotranspiration could also impact paleo records.
This modern variability in dDwax is strongly influenced by soil water evaporation (17), and it is possible that changes in potential evapotranspiration could also impact paleo records.
This modern variability in dDwax is strongly influenced by soil water evaporation (17), and it is possible that changes in potential evapotranspiration could also impact paleo records.
This modern variability in ?Dwax is strongly influenced by soil water evaporation (17), and it is possible that changes in potential evapotranspiration could also impact paleo records.
The carbonate isotope values also show an up core trend to more positive values with a particularly marked step in d13C values at 125 cm from ~-3 per mil to ~+2 per mil, in part explained by the shift in mineralogy; aragonite is 1.9 per mil (0.6 per mil) more positive in d13C (d18O) compared with calcite precipitated in waters with the same temperature and isotopic composition (Grossman, 1984; Grossman and Ku, 1986). Oxygen and hydrogen isotope ratios of the lake waters collected in 2007 show a clear evaporative signal (Supplementary Figure 1, available online), and d18O trends to more positive values likely represents a shift to more evaporative dominant conditions given the associated change from calcite to aragonite (e.g. Jones et al., 2006).
Hydrologically closed lake (Talbot 1990; Leng & Marshall 2004)
Transition from the most positive dD values during the Younger Dryas to the most negative dD values during the early Holocene, followed by a return to more positive values during the middle to late Holocene indicate that moisture availability, moisture source, or most likely a combination of both varied between these time periods, with the wettest interval during the early Holocene.
Today, Lake Miragoane is not a closed lake.
Interpretation of early Holocene (9000 until 7300 14C yrBP) oxygen isotopic results in terms of E/P is at odds with the strong evidence for moist conditions based on the presence of extensive lowland forest. The discrepancy between early Holocene pollen and 18Oresults in the PetenItza core can be reconciled if lakewater 18O is assumed to have been controlled by both regional climate and the changing surface area/volume ratio of the filling lake
higher d18O values should correlate with lower water levels, as inferred from the diatoms, indicating periods of reduced precipitation. In fact, the opposite appears to be the case - these two indicators tend to have a slight positive correlation rather than a negative one.
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.
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.
A. islandica appears to pre-cipitate its aragonitic shell matrix in oxygen isotopic equilib-rium with ambient seawater (e.g., Weidman et al. 1994). Thus,oxygen isotope values ofA. islandicashell material reflect sea-water temperature and seawater isotopic composition (relatedto salinity) (Marsh et al. 1999; Sch€one et al. 2004; Wanamakeret al. 2009).
Plant to plant differences in fractionations between dD values of n-alkyl compounds and their precipitation source may introduce the greatest error in interpreting rainfall amount.
Plant to plant differences in fractionations between dD values of n-alkyl compounds and their precipitation source may introduce the greatest error in interpreting rainfall amount.
Plant to plant differences in fractionations between dD values of n-alkyl compounds and their precipitation source may introduce the greatest error in interpreting rainfall amount.
Plant to plant differences in fractionations between dD values of n-alkyl compounds and their precipitation source may introduce the greatest error in interpreting rainfall amount.
Plant to plant differences in fractionations between dD values of n-alkyl compounds and their precipitation source may introduce the greatest error in interpreting rainfall amount.
Plant to plant differences in fractionations between dD values of n-alkyl compounds and their precipitation source may introduce the greatest error in interpreting rainfall amount.
Plant to plant differences in fractionations between dD values of n-alkyl compounds and their precipitation source may introduce the greatest error in interpreting rainfall amount.
Plant to plant differences in fractionations between dD values of n-alkyl compounds and their precipitation source may introduce the greatest error in interpreting rainfall amount.
Temperature equation of Bohm et al. [2000]. 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.
Temperature equation of Bohm et al. [2000]. 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.
Oxygen isotope values (d18O) are measured in %VPDB for aragonite and % SMOW for water. Ratios of Sr/Ca are measured in mmolmol1, temperature (T) is in C, and salinity (S) is in psu. Equation (3) is constructed from surface waters (<50 m) of the return flow of the N. Atlantic Gyre between 45N and 31N. During transport between these latitudes, this water mass begins adopting the properties of the SMW [Krauss, 1986; Ottens, 1991]. By combining and rearranging these equations to reconstruct the salinity record preserved in Bahamas sclerosponges,
Oxygen isotope values (d18O) are measured in %VPDB for aragonite and % SMOW for water. Ratios of Sr/Ca are measured in mmolmol1, temperature (T) is in C, and salinity (S) is in psu. Equation (3) is constructed from surface waters (<50 m) of the return flow of the N. Atlantic Gyre between 45N and 31N. During transport between these latitudes, this water mass begins adopting the properties of the SMW [Krauss, 1986; Ottens, 1991]. By combining and rearranging these equations to reconstruct the salinity record preserved in Bahamas sclerosponges,
d18O of the leaf water takes part in synthesis of photosynthates.
calibration with instrumental record
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.