There is considerable zonal variability in tropical Pacific climate on mean annual, seasonal, and interannual time scales. To investigate how these zonal climate differences translate into stable isotope space, the stable water isotope and precipitation variability at three grid cells across the tropical Pacific that correspond to the Galápagos archipelago, the island of Kiritimati, and Palau are examined. These locations represent the zonal differences in eastern, central, and western equatorial Pacific climate, respectively, and contain well-known proxy isotope records [Dunbar et al., 1994; Evans et al., 1998; Morimoto et al., 2002; Nurhati et al., 2009; Sachs et al., 2009; Wu and Grottoli, 2010]. Figure 7 shows the mean climatology of precipitation and δ18Op for each site. In both the average model climatology and observations, the Galápagos are driest of the three sites, with a wet season that peaks in February-April and little rain during the rest of the year. The simulations overestimate Galápagos precipitation, particularly the amplitude of the wet season. GNIP station precipitation is much higher than GPCP precipitation for the Galápagos, partly because the GNIP station is located at a higher, relatively moister elevation (194 m); the highlands of the Galápagos receive more precipitation due to orographic penetration of the stable inversion layer and associated stratocumulus cloud deck [Colinvaux, 1984]. Along with overestimating wet season precipitation, on average, models simulate δ18Op values that are also too negative in the wet season. Generally, Galápagos δ18Op values are lowest in observations and simulations from February to April, indicating a climatological amount effect. February and March are the wettest months in the averaged model simulation and in observations, but April has the lowest δ18Op values in observations, and the models simulate the lowest δ18Op values in March and April.
 Like the Galápagos, Kiritimati is also a relatively dry site, but precipitation is higher in the dry period from June to December, reducing the amplitude of the seasonal cycle of precipitation compared to the Galápagos. The climatology of GNIP δ18Op from Kiritimati also shows a smaller amplitude seasonal cycle compared to the Galápagos, with the lowest values in January-April, when precipitation peaks, again indicating a climatological amount effect. On average the simulations overestimate precipitation for Kiritimati throughout the year. Along with simulating too much precipitation, the models simulate δ18Op values that are too negative. The models also produce a δ18Op climatology with a more pronounced seasonal cycle than the limited GNIP data for the site.
 Palau, the westernmost and wettest of the three sites, has a wet season coinciding with early boreal summer, from May to July, as it is located at 6°N, the northernmost of the three sites. The model average underestimates the amplitude of the seasonal cycle of precipitation at Palau, and shows the driest months occurring in April and August, while observations show the driest months are March-April and November-December. Like precipitation, average model δ18Op values also have a lower amplitude seasonal cycle compared to observed δ18Op values. Modeled δ18Op values are lowest from June to December, despite the May-July peak in precipitation, highlighting the reduced importance of local precipitation amount on δ18Op values at this site.
 The strength of the amount effect varies across model simulations at the three tropical Pacific sites considered here, and is generally overestimated by all simulations. Figure 8 shows the correlation coefficient between monthly δ18Op and precipitation values and the linear regression coefficient between monthly δ18Op and precipitation values (in ‰/mm/d), at Galápagos, Kiritimati and Palau for each model simulation and observations. Correlation and regression coefficients from station observations are similar for the three sites, with regression coefficients of ~ –0.2‰ /mm/d, and correlation coefficients that range between –0.4 and –0.6. However, the correlation and regression coefficients vary widely from simulation to simulation, and are too strong overall, as apparent in Figures 1 and 2. Many models simulate the strongest amount effect magnitude for the Galápagos, whereas the weakest modeled amount effect is at Palau.
 The relationships between δDp and δ18Op at each site can provide additional information on the relationship between water isotopes and climate, particularly the impact of kinetic effects related to evaporation [Gat, 1996]. Figure 9 shows the meteoric water lines and deuterium excess values derived from the linear regression lines of both simulated and observed monthly δ18Op and δDp values. Simulated meteoric water lines (MWLs) and d-excess values for Palau, Kiritimati, and Galápagos overlap one another and cluster around the global average values of 8 and 10 for the MWL and deuterium excess (d-excess), respectively. Simulated MWL slopes and d-excess values for the Galápagos are lower compared to values for Palau and Kiritimati. Compared to observations, the simulations underestimate MWL and d-excess values in the Galápagos. At Palau, simulated MWL and d-excess values also diverge from observations, with the simulations overestimating MWL and d-excess values. Simulated MWL and d-excess values for Kiritimati diverge most strongly from observations, and are much higher than observed values.
 Lower local MWL and d-excess values, such as those at Kiritimati, may indicate a stronger evaporative influence on falling rain [Jouzel, 1986; Gat, 1996]. The simulations of higher MWL and d-excess values, compared to the observed values at Kiritimati, suggests the models do not produce enough rain evaporation in the central tropical Pacific. The same model deficiency occurs in Palau, where simulated local MWL and d-excess values are higher than those observed. In the Galápagos, simulated local MWL and d-excess values are lower than observed values, suggesting the models may produce too much evaporation in this region. However, the Galápagos GNIP station is located in a higher-elevation, more humid site that may have reduced evaporation compared to sea level. An additional time series of precipitation isotopes from near sea level in the Galápagos would be useful to explore altitude-induced evaporative effects in the islands.
4.1 Local δ18Op Variability: Local or Regional Hydroclimate Proxy?
 Recent analyses of precipitation and δ18Op values in the western tropical Pacific and parts of monsoon Asia have demonstrated that local δ18Op and local precipitation amount often do not share a strong relationship [Cobb et al., 2007; Kurita et al., 2009; LeGrande and Schmidt, 2009; Dayem et al., 2010; Moerman et al., 2013]. Rather, vapor transport history and the degree of vapor parcel distillation are implicated as the main controls on δ18Op variability, such that western tropical Pacific and east Asian δ18Op are variables that reflects aspects of regional-scale atmospheric circulation, rather than just local rainfall amount [Cobb et al., 2007; Kurita et al., 2009; LeGrande and Schmidt, 2009; Dayem et al., 2010; Moerman et al., 2013]. In this section, we investigate monthly anomalies of δ18Op and precipitation variability at each site to assess how local δ18Op and precipitation values relate to precipitation variability across the entire tropical Pacific basin.
 Given recent observations and modeling results for other parts of the western tropical Pacific, we hypothesize that simulated δ18Op at Palau will be better correlated to regional, rather than local precipitation [Kurita et al., 2009]. Palau precipitation, on the other hand, will likely only correlate with local precipitation, rather than basin-wide precipitation anomalies. We find that the relationship between Palau δ18Op, Palau precipitation, and tropical Pacific precipitation is not consistent among model simulations (Figure 10). In the 7 of out the 12 simulations and 3 out of the 4 nudged simulations—CAM2, ECHAM4, MIROC, GISS nudged, GSM, GSM nudged, and HadAM3—the relationship between Palau δ18Op and basin-wide precipitation is stronger than the relationship between Palau precipitation and basin-wide precipitation, especially in the central and eastern tropical Pacific. In GENESIS, GISS, and HadCM3, both Palau δ18Op and precipitation are only correlated with local precipitation. In LMDZ and LMDZ nudged, both δ18Op and precipitation at Palau have strong relationships with precipitation across the tropical Pacific. None of the simulations accurately reproduce the observed lack of relationship between Palau δ18Op anomalies and local, as well as large-scale, precipitation anomalies. Palau is located near the eastern edge of the warm pool and provides a particular challenge for models to capture precipitation and transport processes near steep gradients in SST. Indeed, the lack of a relationship between large-scale tropical Pacific precipitation and Palau δ18Op in the observational data suggests the model simulations broadly fail to capture the hydrology of the region.
Figure 10. Map of correlation coefficients between monthly anomalies of precipitation and monthly anomalies of δ18Op values at the grid cell representing Palau (black star) and monthly anomalies of precipitation across the tropical Pacific. (left) Palau δ18Op and gridded precipitation. (right) Palau precipitation and gridded precipitation. Only values that are significant at the two-tailed 95% confidence interval are plotted.
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 Recent analysis of a 4 year data set of monthly δ18Op from Palau demonstrates that local δ18Op is correlated with regional (rather than local) precipitation and large-scale convergence and divergence associated with the seasonal march of the ITCZ [Kurita et al. 2009]. However, as seen in Figure 10, observed monthly anomalies of δ18Op at Palau are also not correlated with regional precipitation anomalies; overall correlations are generally weak across the western Pacific. Strong correlations are observed when we include the seasonal cycle in the δ18Op data, correlating monthly δ18Op and monthly tropical Pacific precipitation, rather than the monthly anomalies (see Figure S1 in the supporting information). In this case, we find that Palau δ18Op is negatively correlated with precipitation north of the equator across the tropical Pacific, and positively correlated with precipitation south of the equator across the tropical Pacific. All model simulations also capture this north-south pattern of correlations when the seasonal cycle is included in the precipitation and δ18Op time series, with the exception of the “free” simulations of GISS and GSM.
 The reason for the stronger relationship between monthly Palau δ18Op and regional, rather than local, precipitation is thought to relate to changes in large-scale convergence and divergence [Kurita et al. 2009]. These atmospheric variables share a relationship with δ18Op as they can influence water vapor isotope values through changes in the degree of vapor parcel distillation, and upwind rain evaporation [Lawrence et al., 2004; Risi et al., 2010]. As divergence and convergence are highly seasonal in this region, they likely impart a stronger seasonal cycle to Palau δ18Op values, whereas precipitation lacks strong seasonality and is defined by interannual variability (Figure 11).
Figure 11. Time series of average model simulations of simulated model δ18Op (dotted line) and precipitation (solid line) for (A) Galápagos (B) Kiritimati, and (C) Palau.
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 Both δ18Op and precipitation anomalies at Kiritimati are strongly correlated with precipitation across the central and eastern equatorial Pacific in every model simulation except HadCM3, in which case only local precipitation, not δ18Op, is correlated with central equatorial Pacific rainfall (Figure 12). Although both δ18Op and precipitation at Kiritimati are highly correlated with precipitation across a broad swath of the central and eastern equatorial Pacific, the relationship between δ18Op and precipitation at Kiritimati and western tropical Pacific precipitation varies more from model to model. There is a strong relationship, with increased precipitation and lower δ18Op values at Kiritimati coinciding with decreased precipitation and higher δ18Op values in the western tropical Pacific, in CAM2, MIROC, LMDZ, LMDZ nudged, and HadAM3. In ECHAM4 nudged, GENESIS, GISS, GISS nudged, GSM, GSM nudged, and HadCM3 there is no relationship between either Kiritimati δ18Op or Kiritimati precipitation and western tropical Pacific precipitation, suggesting controls on δ18Op are related more to precipitation than atmospheric circulation.
Figure 12. Map of correlation coefficients between monthly anomalies of precipitation and monthly anomalies of δ18Op values at the grid cell representing Kiritimati (black star) and monthly anomalies of precipitation across the tropical Pacific. (left) Kiritimati δ18Op and gridded precipitation. (right) Kiritimati precipitation and gridded precipitation. Only values that are significant at the two-tailed 95% confidence interval are plotted.
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Figure 13. Map of correlation coefficients between monthly anomalies of precipitation and monthly anomalies of δ18Op values at the grid cell representing Galápagos (black star) and monthly anomalies of precipitation across the tropical Pacific. (left) Galápagos δ18Op and gridded precipitation. (right) Galápagos precipitation and gridded precipitation. Only values that are significant at the two-tailed 95% confidence interval are plotted.
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 The observed spatial relationship between Kiritimati precipitation anomalies and monthly precipitation anomalies across the tropical Pacific includes positive correlation coefficients across the central and eastern tropical Pacific and negative correlations in the western equatorial Pacific. As the Kiritimati GNIP δ18Op time series is from the early 1960s, prior to the period of satellite observations, we are unable to compare observed δ18Op values with GPCP2.2 data. However, based on our comparison to observed precipitation, CAM2, MIROC, LMDZ, LMDZ nudged, and HadAM3 seem to best simulate the relationship between local Kiritimati precipitation variability and precipitation across the tropical Pacific. These simulations all have a negative correlation between Kiritmati precipitation anomalies and western equatorial Pacific precipitation anomalies. Given the strong coherence between modeled precipitation and δ18Op in Kiritimati, we conclude δ18Op in the central equatorial Pacific is a proxy for local rainfall amount. Yet, as observed by the strong correlation coefficients between both δ18Op and precipitation at Kiritimati with basin-scale precipitation in the simulations, both δ18Op and precipitation may be considered proxies for large-scale atmospheric variability as well. This is because interannual variability in local precipitation amount at Kiritimati is driven by changes to horizontal convergence associated with large-scale Walker Circulation, which also modulates hydroclimate across much of the tropical Pacific.
 Galápagos precipitation and δ18Op values are correlated with precipitation in the eastern equatorial Pacific in all model simulations. However, the relationship between both Galápagos precipitation and δ18Op and west-central to western tropical Pacific precipitation is generally weak, and completely absent in GENESIS3, GISS, GSM, GSM nudged, and HadCM3. In MIROC, LMDZ, LMDZ nudged, GISS nudged, and HadAM3, Galápagos precipitation has a stronger relationship with western Pacific precipitation compared to Galápagos δ18Op. Furthermore, in GENESIS3, GSM, and GSM nudged, there are stronger correlation coefficients between Galápagos precipitation and precipitation further west relative to the correlation coefficients between Galápagos δ18Op and gridded precipitation. That is, the relationship between Galápagos δ18Op and large-scale precipitation is more confined to the far eastern equatorial Pacific, compared to the relationship between Galápagos precipitation and basin-wide precipitation. Thus, in the eastern tropical Pacific, model simulations suggest precipitation, rather than δ18Op, may be a better indicator of basin-scale hydroclimatic variability, while δ18Op reflects regional precipitation.
 Galápagos precipitation observations from the GPCP2.2 data set indicate a strong, positive relationship between Galápagos precipitation anomalies and eastern to central equatorial Pacific precipitation anomalies (Figure 13). Galápagos precipitation anomalies also have a significant negative correlation with precipitation in the western-central equatorial Pacific, centered around 150°E. A similar spatial pattern of significant correlation coefficients is also found between monthly anomalies of GNIP Galápagos δ18Op data and precipitation. The relationship between Galápagos precipitation and tropical Pacific precipitation is slightly stronger in the western equatorial Pacific and SPCZ compared to the relationship between Galápagos δ18Op and precipitation in these regions. Thus, there is a stronger relationship between Galápagos precipitation and remote tropical Pacific precipitation in eight of the twelve model simulations that is also weakly expressed in observations. Thus, in the eastern equatorial Pacific, direct proxies of precipitation amount, rather than proxies for δ18Op, may serve as more robust indicators of large-scale tropical Pacific precipitation.