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    Mean winter temperature (°C) and precipitation amount (mm month−1) from the observations and IsoGSM simulations: (a) CRUTS3.1 and (b) IsoGSM temperatures and (c) GPCP and (d) IsoGSM precipitation amounts.

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    Spatial differences in mean climate fields between IsoGSM simulations and observations, for winter (a) temperature (°C) and (b) precipitation (mm month−1).

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    (a) Mean winter precipitation δ18O [‰, Vienna Standard Mean Ocean Water (VSMOW)] of USNIP stations (colored circles) and IsoGSM (background pattern). Sites with labels are used for the analysis of interannual variability in Fig. 4. (b) Comparison between USNIP precipitation δ18O of stations and corresponding IsoGSM precipitation δ18O values. The gray line indicates the 1:1 line.

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    Interannual variability of winter PNA index, temperature (°C), precipitation amount (mm month−1), and precipitation δ18O (‰, VSMOW). The filled and open symbols denote the observations and IsoGSM simulations, respectively. Error bars indicate interannual standard deviations calculated using all the available winter observed and simulated data at each station.

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    Correlation maps of the modeled winter precipitation δ18O anomaly (‰, VSMOW) and climate index for the (a) PNA, (b) ENSO, (c) PDO, and (d) AMO. The areas enclosed by the gray lines show the field significance at the p < 0.1 level.

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    Spatial difference in modeled winter precipitation δ18O (‰, VSMOW) between the positive and negative PNA periods. The areas enclosed by the gray lines show the field significance at the p < 0.1 level. The boxes represent the NW (45°–55°N and 120°–95°W; blue box) and SE (37°–42°N and 95°–75°W; red box) poles used to calculate the time series of the area-averaged anomaly values.

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    Time series of the modeled winter precipitation δ18O anomaly (‰, VSMOW) in the (a) NW and (c) SE poles defined in Fig. 6 and (b) the PNA anomalies.

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    Composite anomaly maps of temperature (°C) and precipitation (mm month−1) fields for the negative and positive PNA periods based on IsoGSM simulations for (a),(b) temperature and (c),(d) precipitation. The areas enclosed by the gray lines show the field significance at the p < 0.1 level.

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    Composite anomaly maps of circulation and moisture fields for the negative and positive PNA periods based on the IsoGSM simulations: (a),(b) 500-hPa geopotential height (m) and (c),(d) the vertically integrated moisture flux (kg m−1 s−1). The arrows denotes moisture flux anomaly vectors. The areas enclosed by (top) gray lines or (bottom) shaded areas show the field significance at the p < 0.1 level.

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    Time series of the isotope difference index (‰, VSMOW; black curve) and climate index anomalies: (a) PNA, (b) PDO, (c) ENSO 3.4, and (d) AMO. The isotope difference index (Δδ18O) is defined by the difference in the δ18O anomaly between the NW and SE poles.

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Pacific–North American Teleconnection Controls on Precipitation Isotopes (δ18O) across the Contiguous United States and Adjacent Regions: A GCM-Based Analysis

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  • 1 Tianjin Key Laboratory of Water Resource and Environment, Tianjin Normal University, Tianjin, China, and Atmosphere and Ocean Research Institute, University of Tokyo, Kashiwa, Chiba, Japan
  • 2 Atmosphere and Ocean Research Institute, University of Tokyo, Kashiwa, Chiba, Japan
  • 3 Department of Geology and Geophysics, University of Utah, Salt Lake City, Utah
  • 4 Department of Biological Sciences, University of Alaska Anchorage, Anchorage, Alaska
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Abstract

The Pacific–North American (PNA) teleconnection pattern has a strong influence on North America’s winter climate, but much less is known about how the PNA pattern controls precipitation isotopes (e.g., δ18O) across the United States. In this study, an isotopically equipped atmospheric general circulation model (isoGSM) is used to investigate how divergent phases of the PNA affect precipitation δ18O values across the United States. A simulation using observational climate and isotope data over the United States is evaluated first. The simulation explains 84% of the spatial variability of winter precipitation δ18O, with an overestimation in the northern Rocky Mountains and the Great Lakes. Temporally, the simulation explains 29%–81% of the interannual variability of winter precipitation δ18O, with typically a higher explained variance in the east than the west. The modeled winter precipitation δ18O exhibits a clear northwest–southeast (NW–SE) dipolelike pattern in response to shifts in the PNA pattern, with the center of positive polarity in the northwestern United States and the Canadian prairies and the center of negative polarity over the Ohio River valley. This dipolelike spatial pattern is a result of the difference in atmospheric circulation and moisture sources associated with the PNA pattern. These results highlight the importance of the PNA-associated circulation dynamics in governing precipitation isotope patterns across the United States. This understanding improves our ability to interpret paleoclimate records of water isotope/hydrologic change across the United States with a much greater appreciation of regional traits. The robust antiphase oscillation in precipitation isotopes in response to shifting the PNA pattern provides a promising opportunity to reconstruct the past variability in the PNA pattern that may be recorded in ice cores, tree rings, lake sediments, and speleothems.

Corresponding author address: Zhongfang Liu, Atmosphere and Ocean Research Institute, University of Tokyo, General Research Bldg. 211a, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568, Japan. E-mail: liuzf406@gmail.com

Abstract

The Pacific–North American (PNA) teleconnection pattern has a strong influence on North America’s winter climate, but much less is known about how the PNA pattern controls precipitation isotopes (e.g., δ18O) across the United States. In this study, an isotopically equipped atmospheric general circulation model (isoGSM) is used to investigate how divergent phases of the PNA affect precipitation δ18O values across the United States. A simulation using observational climate and isotope data over the United States is evaluated first. The simulation explains 84% of the spatial variability of winter precipitation δ18O, with an overestimation in the northern Rocky Mountains and the Great Lakes. Temporally, the simulation explains 29%–81% of the interannual variability of winter precipitation δ18O, with typically a higher explained variance in the east than the west. The modeled winter precipitation δ18O exhibits a clear northwest–southeast (NW–SE) dipolelike pattern in response to shifts in the PNA pattern, with the center of positive polarity in the northwestern United States and the Canadian prairies and the center of negative polarity over the Ohio River valley. This dipolelike spatial pattern is a result of the difference in atmospheric circulation and moisture sources associated with the PNA pattern. These results highlight the importance of the PNA-associated circulation dynamics in governing precipitation isotope patterns across the United States. This understanding improves our ability to interpret paleoclimate records of water isotope/hydrologic change across the United States with a much greater appreciation of regional traits. The robust antiphase oscillation in precipitation isotopes in response to shifting the PNA pattern provides a promising opportunity to reconstruct the past variability in the PNA pattern that may be recorded in ice cores, tree rings, lake sediments, and speleothems.

Corresponding author address: Zhongfang Liu, Atmosphere and Ocean Research Institute, University of Tokyo, General Research Bldg. 211a, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568, Japan. E-mail: liuzf406@gmail.com

1. Introduction

Winter climate in North America is strongly modulated by the Pacific–North American (PNA) teleconnection (Wallace and Gutzler 1981). This large-scale climate mode represents a Rossby wave train spanning the North Pacific and North America. The amplification or dampening of this climatological quasi-stationary wave can be measured using an index derived from a linear combination of standardized 500-hPa geopotential height anomalies (Wallace and Gutzler 1981). A positive PNA (PNA+) index is associated with enhanced meridional flows due to an amplified ridge over western North America and a deepened trough over the southeastern United States. In contrast, a negative PNA (PNA−) index generally corresponds to stronger zonal flows due to a damped ridge–trough pattern. Although the PNA pattern is a natural internal atmospheric variation, it may be strongly influenced by sea surface temperature (SST) variability associated with the El Niño–Southern Oscillation (ENSO), Pacific decadal oscillation (PDO; Mantua et al. 1997), and Atlantic multidecadal oscillation (AMO; Kerr 2000) (e.g., Horel and Wallace 1981; Straus and Shukla 2002; Yu and Zwiers 2007; Zhang and Delworth 2007).

The PNA pattern influences the direction and strength of the prevailing circulation and, thus, affects airmass frequency, moisture variability, temperature, and precipitation across many parts of continental North America. The positive phase of the PNA is associated with an increase in tropical–subtropical airmass frequency and a northward shift of the polar jet over northwestern North America (Sheridan 2003), leading to warmer winter temperatures in the region and drier conditions in the Pacific Northwest (Leathers et al. 1991). Whereas, the enhanced influence of the continental polar air mass and a southward displacement of the polar jet over the eastern United States (Sheridan 2003) result in colder winter temperatures in the region and drier conditions in the Ohio River valley (Leathers et al. 1991; Coleman and Rogers 2003). The opposite is true for the negative PNA phase. Phases of the PNA are also closely linked to variability in mountain snowpack (e.g., Abatzoglou 2010), streamflow (e.g., Rogers and Coleman 2003), and lake level (e.g., Wiles et al. 2009), as well as the occurrence of wildland fires (Trouet et al. 2006) in North America due to changes in the PNA-driven temperature and precipitation regimes. For example, positive PNA yields warmer temperature and weaker precipitation in the western United States, leading to a decline of snowpack in the region (Jin et al. 2006), but may increase the frequency of wildland fire activity (Trouet et al. 2006).

Despite the importance of the PNA pattern in determining hydroclimate variability across the continental North America, only a few studies have quantified how the PNA affects precipitation isotope distributions in space and time (Birks and Edwards 2009; Liu et al. 2010, 2012). Understanding the means by which the modern PNA affects the spatial and temporal patterns of precipitation isotopes across the United States is especially important as we seek to fully interpret climate signals recorded in natural isotopic archives. For instance, analysis of paleo–water isotope proxies preserved in lake sediments, tree rings, and speleothems is increasingly employed to infer PNA fluctuations (Kirby et al. 2001; Edwards et al. 2008; Field et al. 2010; Hubeny et al. 2011). Fully dissecting the climate details stored in paleoisotope proxies thus requires a thorough understanding of the modern water isotopic attributes associated with PNA phases and associated mechanisms. Recently, the PNA pattern has been shown to be positively correlated with precipitation isotopes in western Canada (Birks and Edwards 2009). We have previously extended this work and shown that precipitation isotope correlations with the PNA pattern exhibit an east–west seesaw mode across the continental United States, with positive correlations in the west and negative correlation in the east (Liu et al. 2011), and most recently mapped fluctuations in airmass boundaries associated with positive and negative PNA phases (Liu et al. 2012).

Each of these prior studies linking the PNA and precipitation isotope patterns has indicated that variations in atmospheric circulation, moisture sources, and transport processes influence the spatial patterns of precipitation isotopes. However, our ability to assess the stability of PNA–isotope relationships over space and time has been limited. In particular, the isotopic impact of interactions with other large-scale climate teleconnections such as ENSO, PDO, and AMO that modulate the PNA pattern is still poorly known. This is in part due to the relatively short length of available precipitation isotopic records (only one site with records greater than 15 yr) and their limited spatial coverage and density.

State-of-the-art general circulation models (GCMs) equipped with water isotope tracers can provide a more rigorous investigation of the mechanistic links between precipitation isotope fields and large-scale climate pattern changes (e.g., Field 2010). In this study, we hypothesize that the variability of winter precipitation isotopes across the United States is controlled by the PNA-associated atmospheric circulation. To test this hypothesis, we examine the associations of observed PNA patterns with winter precipitation δ18O and climate fields in an isotope-enabled GCM (IsoGSM; Yoshimura et al. 2008). We first quantify the performance of IsoGSM over the United States using observed climate and isotope datasets. This includes an evaluation of the performance of IsoGSM in terms of mean winter temperature, precipitation, and precipitation δ18O. In addition, we assess the performance of IsoGSM in simulating the interannual variability of winter temperature, precipitation, and precipitation δ18O at eight isotope monitoring sites. Finally, the modeled precipitation δ18O and meteorological parameters are used to understand how the PNA pattern controls the spatial and temporal patterns of winter precipitation δ18O. The objectives of this study are 1) to quantify the impact of the PNA pattern on winter precipitation isotope ratios, 2) to identify the optimal regions within the United States in which to compile δ18O-based proxy records of past PNA variability, and 3) to elucidate how δ18O–PNA relationships might be used to reconstruct paleo-PNA variability.

2. Data and methods

a. Observations

Global community databases (e.g., the Global Network for Isotopes in Precipitation, GNIP) contain a small number of precipitation isotope data available for the study region and time period, and here we use data from the U.S. Network for Isotopes in Precipitation (USNIP; Welker 2000, 2012). USNIP consists of weekly composite δ18O and δ2H values along with precipitation amount and air temperature at 73 sampling sites distributed across the United States (Welker 2000, 2012). We obtained weekly data from eight USNIP stations with relatively long-term (10–15 yr) records representing samples collected from 1989 through 2003. One published USNIP dataset that includes six consecutive years (1989–94) of monthly means of precipitation δ18O across the United States was also used for this study (Vachon et al. 2010b). Raw weekly or monthly δ18O values were used to calculate precipitation-amount-weighted isotope ratios for the winter season (January–March, JFM).

To assess the performance of the model, gridded monthly temperature data from the Climatic Research Unit (CRUTS3.1; Mitchell and Jones 2005; available online at http://www.cru.uea.ac.uk/cru/data/hrg) and precipitation data from the Global Precipitation Climatology Project (GPCP; Adler et al. 2003; available online at http://jisao.washington.edu/data/gpcp) were used in this study. The CRUTS3.1 dataset covers the global land surface (excluding Antarctica) with 0.5° grid cells, spanning a period from 1901 to 2010, and is constructed through interpolation of instrumental measurements. The GPCP precipitation dataset is based on a combination of land-based rain gauge data and satellite-based estimates, providing an estimate of rainfall amount over regions with sparse direct observations. The dataset has a global coverage pattern with 1° × 1° resolution spanning a period from 1979 to 2010.

We used the average winter (JFM) PNA index from the Climate Prediction Center (CPC; available online at http://www.cpc.ncep.noaa.gov) for the period 1950–2010 to identify winters characterized by strong positive or negative PNA pattern. Selecting winters lying outside of a threshold value of +/− 0.5 standard deviations, we identified 14 positive (1981, 1983, 1984, 1986, 1987, 1988, 1992, 1995, 1998, 2000, 2001, 2003, 2004, and 2010) and 6 negative (1979, 1982, 1989, 1990, 2002, and 2009) PNA winters. We also analyzed the isotopic data relative to indices for three large-scale climate modes related to sea surface temperature (SST) structure (ENSO, PDO, AMO). The ENSO index (Niño-3.4) was from the Climate Prediction Center (CPC), while the PDO and AMO indices were from the Climate Diagnostics Center (CDC, available online at http://www.esrl.noaa.gov/psd/data/climateindices/list).

b. IsoGSM model

The isoGSM model is a current-generation global atmospheric general circulation model, which incorporates water isotopes into the Scripps Experimental Climate Prediction Center’s global spectral model. The model uses 28 vertical levels and a spectral horizontal resolution of T62. The model is forced with prescribed sea surface temperatures and sea ice conditions from the optimal interpolation (OI) daily dataset (Yoshimura and Kanamitsu 2008) provided by the National Centers for Environmental Prediction (NCEP) (Reynolds et al. 2007). To better reproduce the observed circulation pattern, the simulations were spectrally nudged at 6-h intervals to wind and temperature fields from the NCEP–Department of Energy (DOE) Reanalysis (Kanamitsu et al. 2002). Such nudging provides more realistic simulations of the climate and isotope information compared with free-running simulations. This simulation provides a 32-yr (1979–2010) precipitation isotope and climate record on a 1.85° × 1.85° global grid.

c. Precipitation isotope and climate pattern analysis

Climate and isotopic output from the IsoGSM are used to investigate the spatial and temporal patterns of winter precipitation δ18O for the PNA+ and PNA− periods. To identify where the isotopic expression of the PNA is strongest and how the PNA controls the isotopic patterns, we explore the isotope–PNA correlation and climate field anomalies (e.g., geopotential height, winds, moisture flux, temperature, and precipitation) that are associated with variations in the PNA mode. To examine the robustness of the spatial precipitation isotope responses to large-scale climate modes, correlation analysis between the simulated precipitation isotope field and the climate indices is performed at each grid point and a two-sided Student’s t test (p < 0.1) is used to check the statistical significance. A two-sample t test is also used to test the significance of simulated local isotope differences between the positive and negative PNA patterns. The statistical significance of the composite average winter climate field anomalies is determined at each grid point using Student’s t test (p < 0.1) to evaluate the null hypothesis that the actual composite anomaly is zero.

3. Results

a. Model evaluation

1) Mean climate and isotope fields

We compare the long-term mean winter temperature and precipitation from the IsoGSM simulation with the CRUTS3.1 temperature and GPCP precipitation data to evaluate the performance of the model (Fig. 1). It should be noted that the model was nudged toward the NCEP–DOE reanalysis atmospheric data (wind speed and surface temperature) with a spectral nudging technique for large-scale (larger than 1000 km) waves (Yoshimura and Kanamitsu 2008). A comparison between model and CRU temperature, therefore, may not be necessary, but it still provides confirmation of the model performance. As shown in Figs. 1a,b, the IsoGSM simulation accurately reproduces the large-scale temperature field within the whole domain defined (R2 = 0.97). The modeled mean winter temperature is −1.58°C, slightly lower than the observed value of −1.25°C. This small offset between the model and observations is derived mainly from a systematic underestimate over most parts of the study domain and an overestimation in the Rocky Mountains (Fig. 2a). This underestimate is also found in other models (Rawlins et al. 2012) and may be related to different processes affecting the surface radiative budget, such as the representation of the planetary albedo. In addition, the warm bias over the Rocky Mountains region may be linked with the relatively coarse resolution of the model that limits its ability to resolve finer-scale topographic effects.

Fig. 1.
Fig. 1.

Mean winter temperature (°C) and precipitation amount (mm month−1) from the observations and IsoGSM simulations: (a) CRUTS3.1 and (b) IsoGSM temperatures and (c) GPCP and (d) IsoGSM precipitation amounts.

Citation: Journal of Climate 27, 3; 10.1175/JCLI-D-13-00334.1

Fig. 2.
Fig. 2.

Spatial differences in mean climate fields between IsoGSM simulations and observations, for winter (a) temperature (°C) and (b) precipitation (mm month−1).

Citation: Journal of Climate 27, 3; 10.1175/JCLI-D-13-00334.1

The modeled winter precipitation pattern well represents that of GPCP precipitation (R2 = 0.90; see Figs. 1c,d). Both the simulation and observations show similar continental effects, with reduced precipitation inland from the West Coast and the Gulf of Mexico. The largest amount of precipitation occurs in the Pacific Northwest, with values of 239.8 and 237.5 mm month−1 for the simulation and observations, respectively. For the driest areas, with precipitation less than 20 mm month−1 (mainly the desert Southwest and northern Great Plains), the model is also in good agreement with the observations. On average, the modeled value is 70.5 mm month−1, slightly higher than the GPCP’s precipitation result (56.5 mm month−1). The wet bias occurs in most parts of the United States except New England, the Gulf coast states, and the west coast (Fig. 2b). Overestimation of winter precipitation in western North America is not unique to the GSM model, but includes over 75% of the models used in the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC; Christensen et al. 2007), due in part to the complex topography of the region (Duffy et al. 2003).

Figure 3 shows a comparison of the USNIP amount-weighted winter mean δ18O with corresponding IsoGSM values. The simulation successfully captures the large-scale patterns described by the observational data, although local deviations between station and modeled values are apparent in some regions (e.g., the northern Rocky Mountains and Great Lakes region) (Fig. 3a). The spatial δ18O pattern exhibits a clear geographical effect, with δ18O values decreasing from the low-latitude coastal regions and lower elevations toward high-latitude inland and inland mountainous regions. The most 18O-depleted values occur in the northern Rocky Mountains, with the most 18O-enriched values along the Gulf of Mexico and over Florida. A spatial correlation analysis shows that the model explains 84% of the observed spatial variance in winter precipitation δ18O, with a mean positive bias of 1.7‰ (Fig. 3b). The positive bias largely reflects an overestimation of values in relatively cold regions such as the northern Rocky Mountains and the Great Lakes. A similar overestimation over the northern Rocky Mountains was also found in other model studies (Schmidt et al. 2005) and was ascribed to the model’s coarse topographic resolution (Berkelhammer et al. 2012). The coarse resolution can cause lower elevations in the model than in the observations, which leads to higher precipitation δ18O values because the falling raindrops are subject to more enrichment in 18O via evaporation and equilibration with surrounding vapor that have enriched isotope values relative to vapor at higher elevations (Buenning et al. 2012). Overestimation in the Great Lakes region may be due to the inadequate representation of the strong lake effect in this region, which is not well resolved by IsoGSM and contributes to winter precipitation with relatively low isotopes ratios (e.g., Burnett et al. 2004).

Fig. 3.
Fig. 3.

(a) Mean winter precipitation δ18O [‰, Vienna Standard Mean Ocean Water (VSMOW)] of USNIP stations (colored circles) and IsoGSM (background pattern). Sites with labels are used for the analysis of interannual variability in Fig. 4. (b) Comparison between USNIP precipitation δ18O of stations and corresponding IsoGSM precipitation δ18O values. The gray line indicates the 1:1 line.

Citation: Journal of Climate 27, 3; 10.1175/JCLI-D-13-00334.1

2) Interannual variability of winter climate and isotope responses to the PNA pattern

As shown above, IsoGSM produces a realistic representation of the spatial pattern of the mean climate and isotope fields across the United States, with modest regional biases. Here, we also evaluate the interannual variability in the climate and isotope responses to the PNA pattern. We use time series data from eight USNIP stations (Fig. 3a) to conduct a direct comparison between the simulation and observations on an annual time scale. The winter climate and isotope data from IsoGSM and USNIP sampling stations in the western (MN16, OR10, CO02, and CA99) and eastern (VT99, NY99, WV04, and WI99) United States are plotted in Fig. 4. Modeled temperature variations for all sites exhibit a significant (p < 0.0001) and strong (R = 0.90–0.99) correlation with CRU data. Except for the MN16 site, the western sites tend to exhibit a warmer winter temperature (1.2°–4.6°C) for the simulation than the observations. Simulated precipitation amount values are moderately (R values vary from 0.57 to 0.93) correlated with the observations at the USNIP sites, and all correlations are significant at the 95% confidence level or higher. Except for two sites (VT99 and NY99) in the northeastern United States, the model tends to overestimate the precipitation amount (15–70 mm month−1) and variability.

Fig. 4.
Fig. 4.

Interannual variability of winter PNA index, temperature (°C), precipitation amount (mm month−1), and precipitation δ18O (‰, VSMOW). The filled and open symbols denote the observations and IsoGSM simulations, respectively. Error bars indicate interannual standard deviations calculated using all the available winter observed and simulated data at each station.

Citation: Journal of Climate 27, 3; 10.1175/JCLI-D-13-00334.1

Temporal correlations between simulated and observed precipitation δ18O values vary among sites (R from 0.54 to 0.90; Fig. 4), with all correlations significant at p < 0.1 (at CA99 and WV04) or p < 0.05 (the other six stations). Although the simulation reproduces winter precipitation δ18O values and the interannual variability reasonably well, IsoGSM tends to produce a positive bias of 1‰–2.5‰, especially for the western stations that are characterized by a high interannual variability of observed winter precipitation δ18O. Overestimation of δ18O values in the western sites may reflect the influence of the warm temperature bias (Fig. 2a).

In addition to reproducing observed temporal climate and isotope variations, IsoGSM simulates previously documented patterns of the PNA-related variation. The modeled winter temperature exhibits regionally contrasting correlations with the PNA across the United States, with weak but partly significant positive correlation (R > 0.54, p < 0.05) in the west and nonsignificant negative correlation in the east, reproducing the general pattern documented in the observations (Leathers et al. 1991). The modeled precipitation shows less sensitivity to the PNA pattern, but the correlation between precipitation and the PNA index is only statistically significant at CA99 (R = 0.67, p < 0.01). The modeled precipitation δ18O show a spatially opposite response to the change in the PNA. At the western sites except CA99, δ18O values are in phase with the PNA variability, though the correlations are not statistically significant (p > 0.1). The eastern sites exhibit a clear inverse correlation between modeled δ18O and the PNA variability, but are only significant at VT99 (R = −0.46, p < 0.1) and WV04 (R = −0.60, p < 0.05). Such a similar dipolelike structure is also identified for the observed precipitation isotope data in the United States (Liu et al. 2011), indicating the spatial structure of how the PNA affects winter precipitation isotope variability at the region scale.

b. PNA pattern influence on precipitation δ18O

Figure 5a presents the correlation pattern of the winter precipitation δ18O field with the PNA index for the 32-yr period 1979–2010. Precipitation δ18O field responses to the PNA pattern display a NW–SE-oriented dipolelike configuration, with significant (p < 0.1) positive correlations over the northwestern United States and southwestern Canada and significant (p < 0.1) negative correlations over southeastern North America. In the NW region, the strongest (R > 0.3) correlations are centered over parts of the Pacific Northwest, eastern Montana, and the Canadian prairies. In contrast, the SE region exhibits stronger and more consistent correlations, with the highest (R < −0.4) correlations located over the Ohio River valley and the northeastern United States.

Fig. 5.
Fig. 5.

Correlation maps of the modeled winter precipitation δ18O anomaly (‰, VSMOW) and climate index for the (a) PNA, (b) ENSO, (c) PDO, and (d) AMO. The areas enclosed by the gray lines show the field significance at the p < 0.1 level.

Citation: Journal of Climate 27, 3; 10.1175/JCLI-D-13-00334.1

The contrasting spatial patterns associated with the PNA are even more apparent when the isotope difference between the periods of PNA+ and PNA− is presented (Fig. 6). The NW and SE regions of the dipole both exhibit widespread, significant δ18O differences between the positive and negative PNA periods, with magnitudes of 1.5‰–3.0‰ and opposite sign.

Fig. 6.
Fig. 6.

Spatial difference in modeled winter precipitation δ18O (‰, VSMOW) between the positive and negative PNA periods. The areas enclosed by the gray lines show the field significance at the p < 0.1 level. The boxes represent the NW (45°–55°N and 120°–95°W; blue box) and SE (37°–42°N and 95°–75°W; red box) poles used to calculate the time series of the area-averaged anomaly values.

Citation: Journal of Climate 27, 3; 10.1175/JCLI-D-13-00334.1

While these general dipolelike patterns describe the PNA-related precipitation isotope variation across the United States and adjacent regions, there are still some uncertainties. For example, in the NW region, weak positive or even negative correlations occur in California and the Great Basin, as well as throughout the mid-Rockies (Figs. 5a and 6). This suggests that other (non-PNA associated) processes may play a more dominant role in these regions (see the discussion).

Based on the assessment of the spatial patterns, we defined two poles of maximal isotope response to the PNA pattern (Fig. 6; NW pole, 45°–55°N and 120°–95°W, blue box; and SE pole: 37°–42°N and 95°–75°W, red box). For each of these poles, we extracted mean winter precipitation δ18O anomalies for each year from the IsoGSM simulations and compared these with a 32-yr PNA index time series (Fig. 7). Values from the NW pole show coherent multiannual fluctuations in winter δ18O anomalies that are positively correlated and synchronous with the fluctuations in the PNA index throughout the period observed (R = 0.66, p < 0.0001). The δ18O anomalies for the SE pole also exhibit temporally coherent variability but are anticorrelated with fluctuations in the PNA pattern (R = −0.55, p < 0.001).

Fig. 7.
Fig. 7.

Time series of the modeled winter precipitation δ18O anomaly (‰, VSMOW) in the (a) NW and (c) SE poles defined in Fig. 6 and (b) the PNA anomalies.

Citation: Journal of Climate 27, 3; 10.1175/JCLI-D-13-00334.1

c. SST influence on precipitation δ18O

To evaluate the relative impacts of SST forcings and the PNA circulation patterns on precipitation isotopes, we perform a spatial correlation analysis of the simulated winter precipitation δ18O with the indices of several climate modes with strong SST expression (Fig. 5). The pattern of precipitation δ18O variability associated with the PNA is somewhat similar to those associated with ENSO, PDO, and AMO modes, especially for the PDO, which show a dipolelike pattern with positive correlation in the northwest and negative correlation in the southeast (Fig. 5). Despite the overall similarity of the winter precipitation δ18O responses to the PNA and these large-scale climate modes, the differences between the PNA and SST correlations are prominent in strength across both of the poles of action identified for the PNA-isotope variation. The PDO correlation pattern exhibits the most similarity to that of the PNA, but the correlations for both poles are clearly weaker, with the NW center shifting west and south and the SE center shifting east (diminished area of positive correlations) (Figs. 5a,c). The correlation patterns for ENSO and AMO are unlike that of the PNA with the isolines oriented nearly east–west (Figs. 5b,d). The SE poles of the ENSO and AMO correlations are substantially decreased and limited to the southeastern coastal states, with their centers over the Gulf coast. In contrast, the NW poles of the correlations are stronger for the ENSO and weaker for the AMO, and spatially broader than those for the PNA correlation.

We also evaluated the explanatory power of these SST forcings for winter precipitation isotope variability over the two previously defined poles of the PNA-δ18O action (NW and SE). The results indicate that only ENSO is significantly (p < 0.05) correlated with precipitation δ18O in the NW pole, explaining 15% of the variance in precipitation δ18O, far lower than the explanatory power (43%) of the PNA pattern. For the SE pole, the PDO and ENSO patterns account for 26% (p < 0.005) and 19% (p < 0.05) of the variance in precipitation δ18O, respectively, still lower than the explanatory power (31%) of the PNA. These findings suggest that circulation-related changes associated with the PNA pattern are a leading cause of the dipolelike spatial pattern of winter precipitation isotope variability we have identified here.

4. Discussion

a. PNA pattern control on precipitation δ18O

Our model results identify a NW–SE-oriented spatial dipole pattern in precipitation δ18O values across the United States that is associated with PNA phases: positive correlations in the northwest and negative correlations in the southeast. This pattern is similar to, but refines, that initially identified by Liu et al. (2011), based on spatially sparse observational records. This spatially dichotomous δ18O pattern bears some resemblance to the spatial pattern of the PNA-associated temperature variation (Figs. 8a,b), which may suggest that local temperature is an important driver in defining spatial precipitation isotope patterns (Kohn and Welker 2005; Vachon et al. 2010a,b). However, previous studies have also demonstrated that spatial and temporal variability in the precipitation isotope ratios across the United States is controlled to a large extent by the variation in moisture source conditions and patterns of moisture transport in the atmosphere (e.g., Welker 2000; Friedman et al. 2002; Sjostrom and Welker 2009; Liu et al. 2010; Vachon et al. 2010a, 2010b). Our recent work incorporating a spatially dense but temporally limited network of precipitation isotope sampling stations has similarly suggested that variations in the synoptic circulation associated with the PNA pattern are an important control on the spatial patterns of precipitation isotopes (Liu et al. 2012).

Fig. 8.
Fig. 8.

Composite anomaly maps of temperature (°C) and precipitation (mm month−1) fields for the negative and positive PNA periods based on IsoGSM simulations for (a),(b) temperature and (c),(d) precipitation. The areas enclosed by the gray lines show the field significance at the p < 0.1 level.

Citation: Journal of Climate 27, 3; 10.1175/JCLI-D-13-00334.1

To explore the circulation associated with the modeled PNA phase isotope anomalies, we used climate fields (temperature, precipitation, geopotential height, wind, and moisture flux) derived from the IsoGSM simulations to construct composite anomaly maps for the negative and positive PNA phases. All the anomalies are computed as the departures from the 1979 to 2010 base-period means. The composite-anomaly patterns of 500-hPa geopotential height and moisture flux fields for negative and positive PNA periods clearly show the PNA-driven shift in atmospheric structure and circulation (Fig. 9). During the negative PNA periods, significant (p < 0.1) negative and positive geopotential height anomalies dominate the northwest and southeast parts of the study domain, respectively (Fig. 9a). This pattern leads to anomalously strong northwesterly moisture flux in the northwest and anomalously strong southerly moisture flux in the southeast (Fig. 9c). The enhanced northwesterly moisture flux is related to the frequent intrusion of cool Arctic and North Pacific air masses to the northwest, leading to significant cold temperature anomalies (exceeding 1.5°C in magnitude) in the region (Fig. 8a). At the same time, southward displacement of the polar jet (Leathers et al. 1991) also results in more precipitation than normal in the region (Fig. 8c). These cool Arctic and North Pacific air masses are typically more depleted in 18O and yield precipitation with low δ18O values (Welker 2000; Friedman et al. 2002) (Fig. 5a). In contrast, the anomalously strong southerly moisture flux in the eastern United States is associated with more tropical moisture from the Gulf of Mexico (Fig. 9c), tending to result in warm temperature anomalies (Fig. 8a) and positive precipitation amount anomalies (except for the Gulf coast and Florida) (Fig. 8c). Increased precipitation derived from 18O-enriched Gulf of Mexico–sourced moisture (e.g., Simpkins 1995; Welker 2000) is likely responsible for the high precipitation δ18O values in this region (Fig. 5a). Other processes associated with this southerly moisture transport are also more likely to contribute to these higher δ18O values, including reduced rainout due to the proximate moisture sources and enhanced evaporative recharge of air masses traversing the Gulf plains.

Fig. 9.
Fig. 9.

Composite anomaly maps of circulation and moisture fields for the negative and positive PNA periods based on the IsoGSM simulations: (a),(b) 500-hPa geopotential height (m) and (c),(d) the vertically integrated moisture flux (kg m−1 s−1). The arrows denotes moisture flux anomaly vectors. The areas enclosed by (top) gray lines or (bottom) shaded areas show the field significance at the p < 0.1 level.

Citation: Journal of Climate 27, 3; 10.1175/JCLI-D-13-00334.1

During the positive PNA periods, however, strong positive and negative geopotential heights dominate the northwest and southeast, respectively (Fig. 9b). This accentuated ridge-and-trough pattern increases southerly flows in the Pacific Northwest and stronger northeasterly moisture flux across the mid- and southeastern continent (Fig. 9d). This synoptic setup increases the flux of marine moisture from the tropical or subtropical Pacific into the northwestern United States, resulting in warm temperature anomalies and negative precipitation anomalies in the region (Figs. 8b,d). The 18O-enriched vapor associated with this tropical or subtropical air (Friedman et al. 2002; Berkelhammer et al. 2012) also contributes to positive precipitation δ18O anomalies in the region (Fig. 5a). The higher temperature and prevailing subsidence due to the strong high pressure ridge further strengthen the isotopic enrichment of precipitation in this region.

In contrast, the accentuated trough over the southeastern United States during the positive PNA phase is associated with more cold Arctic and North Atlantic air masses plunging south into the mid-Atlantic and the southeast, leading to reduced moisture flux (Figs. 9b,d) and slightly cold temperature anomalies in the region (Fig. 8b), as well as reduced precipitation in the Ohio River valley (Fig. 8d). The negative precipitation δ18O anomalies in the southeast may also result from these anomalous strong northeasterly flows, which bring 18O-depleted moisture to the region (e.g., Sjostrom and Welker 2009). In addition, enhanced isotope fractionation during stratiform precipitation occurring in association with this atmospheric configuration (Gedzelman and Lawrence 1990; Burnett et al. 2004) may contribute to the anomalously low precipitation δ18O values in the region.

Weak or even negative correlations over California, the northern Great Basin, and the mid–Rocky Mountains (Figs. 5a and 6) appear to be an exception to the dipolelike pattern of spatial precipitation δ18O distribution. Recent study based on IsoGSM found that local effects such as condensation height, raindrop evaporation, and vapor–precipitation equilibration had a large influence on the interannual variability of precipitation δ18O in California and the interior of the western United States (Buenning et al. 2013). Thus, large uncertainties in the δ18O–PNA relationships in these regions may be related to these local effects.

b. SST forcing of precipitation δ18O

Large-scale climate modes associated with SST such as ENSO, PDO, and AMO modulate the PNA pattern, and can also affect atmospheric circulation and storm tracks over North America, thus affecting precipitation isotope ratios in the region (e.g., Berkelhammer and Stott 2008; Welker 2012). Although these SST forcings can impose an atmospheric response resembling the PNA pattern over the Pacific and North American sector (McCabe et al. 2004), our results reveal that the isotopic expressions of these large-scale climate modes are clearly different in strength and spatial extent from that of the PNA pattern (Fig. 5). The Pacific SST (ENSO and PDO) has a stronger influence on the winter precipitation isotope pattern across the United States and its correlation with precipitation δ18O bears a closer resemblance to that of the PNA. The physical mechanisms responsible for this resemblance between the ENSO or PDO correlation and the PNA correlation are easily understood. The El Niño and positive PDO phases are typically associated with warmer SST anomalies over the northeastern Pacific, which results in a deepened Aleutian low (surface expression of the PNA pattern is the Aleutian low) in the region and then excites enhanced Rossby waves in the upper atmosphere (Yu et al. 2007; Yu and Zwiers 2007). The relationship of the precipitation isotope with the AMO can be largely attributed to changes in meridional oceanic heat flux and the strength of the Aleutian low (Zhang and Delworth 2007). During the positive AMO phase, a warm North Atlantic SST anomaly leads to weakened northward atmospheric eddy heat transport and a storm track over both the North Atlantic and North Pacific Oceans, thus deepening the Aleutian low and the positive PNA pattern (Zhang and Delworth 2007). Such an AMO-forced PNA-like pattern eventually leads to a precipitation δ18O pattern that is spatially similar to that of the PNA.

In contrast to the low-frequency SST (PDO and AMO) forcings, the ENSO shows a relatively strong influence on North American precipitation δ18O, especially for the northwest pole (Fig. 5). Previous simulation studies also suggest that precipitation isotope anomalies largely reflect climatic changes in the tropics (e.g., Schmidt et al. 2007), probably indicating that the leading influence on continental-scale precipitation δ18O patterns is the variation in circulation driven by tropical Pacific SST forcing (e.g., Seager et al. 2005; Cook et al. 2007). The low-frequency SST forcings appear to have a stronger isotopic expression in the southwestern United States. This is also reflected in a recent paleoclimate study that finds the speleothem δ18O record in California is highly correlated with the PDO (McCabe-Glynn et al. 2013). However, given that our simulation only covers one complete cycle of the PDO (a roughly 20-yr cycle) and parts of the AMO cycle (a roughly 60-yr cycle) (Kitzberger et al. 2007), the correlations presented here may have some degree of uncertainty about the low low-frequency SST forcing. Thus, the results that low-frequency SST forcing modulation of the PNA has an impact on the precipitation isotope across the United States may need to be further verified by future observations or analyzing long climate model integrations.

c. Implications for the past PNA studies

A framework for understanding the controls of the PNA pattern on precipitation isotope ratios at continental scales can present a number of opportunities for the study of paleoclimates. Previous studies have suggested that systematic climate oscillation over the past millennium in North America relates to the changes in the intensity of meridional circulation, as seen today with the PNA pattern (Kreutz et al. 1997; D’Arrigo et al. 2006; Edwards et al. 2008). The PNA pattern influences hydroclimate variability across North America by modulating midtropospheric atmospheric circulation patterns and storm tracks. A reconstruction of past PNA pattern variation will represent a step toward constraining changes in circulation patterns (e.g., Kirby et al. 2001), precipitation (e.g., Hubeny et al. 2011), and aridity (e.g., Nelson et al. 2011).

There are a number of existing Holocene isotopic records from northwestern and southeastern North America that can be interpreted and evaluated within the PNA framework. For example, the isotopic records from ice cores (Holdsworth et al. 1992; Fisher et al. 2004) and lake sediment readings (Anderson et al. 2005) in the Canadian Yukon all show a dramatic drop in δ18O values at the end of the Little Ice Age (LIA; a.d. 1850), which has been attributed to a deepening Aleutian low leading to more isotopic distillation due to the long-distance transport of the southerly moisture (Fisher et al. 2004). Based on our present study, however, this dramatic drop in δ18O during the mid–nineteenth century may reflect an enhanced dominance of the negative PNA pattern (weaker Aleutian low), which favors northerly moisture flux (18O depleted) in northwestern North America (Field et al. 2010).

The robust isotopic expression of the modern PNA pattern in the NW and SE poles identified here suggests that the Ohio River valley and Pacific Northwest are optimum regions within which to compile δ18O-based proxy records of past PNA variability. Although the identification of this dipole signature indicates that paleo-isotope records could be a relatively good archive of information on the PNA variability in either the NW or SE poles, this approach would involve significant uncertainty due to the relatively low explanatory power of the regional δ18O–PNA correlations. For example, the IsoGSM analysis suggests that only 43% and 31% of the isotopic variances in paleoprecipitation within the NW and SE domains (respectively) might be attributed to variations in the PNA pattern.

The isotope data from the NW and SE poles contain substantial multiannual variability, characterized by an antiphase oscillatory pattern of behavior (Fig. 7), allowing us to improve the potential accuracy of the PNA reconstructions by combining information from these two poles. Following Liu et al. (2011), we construct an isotopic difference index expressing the differences in the winter precipitation δ18O anomalies in the NW and SE poles (Fig. 10). Compared to correlations based on data from each pole independently, the isotopic difference index provides a higher-fidelity signal of the PNA oscillation (R2 = 0.68, p < 0.0001), demonstrating that the combination of isotope paleorecords from sites within the NW and SE poles could provide a robust proxy of past PNA variability. The isotopic difference index also shows some sensitivity to the SST forcing, especially ENSO and PDO, which each explains about 30% (p < 0.001) of the interannual variance of the isotopic difference index. As suggested above, this association may result from modulation of the PNA atmospheric pattern by SST anomalies.

Fig. 10.
Fig. 10.

Time series of the isotope difference index (‰, VSMOW; black curve) and climate index anomalies: (a) PNA, (b) PDO, (c) ENSO 3.4, and (d) AMO. The isotope difference index (Δδ18O) is defined by the difference in the δ18O anomaly between the NW and SE poles.

Citation: Journal of Climate 27, 3; 10.1175/JCLI-D-13-00334.1

The North America continent has a variety of natural archives such as tree rings, lake sediments, and speleothems capable of recording past annual to millennium-scales changes in the PNA pattern. Some reconstructions of the position and amplitude of the ridge–trough in western and eastern North America over the past millennium have suggested that the PNA-like atmospheric variability has influenced precipitation variability over the late Holocene (e.g., Edwards et al. 2008; Hubeny et al. 2011). Paleo-isotopic records from the two PNA isotope poles identified here, with sufficient resolution and chronological certainty, could provide a promising opportunity to reconstruct past variability in the PNA pattern. Our ability to reconstruct past changes in the PNA pattern from such isotope proxy records rests in part on the assumption that the spatial isotopic expression of the PNA pattern has not changed significantly over the last millennium. Although we cannot conclusively show that this is the case, our study has for the first time demonstrated that coherent, stable patterns of isotopic variation are associated with PNA pattern variability throughout a multidecadal atmospheric GCM simulation and across intervals of contrasting SST patterns.

d. Comparison with previous study by Liu et al. (2011)

The present study considerably extends our previous work both geographically and temporally by using a GCM simulation. The results presented here further reinforce previous findings. First, we find that precipitation δ18O has the strongest positive correlations with the PNA index over the northwestern United States, which is a result not previously obtained by measurement due to poor data quality. This finding explicitly defines a NW–SE dipolar pattern depicting spatial precipitation isotope variation in response to shifts in the PNA pattern. Second, we investigate the space–time responses of the precipitation isotope to the SST forcings and demonstrate that each of the modes discussed herein (ENSO, PDO, and AMO) exhibits a significant relationship with δ18O in some regions, and that much of this influence is associated with PNA-type shifts in circulation over North America.

5. Conclusions

We have investigated PNA-associated atmospheric circulation controls on precipitation isotope distributions across the United States using an atmospheric GCM equipped with explicit water isotope diagnostics. The simulations successfully capture observed spatial and temporal patterns of winter climate and precipitation isotopes and their response to the PNA pattern. The modeled precipitation δ18O features a clear NW–SE dipolelike pattern of response to the PNA pattern, with the center of positive polarity in the northwestern United States and Canadian prairies and the center of negative polarity in the Ohio River valley. This pattern reflects the influence of PNA-associated variation in moisture sources and storm tracks on winter precipitation isotope ratios. The negative PNA periods are associated with the increasing occurrence of northerly flows over the northwestern United States, leading to anomalously low precipitation δ18O values in the region. In the SE region, however, this circulation pattern increases the dominance of tropical moisture from the Gulf of Mexico and results in anomalously high precipitation δ18O values. Positive PNA periods are linked with the introduction of high-δ18O subtropical–tropical air masses to the NW region and the intrusion of cold 18O-depleted Arctic air masses into the SE region.

The results presented here may be of use in designing research projects to study past changes in the PNA pattern. Our work identifies two regions within North America in which precipitation δ18O values are optimally sensitive to the PNA pattern. Generation of paleorecords from these regions could ultimately improve our understanding of paleo-PNA variations and, conversely, our recognition of the influence of the PNA on isotope distributions in these regions should be incorporated into paleo-isotope proxy data interpretations focusing on other climate variables (e.g., temperature). Due to robust antiphase variations of precipitation isotope ratios in the two poles identified here, reconstructions based on the combination of well-dated proxy materials from these two regions will provide enhanced power to deconvolve paleo-PNA pattern variations from changes in other local climate variables or global teleconnection patterns.

Acknowledgments

This work was supported by a JSPS fellowship, National Natural Science Foundation of China grant (41171022), MEXT/RECCA/SALSA, the MEXT/Sousei Program, and National Science Foundation Grants EAR-0602162 to Bowen and ESH 0080952 to Welker. We thank Bin Guan, Takao Kawasaki, and Chan Eun-Chul for discussion. We also thank three anonymous reviewers for their constructive comments and suggestions.

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