1. Introduction
Precipitation variability in present and future climate scenarios has important impacts on local and regional hydrology, ultimately influencing water availability. Year-to-year fluctuations in precipitation, especially winter snowfall in areas with complex mountainous terrain, create challenges for water managers. The Great Basin (GB) watershed is located in the interior western United States (comprising parts of Utah, Wyoming, Idaho, Oregon, Nevada, and California) and is made up of many smaller snowpack-dominated watersheds, including that of the Great Salt Lake (GSL) in northern Utah. The GSL basin, which encompasses the Wasatch Range, is subjected to substantial interannual and multidecadal precipitation variability (Ropelewski and Halpert 1986; Wang et al. 2010, 2012). Orographic precipitation that occurs in the Wasatch Range is stored as snowpack and then delivered as runoff to the Wasatch Front Range, where more than two million people live and work.
Paleoclimate records indicate that, over most of the past millennium, droughts in the GB were generally more intense and lasted longer than those experienced in the twentieth century, which included the “severe droughts” of the 1930s and 1950s. Studies of tree ring–based drought analysis (Cook et al. 1997; Herweijer et al. 2007) have found that drought frequency has shifted from being centennial and more intense in the early millennium (AD 1000–1400) into being multidecadal and less intense in the late millennium (AD 1800–2000). To what extent such a cyclic feature may change or persist into the future is important information for water management.
The knowledge of precipitation drivers in the GSL basin, which is the largest watershed in the eastern GB, is an important tool for local water managers to guide water resource and supply and infrastructure engineering in preparation for drought or flooding. The GSL basin is situated in the transition boundary of the winter weather pattern that is forced by El Niño–Southern Oscillation (ENSO), known as the ENSO dipole or North American dipole. This positioning means that ENSO has both positive and negative effects on the precipitation received in the basin (Wise 2010; Wang et al. 2010) depending on the phase of the Pacific decadal oscillation (PDO) and possibly also the Atlantic multidecadal oscillation. For much of the twentieth century, El Niño was generally associated with a wet, cool southwestern and a dry, warm northwestern United States, while La Niña was associated with the opposite (Ropelewski and Halpert 1986; Dettinger et al. 1998). It is well known that the PDO, defined as the leading principal component of monthly north-central Pacific SST variability (poleward of 20°N; Mantua et al. 1997), has a significant effect on western U.S. precipitation. The PDO is linked to, and can modulate, the phasing of ENSO, and together they can result in prominent precipitation anomalies in the western United States (Gershunov and Barnett 1998; Gershunov et al. 1999; Mauget 2003). The PDO–ENSO coupling results in a shift of the typical dipole western U.S. precipitation pattern that is related to ENSO (Wise 2010; Brown 2011). Because the GB is situated in the transition of the ENSO dipole, which shifts its wet/dry influences based on the phase of the PDO, it is important to determine how the warming climate will affect the PDO–ENSO teleconnection and the precipitation received in the basin.
In addition to ENSO and the PDO, previous studies have revealed a predominant quasi-decadal oscillation (QDO) associated with precipitation and surface runoff in the GSL basin by examining the GSL surface elevation (Wang et al. 2010). The GSL surface elevation is significantly coherent with the Pacific QDO, defined by anomalous sea surface temperatures (SSTs) in the Niño-4 region (5°S–5°N, 160°E–150°W), which means that precipitation in the GSL basin must be phase-shifted from the Pacific QDO by a quarter phase in order to generate the coherent phase between the Niño-4 and the GSL surface elevation; this creates a lag of about three years between the Niño-4 and the precipitation (and between the precipitation and the GSL surface elevation) (Wang et al. 2010). During this transition phase of the Pacific QDO, a short-wave train pattern forms in the western tropical Pacific and emanates toward North America (Wang et al. 2011). This standing short-wave train is associated with enhanced precipitation in the Intermountain West at a quarter-phase lagged response to the Pacific QDO (i.e., a direct response to the transition-phase QDO anomalies of SSTs; Wang et al. 2011).
There has been an increased interest in the effects that climate change may have on GB hydrology (e.g., Bardsley et al. 2013; Mensing et al. 2013). Climate change assessments rely heavily on climate models. The latest generation of global climate models (GCMs) participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) contributed to the recently published Intergovernmental Panel on Climate Change (IPCC) Assessment Report 5 (AR5; Stocker et al. 2013). In addition to the atmosphere–ocean global climate models (AOGCMs), this generation of GCMs includes what are known as Earth system models (ESMs), which are AOGCMs coupled with the carbon cycle fluxes between the ocean, atmosphere, and land surface (Taylor et al. 2012). Other advances with CMIP5 include more models, an increased number of experiments and outputs, and enhanced physics packages (Taylor et al. 2012).
On interannual time scales, precipitation variability in CMIP5 models has been shown to be overenergetic compared to observed precipitation variability (Ault et al. 2012). In the western United States, the CMIP5 models tend to underestimate the precipitation variability on decadal to multidecadal time scales (Ault et al. 2012). Because the hydrology in the GB is driven both by the interannual variability (Dettinger et al. 1998) and by quasi-decadal variability (e.g., Wang et al. 2010, 2012), this study presents a CMIP5 model comparison focused on the interannual to multidecadal connections between historical Pacific Ocean SSTs and GB precipitation. Although the limitations of model ranking have been highlighted in recent research (e.g., Mote et al. 2011), identification of models that realistically capture oceanic modulation of GB precipitation over a range of time scales is important for 1) informing an objective weighting of climate projections and 2) selecting models to be used in dynamical downscaling and/or stochastic climate modeling, thus providing climate information usable for collaborators in fields including hydrology, biology, urban planning, and civil engineering. Moreover, because the projected precipitation changes over the GB differ between CMIP5 and the previous generation of models (CMIP3), as was shown in Brekke (2013), there is an urgent need to evaluate the CMIP5 models for the GB hydroclimate.
2. Data and methods
a. Data
The observational precipitation data used in this study are 1° gridded monthly global land surface precipitation data provided by the Global Precipitation Climatology Centre (GPCC), which span from January 1901 to December 2010 (Schneider et al. 2011). We use version 6 of these data in this study. The analysis domain chosen for this study follows Wang et al. (2010) and encompasses the eastern Great Basin in the western United States (37.5–42.5°N, 115°–110°W; Fig. 1). The eastern Great Basin contains the Wasatch Range, a major contributor of water flow in the Great Salt Lake basin. As noted in previous studies (e.g., Wang et al. 2010), even though the analysis domain covers more than just the eastern Great Basin, the low-frequency precipitation variability is consistent over the extent of this domain.
The portion of the Great Basin used in the study (37.5°–42.5°N, 115°–110°W). The filled circles indicate the points in the GPCC precipitation dataset that were spatially averaged for analysis. The inset map shows the location of region in the contiguous United States. The color bar indicates elevation in meters above mean sea level.
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
The portion of the Great Basin used in the study (37.5°–42.5°N, 115°–110°W). The filled circles indicate the points in the GPCC precipitation dataset that were spatially averaged for analysis. The inset map shows the location of region in the contiguous United States. The color bar indicates elevation in meters above mean sea level.
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
The portion of the Great Basin used in the study (37.5°–42.5°N, 115°–110°W). The filled circles indicate the points in the GPCC precipitation dataset that were spatially averaged for analysis. The inset map shows the location of region in the contiguous United States. The color bar indicates elevation in meters above mean sea level.
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
The observational sea surface temperature data used in this study (HadISST) are reconstructed 1° gridded monthly data based on in situ and satellite observations. The data span from January 1870 to June 2013 (Rayner et al. 2003) and were obtained from the Met Office Hadley Centre.
For inclusion in the study, CMIP5 models were required to have all-forcing historical precipitation output and sea surface temperature output, both back to at least 1900. Twenty models satisfied this criterion, and Table 1 provides the modeling center, number of ensemble members, and literature references for each. The observational data used in the analysis span 1901–2005 to align with the GPCC precipitation data starting in 1901, and the model data span 1900–2005. The year difference is negligible on account of the filtering described immediately below.
List of CMIP5 models used in this study, the number of ensemble members per model [denoted by No(s).], and their corresponding institutions. (Expansions of acronyms are available online at http://www.ametsoc.org/PubsAcronymList.)
b. Methods
To concentrate on the time scales of interest (i.e., the interannual and quasi-decadal frequencies), we filtered the monthly precipitation and SST data three different ways using a Hamming window (HW) filter (Hamming 1998). We use monthly data by following the method described in Wang et al. (2010). In this way, all seasons are included in the analysis, but the seasonal cycle is filtered out. As discussed in Wang et al. (2010), this windowed filter is most appropriately applied to short-length time series because of its ability to eliminate unwanted frequencies (Iacobucci and Noullez 2005). We applied a 3–7-yr bandpass filter to the data to isolate the high-frequency variability associated with ENSO. We applied a 10–15-yr bandpass filter to obtain the quasi-decadal signal such as that found in Wang et al. (2010).
1) Principal component analysis
We applied a low-pass 7-yr filter on the data to analyze frequencies lower than ENSO, focusing primarily on the PDO. To define the PDO, we computed the leading empirical orthogonal function (EOF; Hannachi et al. 2007) of the detrended, filtered monthly sea surface temperature data. The EOF analysis was performed over the northern Pacific Ocean (20°–66°N, 160°E–110°W) and yielded the well-known east–west dipole characterizing the PDO (e.g., Mantua et al. 1997). We set the sign of the EOF to emulate the positive (warm phase) PDO pattern and regressed the sea surface temperatures onto the associated principal component time series, which gives units of kelvin per standard deviation at each location (as opposed to the arbitrary scaling of the EOF; e.g., Thompson and Wallace 2000).
2) Correlating Great Basin precipitation with Pacific SSTs
3) North Pacific streamfunction analysis
3. Results
a. Interannual (3–7 yr) relationship between Pacific SSTs and GB precipitation
Figure 2 shows the interannual (3–7 yr) contemporaneous correlation spatial patterns between SSTs and GB precipitation for each included CMIP5 model and observations. For observations (Fig. 2u), the contemporaneous correlation map of bandpass 3–7-yr filtered Pacific SSTs and Great Basin precipitation highlights higher-frequency Pacific Ocean variability dominated by ENSO in the tropics with variability also in the extratropics. In the CMIP5 ensemble, we show in Fig. 2 only the member from each model with the map that best matches that of observations based on the area-weighted uncentered spatial correlation coefficients [Eq. (1)]. As discussed in Deser et al. (2014), internal variability within each model differs, and this is evident in each model’s area-weighted uncentered spatial correlation coefficients (crosses, Fig. 3a). Most models, including CCSM4 and CESM1(CAM5.1, FV2), exhibit a stronger than observed correlation between the tropics and GB precipitation compared to observations (cf. all panels to Fig. 2u). Despite being excessively strong in the tropics, the majority of the spatial patterns in the mapped ensemble members do resemble observations [
Contemporaneous correlation maps between bandpass-filtered 3–7-yr SSTs and Great Basin precipitation for models (a)–(t) and observations (u). The model’s ensemble member with the map that most closely matches that of the observations is shown [the match is assessed using the area-weighted uncentered spatial correlation 
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
Contemporaneous correlation maps between bandpass-filtered 3–7-yr SSTs and Great Basin precipitation for models (a)–(t) and observations (u). The model’s ensemble member with the map that most closely matches that of the observations is shown [the match is assessed using the area-weighted uncentered spatial correlation
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
Contemporaneous correlation maps between bandpass-filtered 3–7-yr SSTs and Great Basin precipitation for models (a)–(t) and observations (u). The model’s ensemble member with the map that most closely matches that of the observations is shown [the match is assessed using the area-weighted uncentered spatial correlation
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
Bar charts on the left show how well CMIP5 ensemble members capture observed spatial patterns related to Great Basin precipitation (P) and Pacific sea surface temperatures (SSTs), where the statistic used is the area-weighted uncentered spatial correlation [
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
Bar charts on the left show how well CMIP5 ensemble members capture observed spatial patterns related to Great Basin precipitation (P) and Pacific sea surface temperatures (SSTs), where the statistic used is the area-weighted uncentered spatial correlation [
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
Bar charts on the left show how well CMIP5 ensemble members capture observed spatial patterns related to Great Basin precipitation (P) and Pacific sea surface temperatures (SSTs), where the statistic used is the area-weighted uncentered spatial correlation [
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
Area weighted uncentered spatial correlations (
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
Area weighted uncentered spatial correlations (
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
Area weighted uncentered spatial correlations (
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
Bar charts on the left show the Great Basin precipitation variance for (a) bandpass 3–7-yr filtered data, (b) bandpass 10–15-yr filtered data, and (c) low-pass 7-yr data. Bar charts on the right show the variance of (d) bandpass 3–7-yr Niño-4 SSTs, (e) the PDO index of the ensemble members in (b), and (f) the PDO index of the ensemble members in (c). As in Fig. 3, the ensemble member closest to observations is indicated by the bar, and other available ensemble members are indicated by the cross symbol. The order of the models here follows the first three rows in Fig. 3.
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
Bar charts on the left show the Great Basin precipitation variance for (a) bandpass 3–7-yr filtered data, (b) bandpass 10–15-yr filtered data, and (c) low-pass 7-yr data. Bar charts on the right show the variance of (d) bandpass 3–7-yr Niño-4 SSTs, (e) the PDO index of the ensemble members in (b), and (f) the PDO index of the ensemble members in (c). As in Fig. 3, the ensemble member closest to observations is indicated by the bar, and other available ensemble members are indicated by the cross symbol. The order of the models here follows the first three rows in Fig. 3.
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
Bar charts on the left show the Great Basin precipitation variance for (a) bandpass 3–7-yr filtered data, (b) bandpass 10–15-yr filtered data, and (c) low-pass 7-yr data. Bar charts on the right show the variance of (d) bandpass 3–7-yr Niño-4 SSTs, (e) the PDO index of the ensemble members in (b), and (f) the PDO index of the ensemble members in (c). As in Fig. 3, the ensemble member closest to observations is indicated by the bar, and other available ensemble members are indicated by the cross symbol. The order of the models here follows the first three rows in Fig. 3.
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
The influences of ENSO on western U.S. precipitation have been studied extensively (e.g., Ropelewski and Halpert 1986, 1989; Halpert and Ropelewski 1992; Hidalgo and Dracup 2003; Mo and Schemm 2008). The Great Basin is situated between the precipitation dipole anomalies associated with ENSO in the western United States. The relatively weak correlations in the observations between SSTs along the equatorial Pacific and GB precipitation (Fig. 2u) support the variable association between ENSO and GB precipitation due to the shifts of the dipole cancelling out the ENSO signal. Nearly all of the models capture the connection between the ENSO region and GB precipitation, but some of them [e.g., CCSM4 and CESM1(CAM5.1, FV2)] have a stronger correlation between ENSO SSTs and GB precipitation than what is observed.
There has been a growing interest in understanding the linkages between SST variability in the North Pacific and western U.S. precipitation (e.g., Latif and Barnett 1994; Kushnir et al. 2002). The midlatitude storm track is known to be affected by sea surface temperatures in the North Pacific (e.g., Namias et al. 1988; Peng and Whitaker 1999; Frankignoul et al. 2011), and we selected CCSM4 to analyze this more closely in part because of its tightly packed set of 
Correlation maps between the (a)–(h) bandpass 3–7-yr filtered and (q)–(x) bandpass 10–15-yr filtered streamfunction and Great Basin precipitation for the observations, the average of all six ensemble members of CCSM4, and each individual ensemble member. (i)–(p) The streamfunction for the bandpass 3–7-yr filtered data for only the years of positive PDO.
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
Correlation maps between the (a)–(h) bandpass 3–7-yr filtered and (q)–(x) bandpass 10–15-yr filtered streamfunction and Great Basin precipitation for the observations, the average of all six ensemble members of CCSM4, and each individual ensemble member. (i)–(p) The streamfunction for the bandpass 3–7-yr filtered data for only the years of positive PDO.
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
Correlation maps between the (a)–(h) bandpass 3–7-yr filtered and (q)–(x) bandpass 10–15-yr filtered streamfunction and Great Basin precipitation for the observations, the average of all six ensemble members of CCSM4, and each individual ensemble member. (i)–(p) The streamfunction for the bandpass 3–7-yr filtered data for only the years of positive PDO.
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
Links between ENSO and the PDO
As noted in the introduction, there is evidence that the PDO modulates ENSO-driven precipitation anomalies over the western United States. To analyze this effect in CMIP5, we computed the 10-yr running correlation between the bandpass-filtered 3–7-yr spatially averaged Niño-4 SSTs and Great Basin precipitation for all models and the observations to depict decadal fluctuations in the ENSO teleconnection. The time series of the running ENSO–GB precipitation correlation (
(a) For observations, the blue curve shows the PDO index and the green curve shows the 10-yr running correlation between bandpass 3–7-yr filtered Niño-4 SSTs and Great Basin precipitation (
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
(a) For observations, the blue curve shows the PDO index and the green curve shows the 10-yr running correlation between bandpass 3–7-yr filtered Niño-4 SSTs and Great Basin precipitation (
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
(a) For observations, the blue curve shows the PDO index and the green curve shows the 10-yr running correlation between bandpass 3–7-yr filtered Niño-4 SSTs and Great Basin precipitation (
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
To visualize the underlying dynamics, we calculated the correlation coefficients between the 200-hPa streamfunction and GB precipitation for the years of positive PDO (and, consequently, high running correlation coefficients between Niño-4 SSTs and GB precipitation) (Fig. 6i). The trimodal pattern over the Pacific follows that described in Gill (1980) and Alexander et al. (2002) and reflects the canonical atmospheric response to El Niño (note that Fig. 6 only shows the North Pacific portion of the pattern). However, the composite map of all six CCSM4 ensemble members is much weaker than that of the observational map (Fig. 6j), and there is substantial variability among the individual members (Figs. 6k–p).
In general, the models do not capture the observed positive correlation between the PDO and 
b. Quasi-decadal relationship between Pacific SSTs and GB precipitation
The correlation maps between contemporaneous bandpass 10–15-yr filtered SST data and GB precipitation data are generally similar in spatial pattern and strength between the models, although most of them have too-strong correlations in the tropical Pacific compared to observations (Fig. 8). However, a number of the models have relatively high area-weighted uncentered spatial correlations, indicating good overall pattern matches (Fig. 3b). While model agreement with observations seems good at a low frequency, the 
Contemporaneous correlation maps between bandpass 10–15-yr filtered SSTs and Great Basin precipitation for (a)–(t) models and (u) observations. The model’s ensemble member with the map that most closely matches that of the observations is shown.
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
Contemporaneous correlation maps between bandpass 10–15-yr filtered SSTs and Great Basin precipitation for (a)–(t) models and (u) observations. The model’s ensemble member with the map that most closely matches that of the observations is shown.
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
Contemporaneous correlation maps between bandpass 10–15-yr filtered SSTs and Great Basin precipitation for (a)–(t) models and (u) observations. The model’s ensemble member with the map that most closely matches that of the observations is shown.
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
As with the high-frequency data, we calculated the 200-hPa streamfunction from the bandpass 10–15-yr filtered 200-hPa geopotential height data and correlated it with bandpass-filtered GB precipitation (Fig. 6q). In observations, the negative ψ anomaly over the western United States, which has shifted slightly farther north than the bandpass 3–7-yr map, extends zonally out into the North Pacific. The composite of the CCSM4 ensemble members captures the ψ anomaly centered just off the west coast of North America, but the westward extension of the negative ψ anomaly over the North Pacific is not apparent, reflecting marked variability in the upstream pattern among the ensemble members (Fig. 6r). For example, members such as r2 (Fig. 6t) indicate that the CCSM4 model physics capture the observed zonal upstream pattern given some specific initial conditions, but the diversity of patterns in other members suggests strong internal system variability that can affect the teleconnection pattern and GB precipitation (Deser et al. 2012, 2014).
Connectivity with the PDO
The correlation maps in Fig. 8 between bandpass 10–15-yr filtered SSTs and GB precipitation depict a PDO-like pattern in the North Pacific. To examine further how models simulate the PDO modulation of the ENSO teleconnection, we assess PDO spatial patterns and the correlation between PDO and GB precipitation. We calculated the PDO index for each model and observations following the approach described in section 2a. For each model, we chose the ensemble member that had the highest 
The correlation between the PDO index and GB precipitation is maximized at a lag of about two years for observations (Fig. 3g). Only some models capture this lagged correlation (Fig. 3g) even though most can generate realistic PDO spatial patterns (Fig. 3c). MIROC5, HadGEM2-ES, and CCSM4, the models with the highest 
c. Lagged relationship between Pacific QDO and GB precipitation
As noted in the introduction, Wang et al. (2011) described the dynamical connection between the transition phase coupling of the Pacific QDO and GB precipitation. An atmospheric wave train sets up from the western tropical Pacific, where SSTs fluctuate more strongly than in the Niño-4 region, in an arc toward western North America. To show the model representation of this transition-phase teleconnection, we used the bandpass 10–15-yr filtered data to compute correlations with GB precipitation lagging Pacific SSTs by three years, as shown in Fig. 9. The spatial patterns of these correlations are notably variable (recall that we show only the ensemble member best matching the observed pattern). The area-weighted uncentered spatial correlations decrease rapidly across the models and even become negative in some models, indicating only a weak presence of the QDO transition dynamics in CMIP5 (Fig. 3d). One ensemble member of CCSM4 has the highest pattern match in Fig. 3d (shaded bar), yet other members of CCSM4 have maps that are opposite in sign to observed patterns (crosses below zero, Fig. 3d). Generally, as the area-weighted uncentered spatial correlations decrease and become negative, the correlation between Niño-4 SSTs and GB precipitation also decreases and becomes negative (Fig. 3h), suggesting that poor simulations of the Pacific QDO lead to even poorer simulations of its teleconnection impact on GB precipitation. Among all models, CCSM4 stands out as it has some ensemble members that capture both the spatial pattern of this transition-phase teleconnection (Fig. 9c) as well as the contemporaneous relationship between Niño-4 SSTs (where the Pacific QDO manifests itself) and GB precipitation (Fig. 3h).
Lagged correlation maps between bandpass 10–15-yr filtered SSTs and Great Basin precipitation with precipitation lagging SSTs by 3 years for (a)–(t) models and (u) observations. The model’s ensemble member with the map that most closely matches that of the observations is shown.
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
Lagged correlation maps between bandpass 10–15-yr filtered SSTs and Great Basin precipitation with precipitation lagging SSTs by 3 years for (a)–(t) models and (u) observations. The model’s ensemble member with the map that most closely matches that of the observations is shown.
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
Lagged correlation maps between bandpass 10–15-yr filtered SSTs and Great Basin precipitation with precipitation lagging SSTs by 3 years for (a)–(t) models and (u) observations. The model’s ensemble member with the map that most closely matches that of the observations is shown.
Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00488.1
4. Summary and discussion
This study focuses on determining the degree to which CMIP5 models are able to simulate observed interannual to multidecadal connections between Pacific SSTs and Great Basin precipitation. All of the models performed reasonably well on the interannual time scale as far as capturing ENSO; however, not all of the models produced the observed connection between Niño-4 SSTs and precipitation in the Great Basin. This discrepancy could be due to the phase of PDO present, as PDO can alter the effect of ENSO on Great Basin precipitation (recall Fig. 7b). Most models can generate a realistic PDO, but the highly variable correlations between the simulated PDO index and GB precipitation are likely due to the too-strong variance in the PDO. For some of the models, the excessively strong correlations between bandpass 3–7-yr SSTs and GB precipitation could be because the PDO did not appear to modulate ENSO at all. In contrast, the quasi-decadal connection (i.e., a dominant mode in the GB) proved to be more difficult for the models to capture. The lagged connection between Niño-4 SSTs and GB precipitation and therefore the transition phase of the Pacific QDO, as discussed in Wang et al. (2010) and Wang et al. (2011), proves to be a challenge for the models.
Of the 20 models evaluated, CCSM4 consistently produced at least one ensemble member that compared well with observations. CCSM4 had high 
For the most part, models with more than one ensemble member performed more favorably. The models with only one ensemble member (ACCESS1.3, GFDL-ESM2G, HadGEM2-AO, HadGEM2-CC, and INM-CM4.0) had only one opportunity to capture the connection between Pacific SST variability and GB precipitation while models with more than one ensemble member had more opportunities to capture the connectivity. While this observation echoes other studies (e.g., Kirtman and Shukla 2002; He et al. 2014), the implication from this study is that the connectivity between Pacific SSTs and GB precipitation is highly sensitive to the initial conditions, including the state of the Pacific Ocean. Even slight changes in the initial conditions can result in vastly different outcomes (e.g., Deser et al. 2012, 2014). However, it is important to note that after completing an analysis of a multicentury preindustrial control run from CCSM4, we found that the 
Finally, models struggled to capture the impact of the PDO on GB precipitation. This may result from the periodicity of PDO being incorrectly simulated or because the teleconnection associated with the PDO transition, which is much weaker in SST anomalies in the tropics, did not achieve the observed pattern. However, using a 2000-yr simulation by GFDL CM2.1, Wang et al. (2012) showed that long control runs are able to depict the transition-phase effect of the PDO on GB precipitation. This aspect echoes the previous argument of models capturing the full spectrum of internal variability, but it nonetheless requires further investigation.
A goal of this study was to narrow down the field of available CMIP5 models in order to determine the “best” model to use for future studies of precipitation in the GB as well as perform dynamical downscaling analyses on a local scale. Ranking of models has been discussed in previous studies (e.g., Mote et al. 2011), and in some cases, choosing one model over another does not lead to significantly different results. A preliminary analysis of future CMIP5 model precipitation output over the western United States yielded quite different outcomes between the top performing models; however, when taking an average of the top models and comparing it to the average of all models, the areas of increased and decreased precipitation did not differ as strongly. In addition, ranking models based on their performance on these two time scales (one that captures ENSO-like variability and one that captures PDO-like variability) is a challenge because we do not fully understand the processes driving these modes of variability. Models that are able to successfully capture the connection between ENSO variability and GB precipitation in the past may not necessarily be the best under climate change (Guilyardi et al. 2009).
This study evaluated 20 of the coupled atmosphere–ocean global climate models that participated in CMIP5 and their ability to capture the historical connections between GB precipitation and the major modes of variability in the Pacific Ocean. By applying a statistical analysis including a correlation map pattern match, we determined which models performed more favorably at the different frequencies. We also evaluated how successful the models are at coupling the frequencies which result in precipitation in the GB. The results presented here may have relevance extending beyond the Great Basin because of the spatial scale of the relevant teleconnections, and the method can also be applied for more specific regional analyses. While considering model ranking and the limitations of ranking, we are able to make decisions on how to best use the models for future analysis of precipitation, including using the outputs as inputs for dynamical downscaling on a small scale.
Acknowledgments
This material is based upon work supported by the National Science Foundation under Grants EPS-1135482, EPS-1135483, and EPS-1208732. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Support for S. Wang from Grants NNX13AC37G and WaterSMART R13AC80039 and from the Utah Agricultural Experiment Station is appreciated. Additional support for K. Smith was provided by the University of Utah, including a fellowship from the Global Change and Sustainability Center. Provision of computer infrastructure by the Center for High Performance Computing at the University of Utah is gratefully acknowledged.
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