Geophysical Research Letters
RESEARCH LETTER
10.1029/2018GL078312
Key Points:• Most of the drying over the U.S.
Southwest from 1983 to 2012 is driven
by internal variability rather thananthropogenic climate change• Removing the effects of atmosphericcirculation variability largely
reconciles drying trends in
observations and climate model
simulations• Prospects and limitations for thedetection of forced temperature andprecipitation trends in observations
are discussed

Supporting Information:• Supporting Information S1
Correspondence to:F. Lehner,
ﬂehner@ucar.edu

Citation:
Lehner, F., Deser, C., Simpson, I. R., &Terray, L. (2018). Attributing the U.S.
Southwest’s recent shift into drier
conditions. Geophysical Research Letters,
45. https://doi.org/10.1029/
2018GL078312
Received 11 APR 2018
Accepted 1 JUN 2018
Accepted article online 8 JUN 2018

Attributing the U.S. Southwest’s Recent ShiftInto Drier Conditions
Flavio Lehner1, Clara Deser1, Isla R. Simpson1, and Laurent Terray2

1

National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, Boulder, CO, USA, 2Université deToulouse, CERFACS/CNRS, Climate, Environment, Coupling and Uncertainties Group, Toulouse, France

Abstract The U.S. Southwest experienced a strong hydroclimate trend from the 1980s to the 2010s, fromcool and wet to warm and dry conditions. Attribution of this trend is challenging due to the inﬂuence ofinternal variability but desired by water managers eager to plan for robust signals of climate change in thiswater-scarce region. Here we use an empirical method based on constructed circulation analogues toassess the contribution of atmospheric circulation variability to the recent observed hydroclimate trend.
Consistent with other studies, we ﬁnd the observed precipitation trend from 1983 to 2012 to be mainly due
to internal atmospheric circulation variability that is driven in part by decadal-scale tropical Paciﬁc sea
surface temperature changes. Removing this internal dynamical component brings the observed
precipitation trend into closer agreement with the anthropogenically forced response in climate models,demonstrating progress toward an integrated perspective on climate change attribution.

Plain Language Summary The U.S. Southwest has been getting drier and warmer over the last few
decades. These changes ﬁt the common narrative of what might be expected to happen in response toincreasing greenhouse gas concentrations. However, natural variability of precipitation and temperature is
known to be large in this region, making it difﬁcult to clearly attribute the recent drying and warming togreenhouse gas forcing. Here we show that while the warming is largely due to greenhouse gas forcing, thedrying is mostly due to internal climate variability. To date, only an insigniﬁcant drying remains after
accounting for this internal climate variability. Unlike previous studies that relied exclusively on climate
models, we are able to reach these conclusions based on a combination of observations, an empiricalstatistical method, and climate models.
1. Introduction
Recent decades have seen strong hydroclimate trends over the United States (U.S.) Southwest (SW), from
relatively wetter and colder conditions in the 1980s to relatively drier and warmer conditions in the 2000s
and beyond (Seager & Hoerling, 2014). Intermittent drought conditions and below average streamﬂow fromthe early 2000s onward have challenged water management and prompted water agencies to develop new
strategies for drought mitigation (Bureau of Reclamation, 2014; Raff et al., 2013). A critical question is to what
extent are recent trends due to internal variability as opposed to anthropogenic climate change and thus towhat extent are such trends expected to continue? This question is motivated by the potential for futurehydroclimate variability to be outside of the historical envelope (Cook et al., 2015), which might require
fundamentally different water resource management and infrastructure.
Climate models robustly project an expansion of the subtropics with warming (Held & Soden, 2006). This
expansion is often implicated in observed drying trends over subtropical to midlatitude regions (e.g., Preinet al., 2016) but to date remains difﬁcult to actually detect and attribute in observations (Davis & Rosenlof,2012). Also, the expansion is not necessarily expected to manifest itself over land in the form ofcirculation-driven precipitation declines, especially over the U.S. SW (Greve et al., 2014; He & Soden, 2016a;
Schmidt & Grise, 2017). Rather, a thermodynamically driven decrease in mean moisture convergence during
the warm season seems to dominate the projected drying there (Ting et al., 2018). Projected increases in
winter precipitation may partially mitigate this drying trend but only along the U.S. West Coast (Seager
et al., 2014; Simpson et al., 2016).

©2018. American Geophysical Union.
All Rights Reserved.

LEHNER ET AL.

At least two robust arguments emerge from the literature on the topic of the U.S. SW’s recent shift into drier
conditions: (1) the precipitation shift is not primarily forced by anthropogenic increases in greenhouse gas(GHG) concentrations; instead, it is mainly a result of internal decadal climate variability, in particular a shift

1Geophysical Research Letters10.1029/2018GL078312

from the positive to the negative phase of the Paciﬁc Decadal Oscillation (PDO) and ensuing teleconnections
to North America (Delworth et al., 2015; Hoerling et al., 2010; Schubert et al., 2009; Seager et al., 2005; Seager& Ting, 2017; Zhao et al., 2017); (2) the hydrologic impact of this shift is exaggerated by anthropogenic
warming, affecting various aspects of hydroclimate, such as increased evaporative demand leading toelevated drought risk (Diffenbaugh et al., 2015; Lehner, Coats, et al., 2017), lower streamﬂow (McCabe
et al., 2017; Vano et al., 2012; Woodhouse et al., 2016), lower runoff efﬁciency (Lehner, Wahl, et al., 2017),
reduced snowpack (Mote et al., 2005), and altered seasonality of hydrographs (Clow, 2010).
Despite the broad consensus on the relative roles of internal variability and anthropogenic forcing, the exact
quantiﬁcation of the individual factors contributing to the U.S. SW’s recent shift into drier conditions remains
challenging (Schubert et al., 2016). For example, it seems likely that tropical sea surface temperatures (SSTs)
during recent decades were inﬂuenced to some extent by GHG forcing (e.g., Park et al., 2017), although thespatial ﬁngerprint of GHG forcing on tropical SSTs and precipitation appears distinct from the observed trend
over recent decades (Deser & Phillips, 2009). Further, uncertainties remain with regard to observed SST trends(Coats & Karnauskas, 2017; Deser et al., 2010). It is possible that GHG forcing ampliﬁes Paciﬁc decadal variability itself (Liguori & Di Lorenzo, 2018), in turn affecting hydroclimate variability. More generally, we expect
precipitation variability to increase more robustly with warming than mean precipitation over much of the
globe, including the U.S. SW (Pendergrass et al., 2017). Finally, recent studies have suggested an inﬂuenceof Arctic sea ice loss on midlatitude precipitation (Blackport & Kushner, 2017; Cvijanovic et al., 2017; Deser,
Sun, et al., 2016; Lee et al., 2015; Swain et al., 2017; Wang et al., 2018), although such an inﬂuence might
be partially offset by opposing climate change effects (Barnes & Screen, 2015; Deser et al., 2015; Harvey
et al., 2014).
Importantly, most previous studies have relied on model simulations to attribute recent hydroclimate trends
to either forced or internal components of climate change. Such simulations are capable of reproducing
observed hydroclimate trends when forced with observed SSTs in so-called Atmospheric Model
Intercomparison Project (AMIP) modeling experiments (e.g., Hoerling et al., 2010). However, historical simulations with coupled models struggle to reproduce observed hydroclimate trends, due to biases in temporalvariability and spatial gradients of SSTs (Kajtar et al., 2018; Luo et al., 2018; Seager & Vecchi, 2010; Shin &Sardeshmukh, 2011; van Haren et al., 2013). It is thus desirable to approach the attribution of recent U.S.
SW hydroclimate trends from both a modeling and observational perspective and investigate the degreeof consistency that can be achieved from the two information sources.
A promising technique to achieve such comparability is “dynamical adjustment”, in which the effects ofatmospheric circulation variability on surface climate are removed. The residual variability then reveals theinternal and anthropogenically forced thermodynamic signal, absent any forced changes in circulation(Cattiaux et al., 2012; Wallace et al., 2012). New methods of dynamical adjustment based on constructed circulation analogues (CCAs), partial least squares regression, and self-organizing maps have recently been used
to estimate the importance of internal atmospheric circulation variability for Northern Hemisphere temperature (Deser, Terray, & Phillips, 2016; Horton et al., 2015; Lehner, Deser, & Terray, 2017; O’Reilly et al., 2017;
Smoliak et al., 2015), land-atmosphere coupling during summer (Merriﬁeld et al., 2017), and dissection ofmodel biases in future precipitation projections (Fereday et al., 2017).
Here we use a CCA technique to remove the inﬂuence of atmospheric circulation variability from observed
precipitation and temperature anomalies to attribute underlying trends over the U.S. SW. In addition, we
use a set of climate model simulations to assess the relative inﬂuence of GHG forcing and internal variabilityon the observed trends. We focus on the 30-year trend from 1983 to 2012, a common time period between
observations and available model simulations.

2. Materials and Methods
2.1. Observational Data Sets
We use the 1° × 1° gridded Global Precipitation Climatology Centre (GPCC) data set (Schneider et al., 2015)
from 1901 to 2015 and the 1° × 1° gridded Berkeley Earth Surface Temperature (BEST) data set (Rohde et al.,2013) from 1901 to 2015. In addition, we use a 1/8° × 1/8° gridded 100-member observational “ensemble”
data set of precipitation and temperature from 1980 to 2012, which reﬂects uncertainty from various

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station data processing steps (Newman et al., 2015). All precipitation trends are expressed as percent deviations from their 1981–2010 mean. We use gridded sea level pressure (SLP) from the 2° × 2° Twentieth Century
Reanalysis version 2c from 1901 to 2014 (Compo et al., 2011) after regridding bilinearly to 1° × 1°. The SLP
data are extended through 2015 with ERA-Interim (Dee et al., 2011) as described in Lehner, Deser, et al.
(2017). All data are monthly means.
2.2. Dynamical Adjustment Technique
We use a CCA-based dynamical adjustment technique to remove atmospheric circulation variability fromobserved precipitation (GPCC) and surface air temperature (SAT; BEST). The empirical technique is described indetail in Deser, Terray, and Phillips (2016) and summarized here. For each month, for example, January 2012,
the SLP patterns from all other available Januaries are pooled (domain: 20N°–90°N, 180W°–10°W). Out of these114 SLP patterns, 100 are randomly sampled and combined in a linearly optimal way to form a constructed
SLP analogue that resembles most closely the SLP pattern for the month in question. The coefﬁcients derived fromthe optimal linear combination of SLP patterns are then applied to the corresponding precipitation and SAT toform the “dynamically induced” portion of the precipitation and SAT ﬁeld. The procedure of randomly selecting
100 SLP patterns is repeated 100 times with replacement to guard against overﬁtting. The ﬁnal CCA and accompanying dynamically induced precipitation and SAT are the average over those 100 iterations. The dynamicallyinduced portion is then subtracted from the original precipitation and SAT ﬁeld to obtain the “dynamicallyadjusted” portion. Prior to selecting the SLP patterns and constructing the analogues, precipitation, SAT, and
SLP are detrended for each grid cell and calendar month by removing an estimate of the forced response of thatvariable (see section S1 and Figure S1 in the supporting information for more details).
2.3. Model Simulations
We use monthly mean output from a set of climate model simulations (Table S1). First, we use 10-member
ensembles of atmosphere-only simulations with observed SSTs prescribed globally from the Community
Earth System Model version 1 (CESM) and Geophysical Fluid Dynamics Laboratory CM2.1 (GFDL). These simulations are termed Global Ocean Global Atmosphere (GOGA). For CESM, we also use a 10-member ensemble
in which observed SSTs are only prescribed in the tropics (28°S–28°N), with climatological SSTs and sea ice
outside 35° latitude, and a linear interpolation in between, termed Tropical Ocean Global
Atmosphere (TOGA).
Second, we use fully coupled simulations from the Coupled Model Intercomparison Project Phase 5 (Taylor
et al., 2012; 40 models, one simulation each) and large ensembles of fully coupled simulations with CESM
(40 simulations; CESM LE) and GFDL (10 simulations; GFDL LE). The historical and rcp85 portions of these
ensembles are merged at years 2005/2006.
Third, we use a 2,200-year long preindustrial control simulation with CESM and a 2,600-year long simulation
with the atmospheric component of CESM, the Community Atmosphere Model 5 (hereafter CESM atmosphere only). The latter simulation has prescribed climatological SSTs that were derived from the aforementioned preindustrial control simulation and hence excludes any interannual to decadal SST variability such as
El Niño–Southern Oscillation or PDO.
The GOGA and TOGA simulations are used to test whether climate models are able to simulate the observedhydroclimate trends when given the observed lower boundary conditions over the oceans together with realistic historical radiative forcing. The fully coupled simulations are used to investigate how much of theobserved hydroclimate trends can potentially be attributed to anthropogenic forcing and whether coupledmodels are generally able to produce hydroclimate variability of the same magnitude as observed.

3. Results
3.1. Precipitation Trend 1983–2012
The observed water year precipitation trend from 1983 to 2012 (i.e., October–September 1982/1983 to
October–September 2011/2012) over the U.S. SW features widespread drying, with the most pronounced
drying (>50% 30 years 1) in the southern parts of Arizona and New Mexico (Figure 1a). The drying patternencompasses most of the Colorado River and Rio Grande basins, two of the most important rivers in the
U.S. SW, which consequentially saw signiﬁcant depletion of water resources over this time period.

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Figure 1. Water year precipitation trend from 1983 to 2012 in (a) observations, (b) dynamically adjusted observations, (c)CESM GOGA ensemble mean, (d) CESM LE mean, (e) CESM LE member #8, and (f) dynamically adjusted CESM LE member#8, expressed as percent of the respective 1981–2010 mean. Hatching indicates signiﬁcant trends (95% conﬁdence).
1
Contours are sea level pressure trend in 0.5 hPa 30 years increments starting at ±0.25 hPa. Number of simulations of eachensemble is given in brackets. CESM = Community Earth System Model; GOGA = Global Ocean Global Atmosphere.

Can the precipitation trend be attributed to an anomalous atmospheric circulation trend? The SLP trend ﬁeld
indicates a strong ridge over the North Paciﬁc as well as a weaker ridge over the U.S. SW, suggestive of a
dynamical contribution to the dry conditions. After dynamically adjusting the precipitation observations,most of the drying is eliminated (Figure 1b). The lack of appreciable trends in the residual SLP trend ﬁeld
indicates that the CCA method accounts for nearly all of the observed atmospheric circulation trend.
Can the observed atmospheric circulation trend be attributed to the observed SST trend pattern? Indeed,much of the observed precipitation and SLP trend pattern is recovered by the ensemble mean of the
CESM GOGA simulations (Figure 1c), suggesting a governing inﬂuence of SSTs for observed hydroclimate.
Note, however, that the simulated SLP trend pattern over Canada and the western United States isinconsistent with observations and leads to an underestimation of wetting there. These inconsistencies
are present to some degree in all 10 of the individual GOGA ensemble members (not shown), suggesting
there is a systematic error in the model’s response to the prescribed SSTs, rather than this being a featureof internal atmospheric variability. It is worth noting that the positive SLP trend over the U.S. Interior Westis less robust across observational data sets than the large-scale ridge over the North Paciﬁc and could
thus be related to difﬁculties with estimating SLP over sparsely observed and mountainous areas(Figure S2). Like CESM, GFDL GOGA reproduces the large-scale SLP trend pattern but not the smaller features over the continent. However, GFDL GOGA does not simulate the observed drying pattern as well as
CESM GOGA (Figure S3).

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Can the observed atmospheric circulation trend, and by extension the observed SST trend pattern, be attributed to anthropogenic forcing? The CESM LE mean represents an estimate of the forced anthropogenic
response and can be used to answer this question, conditional on the forcing and the model’s forced
response being correct. The forced SLP trend is near zero (Figure 1d), which means we cannot attributethe observed atmospheric circulation trend to anthropogenic forcing according to CESM, nor according toother models (Figure S3). The forced SST trend pattern does not resemble the observed pattern (Figures
S4b and S4d) and thus, to ﬁrst order, cannot be implicated as a dominant driver of the SST trend pattern
and the associated SLP and precipitation response. Rather, the observed SST trend pattern resembles the
PDO, displaying similarities with the previous large PDO switch in 1936–1965 (including its SLP and precipitation response), which occurred in the absence of strong GHG forcing (Figures S4a and S4c).
Thus, assuming that most of the observed drying trend is driven by atmospheric circulation variability forced
by SSTs, and that the SST trend pattern itself is not primarily an expression of anthropogenic forcing but anexpression of internal ocean-atmosphere dynamics, we expect the dynamically adjusted observed precipitation trend to more closely resemble the forced precipitation trend from models than the unadjusted trend.
Indeed, the dynamically adjusted observed precipitation trend pattern shows good agreement with the
CESM LE ensemble mean trend, although some differences remain (compare Figures 1d and 1b). In particular,
the dynamically adjusted observations show drying along the U.S. West Coast and western Nevada, and wetting of much of the U.S. Interior West, Texas, and eastern Mexico (Figure 1b). These residual precipitation
trends are opposite in sign and generally weaker than the unadjusted trends. The patchiness of the dynamically adjusted observations compared to the smoother CESM LE ensemble mean likely reﬂects imperfectionsof the empirical CCA method due to the limited sample of SLP analogues, observational uncertainty, andadditional inﬂuences not directly related to circulation.
The degree of agreement achievable through dynamical adjustment between a single climate realization andthe forced response can be illustrated using the CESM LE. We apply dynamical adjustment to CESM LE member #8 (see section S1 for details), which by chance shows a drying trend that resembles observations(Figure 1e). As in observations, dynamical adjustment eliminates most of the drying and SLP trend(Figure 1f), and while the residual precipitation trend comes closer to the forced response some differences
remain (compare Figures 1f and 1d). To assess the efﬁcacy of dynamical adjustment more generally, Figure
S5a shows the distribution of precipitation trends averaged over the U.S. SW region (box in Figure 1) before
and after dynamical adjustment for all 40 members of CESM LE as well as observations. Dynamical adjustment narrows the distribution of trends by about 50%, with all members falling within the range 12.5 to+7.5% 30 years 1. Importantly, the observed trend, which lies at the lower end of (but within) the model
range before dynamical adjustment, falls near the middle of the model’s distribution after dynamical adjustment: that is, near the model’s forced response. This conﬁrms that dynamical adjustment can help to reveal
the forced response in the U.S. SW over this time period in observations.
3.2. Temperature Trend 1983–2012
The observed water year SAT trend is positive over much of the western United States and Canada, withlargest values over the south-central United States and smallest amplitudes over south-central Canada(Figure 2a). Dynamical adjustment produces a more spatially uniform pattern of warming by reducing themagnitudes over the south-central United States and increasing them over southwestern Canada(Figure 2b). Anthropogenic GHG forcing, as represented by the CESM LE mean, can account for the generalwarming trend in the dynamically adjusted observations, albeit with slightly larger magnitudes (compare
Figures 2d and 2b). Consequently, the unadjusted and dynamically adjusted observations, averaged over
the U.S. SW, fall in the lower half of (but within) the respective ranges of CESM LE (Figure S5b). The efﬁcacy
of dynamical adjustment is again conﬁrmed by using CESM member #8 (Figure 2e), which resembles the
CESM forced response after dynamical adjustment (compare Figures 2f and 2d).
The CESM GOGA simulations reproduce the ampliﬁed (dampened) warming over the south-central UnitedStates (southern Canada) relative to the ensemble mean of CESM LE (compare Figures 2c and 2d).
However, both the ampliﬁcation and damping signal are overestimated signiﬁcantly in CESM GOGA, leading
to warming of >2 °C 30 years 1 over the U.S. SW and even cooling over southern Canada. The same holds
true for GFDL GOGA (Figure S3). The anomalous atmospheric circulation, attributable to the observed SST

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Figure 2. Same as Figure 1 but for surface air temperature. CESM = Community Earth System Model; GOGA = Global Ocean
Global Atmosphere.

evolution (section 3.1), extends too far over land in the GOGA simulations, causing some of the overestimated
SAT response.
The results from Figures 1 and 2 illustrate that while the models are able to recover the general precipitationand SAT features of the teleconnections originating from anomalous SSTs, the magnitude and spatial details
might not be replicated exactly or might even be biased systematically.
3.3. Uncertainty and Seasonality of 1983–2012 Trends
The observed water year precipitation trend from GPCC, averaged over the U.S. SW region, is19.3% 30 years 1 (Figure 3 and Table S2). A range of possible water year precipitation trends as derived
from the observational ensemble by Newman et al. (2015) is given in gray shading in Figure 3 and illustrates
the considerable observational uncertainty, spanning 21.4% 30 years 1 to 12% 30 years 1 (Figure 3a andTable S2). The dynamically adjusted water year precipitation trend is 6.1% 30 years 1, which is close to zero
within the methodological uncertainties, estimated here from the different detrending methods tested (redshading in Figure 3; see section S1 for details).
The water year precipitation trend is composed of drying in all seasons October–December, January–March,
April–June, and July–September (Figures 3b–3e). In all seasons, dynamical adjustment reduces or reverses
the negative precipitation trend to approximately zero within observational and methodological uncertainty.
The Newman et al. (2015) uncertainty data are only shown for unadjusted trends; its short record does notallow us to use it in the CCA method. However, we do not expect the observational uncertainty to be affectedsigniﬁcantly by dynamical adjustment.

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Figure 3. The 1983–2012 trends of (a–e) precipitation and (f–j) surface air temperature over the U.S. Southwest fromobservations and various model simulations. Black horizontal lines and gray shading indicate observations (GPCC andBEST) and observational uncertainty (Newman et al., 2015). Red horizontal lines and pink shading indicate
dynamically adjusted observations and its methodological uncertainty (see section S1 for details). The individualmodel simulations are given as squares; each ensemble’s mean is given with a circle. CESM = Community Earth
System Model; GOGA = Global Ocean Global Atmosphere; GPCC = Global Precipitation Climatology Centre;
BEST = Berkeley Earth Surface Temperature; TOGA = Tropical Ocean Global Atmosphere; GFDL = Geophysical Fluid
Dynamics Laboratory; CMIP5 = Coupled Model Intercomparison Project Phase 5; AMIP = Atmospheric Model
Intercomparison Project.

The ensemble mean precipitation trends in the coupled models are statistically indistinguishable from zero inall seasons (Figures 3a–3e). The spread of these ensembles encompasses the observed drying trend, suggesting that the coupled models are generally able to produce precipitation trends of the magnitude that has
been observed from 1983 to 2012. However, it is also clear that the same models, when run with prescribed
observed SSTs (GOGA), produce stronger precipitation trends than any that occur in the coupled model version (compare the most negative values for GOGA and LE simulations in Figures 3a–3e). Investigation of all
possible 30-year precipitation trends (not just 1983–2012) reveals that CESM GOGA simulations generally
produce stronger trends, positive and negative, than occur in any of CESM LE, CESM preindustrial control
simulation, and CESM atmosphere-only control simulation (Figure S6). This occurs despite an order of magnitude smaller sample size in CESM GOGA (a total of 1,060 30-year trends in CESM GOGA and >10,000 30-year
trends in the CESM coupled and atmosphere-only simulations) and is hence a robust result. It is thus conceivable that observed precipitation trends stronger than 1983–2012 would not be reproduced by coupledmodels but could be simulated in a GOGA setup. This illustrates the ongoing challenge for coupled models
to reproduce the ﬁdelity of observed hydroclimate variability (He & Soden, 2016b; Seager & Hoerling, 2014).
GFDL GOGA simulations generally show stronger drying trends than CESM and overestimate the observed
trend in April–June.
We also found that the CESM atmosphere-only control simulation produces trends as strong as observed during 1983–2012, including similar SLP and precipitation trend patterns (Figures S7a and S7b). Thus, the particular SST pattern during that period cannot be considered a necessary condition for the drying trend over the
U.S. SW, in line with studies on tropical SST forcing of atmospheric ridging in the context of the recent
California drought (Seager & Henderson, 2016; Teng & Branstator, 2017). However, the fact that all GOGA
simulations produce a drying indicates that the chances for drying are greatly increased in the presence ofthe particular 1983–2012 SST trend pattern.

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Finally, we investigate a 10-member ensemble of TOGA simulations with CESM (ﬁrst blue boxplot in eachpanel of Figure 3). The TOGA and GOGA ensembles are not statistically different in the precipitation trends;
they induce over the U.S. SW during 1983–2012 (Figures 3a–3e). This suggests that the extratropical SSTs
were less important in bringing about the full precipitation trend over the U.S. SW, in agreement with earlier
results on the relative roles of tropical and extratropical SSTs for drought over North America (Seager
et al., 2005).
For SAT averaged over the U.S. SW, the observed water year trend is 0.84 °C 30 years 1 (0.68 to 0.85 observational uncertainty range) and the dynamically adjusted trend is 0.49 °C 30 years 1 (Figure 3f and Table S2).
Broadly similar trends exist during individual seasons (Figures 3g–3j). Contrary to precipitation, the observed
SAT trends are mostly consistent with coupled models forced with GHG increase (Figures 3f–3j).
Consequently, the effect of dynamical adjustment on the observed SAT trends is smaller than that for precipitation and never leads to a cancelation or reversal of the positive trend of the observations, except in
October–December when some of the dynamical adjustment methods reduce the SAT trend to almost zero.
As hinted at in Figure 2, the GOGA simulations tend to overestimate the warming trend over the SW UnitedStates, especially in spring (Figure 3i). Since the coupled models always encompass the observed SAT trend
and hence show no clear sign of bias in that regard, this indicates that the SAT response to the prescribed
SSTs in the GOGA simulations is likely overestimated.

4. Summary and Conclusions
Using a dynamical adjustment method, we provide evidence for the importance of atmospheric circulation
variability for the strong drying and warming trend over the U.S. SW from 1983 to 2012. Critically, the method
is based on observations alone. After applying dynamical adjustment, closer correspondence emerges
between the residual precipitation trend and the anthropogenically forced precipitation trend pattern given
by climate models. Using model simulations with prescribed observed SSTs, we show that the anomalous
atmospheric circulation that caused the negative precipitation trend can be attributed with high probability
to teleconnections arising from tropical Paciﬁc SSTs.
Our approach provides a novel attribution perspective on U.S. SW precipitation trends that combines bothobservations and models. In particular, dynamical adjustment enables progress toward the long-elusive
detection of forced precipitation trends in observations in situations where thermodynamically forced trends
have arisen alongside inﬂuences of internal atmospheric circulation variability (Hegerl et al., 2015). However,more sensitivity tests over other regions and time periods, such as the exceptional drought conditions over
California from 2012 to 2016, are planned to conﬁrm the utility of the dynamical adjustment method
for precipitation.
While different models agree on the general precipitation and temperature response to prescribed SSTs, aswell as GHG forcing, there remain considerable differences in the magnitude of those responses. This makes
it difﬁcult to exactly quantify the relative roles of GHG, internal atmospheric variability, and atmosphericvariability forced by SSTs in the observed trends. We also show notable uncertainties in the observed trends
themselves, something that poses a challenge for model evaluation (Deser et al., 2017, 2018).
Coupled models do not suggest a dominant inﬂuence of GHGs on the observed 1983–2012 Paciﬁc SST trend
pattern. However, these models contain biases in tropical SSTs and precipitation, undermining conﬁdence inthe corresponding model-projected forced responses—which in turn could affect U.S. SW precipitation
through teleconnections. Further, dry conditions have prevailed in parts of the U.S. SW even after 2012,
despite a string of positive PDO years since 2013, inviting speculation as to whether the emergence of aforced drying trend is imminent (Seager et al., 2007). Our observed record of SSTs over the last century does,
however, indicate that similar magnitude SST and precipitation trends have occurred in the past under very
different anthropogenic forcing, indicating that natural variability is capable of contributing the dominantfraction of the trends discussed here.
While much of the drought conditions over the U.S. SW since the 2000s were caused by the precipitationdeﬁcit, the accompanying warming helped to deepen the drought through increased evapotranspiration(Weiss et al., 2009). Unlike the precipitation deﬁcit, this warming is driven primarily by anthropogenic forcing
from GHGs rather than atmospheric circulation variability (although some ampliﬁcation might be attributable

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to the particular 1983–2012 SST evolution). Thus, it is important to distinguish between forced and internal
aspects of the recent droughts (e.g., Williams et al., 2015). In this case, drying and warming combined toform what in some regions equates to one of the steepest hydroclimate trends of the last several centuries(Lehner, Wahl, et al., 2017). While precipitation will continue to show signiﬁcant interannual to decadal
variability over the U.S. SW, it will likely do so over a rising background temperature, which in turn will
increase the probability of drought whenever precipitation deﬁcits occur (Udall & Overpeck, 2017;
Woodhouse et al., 2010).
Predictability of decadal hydroclimate trends over the U.S. SW, such as the one from 1983 to 2012, remains
low. State-of-the-art decadal prediction systems struggle to provide skillful forecasts of tropical Paciﬁc SSTs
beyond 2-year lead times (Yeager et al., 2018). However, 2-year La Niñas, which often induce drought conditions over the southern United States (Okumura et al., 2017), can now be successfully predicted 2 years before
their onset (DiNezio, 2017). On seasonal time scales, temperature forecasts over the U.S. SW are now skillful
enough that they can improve streamﬂow forecasts in temperature-sensitive basins such as the Colorado
River or Rio Grande (Lehner, Wood, et al., 2017). Thus, with increasing inﬂuence of temperature on hydroclimate, an opportunity might arise to mitigate some of the negative impacts of such trends on water resources
through better predictability of temperature relative to precipitation.

Acknowledgments
We are grateful to Sloan Coats, Pedro
DiNezio, Ruixia Guo, Andrew Newman,
Angeline Pendergrass, Andreas Prein,
Yaga Richter, Andrew Wood, andStephen Yeager for discussion; toTingting Fan, Yu Kosaka, and Adam
Phillips for sharing simulations and fortechnical support; and to two
anonymous reviewers for constructive
feedback. We acknowledge the efforts
of all those who contributed to
producing the simulations andobservational data sets. All CESM
simulations are available onearthsystemgrid.org; contact Yu Kosaka(ykosaka@atmos.rcast.u-tokyo.ac.jp) for
the GFDL simulations. The National
Center for Atmospheric Research issponsored by the National Science
Foundation. F. L. is supported by a
Postdoc Applying Climate Expertise(PACE) fellowship cosponsored by the
National Oceanic and Atmospheric
Administration and the Bureau of
Reclamation and administered by the
Cooperative Programs for the
Advancement of Earth System Science(CPAESS).

LEHNER ET AL.

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