Geophysical Research Letters
RESEARCH LETTER
10.1002/2017GL074831
Key Points:• Summer temperatures in the central
U.S. are inﬂuenced by circulation andland surface conditions• Circulation eﬀects on summer
temperatures can be removed withdynamical adjustment• Residual temperature variability
in the central U.S. reﬂects the land
surface feedback

Supporting Information:• Supporting Information S1

Correspondence to:A. Merriﬁeld,
almerrif@ucsd.edu

Citation:
Merriﬁeld, A., Lehner, F., Xie, S.-P.,& Deser, C. (2017). Removing
circulation eﬀects to assess central
U.S. land-atmosphere interactionsin the CESM Large Ensemble.
Geophysical Research Letters, 44,9938–9946.
https://doi.org/10.1002/2017GL074831

Received 12 JUL 2017
Accepted 17 SEP 2017
Accepted article online 22 SEP 2017
Published online 13 OCT 2017

Removing Circulation Eﬀects to Assess Central
U.S. Land-Atmosphere Interactionsin the CESM Large Ensemble
Anna Merriﬁeld1, Flavio Lehner2, Shang-Ping Xie1

, and Clara Deser2

1 Scripps Institution of Oceanography, University of California, San Diego, CA, USA, 2 Climate and Global Dynamics Division,
National Center for Atmospheric Research, Boulder, CO, USA

Abstract Interannual variability of summer surface air temperature (SAT) in the central United States(U.S.) is inﬂuenced by atmospheric circulation and land surface feedbacks. Here a method of dynamical
adjustment is used to remove the eﬀects of circulation on summer SAT variability over North America
in the Community Earth System Model Large Ensemble. The residual SAT variability is shown to reﬂect
thermodynamic feedbacks associated with land surface conditions. In particular, the central U.S. is a“hot spot” of land-atmosphere interaction, with residual SAT accounting for more than half of the total SAT
variability. Within the “hot spot,” residual SAT anomalies show higher month-to-month persistence throughthe warm season and a redder spectrum than dynamically induced SAT anomalies. Residual SAT variability
in this region is also shown to be related to preseason soil moisture conditions, surface ﬂux variability,
and local atmospheric pressure anomalies.
1. Introduction
Adverse impacts from anthropogenic climate change are likely to be exacerbated in summer when temperatures are already at their seasonal maximum. Indeed, exceptionally warm summers projected by climate
models will jeopardize crops, strain water resources, and tax human health (Romero-Lankao et al., 2014;
Lehner, Wahl, et al., 2017). With a surfeit of solar radiation present, summer SAT over land is set by large-scaleatmospheric circulation patterns, topography, and cloud cover and modiﬁed regionally by land surface
conditions.
The land surface inﬂuences the atmosphere through a series of nonlinear processes, linking soil moisturewith evapotranspiration, cloud formation and precipitation (e.g., Findell & Eltahir, 2003; Tawﬁk et al., 2015a,
2015b). Modeling studies identify a “hot spot” of land-atmosphere interaction in the central U.S. (e.g., Koster
et al., 2006; Lorenz et al., 2015; Zhang et al., 2008). There are indications, however, that the land-atmosphere
coupling in such hot spot regions might be overestimated in climate models (Fischer et al., 2012; Sippel
et al., 2017; Stegehuis et al., 2013). In the central U.S., for example, models tend to feature warmer mean
SAT (Mueller & Seneviratne, 2014) and more interannual SAT variability (Berg et al., 2014; Merriﬁeld & Xie,
2016) than observed. The land surface inﬂuence on SAT is challenging to quantify (Yang et al., 2004) due tothe confounding inﬂuence of internal atmospheric variability (Deser et al., 2012; Wallace et al., 1995, 2015).
For example, hot extremes brought about by persistent anticyclonic conditions in the atmosphere are often
intensiﬁed by dry soils (Durre et al., 2000; Miralles et al., 2014; Vogel et al., 2017).

©2017. American Geophysical Union.
All Rights Reserved.

MERRIFIELD ET AL.

This study uses the empirical method of “dynamical adjustment” (Deser et al., 2016; Lehner, Deser, et al.,2017) to remove the circulation-induced component of SAT variability in the Community Earth System Large
Ensemble. We examine the relative magnitude, spatial pattern, and temporal persistence of circulation versus
thermodynamic drivers of summer SAT variability over the U.S., with particular attention on the central U.S. hotspot region identiﬁed in previous studies. Speciﬁcally, we evaluate whether the thermodynamic componentof SAT variability helps isolate inﬂuences of anomalous land surface conditions. The remainder of the paper is
structured as follows. Section 2 introduces the model simulations and dynamical adjustment methodology.
Results are presented in Section 3, beginning with two case studies and then generalizing to characterize thedynamic and thermodynamic contributions to SAT variability in the Community Earth System Model Large
Ensemble. Sections 4 and 5 provide a summary and discussion, respectively.
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2. Climate Model Simulations and Dynamical Adjustment Methodology
We analyze the role of the atmospheric circulation and land surface condition on summer SAT variability overthe historical period (1920–2005) in the 30 member ensemble of simulations conducted with the National
Center of Atmospheric Research (NCAR) Community Earth System Model version 1 (CESM1), hereafter referred
to as the CESM Large Ensemble (CESM-LE) (Kay et al., 2015). The CESM-LE is a fully coupled, 1∘ horizontal
resolution initial condition ensemble; each ensemble member is subjected to identical CMIP5-based external
forcing scenarios. The members diﬀer slightly from one another in their initial atmospheric state. Large ensembles allow us to sample internal variability in the presence of forced climate changes, thereby providing a
range of possible climate trajectories to analyze (Deser et al., 2012; Lehner et al., 2016).
We employ constructed circulation analogues to dynamically adjust monthly mean SAT ﬁelds in the CESM-LE.
The method is summarized brieﬂy here and in more detail in the supporting information; the reader is referred
to Deser et al. (2016) for a full description.The method relies on the ability to reconstruct a given monthly
mean circulation ﬁeld (“target month”), represented here by monthly mean 500 mb geopotential height(Z500) as opposed to SLP as in Deser et al. (2016), from a large set of imperfect analogues obtained from the
CESM1 preindustrial control simulation (Kay et al., 2015) with the same model setup. The closest analogues(in terms of Euclidean distance from the target month over the domain 20–90∘ N, 180–10∘ W) are linearly
combined with an optimal set of weights, to reconstruct the target Z500 ﬁeld in the CESM-LE. The same optimal linear combination is then applied to the accompanying SAT ﬁelds in the control simulation to construct
the dynamically induced component of SAT. This dynamical component is then subtracted from the original
SAT ﬁeld of the target month to obtain the residual SAT component, which we interpret as being primarily
thermodynamically induced and potentially land surface driven.

3. Results
3.1. Two Julys With Similar Circulation but Diﬀerent SAT
In the central U.S., diﬀerent SAT anomalies can exist under similar atmospheric circulation conditions(Figure 1). To illustrate this, we select two Julys from the CESM-LE (July 1963 of member 15, and July 1925 ofmember 22) featuring similar midlatitude bands of high pressure at 500 hPa (Z500) with centers in the vicinityof the Aleutian islands and the west coast of the U.S. and a low-pressure center over western Canada, similar
to the pattern described by McKinnon et al. (2016). SAT anomalies in the two cases diﬀer most notably inthe central U.S., while they are broadly similar elsewhere. The conﬁguration of SAT anomalies associated with
the Z500 pattern indicate that atmospheric circulation anomalies are largely responsible for the warm (cool)
anomalies in the western U.S. (Canada) (Figures 1c and 1d). The local warm anomaly over the central U.S. incase 2 (Figure 1f ), which exceeds average central U.S. SAT by more than 5∘ C, is not accounted for by dynamical adjustment and might hence be of thermodynamic origin. In contrast, there is no signiﬁcant central U.S.
SAT anomaly present in case 1 (Figure 1e). To investigate possible mechanisms explaining the diﬀerences inresidual SAT anomaly over the central U.S., we compare area-averaged land surface parameters in the region
shown in Figures 1e and 1f (32.5–41.9∘ N, 90–101.25∘ W). We consider soil moisture, sensible and latent heat
ﬂuxes, the shortwave cloud radiative eﬀect, which is the diﬀerence between all-sky and clear-sky downward
shortwave radiation at the surface (Cheruy et al., 2014), and the diurnal temperature range, which serves as
proxy for local boundary layer moisture conditions (Dai et al., 1999; Lewis & Karoly, 2013). The land surface
anomalies are presented in terms of percent diﬀerence from their long-term averages.
In case 1 (green bars in Figure 1g), soils are 4% wetter than average. The surface heat ﬂux is partitioned infavor of latent (8%) over sensible (−14%) heating. Local atmospheric conditions were largely unremarkable,with modest reductions in cloud cover (−11%) and diurnal temperature range (−2%). In case 2 (blue bars in
Figure 1g), soils are 11% drier than average. Anomalously dry soil conditions exist in the previous spring incase 2, from a −4% anomaly in April to a −10% anomaly in June (not shown). The sensible heat ﬂux in theregion is more than twice the long-term average (102%), in conjunction with a 25% reduction in the latent
heat ﬂux due to drier soils. Cloud cover is below average in case 2 (−30%), which suggests that the increase ofincoming shortwave radiation that accompanies reductions in cloud cover contribute to the SAT anomaly inthe region. The diurnal temperature range is 30% larger than average, which is consistent with drier boundary
layer conditions that result in enhanced radiative warming during the day and cooling at night. The residual
SAT anomaly in case 2 likely originated from heat ﬂux partitioning by the land surface with reduced cloud
cover playing a role, a narrative consistent with other case studies (e.g., Namias, 1982; Orth & Seneviratne,
2017; Perkins, 2015).
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Case 1

10.1002/2017GL074831

Residual

Dynamic

Total

Case 2% difference fromclimatology (box)100

G

Case 180

Case 2

60
40
20
0
-20
-40
Soil
Moisture

Sensible
Heat Flux

LatentHeat Flux

Shortwave Cloud
Radiative Effect

DiurnalTemperature Range

Figure 1. (a, b) Total, (c, d) dynamical, and (e,f ) residual SAT anomalies (color) in two Julys with similar attendantcirculation (Z500; contours) in the CESM-LE. Anomalies are calculated from the historical period mean (1920–2005).
Z500 contours range from 5 to 95 m (solid) and −5 to −50 m (dashed) in 15 m intervals. (g) Area-averaged surface
conditions for each July, expressed as percent diﬀerence from the historical period mean.

3.2. Deﬁning the Hot Spot
The selected cases in Figure 1 demonstrate how residual SAT can help to identify the spatial extent and estimate the magnitude of the land surface’s inﬂuence on summer SAT. To take a more general look at the SAT
variability that might be driven by land surface conditions, we calculate the standard deviation (𝜎 ) of dynamic
and residual SAT anomalies across all summers (June–August; JJA) of the historical portion of each memberof the CESM-LE and then average the 30 𝜎 values. Note that for each year and grid point, the ensemble-mean
dynamic (residual) SAT has been subtracted from each ensemble member’s dynamic (residual) SAT before
computing 𝜎 , thereby isolating the contribution from internal variability. Interannual variability of dynamic
JJA SAT in the CESM-LE is of comparable magnitude over the western and central U.S., though dynamics
explain over three quarters of the total SAT 𝜎 in the former and less than half in the latter region (Figure 2a).
Residual JJA SAT variability is highest along the arc of the Great Plains with almost no residual variability inthe western U.S. (Figure 2b). The lack of residual 𝜎 west of the Rocky Mountains is consistent with the region
being too dry overall for moisture variations to inﬂuence SAT (Kamae et al., 2016; Seneviratne et al., 2010).
In the central U.S., the magnitude of residual variability suggests that the land surface feedbacks may be
responsible for more than a quarter of total SAT 𝜎 . The southern central US (32.5–41.9∘ N, 90–98.75∘ W) stands
out as a “hot spot,” with more than half of the total SAT 𝜎 considered thermodynamic. Hereafter, SAT averaged
over this region is deﬁned as hot spot SAT.
The land surface’s inﬂuence on SAT can also be inferred from temporal characteristics of dynamic and residual SAT within the hot spot region. Evidence of the land surface feedback can be seen on intraseasonal
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10.1002/2017GL074831

25%
50%
50%

25%

% of total
SAT variance2 / cycles per year

JJA standard

month

cycles per year

Figure 2. Standard deviation of (a) dynamic and (b) residual JJA SAT in color, overlaid with percent of total JJA SAT
variance explained in black contours, averaged across 30 members of CESM-LE over the historical period. (bottom)
Temporal features of hot spot region dynamic (blue) and residual (red) SAT. (c) August hot spot SAT autocorrelationand (d) spectra of JJA hot spot SAT, in terms of ensemble average (solid line) and 1𝜎 ensemble spread (shading).

(Figure 2c) to multidecadal (Figure 2d) timescales. Autocorrelation of dynamic (blue) and residual (red) August
SAT anomalies are shown in Figure 2c, where the solid curve shows the average and the shading shows
the range across the 30 members of the CESM-LE. Although there is considerable spread in autocorrelations
among the 30 model runs for both dynamic and residual SAT, on average, residual SAT has a longer decorrelation timescale than dynamic SAT. Residual autocorrelation signiﬁcant at 95% (above gray dashed line in
Figure 2c, see supporting information) arises in June and continues until September, which suggests that
residual SAT originates from a persistent surface forcing which exerts inﬂuence throughout the warm season.
Dynamic SAT reﬂects the shorter-lived inﬂuence of atmospheric circulation that is not signiﬁcantly correlated
from month to month. The spectra of dynamic (blue) and residual (red) JJA SAT anomalies over the 86 year
historical period are shown in Figure 2d, where the solid curve shows the average and the shading shows
the range across the ensemble. The spectrum of dynamic JJA SAT has approximately equal power at all
frequencies, while the spectrum of residual JJA SAT has more energy at lower frequencies. The redder residual spectrum is consistent with the notion that residual SAT reﬂects the integration of stochastic atmosphericforcing by the land surface (Delworth & Manabe, 1993).
3.3. Developing a Thermodynamic Narrative
The above results suggest that residual SAT variability in the hot spot region might be land surface driven.
Within the model, we can attempt to develop a thermodynamic narrative for this land surface inﬂuence.
We explore several aspects of the land surface feedback in the JJA SAT hot spot region: the relationshipbetween SAT and soil moisture (Figure 3) and between SAT and local SLP (Figure 4). We quantify the former byshowing correlation maps of the components of JJA hot spot SAT and total soil moisture from the antecedentspring through the subsequent fall (Figure 3). Correlations (r) are computed from a concatenated record of
2,580 model years (30 simulations, 86 years each) and are determined to be signiﬁcant at 95% if they exceed
about 0.03 in absolute value (see supporting information). Total JJA hot spot SAT is negatively correlatedwith spring (March–May; MAM) soil moisture from the Gulf Coast through the central Great Plains (Figure 3i).
The spatial structure of the correlation suggests that moist southerly ﬂow from the Gulf provides the moisture necessary for the land surface to inﬂuence the atmosphere (Kushnir et al., 2010; Feng et al., 2011).
The maximum of these lagged correlations (r = −0.25) occur in the southern portion of the hot spot. The average correlation of dynamic JJA hot spot SAT with MAM soil moisture in the hot spot is very low (r = −0.05;
Figure 3ii), which suggests that the spring soil moisture state in the central U.S. does not inﬂuence the
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MAM Soil Moisture (-1)
i10.1002/2017GL074831

SON Soil Moisture (+1)
vii

JJA Soil Moisture (0)
iv

Total

0.8

0.6

0.4

0.2
cloud cover
0

viii

v

Dynamic

ii

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0.2
cloud cover
0

vi

ix
0.8

Residual

iii

0.6

0.4

0.2
cloud cover0

Correlation with JJA Hotspot SAT
Figure 3. Correlation maps of (i, iv, vii) total, (ii, v, viii) dynamic, and (iii, vi, ix) residual JJA hot spot SAT with total column soil moisture at each grid point. The left
column shows correlation with preseason (MAM) soil moisture, the middle with concurrent JJA soil moisture, and the right with postseason (SON) soil moisture.
Normalized regression coeﬃcients for JJA cloud cover (bcld ; gray) and sensible heat ﬂux (bshf ; red) regressed on each component of JJA SAT are inlaid in
Figures 3iv–3vi. Bars (whiskers) represent ensemble average (spread).

atmospheric circulation patterns that follow. Removing the inﬂuence of dynamics, however, leads to higher
correlations between MAM soil moisture and JJA SAT (Figure 3iii). In fact, residual JJA hot spot SAT is more
highly correlated with MAM soil moisture in the hot spot than total SAT is (r = −0.36). This supports our
hypothesis that removing circulation-induced SAT variability allows us to better characterize SAT variability
driven by the land surface feedback.
Instantaneous correlations in the hot spot are of similar magnitude for total and residual JJA SAT and JJA
soil moisture, and dynamic SAT correlations remain weaker (Figures 3iv–3vi). Fall (September–November;SON) soil moisture is more highly correlated with JJA SAT and its components than MAM soil moisture is(Figures 3vii–3ix). Correlations of SON soil moisture with dynamic and residual hot spot SAT are of similar
magnitude, which suggests that a hot summer dries soils in the hot spot whether the warm anomaly was circulation induced or land surface driven. To strengthen the case that residual hot spot SAT is land surface driven,
we determine the relative inﬂuence of cloud cover and surface ﬂuxes through a multivariate regression of theshortwave cloud radiative eﬀect and the sensible heat ﬂux on JJA hot spot SAT (bars inlaid in Figures 3iv–3vi).
In each ensemble member, all ﬁelds are normalized by standard deviation at each grid point. A multivariate regression is then carried out at each grid point and the resulting normalized regression coeﬃcients,
bcld and bshf , are averaged over the hot spot to estimate relative contribution of clouds versus the land
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Total

Correlation of hotspot SAT with SLP (color) & Z500 (contours)

ResidualA

B
Difference in JJA Residual SAT , Z500 - SLP (color)r
SLP / Z500 correlation (contours)

C
Figure 4. (a) Correlation map of total JJA hot spot SAT with SLP in color and with Z500 in contours. Z500 correlation
contours range from 0.1 to 0.8 (solid) and −0.1 to −0.3 (dashed) in intervals of 0.1. (b) Same as Figure 4a, but withresidual JJA hot spot SAT. (c) Diﬀerence in the standard deviation of JJA residual SAT (∘ C; color) using Z500 versus SLP torepresent circulation. The residual SAT variability diﬀerence map is overlaid with a map of the correlation between SLP
and Z500 (contours). Correlation contours range from 0.1 to 0.8 (solid) and −0.1 to −0.4 (dashed) in intervals of 0.1.

surface feedback. Bars in Figures 3iv–3vi show the average, and whiskers show the range in bcld and bshf acrossthe ensemble. Cloud cover and the sensible heat ﬂux play comparable roles in setting total JJA hot spot SAT,
with an average bcld of 0.34 and bshf of 0.48 (Figure 3iv). This supports the narrative posited in Figure 1 thatwarm SAT anomalies in the hot spot occur in conjunction with clear skies, more incoming shortwave radiation,
and an increased sensible heat ﬂux. Regression on the components of JJA hot spot SAT conﬁrm that variations
in dynamic and residual SAT have diﬀerent physical underpinnings. Dynamic SAT variability (Figure 3v) relates
primarily to variations in cloud cover (bcld = 0.46), as large cloud systems that impact radiation tend to be coupled to large-scale atmospheric circulation (Bony et al., 2015). Residual SAT variability (Figure 1vi) is primarily
driven by variations in the sensible heat ﬂux (bshf = 0.64), which reﬂects the thermodynamic partitioning of
surface ﬂuxes by soil moisture in the hot spot region (Seneviratne et al., 2010). This illustrates the eﬃcacy ofdynamical adjustment in separating circulation induced from thermodynamic anomalies, as the method isable to reveal relationships between hot spot SAT and multiple aspects of the land surface feedback.
In winter, dynamic SAT and SLP are expected to show a quadrature relationship consistent with horizontal
advection. In summer, surface lows form over hot, dry land surfaces and in regions of diﬀerential heatingsuch as coastlines and are identiﬁable by a local anticorrelation between SAT and SLP (Rowson & Colucci,
1992). Because thermal lows are associated with local land surface conditions, their presence is not necessarily reﬂective of the large-scale circulation patterns that drive dynamic SAT. Over the ocean, the midlatitude
atmosphere tends to feature SLP anomalies of the same sign as Z500 anomalies (Figure 4a). In the hot spot,
positive (negative) correlations between JJA SAT and Z500 (SLP) are highest locally (Figure 4a). Low pressure
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at the surface and high pressure aloft is representative of the atmospheric baroclinicity that sets up thermodynamically over hot, dry land surfaces. Dynamical adjustment removes covariability between hot spot SAT
and Z500 as expected, both locally and remotely (Figure 4b). Correlations between residual hot spot SAT and
SLP, however, remain almost unchanged over the hot spot region. After dynamical adjustment, local correlation between SAT and Z500 is reduced from 0.80 to 0.31 while local correlation between SAT and SLP is
only reduced from −0.73 to −0.60. The magnitude of the remaining residual SAT-SLP correlation supports
the thermodynamic interpretation of SAT variability in the hot spot region, that the state of the land surface (e.g., soil moisture) inﬂuences local SAT, which in turn sets up a spatially concomitant SLP signature.
The remaining residual SAT-Z500 correlation is substantially reduced but not negligible, which is expected ina coupled system. The relationship may reﬂect the inﬂuence of large-scale circulation on soil moisture or viceversa (Fernando et al., 2016; Koster et al., 2016).
We have repeated the dynamical adjustment method using SLP (rather than Z500) as an indicator of
circulation. Residual SAT 𝜎 from the two methods are compared in Figure 4c. More SAT variance is removed
with SLP analogues than with Z500 analogues (i.e., Z500-derived residual 𝜎 is larger than SLP-derived
residual 𝜎 ) along the arc of the Great Plains, in the U.S. Southwest and Northern Mexico, and in coastal regions.
These are regions where thermal lows are known to occur (Johnson, 2003) with feature of a negative correlation between Z500 and SLP (Figure 4c; dashed contours) representative of the baroclinic thermal low
signature.

4. Summary
We have presented evidence that a method of dynamical adjustment developed by Deser et al. (2016) can beused to evaluate land surface-driven SAT variability in the CESM-LE. The dynamic and residual componentsof summer SAT are presented in terms of interannual variability, and the central U.S. hot spot of land surfacedriven SAT is deﬁned where residual variability accounts for more than half of the total JJA SAT variance.
Temporal characteristics of the dynamic and residual (i.e., thermodynamic) hot spot SAT suggest that thelatter is land surface driven. Finally, a general thermodynamic narrative is developed for residual SAT using
the entire CESM-LE. Dynamical adjustment is able to reveal clear relationships between summer SAT and two
key aspects of the land surface feedback (preseason soil moisture and sensible heat ﬂux variability) that arenormally obscured by the inﬂuence of atmospheric circulation. Dynamical adjustment performed with Z500
rather than SLP also leaves behind a thermodynamic, surface low signature that accompanies warm SAT in
the hot spot. We conclude that dynamical adjustment can be used to empirically estimate the magnitude andspatial extent of land surface-driven SAT variability.

5. Discussion
Dynamical adjustment was previously employed to develop a physical understanding of SAT trends(Deser et al., 2016; Lehner, Deser, et al., 2017). The method also gives insight into interannual variability,
allowing us to determine why one summer is hotter than another. Residuals tend to be larger in the summer
than in the winter when horizontal advection plays a more prominent role in setting SAT (Deser et al., 2014;
Sheﬃeld et al., 2013). This does not necessarily mean that dynamical adjustment is not useful in the summer,
just that thermodynamic processes play a role in setting SAT that is comparable in magnitude to dynamics.
Determining the thermodynamic basis for the summer residual both validates dynamical adjustment as amethod and indicates where land-atmosphere interactions may be meaningful.
We evaluate dynamical adjustment in a model, where there are thousands of years of simulation from which
to pick analogues, and land surface information is temporally and spatially complete. Because dynamical
adjustment is an empirical method, having as many analogues as possible is key to eﬀectively characterizing
circulation (van den Dool, 1994). Analogues may capture one aspect of circulation in the domain but have different features elsewhere and are therefore “imperfect.” Additionally, care must be taken when using a linear
method to explain features of a nonlinear coupled system. Land-atmosphere interactions are two way, whichmakes separating the forcing and response a challenge, particularly on monthly timescales (Levine et al.,2016). Further assessment of dynamical adjustment is warranted, particularly regarding its application toobservational data sets (Lehner, Deser, et al., 2017). Temperature is a well-measured ﬁeld, both spatially andtemporally, while land surface observations tend to be limited. Developing a land-atmosphere hot spot
deﬁnition based solely on temperature would allow us to leverage the observational record prior to thesatellite era, ensuring robust statistics.
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Acknowledgments
We thank Laurent Terray forconceiving the method of dynamical
adjustment used in this study andKaren McKinnon, Adam Phillips,
Nicolas Siler, and Jeﬀery Strong forhelpful discussion. We also thanktwo anonymous reviewers for theirconstructive feedback. A. M. was supported by National Science Foundation(NSF) Graduate Research Fellowship
Program under grant DGE-1144086
and the Graduate Student Visiting
Fellowship at the National Center
for Atmospheric Research. F. L. is
supported by a Postdoc Applying
Climate Expertise (PACE) fellowshipcosponsored by NOAA and the Bureau
of Reclamation. The CESM-LE simulations are available on the Earth System
Grid (www.earthsystemgrid.org).
The National Center for AtmosphericResearch is sponsored by NSF.

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