RESEARCH LETTER
10.1029/2018GL081316
Key Points:• Removing the inﬂuence of
atmospheric circulation variability
reveals the thermodynamic
component of observed precipitation
trends• The observed thermodynamic trend
is in good agreement withanthropogenically forced trends
from historical climate model
simulations• Our approach provides analternative to formal “detection andattribution” methods for revealing
anthropogenic changes inobservations
Supporting Information:• Supporting Information S1

Correspondence to:
C. Deser,cdeser@ucar.edu

Citation:
Guo, R., Deser, C., Terray, L., &Lehner, F. (2019). Human inﬂuence on
winter precipitation trends (1921–2015)
over North America and Eurasia
revealed by dynamical adjustment.
Geophysical Research Letters, 46.
https://doi.org/10.1029/2018GL081316
Received 13 NOV 2018
Accepted 24 FEB 2019
Accepted article online 4 MAR 2019

Human Inﬂuence on Winter Precipitation Trends(1921–2015) over North America and Eurasia
Revealed by Dynamical Adjustment
Ruixia Guo1,2, Clara Deser1, Laurent Terray3, and Flavio Lehner11

Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, USA, 2Key
Laboratory for Semi‐arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou
University, Lanzhou, China, 3Climate, Environment, Coupling and Uncertainties group, Université de Toulouse,
CERFACS/CNRS, Toulouse, France

Abstract Detecting and attributing a human inﬂuence on observed rainfall trends is a major challenge
due to the presence of large amplitude internal variability on all time scales and by limited temporal andspatial data coverage. Here we apply a “dynamical adjustment” methodology to a gridded archive of monthly
precipitation to estimate an anthropogenic inﬂuence on long‐term (1920–2015) trends over North America
and Eurasia during winter (November–March). This empirical approach aims to remove atmosphericcirculation inﬂuences from precipitation variability and trends, thereby revealing the thermodynamicallyinduced component as a residual. The geographical pattern and amplitude of this observed thermodynamic
residual precipitation trend are in good agreement with anthropogenically forced trends obtained fromensembles of historical climate model simulations. Such consistency helps to reconcile observations andmodels and provides compelling evidence for a human inﬂuence on century‐scale precipitation trends over
North America and Eurasia during the cold season.
Plain Language Summary It is difﬁcult to isolate the anthropogenic inﬂuence on long‐term
precipitation trends due to confounding effects from internal variability. Here we remove the inﬂuence of
atmospheric circulation variability, which is primarily unforced, from observed precipitation trends using anempirical approach called “dynamical adjustment.” This removal isolates the thermodynamic component ofobserved precipitation trends as a residual. We ﬁnd that this thermodynamic component is in good
agreement with the anthropogenic component determined from historical simulations from climate models.
Thus, we conclude that we are able to identify a human inﬂuence on observed century‐scale precipitation
trends over North America and Eurasia.
1. Introduction
Precipitation plays a crucial role in climate and sustains life on Earth. Without efforts to reduce greenhouse gas emissions from the burning of fossil fuels, humans will cause unprecedented changes in precipitation worldwide, with dire consequences for society, agriculture, and the environment (IPCC, 2013).
Globally, precipitation is projected to increase at an approximate rate of 1–2% per K warming; regionally,
however, rainfall changes will be more complex depending on local horizontal and vertical temperature
gradients and associated changes in atmospheric circulation. For example, intensiﬁcation and poleward
expansion of the Hadley Circulation will lead to zonal‐mean precipitation increases in the deep tropics
and middle latitudes and reductions in the subtropics (Held & Soden, 2006; IPCC, 2013; O'Gorman &Schneider, 2009).

©2019. The Authors.
This is an open access article under theterms of the Creative Commons
Attribution‐NonCommercial‐NoDerivs
License, which permits use and distribution in any medium, provided theoriginal work is properly cited, the use
is non‐commercial and no modiﬁcations or adaptations are made.

GUO ET AL.

While theory and climate models provide a physical framework for understanding the projected changes inrainfall, detecting and attributing a human inﬂuence on observed rainfall trends is a major challenge (Hegerl
et al., 2015; Knutson & Zeng, 2018; Sarojini et al., 2016). This challenge stems from the high degree of internal variability of precipitation at all time scales, and by limitations of the observational record including data
quality issues and restricted spatial and temporal coverage (Hawkins & Sutton, 2009; Hegerl et al., 2015;
Sarojini et al., 2016). Nonetheless, formal “detection and attribution” methods based on model “ﬁngerprints” of externally forced signals have revealed a role for anthropogenic climate change in observed precipitation trends over limited latitudinal bands, regions, and seasons, including parts of the NH continents inwinter and spring, as reviewed in Sarojini et al. (2016). Such methods have also been used to study changes

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in the annual cycle of zonal‐mean precipitation equatorward of 50° latitude during the satellite era (1979–2014), a period confounded by the global surface warming hiatus (Marvel et al., 2017). Most recently,
Knutson and Zeng (2018) assessed the degree to which regional terrestrial precipitation trends are consistent
with historical climate model simulations. They concluded that models possibly underestimate century‐
scale trends over extratropical land in comparison with observations, with important implications for theveracity of model projections over the coming decades. They ascribed the underestimation to a combinationof model and forcing errors, in addition to data quality issues.
Here, we present a different approach to the problem of identiﬁcation of a possible human inﬂuence on
century‐scale (1921–2015) precipitation trends over North America and Eurasia during the cold season using
a “dynamical adjustment” methodology based on Constructed Circulation Analogs (CCAs). The beneﬁt ofthis approach is that it provides an observationally based estimate of response to anthropogenic forcing thatis independent of climate models. Versions of this methodology have been applied to terrestrial surface airtemperatures to understand and attribute warming trends over the NH (Deser et al., 2016; Lehner et al.,2017; Smoliak et al., 2015) and to identify hotspots of land‐atmosphere coupling (Merriﬁeld et al., 2017).
To the best of our knowledge, this is the ﬁrst time dynamical adjustment has been used to study and attributecentury‐scale precipitation trends. O'Reilly et al. (2017) used dynamical adjustment to isolate residual thermodynamic effects of the Atlantic Multidecadal Oscillation upon European rainfall, while Lehner et al.
(2018) used it to attribute changes in hydroclimate over the southwestern United States over the lastfew decades.

2. Data and Methods
2.1. Observational Data Sets
We use precipitation data from the Global Precipitation Climatology Centre (GPCC) version 7 (Schneider
et al., 2015). This is a monthly gridded (2.5° latitude × 2.5° longitude) gauge‐analysis product derived from
quality‐controlled station data covering the period 1901–2015. This data set includes information on thenumber of stations in each grid box for each month and year, which we use to mask out grid cells with insufﬁcient data during our study period 1920–2015 using the criterion of at least one station with continuous
data every decade on average from the 1920s through the 2000s. According to a recent comprehensive review
of global precipitation data sets by Sun et al. (2018), GPCC contains approximately ﬁve times more station
data than the Climate Research Unit archive (Harris et al., 2014; New et al., 2000), which is the only othercentury‐long gridded gauge product. However, the patterns of precipitation trends based on Climate
Research Unit are similar to those based on GPCC, with only small differences in amplitude, and our conclusions regarding dynamical and thermodynamic contributions to these trends are not affected by the
choice of data set (see Figure S1).
For the dynamical adjustment procedure, we use monthly sea level pressure (SLP) data from the TwentiethCentury Reanalysis (20CR) version 2c (Compo et al., 2011). This gridded product has a spatial resolution of
2° latitude × 2° longitude, and extends from 1920 to 2014. We have updated the 20CR through 2015 with datafrom ERA‐interim (Dee et al., 2011). Long‐term SLP trends over the NH extratropics in winter are consideredmore reliable in the 20CR than the ECMWF 20th Century Reanalysis (Poli et al., 2016) as documented byBloomﬁeld et al. (2018).
2.2. Model Data Sets
We make use of a 40‐member ensemble of simulations with the Community Earth System Model, version 1(CESM1), a fully coupled state‐of‐the‐art climate model at a spatial resolution of approximately 1° in both
latitude and longitude (Kay et al., 2015). Each ensemble member is subject to the same radiative forcing (historical for 1920–2005 and representative concentration pathway 8.5 emissions scenario for 2006–2100), but
starts from slightly different atmospheric temperatures on 1 January 1920. Averaging across the 40 members
provides a robust estimate of the model's forced response to climate change (Deser et al., 2012). We also
make use of the Fifth Coupled Model Intercomparison Project (CMIP5) archive (Taylor et al., 2012), which
consists of 37 models with historical and representative concentration pathway 8.5 simulations. We selectthe ﬁrst ensemble member from each of the 37 models and average them together to form the CMIP5 multimodel ensemble‐mean, which we interpret as another estimate of the response to external radiative forcing.
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Geophysical Research Letters10.1029/2018GL081316

2.3. Analysis Methods
Our study period is 1920–2015, dictated by the availability of the precipitation station data and the start year
for the CESM1 ensemble. We compute monthly anomalies by subtracting the long‐term (1920–2015)
monthly means from the corresponding month of each year, and then form cold season (November throughMarch) averages from the monthly anomalies. The 1921 cold season consists of data averaged from
November 1920 through March 1921, and so on. We compute linear trends using least‐squares regression
analysis and assess statistical signiﬁcance at the 90% conﬁdence level using a two‐tailed Student's t test.
Similar results are obtained with more sophisticated trend ﬁtting procedures such as Extended Empirical
Model Decomposition (Wu & Huang, 2009; not shown). In addition, the results are not sensitive to the exact
start date, with similar dynamically adjusted patterns for trends that begin in 1930, 1940, and 1950 (notshown). All model precipitation (SLP) data are regridded to the GPCC (20CR) grid usingbilinear interpolation.
2.4. Dynamical Adjustment Technique
We remove the inﬂuence of atmospheric circulation variability from observed and simulated monthly precipitation following the CCA approach of Deser et al. (2016). First, we reconstruct the SLP pattern for a given
month and year (Ti) using an optimal linear combination of SLP patterns in the same month from 40 randomly selected years in the data set (excluding the year in question) based on multiple linear regression.
For example, to reconstruct the SLP pattern in January 1920, we optimally combine 40 randomly selected
Januaries during 1921–2015. We then apply the same set of optimal linear weights to the corresponding precipitation ﬁelds to derive the “dynamically induced” component of precipitation at time Ti. This random
selection and optimal reconstruction procedure is repeated 100 times (with replacement) for each monthand year in order to sample different land and ocean states that might otherwise inﬂuence the estimate ofdynamically induced precipitation. Finally, for each month and year, the 100 reconstructed SLP patterns
and the accompanying dynamical‐induced precipitation ﬁelds are averaged together, providing “best estimates” of these ﬁelds and to guard against overﬁtting.
The dynamical adjustment procedure is conducted separately for North America and Eurasia to improve thereconstruction of the SLP patterns and associated estimates of dynamically induced precipitation (see Figure
S2 for the domains used). To obtain the thermodynamic contribution to precipitation in each month andyear, we subtract the dynamically induced component from the total precipitation. It is important to notethat the dynamically induced precipitation estimates include radiative and/or thermodynamic feedbacks
that may arise from changes in, for example, cloud cover or land surface conditions that accompany the
SLP patterns. Thus, the term dynamically induced precipitation refers to the combination of circulationinﬂuence and these potential local feedbacks (see Deser et al., 2016, and Lehner et al., 2017, for further discussion). We emphasize that no model information is used in our determination of the dynamical and thermodynamic contributions to observed precipitation.
We have examined the sensitivity of our results to the number of years chosen for our random samples, thenumber of repetitions used, and the size of the SLP domain and ﬁnd only small differences for reasonablechoices of these parameters, differences that are within the error of the method as determined by applying
the same dynamical adjustment procedure to each ensemble member of CESM1 where the true forced
response is known (see Figure S3).

3. Results
The map of observed winter precipitation trends during 1921–2015 is displayed in Figure 1. NorthernEurasia (north of approximately 40°N) shows widespread and statistically signiﬁcant increases in winter precipitation during 1921–2015, with values exceeding 1.2–1.6 mm mo−1 per decade west of the Ural Mountains
and along the east coast, while southern Europe exhibits coherent albeit weaker amplitude drying trends
that attain statistical signiﬁcance over the eastern Mediterranean (Figure 1a). These precipitation trends
occur in the context of changes in the large‐scale atmospheric circulation, with negative SLP trends overnorthern Eurasia and positive SLP trends over the central North Atlantic extending into southwestern
Europe (Figure 1a). Precipitation trends over North America are generally positive but statistically insignificant, accompanied by relatively weak trends in SLP (Figure 1a).
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Geophysical Research Letters10.1029/2018GL081316−1

Figure 1. (a) Observed winter (November–March) precipitation (color shading; mm mo per decade) and SLP (contours, hPa per decade) trends during 1921–
2015. (b) Dynamical contribution to (a). (c) Thermodynamic residual (a minus b). Ensemble‐mean precipitation and SLP trends from (d) CMIP5 and (e)CESM1. In all panels, the SLP contour interval is 0.1 hPa per decade, with positive (negative) values in red (blue) and the zero contour in black. Stippled regions
denote precipitation trends that are insigniﬁcant at the 90% conﬁdence level based on a two‐sided Student t test. The zero contour is suppressed for clarity on panel(c). CMIP5 = Fifth Coupled Model Intercomparison Project; CESM1 = Community Earth System Model, version 1; SLP = sea level pressure.

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Figure 2. Observed time series of winter (November–March) precipitation anomalies (mm mo ) averaged over(a) Northern Europe and (b) Eastern North America (regions outlined in Figure 1c). The upper curve shows the total
precipitation, middle curve shows the dynamical contribution, and the bottom curve shows the thermodynamic residual.
Also shown are the best‐ﬁt linear trend lines. The thick red and blue curves show the CESM1 and CMIP5 ensemble‐mean
total precipitation time series, respectively, and the thin red curves show the thermodynamic residual time series foreach of the 40 CESM1 ensemble members. CMIP5 = Fifth Coupled Model Intercomparison Project; CESM1 = Community
Earth System Model, version 1.

The dynamical contribution to these observed trends as estimated with our CCA procedure is shown in
Figure 1b. Atmospheric circulation changes account for approximately half of the wetting over northernEurasia, and most of the drying over southern Europe (and extending northward into France and the southern United Kingdom), consistent with the cyclonic and anticyclonic SLP trends in these two regions, respectively (Figure 1b). These circulation‐induced precipitation trends are generally statistically signiﬁcant for
magnitudes exceeding 0.4 mm mo−1 per decade (Figure 1b). Over North America, anticyclonic circulation
trends produce signiﬁcant drying in the east, while cyclonic trends contribute to wetting over the western
United States (albeit insigniﬁcant; Figure 1b). It is evident by comparing the total (Figure 1a) and reconstructed (Figure 1b) SLP trends that our CCA procedure is highly skillful, with SLP trend differences nearzero throughout the Northern Hemisphere (Figure 1c).
Subtracting the dynamically induced component of observed precipitation trends from the total yields anestimate of the thermodynamic contribution, hereafter referred to as “thermodynamic residual”(Figure 1c). The spatial pattern and amplitude of this observed thermodynamic residual precipitation trend
are in good agreement with the radiatively forced component of trends simulated by climate models, here
given by the ensemble‐mean of 37 models in the CMIP5 (Figure 1d) and of the 40‐member CESM1(Figure 1e), more so than are the original (total) precipitation trends. For example, over Eurasia the thermodynamic residual trends show a more zonally uniform structure compared to the total trends, with precipitation increases (decreases) north (south) of approximately 35°N, similar to the models' forced (ensemble‐mean)patterns. In addition, the reduced magnitudes of the thermodynamic residual wetting and drying trends are inbetter agreement with the amplitudes of the models' forced responses than are the total trends. Over North
America, the thermodynamic residual trends show an area of signiﬁcant positive values over the eastern thirdof the continent, a feature that is absent from the total trends and which corresponds well with the region ofenhanced wetting in the models' forced responses, especially CMIP5. The thermodynamic residual trends over
the rest of North America are generally insigniﬁcant and do not resemble the models' ensemble‐mean trends.
More quantitatively, the spatial correlation coefﬁcient between the observed thermodynamic residual trend
and the CMIP5 (CESM1) ensemble‐mean trend is 0.63 (0.41), and the root‐mean‐square (rms) amplitude of

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Table 1
Observed Total, Dynamical, and Thermodynamic Residual Precipitation (Top) Trends (1921–2015) and Associated 90%−1−1
Conﬁdence Intervals ((mm mo ) decade ) and (Bottom) Interannual Variance for Northern Europe and Eastern
North America
Ensemble‐mean
Trend(1921–2015)−1−1(mm mo ) dec

Observed
total

Observed
dynamical
contribution

Observed
thermodynamic
residual

Northern Europe

0.79 ± 0.25

0.32 ± 0.22

0.47 ± 0.12

Eastern North
America

0.27 ± 0.46

−0.32 ± 0.37

0.59 ± 0.36

CMIP5
CESM1
a

0.30 ± 0.06
a0.29 ± 0.05
a0.44 ± 0.08
a0.38 ± 0.10
Ensemble‐mean
thermo. resid.

Observed
total

Observed
dynamical
contribution

Observed
thermodynamic
residual

Northern Europe

20.2

13.1

5.2

Eastern North
America

54.5

34.9

35.6

Interannual
variance−1 2(mm mo )

CMIP5
CESM1
b

3.0
b
2.4
b
30.9
b
31.4

Note. For trend, values in bold font are signiﬁcant at the 90% conﬁdence level. For interannual variance, all data are
detrended prior to computing the variance over the period 1921–2015. CMIP5 = Fifth Coupled Model
Intercomparison Project; CESM1 = Community Earth System Model, version 1.
aEnsemble‐mean trend values and 90% conﬁdence intervals based on (top) CMIP5 and (bottom) CESM1.
b
Interannual variance of thermodynamic residual precipitation averaged across all members of (top) CMIP5 and(bottom) CESM1.

the observed thermodynamic residual trend is 0.46 mm mo−1 per decade which is closer to the rms amplitudeof the CMIP5 (0.31 mm mo−1 per decade) and CESM1 (0.33 mm mo−1 per decade) ensemble‐mean trend thanis the observed total trend (rms amplitude = 0.72 mm mo−1 per decade). Note that the ensemble‐mean
precipitation trends in CMIP5 and CESM1 show a high degree of statistical signiﬁcance due to the large
number of simulations averaged together, which reduces the noise from internal variability, while somenoise remains in the observed thermodynamic residual due to errors in our dynamical adjustment
methodology and potential internal thermodynamic inﬂuences.
The forced SLP trends in both CMIP5 and CESM1 are very weak compared to the observed SLP trends,
underscoring that the radiatively forced precipitation trends in the two model ensembles are predominantly
thermodynamic in origin (Figures 1d and 1e). This lends credence to our physical interpretation of theobserved residual precipitation trends (Figure 1c). Indeed, we have veriﬁed that removing the forced SLP
response (by subtracting the CESM1 or CMIP5 ensemble‐mean SLP in each month and year) before performing dynamical adjustment on the observations does not appreciably alter the thermodynamic residual
precipitation trends (see Figure S4).
To complement the trend maps, we show time series of total, dynamical, and thermodynamic residual coldseason precipitation anomalies for two regions (outlined in Figure 1c): Northern Europe (42–75°N, 12°W–
60°E) and Eastern North America (30–55°N, 55–95°W). The signiﬁcant upward trend in total precipitation
over Northern Europe (0.79 ± 0.25 mm mo−1 per decade) is superimposed upon large interannual variability(Figure 2a, top panel). Atmospheric circulation changes account for 40% of the total long‐term trend, and
74% of the total interannual variance (Figure 2a, middle panel; Table 1), leaving a substantial upward trend(0.47 ± 0.12 mm mo−1 per decade) and minimal variance in the residual time series (Figure 2a, bottom
panel; Table 1). Note that the total interannual variance slightly exceeds the sum of the individual dynamical
and thermodynamic residual interannual variances due to nonzero temporal correlation between thesecomponents (Table 1).
The magnitude of the observed thermodynamic residual trend for Northern Europe is similar to the anthropogenic (ensemble‐mean) trends in CMIP5 and CESM1 (0.30 ± 0.06 and 0.29 ± 0.05 mm mo−1 per decade,GUO ET AL.

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Figure 3. Observed and CESM1 winter (November–March) precipitation (a, d) climatology (mm mo ), (b, e) interannual−1 2variance (mm mo ) , and (c, f) percentage of interannual variance explained by dynamical adjustment (%) based on
detrended data during 1921–2015. Results for CESM1 are based on averaging across all 40 members of the ensemble.
CMIP5 = Fifth Coupled Model Intercomparison Project; CESM1 = Community Earth System Model, version 1.

respectively; Table 1). In addition, the interannual variance of the observed (detrended) residual time series[(5.2 mm mo−1)2] is similar to that of the models' (detrended) residual time series [(3.0 mm mo−1)2 and(2.4 mm mo−1)2] for CMIP5 and CESM1, respectively, obtained by applying dynamical adjustment toeach ensemble member and then averaging the residual variances across the members (Table 1). These
results are visually apparent in Figure 2a (bottom panel), which shows that the observed residual timeseries lies within the spread of the 40 individual CESM1 residual time series in all but 7 years of therecord (similar results are obtained for CMIP5; not shown). The CESM1 and CMIP5 ensemble‐mean timeseries (thick red and blue curves in the bottom panel of Figure 2a) exhibit less interannual variability thanthe observed thermodynamic residual time series (Table 1), a reﬂection of the fact that with so many
ensemble members, most of the noise from internal variability has been averaged out, while some noise
remains in the observed thermodynamic residual due to errors in our dynamical adjustment methodology
and potential internal thermodynamic inﬂuences.
Compared to Northern Europe, total precipitation over Eastern North America displays a much smaller (andstatistically insigniﬁcant) upward trend (0.27 ± 0.46 mm mo−1 per decade) and a higher level of interannual
variance (Figure 2b, top panel; Table 1). Atmospheric dynamics causes a drying trend (−0.32 ± 0.37 mm mo−1
per decade), leaving a statistically signiﬁcant wetting trend in the residual time series (0.59 ± 0.36 mm mo−1
per decade: Figure 2a, middle and bottom panels; Table 1). Like Northern Europe, the magnitude (and sign) of
the residual trend is similar to the anthropogenic trends in CMIP5 and CESM1 (0.44 ± 0.08 and0.38 ± 0.10 mm mo−1 per decade, respectively), and the residual variance is comparable to that in both model
ensembles (Table 1). Indeed, the observed residual time series lies within the ensemble of residual time series
from CESM1 (Figure 2b, bottom panel) and CMIP5 (not shown). A larger proportion of interannual varianceremains in the observed residual time series for Eastern North America (65%) compared to Northern Europe(25%; Table 1).
Figures 3a and 3b show maps of observed cold season precipitation climatology and its interannual variance(based on detrended data), respectively. There is a strong correspondence between the climatology and theamplitude of variability. For example, coastal regions are wetter and more variable than continental interiors. The variance attributable to atmospheric dynamics maximizes over western North America andEurasia, exceeding 50% within these areas and more than 70% along the European coast (Figure 3c). Lessvariance is explained elsewhere, even in the highly variable regions of eastern North America and Japan.
Both CESM1 and CMIP5 show realistic simulations of the geographical patterns and amplitudes of precipitation climatology and total interannual variance, as well as the proportion of variance attributable to
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dynamics (Figures 3d–3f and S5, respectively). The dominant modes of interannual precipitation variability
over North America and Eurasia are associated with large‐scale SLP anomaly patterns that are indicative of acirculation‐driven inﬂuence (Figure S6); these relationships are realistically simulated by the models(Figure S7).

4. Summary and Discussion
To the best of our knowledge, this is the ﬁrst use of dynamical adjustment to identify an anthropogenic inﬂuence on observed long‐term (95‐year) precipitation trends. In particular, we have applied a CCA‐based procedure to estimate the dynamically induced portion of winter (November–March) precipitation trends
during 1921–2015 over North America and Eurasia. Subtracting this dynamical component from the total
trend yields the thermodynamic residual precipitation trend. The geographical pattern and amplitude of thisobserved thermodynamic residual trend are in good agreement with anthropogenically forced trends
obtained from two different climate model large ensembles: the 37‐model CMIP5 archive and the
40‐member CESM1. In particular, the observed thermodynamic residual trends over Eurasia show a more
zonally uniform structure compared to the total trends, with precipitation increases (decreases) north(south) of approximately 35°N, similar to the models' forced (ensemble‐mean) patterns. In addition, themagnitudes of the residual wetting and drying trends are in better agreement with the models' forced
responses than are the total trends. Over North America, the thermodynamic residual trends show enhanced
wetting over the eastern third of the continent, similar to the forced responses in the model ensembles. In
observations, this thermodynamic wetting trend is nearly completely offset by a dynamically induced
drying trend.
The fact that the forced circulation (SLP) trends in both model ensembles are close to zero over the time period considered leads us to conclude that the dynamically induced portion of the observed precipitation
trends is almost entirely internally generated. This, in turn, leads us to the interpretation that the observed
residual precipitation trends are externally forced. The close similarity between the models' forced trends
and the observed thermodynamic residual trend lends strong support to our inference and helps to reconcile
models and observations. We emphasize that the dynamical adjustment procedure we have applied to observations is entirely independent of information from climate models, unlike other approaches such as thoseused in formal detection and attribution studies.
We have focused on winter in the extratropical NH for which internal circulation variability is large relative
to the forced circulation response over the historical period. Our method may be less suited to future time
periods, other seasons and regions for which radiatively forced circulation changes are more important(Seager et al., 2010; Byrne & O'Gorman, 2015; Simpson et al., 2016), although model information on forced
circulation changes could be incorporated into the dynamical adjustment protocol. Finally, there is scope for
improvement of our methodology by tailoring the SLP domain to different precipitation subregions andapplying it to daily data.
Acknowledgments
We thank Isla Simpson and Angeline
Pendergrass for useful discussions
during the course of this work. The ﬁrst
author was supported by China
Scholarship Council and National
Natural Science Foundation of China(41722502, 41575006, 41521004,
91637312) and the China 111 project (B13045). NCAR is sponsored by the US
National Science Foundation. Data
supporting the conclusions can beobtained from the references, ﬁgures,the table, and the supporting information. We acknowledge the efforts of all
those who contributed to producing thesimulations and observational data sets.
All CESM simulations are available
online (earthsystemgrid.org). We thankthe two anonymous reviewers for theirhelpful comments.

GUO ET AL.

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