Supplementary MaterialtoAttributing the US Southwest’s recent shift into drier conditions

Flavio Lehner1*, Clara Deser1, Isla R. Simpson1, Laurent Terray2
1

Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder,
USA2

Climate, Environment, Coupling and Uncertainties group, Université de Toulouse,
CERFACS/CNRS, Toulouse, France*

flehner@ucar.eduContent•

Table S1 and S2•Section S1•Figures S1 to S71

Table S1: List of model simulations used in this study. Historical, rcp45, and rcp85 refer to theCMIP5 radiative forcing protocols (Meinshausen et al. 2011). ERSSTv3b and ERSSTv4 refer tothe Extended Reconstructed Sea Surface Temperature version 3b (Smith et al. 2008) and 4(Huang et al. 2015). HadISST refers to the adley Centre Sea Ice and Sea Surface Temperature
data set (Rayner et al. 2003). More details on the simulations can be found in the respective
references. CESM website refers to http://www.cesm.ucar.edu/experiments/cesm1.1/LE.

Model
familyCESM

TimeperiodEnsemblemembers or
number ofmodels
Reference

historical, rcp85

1920-210040(Kay et al. 2015)

ERSSTv4, HadISST

1880-201410

CESM website

ERSSTv4, HadISST

1880-201510

CESM website

Preindustrial control

2,200 years

1(Kay et al. 2015)

2,600 years

1

CESM website

CAM5

Climatological SSTs
from CESM
preindustrial control

Coupledhistorical, rcp45

1861-2015

10(Kosaka and Xie 2016)

ERSSTv3b,HadISST

1950-2012

10(Kosaka and Xie 2016)

historical, rcp85

1871-2099

40

variousExperimental
setup
Forcing details
CoupledCESM1-CAM5

CESM

TOGA
CAM5

CESM

GOGACAM5

CESM

CoupledCESM1-CAM5

CESM

GFDL

Atmosphere-onlyCM2.1-AM2.1

GFDLGOGAAM2.1

CMIP5

Coupled2

Table S2: 1983-2012 trends over the US Southwest (region marked in Fig. 1a) for unadjusted
and dynamically adjusted observed precipitation (GPCC) and surface air temperature (BEST).
Uncertainty for unadjusted observations is based on the minimum and maximum trend in
Newman et al. (2015) and given in square brackets. For dynamically adjusted trends, the result
from EEMD/EEMD detrending is given, while the uncertainty is based all the different
detrending methods tested (see section S1) and given in square brackets.
Water year
Precipitation Unadj.
(%/30 yrs)

Dyn. adj.

Temperature Unadj.
(°C/30 yrs)

Dyn. adj.

Oct-Dec

Jan-MarApr-Jun

Jul-Sep

-19.3

-19.0

-26.7

-9.1

-29.3

[-21.4, -12.0][-26.2, -9.2][-29.7, -15.9][-14.0, 1.5][-36.6, -19.3]

-6.13

-3.4

-0.8

1.3

6.2

[-6.13, -0.8][-9.6, -2.0][-0.8, 11.9][1.3, 15.2][-5.1, 6.2]

0.84

1.01

0.38

0.46

1.32

[0.68, 0.85][0.76, 1.08][0.28, 0.64][0.37, 0.56][1.09, 1.44]

0.49

0.31

0.34

0.50

0.28

[0.35, 0.79][-0.07, 0.61][0.34, 1.01][0.50, 0.89][0.28, 0.88]

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S1. Detrending method prior to dynamical adjustment
For dynamical adjustment with constructed circulation analogues (CCA), a large number ofanalogues are required. Ideally, these analogues are not systematically skewed in theirdistribution of climate states they represent. In context of surface air temperature (SAT) it can beproblematic to use CCAs from a climate significantly warmer or colder than the climate that oneis trying to reconstruct, since an analogue from a warmer climate will falsely give the impressionthat a particular circulation pattern induced warmer conditions, when in the respective climatethat particular circulation pattern might not have a tendency to induce warmer conditions(Smoliak et al. 2015). Note that this issue can also be exploited to study the changing influenceof particular circulation patterns under transient climate conditions (Cattiaux et al. 2012). Whenusing climate models, the issue can be avoided for example by creating CCAs from control
simulations, which represent stationary climate states. For observations this is not possible,
which is why an estimate of the climate non-stationarity, i.e., the forced anthropogenic trend, has
to be removed before the observations can be used to create an un-skewed distribution of CCAs.
Different approaches to detrend SAT have been tested in Deser et al. (2016). Here we test
whether and how precipitation (P) and sea level pressure (SLP) should be detrended before
constructing the CCAs, since it is less clear a priori whether to detrend or not, due to a lessrobust understanding of the anthropogenically forced trend in these two variables compared toSAT (Gillett and Stott 2009; Hegerl et al. 2015). The following detrending methods are tested:(i) no detrending, (ii) removing the CESM LE mean, (iii) removing the CMIP5 multi-model
mean across 40 models, and (iv) removing the first intrinsic mode function of the ensemble
empirical mode decomposition (EEMD; Wu and Huang 2009). EEMD has the benefit of being
non-parametric and is suited to deal with non-stationary and non-linear time-space phenomena;
however, it is purely statistical. The CESM LE mean and CMIP5 multi-model mean have thebenefit of being informed by physics; however, they include possible model biases. We apply
these detrending methods in different combinations to P and SLP observations over the period
1901-2015 and investigate their impact on the 1983-2012 dynamically adjusted P and SLP trend
over Western North America, i.e., same domain and time period as in the main paper (Fig. S1).
While it is clear that the different methods yield slightly different results, they ultimately allreduce the drying trend over the US Southwest. The range of area-averaged P trends resulting
from the different detrending methods is given in Table S2 and is comparable to the uncertainties
in the unadjusted observations themselves. We thus conclude that it does not matter critically forthe study here which detrending method is chosen. Maybe more interestingly, this also suggeststhat any forced gradual changes in SLP (if they exist in reality), do not notably affect ourestimate of the general SLP influence on the observed P and SAT trends. For consistency, in themain paper we focus on the results from the detrending of SAT, P, and SLP with EEMD.

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Figure S1: Water year precipitation (P), surface air temperature (SAT), and sea level pressure(SLP) trend from 1983 to 2012 from (a) observations (GPCC and TCR version 2c) and (b-h)
after dynamical adjustment. Prior to dynamical adjustment, P, SAT, and SLP have been
detrended with the methods indicated in the individual panels.

We dynamically adjust ensemble member #8 of the CESM LE, since it happens to show a similarshift from positive to negative PDO during 1983-2012 as observed (not shown) as well as a similar
drying trend over the US SW. The dynamical adjustment of this simulation (and all other 39 CESM
LE members, used in Fig. S4) is conducted in a similar fashion as for observations (Section 2.2).
That is, 95 model years of CESM LE member #8 are used (1920-2015), 80 of which are selected
randomly to form the CCAs, and the procedure is repeated 100 times. Instead of EEMD, we usethe CESM LE 40-member mean (a good estimate of the model’s forced response; Deser et al.
2016) to detrend precipitation, SAT, and SLP prior to dynamical adjustment.

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Figure S2: Water year sea level pressure (SLP) trend from 1983 to 2012 from different reanalysis
datasets. While the positive SLP trend in the Aleutian Low is robust across datasets, the anomaly
over the Western US varies across datasets in terms of location, magnitude, and sign. Similar
results are seen in surface pressure (not shown).

6Figure S3: Water year precipitation and sea level pressure (SLP) trend from 1983 to 2012 from(a) GFDL GOGA, (b) GFDL LE, (c) CMIP5. (d-f) same as (a-c), but for surface air temperatureand SLP. Number of simulations of each ensemble is given in brackets. Contours are sea levelpressure trend in 0.5hPa 30 years-1 increments starting at ±0.25hPa.

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Figure S4: Water year sea surface temperature (SST), precipitation, and sea level pressure (SLP)
trends for (a) observations 1936-1965, (b) observations 1983-2012, (c) ensemble mean of CESM
LE 1936-1965, (d) ensemble mean of CESM LE 1983-2012. Contours are sea level pressure
trend in 0.5hPa 30 years-1 increments starting at ±0.25hPa.

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Figure S5: 1983-2012 water year trend averaged over the US Southwest for (a) precipitation and(b) surface air temperature from unadjusted and dynamically adjusted observations and all 40
CESM LE members. The uncertainty range for the dynamically adjusted observations (redshading) is based on the 7 different detrending approaches discussed in Section S1. The reddashed line marks the detrending approach used in the main text (EEMD/EEMD).

9Figure S6: 30-year precipitation trends over the US SW from (red) CESM GOGA, (blue) CESM
LE, (light blue) a 2,200-year long CESM preindustrial control simulation, and (black) a 2,600year long atmosphere-only simulation with CAM5, the atmospheric component of CESM, inwhich climatological SSTs from the CESM preindustrial control simulation are prescribed
perpetually. For the specific period 1983-2012, the individual simulations from CESM GOGAand CESM LE are shown with circles; all the other trends are summarized with vertical bars that
represent the 5-95% range of the trend distribution. The seasons are marked on the x-axis as
water year (WY), Oct-Dec (OND), Jan-Mar (JFM), Apr-Jun (AMJ), and Jul-Sep (JAS).

10Figure S7: 30-year water year precipitation and sea level pressure trends from (a) observations,(b) CESM atmosphere-only control simulation with CAM5, and (c) CESM coupled preindustrial
control simulation. For observations, the 1983-2012 trend is shown. For the two model
simulations, the 30-year trend period with the highest precipitation pattern correlation with theobserved trend pattern (over the entire domain of the Figure) is shown. Model trends are
expressed relative to their long-term mean.

11References
Cattiaux, J., P. Yiou, and R. Vautard, 2012: Dynamics of future seasonal temperature trends andextremes in Europe: A multi-model analysis from CMIP3. Clim. Dyn., 38, 1949–1964,
doi:10.1007/s00382-011-1211-1.
Deser, C., L. Terray, and A. S. Phillips, 2016: Forced and internal components of winter airtemperature trends over North America during the past 50 years: Mechanisms andimplications. J. Clim., 29, 2237–2258, doi:10.1175/JCLI-D-15-0304.1.
Gillett, N. P., and P. a. Stott, 2009: Attribution of anthropogenic influence on seasonal sea levelpressure. Geophys. Res. Lett., 36, L23709, doi:10.1029/2009GL041269.
Hegerl, G. C., and Coauthors, 2015: Challenges in quantifying changes in the global water cycle.
Bull. Am. Meteorol. Soc., 96, 1097–1115, doi:10.1175/BAMS-D-13-00212.1.
Huang, B., and Coauthors, 2015: Extended reconstructed sea surface temperature version 4(ERSST.v4). Part I: Upgrades and intercomparisons. J. Clim., 28, 911–930,
doi:10.1175/JCLI-D-14-00006.1.
Kay, J. E., and Coauthors, 2015: The community earth system model (CESM) large ensemble
project : A community resource for studying climate change in the presence of internal
climate variability. Bull. Am. Meteorol. Soc., 96, 1333–1349, doi:10.1175/BAMS-D-1300255.1.
Kosaka, Y., and S.-P. Xie, 2016: The tropical Pacific as a key pacemaker of the variable rates ofglobal warming. Nat. Geosci., 9, 669–673, doi:10.1038/ngeo2770.
Meinshausen, M., and Coauthors, 2011: The RCP greenhouse gas concentrations and theirextensions from 1765 to 2300. Clim. Change, 109, 213–241, doi:10.1007/s10584-011-0156z.
Newman, A. J., and Coauthors, 2015: Gridded Ensemble Precipitation and Temperature
Estimates for the Contiguous United States. J. Hydrometeorol., 16, 2481–2500,
doi:10.1175/JHM-D-15-0026.1.
Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C.
Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and nightmarine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407,doi:10.1029/2002JD002670.
Smith, T. M., R. W. Reynolds, T. C. Peterson, and J. Lawrimore, 2008: Improvements to
NOAA’s historical merged land-ocean surface temperature analysis (1880-2006). J. Clim.,
21, 2283–2296, doi:10.1175/2007JCLI2100.1.
Smoliak, B. V., J. M. Wallace, P. Lin, and Q. Fu, 2015: Dynamical Adjustment of the NorthernHemisphere Surface Air Temperature Field: Methodology and Application toObservations*. J. Clim., 28, 1613–1629, doi:10.1175/JCLI-D-14-00111.1.
Wu, Z., and N. E. Huang, 2009: Ensemble Empirical Mode Decomposition : A Noise Assisted
Data Analysis Method. Adv. Adapt. Data Anal., 1, 1–41, doi:10.1142/S1793536909000047.

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