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Projecting North American Climate over the Next 50 Years: Uncertainty due
to Internal Variability*
CLARA DESER AND ADAM S. PHILLIPS
National Center for Atmospheric Research,1 Boulder, Colorado

MICHAEL A. ALEXANDERNOAA/Earth System Research Laboratory, Boulder, Colorado

BRIAN V. SMOLIAK
University of Minnesota, Minneapolis, Minnesota(Manuscript received 29 July 2013, in final form 30 October 2013)
ABSTRACTThis study highlights the relative importance of internally generated versus externally forced climate trends
over the next 50 yr (2010–60) at local and regional scales over North America in two global coupled model
ensembles. Both ensembles contain large numbers of integrations (17 and 40): each of which is subject toidentical anthropogenic radiative forcing (e.g., greenhouse gas increase) but begins from a slightly different
initial atmospheric state. Thus, the diversity of projected climate trends within each model ensemble is due
solely to intrinsic, unpredictable variability of the climate system. Both model ensembles show that natural
climate variability superimposed upon forced climate change will result in a range of possible future trends forsurface air temperature and precipitation over the next 50 yr. Precipitation trends are particularly subject touncertainty as a result of internal variability, with signal-to-noise ratios less than 2. Intrinsic atmosphericcirculation variability is mainly responsible for the spread in future climate trends, imparting regional coherence to the internally driven air temperature and precipitation trends. The results underscore the importance of conducting a large number of climate change projections with a given model, as each realization
will contain a different superposition of unforced and forced trends. Such initial-condition ensembles are alsoneeded to determine the anthropogenic climate response at local and regional scales and provide a newperspective on how to usefully compare climate change projections across models.

1. Introduction
The combined effects of anthropogenic climate change
and natural climate variability will determine Earth’s climate trajectory in the coming decades. At the global scale,human-induced climate change is likely to dominate overinternally generated variability for time periods longer* Supplemental information related to this paper is available atthe Journals Online website: http://dx.doi.org/10.1175/JCLI-D-1300451.s1.
1
The National Center for Atmospheric Research is sponsoredby the National Science Foundation.

Corresponding author address: Clara Deser, Climate and Global
Dynamics Division, National Center for Atmospheric Research,
Boulder, CO 80307.
E-mail: cdeser@ucar.edu
DOI: 10.1175/JCLI-D-13-00451.1
Ó 2014 American Meteorological Society

than about a decade (e.g., Santer et al. 2011; Meehl et al.
2013). However, at regional and local scales, internal variability may be as important as anthropogenic climatechange, even for intervals as long as the next 50 yr at
middle and high latitudes (Deser et al. 2012a,b; Wallace
et al. 2014). The partial masking of human-induced climatechange by internal multidecadal variability is an importantconsideration for policy and planning efforts.
Internal climate variability arises from processes withinand coupled interactions among the atmosphere, oceans,land, and cryosphere. While the ‘‘memory’’ of the atmosphere is generally limited to a few weeks, the atmospheric circulation exhibits long time-scale fluctuations
characteristic of a random stochastic process (e.g., Lorenz
1963; Wunsch 1999; Feldstein 2000; Deser et al. 2012b).
Indeed, extended atmospheric model control integrations
exhibit decadal and longer-term fluctuations despite the2272

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constant lower boundary conditions (Deser et al. 2012b).
Such intrinsic atmospheric circulation variations induce
regional changes in air temperature and precipitationon the multidecadal time scale (e.g., Deser et al. 2012b;
Wallace et al. 2014, and references therein). Slow oceanic fluctuations may impart added persistence to theatmosphere. A prime example is the El Ni~
no–Southern
Oscillation (ENSO) phenomenon, a coupled ocean–
atmosphere mode of interannual variability originatingin the tropical Pacific that affects climate worldwide via
atmospheric teleconnections (e.g., Horel and Wallace1981). On longer time scales, fluctuations of the Atlantic
thermohaline circulation and the Pacific and Atlantic
Ocean gyre circulations contribute to low-frequency climate variability over the adjacent continents. In particular, the Atlantic multidecadal oscillation (AMO) has
been implicated in long-term changes in Atlantic hurricane activity and precipitation over the Sahel, southeastern United States, and Brazil (e.g., Enfield et al. 2001;
Zhang and Delworth 2006; Ting et al. 2009, 2011). Similarly, the Pacific decadal oscillation (PDO; Mantua et al.
1997) and interdecadal Pacific oscillation (IPO; Poweret al. 1999) have contributed to low-frequency climate
fluctuations over North America (Minobe 1997; Deseret al. 2004; Meehl et al. 2012) and Australia (Power et al.
1999). Despite their multidecadal character, the predictability of the AMO and PDO/IPO is limited to much
shorter intervals (,;10 yr) because of their stochastic
nature (Newman 2007; Msadek et al. 2010; Matei et al.
2012; Branstator et al. 2012; Newman 2013).
The climate response to rising concentrations of radiatively active chemical species including well-mixed
greenhouse gases (GHGs) and tropospheric sulfate aerosols associated with human activities involves a complex
set of feedbacks within the climate system: some of which
are more certain than others (e.g., Solomon et al. 2007).
In addition to anthropogenic factors, natural changes inradiative forcing of the climate system occur in association with variations in volcanic activity and solar output.
Understanding such externally forced climate change,
both human and natural in origin, remains a pressing
challenge as evidenced by the considerable range ofmodel sensitivities to the identical set of radiative forcings(Solomon et al. 2007).
Isolating the effects of anthropogenic climate change
from those of internally generated variability requires
ensembles of simulations with a given climate model,
with each subject to the identical external forcing. This
follows from the fact that any single model projection
contains both intrinsic and externally forced contributions: it is only by averaging across ensemble membersthat the random sequences of internally generated variability in the individual realizations can be sufficiently

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muted to reveal the model’s response to external forcing. [Note that this approach does not preclude thepossibility that the forcing may affect the properties of
the internal variability: for example, by changing its
variance.] Once the externally forced response is obtained, it can be subtracted from each model run to findthe contribution from internal variability. While it iscommon practice to average single runs from multiple
models to obtain a robust estimate of anthropogenic
climate change (e.g., the ‘‘multimodel mean’’; Solomon
et al. 2007), this approach does not allow the isolation ofinternal variability in any given model simulation because structural differences between models can lead todifferent externally forced responses.
Using the phase 3 of the Coupled Model Intercomparison
Project (CMIP3) multimodel archive, Hawkins and Sutton(2009, 2011) highlighted that structural differences amongmodels constituted a greater source of uncertainty thaninternal variability in regional surface air temperature(precipitation) projections on time scales longer than 15(30) yr. In their studies, internal variability was defined as
the residual from a fourth-order polynomial fit to the timeseries over the next 100 yr in each run; that is, the externally forced response was equated with the fourth-orderpolynomial fit. With this definition, they effectively eliminated internal climate fluctuations with time scales longer
than a few decades. Thus, they were unable to assess thefull contribution of internal variability to uncertainty inregional climate projections over the next 50 yr.
The spread in projected climate trends over the next
half century resulting from the superposition of internalvariability and anthropogenic forcing has been examined with a 40-member ensemble of climate change
simulations conducted with the National Center for
Atmospheric Research (NCAR) Community Climate
System Model, version 3 (CCSM3) (Deser et al. 2012a,b;Oshima et al. 2012; Kang et al. 2013; Hu and Deser 2013;
Wettstein and Deser 2014; Wallace et al. 2014). Thesestudies focused on various aspects of uncertainty due tointernal variability, including signal-to-noise analysis;
minimum ensemble size requirements; and time of
emergence of the forced signals in near-surface air temperatures, precipitation, and atmospheric circulation
patterns around the globe (Deser et al. 2012b; Oshima
et al. 2012; Kang et al. 2013; Wallace et al. 2014). In addition, uncertainty in the magnitudes of future regionalsea level rise and Arctic sea ice loss were assessed in Hu
and Deser (2013) and Wettstein and Deser (2014), respectively. The impact of internal variability on 50-yr
climate projections over North America was highlighted
briefly in Deser et al. (2012a) and Wallace et al. (2014).
In this paper, we expand upon the early results ofDeser et al. (2012a) and Wallace et al. (2014) to more

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fully document the interplay between anthropogenic
climate change and natural climate variability over
North America during the next half century using twomodel ensembles, the 40-member CCSM3 discussed
above and the 17-member ECHAM5/Max Planck Institute Ocean Model (MPI-OM; referred to as ECHAM5
for short). With such large sample sizes and two different
models, we can more robustly determine the range of
future climate outcomes. To the extent that natural variability over the next 50 yr is unpredictable, this range of
future states represents the irreducible spread of the climate forecast. Knowledge of this spread will be important
for the policy and decision-making communities.
We focus on two key climate parameters, near-surface
air temperature and precipitation, in both winter(December–February) and summer (June–August). In
addition to quantifying the relative amplitudes and spatial patterns of the externally forced and internally generated components of the climate projections in eachmodel run, we investigate the contribution of the atmospheric circulation to the forced and free climate trends.
Additionally, we apply a methodology for empiricallyremoving from each realization the signature of atmospheric circulation-driven internal variability sothat individual runs from different models can be directly compared.
The rest of the paper is organized as follows: Section 2
describes the models and experimental design, as well as
the observational datasets used to evaluate aspects ofthe models’ low-frequency variability. Section 3 provides
results on the range of climate trends over the next 50 yr
from each model ensemble, showing the relative contributions from internal variability and external forcing interms of spatial maps, signal-to-noise ratios, and thelikelihood that the trend will have a particular sign (e.g.,
warming, drying). It also demonstrates the role of theatmospheric circulation in the spread of climate trajectories and briefly compares decadal variability in themodels with nature. Section 4 concludes with a summaryof the main findings and a discussion of the results.

2. Models, experimental design, methods, anddatasets
The primary model output used in this study is the
40-member ensemble of climate change simulations
with the CCSM3 for the period 2000–60 described in
Deser et al. (2012b), to which the reader is referred forcomplete details. CCSM3 is a comprehensive coupledatmosphere–ocean–sea ice–land general circulation
model (for extensive documentation, see special issueof Journal of Climate, 2006, Vol. 19, No. 11) and a participant in CMIP3. The simulations are conducted at

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a horizontal resolution of approximately 2.88 latitude
and 2.88 longitude (T42 spectral truncation). Each ensemble member undergoes the same external forcing:the main components of which are the Special Report on
Emissions Scenarios (SRES) A1B greenhouse gas scenario (in which carbon dioxide concentrations increase
from approximately 380 ppm in 2000 to approximately
570 ppm in 2060) and stratospheric ozone recovery by
2060, as well as smaller contributions from sulfate aerosol and black-carbon changes (see Meehl et al. 2006).
Each ensemble member begins from identical initialconditions in the ocean, land, and sea ice model components (taken from the conditions on 1 January 2000
from a single twentieth-century CCSM3 integration) andslightly different initial conditions in the atmosphericmodel (taken from different days during December 1999
and January 2000 from the same twentieth-century
CCSM3 run). Thus, the spread amongst the different
members represents the irreducible uncertainty of theforecast (e.g., arising from unpredictable internallygenerated variability). The sensitivity of the climate
system to small perturbations in initial conditions ischaracteristic of a nonlinear deterministic system as
elucidated by Lorenz (1963). Perturbing the initial stateof the ocean in addition to the atmosphere may add tothe uncertainty of the climate forecast, but we have notexplored this aspect here.
We also make use of the 17-member ensemble ofclimate change simulations with ECHAM5, which isalso a CMIP3 model (for details, see Roeckner et al.
2003; Sterl et al. 2008). The ECHAM5 runs are conducted
at a horizontal resolution of approximately 1.8758 in latitude and longitude (T63 spectral truncation). The experimental design for the ECHAM5 ensemble is very
similar to that for CCSM3. In particular, the ECHAM
runs are subject to the same external forcing as CCSM3,
except for stratospheric ozone, which was kept constant.
This difference is expected to have minimal impact on thecomparison between the two models, given that our focus
is on North America and the largest ozone changes occur
over the South Pole. Each ensemble member begins fromidentical initial conditions in the ocean, land, and sea ice
model components (taken from the conditions on 1 January 1950 from a single twentieth-century integration)and slightly different initial conditions in the atmosphericmodel obtained by perturbing the initial state of the atmosphere using Gaussian noise with an amplitude of
0.18C (Roeckner et al. 2003). The ECHAM ensemble
covers the period 1950–2100.
We analyze the period 2010–60 from each model ensemble and compute linear trends over this 51-yr period
for winter (December–February) and summer (June–
August) separately. The trend values are reported in

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FIG. 1. Winter SAT trends [2010–60; 8C (51 yr)21] from each of the 40 CCSM3 ensemble members.

physical units (e.g., as the total linear change over the
51-yr period; e.g., in 8C for air temperature and mm day21
for precipitation). Similar results are obtained using epoch differences between 2051–60 and 2010–19 in place of
linear trends (see also Deser et al. 2012b).
We make use of the following observational datasets
to validate the models’ decadal variability: air temperature from the Merged Land–Ocean Surface Temperature analysis (MLOST) version 3.5 (Vose et al.
2012) on a 58 latitude/longitude grid; precipitation fromthe Global Precipitation Climatology Centre (GPCC;
Becher et al. 2013) dataset on a 18 grid; and sea levelpressure from the Twentieth-Century Reanalysis (20CR;
Compo et al. 2011) on a 28 grid. We use data over theperiod 1900–2010 for each dataset.

3. Resultsa. Overview of SAT and precipitation trends
The complete set of seasonal surface air temperature(SAT) and precipitation trends projected for 2010–60
from each run of CCSM3 and ECHAM5 is presented in
Figs. S1–S6 in the supplementary materials. Here weshow a subset of the results to illustrate some key points.

Winter SAT trends over the next 50 yr display considerable diversity across the CCSM3 40-member ensemble, despite each simulation being subject to identicalradiative forcing (Fig. 1). For example, some ensemble
members exhibit amplified warming (.58C) over Canada (runs 6, 15, 26, and 36) while others show coolingover portions of the United States (runs 4, 16, 28, 36,
and 40). The canonical signature of poleward amplification is evident in many of the runs, while others lack
this structure, favoring instead an east–west contrast in
warming magnitude (runs 12, 13, 29, 33, and 35). The
17-member ECHAM5 ensemble exhibits qualitatively
similar diversity in winter SAT trends (Fig. S3).
Figure 2 shows the projected (2010–60) trends in summer precipitation for each of the 40 CCSM3 ensemble
members. The largest amplitude trends (61 mm day21)
generally occur over the central United States: however,their polarity varies considerably from member to member. For example, runs 3, 11, 12, 26, and 27 exhibit positive
precipitation trends in this region while runs 1, 9, 16, 21,
and 39 show negative trends. A similar diversity in thepolarity of summer precipitation trends is present in the
17-member ECHAM5 ensemble (Fig. S6). In fact, summer precipitation trends in ECHAM5 show somewhat

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FIG. 2. Summer precipitation trends [2010–60; mm day21 (51 yr)21] from each of the 40 CCSM3 ensemble members.

larger amplitudes and member-to-member variation compared to CCSM3. Recall that each individual model realization represents a plausible outcome (subject tomodel limitations) for future precipitation trends over
North America and that the differences within a single
model ensemble are due solely to internally generated
climate variability.

b. Partitioning of total trends into internal and forced
components
The variety of projected climate trends in individualmodel realizations results from the superposition of internal climate variability and the response to external
forcing (i.e., GHG increases). To illustrate this point, wepartition the total trends into contributions from theforced response (obtained by averaging all ensemble
members) and the internal variability (obtained bysubtracting the forced response from the total trend).
Examples of this decomposition are given in Figs. 3
and 4 based on CCSM3; similar results are obtained fromECHAM5 (not shown). Figure 3a shows winter SAT
trends for members 16 and 22, the runs with the least andmost warming over the contiguous United States, respectively. The total trends (Fig. 3a, left) are markedly

different, with run 16 showing little warming over mostof the contiguous United States (,18C in the west andslight cooling in the east) coupled with high amplitude(.68C) warming near the Arctic border; whereas run 22
shows a more diffuse pattern of warming that extends
southward over much of the continent. The forced
component of these trends exhibits the expected signature of poleward amplification, with regions adjacentto the Arctic Ocean warming by 58–68C compared to
18–28C over most of the contiguous United States (Fig. 3,
right). The internally generated or natural componentof the trends exhibit continental-scale patterns withmaximum amplitudes of approximately 18–38C (e.g.,comparable to the forced response over the contiguousUnited States; Fig. 3a, center). Specifically, internalvariability contributes to widespread cooling over theUnited States and southern Canada, as well as warming
over northern Canada and Alaska, in run 16, whereas it
contributes to warming over the middle of the continentand slight cooling to the north and south in run 22.
Averaged over the contiguous United States, the SAT
trend in run 16 (0.88C) results from a balance between
the response to radiative forcing (2.18C) and internalvariability (21.38C). For run 22, the U.S.-average

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FIG. 3. (left) Total 2010–60 SAT trends decomposed into (center) internal and (right) forced components for two contrasting CCSM3
ensemble members. Results shown for (a) winter, runs 16 and 22, and (b) summer, runs 3 and 31. Color bar units are degrees Celsius per
51 yr.

SAT trend (3.48C) is composed of the forced response(2.18C) augmented by internal variability (1.38C).
Summer SAT trends also exhibit considerable range
in magnitude and spatial pattern across the CCSM3
ensemble (Fig. 3b; see also Fig. S1). For example, run
3 shows muted warming (,1.58C) over much of thecontiguous United States accompanied by larger warming (1.5–3.58C) over northern Canada and Alaska, while
run 31 shows strong SAT increases (.38C) over theUnited States accompanied by weaker warming to
the north (,18C; Fig. 3b). The contribution of internalvariability to the summer SAT trends in the two runs
shows similar large-scale patterns to those in winter, with

out-of-phase behavior between northern Canada/Alaska
and the contiguous United States. In terms of magnitude,
the unforced contribution is generally less than the forced
contribution, except over parts of the U.S. Midwest,
where they are comparable. Averaged over the contiguous United States, the SAT trend in run 3 (1.18C) resultsfrom radiative forcing (2.18C) partially offset by internalvariability (21.08C). For run 31, the U.S.-average SAT
trend (3.38C) is composed of the forced response (2.18C)
augmented by internal variability (1.28C).
The diversity of precipitation trends across the CCSM3
ensemble indicates a large role for internal variability
compared to radiative forcing in both seasons (Fig. 4; see15 MARCH 2014

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FIG. 4. As in Fig. 3, but for precipitation. Results shown for (a) winter, runs 30 and 31, and (b) summer, runs 9 and 17. Color bar units are
millimeters per day per 51 yr.

also Figs. 2 and S2). For example, in winter, the total
precipitation trend in run 30 shows a dipole pattern along
the west coast of North America, with large increases(.1 mm day21) over Canada and similar amplitude decreases over the United States (Fig. 4a). A contrasting
pattern is found for run 31, which shows widespread precipitation increases (;0.5 mm day21) across the southern
United States, accompanied by drying (moistening) along
the west coast of Canada (Alaska) in the total field(Fig. 4a). It is clear that internal variability dominates
over radiative forcing in the areas of maximum trend
amplitude and that radiative forcing is responsible for themodest moistening (0.2–0.3 mm day21) across Canada
common to both runs (Fig. 4a).

In summer, CCSM3 ensemble member 9 shows drying
over the interior United States in the total field, where
all of which derives from internal variability, while
ensemble member 27 shows broadscale moistening
over the same region, where nearly all of which is due tointernal variability (Fig. 4b). Averaged over the contiguous United States, the total summer precipitation
trend in run 9 (20.18 mm day21) results from internalvariability (20.31 mm day21) partially offset by theforced response (0.13 mm day21), while that in run 27(20.44 mm day21) derives from mainly internal variability (0.31 mm day21) plus the forced response.
Two important points emerge from Figs. 3 and 4. First,
the unforced component of SAT and precipitation

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trends over the interval 2010–60 exhibit large-scalespatial coherence as opposed to small-scale noise. Second, the magnitude of the unforced component of the
2010–60 trend can be as large as (in the case of SAT,
especially in winter) or larger than (in the case of precipitation) that of the forced component. Similar points
obtain for ECHAM5 (not shown). Taken together,
these results have important implications for resource
management and adaptation. For example, water resource managers must plan for a range of future conditions that span broad geographical regions.
The diversity of SAT and precipitation trends within
the CCSM3 and ECHAM5 ensembles indicates that
not only is a large set of simulations needed to define
the forced climate response in a single model but also
the interpretation of any single model realization as theforced climate response is problematic, especially on
local and regional scales over North America. Additionally, both models give the qualitative impressionthat future precipitation trends exhibit higher uncertainty than future SAT trends, an aspect that will bequantified in the next section.

c. Quantifying the relative contributions of internalvariability and external forcing
The results shown above give a qualitative impressionof the range of patterns and amplitudes of projected
SAT and precipitation trends over the next 50 yr becauseof external radiative (i.e., GHG) forcing and internalvariability. Here we provide a quantitative assessment
using a simple signal-to-noise analysis. Figure 5a compares the forced SAT trends in the two models, estimated by averaging over all ensemble members, andFig. 5b shows the standard deviation of the SAT trends(e.g., internal variability) across the ensemble members
in each model. The signal-to-noise ratio (SNR) between
the forced SAT trend and the internal variability of the
SAT trends is shown in Fig. 6 (top). These standard
metrics convey useful information about the magnitudesof the forced and internally generated components offuture climate change, although they do not convey
anything about the spatial coherence of the internal
contribution (recall Figs. 3 and 4).
The forced SAT responses in the two models are
generally comparable in terms of pattern and amplitude,with poleward-intensified warming in winter and relatively homogenous and weak warming in summer (Fig.
5, top). The more mottled appearance of the warming in
ECHAM5 compared to CCSM3 may be partly due to its
smaller ensemble size and higher spatial resolution. Theinternal variability of the SAT responses is stronger inwinter than summer in both models, especially overCanada and Alaska (Fig. 5, bottom). However, CCSM3

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shows larger internal variability of SAT trends compared to ECHAM5 at many locations in both seasons.
Consequently, SNRs are somewhat higher for ECHAM5
compared to CCSM3 (Fig. 6, top). However, the SNR
spatial distributions are similar between the models, withrelatively low values over the midsection of the continent
in winter (1–2 in CCSM3 compared to 3–4 in ECHAM5),and high values (4–5) over the western and eastern
United States in summer (and extending to southern
Canada in ECHAM5). Over the United States, the SNR
is generally higher in summer than winter in both models.
It is worth noting that the spatial pattern of the SNR ismainly determined by the pattern of the internal variability in summer and by a combination of the patterns of
the forced response and the internal variability in winter.
The forced precipitation trends at high latitudes arebroadly similar between the two models, with the largest
increases along the Gulf of Alaska coast in winter andmore moderate but widespread increases over Canada
and Alaska in both seasons (Fig. 7, top). The forced trends
over the contiguous United States show less agreement
between the two models, especially in summer, with many
regions lacking a statistically significant response. In particular, ECHAM5 shows statistically significant summer
drying over Mexico and parts of Texas, New Mexico, andArizona, whereas CCSM3 exhibits wetter conditions ofmarginal statistical significance in these areas. In addition,
the eastern United States shows significant precipitation
increases in both seasons in ECHAM5 but only in summer in CCSM3. The spatial patterns of internal precipitation trend variability are similar for the two models,with the largest values along the entire Pacific coast and insoutheastern United States in winter and in the central
and eastern United States and southern Alaska in summer(Fig. 7, bottom). Over the United States, variability is
generally higher in ECHAM5 than CCSM3 in winter andvice versa in summer (comparison to observations is given
in section 3g). An important result is that SNR is considerably lower for precipitation trends than SAT trends,
with values generally ,1 over the contiguous UnitedStates and values mostly ,2 over Canada and Alaska inboth models and seasons (Fig. 6, bottom).

d. Chance of positive trends
From these large ensembles, one can quantify the
chances that temperature or precipitation will increase(or decrease) over the coming decades by counting thenumber of runs with a positive trend divided by the total
number of runs for each model. We reiterate that inthese model ensembles, the reason why individual runs
may show opposite-signed trends at a given location is due
to unpredictable, internally generated variability. Figure 8
shows the results for winter SAT and precipitation trends

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FIG. 5. SAT trends 2010–60 [8C (51 yr)21]: (a) ensemble-mean and (b) standard deviation from (left) CCSM3 and(right) ECHAM5. In (a),(b): for (top) December–February (DJF) and (bottom) June–August (JJA). All values in(a) are significant at the 95% confidence level.

over the next 50 yr (2010–60) in winter (top panels) andsummer (bottom panels) for both CCSM3 and ECHAM5.
Over the next 50 yr, both models show a better than 95%
chance that winters will warm at all but a few locations;winter precipitation trends, on the other hand, are lesscertain over much of the contiguous United States, withthe most robust precipitation trends occurring overnorthern Canada where chances of moistening generally
exceed 85% and to a lesser extent over the western
United States, where chances of drying exceed approximately 65% (note that a low chance of a positive precipitation trend implies a high chance of a negative

precipitation trend). Broad areas of the eastern UnitedStates in winter and much of the continent in summer
exhibit approximately equal odds (35%–65%) of drying
and moistening trends over the next 50 yr; a notable exception is over the desert southwest in summer wherechances of a drying trend are .75% in ECHAM5.
The sign of the trends is less certain over the next 25 yr(2010–35) compared to the next 50 yr (Fig. 9). Chancesof warming over the next 25 yr are 55%–75% over thenorthwestern and southeastern portions of the continent
in winter, depending on the model. Even in summer overthe next 25 yr, there are regions where the chance of2280

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FIG. 6. Signal-to-noise ratio maps for (a) SAT and (b) precipitation trends during 2010–60 from (left) CCSM3 and(right) ECHAM5 in DJF and JJA. Signal to noise is defined as the absolute value of the forced (ensemble mean)
trend divided by the standard deviation of trends across the individual ensemble members.

warming is reduced to 65%–75%, although the twomodels disagree on their location. The models show
consistent results for the western United States, which
has a better than 95% chance of warming over the next
25 yr. Unlike SAT, the sign of precipitation trends overthe next 25 yr is highly uncertain in both model ensembles, with most areas falling in the 35%–75% range.
These results have implications for the detection of

future forced climate change at any given location, as
well as planning and mitigation efforts.

e. Internal variability of future climate trends: Role of
the atmospheric circulation
What are the physical mechanisms underlying internalvariability of projected climate trends in the next 50 yr
over North America? It is useful to consider two broad

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FIG. 7. As in Fig. 6, but for precipitation trends for 2010–60 [mm day21 (51 yr)21]. (a) Stippling indicates values not
significant at the 95% confidence level.

classes of mechanisms: thermodynamic and dynamical.
Thermodynamic processes include effects of internalvariability in clouds, water vapor, snow cover, soil
moisture, sea surface temperatures, and sea ice conditions upon the heat and moisture budget of the overlying
atmosphere, in the absence of atmospheric circulation
changes. Dynamical mechanisms relate to internal variations in the atmospheric circulation, which alter SAT
and precipitation via changes in airmass trajectories andtheir associated advection of heat and moisture. Numerous studies have demonstrated the importance offluctuations in the large-scale atmospheric flow, whichhave been shown to account for a large fraction of thevariability in SAT and precipitation over North America on time scales ranging from weeks to decades (e.g.,
Wallace et al. 1995; Hurrell 1996; Thompson et al. 2009;
Wallace et al. 2012). Although it is conceptually useful
to consider the two classes of mechanisms independently, significant feedbacks may exist between them.
For example, circulation-induced changes in precipitation may alter snow cover, soil moisture, and cloudiness;these in turn may affect SAT by altering the surface
radiation balance. Such feedbacks may be particularly
strong in summer (e.g., Fischer et al. 2007; Hoerling et al.
2014), evidenced in part by the inverse correlation between interannual variations in SAT and precipitation

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FIG. 8. Chance (%) of a positive trend in SAT and precipitation over the period 2010–60 during (a) winter and(b) summer from the (left) CCSM3 and (right) ECHAM5 ensembles. Note that a low chance implies a high chance ofa negative trend.

over much of the United States (e.g., Trenberth andShea 2005).
We hypothesize that the spread in climate change
projections within a given model ensemble is largely due
to dynamically induced internal variability, particularly
during winter. As a first step in evaluating the role of theatmospheric circulation, we show winter sea level pressure (SLP) trends in two contrasting runs from the

CCSM3 ensemble, along with accompanying trends in
SAT and precipitation. As before, the total trends aredecomposed into contributions from internal variability
and the forced response. Run 29 features positive SLP
trends over the North Pacific, with maximum amplitude ofapproximately 6 hPa, compared to negative SLP trendsof similar magnitude in run 6 (Fig. 10, left). The
opposite-signed circulation trends are associated with

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FIG. 9. As in Fig. 8, but for 2010–35.

different airmass trajectories, with run 29 bringing cold
air from the north down into Canada and run 6 bringing
warm maritime air up from the south into western
Canada. The imprint of these different trends in airflow
can be seen in the total SAT trend field. The effect of thecirculation on SAT is more readily discernible in theunforced component of the trends (Fig. 10a, center).
The internal component of the two SLP trend patterns isnearly equal and opposite, with positive values over the
North Pacific and negative values over eastern Canada

in run 29 and reversed polarity in run 6. These SLP trend
patterns give rise to northerly (southerly) wind trends
over Canada in run 29 (6), qualitatively explaining thecooling (warming) trends (Fig. 10a, center).
The relationship between the SLP and SAT trends isvery different for the forced component compared tothe unforced component (Fig. 10a, right). The forced
SAT trends are dominated by a northward-intensified
warming pattern, with little discernible influence fromthe forced SLP trend distribution. It is also noteworthy

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FIG. 10. (left) Total 2010–60 winter trends decomposed into (center) internal and (right) forced components for two contrasting CCSM3
ensemble members (runs 29 and 6) for (a) SAT [color shading; 8C (51 yr)21] and SLP (contours) and (b) precipitation [color shading;
mm day21 (51 yr)21] and SLP (contours). SLP contour interval is 1 hPa (51 yr)21, with solid (dashed) contours for positive (negative)
values; the zero contour is thickened.

that the forced SLP trends are considerably smaller inamplitude than the unforced SLP trends (maximum
values ; 1–2 hPa compared to 6–8 hPa; see also Fig. S7).
Indeed, SLP trend signal-to-noise ratios based on the fullset of ensemble members are ,2 (and mostly ,1) over theentire domain in both models and both seasons (Fig. S8).
In addition to SAT, the circulation trends have a clear
influence on winter precipitation trends, discernible
even in the total fields (Fig. 10b, left). For example, thenegative SLP trend in the Gulf of Alaska in run 6 leads
to a strong increase in precipitation along the west coastof Canada and southern Alaska compared to run 29,
which is under the influence of a blocking high pressure

center. In addition, the trough over northeastern Canada in run 29 favors enhanced precipitation compared torun 6, which lacks a pronounced circulation trend in thisregion. These circulation effects are even more apparent
in the unforced component (Fig. 10b, center): the entire
west coast from Northern California to eastern Alaska
shows reduced (enhanced) precipitation under the influence of the high (low) pressure center to the west inrun 29 (6), and much of northeastern Canada showsincreased (diminished) precipitation in association withthe trough (ridge) to the north in run 29 (6). Unlike
SAT, the forced component of the SLP trends has
a discernible influence on the forced component of the15 MARCH 2014

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FIG. 11. Winter SLP trends [2010–60; hPa (51 yr)21] from (a) nine individual realizations of CCSM3 (runs 21–29) and (b) nine CMIP3
models, selected alphabetically.

precipitation trends (Fig. 10b, right). In particular, locally enhanced drying (moistening) along the west coastof the United States (Canada) is plausibly a result of theoffshore (onshore) flow around the high pressure center.
However, the ubiquitous precipitation increase overCanada (;0.2 mm day21) appears to be thermodynamically induced.
Although the atmospheric circulation is less variablein summer than winter, it also appears to play a role ingenerating diversity of warm season SAT and precipitation trends within each model ensemble (Figs. S9 andS10). However, as discussed earlier, it may not be possible

to confidently separate internal dynamical forcing fromthermodynamic land surface feedbacks in some areas suchas the central and western United States in summer.
Additional examples of the spread in winter SLP
trends within the CCSM3 ensemble are presented inFig. 11a. These show a wide variety of patterns andpolarities among the individual realizations. For example, runs 22, 24, 26, and 28 all show negative SLP trends
over the North Pacific and/or Gulf of Alaska, while runs
21, 23, 25, 27, and 29 are dominated by positive trends
in these areas. It is clear from this figure that, in any
single realization, internal variability makes a larger

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contribution than does the GHG-forced response.
Similar results obtain for the ECHAM5 ensemble (notshown). For comparison, Fig. 11b shows the winter SLP
trends (2010–60) in a single realization from nine different CMIP3 models, chosen alphabetically. The SLP
trend patterns and magnitudes within this multimodel
ensemble are even more diverse than within the CCSM3
ensemble. For example, the Canadian Centre for Climate
Modelling and Analysis (CCCma); Coupled Global Climate Model, version 3.1 (CGCM3); Commonwealth
Scientific and Industrial Research Organisation Mark(CSIRO); and Goddard Institute for Space Studies,
Atmosphere–Ocean Model (GISS-AOM) models exhibit negative SLP trends over the North Pacific, while
the Centre National de Recherches Met
eorologiques(CNRM); Geophysical Fluid Dynamics Laboratory(GFDL; both versions); and Goddard Institute for SpaceStudies Model E2, coupled with the Hybrid Coordinate
Ocean Model (HYCOM) (GISS-E2H) models havepositive trends. The interpretation of the spread in SLP
trends within the multimodel ensemble is problematic
because of the inclusion of both structural differencesamong models and internal variability within models.
However, visual comparison with the set of CCSM3 runsin Fig. 11a strongly suggests that internal variability may
be an important factor, a point also emphasized in Deseret al. (2012b) for the extratropics of both hemispheres. A
similar comparison between CCSM3 and CMIP3 forsummer SLP trends is shown in Fig. S11.
Empirical orthogonal function (EOF) analysis provides
an objective way to identify the most prominent patternsof variability. Here, we have computed the leading EOF
of the set of 40 CCSM3 winter SLP trend patterns over the
Pacific–North American domain [258–758N, 1808–608W].
Note that, unlike conventional applications of EOF
analysis in the time domain, we have applied it in the‘‘ensemble trend’’ domain to find the dominant pattern ofinternally generated SLP trend variability (see also Deseret al. 2012b). The leading EOF accounts for 59% of thevariance in winter SLP trends across the CCSM3 ensemble and is distinct from the second EOF (which accounts
for 16% of the variance) according to the criterion of
North et al. (1982). This ‘‘dominant’’ mode of winter circulation trend variability exhibits, in its positive phase,
negative SLP trend anomalies over the North Pacificand weaker downstream centers of alternating sign overthe central and southeastern United States (Fig. 12;
the downstream features are more apparent at upper
levels; not shown). This structure resembles the surface
manifestation of the Pacific–North American (PNA)
teleconnection pattern (Wallace and Gutzler 1981), a
prominent mode of interannual variability in both natureand the model (not shown).

VOLUME 27

The impact of this circulation pattern upon SAT and
precipitation trends is assessed by linearly regressing the setof 40 SAT and precipitation trend values at each grid box
upon the standardized SLP principal component (PC)record. Note that the ensemble-mean trends are removed
in the linear regression procedure, thereby isolating theinternal component of variability. In its positive phase, the
SLP trend EOF is accompanied by positive (negative) SAT
trend anomalies over the central and western (southeastern) portion of the continent and by positive precipitation
trend anomalies along the west coast (Figs. 12a,b). The
similarity between these patterns and the unforced trends
in ensemble member 6 is noteworthy (recall Figs. 10a,b).
By adding/subtracting this dominant dynamically induced pattern of unforced SAT and precipitation trend
variability to/from the forced trends, one obtains the
expected range of future changes in SAT and precipitation that results from the superposition of GHG forcing
and internal circulation variability (Figs. 12a,b, bottom).
Here we have doubled the internal (SLP EOF) contribution to span the 95% range assuming a normally distributed SLP PC. For a 22 standard deviation departureof the SLP PC (sPC), the total (forced plus internal)
trends exhibit a strong anticyclonic circulation over the
North Pacific (maximum values ; 9 hPa) and a weaker
cyclonic circulation centered over Hudson’s Bay, accompanied by relatively modest warming over the northwestern United States (,18C) and Canada (and slightcooling in Washington and Oregon). This contrasts withthe 12 sPC case, in which the North Pacific is dominatedby negative SLP trends (maximum values ; 28 hPa),accompanied by considerably larger warming (38–48C)
over the Pacific northwest and muted warming (,18C)
over the southeast (Fig. 12a, bottom right). Similarly, the
22 sPC case shows a north–south dipole pattern of precipitation trends along the west coast of North America
with a nodal line over British Columbia, compared to the
12 sPC case in which the nodal line is shifted southward
over Northern California (Fig. 12b, bottom). The correspondence between these ‘‘endmember’’ precipitation
trend patterns and the raw trends in ensemble members 6
and 29 (recall Figs. 10a,b) is noteworthy.
This example serves to illustrate the effects of unforced
circulation-induced trends in the presence of GHG-forced
climate change. Of course, other circulation trend patterns
will complicate the picture in any given model run but, tothe extent that this is the dominant mode of circulation
trend variability, it provides some guidance on the range of
unforced circulation-induced SAT and precipitation trends
that will be superimposed upon the GHG-forced response.
Analogous results are found for summer, although theleading EOF of SLP trends accounts for a smaller proportion of variance (40%) than in winter (Fig. S12).

15 MARCH 2014

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2287

FIG. 12. a) (top left) Regressions of winter SLP and SAT trends upon the leading PC of winter SLP trends in the
40-member CCSM3 ensemble, where values have been multiplied by 2; (top right) CCSM3 ensemble-mean winterSLP and SAT trends; (bottom left) difference of (top right) minus (top left); and (bottom right) sum of (top right) plus
(top left). (b) As in (a), but substituting precipitation for SAT. Contour interval is 1 hPa (51 yr)21; color shading inunits of degrees Celsius per 51 yr for SAT and millimeters per day per 51 yr for precipitation.

f. Dynamical adjustment of the SAT and precipitation
trends
It is clear from the results shown above that the largescale atmospheric circulation affects the spatial patterns
and magnitudes of regional SAT and precipitation

trends in any given model realization by virtue of
its large natural variability, obfuscating the anthropogenically forced signal. To further demonstrate theconfounding impact of dynamically induced internalvariability upon climate trends in any single GHGforced simulation, we have constructed ‘‘dynamically2288

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TABLE 1. Cumulative percentages of winter (DJF) and summer (JJA) SAT and precipitation (PPT) 2010–60 trend variance explainedby the first, second, and third SLP predictor patterns, averaged over all North American grid boxes in the top half of the table. The bottom
half of the table is as in the top but for the signal-to-noise ratios of the dynamically adjusted trends. C40 refers to the 40-member CCSM3
ensemble, and E17 refers to the 17-member ECHAM5 ensemble. See text for details.
Cumulative percentage of trend variance (%)
SLPpredictor
pattern

C40
SAT
DJF

C40
SAT
JJA

C40
PPT
DJF

C40
PPT
JJA

E17
SAT
DJF

E17
SAT
JJA

E17
PPT
DJF

E17
PPT
JJA

1
2
3

55
70
80

54
67
78

45
66
75

35
52
65

54
80
90

55
77
90

46
75
88

50
73
87

SLP
predictor
pattern

C40
SAT
DJF

C40
SAT
JJA

C40
PPT
DJF

C40
PPT
JJA

E17
SAT
DJF

E17
SAT
JJA

E17
PPT
DJF

E17
PPT
JJA

1
2
3
Unadjusted

3.8
4.7
5.6
2.6

4.6
5.4
6.7
3.1

1.2
1.4
1.7
0.9

0.8
0.9
1.1
0.6

5.2
7.8
11.7
3.4

6.3
8.9
13.8
4.2

1.2
1.7
2.4
0.9

0.8
1.1
1.6
0.5

Signal-to-noise ratios of the dynamically adjusted trends

adjusted’’ versions of the SAT and precipitation trends
for each member of the CCSM3 and ECHAM5 ensembles following the method of Wallace et al. (2012)
and Smoliak (2013). Briefly, three orthogonal SLP trend
predictor patterns are determined for SAT (or precipitation) trends at each grid box using the method ofpartial least squares. Our choice of three SLP predictors
is motivated by the fact that the additional trend variance explained by the fourth predictor is small (,5%)
and because the first three predictor patterns are all
clearly physically meaningful (not shown). However, theresults are not sensitive to the exact number of SLP
predictor patterns or to the precise domain used for
calculating them (here we use the same domain as in the
SLP EOF analysis above). Other methods of dynamical
adjustment—for example, using SLP trend EOFs as
predictor patterns—yield similar results (not shown).
Table 1a shows the cumulative percentages of SAT
and precipitation trend variance explained by the first,
second and third SLP predictor patterns, averaged over
all North American grid boxes. These predictors achieve
cumulative reductions in trend variance of 35%–55%,
52%–70%, and 65%–80%, respectively, for CCSM3(46%–55%, 73%–80%, and 87%–90%, respectively, for
ECHAM5), with the ranges denoting the dependence
on season and reference variable. The remaining fraction of unexplained trend variance is attributable tothermodynamic processes and to unresolved dynamical
influences.
Figures 13 and 14 compare the total and dynamicallyadjusted SAT and precipitation trends for the contrasting CCSM3 ensemble members discussed earlier
for winter (Fig. 10) and summer (Fig. S8). It is clear thatby reducing the circulation-induced component of internal trend variability, the individual runs are brought
into much better agreement, consistent with the resultsin Table 1a. Additionally, the dynamically adjusted
trend patterns resemble more closely the forced component of the response than the raw trends, as expected.
These examples serve as powerful demonstrations of the
impact of internally generated circulation trends upon
SAT and precipitation trends in any single GHG-forcedmodel simulation.
As alluded to earlier, comparing projected climate
trends from single realizations of different models
is problematic because of the confounding effects ofstructural differences between models (which can leadto different forced responses) and the presence of internal variability. Figures 15 and 16 illustrate how the
reduction of internally generated dynamical contributions to single contrasting runs from the CCSM3 andECHAM5 models clarifies their comparison. The SAT
and precipitation trends in the two models show muchbetter agreement after accounting for the effects of internally generated circulation trends. Remaining differences can be largely attributed to differences in theirforced responses (Figs. 15 and 16, right).
Reducing the influence of internal circulation trends
from the SAT and precipitation trends also augments
their SNR (Table 1b). The average SNR over North
America increases by approximately a factor of 2–3 after dynamical adjustment with three orthogonal SLP
predictor patterns. In particular, SAT SNR increases
from ;3 to ;6 in CCSM3 and from ;4 to ;12 in
ECHAM5 in both seasons. Although the fractional increase in SNR for precipitation is similar to that for15 MARCH 2014

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FIG. 13. Winter (a) SAT [8C (51 yr)21] and (b) precipitation [mm day21 (51 yr)21] trends (2010–60) for two contrasting CCSM3 ensemble members (runs 29 and 6) based on (left) total and (right) dynamically adjusted (‘‘circulation residual’’) fields. See text for details.

SAT, it remains relatively low even with three SLP
predictors (;2 in winter and ;1 in summer). This may
reflect a combination of factors, including a weak forced
signal (Fig. 7a), thermodynamically generated internalvariability, and dynamically induced internal variability

unresolved by three large-scale predictor patterns. We
note that the average precipitation SNR after dynamical
adjustment is higher for Canada and Alaska (1.6 in
CCSM3 and 2.7 in ECHAM5) than for the contiguousUnited States (0.8 in CCSM3 and 1.9 in ECHAM5),

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FIG. 14. As in Fig. 13, but for summer: CCSM3 runs 28 and 30.

consistent with the stronger forced component of the
response at high latitudes (recall Fig. 7a).

g. Assessing the models’ multidecadal variability
An important consideration in assessing the relevanceof the results from the multimember CCSM3 andECHAM5 ensembles for the real world is the fidelity

with which the models simulate the variability of 50-yr
climate trends. While the limited duration of the observational record precludes a direct assessment of 50-yr
trend variance, the magnitudes of observed and simulated variability on decadal-to-multidecadal time scales
can be compared. Figure 17 shows maps of the standard
deviation of 8-yr low-pass filtered SAT, precipitation,

15 MARCH 2014

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2291

FIG. 15. SAT trends (2010–60) in (a) winter and (b) summer for a single realization of (top) CCSM3 and (bottom) ECHAM5 based on(left) total and (center) dynamically adjusted (circulation residual) fields. (right) The ensemble-mean (e.g., forced) trends in (top) CCSM3
and (bottom) ECHAM5. Color bar is in units of degrees Celsius per 51 yr.

and SLP anomalies in winter and summer from themodels and ;110 yr of observations (see datasets listed
in section 2; similar results are obtained with other datasets: not shown). To reduce the influence of potential
externally forced signals, we have removed the linear
trend from the observations, although this has almost no
effect (not shown). For the same reason, we also removed the ensemble mean from each model run at eachtime step. The low-pass filtered time series from eachmodel run were appended to one another before computing the standard deviation of this overall record.
The magnitudes and spatial patterns of decadal SAT
and precipitation variability are reasonably well simulated
by both models (Figs. 17a,b, respectively). Observed SAT

in winter shows a band of high decadal variance extending
from Alaska southeastward across the continent: thisstructure is relatively well captured by ECHAM5 and lesswell simulated by the coarser-resolution CCSM3 but
overall magnitudes are similar. Observed SAT in summer
shows a more homogenous pattern of decadal variancecompared to winter, an aspect that is well simulated by
the models; the magnitudes are well represented by
ECHAM5 and overestimated by ;20%–30% in CCSM3.
The observed winter precipitation variance maxima along
the west coast of North America and the southeastern
United States are reproduced in the models, with themagnitudes somewhat underestimated in CCSM3. Observed summer precipitation variance is more evenly

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FIG. 16. As in Fig. 15, but for precipitation [mm day21 (51 yr)21].

distributed across the continent than in winter, increasing
gradually from west to east across the United States. This
aspect is generally captured by the models, in terms ofboth pattern and overall magnitude.
Observed winter SLP shows a center of enhanced
decadal variance over the North Pacific (maximum
values ; 3 hPa) as well as higher variance over Canada(;1–2 hPa) compared to the United States (;0.5–1 hPa;
Fig. 17c). The models simulate these features, although
CCSM3 overestimates the variance over the North Pacific and northwestern North America by ;20%–30%.
The observed reduction in SLP variance in summer
compared to winter is captured by the models, witha slight underestimate of the simulated summer SLP
variance over the United States.
In summary, the comparisons shown in Fig. 17 indicatethat the models simulate generally realistic distributionsof low-frequency (.8 yr) variability in SAT, precipitation, and SLP, with magnitudes mostly within 20%–30%
of observations. Additional information on the temporalcharacteristics and spatial patterns of simulated multidecadal climate variability may be found in special issue
on CCSM3 of the Journal of Climate (2006, Vol. 19,
No. 11) and the special issue section on ECHAM5 of the
Journal of Climate (2003, Vol. 19, No. 16). Further work isneeded to assess the fidelity of simulated multidecadal
variability, including the models’ representations ofmechanisms and feedbacks.

4. Summary and discussion
We have examined the contribution of internal variability to uncertainty in projected SAT and precipitation
trends during the next 50 yr at local and regional scales15 MARCH 2014

DESER ET AL.

FIG. 17. Amplitude of decadal variability in CCSM3, ECHAM5, and observations for (a) SAT (8C), (b) precipitation(mm day21), and (c) SLP (hPa) in (top) winter and (bottom) summer.

2293

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over North America from large ensembles of simulations with two comprehensive climate models. Eachsimulation is subject to the identical GHG forcing scenario (SRES A1B) but begins from a different atmospheric initial condition: thus, the spread of futureclimate trends is attributable to unpredictable internallygenerated variability. Unlike multimodel ensembles
such as CMIP3 and CMIP5, which confound internalvariability and structural differences among models, ourexperimental design allows us to isolate the contributionof internal variability to the spread of anthropogenicallyforced climate projections. Consistent results are foundfor both model ensembles, including higher relative
uncertainty in projected trends for precipitation compared to SAT regardless of season and for SAT in wintercompared to summer. This uncertainty stems in largepart from the dynamical effects of atmospheric circulation trends that contain a high proportion of unforced
variability compared to GHG-forced signals. The broad
spatial scale of these circulation changes imparts regional coherence to the internal component of surface
climate trends in any single model run. This in turn has
important consequences for impacts upon agriculturaland water resources, as wide areas within the UnitedStates and Canada are affected in a similar manner.
By elucidating the impact of natural circulationinduced climate trends, we provide a new perspective
on how to usefully compare climate change projections
across models and how to properly interpret the single
climate trajectory that nature will produce. This new
perspective highlights the importance of conducting
numerous simulations with a given climate model, as
each realization will contain a different mix of unforced
and forced changes. Indeed, we advocate that, for the
next International Panel on Climate Change (IPCC)
assessment, modeling centers consider allocating resources in favor of large ensembles, even at the cost ofprescribing alternate forcing scenarios. The minimum
number of ensemble members needed will depend on
the climate parameter, geographical location, spatial
scale, and time horizon of interest (see Deser et al.
2012b; Screen et al. 2013). Lengthy model control integrations provide an additional resource for obtaining
relevant information on internal variability, and whencombined with estimates of the forced response can beused to enhance ensembles of climate change simulations. Alternatively, statistics of unforced variability
derived from observations can be used in place of model
estimates of internal variability.
In analogy with ensemble weather forecasting, thisstudy underscores the need for ensemble climate forecasts to inform public policy, planning, and mitigation
efforts. Even over time spans as long as the next 50 yr,

VOLUME 27

natural climate variability superimposed upon forced
climate change will result in range of possible futureclimate trends. As in seasonal forecasting, this range canbe represented as a distribution from which the likelihood of a particular trend outcome or threshold exceedance can be determined. Such distributions can beused to assess vulnerability and risk for a broad range ofclimate impacts: for example, wildfires and humanhealth hazards, including effects on extreme events suchas heat waves, floods, and droughts. We reiterate thatany single model run or any multimodel average (e.g.,
from CMIP3/5) lacks information on the expected rangeof climate trend outcomes associated with natural variability, limiting utility for decision-making purposes. By
the same token, regional climate models and statistical/
dynamical downscaling procedures should make use of
the range of ensemble climate forecasts conducted with
global models. These efforts should also incorporate
information on the reliability of ensemble climate
forecasts as discussed in van Oldenborgh et al. (2013).
Our study has focused on North America. Preliminary
results indicate that similar uncertainties in future climate trends are present over Eurasia (not shown). Other
regions of the globe, however, may contain a higher
proportion of human-induced climate change comparedto natural variability than the northern continents: inparticular, low-latitude areas whose populations are
particularly vulnerable to climate fluctuations (Mahlstein
et al. 2011; Wallace et al. 2014). Further work on therelative roles and mechanisms of internally generated andforced climate change in other regions is warranted.
Acknowledgments. We thank Dr. Haiyan Teng forassistance with the CCSM3 ensemble and Dr. Grant
Branstator and Professor John M. Wallace for useful
discussions during the course of this work. We also
thank the three anonymous reviewers for their comments and suggestions.

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