DISTINGUISHING THE
ROLES OF NATURAL AND
ANTHROPOGENICALLY FORCED
DECADAL CLIMATE VARIABILITY
Implications for Prediction
by U.S. CLIVAR Decadal Predictability Working Group : Amy Solomon, Lisa Goddard, Arun Kumar,

James Carton, Clara Deser, Ichiro Fukumori, Arthur M. Greene, Gabriele Hegerl, Ben Kirtman,
Yochanan Kushnir, Matthew Newman, Doug Smith, Dan Vimont, Tom Delworth,
Gerald A. Meehl, and Timothy Stockdale

To assess decadal forecasts it is necessary to identify to what extent regional changes
are due to natural climate variations, and are thus transitory, and to what extent they
are due to anthropogenic forcing, and are likely to continue.

M

OTIVATION. An ambitious effort to produce
experimental near-term decadal forecasts has
begun, motivated by the possibility that theclimate models used for climate change projections
can capture not only the impact of the changing
atmospheric composition but also the evolution ofslow natural variations of the climate system when
initialized with ocean observations. In the cases
where initialization improves the forecast, addressing
the question of how much of that improvement is dueto the natural versus the forced climate components
is important to understanding the benefits of theinitialization. Untangling the natural and forced
components of the climate is necessary because theresponse to external forcing may project onto or comingle with natural climate variability. As the scienceof decadal prediction is in its infancy, one would liketo assess and understand the following:
AMERICAN METEOROLOGICAL SOCIETY

1) the expectations for added regional climate information and skill achievable from initialized
decadal predictions;2) what physical processes or modes of variability
are important for the decadal predictability andprediction problem, and whether their relevance
may evolve and change with time;3) what elements of the observing system are important for initializing and verifying decadal
predictions; and
4) in terms of attribution, to what extent are regionalchanges in the current climate due to natural climate variations and thus transitory, and to what
extent are they due to anthropogenic forcing andthus likely to continue.
As with the preceding decade, the climate evolution in the near term will be a combination of forced
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climate change and natural variability. As an example, consider the prolonged drought conditions of
the American West since the late 1990s. Most of thetwenty-first-century climate change projections usedin the Intergovernmental Panel on Climate Change(IPCC) Fourth Assessment Report (AR4) suggestthat this region will become drier as precipitation
decreases and evaporative demand increases with
future warmer temperatures (Seager et al. 2007).
However, since dry conditions in this part of theworld are also associated with natural interannualto-decadal variability in sea surface temperatures inboth the Atlantic and the Pacific basins (e.g. McCabe
et al. 2004; Seager et al. 2005; Schubert et al. 2009)
and appear to have occurred before the twentiethcentury (see Jansen et al. 2007), how much of the recent drought can be attributed to natural variability
and how much can be attributed to ongoing climatechange? An answer to this question could greatly
aid western water resource managers in developing
informed adaptation strategies.
The purpose of this paper is to describe existing
methodologies to separate decadal natural variability
from anthropogenically forced variability, the degree
to which those efforts have succeeded, and the ways
in which the methods are limited or challenged byexisting data. Note that the separation of decadal
natural variability from anthropogenically forced
variability goes beyond what has already been accomplished in previous studies that focused primarily on the detection of a long-term anthropogenic
signal (Hegerl et al. 2007b) because on decadal timescales anthropogenic effects may be nonmonotonic,
regionally dependent, and/or convolved with naturalvariability.
The World Climate Research Programme (WCRP)
is coordinating a set of decadal prediction experiments

AFFILIATIONS: Solomon and Newman —University of Colorado,and NOAA/Earth System Research Laboratory, Boulder, Colorado;
Goddard and Greene —NOAA/International Research Institutefor Climate and Society, Palisades, New York; Kumar—NOAA/
Climate Prediction Center, Camp Springs, Maryland; Carton —University of Maryland, College Park, College Park, Maryland;
Deser and Meehl—National Center for Atmospheric Research,
Boulder, Colorado; Fukumori —NASA Jet Propulsion Laboratory,
Pasadena, California; Hegerl—University of Edinburgh, Edinburgh,
United Kingdom; Kirtman —University of Miami, Miami, Florida,
and Center for Ocean–Land–Atmosphere Studies, Calverton,
Maryland; Kushnir—Lamont-Doherty Earth Observatory
Columbia University, New York, New York; S mith —Met Office
Hadley Centre, Exeter, United Kingdom; Vimont—University of

142 |february 2011

(Taylor et al. 2008; Meehl et al. 2009a) that are being
conducted by modeling centers around the world. It
must be clearly emphasized that these are preliminary experiments to assess the current feasibility of
decadal predictions. The approaches for separating
natural and forced variability discussed in this paper,presented with their benefits and limitations, are intended to serve as a starting point from which these
decadal prediction experiments can be assessed andfrom which the processes and potential predictabilityof decadal variations can be better understood.
PHYSICAL PROCESSES INVOLVED WITH
DECADAL TIME SCALES IN THE CLIMATESYSTEM. To assess naturally occurring decadal
variability in the climate system and the ability ofmodels to simulate and forecast it, one must identify
the relevant physical processes. Most studies point tooceanic mechanisms as central to climate memory,
particularly those related to reservoirs of ocean heator slowly evolving circulation and their interaction
with the atmospheric variability. For example, in
midlatitudes sea surface temperatures (SSTs) are welldescribed by the stochastic climate model paradigm(Frankignoul and Hasselmann 1977), where random
atmospheric surface forcing with a “white noise”
spectrum, or equivalent power at all frequencies,
is integrated by the ocean mixed layer to produce a“red noise” spectrum, in which power is amplified
at lower frequencies [see Deser et al. (2010a) for areview]. Additionally, a number of ocean processes(e.g., overturning and gyre circulations, the triggering of Rossby waves, and advection of temperature/salinity anomalies by the mean currents) are potential
candidates that may provide additional predictability by influencing atmospheric and thus terrestrial
variability.

Wisconsin—Madison, Madison, Wisconsin; Delworth —NOAA/
Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey;
Stockdale —European Centre for Medium-Range Weather
Forecasts, Reading, United Kingdom
CORRESPONDING AUTHOR: Amy Solomon, NOAA/ESRL/PSD,
325 Broadway, R/PSD1, Boulder, CO 80305–3328
E-mail: amy.solomon@noaa.gov
The abstract for this article can be found in this issue, following the tableof contents.
DOI:10.1175/2010BAMS2962.1
In final form 7 October 2010
© 2011 American Meteorological SocietyOne example of potentially predictable natural
climate variability is that
produced by wind-forced
extratropical ocean Rossby
waves that propagate acrossan ocean basin and create
thermocline anoma lies
near the western boundary.
These signals in the ocean
are then communicatedto t he surface t hroughwintertime heat fluxes andFig. 1. Strength of the AMOC at 30°N in a variety of 19 AR4 coupled models
wind stress. Schneider andforced with observed greenhouse gas and aerosol forcing until 1999 and theMiller (2001) demonstrate
Special Report on Emissions Scenarios (SRES) A1B scenario of greenhousegas forcing after 1999. Bars on the left show various observational estimates.
that such a process in the
From Meehl et al. (2007).
North Pacific can yieldpredictable wintertime
SST anomalies in the Kuroshio–Oyashio Extension warm upper-ocean water northward in the Atlantic
at lead times of up to 3 years. Decadal timescale vari- and releasing that heat to the atmosphere; the cooled
ability in patterns of Pacific basin SSTs associated water sinks and returns southward at depths below
with the interdecadal Pacific oscillation (IPO; Power 1000 m. The Atlantic shows evidence of multidecadal
et al. 1999) has been connected to wind-forced ocean climate variations generally referred to as the Atlantic
Rossby waves near 25°N and 25°S that are central multidecadal oscillation (AMO; Enfield et al. 2001),
to a mechanism that produces the IPO (White et al. with a basin-scale signature in SST. It has been hy2003; Meehl and Hu 2006; McGregor et al. 2007, pothesized that the multidecadal fluctuations in SST2008). The IPO has subsequently been used as the may be related to AMOC fluctuations. AMO-like SST
basis for decadal predictions of Pacific SSTs and fluctuations are found in many coupled models (e.g.,associated precipitation over North America and Latif et al. 2006), and while different models seem toAustralia in a perfect model study using a large produce fluctuations for different reasons and withensemble of climate model simulations (Meehl et al. different time scales (see Fig. 1), all seem to involve a2010). Another example of a source of predictability link to the AMOC. The presence of feedbacks linking
may come from the shallow wind-driven meridional AMOC, SST, and the atmospheric circulation opens
overturning ocean circulations called subtropical up potential for predictability of decadal climate
cells (STCs), which connect the subtropical atmo- variability over land associated with predictability ofsphere to the equatorial region through the ocean in AMOC variations (see Knight et al. 2006).
both the Atlantic and Pacific basins [see Schott et al.
A principal assertion behind the decadal predic(2004) for a review]. STCs have been hypothesized tion experiments is that initialization of decadalto play a role in decadal climate variability by the scale ocean processes, such as those mentioned
advection of salinity/temperature anomalies along above, will provide additional predictability beyond
STC pathways to the equator (Gu and Philander that due to the radiative forcing from increasing
1997; Yeager and Large 2004) or by changes in STC greenhouse gases. The extent to which this is true
strength, which controls the amount of cold water using the current generation of GCMs and datathat upwells at the equator, both in models (Kleeman assimilation systems has yet to be determined, alet al. 1999; Solomon et al. 2003) and observations though hindcast experiments appear promising (e.g.,(McPhaden and Zhang 2002).
Smith et al. 2007). As described in the next section,
A potentially large source of predictability of several approaches have been proposed to separate
natural climate variability on decadal time scales natural from externally forced variability in order
may also come from f luctuations in the Atlantic to highlight the added value of ocean initialization
meridional overturning circulation (AMOC) (e.g., on predictions of the future or to help better unsee Delworth and Mann 2000; Knight et al. 2005; derstand or describe past trends and variability. To
Dijkstra et al. 2006; Zhang and Delworth 2006). This date, no superior approach exists; all have benefits
circulation plays a key role in climate by transporting and limitations.
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APPROACHES TO SEPAR ATE NATU RAL INTERNAL VARIABILITY FROM ANTHROPOGENICALLY FORCED DECADALVARIATIONS. Analysis of initialized decadal prediction studies. Natural and forced variability may beseparated to a certain extent by comparing parallel setsof initialized and uninitialized hindcast experiments
made with the same climate model (Smith et al. 2007;
Keenlyside et al. 2008; Pohlmann et al. 2009). If all
external forcing (i.e., from anthropogenic greenhousegases and aerosols, solar irradiance, and volcanic
eruptions) is identical, then differences between thetwo sets of hindcasts arise purely from initialization.
Since natural internal variability can only be predicted by starting from its correct phase, improved
skill in initialized over uninitialized hindcasts may
indicate skillful prediction of some aspects of natural variability. However, improved skill in initialized
hindcasts may also arise from removing biases that exist in uninitialized climate models forced by observedchanges in external forcing and that may be smallerat the start of initialized decadal predictions. This
source of additional skill is potentially important for
improving predictions of climate change commitment
or short-term response to volcanic eruptions, but itwould need to be taken into account in any attempt
to separate natural and forced variability.
There are also other issues to be considered whenanalyzing decadal hindcasts. Climate models cannot be
initialized perfectly with incomplete observations. This
usually leads to an initialization shock, during which
the model rapidly adjusts to imbalances introduced
by imperfect initialization, causing a degradation of
forecast skill that could mask any signals from naturalvariability. It is also possible that unrealistic model
responses to imperfectly estimated initial conditions(Acero-Schertzer et al. 1997; Ji et al. 2000; Masina
et al. 2001) could lead to apparent hindcast skill that
could be incorrectly attributed to natural variability.
For example, in initialized hindcasts of the mid-1990s
warming of the North Atlantic subpolar gyre, J. Robson
et al. (2009, unpublished manuscript) found that skill
was due to errors in assimilated density anomalies.
Furthermore, initializing and assessing decadal
hindcasts is severely hampered by the sparseness ofhistorical subsurface ocean observations. For example,
natural variations of the Atlantic AMOC are predictable in idealized model experiments (Collins et al.
2006), but our ability to confirm such predictability inreality is compromised by the lack of historical ocean
observations. Ultimately these issues must be overcome
in order to capitalize on the predictability from thenatural variability to improve decadal forecasts.
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Analysis of model ensemble means and variance. A
large ensemble of climate simulations can be used
to separate the model response to external forcing
from the variations that are internal to the system.
The former is referred to as external variability, while
the latter is referred to as internal or the natural
climate variability. The approach described closely
follows a similar approach used in seasonal climate
predictions, where seasonal atmospheric variability
is decomposed into external variability because of
SST and internal variability due to atmospheric processes alone (e.g. Kumar and Hoerling 1995; Rowell
et al. 1995).
For coupled general circulation models (CGCMs)
used in climate change projections, in which theocean has not been initialized, the mean over an
ensemble of CGCM simulations is the least biased
estimate of the response of the model to the specified
time evolution of external forcings (e.g., CO2, solar
variability, volcanic aerosols). The departure in eachclimate simulation from the ensemble mean then provides an estimate of the model’s internal variability
due to natural fluctuations. This approach can be applied to any time average extending from seasonal toannual to decadal. However, for longer time averages,
the removal of weather or climate noise by ensemble
averaging is more effective.
For large enough ensembles with specified external forcing, this approach also allows for theinvestigation of how external forcing may project
onto dominant modes of internal variability. In one
example, a 40-member ensemble of CGCM integrations with changing atmospheric composition andozone recovery for the period 2005–60 was compared
to a long (10,000 yr) unforced control run of the atmospheric model component with a specified repeating
annual cycle of sea surface temperatures and sea ice
conditions (Deser et al. 2011). The ensemble mean atmospheric circulation trend, interpreted as the forced
response, exhibits a statistically significant weakening of the Southern Hemisphere polar vortex during
austral summer (positive sea level pressure trends athigh latitudes and negative ones at middle latitudes;
Fig. 2, left panel). The spread of the response among
the individual ensemble members, or intraensemble
noise, is also characterized by an annular patternreminiscent of the forced response (Fig. 2, middle
panel). Further, the pattern of the noise closely resembles the leading EOF from the unforced atmosphericmodel control run (Fig. 2, right panel). This study
illustrates that the pattern of the forced response may
have similar structure to the natural variability in themodel, as also noted by Meehl et al. (2009b) for theclimate shift that occurredin the Pacific in the mid1970s. These results further
demonstrate that externallyforced multidecadal trendsof some variables can be
subject to large uncertainties owing to noise, thus
requiring analysis of verylarge ensembles.
This approach to separating the natural andexternally forced variability, based on ensembles ofclimate simulations, is aconceptually simple methF ig . 2. Projected November–February sea level pressure trends duringodology in its formulation.
2005–
60 over the Southern Hemisphere. (top left) Forced 40-member
The approach, however,coupled model ensemble mean. (top middle) Leading EOF of the deviation
also has some limitations,of each coupled model ensemble member’s trend from the coupled model
including the fact that esensemble mean trend. (top right) Leading EOF of a 178-member ensemble oftimates of internal and ex56-yr trends from a 10,000-yr atmospheric model control integration. (bottom
ternally forced variabilityleft) PDF of the trends in the index of the southern annular mode from eachcoupled model ensemble member (red bars) and from each atmospheric
are model dependent. On
control member (gray). (bottom right) As at bottom left, but the coupled
the other hand, based onmodel ensemble mean trend has been removed from each individual coupled
an analysis of simulations
model ensemble member. From Deser et al. (2011).
from multiple CGCMs anda comparison of total variability against the observed estimates, some confi- externally forced signal. In a small ensemble, say, ofdence in the model-based estimates can be gained. the size envisioned in the Fifth Assessment Report
However, the approach, by construction, requires a (AR5) decadal prediction experiments, the intralarge ensemble of simulations and can be computa- ensemble noise due to energetic internal variability
tionally taxing. For example, existing model archives with coherent spatial structure will impact the EOFs
used in the third phase of the Coupled Model Inter- of the ensemble mean and may make it difficult tocomparison Project (CMIP3) generally do not have distinguish between the patterns of signal and noise.
large enough ensembles from individual models for To overcome this problem, a spatial prewhitening
this approach to be viable.
transformation is applied to the ensemble mean data
to remove the spatial correlations from the noise
Signal-to-noise maximizing EOFs. The signal-to-noise structures and thus remove the impact of the climate(S/N) maximizing EOF analysis is an effective meth- noise on the ensemble mean (see Allen and Smith
od to distinguish between externally forced climate 1997; Venzke et al. 1999; Chang et al. 2000). This
responses, which are common to all ensemble mem- analysis is equivalent to identifying the predictablebers, and natural internal climate variability. This components that maximize the average signal-toapproach can be used with small ensembles and can noise ratio (see DelSole and Tippett 2008).
be used when the signal due to external forcing is on
Ting et al. (2009) applied S/N maximizing EOFs
the order of, or weaker than, the internal variability to twentieth-century SST variability over the Northin the model.
Atlantic basin to distinguish natural variability [inIn signal-to-noise maximizing EOFs, the predict- particular, Atlantic multidecadal variability (AMV)]
able patterns in ensemble prediction experiments are from the externally forced signal in six small CMIP3
sought by calculating the dominant patterns (EOFs) ensembles performed with several different CGCMs.
of the covariance matrix of the ensemble-average The results exhibited a cleaner and better betweenoutput (e.g., Seager at al. 2008). In large ensembles, model agreement of the global forced signal thaninternal variability of each ensemble member using a simple ensemble mean temperature. The
largely cancels out in the ensemble mean, leaving the estimate of the forced signal can then be subtracted
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to determine the pattern of internal interdecadal
variability in the observed (and modeled) North
Atlantic SST (see Fig. 3). This yields a much different
impression of the magnitude of the AMV in the earlytwenty-first century than more subjective estimatesof AMV [e.g., the departure from a linear trend, orfrom the global mean; see Enfield and Cid-Serrano(2010) or Ting et al. (2009) for a discussion].
While S/N EOF analysis can be useful for identifying a common signal in small ensembles offorced CGCM integrations, even in the presence ofsignificant levels of climate noise, the method does
have limitations in the context of decadal predictions.
In particular, S/N maximizing EOFs would need an

additional set of model simulations to separate information on the patterns of externally forced variability
from any predictable or persistent patterns related tothe initial conditions, as the initialized signal would
also be part of the output common to all ensemble
members.

Linear inverse models. Linear inverse modeling (LIM)
is an empirical technique to fit a multivariate rednoise model to observations or model output. Thesemodels have been very successful in simulating ENSO
variability and can reproduce the observed power
spectrum on seasonal-to-interannual time scalesof the dominant pattern of tropical SST variability(see Newman et al. 2009). Since LIM
determines empirical, potentiallynonorthogonal, dynamical modes,
it is useful for identifying how thesemodes contribute to a physical phenomenon. For example, LIM has beenused to show that the Pacific decadaloscillation (PDO; Mantua et al. 1997)
may not be a single physical mode but
a superposition of a number of processes with different dynamical origins (Newman et al. 2003; Schneider
and Cornuelle 2005; Newman 2007).
For example, LIM of Pacific basin
SSTs finds that the “decadal ENSO”
pattern with PDO signature in the
North Pacific (Fig. 4c; e.g., Zhang
et al. 1997; Deser et al. 2004) has adecay time that is far shorter than its
period. As a result, little long-range
forecast skill is associated with thiseigenmode. Instead, predictability inthis system on greater than interannual time scales comes from the twoleading stationary eigenmodes: aleading eigenmode with a 100-yr trend(Fig. 4a) and a second eigenmode(Fig. 4b) that has a pattern somewhat
similar to the multidecadal signal
Fig. 3. (a) Projection of SST averaged in the North Atlantic basin found by Deser et al. (2004) (see also
onto the leading S/N maximizing principal component (PC) in each D’Arrigo et al. 2005). The combinedof the participating models [see list in figure and information in Ting effects of these two eigenmodes alone
et al. (2009)]. Each model PC is depicted by a different color, and the dominate the patterns of Pacific SST
dashed line is the ensemble average. The observed SST average, sug- trend in this dataset over both thegesting a superposition of a forced trend and internal, multidecadal
entire century and the last 50 years. In
variability, is shown with the solid black line. (b) The observed AMO
addition, LIM is useful for identifying
index constructed by subtracting from the observed North Atlantic
SST average the model estimates of the forced North Atlantic SST optimal initial conditions that proshown in (a). The black dashed line shows the forced response average duce the largest variability in a linearlystable stochastically forced system.
across all six participating models. From Ting et al. (2009).
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Such initial conditions thatwould portend potential
predictability have been
identified for the North
Atlantic (see Tziperman
et al. 2008; Hawkins andSutton 2009) and the tropical Pacific (see Penland andSardeshmukh 1995).
As a consequence, the estimates of observed empirical modes from LIM can beused to assess the properties of empirical modes calculated from model output.
Also, by comparing LIM
simulations with external forcing to their corresponding control runs, one
can gain some insight into
how dynamical modes may
be impacted by external
forcing. For example, comFig. 4. (left) Leading empirical eigenmodes and (right) their corresponding
paring empirical modes
time series from the LIM of annual-mean HadISST SST anomalies. The LIM
estimated from the outis constructed as in Newman (2007), except that the EOF basis is determinedput of twentieth-century
over the entire Pacific domain (20°S–60°N); the leading 12 PCs are retained,
simulations to empiricalexplaining 92% of the variance in both the tropics and in the North Pacific,
modes determined fromunlike in Newman (2007), where less than two-thirds of the North Pacificboth the corresponding
variance was retained. Contour interval is the same in all panels but is arbitrary. Red (blue) shading indicates positive (negative) values; zero contour
control simulations andis removed for clarity. (a) Leading eigenmode, stationary with decay time offrom observations indi13 yr. (b) Second eigenmode, stationary with decay time of 6.4 yr. (c) Mostcates that external forcenergetic phase of third (“decadal ENSO”) eigenmode, propagating with aing substantially impacts
period of 16 yr and decay time of 2.1 yr.
t he leading eigenmode(Fig. 5a). Moreover, theclimate models might be underestimating the po- fingerprints” or best linear unbiased estimators (seetential predictability of natural variability since in review in Hegerl et al. 2007b). For detection and atvirtually all of the twentieth-century simulations, the tribution, all relevant external influences on climate
second eigenmode is not only poorly captured but is must be considered. The attribution methods thenalso much less persistent than in the observed LIM, attempt, with uncertainty estimates, to identify thefor reasons that are presently not understood (e.g., contribution of each external forcing factor to theNewman 2007) (Fig. 5b).
observed change. The shape of the fingerprints is
assumed known, and their magnitude is estimated,
Application of detection/attribution studies. Climate allowing the results to account for uncertainties,change detection and attribution studies aim to such as errors in a model’s sensitivity to a particularisolate the anthropogenically forced component forcing or in the magnitude of external forcings inof the evolving climate. They generally use infor- general.
mation about the shape of the expected climate
The results from detection and attribution
response to forcing (the “fingerprint”) and are methods, however, go further. The best-guess andtargeted to isolating the role of these fingerprints uncertainty ranges of the greenhouse gas contribuin observed climate change as clearly as possible tion in the observed temperature changes can befrom internal climate variability. Often, this is done used directly to predict future changes (Stott andusing signal separation techniques, such as “optimal Kettleborough 2002) and have been used, among
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other methods, to provide uncertainty ranges for
future climate change in the IPCC assessment (Knutti
et al. 2008). Lee et al. (2006) demonstrated that overa large part of the twentieth century, the forced
component, determined by optimal fingerprints, canproduce skillful hindcasts of decadal global temperature variability.
The success of fingerprint methods in separating
different factors influencing climate suggests thatthey may also be useful in separating the influenceof initial conditions from those of external forcing,thus allowing us to trace where the initial conditions
have made significant differences in hindcasts andhow long this influence has lasted. The fifth phase of

Fig. 5. Comparison of the (a) leading and (b) second
observed eigenmodes with the corresponding eigenmodes based on each 100-yr ensemble member fromthe twentieth-century AR4 coupled GCMs (blue) andthe associated control runs (green). Both plots show
the decay time scale of each modeled eigenmode vs. its
pattern correlation with the corresponding observed
eigenmode. The red circle in each panel indicates theobserved eigenmode.

148 |february 2011

the CMIP (CMIP5) simulations will provide a useful
test bed for such an extension of the detection andattribution method.
However, when it comes to applying such approaches on regional scales and to variables otherthan temperature, a number of difficulties loom.
One important shortcoming is that on smaller thancontinental scales, the uncertainty in forcings otherthan greenhouse gases is large; the exact time–spacepattern of aerosols, land use change, and other forcings is often poorly known and poorly represented
in models. This would hamper the ability to reliably
attribute successes and failures in regional hindcasts
to particular causes. When applying this approachto variables other than temperature, the difficulties
increase. Only recently, for example, has the effectof anthropogenic forcing on precipitation beenformally detected (Zhang et al. 2007). However, themultimodel fingerprint produces smaller changes
in zonally averaged precipitation than observed,
indicating that the understanding and simulation of
precipitation variability is still limited.
CHALLENGES. Interaction between natural andexternally forced variability. As discussed earlier, theresponse to external forcing may resemble the natural modes of variability on regional and hemisphericscales. This was seen to be the case in the modelingstudy of Meehl et al. (2009b), where natural andexternally forced patterns of variability with similar
structure contributed to the mid-1970s climate shift
over the Pacific basin, from relatively cool to relatively
warm conditions along the equator. Indeed, theyargued that an anthropogenically forced shift would
have occurred in the 1960s if it were not for thepresence of large-amplitude natural variations thatdelayed the shift into the 1970s.
Just how external forcing interacts with naturalmodes of variability remains an important but unresolved issue. The process may be fundamentally
linear with external forcing selecting certain naturalinternal modes because of their inherent time scales
and spatial structures, or nonlinear where the impactof the external forcing on the modes of variability
has a net effect on the long-term trend signal (e.g.,
see Branstator and Selten 2009). In the linear case,the forcing and the response may not have similar
patterns because of the nonnormal growth of naturalmodes. In the nonlinear case, the external forcing
may cause changes in the frequency of occurrence ofclimate modes with or without changing the spatial
structure of the leading modes of variability (seeCorti et al. 1999; Hsu and Zwiers 2001; Brandefelt

2006; Branstator and Selten2009).
Observational uncertainties.
Verification of the forced
component of twentiethcentur y climate trends
simulated in model experiments depends on theexistence of accurate estimates of these trends in
observations. Given thelimited sampling in both
space and time of the observations and proxy records, these verifications
must be handled carefully.
In particular, knowledgeof the spatial patterns andmag nitudes of cli matetrends over the oceans ishampered by the unevenand changing distributionof commercial shipping
routes (Fig. 6) and otherobservational inputs as well
as different approaches tomerging analyses of theobservations (Rayner et al.
Fig. 6. Distribution of surface marine observations from the International2011).
Comprehensive Ocean–Atmosphere Data Set (ICOADS), shown as the
An example of the impercent of months with at least one observation per 2° lat × 2° lon grid box
pact of observational unduring the 20-yr period indicated. Adapted from Deser et al. (2010a).
certainties on the interpretation of twentieth-century
SST trends is shown in Fig. 7 based on an uninter- realistic ones (Deser et al. 2010b). These observational
polated dataset [version 2 of the Hadley Centre SST sampling issues underscore the challenge of providing
dataset (HadSST2); Rayner et al. 2006] and two a robust target for model validation of twentieth-cenoptimally interpolated reconstructions [the Hadley tury surface marine climate trends and perhaps theCentre Sea Ice and SST dataset (HadISST; Rayner need to consider a suite of complementary measures
et al. 2003) and version three of the National Oce- for poorly sampled variables and/or regions.
anic and Atmospheric Administration’s (NOAA’s)
A limitation of the instrumental record is that it
extended reconstructed SST (ERSSTv3; Smith et al. spans at most a few realizations of decadal variability.
2008)]. Although trends from the three datasets share Paleoclimate records—derived from tree rings, cormany features in common, such as a strengthening als, lake sediments, or other “proxies”—have beenof the equatorial Pacific zonal temperature gradient used to extend this record to hundreds of years or(Karnauskas et al. 2009), there are also differences. more and are generally believed to be free of anthroMost notably, the eastern equatorial Pacific shows pogenic influence prior to the industrial age (Brookcooling in HadISST and warming in HadSST2 and 2009; Jansen et al. 2007), thus constituting a potenERSSTv3 (see also Vecchi et al. 2008). However, in- tial means of model verification. Particular proxydependently measured but related variables, such as types are generally restricted to specific ecologicalnighttime marine air temperatures, provide some domains and spatial coverage can be patchy, but there
evidence that the eastern Pacific trends represented in has been a recent emphasis on the reconstruction of
the HadSST2 and ERSSTv3 datasets may be the more complete climate fields (Luterbacher et al. 2001; CookAMERICAN METEOROLOGICAL SOCIETY

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and Krusic 2004; Mann et al. 2007; Riedwyl et al.
2009; Cook et al. 2010; Neukom et al. 2011). Because
paleodata constitute the sole records of Earth’s preinstrumental climate, such reconstructions meritattention as a potential means of model verification
with respect to both unforced and naturally forced
climate fluctuations (Jansen et al. 2007; Hegerl et al.
2007b).
Additional challenges are faced in the assessmentof ocean processes below the surface, even for the
recent past. The available analyses of ocean observations span a wide range of products aimed at climate
studies as well as ocean “nowcasting” and short-term
forecasting applications. The ocean analysis products differ in the underlying models and estimation

Fig. 7. Twentieth-century SST trends [°C (100 yr) –1]
from the (top) uninterpolated HadSST2, (middle)
reconstructed HadISST, and (bottom) reconstructed
ERSSTv3 datasets, based on monthly anomalies during
1900–2008. A minimum of 3 months per decade ineach decade was required to compute a trend fromthe HadSST2 dataset. Adapted from Deser et al.
(2010b).

150 |february 2011

methods, as well as the suite of observations thatare assimilated. Many of the analysis products span
multiple decades from the 1980s to the present, withsome also reaching back to the 1950s, and provide aconvenient means for retrospective studies of climate
variability; however, the relative accuracy and fidelityof the analyses depend in part on the specific variables used and are active areas of study. While manyof the ocean syntheses employ estimation methods
based on those first developed in weather forecasting,some employ so-called smoothing methods that estimate the source of the model inaccuracies correctedby combining with data [see review by Balmaseda
et al. (2010)]. The assimilation of Argo data in theseanalyses may remove biases in the upper ocean andallow for the initialization of ocean circulations andtransports (e.g., see Forget et al. 2007); however, suchrecords are limited to after 2000.
Modeling uncertainties. The spatial structure anddominant time scales of natural variations differ
across models (see discussion of Fig. 5). Additionally,
coupled climate models produce a range of responses,
in space and time, to anthropogenic radiative forcing(Fig. 8). Such differences in model estimates of internal variability and response to external forcing limit
our understanding for the potential of the decadal
climate predictions.
As an example, the historical changes and future
response of the tropical Pacific mean state have been
subjects of debate. Different proposed mechanisms
disagree on the expected sign of change in the zonal
SST gradient in the tropical Pacific (Knutson andManabe 1995; Meehl and Washington 1996; Caneet al. 1997; Clement and Seager 1999) in response toanthropogenic forcing. The observational record does
little to clarify the situation, as trends in different
observed SST records differ in even their sign (seeFig. 7). Models that simulate the largest El Niño–like
response have the least realistic simulations of ENSO
variability, while models with the most realistic simulations of ENSO project little change in the Pacificzonal SST gradient (Collins 2005). These differencesin tropical Pacific interannual variability and change
have implications for Pacific decadal variability
through their impact on large-scale changes in theatmospheric circulation (e.g., Alexander et al. 2002;
Vimont 2005).
Different climate model responses to radiative
forcing may lead to differences in the slowly varying
base state of the oceans. Differences in the oceanbase state, in turn, may alter the character of naturalvariability by changing the advective time scale ofF i g . 8. First EOF andassociated principal
component (PC) of
annual-mean sea surface temperature fromobservations and three
twentieth-century simulations for years 1890–
1999: (a),(b) HadISST
dataset (Rayner et al.
2003); (c),(d) National
Center for AtmosphericResearch (NCAR)– U.S.
Department of Energy
Parallel Climate Model,
version 1 (PCM1; www.
cg d . u c a r. e d u / p c m / ) ;(e),(f) The GeophysicalFluid Dynamics Laboratory (GFDL) climate
m o d e l , ve r s i o n 2 .1
(CM2.1; Delworth et al.
2006); (g),(h) The NCAR
Communit y C lim ate
System Model, version
3.0 (CCS M3.0 ; w w w.
ccsm.ucar.edu/models/
ccsm3.0). All data havebeen smoothed with a
10-yr low-pass Lanczos
filter using 21 weights.
EOF patterns are normalized. PCs are in unitsof degrees Celsius. The
percent in the upper rightof each figure indicatesthe amount of variance explained by each pattern. Note that the PC time series from the climate model simulations show fluctuations with larger amplitude than observations, all of which fluctuate on different time scales.

density/salinity anomalies and pathways between theextratropics and tropics. Thus, the ability of models
to reproduce the observed spatial patterns of forced
variability is important to realizing the full benefitsof ocean initialization of natural variability; however,validation of the forced patterns of variability is notstraightforward, given the observational uncertainty
present in identifying even the natural, internal patterns of low-frequency variability.
DEVELOPING A FRAMEWORK TO ASSESSDECADAL PREDICTIONS. Given that overthe course of the next 10–30 years the magnitude ofnatural decadal variations may rival that of anthropogenically forced climate change on regional scales,
initialized decadal predictions have the potential toprovide important information for climate-related
management and adaptation decisions. Such predicAMERICAN METEOROLOGICAL SOCIETY

tions are presently one of the grand challenges forthe climate community. Long experience in weather
and climate forecasting has shown that forecasts areof little utility without a priori assessment of forecast
skill and reliability. This will be no less true for decadal forecasts if they are to be useful. However, evencrudely estimating skill for a forecast system requires
some understanding of the sources for potential
skill, especially when expected skill depends upon
the initial conditions themselves, and the expected
evolution of forecast spread, which is one measureof uncertainty.
For decadal predictions, understanding of thesources for potential skill requires identifying thosephysical phenomena—and their model equivalents or lack thereof—that may provide additional
predictability on decadal time scales. This includes anassessment of the physical processes through which
february 2011

| 151

Table 1. Comparison of methods to separate natural and forced decadal variability described in the paper.
Note that all methods are still subject to conflation of natural and forced patterns.
Requires large
ensembles

Property method

Distinguishes naturaland externally forced
trendsIsolates dynamical
modes of naturalvariabilityNo

Can identify skill due
to initialization andexternal forcing

Identifies skill due
to initialization

Analysis of ensemblemeans and variance(ANOVA)

Yes

Identifies change in statistics
due to external forcing
by comparing forced andunforced runs

Optimal fingerprinting

Yes, to identify fingerprints
of response to external
forcing

No

No

PotentiallyLess sensitive to numberof ensemble members than
ANOVA

Identifies change in statistics
due to external forcing
by comparing forced andunforced runs

NoNo

Linear inverse models

No

Identifies change in statistics
due to external forcing
by comparing forced andunforced runs

Identifies empirical
modes

Yes

Initialized hindcasts

Potentially

Potentially

S/N maximizing
EOF

anthropogenic forcing interacts with or projects
upon natural variability. However, it is important tonote that the rate at which forecast experience willaccumulate on the decadal time scale is necessarilymuch slower than the rate at which it accumulates forweather forecasting. Given this, a physical framework
is necessary to provide a consistent assessment of thedifferent decadal prediction experiments planned
for the AR5.
The main conclusion drawn from the body ofwork reviewed in this paper is that distinguishing
between natural and externally forced variations isa difficult problem that is nevertheless key to any
assessment of decadal predictability and decadal
prediction skill. Note that all the techniques are limited by some assumption intrinsic to their analysis,
such as the spatial characteristics of the anthropogenic signal, independence of noise from signal,
or statistical stationarity. Benefits and limitationsof techniques described in this paper are listed inTable 1. Also, all the techniques utilize either shortand potentially inaccurate observational datasets
and/or potentially biased but lengthier CGCM
datasets. The analysis techniques discussed in thepaper should be applied to the long control CGCM
runs and both the twentieth-century simulations andtwenty-first-century projections, which will serve asa critical test bed for analysis of the relationship between natural and anthropogenic variability. These
strategies can also be applied to existing decadal
152 |february 2011

Yes

prediction experiments and climate change projections in order to develop a series of metrics that canbe used to assess the predictions to be done for theAR5. These metrics could help identify, to the extent
possible with limited ensemble sizes, the impact ofdifferent initialization strategies, model biases, anderrors in model physics on the response to external
forcing and the predictable and unpredictable natural variations.
A reasonable starting point for these metrics is tofocus on decadal variability due to ocean processes,
as discussed earlier. This requires analyses that assess the spatial patterns and associated time scalesof natural variations, and their potential change instructure and frequency due to external forcing. A
starting point for such analyses could be to compare
the existing climate change projections against theircompanion control runs. In addition, since externallyforced SSTs play an important role in climate variations over land through atmospheric teleconnections,
it is necessary to develop metrics that assess the spatial pattern of externally forced SST variability, as wellas upper-ocean structure and variability. To quantify
signal-to-noise ratios, it is necessary to develop metrics that can properly validate ensemble simulations
and predictions. The development of these metrics
will help guide the assessment of decadal forecasts
and will provide a framework for identifying potential directions to improve our ability to make decadal
predictions.

ACKNOWLEDGMENTS. The authors of this paper
are members of the Decadal Predictability Working Group,sponsored by U.S. CLIVAR. We appreciate the support
from the U.S. CLIVAR office. We thank two anonymous
reviewers for their helpful and thoughtful suggestions.

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