COMMENTARY
10.1029/2020EF001854
This article is a companion to Mankin
et al. [2020] https://doi.org/10.1029/
2020EF001610.

“Certain Uncertainty: The Role of Internal Climate
Variability in Projections of Regional Climate
Change and Risk Management”
Clara Deser1
1

Key Points:• Internal climate variability
introduces unavoidable uncertainty
in regional climate projections dueto its limited predictability• Initial‐condition "Large Ensembles"
conducted with global coupledclimate models provide an expected
range of future climate outcomes• Their value for climate risk
assessment, adaptation
management, and decision‐making
has yet to be fully realized

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

Citation:
Deser, C. (2020). “Certain uncertainty:
The role of internal climate variability
in projections of regional climatechange and risk management”. Earth's
Future, 8, e2020EF001854. https://doi.
org/10.1029/2020EF001854
Received 14 OCT 2020
Accepted 10 NOV 2020
Accepted article online 17 NOV 2020

Invited Commentary on “The value of
initial condition large ensembles torobust adaptation decision‐making” by
Mankin et al., Earth's Future, 2020

©2020. The Authors.
This is an open access article under theterms of the Creative Commons
Attribution License, which permits use,
distribution and reproduction in any
medium, provided the original work isproperly cited.

DESER

NCAR, Boulder, CO, USA

Abstract Internal climate variability co‐exists with anthropogenic climate change and places limits on
the accuracy of regional climate projections due to its inherent unpredictability. This “certain”
uncertainty in regional projections introduced by internal variability contrasts with uncertainty resulting
from structural differences amongst climate models, which is potentially reducible as climate models
improve. Initial‐condition “Large Ensembles” of simulations with individual climate models provide a newperspective on the expected range of future climate change outcomes. Their value for climate risk
assessment, adaptation management, and decision‐making has yet to be fully realized.

1. Introduction
The familiar adage “climate is what you expect; the weather is what you get” reminds us that the atmosphereis a chaotic system with limited predictability. For example, while we can predict with certainty that July will
be warmer than January in the Northern Hemisphere, we cannot forecast the occurrence of a particular heatwave more than a few weeks ahead of time, at best. More broadly, this adage is also applicable to future
projections of regional climate change. In the case of climate change, what we expect derives from theimpacts of fossil fuel burning and other human activities which release heat‐trapping gases (carbon dioxide,methane, etc.) into the atmosphere. What we get, however, is the combined effect of anthropogenic inﬂuences and natural climate variability, where “natural” refers to exogenous factors such as volcanic eruptions
and solar and orbital cycles and intrinsic ﬂuctuations of the climate system caused by the chaotic dynamics
of the atmosphere and oceans. Intrinsic (also termed internal) climate ﬂuctuations are generally unpredictable more than a few years ahead of time and hence play an analogous role to “weather” on time scales of
decades and longer. That is, while we may be able to predict the impacts of anthropogenic climate change
based on the laws of physics, the presence of internal climate variability introduces an unavoidable elementof randomness to the actual outcomes we will experience. The co‐existence of internal climate variability
and anthropogenic climate change thus places limits on our ability to make accurate climate projections
for the coming decades, especially in regions where their effects are of similar magnitude. This inherent
uncertainty in regional climate projections is certain, and must be taken into account in climate risk assessment, adaptation management and decision‐making.
The “certain uncertainty” that stems from the superposition of unpredictable internal climate variability andanthropogenic climate change stands in stark contrast to a different type of uncertainty associated with climate projections, namely, “model structural uncertainty.” “Model structural uncertainty” arises from ourincomplete understanding of the physical and biological processes within the climate system, as well as fromthe imperfect representation of the known processes in numerical models. For this reason, it is important toconsider projections from many different climate models to assess sensitivity to model formulation. As ourunderstanding of the climate system advances, and as climate models improve in their representation of thesalient processes, the contribution of models' structural uncertainty to regional climate projections isexpected to reduce. However, advances in climate modeling will not alleviate the “certain uncertainty” inprojections arising from unpredictable internal climate variability, although it may affect the magnitudeof this uncertainty (note that some models produce excessive natural variability while others simulate insufﬁcient variability).
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Earth's Future10.1029/2020EF001854

Figure 1. Projected changes in winter precipitation over the next 50 years (2010–2060) for North America based on the40‐member initial‐condition Large Ensemble conducted with Community Earth System Model version 1 under theRCP8.5 radiative forcing scenario. The top panel shows “what we expect”: that is, the change in winter precipitation due
to anthropogenic inﬂuences. The bottom panels show the plausible range of “what we get”: that is, the combined
inﬂuence of anthropogenic climate change and natural climate variability. See text for details.

2. A Range of Outcomes: The Value of Large Ensembles
We can reframe the adage “climate is what you expect; the weather is what you get” to “anthropogenic
climate change is what you expect; anthropogenic climate change and natural climate variability is what
you get.” In other words, the presence of unpredictable internal climate variability results in a range of possible outcomes for human‐caused climate change. Climate models provide a direct tool for assessing thisrange of outcomes, obtained by conducting a large ensemble of simulations with the same model and thesame radiative forcing protocol (i.e., scenario of greenhouse gas emissions) but varying the initial conditions(sometimes by extremely small amounts on the order of the model's numerical round‐off error: for example,
10−14 K for atmospheric temperature). Once the memory of the initial conditions is lost, the resulting spread
across the ensemble members is due solely to unpredictable internally generated natural climate variability.
It is important to note that each simulation in the ensemble contains a common response to the imposedradiative forcing superimposed upon a different sequence of internal variability. The ensemble size needed
to bracket the range of plausible outcomes depends upon the relative magnitudes of internal climate variability and forced response, which in turn depends upon the quantity of interest, location, season, and timehorizon (Deser et al., 2012; Milinski et al., 2019). Generally speaking, internal climate variability is larger
in the extra‐tropics than the tropics, greater in winter than summer, larger for precipitation than temperature, and relatively stronger compared to forced climate change at shorter time horizons.
An example of the range of possible outcomes for winter precipitation changes during the next 50 years over
North America is illustrated in Figure 1 based on the 40‐member initial‐condition Large Ensemble (“LE” forshort) conducted with Community Earth System Model version 1 (CESM1) under the RCP8.5 radiative forcing or “business as usual” scenario (Kay et al., 2015). The top panel of this ﬁgure shows “what we expect”:
that is, the change in winter precipitation due to anthropogenic inﬂuence, obtained by averaging the40 members together. It shows that the United States and Canada will get progressively wetter, especiallyalong the west and east coasts, while Mexico will become drier. The bottom panels show the plausible rangeof “what we get”: that is, the combined inﬂuence of anthropogenic climate change and natural climateDESER

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Earth's Future10.1029/2020EF001854

variability. These two maps bracket the 5th to 95th percentile range of outcomes, obtained by selecting theensemble member with the second wettest trend over the United States and the ensemble member with thesecond driest trend over the United States. It is clear that “what we get” can differ substantially from “whatwe expect”: precipitation along the East and West Coasts of the United States may increase by more than
2 mm day−1 (left panel) or it may decrease over the southern half of the central and western UnitedStates (right panel), depending on how internal variability unfolds over the next 50 years. Note that it is
not possible to predict which of the two outcomes (or any other outcome) will occur, since internal climate
variability over such a long (50 year) time period is random (a “roll of the dice”). This unavoidable uncertainty in future precipitation change risk is important information for water resource managers, especially
if modiﬁcations to infrastructure are needed.

3. Additional Applications of Large Ensembles
In addition to providing a range of plausible future climate outcomes for a given model and radiative forcing
scenario, LEs have proven to be enormously useful for model evaluation and inter‐comparison purposes. In
particular, they facilitate a clean separation of the forced response from internally generated variability,
which is otherwise confounded in individual simulations on regional/decadal scales (e.g., Deser et al.,2012, 2020; Kay et al., 2015; Lehner et al., 2020; Maher et al., 2019). The forced response may be directly estimated from the ensemble average at each time step, since the internal variability is by deﬁnition randomly
phased among the individual members after the memory of the initial conditions is lost. Until the advent ofLEs, it was problematic to isolate the various sources of inter‐model differences in the Coupled Model
Inter‐comparison Project (CMIP) archives due to the limited number of simulations (generally 1–3) for eachmodel (i.e., structural uncertainty was confounded with uncertainty due to internal variability; see Deseret al., 2012, and Lehner et al., 2020). An additional advantage of LEs is their “strength‐in‐numbers” withregard to sampling of internal variability. In particular, they facilitate examination of externally forcedchanges in the characteristics of internal variability, for which large sample sizes are needed (obtained bypooling together the individual ensemble members). Importantly, this includes extreme events, which arerare by deﬁnition in any single simulation (see Deser et al., 2020 for an example).
LEs provide crucial context for understanding and interpreting the observational record. Just as in a modelLE, the sequence of internal variability in the real world could have unfolded differently. That is, the observational record traces only one of many possible climate histories that could have happened under the same
exogenous natural and anthropogenic forcing (i.e., volcanoes, solar and orbital cycles, and greenhouse gas
emissions). For example, El Niño and La Niña events could have occurred in different years, ordecadal‐multidecadal ﬂuctuations in the Paciﬁc and Atlantic Oceans could have had a different chronology.
This concept, sometimes referred to as the “Theory of Parallel Climate Realizations” (Tél et al., 2020) or thenotion of “Contingency” (Gould, 1989), has enormous implications. For one, it means that a single model
simulation of the historical period need not match the observed record, even if the model is “perfect” in
its representation of statistical characteristics of the real world's climate. However, the statistical characteristics of the model's internal variability must match those of the real world, taking into account the limited
sampling in the observational record due to the limited ensemble size of one, and the limited duration andspatial coverage of the measurements. Further, the spread across a model LE must encompass the observational record (subject to the above observational uncertainties) for the model to be credible, provided that
there are enough members to adequately span the range of possible sequences of internal variability.
However, this is not a sufﬁcient criterion, since a model with unrealistically large internal variability may
encompass the observational record for the wrong reason. These considerations emphasize the importanceof evaluating both the amplitudes and patterns of internal variability in models (Fasullo et al., 2020; Suarez‐
Guiterrez et al., 2020).
To date, the most common applications of LEs have been (1) to assess the range of future climate outcomesarising from the combined effects of internal variability and forced climate change (Deser et al., 2014, 2020);(2) as methodological testbeds for evaluating approaches to detection and attribution of anthropogenic
climate change in the observational record (e.g., Barnes et al., 2019; Bonﬁls et al., 2019; Deser et al., 2016;
Santer et al., 2019; Stephan et al., 2019; Wills et al., 2020); and (3) to partition the sources of uncertainty

DESER

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Earth's Future10.1029/2020EF001854(internal variability, model structure, and radiative forcing scenario) in the multi‐model CMIP archives(Deser et al., 2012; Kumar & Ganguly, 2018; Lehner et al., 2020; Schlunegger et al., 2020).

4. Looking Ahead: The Power of Large Ensembles for Risk Assessment
and Decision‐Making
The power of LEs for climate impacts risk assessment and decision‐making is largely untapped. Pioneering
work by Garcia‐Menendez et al. (2017) and Saari et al. (2019) demonstrated the value of LEs with regard toair quality and associated health risks in a warming world. In this issue of Earth's Future, Mankin et al.
(2020) go one step further and show how LEs can support regional‐scale robust adaptation decision‐
making with regard to freshwater resources. Further studies of this kind are urgently needed to ensure thatclimate adaptation policies are based on the most complete scientiﬁc information available.
Acknowledgments
I thank Adam Phillips for creating theﬁgure. NCAR is sponsored by theNational Science Foundation.

DESER

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