REVIEW ARTICLE
PUBLISHED ONLINE: 7 SEPTEMBER 2015 | DOI: 10.1038/NCLIMATE2689

Towards predictive understanding of regional
climate change
Shang-Ping Xie1*, Clara Deser2, Gabriel A. Vecchi3, Matthew Collins4, Thomas L. Delworth3,
Alex Hall5, Ed Hawkins6, Nathaniel C. Johnson1,7,8, Christophe Cassou9, Alessandra Giannini10
and Masahiro Watanabe11
Regional information on climate change is urgently needed but often deemed unreliable. To achieve credible regional climate
projections, it is essential to understand underlying physical processes, reduce model biases and evaluate their impact on projections, and adequately account for internal variability. In the tropics, where atmospheric internal variability is small comparedwith the forced change, advancing our understanding of the coupling between long-term changes in upper-ocean temperatureand the atmospheric circulation will help most to narrow the uncertainty. In the extratropics, relatively large internal variability
introduces substantial uncertainty, while exacerbating risks associated with extreme events. Large ensemble simulations are
essential to estimate the probabilistic distribution of climate change on regional scales. Regional models inherit atmosphericcirculation uncertainty from global models and do not automatically solve the problem of regional climate change. We conclude that the current priority is to understand and reduce uncertainties on scales greater than 100 km to aid assessments at
finer scales.

C

limate change is one of the most serious challenges facing
humanity, and extends far beyond the rise in global mean temperatures. Regional manifestations of climate change, includingchanges in droughts, floods, storminess, wildfires and heat waves, willaffect societies and ecosystems. Information about regional impacts is
crucial to support planning in many economic sectors, including agriculture, energy and water resources. Despite their importance, reliable projections of regional climate change face ongoing challenges1.
Here we review recent advances in understanding regional climate change, offer a critical discussion of outstanding issues, andmake recommendations for future progress. We start by highlighting
robust regional climate change patterns and their physical underpinnings, with a focus on temperature, precipitation and atmosphericcirculation. Next we discuss outstanding challenges, including thoserelated to physical understanding, model biases and internal variability effects, all of which contribute to uncertainty in projectedchanges of regional climate and extreme events. We conclude witha perspective on emerging opportunities in regional climate change
research, including efforts to better understand and quantify projections of extreme events enabled by increasing model resolution andensemble size.

Mechanisms for regional climate change

Regional climate projections are often perceived as synonymouswith downscaling, but a better understanding of the physical origins
of regional changes is essential to achieve more reliable projections.
Regional models and global climate models (GCMs) alike can aid
this understanding. Here we use the term ‘regional’ in a broad sense,

considering scales as large as whole continents and ocean basins(thousands of kilometres) or as small as a few hundred kilometres,
limited by the resolution of GCMs and long historical observations.
Regional models can achieve finer resolution than GCMs.
Climate anomalies are made up of a response to radiative changes
and variability generated internally within the ocean–atmosphere–land–cryosphere system. Projections rely on assumptions about
future changes in greenhouse gases (GHGs), aerosols and land use.
Radiative forcing will probably continue increasing for the rest of the
century, although the rate of increase is uncertain. Over time, theforced response will strengthen, diminishing the relative contribution
from internal variability. Unless aggressive mitigation policies curb
GHG emissions, the forced response is expected to dominate regionaltemperature change by the end of the century 2.
Uncertainty in regional climate projections arises from internalvariability as well as differences in model structure and forcing scenario, with the relative importance of these factors varying with time
horizon3. This section highlights robust patterns of regional climatechange, and the next section discusses uncertainties due to model
biases and internal variability. GHG forcing uncertainty will not be
addressed in detail, as at the regional scale it can be nearly eliminatedsimply by scaling with global mean temperature change. However,aerosols are an important regional-scale forcing, and their imprint on
regional climate change patterns will be discussed.

Temperature. For timescales of a century and longer, the magnitude of global mean temperature change under any emissions scenario is related to the equilibrium climate sensitivity (ECS)4 and the

Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093-0206, USA. 2National Center
for Atmospheric Research, Boulder, Colorado 80307-3000, USA. 3Geophysical Fluid Dynamics Laboratory, 201 Forrestal Road, Princeton, New Jersey
08540-6649, USA. 4College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, EX4 4QF, UK. 5Department of Atmosphericand Oceanic Sciences, UCLA, 405 Hilgard Avenue, Los Angeles, California 90095, USA. 6National Centre for Atmospheric Science, Department ofMeteorology, University of Reading, Reading, RG6 6BB, UK. 7International Pacific Research Center, University of Hawaii at Manoa, Honolulu, Hawaii
96822, USA. 8Cooperative Institute for Climate Science, Princeton University, Princeton, New Jersey 08540, USA. 9CNRS/CERFACS, 42 avenue Gaspard
Coriolis, Toulouse F-31057 Cedex, France. 10International Research Institute for Climate and Society, Columbia University, 61 Route 9W, Palisades,
New York 10964-8000, USA. 11Atmosphere and Ocean Research Institute, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568, Japan.
*e-mail: sxie@ucsd.edu
1

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a(°C)−2 −1.5 −1 −0.5 0 0.5 1 1.5 2

3

4

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9 11

b

(mm d−1)−2−1 −0.5 −0.2 −0.1

0

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Figure 1 | CMIP5 multimodel mean changes. a, Surface air temperatureand b, precipitation under Representative Concentration Pathway(RCP) 4.5 for the period 2081–2100 expressed as anomalies from1986–2005, as the ensemble mean of 42 models available in CMIP5.
Hatching indicates regions where the multimodel mean change is lessthan the natural variability (computed from 20-year averages takenfrom pre-industrial control experiments). Images generated using
http://climexp.knmi.nl/plot_atlas_form.py.

rate of deep oceanic heat uptake, which determines how quickly
ECS is approached. Different models produce different values ofthese key metrics. The ECS of a GCM can be approximated as thesum of albedo, water vapour, lapse rate and cloud feedbacks. Cloud
feedback is the dominant source of model spread5. Such feedbacks
are strongly related to regional phenomena, so that the global mean
is determined by integrated regional-scale effects (for example, ice
albedo feedback).
At continental scales, robust features of change in surface airtemperature have been found in observations and model projections (Fig. 1a). Polar amplification is a hallmark of surface temperature change in the Northern Hemisphere. It is largely a consequenceof sea ice and snow albedo feedbacks, although poleward energy
transport and feedbacks from clouds and water vapour may also be
important 6,7. The ratio of land warming to ocean warming is foundto be greater than unity across all scenarios and models for bothtransient and equilibrium warming, owing to differences in surface sensible and latent heat fluxes, boundary layer lapse rate andrelative humidity, and cloud cover 8. Muted warming is found in theSouthern Ocean where excess surface heat is mixed into the oceaninterior more effectively 9,10. A similar feature is found in the North
Atlantic subpolar gyre. These large-scale features are amenable to‘pattern scaling’, where fixed patterns of surface temperature change
are scaled by the global mean temperature response across scenarios
and through time11.
922

Precipitation. Whereas surface temperatures rise everywhere in
future projections, precipitation change is highly variable spatiallyin sign and amplitude, with a relatively small global mean change(Fig. 1). The fundamentally regional character of forced precipitation change highlights the challenge for predicting precipitation.
In the absence of major circulation changes, atmospheric moisture increases with warming, strengthening the climatologicaldistribution of precipitation minus evaporation (P – E)12,13. This
explains the general rainfall increase in summer monsoon regions14,
for example. At high latitudes, precipitation increases as storms
transport more moisture poleward15. Over tropical oceans, the wetgets-wetter pattern is realized in atmospheric models in the idealized case where sea surface warming is spatially uniform (Fig. 2a).
Spatial patterns of sea surface temperature (SST) changes affecttropical convection. Fast equatorial waves flatten horizontal temperature gradients in the tropical free troposphere, so that convective
instability, measured by the moist static energy difference between
the surface and upper troposphere, largely follows the SST pattern.
As a result, tropical rainfall change follows a warmer-gets-wetter
pattern (that is, positive where the local warming exceeds the tropical average)16. Enhanced warming over the equatorial Pacific andAtlantic anchors a band of rainfall increase where rainfall is currently low (Figs 1b and 2b). Ocean–atmosphere feedback is important in coupled SST–rainfall pattern formation. For example, muted
surface warming in the tropical Southeast Pacific is associated withacceleration of the southeast trade winds, which suppresses therainfall increase along the southeastward slanted rain band called
the South Pacific Convergence Zone (Fig. 2)17. The equatorial peakin SST warming is a robust feature across models owing to reduced
evaporative damping 18. The ongoing decadal cooling of the equatorial Pacific19 is, however, a sober reminder of the difficulty indetecting anthropogenically forced ocean warming patterns amidst
internal variability.
Competing effects of moisture and circulation change on P – E
can be understood by decomposing the P – E response into a thermodynamic component due to moisture increase with no circulation change, and a dynamic component due to circulation changewith no moisture change. The thermodynamic component gives riseto the wet-gets-wetter effect, but overestimates it because of partial
compensation by the tropical circulation slowdown15,20. Sea surface
warming patterns induce atmospheric circulation change, so thatthe warmer-gets-wetter effect is part of the dynamic component.
Although SST patterns do not change much through the year, thethermodynamic component strengthens in the rainy season, andwet regions in the rainy season tend to get wetter 21.
In monsoon regions, precipitation is concentrated in the summer season. Summertime monsoon rains are projected to intensify
because of moisture increase, a change especially pronounced for
the Asian–Australian monsoons14. A robust shift in the seasonal
cycle is apparent in GCMs, characterized by a delay in monsoon
onset and an increase in precipitation later in the season22. The
delay in onset is consistent with a vertical stability increase, similar to a developing El Niño event 23. This effect is compensated by alater increase in moisture convergence24. Over tropical continents,
remote oceanic influence on rainfall changes is also important 25.
Over the African Sahel, for example, precipitation change follows
the SST difference between the neighbouring subtropical North
Atlantic and global tropics26.
Relatively high consistency in rainfall change emerges overtropical oceans from model projections (Fig. 1b), but large intermodel variability remains (Fig. 3a). Decomposition of intermodel
variability shows that the dynamic component (due to uncertainties in atmospheric circulation change) dominates the uncertainty(Fig. 3b). The intermodel variability in tropical circulation can betraced further to differences in sea surface warming patterns. For
example, an anomalous interhemispheric Hadley cell tied to a

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REVIEW ARTICLE

NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE2689
cross-equatorial SST gradient dominates the intermodel variability
in the zonal mean. This displaces the band of increased rainfall into
the anomalously warm hemisphere27. The SST pattern effect has also
been identified in intermodel variability of rainfall change in the
Sahel26 and Amazon28,29, though land surface feedback is also important in these cases. In the tropics, the tight relationship between circulation uncertainties and SST patterns points to the importanceof ocean–atmosphere interaction. Ultimately, the coupled SSTcirculation uncertainty originates from parameterized physics suchas convection, land surface processes and aerosol effects.

Circulation. As climate warms, atmospheric moisture content increases at a rate of 6–7% per degree of warming, set by theClausius–Clapeyron equation. The global mean precipitation
increase is much less (2–3% K–1) because it is constrained by tropospheric radiative cooling 13. The difference between these rates ofincrease is consistent with a general decrease in the tropical overturning circulation13. In climate model projections, the east–west
Walker circulation shows such a robust slowdown30, but changes in
the north–south Hadley circulation strength are more varied anddepend on the cross-equatorial ocean warming gradient 27. In addition, the Hadley circulation expands poleward31. What determines
its poleward expansion has not been fully explained, but relates tothe latitude at which the associated westerly flow becomes baroclinically unstable31. The expansion coincides with poleward shifts
in arid zones, with important implications in sensitive regions (forexample, the Mediterranean climate zones)32,33. It is also consistent
with an intensification of summertime subtropical anticyclones34.
Aerosol forcing is an important driver of atmospheric circulation change. Unlike GHGs, anthropogenic aerosols are geographically distributed because of their short atmospheric residence time(of the order of a week), with high concentrations in the source
regions of southeastern Asia, Europe and the Americas. Because of
their strong spatial gradients, anthropogenic aerosols induce atmospheric circulation change more effectively than GHGs per unit radiative forcing 35. Larger in the Northern Hemisphere, aerosol forcing
generates an anomalous Hadley circulation that displaces tropicalrainfall into the relatively warm Southern Hemisphere36. A striking regional manifestation of this aerosol effect is the precipitation
decline in the African Sahel from the 1950s to the 1980s37,38. Over
the Asian monsoon region, model results show that aerosol-induced
cooling drives a divergent circulation in the lower troposphere. This
dominates over the thermodynamic effect of GHG-induced temperature increase, causing monsoon rainfall to decrease over thetwentieth century 39.
Despite their distinct geographical distributions, aerosols and
GHGs induce surprisingly similar patterns of SST and oceanic precipitation change40. Such robust macrostructures emerge despitelarge uncertainties in representing microphysical aerosol effects41.
This is because the climate system adjusts to radiative forcing
through common ocean–atmospheric feedbacks that imprint characteristic patterns on the response. Because GHG and aerosol forcings oppose one another, and because aerosols are more effective
per unit forcing in inducing atmospheric circulation and precipitation response, twentieth-century tropical rainfall change is relatively
small and hard to detect. However, this may change in the future
as anthropogenic aerosol loading is projected to decline, while the
GHG signal is projected to continue growing.
In the Southern Hemisphere, GHG forcing causes the westerly
wind jets and stormtracks to shift poleward in association with theincreased Equator-to-pole temperature gradient in the upper troposphere42,43. Ozone depletion in the southern polar stratosphere also
contributes to poleward movement of the westerly jets and changes insubtropical precipitation patterns44. Forced changes in the Northern
Hemisphere westerly jets are less pronounced. Compared with thetropics, coupling between large-scale atmospheric circulation and

a

SUSI

60° N
30° N
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140

Figure 2 | Effect of ocean warming pattern on precipitation change.
Precipitation change (colour shading, mm month–1) for: a, spatially uniform
SST increase (SUSI) of 4 K; b, spatially patterned SST increase (SPSI); andc, the difference between the runs. SPSI is derived as the CMIP3 meanfrom the runs with 1% per year CO2 increase at the time of quadrupling.
All the results are scaled to a tropical (25° S to 25° N) mean SST increaseof 4 K, based on the ensemble average of 11 atmospheric GCMs available
in CMIP5. Line contours are for climatological precipitation (150, 200,
250 and 300 mm month–1 contours) in a, and for SST deviations from thetropical mean warming (0.4 K intervals; zero contour thickened) in b.

the SST pattern is weak in the extratropics. Atmospheric internalvariability is also large, making it difficult to isolate the forced
response. Finally, nonlinear interactions between the mean flow
and weather systems create blocking events, which are poorly
understood and may be inadequately represented by models.
Shepherd45 reviews midlatitude atmospheric dynamics related toclimate change.

El Niño. The above discussion relates to changes in mean climate,
but large-scale modes of internal variability greatly affect regional
weather and climate over a broad temporal spectrum, from daily
extremes to decadal changes. Their possible alteration in both
frequency and amplitude under climate change is a key source of
uncertainty at the regional scale.
In the tropics, El Niño–Southern Oscillation (ENSO) is thedominant source of fluctuations in present climate and is expected
to remain so14. Despite common future changes in mean states
potentially affecting ENSO growth (for example, equatorial tradewind weakening and shoaling of the thermocline30), climate models do not show any systematic change in the typical amplitude ofeast Pacific SST variations46,47. The spread among model responses
is likely to be due to systematic errors in simulating present-day

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a

NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE2689∆P intermodel standard deviation

60° N
30° N
0°
30° S
60° S

60° E

120° E

180°

120° W60° W

0°10 15 20 25 30 35 40 45 50 55 60

Standard deviation (mm month−1)bStandard deviation of ∆P’(x,y)

60
50
40
30
20
10

∆P’

∆P’dyn∆P’therm

Figure 3 | Intermodel spread of tropical precipitation change.
a, Intermodel standard deviation of precipitation change σ(ΔP), along withclimatological precipitation (150, 200, 250 and 300 mm month–1 contours).
Precipitation change ΔP is given in mm month–1. Here ΔP = ΔPMME + ΔP,
where the prime denotes the intermodel deviation from the multimodel
ensemble (MME) mean. b, Standard deviation of spatial variations of ΔP
within 30° S to 30° N (cross marks for individual models) as a measure of
uncertainty, based on 70-year trends from 1% CO2 increase to quadruplingruns with 20 CMIP5 models. ΔP is decomposed into dynamic andthermodynamic components. The open circle denotes the ensemble mean,
and the error bar one standard deviation. The dynamic component is highlyvariable among models and the largest uncertainty of rainfall projections.
s)s)∆Pdyn = –(1/ρwg)∫(p∆ω(∂q/∂P)dp and ∆Ptherm = –(1/ρwg)∫(p
ω(∂∆q/∂P)dp,
00
where p is pressure, q specific humidity, ω pressure velocity, ρw the densityof water, g gravity, and the subscript s denotes surface value. All results
are scaled to a tropical (25° S to 25° N) mean SST increase of 4 K ineach model.

ENSO48. In addition, there is a delicate balance between amplifying
and decaying feedbacks in the ENSO cycle, and their relative
modifications by climate change differ among models49,50. Lowfrequency ENSO modulation, independent of radiative forcingchanges, also makes detection of the anthropogenic response achallenge51. Nevertheless, there is increasing evidence that ENSO
properties besides SST amplitude will change robustly because of
the patterned increase in the background SST. For instance, positive rainfall anomalies during ENSO warm phases over the central
equatorial Pacific will intensify 52,53 because locally enhanced surface
warming reduces the barrier to atmospheric convection. In turn,more frequent extreme tropical rainfall events during El Niño may
affect weather patterns worldwide via atmospheric teleconnections.
Associated with the enhanced convective response over the easternequatorial Pacific, the ENSO-forced Pacific North American patterntends to intensify and shift eastward in a warmer climate14.

Extremes. Changes in temperature extremes often scale withchanges in the mean54,55, indicating that local temperature variance
has changed little throughout the globe56. Variance in individual
climate realizations, however, may change under continued global
warming, altering tails of probability distributions and frequencies ofextreme events. Such projected changes include reduced wintertime924

mid- and high-latitude temperature variability owing to Arctic
amplification57, and increased summertime temperature variability
in some midlatitude regions owing to soil moisture feedback58.
Precipitation intensity is projected to increase globally. Water
vapour increases contribute most strongly to these changes in thetropics, but atmospheric circulation changes also play a role in midlatitudes59. For example, the projected poleward shift of the storm
tracks42 increases precipitation variance in some regions, exacerbating the risk of extremes, while decreasing it and alleviating the risk
in other regions. On seasonal and interannual timescales, the robust
projection of increased extreme El Niño frequency 53 would alter
extreme precipitation patterns linked to El Niño.
Tropical cyclones (TCs) are among the most destructive storms.
Some key TC statistics, such as count and track density, are tied tolarge-scale environmental factors such as SST and vertical shear.
Atmospheric models of resolution finer than 100 km show remarkable skill in capturing this environmental control and simulating spatial and temporal variability of TCs60. In a warmer climate,global TC counts tend to decrease in GCMs, but intense storms
may become more frequent, and TC rainfall is likely to intensify 14,61.
Studies projecting TC counts for individual basins show large variability among models, with SST change relative to the tropical mean
warming accounting for much of this variability 62,63. Because of
the interhemispheric gradient in the SST increase, the TC countdecrease is more pronounced in the Southern Hemisphere. The
western Pacific is an exception because of strong remote SST effects,
similar to what is found for ENSO-induced variability in TC genesis63. Mid-tropospheric vertical velocity seems to be a robust predictor of basin count change, and is tied to the distribution of SSTchange. In addition to TC genesis, atmospheric circulation change
impacts TC tracks, affecting the statistics of TC landfall64.

Challenges

For global-mean temperature projections, aerosol effects and cloud
response are leading sources of uncertainty in radiative forcing andclimate feedback, respectively 2. For regional precipitation projections, we have shown that atmospheric circulation change is the
major source of uncertainty (Supplementary Fig. S1). In the tropics, the circulation is coupled with patterns of SST change, whereas
in the extratropics, internal variability, random but organized intolarge-scale spatial patterns, exacerbates the circulation uncertainty.
The problem of regional climate change projections presents a
range of challenges in terms of physical understanding, the observational record, climate models and the simulations that we perform with them. For example, what are the long-term observational
trends, and what are their causes? How sensitive are regional climate change patterns to forcing types with different spatial distributions (GHGs versus aerosols)? How can we predict robust patternsof circulation and precipitation change? How do systematic errors
in models affect the change patterns? What are the relative roles ofinternal variability and forced response? These questions pose newproblems of ocean–atmosphere–land interactions. Understanding
these interactions will allow us to reduce circulation uncertainty
and build confidence in regional climate projections.

Observations. The quality of the observational record is an inherent source of uncertainty, particularly pertaining to variability on
decadal and longer timescales. Limited duration, incomplete spatial
coverage and observational errors hinder our ability to characterize
past changes and attribute them to anthropogenic forcing, and limit
our ability to evaluate models65.
The tropical Pacific provides an example. Observational data
sets disagree on the pattern of tropical Indo-Pacific SST change30,66.
Spatial variations in SST trends (0.2 oC per century) are generally smaller than the global SST increase (0.6 oC per century),
approaching observational errors and/or internal variability. These

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NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE2689
0.8b1976−2005
1976−2080

Frequency of exceedance (%)

aFrequency

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2016−2030 (ensemble)−2

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SAT anomaly (°C)

Figure 4 | Probabilistic representation of regional climate change at a grid box near Vienna, Austria (48.5° N, 16.2° E). a, Frequency distributions, binned
at intervals of 0.5 °C per 50 years, of the 1976–2005 and 1976–2080 wintertime (December–February) SAT trends from a 30-member CESM ensemble
under the Representative Concentration Pathway (RCP) 8.5. b, The frequency of linear trend exceedance for trends that begin in 1976 and end in different
years (x axis) at the grid point. The trend threshold (filled contours at intervals of 0.25 °C per 50 years) at a frequency of exceedance α is determined bythe (100 – α) percentile of the trends for the 30 ensemble members. The plotted exceedance frequency limits are 2.5% and 97.5%. The radiatively forced
trend is approximated by the median trend. c, Estimates of probability distribution functions (PDFs) of summer (June–August) mean SAT anomalies,
defined by the 1951–2000 base period. The PDF of a ‘typical’ realization for 2001–2015 (dashed black) is determined as the normal distribution withmean and standard deviation of the 30-member ensemble. The purple and orange curves are 2016–2030 PDF estimates from two individual ensemble
members, obtained by kernel density estimation. Deviations from the seasonal mean for the PDFs are obtained by subtracting the seasonal SAT anomaly
from the 2001–2030 linear trend. d, As in c, but the thick red curve represents the 2016–2030 estimated PDF from the full ensemble by adopting thenormal distribution with variance equal to (σ02 + σμ2), where σ0 is the ensemble mean of the seasonal standard deviation from the 2016–2030 mean
(0.85 °C) and σμ is the ensemble standard deviation of the 2016–2030 mean SAT anomalies (0.23 °C), indicating the widening impact of trend uncertainty
on the ensemble PDF. The dashed red curve is the estimate derived directly from the histogram of the 30 ensemble members. The expected increase in
hot extremes, depicted by the area in red shading, is due to both rightward shift of the PDF and the PDF broadening. The broadening is owing to trend
uncertainty from natural variability and an increase in σ0 from 0.80 to 0.85 °C.

spatial patterns drive atmospheric circulation changes, which in turn
determine rainfall change patterns, as described above. Since all datasets are imperfect, seeking physical consistency among observations,for example between the tropical SST gradient and trade winds67,
is a way to infer regional change patterns. The assimilation of data
into models seeks such consistency, and proves effective for studying variability on synoptic to decadal timescales. Reanalysis products, however, often are not appropriate for climate change studies67,
as the quality and quantity of assimilated data change over time. A
new generation of reanalysis suitable for climate change research isnecessary, with use of coupled assimilation to improve consistency
between ocean and atmospheric data.
Knowledge of the strengths and limitations of observational data
sets is imperative for understanding past climate change, evaluating models and constraining projections. Community efforts togather such knowledge from experienced data users and developers, and to share it with the wider climate community via ‘opensource’ platforms (for example, https://climatedataguide.ucar.edu/)
are essential68. To facilitate multimodel assessments, open-source
assessment packages for climate models can be valuable resources.
For example, the Climate Variability Diagnostics Package(http://www2.cesm.ucar.edu/working-groups/cvcwg/cvdp) provides
key metrics of internal climate variability across models, with comparison to observations69. Ongoing efforts to produce a meaningful setof metrics on mean states, internal variability, and response to external forcing are integral to advancing regional-scale model evaluation(http://www-metrics-panel.llnl.gov/wiki/FrontPage). The challenge
is to convert insights from model evaluation to model improvements.

Impact of model errors on projections. Despite limitations ofobservational records, model biases are clearly evident, reducing
confidence in regional projections. A common problem is excessive summertime drying of soils in continental interiors, which
may impact the land–sea warming ratio. Models simulating excessive summer Arctic sea-ice may have too weak polar amplification70. In the tropics, convection and rainfall are organized into
east–west elongated bands called the intertropical convergencezone (ITCZ). A long-standing bias is the so-called ‘double’ ITCZ,
referring to models’ failure to keep the ITCZ north of the Equator
over the eastern Pacific and Atlantic. The double ITCZ bias is related
to atmosphere–ocean coupling errors and is likely to affect rainfallchange projections in the South Pacific Islands71 and elsewhere.

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Tropics:
air−sea coupling

NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE2689
observable quantities and future projection variables in multimodel
ensembles and uses the relationship to re-weight the multimodel
projections in a similar way to the Bayesian approach70,78. Emergent
constraints cannot deal with errors common to all models, highlighting the need for innovative complementary approaches toimproving models.

Extratropics:
atmospheric internal variability

Atmospheric circulation change

Regional climate change

Regional forcing:aerosols, land use...

Thermodynamic effect:
wet-gets-wetter...

Figure 5 | Schematic of physical origins of regional climate change.

The 30–60-day Madden–Julian Oscillation is another phenomenon
poorly represented in many models72 and affecting confidence inprojections of the South Asian monsoon, especially the subseasonalvariability such as active/break cycles. Thus, despite a relatively
robust understanding of tropical rainfall changes (see ‘Mechanisms
for regional climate change’ above), the precise pattern in any particular model may not be credible.
Some biases persist over multiple model generations. It is important to move beyond routine model evaluation (for example, root
mean square errors) and develop innovative techniques to evaluate processes impacting regional projections. The equatorial Pacificcold tongue, for example, results from interaction of trade winds
and ocean upwelling (Bjerknes feedback). The cold tongue extends
too far west in most models, skewing ENSO SST anomaly patterns
and hence atmospheric teleconnections. The balance between the
Bjerknes feedback and damping by upwelling and surface heat fluxes
determines the magnitude and pattern of SST response to global
warming 16,18. This balance varies considerably among models. Most
CMIP5 models project larger warming in the eastern than westernequatorial Pacific14. But if the upwelling damping were stronger,
this change in east–west gradient could reverse73, altering ENSO’smagnitude and spatial pattern49. Model evaluation should quantify
these ocean–atmospheric feedbacks and their role in determining
the spatial pattern of SST change. Such process-based model evaluation challenges the observational record, as estimates of processlevel variables may only be available from field campaigns in sparse
regions and times.
A further challenge is that model processes often involve complex
interactions between resolved dynamics and multiple parameterization schemes. It is not the best strategy to update parameterization
schemes in isolation, as physical consistency of multiple processes isrequired. The ‘assembly’ stage of model development, often erroneously called ‘model tuning’, would benefit from tighter integration
with process-based model evaluation. For example, long-standing
tropical biases like the double ITCZ may be influenced by extratropical errors, such as Southern Ocean clouds74 and the Atlantic
meridional overturning circulation75.
Statistical methods have been suggested to adjust regional projections based on evaluation of model errors. Bayesian techniques
use large model ensembles with perturbed parameters and weight
each member according to its ability to reproduce observations76,77.
Such approaches take into account uncertainties from multiple
sources: models, observations and physical understanding. This
allows us to move beyond simple ensemble mean and standard
deviation approaches common in regional assessments (Fig. 1).
The concept of ‘emergent constraints’ derives relationships between
926

Effects of internal variability. Any individual observed or simulated climate trajectory contains contributions from internal variability and external forcing. The relative importance of these twocontributions depends on temporal and spatial scale, and on thevariable of interest 3,79,80. In the extratropics, internal variability
plays a dominant role in multidecadal atmospheric circulation
changes, shaping regional patterns of temperature and precipitation changes80. For example, large uncertainties in North American
air temperature and precipitation trends projected over the next
50 years stem mostly from internal circulation variability 81. To theextent this internal variability is unpredictable, the resultant uncertainty is irreducible. This ‘single realization effect’ is large enough tomask the forced regional response, presenting a major challenge forunderstanding and communication of regional climate change45,82.
Owing to internal variability, ensemble-mean regional climate
trends may be misleading 83,84. The top panels of Fig. 4 provide an
example of a probabilistic representation of winter SAT trends at a
grid point near Vienna, Austria, based on a 30-member initial condition ensemble81. The trend distribution is broad for 1976–2005; evenwith the forced response of 0.2 °C per decade, there is a 20% chancethat the 30-year SAT trend is negative. As trend length increases, theradiatively forced trend increases while the trend distribution narrows, indicating reduced importance of internal variability.
Internal variability has a particularly important impact on projected changes in extreme events, as illustrated in the bottom panelsof Fig. 4 for summertime temperature at the same grid box fromthe 30-member ensemble. Trend uncertainty over the 2001–2030
period results in substantial divergence among summertime temperature distributions (Fig. 4c), with great increases in hot extremes
for some realizations (for example, realization 2) but modest
increases in others (for example, realization 1). Variance changes,
depicted by changes in the width of the distributions, are modest
in this example. However, uncertainty in temperature trend owing
to decadal internal variability broadens the ensemble’s probability
distribution function (Fig. 4d). This broadening indicates that internal variability averages out across realizations in climate means,
but not in extremes. Thus decadal internal variability increases theprobability of extreme events by widening the tails of the distribution. When coupled with potential socio-economic consequences,
this would result in an increase of disaster risk. Whereas nature
produces only one realization, risk assessment (for example, forinsurance) must consider all possible outcomes based on large initial-condition ensembles from different models under a variety offorcing scenarios.
Changes in variance and skewness are also important for extreme
events. The summertime temperature variance at the central
European location in Fig. 4 increases by about 7% between 2001–2015
and 2016–2030, consistent with the projected increase in European
summertime temperature variability 56, contributing to widening of
the probability distribution. There is evidence that GCMs have considerable errors in their simulation of internal variability 3,85,86, butsuch evaluations are limited by an observational record that is too
short to be representative of the true range of decadal variability 87.
This verification challenge is even greater for extreme events. Such
events are rare by definition and therefore are even more affectedby the observational record’s limitations55. Climate model improvements, increased understanding of radiatively forced dynamicalchanges and large-ensemble simulations are required to alleviate thestatistical limitations of small sample sizes in a single realization.

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REVIEW ARTICLE

NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE2689

tyrie
Va allel)r(Pa(Parallel)ts
Experimen

(Se
ria
l)
Do
ma
in

Leng
th
(Ser
ial)

)

le

emb

We have identified key physical mechanisms for regional climatechange (Fig. 5). The thermodynamic response to radiative forcing is best understood and most robust across models. Examples
include enhanced continental warming, polar amplification andthe wet-gets-wetter effect. Decomposition of rainfall change into
thermodynamic and dynamic components shows that atmospheric circulation change is the main source of uncertainty

Re (Serso
lu ial)
tio
n

allel
(Par

Recommendations for research

CMIP5 core
Near-term
Regional
D&A

Ens

With limited computational resources, it is critical to make optimaluse of computing resources to advance regional climate change
projections and to correctly assess uncertainties, reducing themwhen possible.
There are a number of demands on computer and humanresources (see figure). A variety of independent models, differingsignificantly in their underlying physics and numerics, is required
to provide assessments of the range of possible climate change.
Models are also being developed that contain ever more complete
representations of the climate system, including processes such as
biogeochemical cycles, atmospheric chemistry and aerosols, clouds
and convection, land processes and ice sheets. Process-oriented
experiments are needed to better understand model behaviour,
including internal variability and the response to various radiative
forcing. The following factors increase demands on computational
resources. First, internal variability has a very strong imprint on
climate trends even on timescales as long as several decades andspatial scales as large as continents81. This calls for large ensemble
simulations96. Second, when spatial resolution is high (25–50 km),many phenomena are reasonably well simulated in GCMs97,
including tropical cyclones63,91 and extratropical weather regimes
such as blocking 98,99. This makes higher resolution desirable.
Regional models are useful to understand the role of smallscale processes in shaping the regional climate response. These
processes include orographic precipitation, snow-albedo feedback,land–sea breeze circulation systems, mesoscale convective systems,
and ocean feedbacks on tropical cyclone intensity. Orography andcoastline geography unresolved by global models can introduce
credibility into regional patterns obtained with downscaling techniques. Such smaller-scale mechanisms need to be carefully evaluated to establish credibility 100.
We recommend the following modelling strategies to achieve
more reliable regional climate projections. These recommendations
contribute to the ongoing planning for the next phase of CMIP and
grand challenges of the World Climate Research Programme:• To develop innovative experiments to shed light on atmosphericcirculation response to radiative forcing, and to explore thesensitivity to ocean coupling, land surface processes and otherimportant physical processes such as convection;• To perform large ensemble simulations to isolate forced change
and internal variability, and estimate the probability distribution of regional change;• To exploit the emerging capability of high-resolution modelling to simulate important extreme phenomena such as tropical cyclones, and take advantage of resolved local geographicalfeatures such as the coastline and orography;• To run the models for scenario projections in initialized modeand verify their subseasonal to interannual climate predictions,and to test the models’ skill in simulating important climate
events such as mega droughts;• To explore model development practices that effectively incorporate insights from process-based model evaluation and integrate multiple coupled processes for overall physical consistency.

Complexity(Serial)Box 1 | Modelling strategies.

Competing priorities for running climate simulations. Many choices
have to be made in designing an ensemble to produce information
about past and future climates. These choices include: (i) ‘Variety’, thenumber of (pseudo-) independent simulators; (ii) ‘Complexity’, thenumber of physical, chemical and biological processes included in thesimulator; (iii) ‘Resolution’, the grid spacing; (iv) ‘Experiments’, howmany different types of simulation are to be performed; (v) ‘Domain’,
whether the simulation needs to be global and coupled, or regional
atmosphere-only; (vi) ‘Ensemble’, the number of independent
realizations; and (vii) ‘Length’ of the simulation. Different purposes
and questions require different ensemble design strategies. For
example, CMIP5 made a set of core choices (grey) to use manydifferent global simulators, to run several different long experimentswith medium complexity and resolution with small ensemble sizes.
This core ensemble was designed to answer specific questions about
how climate has changed in the recent past and may change in the
future with different emission scenarios. If the question is to determine
how the probabilities of certain outcomes may change in the nearterm (next 20 years) on regional scales, a different design is required(orange); or if the focus is on detailed downscaling using regionalmodels for future time slices then a different set of decisions needs tobe made (green). For detection and attribution (D&A) of past climaticchanges, a large number of experiments are needed (blue). Note thatsome of these categories are serial, that is, more time is required tocomplete the simulations, and some are parallel, which means that
additional processors could be used to perform the simulations in thesame time.

in regional projections. Understanding the mechanisms forcirculation change is essential to reduce this uncertainty, butthey have only begun to be explored. More research is needed on
how aerosol forcing can induce regional atmospheric circulation
change (for example, the Asian summer monsoon). Recent studies
suggest that despite large uncertainties in aerosol radiative forcing, there are robust planetary-scale response patterns, mediatedby ocean coupling.

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NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE2689

Our review suggests distinct regimes of atmospheric circulation
change in the tropics versus the midlatitudes, calling for different
approaches. In the tropics, internal variability on decadal timescales
and longer is relatively small in comparison with the forced signal
on the centennial horizon, and models now agree on some aspectsof the pattern of rainfall change that are projected to emerge by theend of this century (for example, an increase in the equatorial Pacificand Atlantic, and a decrease in the southeastern tropical oceans).
Precipitation and atmospheric circulation are tightly coupled with
the SST change pattern in both the multimodel mean projection
and intermodel variability. Elucidating this coupling, and developing observational constraints, can narrow uncertainties of regional
projections in the tropics. An analogue may be the historical development of ENSO prediction, where theory initially explained how
coupled modes emerge from ocean–atmosphere feedback, ultimately laying the foundation for seasonal climate prediction. The
challenge is to extend this success to radiatively forced problems,
and to design observing systems that monitor key processes associated with ongoing climate change.
In the midlatitudes, by contrast, coupling between large-scaleatmospheric circulation and local SSTs is weak. Internal variability
plays a much larger role in generating differences among regionalscale projections. Nevertheless, the lack of a robust circulation
response in midlatitudes in models does not preclude potential
shifts in storm tracks or changes in blocking frequency that models
cannot (yet) represent. Random internal variability and the nonlinear nature of the midlatitude circulation render regional climate
projections inherently probabilistic.
We recommend a coordinated multimodel set of large initialcondition ensembles to further regional climate change research(Box 1). First, such a set of experiments would quantify probabilities
of changes in means and extremes across models, including not only
structural uncertainty but also irreducible uncertainty due to internal variability. Quantification of changes in risks is necessary forinsurance, and for infrastructure planning. To quantify probability
distributions and occurrence of extremes, we need research into
dynamical processes governing changes in higher-order moments
such as variance and skewness. Second, the set of experiments
would enable isolation of uncertainties due to internal variability
from those due to model structure. Large ensembles also open new
possibilities for studying radiatively forced changes in extratropicalatmospheric circulation.
Computing advances have benefited climate modelling throughenhanced complexity and increased resolution. A threshold has
recently been crossed: at 50-km resolution, atmospheric models
demonstrate marked skill in simulating TC statistics. This opens up
new opportunities for studying climate change effects on TC variability, much as happened in the 1970s to 1980s, when explicit simulations of extratropical cyclones vastly improved weather forecasts.
High-resolution large-ensemble simulations could greatly advanceour understanding of internal variability and forced change in TC
metrics and processes, especially track density, landfall statistics
and ocean feedback. Higher resolution also improves simulationof blocking events, a phenomenon linked to extreme weather inthe extratropics.
Robust precipitation changes are projected over land: increases
at high latitudes and in the Asian monsoon result from enhanced
atmospheric moisture content, whereas decreases in the subtropics arise from Hadley cell expansion. The ocean warming patternalso changes atmospheric circulation over the Sahel and Amazon,
although the robustness of these changes remains to be tested.
In addition to such non-local atmospheric changes, improved
understanding of land surface processes is key to more credible
projections of human impacts58,88. For example, soil moisture andnear-surface relative humidity are projected to decrease globally 89,
probably exacerbating drought when it does occur, and potentially

increasing the frequency and intensity of heat waves. More realistic
simulation of snow albedo feedback and snow processes would also
reduce uncertainty surrounding continental warming, runoff timing and soil moisture at high latitudes90.
Agreement among models is an indicator of robust change, butshould be viewed in the context of model biases and weak observational constraints on forced regional response. Evaluating the impact
of common biases and ultimately reducing them is a grand challenge. The daily verification cycle has enabled weather forecasts toimprove steadily by exposing model errors and observational needs.
Similarly, seasonal prediction91 and attribution studies of extreme
climate events92 can improve physical understanding and build
model confidence. In this context, pacemaker experiments — thatis, experiments with partial coupling that prescribes observed SST
or wind evolution in tropical oceans19,93,94 — are useful to identify
key drivers of regional change. Further innovations in experimental
design are necessary to expose model problems. For example, fluxadjusted models can be run in parallel with freely evolving models
to evaluate effects of model biases on regional projections.
Regional climate projections are often taken as synonymouswith downscaling global scenarios. The misconception is that withenhanced resolution, regional models will automatically solve theproblem of producing regional climate projections. Regional climate
models require lateral boundary conditions, which are subject tolarge uncertainties in atmospheric circulation change. Without carefully considering the uncertainty in lateral boundary conditions andmodel biases, downscaling global model projections adds essentiallymeaningless spatial detail1. Regional models may be useful to understand physical processes in areas of complex coastlines and orography, and may provide useful climate change impact informationon the kilometre scales relevant to climate adaptation planning 95.
We suggest, however, that the current priority is to understand andreduce GCM uncertainties on regional scales (>100 km), which
often dictate changes on finer scales. To achieve reliable regional climate projections, it is essential to understand the underlying physics,
reduce model biases and adequately account for internal variability.

928

Received 16 February 2015; accepted 22 May 2015;
published online 7 September 2015

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Acknowledgements

S.M. Long drew Figures 2 and 3. S.P.X. is supported by the National Science Foundation(NSF) and National Oceanic and Atmospheric Administration (NOAA); and M.C. by
NERC NE/I022841/1. NCAR is supported by the NSF.

Author contributions

S.P.X., C.D. and M.C. led the writing of the paper. All authors contributed to thedevelopment and writing of the paper.

Additional information

Supplementary information is available in the online version of the paper. Reprints
and permissions information is available online at www.nature.com/reprints.
Correspondence should be addressed to S.P.X.

Competing financial interests

The authors declare no competing financial interests.

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