1

NOAA Technical Report

Regional Climate

Processes and 

Projections for

North America: 

CMIP3/CMIP5 Differences, 

Attribution and 

Outstanding Issues
2

NOAA Technical Report OAR CPO-2

RegiONAl ClimATe PROCesses ANd PROjeCTiONs fOR NORTh 

AmeRiCA: CmiP3/CmiP5 diffeReNCes, ATTRibuTiON ANd OuTsTANdiNg 

issues

ClimATe PROgRAm OffiCe

silver spring, md

december 2014

UNITED STATES

DEPARTMENT OF COMMERCE

Dr. Rebecca Blank

Acting Secretary

NATIONAL OCEANIC AND

ATMOSPHERIC ADMINISTRATION

Dr. Jane Lubchenco

Undersecretary for Oceans and

Atmospheres

Office of Oceanic and

Atmospheric Research

Craig McLean

Acting Assistant Administrator

NOTICE FROM NOAA

Mention of a commercial company or product does not constitute an endorsement by NOAA/OAR. Use of 

information from this publication concerning proprietary products or the tests of such products for publicity or

advertising purposes is not authorized. Any opinions, findings, and conclusions or recommendations expressed 

in this material are those of the authors and do not necessarily reflect the views of the National Oceanic and 

Atmospheric Administration.

doi:10.7289/V5DB7ZRC 
3

Report Team Authors

Regional Climate Processes and Projections for North America:

CMIP3/CMIP5 Differences, Attribution and Outstanding Issues

Justin Sheffield 1, Andrew Barrett 2, Dan Barrie 3, Suzana J. Camargo 4, Edmund K. M. Chang 5, Brian Colle 5, 

D. Nelun Fernando 6,7,8 Rong Fu 6, Kerrie L. Geil 9, Qi Hu 10, Xianan Jiang 11, Nathaniel Johnson 12, 

Kristopher B. Karnauskas 13, Seon Tae Kim 14, Jim Kinter 15, Sanjiv Kumar 15, Baird Langenbrunner 16, 

Kelly Lombardo 5, Lindsey N. Long 17,18 Eric Maloney 19, Annarita Mariotti 3, Joyce E. Meyerson 16, 

Kingtse C. Mo 18, J. David Neelin 16, Sumant Nigam 21, Zaitao Pan 20, Tong Ren 26, Alfredo Ruiz-Barradas 21, 

Richard Seager 22, Yolande L. Serra 23, Anji Seth 25, De-Zheng Sun 24, 25 Jeanne M. Thibeault 26, 

Julienne C. Stroeve 2, Chunzai Wang 27, Shang-Ping Xie 28, Ze Yang 6, Lei Yin 6, Jin-Yi Yu 29, Tao Zhang 24,25 

Ming Zhao 12

Affiliations

1 Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ

2 National Snow and Ice Data Center, Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO

3 National Oceanic and Atmospheric Administration, Office of Oceanic and Atmospheric Research, Climate Program Office Silver Spring, MD

4 Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY

5 School of Marine and Atmospheric Sciences, Stony Brook University – SUNY

6 Jackson School of Geosciences, University of Texas at Austin, TX 

7 University Corporation for Atmospheric Research, Boulder, Colorado

8 Surface Water Resource Division, Texas Water Development Board, Austin, TX

9 Kerrie L. Geil, Department of Atmospheric Sciences, University of Arizona, Tucson, AZ

10 School of Natural Resources and Department of Earth and Atmospheric Sciences, University of Nebraska-Lincoln, Lincoln, NE

11 Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, CA

12 Nathaniel Johnson, NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ

13 Woods Hole Oceanographic Institution, Woods Hole, MA

14 CSIRO, Marine and Atmospheric Research, Aspendale, Victoria, Australia

15 Center for Ocean-Land-Atmosphere Studies, Fairfax, VA

16 Department of Atmospheric and Oceanic Sciences, University of California Los Angeles

17 Wyle Science, Technology and Engineering, College Park, MD

18 Climate Prediction Center/NCEP/NWS/NOAA, College Park, MD

19 Department of Atmospheric Science, Colorado State University, Fort Collins, CO

20 Saint Louis University, St. Louis, MO

21 Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD

22 Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY

23 Department of Atmospheric Sciences, University of Arizona, Tucson, AZ

24 Cooperative Institute for Environmental Studies/University of Colorado 

25 NOAA Earth System Research Laboratory, Boulder, CO

26 Department of Geography, University of Connecticut, Storrs, CT

27 Physical Oceanography Division, NOAA Atlantic Oceanographic and Meteorological Laboratory, Miami, FL

28 Atmospheric Science & Physical Oceanography, Scripps Institution of Oceanography, University of California, San Diego, CA

29 Department of Earth System Science, University of California, Irvine, CA

The authors wish to thank the following individuals for their careful review of the report:

Ruby Leung (Pacific Northwest National Laboratory), Michael Wehner (Lawrence Berkley National Laboratory), 

Tom Knutson (NOAA Geophysical Fluid Dynamics Laboratory), and one additional anonymous reviewer.
4

Table of contents

1. Introduction 5

2. Data and Methods 7

2.1. Emission Scenarios in CMIP3 and CMIP5 7

2.2. Model and Observational Data 7

2.3. Some Definitions and Caveats 10

3. Representation of Means and Variability of Basic Climate Variables 12

3.1. Temperature 12

3.2. Precipitation 16

3.3. Sea Surface Temperature 18

4. Representation and Projections of Temperature and Precipitation Extremes 20

4.1. Temperature Extremes 20

4.2. Precipitation Extremes 20

5. Representation of Inter-Annual to Decadal Variability and Teleconnections 

 with North American Climate 25

5.1. El Niño and the Southern Oscillation (ENSO) 25

5.2. Pacific Decadal Variability 27

5.3. Atlantic Multidecadal Variability 30

6. Regional Processes 32

6.1. Projections of Drying for the Caribbean and Mexico 32

6.2. North American Monsoon 32

6.3. Future Precipitation in the Southwest 34 

6.4. The US Warming Hole 37

6.5. Extra-tropical Cyclone Activity 38

7. Summary and Recommendations 39

7.1. Summary 39 

7.2. Future Research Directions 39

7.3. Implications for Design and Execution of CMIP6 42

Acknowledgments 43

References 44
5

Abstract

The Coupled Model Intercomparison Project, 

phase 5 (CMIP5) provides an unprecedented 

set of climate model data from coordinated 

experiments that can be used to address a 

variety of questions related to climate change 

and climate variability. The CMIP5 builds on 

the previous model intercomparison phase 

(CMIP3) in several ways, including a larger 

number of modeling centers and models, the 

use of generally moderately higher resolution 

models, and the inclusion of more complex 

and complete representation of Earth system 

processes. A key question is whether the CMIP5 

results have improved since CMIP3 in terms of 

the representation of observed climate features 

and processes, and whether the future projected 

changes are more robust, and why. This report 

addresses these questions for a suite of climate 

variables and regional processes for North 

America and provides recommendations for future 

analyses and experiments to resolve some of the 

ongoing issues. 

Overall, the multi-model ensemble (MME) mean 

performance has not improved substantially 

in CMIP5 relative to CMIP3 for climatological 

variables (precipitation, sea surface temperature 

- SST), except for a slight improvement for near 

surface air temperature over land. CMIP5 models 

tend to underestimate the frequency of heavy 

and extreme daily precipitation events, despite 

a slight improvement over CMIP3, especially 

in the southeastern US. Projected increases in 

moderate to extreme precipitation events are 

similar to CMIP3. Generally, the CMIP5 models 

show better skill for basic attributes of El Niño and 

the Southern Oscillation (ENSO) with performance 

related to the mean SST state. It is unclear 

whether the representation of teleconnections with 

North American climate has improved. It is likely 

that the structure of Pacific Decadal Variability 

(PDV) as indexed by the PDO is slightly better 

simulated and teleconnections for precipitation 

also are improved slightly but remain poor 

overall. Atlantic Multidecadal Variability (AMV) 

as represented by the AMO is better represented 

in CMIP5 models in terms of decadal variability 

and persistence than CMIP3 models, but its SST 

footprint and teleconnections with North American 

climate are still poorly represented. Regionally, 

projections of changes in precipitation from CMIP5 

for the sub-tropics tend to be more robust overall 

than CMIP3, in particular for summer drying 

in the Caribbean and southwest Mexico. The 

boundary between winter wetting and drying in 

the Southwest US is projected to move southward 

in CMIP5 relative to CMIP3 results, although 

the changes are highly dependent on the region 

and season. The CMIP5 models project a more 

significant decrease in extra-tropical storm track 

activity than CMIP3, which may be related to a 

larger projected decrease in the temperature 

gradient between lower and higher latitudes.

1. Introduction

This report summarizes the results of an analysis 

of the Coupled Model Intercomparison Project, 

phase 5 (CMIP5; Taylor et al., 2012; http://cmippcmdi.llnl.gov/cmip5/) data sets for North America 

with special emphasis on the qualitative and 

quantitative changes in results from the previous 

model intercomparison phase (CMIP3) for regional 

climate processes, attribution of changes where 

possible, as well as ongoing issues in model 

evaluation. This report has been developed as 

an aid in communications with the community 

interested in the results from CMIP5, to address 

specific questions on climate impacts, adaptation 

and vulnerability that are of high interest, and 

in particular the National Climate Assessment 

community (NCA; http://globalchange.gov/whatwe-do/assessment/nca-overview.html). This 

report is not an exhaustive list of questions/issues 

– rather it is intended as an initial effort that can 

determine the efficacy of such an assessment and 

advise future such efforts. 

The CMIP5 provides an unprecedented set of 

climate model output data that can be used 

to address a variety of questions related to 

climate change and climate variability, including: 

the assessment of future climate projections,

evaluations of models for historical climate, 

the attribution of observed climate change 

and understanding of climate processes and

feedbacks. The CMIP5 builds on the previous 

CMIP3 project in several ways that are expected

Abstract
6

to result in better skill in representing current 

climate and improved understanding of the 

uncertainties in future projections: 1) a larger 

number of modeling centers and models have 

participated in the experiments; 2) the models are 

generally run at higher spatial resolution, which 

helps better resolve topographic and coastline 

features that are important for representing 

regional processes; 3) some models are more 

comprehensive in the processes they represent; 

4) many modeling centers have provided multiple 

realizations of simulations that permit quantification

of uncertainties.

A key question is whether the CMIP5 results have 

improved compared with CMIP3, and why. While 

it is possible to compare published results from 

CMIP5 models with earlier publications based on 

CMIP3 models, a direct comparison of CMIP5 and 

CMIP3 data is more conducive to establishing 

differences between the performance of the 

models. This report takes both approaches to 

provide a more comprehensive evaluation of the 

representation of climate processes in the historical 

simulations and the robustness of changes for the 

future projections. 

This report was compiled under the auspices 

of the National Oceanic and Atmospheric 

Administration (NOAA) Modeling, Analysis, 

Predictions and Projections (MAPP) program, 

which is managed by the Climate Program Office 

(CPO) in the Office of Oceanic and Atmospheric 

Research (OAR). A special CMIP5 Task 

Force (http://cpo.noaa.gov/ClimatePrograms/

ModelingAnalysisPredictionsandProjections/

MAPPTaskForces/CMIP5TaskForce.aspx) 

composed of MAPP principal investigators was 

formed, originally intended to evaluate the fidelity 

of CMIP5 simulations of the past century of climate 

and to determine the scientifically-defensible 

conclusions that can be drawn from CMIP5 

projections of future climate, focused on the climate 

of North America. The results of the Task Force’s 

analysis have been documented in a Special 

Collection of the Journal of Climate including 22 

papers and three summary papers (Sheffield et 

al., 2013a,b; Maloney et al., 2013) that provide 

an overview of the scientific results. The results 

reported in this and other relevant literature are 

provided in this report together with new analysis 

of the CMIP3 and CMIP5 databases to provide a 

consensus on the differences and ongoing issues. 

Where there is a lack of information or consensus, 

recommendations for future research are also 

made, as appropriate. The results are intended to 

be complementary to Sun et al. (2014), who discuss 

regional-scale differences between CMIP3 and 

CMIP5 for surface temperature and precipitation 

means and extremes over the US.

This report is organized into seven sections, 

including the introduction. Section 2 describes the 

differences in the emission scenarios between 

CMIP3 and CMIP5 and discusses the implications 

for comparisons between the two sets of 

simulations. This section also describes the model 

simulations and observations used. We begin 

the analysis in section 3 by discussing changes 

in basic surface climate variables (precipitation, 

land air and sea surface temperature) and then 

extremes of precipitation and temperature at the 

daily time scale (section 4). Section 5 addresses 

inter-annual to decadal variability, in terms of the 

representation of the El Niño Southern Oscillation 

(ENSO), the Pacific Decadal Oscillation (PDO) 

and Atlantic Multidecadal Variability (AMV) 

and their teleconnections with North American 

near-surface climate. Some regional climate

processes are evaluated in section 6, including 

changes in precipitation in the Southwest, a region 

expected to get drier overall in the future with 

implications for water resources, and the warming 

hole phenomenon.

Differences between CMIP5 and CMIP3 

include a larger number of participating 

modeling centers, generally higher 

model resolution, more comprehensive 

representation of Earth system processes, 

and more ensemble members for 

each model.

1. introduction
7

2. Data and Methods

2.1. Emission Scenarios in CMIP3 

 and CMIP5

A key difference between the CMIP3 and CMIP5 

is the set of redesigned emissions scenarios that 

were used to force the models. The CMIP3 models 

were forced by the emissions scenarios from the 

Special Report on Emissions Scenarios (SRES; 

Nakićenović et al., 2000) while the CMIP5 models 

were forced by Representative Concentration 

Pathways (RCPs; van Vuuren et al., 2011). The 

CMIP3 scenarios represent futures with different 

mixes of population growth and policies on energy 

sources. In contrast, the CMIP5 RCP scenarios 

do not relate to particular policy actions but reflect 

mitigation scenarios that lead to one of the RCPs. 

Figure 1 shows the total radiative forcing for a 

selection of the SRES scenarios and RCPs for 

CO2 from fossil fuel sources, and indicates those 

scenarios that are comparable. Furthermore, 

there are differences in the specification of 

other anthropogenic greenhouse gases (GHG) 

and aerosols, with more species prescribed 

in CMIP5, such as black and organic carbon, 

alongside the inclusion of land use change in 

the historic and future projections for CMIP5 

(Taylor et al., 2012).

Recent studies indicate that the equilibrium 

climate sensitivity (ECS) to a doubling of CO2

concentration (2xCO2) and the transient response 

of global/regional temperature and precipitation to 

increasing CO2 remain similar in CMIP5 and that 

differences in projected changes are in large 

part due to different assumptions about non-GHG

forcing between the CMIP3 and CMIP5 scenarios, 

in particular the specification and treatment of 

aerosols. For example, initial investigation of ECS

for temperature by Andrews et al. (2012) indicates 

similar behavior between CMIP3 and CMIP5. 

Analysis of global-mean temperature, tropical 

Atlantic temperature, their difference, and inferred 

hurricane activity by Villarini and Vecchi (2012, 

2013), show similar responses for CMIP3 and 

CMIP5 models in terms of the response to CO2

but differences among scenarios. For one model, 

GFDL-CM3, they showed that the difference 

could be attributed to the treatment of aerosols. 

Further work by Knutson et al. (2013) shows that 

the difference in Atlantic SST, wind shear, and 

potential tropical cyclone intensity projections 

between CMIP3 and CMIP5 are not attributable 

to different response to GHGs, but to different 

non-GHG forcing. This also appears to be true for 

precipitation (Fig. 2; G. Vecchi, 2013, unpublished 

manuscript), implying that differences from model 

changes are small, and that aerosol treatment 

and GHG forcing differences are responsible for 

the differences seen in the transient scenario 

projections. As discussed later in the context of 

regional climate processes, further analysis is 

needed to see if these results extend to other 

models and whether non-GHG forcings are driving 

differences in the projections.

2.2. Model and Observational Data

We use data from multiple climate models from 

the CMIP3 and CMIP5 databases for the historic 

simulations (“20C3M” for CMIP3; “historical” 

for CMIP5) and a range of future scenarios. 

The “historical” simulations are run in coupled 

atmosphere-ocean mode forced by historical 

estimates of changes in atmospheric composition 

from natural and anthropogenic sources, 

volcanoes, GHG and aerosols, as well as changes 

Differences in projected changes are in 

large part due to different assumptions 

about non-greenhouse gas forcing between 

the CMIP3 and CMIP5 scenarios, in 

particular the specification and treatment 

of aerosols.
8

2. Data and Methods

Figure 1. Total radiative forcing from fossil fuel CO2 emissions for selected SRES and RCPs (Figure created at 

live.magicc.org; Meinshausen et al., 2011).
9

2. Data and Methods

Figure 2. Precipitation response (change per K of warming) to doubling of CO2 for CMIP5 (top) and 

CMIP3 (bottom) models.
10

2. Data and Methods

in solar output and land cover. Note that only 

anthropogenic GHGs and aerosols are common 

prescribed forcings for all models; each model 

differs in the set of other forcings that it uses and 

how it implements those forcings, such as land 

use change. For Earth system models (ESMs), the 

carbon cycle and natural aerosols are modeled 

and can therefore introduce feedbacks. 

The historical simulations are generally carried 

out for the period 1850-2005 but we evaluate the 

models for the most recent 30 years, depending 

on the availability of observations. In the case of 

remote sensing observations, this restricts the 

analysis to the satellite period from about 1979 

onwards. In some cases, multiple observational 

datasets are available and are used to estimate 

the uncertainties in the observations. Details of 

the datasets and data processing are given in the 

relevant sub-sections and figure captions. Most 

of the models have multiple ensemble members 

and in general we use the first ensemble member 

(all members have equal utility for the analysis 

and the first member is only chosen as a matter 

of convenience, possibly at the expense of overor under-estimating model errors especially 

at smaller scales). In some cases, the results 

for multiple ensembles are averaged where 

appropriate or used to assess the variability across 

ensemble members. Results are generally shown 

for the multi-model ensemble (MME) mean and for 

the individual models using performance metrics 

that quantify the errors relative to the observations 

or the change in a future period relative to the 

historic period.

2.3. Some Definitions and Caveats

The CMIP models are evaluated on an individual 

and ensemble basis, also drawing from existing 

evaluations in the literature. Model performance is 

dependent on the variable and scale of the 

evaluation and as the report covers a wide range 

of climate processes, it is not possible to have a 

universal definition of performance. In general, 

the performance of the MME is based on whether 

the ensemble envelops the observation, and 

the performance of individual models is based 

on measures of distance from the observation, 

relative to other models. In each case there are 

uncertainties associated with the choices made 

in evaluating the models and assessing whether 

attribution of historic changes and projected 

changes are robust. Here, robustness generally 

refers to the level of indifference of the results 

to differences in these choices, for example, to 

different models or to alternative observations. 

There are several factors that can influence 

the evaluations, including: model selection and 

ensemble size, model internal variability, climate 

drift and observational uncertainties. Although we 

generally do not take these factors into account 

because they are deemed to have a relatively 

small influence on the overall results (e.g. internal 

variability versus inter-model differences), they 

are discussed next and also within sub-sections 

because they may be important for particular 

models and metrics. These caveats also have 

implications on whether we can determine if 

an individual model or the MME has improved 

from CMIP3 to CMIP5. For individual models, 

in particular, the concept of improvement is 

muddled because newer models will generally 

incorporate better physics and can therefore be 

considered more comprehensive, but this does not 

necessarily lead to improved performance against 

observations. The conclusions of this report should 

therefore be interpreted with this in mind. 

The set of models analyzed here and within the 

cited references varies in terms of the number and 

membership. The analysis is generally based on a 

core set of about 15 models that was used 
11

2. Data and Methods

by Sheffield et al. (2013a,b) and Maloney et al. 

(2014) (and references within) and selected to 

subjectively span a range of modeling centers 

and model types (AOGCMs, ESMs). The actual 

number used in each analysis varied based on 

data availability and the effort required to process 

high temporal resolution data. No account was 

taken to determine model independence or the 

sensitivity of results to different subsets of models. 

Studies of model selection are generally geared 

towards robust estimates of future projections (e.g. 

McSweeney et al., 2012; Thober and Samaniego, 

2014) and are based on model performance, 

physical plausibility of projections and spanning 

the range of projections, rather than quantifying 

the uncertainty in sub-sampling. However, we 

expect that the error associated with using an 

ensemble of at least 15 models is small compared 

to the inter-model uncertainty.

Evaluations of individual models against 

observations are subject to errors induced by 

internal climate variability that ensures that 

coupled models will not replicate observed 

variations in climate on annual to decadal 

time scales, unless there is a strong external 

forcing (e.g. volcanic eruption). For this reason, 

evaluations against observations are subject to 

differences in the current states of the observed 

and modeled climates. Evaluating the models 

using metrics calculated over multiple decades 

and large regions, and for multiple model 

realizations, will reduce the impact somewhat. 

However, there is potential to unduly penalize 

models, for example, for extreme events for which 

the number of events is small, or for decadal 

climate variability for which the modeled phase will 

not necessarily line up with the observations. We 

note throughout the report where there is potential 

for these types of discrepancies. For future 

projections, internal variability can obfuscate the 

detection of forced changes for several decades, 

with changes in temperature being detectable 

earlier than for precipitation (e.g. Hawkins and 

Sutton, 2009, 2010). This is dependent on the 

size of the ensemble (Deser et al., 2012). For this 

reason, we evaluate projected changes at the end 

of the 21st century when the forced signal should 

be detectable.

Climate drift in coupled models is another aspect 

that can affect the evaluation of historic climate 

and future projections. Drift is generally a result 

of model component coupling and deficiencies in 

model physics and numerical schemes (Sen Gupta 

et al.,2012), and can be significant especially at 

smaller spatial scales and for the deep ocean 

for which equilibrium has not been reached. The 

magnitude of the drift is relatively small for the 

upper ocean, atmospheric and land processes 

considered in this report and has improved 

considerably in CMIP5 relative to CMIP3 (Sen 

Gupta et al., 2013). Furthermore, the magnitude 

and direction of drift is model dependent and so 

evaluations based on the MME are less affected 

than for individual models. Individual model drift 

in precipitation (which is highly dependent on SST 

drift) for CMIP5 models is generally less than 10% 

of the historical model trend, but for some models 

this can exceed 30%.

The magnitude of climate drift in 

the models is relatively small for the 

upper ocean, atmospheric and land 

processes considered in this report and 

has improved considerably in CMIP5 

relative to CMIP3.
12

A limited analysis of errors for North American 

winter/summer precipitation (P), surface air 

temperature (T) and sea surface temperature 

(SST) was carried out in Sheffield et al. (2013a). 

In analyzing errors, model outputs were compared 

with the best available observational or reanalysis 

data sets. Figure 3 shows a direct comparison of 

CMIP5 results with CMIP3 results for basic climate 

variables over the period 1971-1999 (Sheffield et 

al. 2013a). The figure shows root-mean-square 

error (RMSE) values for CMIP5 and CMIP3 

models for seasonal mean P and T over North 

America and SST over the surrounding oceans. 

The majority of the CMIP5 models analyzed have 

an equivalent CMIP3 model, that is either the 

same model (HadCM3), a newer version, or an 

earlier related version, and so a direct comparison 

of changes since CMIP5 is feasible but subject to 

uncertainties in how the two versions are related 

(see Section 2.3).

Overall, the MME mean performance has 

improved slightly in CMIP5 for nearly all 

climatological variables. For example, there is a 

reduction in the MME mean RMSE for summer 

P (0.90 mm day-1 for CMIP3, 0.86 mm day-1 for 

CMIP5), and for winter SST (1.72°C to 1.55°C). 

The largest percentage reduction in RMSE for 

the MME mean is for summer T (11.8% reduction 

in RMSE). The spread in model performance 

(as quantified by the standard deviation) has 

remained about the same for P, increased for T 

and decreased for SST. The increase in spread 

for T is due to both increases and decreases 

in individual model performance relative to the 

CMIP3 models. Several models have improved 

considerably and across nearly all variables and 

seasons, such as the CCSM4, INMCM4, IPSLCM5A-LR, and MIROC5, although it is unclear 

how these models are related to their previous 

versions. Reductions in performance for individual 

models are less prevalent across variables, but 

are large for CSIRO-Mk3.6.0, HadCM3, and MRICGCM3 for SST in both seasons. The CanESM2 

has worse performance than its CMIP3 equivalent 

(CGCM3.1) for all variables, although it is likely 

that these are very different models and so a direct 

comparison may be unwarranted. The HadCM3 

model, which is used for both the CMIP3 and 

CMIP5 simulations, appears to have degraded in 

performance for SST. We assume that the model 

has not changed and that changes in variability 

derived from different initial conditions and/

or external forcings have caused the changes 

in performance. 

A more detailed regional examination of the errors 

is shown in the next sections in terms of maps of 

the MME mean fields for climatological winter and 

summer in Figs. 4-6 (Ruiz-Barradas et al. 2013). 

As for the continental evaluations above, the MME

mean fields have not changed much from CMIP3 

to CMIP5 models, except for a slight improvement 

in T.

3.1. Temperature

The MME mean of CMIP5 models shows a slight 

improvement over the corresponding mean of 

CMIP3 models of winter and summer T over 

North America (Fig. 4). Winter T is characterized 

by below zero (Celsius) temperatures in the 

inland northern latitudes with a minimum over the 

Rocky Mountains and a maximum over Mexico 

and Central America; the effect of the oceans 

in the northern latitudes temperatures is seen 

through the above-zero T along the coastal strips 

3. Representation of Means and variability

 of Basic Climate Variables

Overall, the multi-model ensemble mean 

performance has improved slightly in CMIP5 

for nearly all climatological variables.
13

Figure 3. Comparison of CMIP5 and CMIP3 model performance for metrics of relevance to North America for 

seasonal (DJF and JJA) precipitation (P), surface air temperature (T) and SST. Results are shown as RMSE

values calculated for 1971-1999 relative to the GPCP, CRU and HadISST observational datasets. Precipitation and 

temperature RMSE values are calculated over North America (130°-60°W, 0°-60°N) and SST RMSE values are 

calculated over neighboring oceans (170°-35°W, 10°S-40°N). The core set of CMIP5 models and their equivalent 

CMIP3 models where available (otherwise indicated by N/A) are shown. The MME mean values are also shown. 

This is Figure 20 in Sheffield et al. (2013a).

3. Representation of Means and variability

 of Basic Climate Variables
14

Figure 4. Mean winter and summer climatological surface air temperature for the period 1971-1999. Observations 

(upper row from CRUTS3.1), CMIP3 and CMIP5 multi-model means (middle row) and difference of multi-model 

mean minus observations (lower row). Red/blue shading denotes positive/negative temperatures (upper and 

middle rows) and their differences (lower row); contour interval is 1mm day-1 for both mean values and differences. 

Statistically significant differences calculated at the 5% level using a two-tailed Student’s t-test are shaded. Fields 

have been interpolated to a common 1.5°×1.5° grid. Figure adapted from Ruiz-Barradas et al (2013).

3. Representation of Means and variability

 of Basic Climate Variables
15

of the Pacific Northwest and Northeastern US. 

The structure of the CMIP3 and CMIP5 MME

means agree with each other and with the general 

structure of the observed winter T; however 

the extension of the minimum over the Rocky 

Mountains and the southward extension of cool 

temperatures over the highlands of Mexico are 

larger than observed in both of the MME means. 

Differences from observations of the MME means 

are smaller in the CMIP5 than in the CMIP3, 

especially over the East South Central and 

South Atlantic states of the US and to a lesser 

degree over the Rocky Mountain states of the 

US and the highlands of Mexico. Mean summer 

T is characterized by relatively low values over 

the Pacific Northwest (15-18°C) and the Rocky 

Mountains where they reach a minimum (~9°C), 

and a tongue of warm T penetrating the US from 

the Gulf of Mexico through the South Central 

states and extending to the northern Great Plains. 

High T (larger than 27°C) is evident over the South 

Central states of the US as well as over southern 

California and Arizona and the Sonora Desert 

over Mexico; minimum T in Mexico is found over 

the highlands (18-21°C). The CMIP3 and CMIP5 

MME means of summer T have structures similar 

to the observations with some differences between 

them, mainly on the extension of the minimum 

over the Rocky Mountains and the Mexican 

highlands; the extension of these minima is closer 

to observations in the CMIP5 mean than in the 

CMIP3 mean. The CMIP5 MME mean error over 

the Rockies and the Mexican highlands is smaller 

than for the CMIP3 mean; errors also show that 

the tongue of warm air over the southern Great 

Plains and the high T over northwestern Mexico 

(including the Sonora desert) are better captured 

by the CMIP5 mean than by the CMIP3 mean.

In terms of variability, Knutson et al. (2013b) 

showed that decadal-scale variability of surface 

air temperature is generally overestimated by 

CMIP3 and CMIP5 models (based on long 

control simulations) in high latitudes globally, 

and generally overestimated across most of 

North America, with underestimation in northwest 

Mexico. The patterns of variability are reasonably 

well reproduced (spatial correlation coefficients of 

0.5 – 0.7) by individual models from both CMIP3 

and CMIP5. Substantial differences between 

CMIP3 and CMIP5 were not found.

Temperature is projected to increase in CMIP5 

models in all seasons under RCP8.5 with the 

greatest warming in wintertime at high latitudes 

(up to 15°C increase), but with higher intermodel variability (Maloney et al., 2014). Direct 

comparison of CMIP3 and CMIP5 projections is 

difficult because of the different scenario forcings 

(see section 2.1). However, as the mean and 

range of the climate sensitivity and transient 

climate responses are similar (Andrews et al., 

2012), the differences are likely to be due more 

to the scenario forcing rather than changes in 

the models. Direct comparison by Knutti and 

Sedlácek (2012) showed that the there is little 

difference in the projections, although warming 

globally and over North America is slightly higher 

in CMIP5. When changes are normalized by 

the global temperature change, the geographic 

patterns of mean changes between CMIP3 and 

CMIP5 are very consistent (pattern correlation = 

0.98 globally), although the absolute changes are 

statistically significantly different in high latitudes 

and parts of Mexico (Collins et al., 2013). 

3. Representation of Means and variability

 of Basic Climate Variables

When temperature changes are 

normalized by the global temperature 

change, the geographic patterns of mean 

changes between CMIP3 and CMIP5 are 

very consistent.
16

3.2. Precipitation

The CMIP5 MME mean shows no improvements 

over the corresponding mean of CMIP3 models of 

mean winter and summer P over North America 

(Fig. 5). Mean winter P is characterized by 

maximum P over the Pacific Northwest and the 

East South Central regions of the US, as well as 

over Central America and southern Mexico; the 

Mountain and Midwest regions of the US as well 

as the majority of Mexico receive less P in this 

season. The CMIP3 and CMIP5 MME means of 

winter P agree in their general structure with the 

observed P over the US, but the maximum in the 

Pacific Northwest extends toward the Mountain 

region and the maximum over the South Central 

region is absent in both sets of models. Differences 

with observations show that the MME mean P is 

larger than observations over practically the whole 

of North America except over the East South 

Central region of the US and Central America and 

southern Mexico where P is less than observed. On 

the other hand, mean summer P is characterized 

by a wetter eastern US with maximum values along 

the narrow coastal region of the South Atlantic 

states of the US, and Mexico and Central America; 

western US and northern Mexico, along the Texas 

border, have less P in this season. The general 

structure of the CMIP3 and CMIP5 MME means 

of summer P are similar to each other and to the 

observed summer P, however differences are 

apparent over the Mountain, West North Central 

and South Atlantic states of the US and over the 

high plains of Mexico. Excessive P is present in the 

CMIP3 and CMIP5 MME mean over the Mountain 

and inland of the South Atlantic states of the US

and the high plains of Mexico. Conversely, reduced 

P is simulated over the central, western south 

central and the coastal band of the southern states 

of the US (particularly Florida and along the Gulf 

of Mexico coast), as well as over the mountain 

regions of Mexico and Central America.

Future projections of wintertime P for 2070-2099 

in CMIP3 (SRESA2) and CMIP5 (RCP8.5) models 

show a similar large-scale pattern of increases 

in mid- to high latitudes and decreases in the 

subtropics (Maloney et al., 2014). There are 

projected increases along the western coast of 

North America from California northwards and 

the eastern coast from the mid Atlantic states 

northward with good model agreement on the 

sign of the changes. However, the boundary 

between the increases and decreases is shifted 

slightly south in the CMIP5 ensemble, to give 

increases over parts of California (Neelin et al., 

2013; see section 6.3 for more details). During 

the summer, higher precipitation is projected 

in Alaska and the Yukon, with complete model 

agreement on the sign, and along the entire Arctic 

coast. Summertime reductions in precipitation 

are focused on the east Pacific warm pool and 

the Caribbean (Maloney et al. 2014). The model 

agreement on these summertime decreases 

has increased from CMIP3 to CMIP5, with 

all CMIP5 models examined by Maloney et 

al. (2014) in agreement for major parts of the 

region (see section 6.1). When normalized to 

global temperature change, the spatial patterns 

of precipitation change over North America 

are similar between CMIP3 and CMIP5, with 

significant differences in the magnitude where 

there is a northward shift in the boundary between 

wetting and drying in the southwestern US, and 

also across parts of Mexico, possibly reflecting 

the differences in scaled temperature change 

(Collins et al., 2013). 

3. Representation of Means and variability

 of Basic Climate Variables

When normalized to global temperature 

change, the spatial patterns of 

precipitation change over North America 

are similar between CMIP3 and CMIP5
17

Figure 5. Mean winter and summer climatological precipitation for the period 1971-1999. Observations (upper 

row, from GPCP), CMIP3 and CMIP5 multi-model means (middle row) and difference of multi-model mean minus 

observations (lower row). Green/brown shading denotes positive/negative differences in precipitation (lower row); 

contour interval is 1mm day-1 for the mean values and 0.3 mm day-1 for the differences. Statistically significant 

differences calculated at the 5% level using a two-tailed Student’s t-test are shaded. Fields have been interpolated 

to a common 1.5°×1.5° grid. Figure adapted from Ruiz-Barradas et al (2013).

3. Representation of Means and variability

 of Basic Climate Variables
18

3.3. Sea Surface Temperature

The MME mean of CMIP5 models for SST shows 

no improvement over the corresponding mean 

of CMIP3 models of winter-to-spring (DJFMAM) 

and summer-to-fall (JJASON) SST in the adjacent 

oceans of North America (Fig. 6). The Western 

Hemisphere Warm Pool (WHWP), where SST is 

equal to or larger than 28.5°C (thick black line 

on maps), usually is absent from December to 

February and appears in the Pacific from March to 

May, while it is present in the Caribbean and Gulf 

of Mexico, or Intra-Americas Sea, from June to 

November (Wang and Enfield, 2001). The cooler 

part of the year (winter to spring) is characterized 

by the small extension of SST in excess of 27°C 

and a suggestion of a cold tongue in the eastern 

equatorial Pacific, while during the warmer part 

of the year (summer to fall) the extension of SST 

in excess of 27°C is at a maximum and the cold 

tongue is well defined over the eastern Pacific. 

High P along the Mexican coasts, Central America, 

the Caribbean Islands and the central-eastern US

is associated with tropical SST in excess of 27°C 

during the warm half of the year; a decrease in the 

regional precipitation south of the equator is also 

evident in this warm half of the year. The structure 

of the CMIP3 and CMIP5 MME means agree with 

each other and with the general structure of the 

observed winter-to-spring SST, except that they do 

not show the high SST in the eastern Pacific and 

Intra-Americas Sea around Central America, or the 

weak cold tongue in the equatorial eastern Pacific 

off the coasts of Ecuador and Peru. The errors are 

particularly characterized by warm biases off the 

northeast coast of the US, especially during the 

cold season; off California and the Pacific side of 

northwestern Mexico, especially during the warm 

half of the year; and off the coasts of Ecuador and 

Peru, a problem that may be related to the models’ 

poor ability to simulate stratus clouds and transport 

by Ekman currents (Zheng et al. 2011). Cold 

biases are extensive over both oceans. The cold 

SST bias in the equatorial Pacific seems to agree 

with the wet/dry bias over the US (resembling 

La Niña conditions), while the warm SST bias off 

the coasts of Ecuador and Peru agree with the 

wet coastal bias over these countries and the dry 

bias over the countries to the east (resembling El 

Niño conditions). The structures of the CMIP3 and 

CMIP5 MME means also agree with each other 

and with the general structure of the observed 

summer-to-fall SST, except that the SST in the 

Atlantic Warm Pool (AWP) region is cooler than 

observed (Liu et al., 2013). The AWP SST bias 

in CMIP5 is more modulated by an erroneous 

radiation balance due to misrepresentation of highlevel clouds rather than misrepresentation of lowlevel clouds as was the case for CMIP3 (Liu et al. 

2013). The CMIP3 and CMIP5 MME mean errors 

highlight similar areas of cold/warm bias to those 

in the winter-to-spring part of the year except that 

the cooling bias over the Pacific side of Central 

America is no longer present.

The multi-model ensemble mean of CMIP5 

models for sea surface temperature shows 

no improvement over the corresponding 

mean of CMIP3 models of winter-tospring and summer-to-fall sea surface 

temperature in the adjacent oceans of 

North America

3. Representation of Means and variability

 of Basic Climate Variables
19

Figure 6. Mean winter-to-spring and summer-to-fall climatological sea surface temperature and precipitation for 

the period 1971-1999. Observations (upper row from HadISST and GPCP), CMIP3 and CMIP5 multi-model means 

(middle row) and difference of multi-model mean minus observations (lower row). Red/blue shading denotes 

positive/negative differences in temperature while green/brown shading denotes positive/negative differences in 

precipitation (lower row); contour interval is 1°C (1 mm day-1) for mean values for temperature (precipitation) and 

0.3 mm day-1 (0.3°C) for the differences. Statistically significant differences calculated at the 5% level using a 

two-tailed Student’s t-test are shaded. The thick black line is the 28.5°C isotherm which is used as a marker for the 

Western Hemisphere Warm Pool. Temperature (precipitation) fields have been interpolated to a common 5°×2.5° 

(1.5°×1.5°) grid. Figure adapted from Ruiz-Barradas et al (2013).

3. Representation of Means and variability

 of Basic Climate Variables
20

A comprehensive evaluation of temperature and 

precipitation extremes is given in Sun et al. (2014). 

Here we present additional analysis and literature 

review on the projected changes in extremes and 

partial attribution of differences between CMIP3 

and CMIP5. 

4.1. Temperature extremes

Sillman et al., (2013a) provide an initial global 

evaluation of CMIP3 and CMIP5 models in their 

ability to simulate the climatology and trends 

for a range of extreme temperature indices as 

well as precipitation extreme indices. These 

indices include the maximum and minimum daily 

maximum and minimum temperatures, the number 

of cold nights/days and warm nights/days, the 

duration of warm/cold spells and the number of 

frost days, ice days, summer days and tropical 

nights. For the temperature indices they found 

that the performance of the CMIP5 ensemble is 

similar to the CMIP3 ensemble, but that the spread 

amongst the CMIP3 models is larger than that 

for CMIP5. This is despite the larger number of 

CMIP5 models examined (31 versus 18). Although 

Sillmann et al. (2013a) found no clear relationship 

between model resolution and performance, higher 

resolution CMIP5 models tend to be closer to the 

ensemble median for some temperature extreme 

indices, suggesting that resolution plays a role. 

Both sets of models are also able to simulate the 

long-term trends in temperature indices and, in 

the case of CMIP5 (and some CMIP3 models with 

volcanic forcing), simulate the short-term changes 

related to volcanic eruptions.

Projected changes in temperature extremes are 

expected across North America, with the number 

of 90-degree days increasing by 50-100% across 

the Midwest and Northeast by end of century in 

CMIP5 models (Maloney et al., 2014). Projected 

changes under the CMIP3 SRES scenarios are 

generally comparable to those of the CMIP5 RCP 

scenarios, although projected changes under 

RCP4.5 are larger than for SRES B1 despite 

similar radiative forcing (Sillman et al., 2013b). 

RCP8.5 changes exceed changes under any 

SRES scenario as expected because of the higher 

radiative forcing.

4.2. Precipitation extremes 

An analysis of extreme precipitation in CMIP5 for 

North America by Sheffield et al. (2013a) (Fig. 

7) showed that models tend to underestimate 

the frequency of precipitation extremes in the 

southeast and south central regions of the US, but 

overestimate extremes in the Southwest (although 

the number of events is generally small), and 

do better at representing the spatial variability 

in the southwest and south central regions, than 

the Southeast (Fig. 7). Some models do well 

across all regions, although it is unclear why. 

In the Northeast, mean precipitation biases are 

driven mainly by biases in heavy precipitation, 

and the set of models spans the observed heavy 

precipitation frequency. Sheffield et al. (2013a) 

noted that these biases could not be explained by 

individual model biases in the representation of 

extra-tropical cyclones. 

DeAngelis et al. (2013) evaluated daily 

precipitation statistics for CMIP3 over North 

America and found robust underestimation 

across models (at least 3⁄4 of models) of the 

intensity of heavy and extreme precipitation 

along the Pacific coast, southeastern United 

States, and southern Mexico. Overestimation 

of light precipitation offsets these biases to give 

generally realistic mean precipitation (Stephens 

et al., 2010). Biases appear to be associated 

with the representation of local forcing rather 

than large-scale circulation. DeAngelis et al. 

(unpublished manuscript) extended their original 

4. Representation and Projections of Temperature

 and Precipitation Extremes
21

Figure 7. Comparison of regional precipitation extremes between CMIP5 models and the CPC daily 

observational dataset. (Adapted from Sheffield et al., 2013a). (a)-(c) Taylor diagrams of the spatial pattern of 

annual number of days when precipitation > 10 mm day-1 over the (a) southwest, (b) south central, and (c) 

southeast US. The standard deviations have been normalized relative to the observed values. (d) Frequency 

distribution of daily average precipitation (in mm day-1) for the Northeast region. Region definitions are given in 

Sheffield et al. (2013a). (A: CanESM2, B: CCSM4, C: GFDL-CM3, D: GFDL-ESM2G, E: GFDL-ESM2M, F: GISSE2R, G: HadCM3, H: HadGEM2-CC, I: HadGEM2-ES, J: IPSL-CM5A-LR, K: MIROC4h, L: MIROC5, M: MPI-ESMLR, N: MRI-CGCM3). Adapted from Sheffield et al. (2013a).

4. Representation and Projections of Temperature

 and Precipitation Extremes
22

evaluation to CMIP5 models, which show smaller 

biases than CMIP3 in heavy precipitation over the 

southeastern United States (i.e., the magnitude 

of heavy to extreme daily precipitation is 

somewhat higher and more realistic; Fig. 8). They 

hypothesize that improvements in CMIP5 are due 

to increased resolution (although the increase in 

resolution from CMIP3 to CMIP5 is modest) and 

perhaps better parameterization of convective 

precipitation in some of the models, as the largescale circulations during extreme events in the 

southeastern US are very similar between CMIP3 

and CMIP5. For the summer in the Southeast, the 

circulation anomalies during extreme events are 

slightly stronger in CMIP5, and the relationship 

between horizontal resolution and biases is 

weaker than in winter. Thus, larger magnitudes 

of summer heavy precipitation in CMIP5 may be 

from a combination of higher resolution, better 

convective parameterizations, and slightly stronger 

circulation features. 

Overall, the CMIP5 models tend to underestimate 

the frequency of heavy and extreme daily 

precipitation events for North America, but show 

a slight improvement over CMIP3 (Sillman et al., 

2013a; DeAngelis et al. (unpublished manuscript)), 

especially in the southeastern US. Increasing 

resolution appears to help (e.g. Wehner et al. 

2010; Li et al., 2011; DeAngelis et al., 2013; 

Kinter et al. 2013) but further investigation is 

required to determine whether there is a limit to 

the increase in skill. The general underestimation 

of extreme daily precipitation events by CMIP5 

and CMIP3 models may be, in part, due to the 

underestimation of observed increasing trends 

over the past century (Min et al., 2011), which 

is notable in the Northeast, Midwest, and upper 

Great Plains (Karl et al., 2009; Walsh et al., 2014). 

A more comprehensive analysis of extremes 

across regions is required; some such analysis is 

described in Sun et al. (2014). Additional analysis 

should include attribution of events (e.g. to tropical 

cyclones (TC), extra-tropical storms, convective 

activity, topography, weather types, atmospheric 

circulation versus local processes, moisture 

sources, ENSO, etc.). For example, TCs contribute 

up to 30% of annual precipitation and up to 20-

25% of extreme precipitation in the southeast 

(Knight and David, 2009) with implications 

for modeled extremes, as models severely 

underestimate the number and intensity of 

landfalling TCs. Furthermore, account needs to be 

taken of scale mismatches between models and 

observations that may hamper evaluations (see 

e.g. King et al. 2013), and the limitation of using 

small sample sizes (e.g. single model ensemble 

member) to detect and compare extreme events.

Projected future changes in heavy precipitation 

in CMIP5, over North America (DeAngelis 

and Broccoli, 2013; Maloney et al., 2014) and 

globally (Scoccimarro et al., 2013), appear to be 

qualitatively consistent with earlier studies using 

CMIP3 data (DeAngelis et al., 2013). For CMIP5, 

DeAngelis and Broccoli (2013) note that heavy 

precipitation (defined as the precipitation from the 

99th percentile and above) increases over much 

of North America by the end of the 21st century, 

with generally larger increases in higher latitudes 

and near the coasts, and mainly during the winter. 

The domain-averaged results from Maloney et al. 

(2013) indicate an increase of 20-30% for the late 

21st century. Summertime increases are generally 

confined to very high latitudes, with only small 

4. Representation and Projections of Temperature

 and Precipitation Extremes

The CMIP5 models tend to underestimate 

the frequency of heavy and extreme daily 

precipitation events for North America, but 

show a slight improvement over CMIP3, 

especially in the southeastern US.
23

Figure 8. Comparison of annual extreme precipitation (average precipitation over all days when precipitation 

equals or exceeds the 99th percentile, P99M) from the CPC daily dataset and the CMIP3 and CMIP5 multimodel 

ensembles. Adapted from DeAngelis et al. (unpublished manuscript).

4. Representation and Projections of Temperature

 and Precipitation Extremes
24

changes elsewhere. For low latitude regions, 

DeAngelis and Broccoli (2013) found that heavy 

precipitation decreases in intensity and frequency 

over regions such as western Mexico and the 

adjacent Pacific Ocean during winter and spring. 

For the northeast US, the changes are on 

the order of 7%/K-1 in the winter (3-5%/K-1 in 

summer) in mean 99th percentile precipitation, 

which translates to about a 20-35% absolute 

increase across models (DeAngelis and Broccoli, 

2013). Scoccimarro et al. (2013) indicate a 20-

30% increase in moderate (75th percentile) to 

extreme (99.9th percentile) winter precipitation in 

CMIP5 for the eastern US (Florida to southeast 

Canada) when comparing the periods 1965-

2005 and 2061-2100 for RCP8.5. The results 

from Maloney et al. (2013) indicate changes on 

the order of 4-5 times for events > 25 mm/day-1

(equivalent to about the 99th percentile) for the 

northeast US. However, it should be noted that 

this relates to a relatively small part of the domain 

(northeast US) where some of the largest changes 

are projected, and that the sample size (number 

of heavy events) is relatively small (change from 2 

events to 8 events). 

Overall, projected changes by the end of the 

21st century (2070-2099) in moderate to extreme 

events show a 20-30% increase over the US, 

which is similar to CMIP3, with much higher 

increases in the northeastern US, especially 

in winter (Fig. 9). The wintertime changes 

are consistent with an upper limit for daily 

precipitation extremes as expected from the 

Clausius-Clapeyron relationship (Kharin et al., 

2013). Min et al. (2011) note that a subset of 

CMIP3 models underestimate the observed 

wetting of extreme precipitation across the 

northern hemisphere during the second half of 

the 20th century, suggesting that the projections 

of future increases may be underestimated, 

although their analysis did not discriminate 

between warm and cool season changes, and 

other methodological choices may have muted the 

model trends. A more detailed regional analysis of 

the representation of extreme precipitation events 

and projected changes is required, including 

the mechanisms of projected changes and the 

relationship with warming. 

Figure 9. Difference in the number of precipitation 

days and percentage change for each amount bin 

between the 2009-2038, 2038-2068, and 2069-2098 

and the historical 1979-2004 period for the northeast 

land region define in Maloney et al., (2013). (Adapted 

from Maloney et al., 2013).

4. Representation and Projections of Temperature

 and Precipitation Extremes

Overall, projected changes by the end of 

the 21st century (2070-2099) in moderate 

to extreme precipitation events show a 20-

30% increase over the US, which is similar 

to CMIP3, with much higher increases in 

the northeastern US, especially in winter.
25

5.1. El Niño and the Southern 

 Oscillation (ENSO)

Guilyardi et al. (2012) and Bellenger et al. 

(2013) showed that, in general, the representation 

of ENSO in CMIP5 has improved over CMIP3. 

In particular, there is a 30% reduction of the 

cold bias in the west Pacific, and the large intermodel spread in the amplitude of ENSO has 

been reduced by a factor of 2, although it should 

be noted that model control simulations suggest 

multi-decadal to centennial modulations of ENSO 

amplitude (e.g. Wittenberg et al., 2009). Kim 

and Yu (2012) also compared ENSO simulations 

between CMIP3 and CMIP5 and concluded 

that the CMIP5 models show less inter-model 

spread, with the CMIP3 models more clearly 

separated into a group that produces strong 

ENSO intensities and a group that produces weak 

ENSO intensities, whereas the CMIP5 model 

ensemble has converged to a single group that is 

closer to observations. They find the reduction in 

spread to be different for the two types of ENSO 

(Eastern Pacific, EP; and Central Pacific, CP; 

Yu and Kao 2007 and Kao and Yu 2009). The 

reduction is particularly significant for the EP type, 

whose generation depends more on thermocline 

variations over the cold tongue region and their 

coupling with the atmosphere. The generation 

of the CP type has been suggested to be less 

sensitive to thermocline variations (e.g., Kao and 

Yu 2009, Yu et al. 2010, Yu and Kim 2011). It is 

possible that efforts aimed at improving model 

parameterizations (such as those associated 

with cumulus convection and ocean mixing) have 

enabled the CMIP5 models to more realistically 

simulate ocean-atmosphere coupling over the 

cold tongue region and the resulting EP ENSO 

variability in the region. Furthermore, the ENSO 

life cycle, as represented by the seasonal phase 

locking and the location of surface temperature 

anomalies is slightly improved since CMIP3 

(Guilyardi et al. 2009). Sheffield et al. (2013b) 

examined the phase locking in CMIP5 models 

and showed that 68% of El Niño and 65% of La 

Niña episodes have peak amplitudes in fall or 

winter in the models, compared to 90% and 89%, 

respectively, in the observations, with implications 

for the representation of teleconnections with land 

surface climate. They note, however, that several 

of the models (CanESM2, CNRM-CM5, HadCM3 

and NorESM1-M) do have fall/winter peak 

frequencies exceeding 80% for both El Niño and 

La Niña episodes. Bellenger et al. (2013) also note 

that fundamental characteristics of ENSO, such 

as its spectrum and central Pacific precipitation 

anomalies, are still poorly represented. 

Sheffield et al. (2013b) examined teleconnections 

with North American climate in CMIP5 models, and 

noted that model skill was related to mean state 

biases in SST (Figure 10). Performance based 

on the representation of composite (El Niño/ La 

Niña) 300hPa geopotential height patterns versus 

NCEP/NCAR reanalysis indicated that the betterperforming models (top half of models) did well in 

representing teleconnection patterns in T and P 

(with the exception being the failure to capture the 

negative P anomaly in the Tennessee and Ohio 

valleys). The lower-performance models had much 

weaker teleconnection patterns and a westward 

shift in height anomalies. This may be explained

5. Representation of Inter-Annual to Decadal Variability

 and Teleconnections with North American Climate

The representation of ENSO 

in CMIP5 has improved 

over CMIP3.

CMIP5 models with better ENSO 

performance did well in representing 

teleconnection patterns in temperature and 

precipitation over North America.
26

by stronger climatological (rather than interannual 

variability) SST cool biases (> 1.5C) in the lowperformance models that drive lower P/convection, 

and weaker and more westward-shifted 

teleconnections. Langenbrunner and Neelin (2013) 

note that there has been little improvement in 

model performance for P teleconnections relative 

to CMIP3. 

Furthermore, there is evidence that model errors 

are related to the different teleconnection patterns 

associated with the two types of ENSO (EP and 

CP), notably for winter temperatures (Sheffield 

et al., 2013b). Zou et al. (2014) show that CMIP5 

models are more capable of simulating the El Niño 

impact on US winter climate for the traditional 

Eastern-Pacific type but not for the emerging 

Central-Pacific type. They offer the following 

explanation for this: during the EP El Niño, the 

largest SST anomalies are located in the eastern 

equatorial Pacific and influence the strength of 

the Walker circulation to give rise to basin-wide 

outgoing longwave radiation (OLR) anomalies. 

The modeled atmospheric responses to the EP El 

Niños are thus not sensitive to the SST anomaly 

structure and can be well simulated by most of the 

CMIP5 models. In contrast, the SST anomalies 

of the CP El Niño are in the central equatorial 

5. Representation of Inter-Annual to Decadal Variability

 and Teleconnections with North American Climatees

Figure 10. Composites of (a)–(c) 300-hPa height (z300; m), (d)–(f) surface air temperature (SAT) (°C), (g)–(i) 

precipitation (mm day-1), and (j)–(l) SST (oC) anomalies during DJF El Niño episodes (left) in observations and in 

(middle) high and (right) low performance CMIP5 ensembles. The observational SAT and precipitation composites 

are based on the CRU TS3.1 land near-surface temperature and precipitation datasets for 1901–2009. The z300, 

SAT, and precipitation composites are normalized by the Niño-3.4 SST anomaly. Stippling in the observed (a) 

z300, (d) SAT, and (g) precipitation composites indicates anomalies that are statistically significant at the 5% level. 

(reproduced from Sheffield et al., 2013b)
27

Pacific and induce only local OLR anomalies to 

the west of the SST anomalies. The modeled 

atmospheric responses to the CP El Niño are 

different among the models depending on the 

simulated magnitudes and locations of the CP El 

Niño SST anomalies. Kug et al. (2012) showed 

that CMIP5 models on average are slightly better 

at representing the two types of ENSO and that 

this was caused by the precipitation response to 

cold tongue El Niño, which is closely related to a 

dry bias over the equatorial eastern Pacific. Yu 

and Zou (2013) showed that the CP El Niño has a 

tendency to enhance the dry impacts and weaken 

the wet impacts produced by the EP El Niño on US

winter precipitation.

Projected changes in ENSO are difficult to 

detect because of the large internal variability 

(Wittenberg, 2009; Maloney et al., 2014). However, 

changes in the base state of tropical SSTs may 

affect the balance of ENSO cycle feedbacks, 

changing the average amplitude of events 

(Guilyardi et al., 2012), and impact on ENSO 

teleconnections (Maloney et al., 2014). Projected 

changes for CMIP5 models under RCP8.5 show 

no change in ENSO event frequency and a slight 

increase in ENSO amplitude, although the intermodel spread is high enough, relative to the 

magnitude of the increase, that these changes are 

not robust (Maloney et al., 2014). Comparison of 

CMIP3 and CMIP5 (Guilyardi et al., 2012) shows 

that both sets of projections display a similar 

diverse range of changes in ENSO characteristics 

with the different model responses due to 

differences in the balance of feedbacks in each 

model. A robust finding, in CMIP5 at least (because 

of improved model historical performance and 

lower inter-model spread in the projections relative 

to CMIP3), may be an increase and decrease, 

respectively, of the intensity of the CP and EP 

ENSO types under RCP4.5 (Kim and Yu, 2012). 

Teleconnections with North American temperature 

and precipitation in CMIP5 models were found to 

strengthen, associated with increases in tropical 

convective forcing driven by the overall increase 

in SSTs (Maloney et al., 2014), but may also be 

related to the changes in the intensity of CP and 

EP ENSO types (Zou et al., 2014). In particular, 

the emerging CP El Niño may produce an overall 

drying effect on the US winter, particularly over 

the Ohio–Mississippi Valley, Pacific Northwest and 

Southeast (Yu and Zou, 2013). 

5.2. Pacific Decadal Variability

Sheffield et al. (2013b) showed that CMIP5 models 

replicate the basic horseshoe SST pattern of the 

Pacific Decadal Oscillation (PDO; defined as the 

leading empirical orthogonal function of extended 

winter (November–April) monthly-mean SST 

anomalies in the North Pacific poleward of 20oN), 

which is similar to CMIP3 models (Oshima and 

Tanimoto, 2009; Furtado et al., 2011). However, 

the CMIP5 models show a westward shift of the 

North Pacific center of action in models with 

respect to observations. Figure 11 shows an 

updated analysis comparing the PDO North 

Pacific SST patterns in CMIP3 and CMIP5 using 

Taylor diagrams. The left figure is taken from 

Oshima and Tanimoto (2009) for CMIP3 and 

a similar calculation is made on the right with 

CMIP5 models from Sheffield et al (2013b). The 

two PDO definitions are slightly different so the 

comparison is not exact, but it does suggest that 

the CMIP5 models do slightly better, with higher 

pattern correlations between model and observed 

regressions. The CMIP3 PDO pattern from 

Oshima and Tanimoto (2009) is based on linear 

regression against the mean SST over a box in 

the central North Pacific. The CMIP5 PDO pattern 

definition is the EOF-based regression pattern 

definition from Sheffield et al. (2013b). The CMIP5 

regression was performed with unfiltered monthly 

5. Representation of Inter-Annual to Decadal Variability

 and Teleconnections with North American Climate
28

SST data, whereas Oshima and Tanimoto (2009) 

first applied a 5-year running mean smoother to 

the time series. It is unlikely that either difference 

in definition would have a significant impact on 

the results, because the leading EOF of North 

Pacific SST is dominated by variability in the 

region that Oshima and Tanimoto (2009) use, 

and the additional interannual variability in the 

CMIP5 definition would not impact the pattern 

much. Although the CMIP5 pattern correlation 

appears to be better than in CMIP3, the spatial 

standard deviation is overestimated. In the CMIP5 

regression, the SST anomalies in the KuroshioOyashio extension (KOE) region are too low, which 

exaggerates the SST anomaly difference between 

the eastern and western North Pacific. It is unclear 

from Oshima and Tanimoto (2009) whether this 

is a common feature in CMIP3 models, but this 

general feature is discernible in the CMIP3 PDO 

analysis of Overland and Wang (2007) (http://www.

pmel.noaa.gov/foci/publications/2007/over0633.

pdf). Therefore the hypothesis is that more models 

are getting the basic PDO structure correct, but 

these models generally suffer from a bias in 

exaggerated SST variability over the KOE region.

In terms of PDO teleconnections, Sheffield et 

al. (2013b) showed that the CMIP5 models do 

well in representing the PDO influence on North 

American surface air temperature, with positive 

(negative) anomalies in the Northwest (Southeast) 

during the positive phase of the PDO. However, 

the models cannot reproduce the precipitation 

teleconnection patterns over large parts of North 

America, with performance generally better in 

western North America (especially the Southwest), 

and worse in the east. In particular, Sheffield 

et al. (2013b) and Polade et al. (2013) note the 

deficiency over the Ohio Valley. There is little 

literature on comparisons between CMIP3 and 

CMIP5 for PDO teleconnections. Leinert (2011) 

discussed the results from CMIP3 and concluded 

that the models do reasonably well at reproducing 

the influence of both the tropical-Pacific-related 

and the extratropical part of the PDO on North 

American surface temperature, but only some of 

the influence of the PDO on precipitation that is 

mainly related to the tropical Pacific connection. 

Leinert (2011) shows that CMIP3 models tend to 

have a delayed response of the North Pacific to 

ENSO forcing due to model biases in the mixed 

layer depth and air-sea feedbacks, and they tend 

to overestimate the lower frequency variability due 

to model errors in the tropics and extra-tropics. 

The Polade et al. (2013) study notes that 9 of the 

14 CMIP5 models evaluated showed improvement 

in simulating the PDO-like mode and its North 

American precipitation teleconnections, which 

they attribute generally to improved resolution 

and model physics. Despite the generally good 

performance for temperature teleconnections and 

possible improvements for precipitation in CMIP5, 

the deficiencies in the observational record in 

representing the full spectrum of Pacific variability 

should be noted. Any conclusions on model 

performance must be made with respect to the 

possible non-stationarity of PDO patterns and the 

modulation of teleconnections depending on the 

phase of ENSO (e.g. Wise et al., 2014; Kam et 

al., 2014).

5. Representation of Inter-Annual to Decadal Variability

 and Teleconnections with North American Climatees

28

CMIP5 models do well in representing the 

Pacific Decadal Oscillation’s influence on 

North American surface air temperature 

while not reproducing the precipitation 

teleconnection patterns over large parts 

of North America. Performance for 

precipitation is generally better in western 

North America (especially the Southwest), 

and worse in the east.
29

5. Representation of Inter-Annual to Decadal Variability

 and Teleconnections with North American Climate

Figure 11. Evaluation of the PDO sea surface temperature (SST) patterns. Taylor diagrams for CMIP3 (Oshima and Tanimoto 

2009) and CMIP5 (Sheffield et al. 2013b) PDO SST patterns in various climate models, each designated by a letter (the letters 

are unrelated in the two plots). Although the PDO definitions vary slightly in the two studies, the CMIP5 models seem 

to perform somewhat better than CMIP3 models, with most pattern correlations less than 0.8 in CMIP3 but above 0.8 in 

CMIP5 models.
30

In terms of future projections, there has been 

no systematic comparison of CMIP3 and CMIP5 

results for either a shift in the spatial pattern 

or intensity of the PDO, or its teleconnections 

with North American climate. Lapp et al. (2012) 

showed a weak shift in the CMIP3 MME mean 

to more occurrences of the negative phase of 

the PDO during the 21st century for three SRES

scenarios (B1, A1B and A2), although the models 

were split among those showing a shift towards 

more negative conditions and those towards more 

positive conditions. For CMIP5 models, there is no 

equivalent study of changes in the PDO. However, 

there is some consistency with the results of Yeh 

et al. (2013), who showed that the negative phase 

of the PDO shifts to cooler SSTs for 9 CMIP5 

models for RCP4.5 for 2100-2200. This shift may 

be associated with a shift towards more CP type El

Niño events due to a shallowing of the thermocline 

(Yeh et al., 2009). For teleconnections, there is 

a statistically significant strengthening in CMIP5 

models of teleconnections with North American 

temperature for all seasons by the end of the 

century for the model ensemble mean with the 

largest strengthening over western North America, 

which is consistent with an increase in tropical 

convective forcing (Maloney et al., 2014). 

5.3. Atlantic Multidecadal 

 Variability

The representation of the AMV has generally 

improved in CMIP5 models (Sheffield et al., 2013b; 

Zhang and Wang 2013; Wang and Zhang 2013) 

compared to CMIP3 (Medhaug and Furevik, 2011), 

particularly after 1960. Sheffield et al. (2013b) 

speculated that this might be due to higher 

resolution models, improved parameterizations 

and the addition of time-evolving land cover. 

Furthermore, the representation of AMV in terms 

of the standard deviations (0.09 to 0.19°C) 

is comparable to, or slightly weaker than, 

the observations (~0.18°C) (Zhang and Wang 

2013), which is an improvement from CMIP3 

models (Ting et al., 2009). The models have also 

improved in terms of persistence (defined as the 

maximum time lag when the autocorrelation first 

crosses the significance line at the 90% level), 

which varies from 5 to 25 years in CMIP5, with 

an average of about 12 years, compared to an 

average of about 5 years in CMIP3 (Medhaug 

and Furevik 2011). Similar to the PDO, any 

conclusions must be tempered by the relatively 

short observational record with respect to the 

time scale of variability, although proxy records 

(e.g. Knudsen et al., 2011) and long-term climate 

simulations (Knight et al., 2005) support the 

existence of AMV over the past millennia. 

The mechanism for AMV continues to be unclear 

despite its importance, and there exist the 

possibilities that it is forced to some degree or it 

is just noise. One potential mechanism is the role 

of variations in the Atlantic Meridional Overturning 

Circulation (AMOC) and associated heat transport 

fluctuations (Delworth and Mann 2000; Knight et 

al. 2005). The analysis of historical simulations 

for most of the CMIP5 models shows that an 

interaction between the AMV and AMOC can 

produce a multidecadal oscillation in the North 

Atlantic Ocean (Zhang and Wang 2013) consistent 

with the delayed advective oscillation mechanism 

proposed by Lee and Wang (2010). Modeling 

studies have also suggested that solar variability 

5. Representation of Inter-Annual to Decadal Variability

 and Teleconnections with North American Climatees

The representation of the Atlantic

Multidecadal Variability has generally

improved in CMIP5 models compared to

CMIP3 with the improvement potentially

due to higher resolution, improved

parameterizations and time-evolving

land cover.
31

and/or volcanoes are important (Hansen et al. 

2005; Ottera et al. 2010), or that aerosols can be 

a primary driver (Dunstone et al., 2013; Booth et 

al. 2012), although the robustness of the latter 

has been questioned because of discrepancies 

in the representation of observed ocean states 

(Zhang et al. 2013). A recent observational study 

suggests that a positive feedback between SST 

and dust aerosols in the North Atlantic via Sahel 

rainfall variability may be a mechanism (Wang et 

al. 2012).

More recent work has shown that the speed up 

(slow down) of the AMOC favors the generation 

of the warm (cold) phase of the AMV via the 

anomalous northward (southward) transport of 

heat in the upper ocean, which conversely leads to 

a weakening (strengthening) of the AMOC through 

changes in the meridional density gradient after 

a delayed period of ocean adjustment (Zhang 

and Wang 2013; Wang and Zhang 2013). Kumar 

et al. (2014) evaluated North Atlantic variability 

(and connections with the “warming hole” in the 

central US) and suggested that anthropogenic 

forcing plays a role in the AMV by increasing its 

persistence slightly more than expected based 

on CMIP5 pre-industrial control simulations. 

The detailed mechanisms of the AMV and 

the separation of natural and anthropogenic 

components remain challenging issues.

Several studies have highlighted the importance 

of the AMV in forcing precipitation variability over 

North America (e.g. Enfield et al. 2001, Sutton 

and Hodson 2005, Wang et al. 2006, Schubert 

et al. 2009, Nigam et al. 2011). However, the 

CMIP5 models do not simulate the observed 

teleconnection patterns well (Sheffield et al., 

2013b), in part because of the poor representation 

of the spatial footprint of AMV SST variability 

-- a situation that has not improved since CMIP3 

(Ruiz-Barradas et al. 2013). The AMO has a much 

stronger and clearer signal in precipitation in the 

southwest US and northwest Mexico (Hu and 

Feng, 2008) and so examining this relationship 

may provide a clearer way to examine how 

well the models simulate AMV remote effects 

on North America. The poor performance is 

also related to the effect of AMV on the lower 

level circulation, which modulates the Great 

Plains low-level jet (LLJ) and the convergence/

divergence of moisture fluxes. AMV has a strong 

effect on upper troposphere circulation as well, by 

creating a favorable upper level front that nurtures 

convection and precipitation in the central US as 

shown in the modeling study of Hu et al. (2011). 

This is consistent with the result that the LLJ is less 

of a key factor for future summer precipitation in the 

Great Plains (see below). Improved understanding 

of teleconnections may be sought by examining the 

upper troposphere circulation in the models, and 

whether this is related to skill in simulating summer 

precipitation in the Great Plains. Furthermore, AMV 

effects on North American summer precipitation 

appear to be primarily to change the interannual 

variability of rainfall as opposed to providing 

a persistent decadal forcing, as suggested by 

Hu and Feng (2012). 

There appears to be no literature on future 

projected changes in AMV in CMIP3 and CMIP5 

simulations. In terms of future research, analysis 

of long control runs (1000 years or longer) would 

be helpful in clarifying the role of anthropogenic 

forcing versus natural variability for AMV and 

the relationship with the AMOC. Improved 

understanding of teleconnections may be sought 

by examining the upper troposphere circulation 

in the models, and whether this is related to skill 

in simulating summer precipitation in the Great 

Plains. Furthermore, AMV has a much stronger and 

clearer signal in precipitation in the southwest US

and northwest Mexico (Hu and Feng, 2008), and 

examining this relationship may provide a clearer 

way to examine how well the models simulate AMV 

remote effects on NA.

31

5. Representation of Inter-Annual to Decadal Variability

 and Teleconnections with North American Climate
32

6. Regional Processes

6.1. Projections of Drying for 

 the Caribbean and Mexico 

Drying of sub-tropical regions is evident in CMIP3 

and CMIP5 model projections, in particular for 

the Caribbean and parts of Mexico. Specifically, 

Maloney et al. (2013) found that CMIP5 model 

projections showed reduced summertime 

precipitation in the east Pacific warm pool and 

the Caribbean, with agreement among all models 

for several regions including the major Caribbean 

islands, the Yucatan Peninsula and southwestern 

Mexico. This intermodel agreement is even higher 

than for CMIP3 (e.g. Neelin et al. 2006) and may 

be related to improved representation of regional 

precipitation. Ryu and Hayhoe (2013) showed 

that precipitation biases over the Caribbean 

region have decreased in CMIP5, and therefore 

more models are able to simulate the two rainy 

peaks that characterize the annual cycle of 

regional precipitation. Furthermore, the better 

models realistically reproduce summer changes 

in the meridional gradient of SST and westward 

extension of the North Atlantic Sub-tropical High 

(NASH). Further research is needed to identify 

the reasons for the drying signal in the region 

and whether this is related to model performance 

in representing the seasonal dynamics of 

precipitation and its relationships with the changes 

in SSTs and the NASH. 

6.2. North American Monsoon

The seasonal cycle of monthly precipitation in 

the core monsoon region of northwest Mexico 

(23.875°-28.875°N, 108.875°-104.875°W) has 

been evaluated in CMIP5 models and compared 

to CMIP3 in Sheffield et al. (2013a). This region 

was selected specifically for its uniformity in 

the seasonal cycle of precipitation, reducing 

spatial variability in the calculation of the mean. 

This region is also at the core of the monsoon, 

which separates model errors in determining the 

northern boundary of the monsoon from errors 

in monsoon seasonality and intensity. The large 

majority of models are biased wet, with an average 

bias of 51.3%. Positive biases occur throughout 

the year with the largest biases seen from July 

through December (Geil et al. 2013). Similar 

monthly errors are seen in CMIP3 models (Liang 

et al., 2008), indicating that there has been no 

improvement in the magnitude of the simulated 

annual cycle of monthly precipitation. On the other 

hand, there does seem to be improvement in the 

timing of seasonal precipitation shifts, with 13 out 

of 21 (62%) CMIP5 models having a phase lag 

of zero months as compared to 6 out of 17 (35%) 

CMIP3 models in Liang et al. (2008).

Several studies have shown that the North 

American monsoon region is expected to become 

drier on an annual basis over the 21st century 

(Maloney et al., 2013; Cook and Seager, 2013). 

However, this decrease is not evenly distributed 

throughout the year, with the majority of models 

showing decreases from November through 

August and increases for September through 

October. This increase in late season rainfall 

suggests that the monsoon is arriving later and 

ending later than during the historical period, 

although these results should be interpreted with 

caution as the models have a wet bias in the 

fall during the historical period (Geil et al. 2013) 

which may simply be growing in time. On the 

other hand, a drier July and August is particularly 

concerning since the result is more robust in terms 

of model consensus on the sign of the change 

and these months tend to be overestimated for 

the model’s historical period (Geil et al., 2013; 

Sheffield et al., 2013a). Analysis of CMIP3 data 

suggest that, in general, precipitation in monsoon 

There has been no improvement in the 

magnitude of the simulated annual cycle 

of monthly precipitation for the North 

American monsoon, although there 

appears to be an improvement in the 

timing of seasonal precipitation shifts.
33

Table 1. CMIP5 projected changes in the relative onset and retreat of the monsoon in the core region in terms of the 

median of the model ensemble.

Historic (1979-2005) RCP8.5 (2070-2099) Difference (RCP8.5 – Hist.)

Onset date 8 Jun +- 3 days 16 Jun +- 4 days 8 +- 5 days *

Retreat date 25 Sep +- 3 days 24 Sep +- 3 days 0 +- 4 days

Monsoon duration 109 +- 4 days 101 +- 4 days -8 +- 6 days *

* statistically significant

regimes such as the SW will be redistributed 

in the future with reduced spring rainfall and 

increased late season rainfall (Seth et al. 2010, 

2011; Biasutti and Sobel 2009). The CMIP5 results 

are qualitatively similar to those from CMIP3 with 

high model agreement on reduced winter through 

early rainy season rainfall (Dec-Jul) but less of 

a consensus on increased late season rainfall 

(Maloney et al., 2013). 

Overall, current analyses indicate early season 

drying and late season wetting that is seen in 

the monsoons of other regions of the Americas 

and Africa although the changes are less clear 

over Asia (Seth et al., 2013). However, these 

CMIP5 results and previous CMIP3 studies are 

generally based on analysis of monthly data and 

so the question of whether the SW monsoon (and 

other global monsoons) will have an increasingly 

later onset is difficult to determine. Sheffield et 

al. (2013a) note that more CMIP5 models show 

zero lag in the start of the monsoon than CMIP3 

models. These zero-lag (unbiased) models were 

analyzed for future changes in the monsoon by 

Maloney et al. (2013) at monthly scale, which 

showed no evidence of a shift in the monsoon, 

based on this monthly analysis. A daily analysis of 

the historic CMIP5 simulations by Geil et al. (2013) 

showed that the models on average tend to start 

early compared to the daily TRMM Multi-satellite 

Precipitation Analysis (TMPA) satellite precipitation 

dataset for the core North American Monsoon 

(NAM) region, which is not reflected in the monthly 

analysis of Sheffield et al. (2013a).

An updated analysis based on daily precipitation 

data from 18 CMIP5 models (Y. Serra, unpublished 

results; Table 1) indicates a slight shift to a later 

onset date (6-8 days in the mean or median) in the 

SW monsoon by 2070-2099 for RCP8.5, but no 

late retreat date, so the duration of the monsoon is 

shortened somewhat. The change in retreat date 

is much harder to determine because of the higher 

variability in the models at the end of the season 

(Fig. 11), which is similar to the historical period 

(Geil et al., 2013). Note that the standard deviation 

of the MME mean is larger than the shift for both 

the onset and retreat, that the observed duration 

as estimated from the TMPA satellite data is 101 

days, and that the models tend to start earlier and 

end earlier in comparison.

Further work is required on several fronts, 

including 1) to understand if the changes for 

broader monsoon regions are similar, such as 

into the southwest US and down into Mexico, and 

whether these results are similar to CMIP3, 2) to 

determine the physical reason for the shift in the 

monsoon timing and therefore its robustness, and 

3) to understand changes in seasonal precipitation 

and its timing in other regions of North America. 

For the second area where extra work is required, 

the locations of the North Atlantic and North Pacific 

Sub-tropical Highs (NASH; NPSH) are relevant for 

the historical simulations and may help determine 

the shift. For example, a change in their positions 

and structure associated with warmer SSTs may 

be relevant. 

6. Regional Processes
34

6.3. Future Precipitation 

 in the Southwest

All generations of CMIP model simulations agree 

on the basic response of the mean hydrological 

cycle to global warming. For example, Maloney 

et al. (2013) examined changes in precipitation 

patterns over North America based on CMIP5 

models and showed that the overall patterns of 

change are similar to CMIP3, with large scale 

increases in mid- to high-latitudes and decreases 

in the sub-tropics. This is the generic ‘wetget-wetter, dry-get-drier’ change in which the 

subtropics dry and the deep tropical and mid- to 

high-latitude regions get wetter. While Mexico fits 

within the ‘dry-get-drier’ region, the southwest 

US has, within the history of model simulations, 

always been at the poleward edge of the region of 

subtropical drying with areas of the US to the north 

projected to get wetter. One notable difference 

between the CMIP3 and CMIP5 results is that 

the boundary between these two regions has 

shifted southward slightly, resulting in increases 

in projected precipitation over parts of California 

(Neelin et al., 2013).

Figure 12 examines this in more detail at regional 

and seasonal scales using a larger set of CMIP5 

models (28) for 1900 to 2100. For the wintertime, 

the CMIP5 model projections of precipitation across 

the southwest vary in space and by season. 

In the mid-winter season, almost all of California, 

Nevada, Utah and Colorado are projected to 

have increased precipitation, although there is no 

consensus on the changes in the southern part of 

this region, while Mexico, most of Arizona, New 

Mexico and Texas have decreased precipitation. 

In the spring season, almost all regions of 

southwest North America are projected to dry 

except for Texas, and the models project a notable 

reduction of precipitation across California, 

Nevada, Colorado and Utah. Projections for the 

summer season changes in the southwest are 

quite weak and not consistent across models. 

For the fall season, the models project decreases 

in precipitation for California, Mexico and 

southern and western parts of Nevada, Arizona, 

New Mexico and Texas, but with weaker model 

agreement, while there are projected declines 

in precipitation across all of southwest North 

America with model agreement in the regions of 

strongest drying. 

When projected changes in precipitation minus 

surface evapotranspiration (P-ET, i.e. the net flux 

of water at the Earth’s land surface which sustains 

soil moisture and surface and subsurface flows) 

are examined, the boundary between drying to 

the south and wetting to the north is farther north 

than that for precipitation alone. This is because 

warming drives increased evapotranspiration so 

that more of what falls as precipitation is recycled 

to the atmosphere with less available to replenish 

soil moisture and streamflow. 

Winter precipitation is very important to water 

resources in the southwest US Winter precipitation 

is primarily delivered by storms propagating into 

6. Regional Processes

One notable difference between the 

CMIP3 and CMIP5 results is that the 

boundary between the projected ‘wetget-wetter’ and ‘dry-get-drier’ regions has 

shifted southward slightly, resulting in 

increases in projected precipitation over 

parts of California.

When projected changes in precipitation 

minus surface evapotranspiration (P-ET, 

i.e. the net flux of water at the Earth’s 

land surface which sustains soil moisture 

and surface and subsurface flows) are 

examined, the boundary between drying to 

the south and wetting to the north is farther 

north than that for precipitation alone.
35

Figure 12. Multimodel mean seasonal changes in P and P-E between 1979-2005 and 2021-2040 for the RCP8.5 

scenario based on 37 CMIP5 models. The stippling is where 3/4 or more of the model ensemble means agree with 

the sign of the multimodel mean change.

6. Regional Processes
36

6. Regional Processes

Figure 13. Evaluation of the simulation of a “warming hole” as a decadal climate variability signal in (a) CMIP3 

and (b) CMIP5 models. Thirty-year running temperature trends are calculated for each year from 1901 to 1975, 

i.e., a trend at 1901 represents the trend for 1901-1930, and similarly a trend at 1975 represent the trend for 

1975 to 2004. The length of the longest continuous negative temperature trend and the average negative 

temperature trend (oC/decade) during that period are shown (see Figure Key). In the model simulations the 

negative temperature trend can occur during any part of the 20th century and does not necessarily coincide 

with the observed negative temperature trend period. We use 66 historical climate simulations from 22 CMIP3 

models, and 92 simulations from 20 CMIP5 models. The CMIP3 models are: bccr_bcm2_0, cccma_cgcm3_1_t63, 

cnrm_cm3, csiro_mk3_0, csiro_mk3_5, gfdl_cm2_0, gfdl_cm2_1, giss_aom, giss_model_e_h, giss_model_e_r, 

iap_fgoals1_0_g, ingv_echam4, inmcm3_0, ipsl_cm4, miroc3_2_hires, miroc3_2_medres, mpi_echam5, 

mri_cgcm2_3_2a, ncar_ccsm3_0, ncar_pcm1, ukmo_hadcm3, and ukmo_hadgem1. The CMIP5 models are: 

CanESM2, CCSM4, CESM1-CAM5, CNRM-CM5, CSIRO-Mk3-6-0, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, 

GISS-E2-H, GISS-E2-R, HadGEM2-CC, HadGEM2-ES, inmcm4, IPSL-CM5A-LR, IPSL-CM5A-MR, MIROC5, 

MIROC-ESM, MPI-ESM-LR, MRI-CGCM3, NorESM1-M. All available ensemble members are considered 

individually. Trends are calculated at the grid cell and then averaged for the Eastern United States [30o-47o N, and 

80o-100o W , land only].
37

the region within the Pacific storm track, with 

the storms steered by the Pacific jet stream. In 

global warming simulations, the mid-latitude jet 

stream commonly shifts poleward and upward 

and strengthens. However, this rule-of-thumb 

description applies primarily to the changes 

averaged around latitude circles, while projected 

shifts also have important dependence on 

longitude and season. Over the eastern Pacific 

Ocean in winter, the strengthening of the Pacific jet 

stream in the current CMIP5 models occurs where 

the climatological storm track veers northward. 

Here models predict a northward shift of the storm 

track, as measured by upper troposphere eddy 

meridional velocity variance, but a southward shift 

of the jet stream (Neelin et al. 2013, Simpson et 

al. 2014, Seager et al. 2014). The winter wetting 

in central to northern California in the CMIP5 

models appears to be related not to a storm track 

shift but due to increased mean flow moisture 

convergence and a wet area getting wetter 

(Seager et al. 2014). This should not be viewed 

as contradicting previous CMIP3 model results, 

but as a local southward revision of the boundary 

between subtropical drying and mid-latitude 

precipitation increases. Despite model agreement 

in the CMIP5 ensemble, confidence in this change 

is not high because it depends on regional jet 

stream dynamics that is not yet fully understood. 

Longitudinally and seasonally varying changes 

in the jet stream and storm track location and 

intensity are important for regional hydroclimate 

projections and for determining the mechanisms 

responsible. Further assessment of fidelity of the 

model simulations of these features should be a 

research priority.

In summary, the boundary between winter wetting 

and drying in the southwest US is projected to be 

farther south in CMIP5 relative to CMIP3 results, 

although the changes are likely not statistically 

significant, highly dependent on the region 

and season, and the fidelity of the models in 

reproducing jet stream dynamics. In spring almost 

all regions are expected to dry, while summer 

changes are weak. The boundary between wetting 

and drying moves further northward when P-ET

is considered. Given that the CO2 sensitivity of 

CMIP5 models is similar to CMIP3 models (see 

section 2.1), the shift in the boundary between 

wetting and drying is likely to be related to the 

treatment of aerosols and differences in GHG

scenarios in CMIP5.

6.4. The US Warming Hole

Several studies have explored reasons for the 

warming hole (WH) – a regional cooling or lack of 

warming in the central and southeast US relative 

to the general warming nearly everywhere else. 

The identified mechanisms include connections 

with internal climate variability (Christidis et al., 

2010) and large-scale decadal variability such as 

the PDO and AMV (Robinson et al., 2002, Kunkel 

et al., 2006, Wang et al., 2009; Meehl et al., 2012; 

Weaver, 2013; Kumar et al., 2013a,b), regional 

scale hydrological processes (Pan et al., 2004), 

land surface interactions (Liang et at. 2006), 

secondary organic aerosols during the growing 

season (Portmann et al., 2009), and aerosol 

effects (Leibensperger et al., 2012). The WH

has lessened in intensity since the 1980s, which 

may be related to the reduction in aerosols since 

that time (Leibensperger et al., 2012), although a 

change in trend over just 30 years is unlikely to be 

statistically significant. 

Analysis of CMIP5 model simulations shows that 

they generally do not replicate the magnitude and 

timing of the WH, which was also found for the 

CMIP3 ensemble (Kunkel et al., 2006; Pan et al. 

6. Regional Processes

Analysis of CMIP5 model simulations 

shows that they generally do not replicate 

the magnitude and timing of the Warming 

Hole, which was also found for the 

CMIP3 ensemble.
38

2013). Figure 13 analyzes the WH as a decadal 

climate variability phenomenon in CMIP3 and 

CMIP5 simulations. All models show a negative 

30-year running trend over some part of the 20th

century in the eastern US, although most models 

underestimate the magnitude as well as the 

persistence (length) of the observed WH. The 

observed trend is contained within the CMIP5 

ensemble (but at the upper end) in contrast with 

the CMIP3 models, for which the observed trend 

falls outside of the ensemble. This suggests that 

the inclusion of non-greenhouse gas forcings in 

CMIP5, e.g. aerosols and land use change (a 

negative radiative forcing), have increased the 

probability of simulating long-term negative trends 

in temperature. A forced response would tend to 

coincide in time with the observed signal and a 

small fraction of the CMIP5 models do show some 

skill in the timing of the WH (Kumar et al., 2013b; 

Pan et al. 2013). However, this is not a robust 

response across models and further investigation 

is required to determine the role of forcings and 

the ability of the models to translate this into a 

response. Most CMIP5 model projections (95% 

range from individual simulations) do not show a 

WH in the first half of the 21st century for annual 

or JJA means under RCP4.5 and RCP8.5 (Kumar 

et al. 2013b, Pan et al. 2013, and Maloney et al. 

2014). While a WH does not emerge in the climate 

projections of the second half of the 21st century 

under RCP8.5, there is a slight chance (~10%) of 

a WH under RCP4.5 that may be related to CO2

stabilization after 2050 in this emission scenario 

(Kumar et al. 2013b; Maloney et al. 2014).

6.5. Extra-tropical cyclone

 activity

Future projections of North American storm track 

activity show a much more significant decrease 

in CMIP5 models for the RCP8.5 scenario than 

CMIP3 for SRES A2, with the largest decrease in 

summer and smallest decrease in spring (Chang, 

2013). While most models agree on the sign of 

the change, there is significant model-to-model 

spread, with the models projecting large storm track 

decreases also projecting negative precipitation 

changes over much of eastern North America and 

the southern U.S. Across the combined CMIP5 and 

CMIP3 ensemble, model-to-model differences in 

projected storm track change are found to correlate 

significantly with model-to-model differences 

in projected change in locally defined mean 

available potential energy (MAPE), suggesting that 

differences in the projected change in local MAPE

can partly account for not only model-to-model 

differences, but also differences between CMIP5 

and CMIP3 projections. 

Based on preliminary analyses by Edmund Chang 

(Stony Brook U.), part of the difference between 

the projected storm track change by CMIP3 and 

CMIP5 can be explained by differences between 

the projected change in meridional temperature 

gradient between the lower and higher latitudes 

(polar amplification), not only at the surface, but 

also in the mid-troposphere. CMIP5 models as 

an ensemble project a larger decrease in both 

the surface and mid-tropospheric temperature 

gradient than CMIP3 models. The reasons for the 

differences in gradients are unclear, but may be 

related to the more comprehensive treatment of 

aerosols in CMIP5 and/or different GHG forcing 

scenarios. It should be noted, however, that 

there is large spatial variability in the future extratropical cyclone changes. Many regions show 

future cyclone decreases as noted above, but 

in some areas of the storm track, the decreases 

are smaller or there are increases, for example 

over the northeastern US, as found by Colle et 

al. (2013). Some of this variability may be an 

artifact of how cyclones are defined and tracked 

(Chang, 2014).

6. Regional Processes

Future projections of North American storm 

track activity show a much more significant 

decrease in CMIP5 models for the RCP8.5 

scenario than CMIP3 for SRES A2.
39

7.1. Summary

Overall, the multi-model ensemble (MME) mean 

performance has not improved substantially 

in CMIP5 relative to CMIP3 for climatological 

variables (precipitation, sea surface temperature) 

over North American regions, except for a slight 

improvement for near surface air temperature over 

land. Some models have improved considerably, 

while others have surprisingly gotten worse. 

Projected changes in moderate to extreme 

precipitation events show a 20-30% increase 

over the US, which is similar to CMIP3, with 

much higher increases in the northeastern US, 

especially in winter. However, CMIP5 models 

tend to underestimate the frequency of heavy 

and extreme daily precipitation events, despite 

a slight improvement over CMIP3, especially in 

the southeastern US. Further work is required 

to understand the regional variability in model 

performance and the attribution of extreme events.

Generally, the CMIP5 models show better skill for 

basic attributes of ENSO with performance related 

to the mean SST state. It is unclear whether the 

representation of teleconnections with North 

American climate has improved. It is likely that the 

structure of Pacific Decadal Variability (PDV) as 

indexed by the PDO is slightly better simulated, 

albeit with larger biases in variability in the 

northwest Pacific. Teleconnections for precipitation 

also are improved slightly but remain poor 

overall. Atlantic Multidecadal Variability (AMV) 

as represented by the AMO is better represented 

in CMIP5 models in terms of decadal variability 

and persistence than CMIP3 models, but its SST 

footprint and teleconnections with North American 

climate are still poorly represented. The “warming 

hole” observed in the southeast US during the 

course of the 20th century is not replicated in 

the CMIP5 ensemble, and appears to be related 

to multi-decadal variability in the north Atlantic 

rather than a forced signal. Model projections 

show no warming hole in the future, although 

reduced warming is related to the negative phase 

of the AMO.

In southwest North America, projections of 

changes in precipitation from CMIP5 tend to be 

more robust overall than CMIP3 based on model 

consensus on the sign of the change. In particular, 

a more robust signal of summer drying in the 

Caribbean and southwest Mexico is evident in 

CMIP5 models. For the Southwest monsoon, 

previous studies of CMIP3 and CMIP5 models 

indicate a redistribution of Southwest monsoon 

rainfall with reduced early season amounts 

and increased late season amounts, although 

the latter is less robust because of large intermodel variability. New analysis based on CMIP5 

daily data shows a slight shift to a later onset 

of about 6-8 days but no late retreat date. The 

boundary between winter wetting and drying in 

the Southwest US is projected to move southward 

in CMIP5 relative to CMIP3 results, although 

the changes are highly dependent on the region 

and season, and the fidelity of the models in 

reproducing jet stream dynamics. In spring, almost 

all regions are expected to dry, whilst summer 

changes are weak. The boundary between 

wetting and drying moves farther northward 

when precipitation minus evapotranspiration is 

considered. The CMIP5 models project a more 

significant decrease in extra-tropical storm track 

activity than CMIP3 that may be related to a larger 

projected decrease in the temperature gradient 

between lower and higher latitudes.

7.2. Future Research Directions

This report provides an overview of current 

understanding of differences in CMIP3 and CMIP5 

model performance and projections, but by no 

means is comprehensive in terms of the literature 

reviewed and analyses carried out. 

7. Summary and Recommendations
40

A more comprehensive evaluation is needed, 

especially at regional scales and in terms of direct 

comparisons of CMIP3 and CMIP5 ensembles, 

for several climate features and processes. 

Furthermore, there is considerable scope for 

improving our understanding of the attribution 

of the differences, which may ultimately help 

understand errors in current models and therefore 

the robustness of future projections. There are 

also several aspects of the model evaluation 

process that need to be improved or better 

understood, in particular for extremes, including 

observational uncertainties and their impact on 

evaluations, scale mismatches between models 

and observations (e.g. King et al. 2013), and 

the impact of small sample sizes (e.g. single 

model ensemble member) to detect and compare 

extreme events.

In terms of specific climate processes, there 

are several avenues of future research that are 

recommended in the individual sections above, 

for example, in understanding the attribution 

of extreme events and the relationship with 

biases in larger scale climate processes. There 

are hints that increasing resolution appears to 

help (e.g. Wehner et al. 2010; Li et al., 2011; 

DeAngelis et al., 2013) but as the increases in 

model resolution from CMIP3 to CMIP5 have 

been modest and performance is influenced by 

changes in other factors, such as forcings and 

model parameterizations, conclusions on the 

impact of resolution are not possible. Further 

investigation is required to determine the impact 

of resolution and whether there is a limit to the 

increase in skill, which has been documented for 

some models as resolution approaches 30km, 

after which skill plateaus or even degrades, likely 

due to the breakdown of the assumptions in 

the convection parameterizations (Kinter et al., 

2013). Understanding the influence of increasing

resolution versus improved parameterizations 

through controlled experiments should be a 

research priority.

Differences between CMIP3 and CMIP5 projected 

changes for several aspects of climate may be 

related to the updated treatment of aerosols in 

CMIP5, given that the CO2 sensitivity of CMIP5 

models is similar to CMIP3 models. For example, 

the shift in the boundary between wetting and 

drying in the southwestern US and the projected 

decrease in extra-tropical storm track activity is 

possibly related to the treatment of aerosols and 

differences in GHG scenarios in CMIP5. However, 

this needs to be confirmed across a range of 

models – something that is not possible with the 

handful of CMIP5 models that have run the full set 

of forcing experiments. 

7. Summary and Recommendations

Future Research Directions:

- Regional-scale evaluation of 

 CMIP3/CMIP5 differences.

- Evaluation of model performance for

 extremes needs improvement taking into 

 account observational uncertainties, scale 

 mismatches, and small sample sizes.

- Further evaluation of the impact of 

 increased resolution and thresholds of 

 model parameterization performance 

 and suitability.

- Better understanding of the impact of new 

 GHG scenarios, aerosol treatments, and 

 enhanced land surface processes on 

 the projections.

- Continued review of model evaluations 

 based on historical simulations and 

 implications for uncertainty in 

 future projections.

- Comprehensive analyses including diverse 

 metrics and evaluations of means, 

 extremes, variability, and teleconnections.
41

Figure 14. Comparison of 17 CMIP5 models across a set of North American continental performance metrics 

based on bias values. (top) Biases normalized relative to the range of bias values across models, with lower values 

indicating lower bias. (bottom) Models ranked according to bias values, with 1 indicating the model with the lowest 

bias and 17 the model with the highest bias. Results for models without available data are indicated in white. The 

bias metrics shown (in order from left to right) are for regional precipitation (P) for DJF and JJA, regional temperature 

(T) for DJF and JJA, annual SSTs for surrouding oceans, annual runoff ratios (Q/P), the annual number of summer 

days (SuDays), frost days (FrDays) and growing season length (GSL), and east-west gradient in the number of 

persistent precipitation (P Persist) and soil moisture (SM Persist) events. (from Sheffield et al., 2013a).

7. Summary and Recommendations
42

The impact of vegetation on climate and their 

feedbacks have not been well evaluated, but 

may have important implications in the context 

of increasing CO2 that may contribute to CMIP3/

CMIP5 differences and the uncertainties in 

future projections Global coupled climate-carbon 

models have shown that the reduction in ET 

due to elevated atmospheric CO2 can reduce 

surface relative humidity, increase the depth of 

the atmospheric boundary layer, reduce low cloud 

cover and increase runoff (Sellers et al. 1996, 

Gedney et al. 2006, Betts et al. 2007). These 

physiological-response-induced land surface and 

cloud feedbacks can lead to a 10-20% increase 

in surface solar radiation and about 0.5-0.7K 

surface temperature increase over global land 

under the double CO2 concentrations (e.g. 

Boucher et al. 2008; Andrews et al. 2011; Pu and 

Dickinson 2014). These effects are particularly 

strong over the eastern US relative to global land 

(e.g., Doutriau-Boucher et al. 2009, Betts and 

Chiu 2010). The impact on precipitation is highly 

uncertain. For example, simulations based on 

the HadCM3LC model suggest a 3% decrease of 

rainfall over global land due to a drier atmospheric 

boundary layer (Andrews et al. 2011), whereas 

simulations of the CESM1.0.2 model suggest a 

0.03 mm/day increase of rainfall over global land 

due to enhanced moisture transport from the 

ocean as a result of stronger land warming (Ping 

and Dickinson 2013). A clearer understanding 

is therefore required of the impacts of plant 

physiological response to elevated atmospheric 

CO2 on the surface water and energy budgets, 

and its contribution to the observed and projected 

changes of temperature, humidity, precipitation 

and clouds, over vegetated land regions such as 

eastern US.

One aspect that has not been well evaluated 

is the performance of individual models across 

different climate processes and whether their 

skill has any bearing on the plausibility of the 

future projections. A limited analysis of model 

errors across different metrics was carried out 

in Sheffield et al. (2013a) for regional/seasonal 

precipitation and near surface air temperature, 

annual sea surface temperature, regional/annual 

runoff over precipitation, summer days/frost 

days, growing season length, and precipitation/

soil moisture persistence (Fig. 14). Sheffield et 

al. (2013a) note that no model stands out as 

being better than the others, although there are 

indications that higher resolution models tend 

to perform better for some climate features, 

especially for the regional features as expected, 

but not universally so and not for basic climate 

variables. The historical performance of a climate 

model depends on the variable examined and the 

metrics used, and may not necessarily give an 

indication of the robustness of its future projections 

(Glecker et al., 2008; Knutti and Sedláček, 2013). 

Further review is required on model evaluations 

and the relationship with future projections. A 

more comprehensive analysis is required across 

metrics, including representation of means, 

extremes, variability and teleconnections, 

and in the context of impacts of interest (e.g. 

regional precipitation). Potential benefits may be 

gleaned from analysis of whether an improved 

mean state leads to better representation of 

variability, for example in ENSO, and therefore to 

improved teleconnections and less uncertainty in 

future projections. 

7.3. Implications for Design and 

 Execution of CMIP6

The results of this report indicate a slight 

overall improvement in the representation of 

historic climate in CMIP5 relative to CMIP3 

and some aspects of regional climate change 

that are more robust in terms of model skill and 

model consensus. 

7. Summary and Recommendations
43

However, there remain many climate features 

that continue to be poorly represented (e.g. 

precipitation extremes and teleconnections) and 

large uncertainties in future projections overall 

and especially relative to their skill in replicating 

relevant processes. The next CMIP phase 

(CMIP6) is currently in the initial design phase 

(Meehl et al., 2014) and affords the opportunity 

to make progress towards understanding these 

ongoing issues through targeted experiments 

and analyses. The design of CMIP6 is set against 

the background of the World Climate Research 

Programme (WCRP) Grand Challenges that 

cover questions related to clouds, circulation and 

climate sensitivity; changes in the cryosphere; 

climate extremes; regional climate information; 

regional sea level rise; and water availability; plus 

an additional theme covering questions related 

to biospheric forcings and feedbacks. In addition 

to core experiments that provide continuity from 

previous CMIPs (such as AMIP type experiments; 

pre-industrial control runs; and climate sensitivity 

experiments), CMIP6 allows for the proposal of 

model intercomparison projects (MIPs) that target 

a specific process. Given the main results in this 

paper, the following experiments and targeted 

analyses may warrant exploration:

1. Climate Variability and Surface Climate 

Teleconnections. AMIP-type runs may help 

understand the performance of models, especially 

in the context of biases in the mean SST state and 

observed non-stationarities in teleconnections. 

For example, teleconnections of North American 

surface climate with ENSO are non-stationary, in 

part due to the influence of decadal variability in 

the north Pacific and Atlantic oceans (Kam et al., 

2014a,b). Proposed CMIP6 AMIP type runs are 

for 1979-2010, but longer-term runs (~100-years) 

would be necessary. Regionally coupled 

experiments would also have merit to isolate the 

influence of different ocean basins on land surface 

climate, for example by allowing full coupling in 

the ENSO region to determine the role of the north 

Pacific and north Atlantic Oceans.

2. Temperature and Precipitation Extremes.

Coordinated analysis of attribution of types 

of extreme events is required (e.g. to tropical 

cyclones, extra-tropical storms, convective activity, 

topography, weather types, atmospheric circulation 

versus local processes, moisture sources, ENSO, 

etc.). Coordinated intercomparisons on identified 

key processes in models perhaps complemented 

by experiments across sets of models related to 

model resolution versus physics parameterizations 

may help researchers understand biases. 

Larger ensembles will be useful to improve the 

robustness of calculated statistics of extremes. 

Issues related to observational uncertainties 

and scale mismatches need to be addressed, 

for example, through improved representation of 

scaling behavior of extremes.

Acknowledgments

This report was compiled the CMIP5 Task Force 

under the auspices of the National Oceanic and 

Atmospheric Administration (NOAA) Modeling, 

Analysis, Predictions and Projections (MAPP) 

program, which is managed by the Climate 

Program Office (CPO) in the Office of Oceanic 

and Atmospheric Research (OAR). The authors 

acknowledge financial support from the CPO for 

this work, and thank four reviewers who provided 

comprehensive and useful comments.

7. Summary and Recommendations
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NOAA Technical Report OAR CPO-2

Regional Climate Processes and Projections for North America: 

CMIP3/CMIP5 Differences, Attribution and Outstanding Issues

How to cite:

Sheffield, J., A. Barrett, D. Barrie, S.J. Camargo, E.K.M. Chang, B. Colle, D.N. Fernando, R. Fu, K.L. Geil, Q. 

Hu, X. Jiang, N. Johnson, K.B. Karnauskas, S.T. Kim, J. Kinter, S. Kumar, B. Langenbrunner, K. Lombardo, L.N. 

Long, E. Maloney, A. Mariotti, J.E. Meyerson, K.C. Mo, J.D. Neelin, S. Nigam, Z. Pan, T. Ren, A. Ruiz-Barradas, 

R. Seager, Y.L. Serra, A. Seth, D.-Z. Sun, J.M. Thibeault, J.C. Stroeve, C. Wang, S.-P. Xie, Z. Yang, L. Yin, J.-Y. 

Yu, T. Zhang, M. Zhao (2014), Regional Climate Processes and Projections for North America: CMIP3/CMIP5 

Differences, Attribution and Outstanding Issues, NOAA Technical Report OAR CPO-2