Climate Projections FAQ

United States Department of Agriculture / Forest Service

Rocky Mountain Research Station

General Technical Report RMRS-GTR-277WWW

April 2012

Amy E. Daniels, James F. Morrison, Linda A. Joyce, 

Nicholas L. Crookston, Shyh-Chin Chen, Steven G. McNulty
Daniels, A.E.; Morrison, J.F.; Joyce, L.A.; Crookston, N.L.; Chen, S.C.; McNulty, S.G. 2012. 

Climate projections FAQ. Gen. Tech. Rep. RMRS-GTR-277WWW. Fort Collins, CO: 

U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 32 p. 

Abstract

Climate scenarios offer one way to identify and examine the land management challenges 

posed by climate change. Selecting projections, however, requires careful consideration of 

the natural resources under study, and where and how they are sensitive to climate. Selection 

also depends on the robustness of different projections for the resources and geographic 

area of interest, and possibly on what climate projections are available for a region. Rather 

than a misguided attempt to identify the “most accurate” climate scenario, managers are 

strongly encouraged to explore variability through the use of multiple climate scenarios. 

Considering a range of possible future climates facilitates the identification of management strategies to help ensure resilience of natural resource systems across a broad set 

of potential conditions. Downscaling climate projections increases the spatial resolution of 

climate information and can make projections more relevant to natural resource managers 

by allowing decision-makers to better visualize what these different futures imply locally 

and regionally. The following series of questions describes key concepts that end-users 

of climate projection products should understand to appropriately interpret downscaled 

climate projections, including various sources of uncertainty. The selection used for each 

component of a downscaled climate projection has implications for interpreting the resulting climate scenario. Understanding the merits and limitations of the downscaling method 

employed is also important since downscaling approaches vary in their dependence on 

observed data availability, computational requirements, and in resultant uncertainty owed 

to biases of the method or the spatial scale of the downscaling.

Keywords: climate change, climate projections, downscaling, general circulation models, 

vulnerability assessment

Cover: Mean annual temperature (1961-1990) presented at different resolutions: 

500 km (311 mi) typical of a GCM grid cell; 50 km (31 mi) typical of RCM grids; and 

10 km (6 mi) and 1 km (0.6 mi), which represents some GCM downscaled outputs.

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Authors

Amy E. Daniels, USDA Forest Service, Research & Development, Washington, DC; 

adaniels02@fs.fed.us; 703-605-5251. 

James F. Morrison, USDA Forest Service, National Forest System, Northern Region, 

Missoula, MT.

Linda A. Joyce, USDA Forest Service, Research & Development, Rocky Mountain Research Station, Fort Collins, CO.

Nicholas L. Crookston, USDA Forest Service, Research & Development, Rocky Mountain 

Research Station, Moscow, ID.

Shyh-Chin Chen, USDA Forest Service, Research & Development, Pacific Southwest 

Research Station, Riverside, CA.

Steven G. McNulty, USDA Forest Service, Research & Development, Southern Research 

Station, Raleigh, NC.

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Acknowledgments

We thank the following technical reviewers for their helpful comments and suggestions: 

Imtiaz Rangwala (Institute of Marine and Coastal Sciences, Rutgers University), Jeremy 

Littell (CSES Climate Impacts Group, University of Washington), Michael Notaro (Center 

for Climate Research, University of Wisconsin-Madison), Jeffrey Lukas (Western Water 

Assessment, CIRES, University of Colorado and Joseph Barsugli (NOAA Earth System 

Research Laboratory and CIRES, University of Colorado). We thank Douglas Boyce, 

Leslie Brandt, Randy Johnson, Greg Kujawa and Borys Tkacz for their user’s reviews, 

as well as feedback from members of the Forest Service Adaptation Working Group during 

the April 2011 National Field Team Meeting in Baltimore, Maryland.
Foreword

The Forest Service is now engaged in an agency-wide effort to respond to climate 

change. As laid out in the National Roadmap for Responding to Climate Change, 

the Forest Service is assessing current risks, vulnerabilities, policies, and gaps 

in knowledge; engaging internal and external partners in seeking solutions; and 

managing for resilience in coupled human and natural systems through adaptation, 

mitigation, and sustainable consumption strategies. Each of the national forests and 

grasslands is involved in this effort, with support from Research Stations, Regional 

Offices, and National Programs.

Understanding current climate variability, as well as the range of plausible climate futures, is a cornerstone to this effort. The implications of climate change at 

local and regional scales are complex. Not only mean temperature, but changes in 

temperature extremes, length of growing season, plant moisture stress, and extreme 

events such as wind and heavy rainfall factor in to stress on resources and must be 

considered in developing adaptive strategies.

Responding effectively to climate change requires us to use the best available 

science to anticipate expected changes, and understand what they mean to resources 

and management alternatives. As Chief Tidwell has reminded us, “sound climate 

science is the foundation for an effective management response.” Using science 

to help us deal with change is not new to us. We are a science-based organization. 

The science behind climate change projections, however, is new to many of us, as 

is the exercise of scenario planning. Climate projections are typically developed 

at broad scales using a number of alternative general circulation models, driven by 

various socioeconomic scenarios. The output may then be “downscaled” to finer 

spatial resolution, using a variety of techniques, to develop more location-specific 

information. It is our hope that you will find this document helpful as you navigate 

this complex arena and develop or select projections that will be most useful in 

assessing vulnerability of the resources you manage and in crafting prudent management responses.

David A. Cleaves

USDA Forest Service Climate Change Advisor

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Preface

In the spring of 2010, the Forest Service Washington Office of Research & 

Development began to catalogue and organize metadata for all downscaled 

climate projection data holdings and uses within the Forest Service. The 

agency has over 140 datasets across 15 different groups. There is relatively 

little coordination or communication regarding dataset use and application. 

This effort highlighted considerable confusion about both the availability and 

use of downscaled climate projections. It also underscored the importance of 

beginning a coordinated national dialogue, as well as the need for educational 

guidance about climate projections applicable to multiple purposes. A team was 

formed in March 2011 to assess the availability and applicability of downscaled 

climate projections for Forest Service research and land management use. This 

document is an output from that assessment. 

Our main objective is to develop a shared understanding within the Forest Service 

of what climate projections are, their strengths and limitations, and to provide some 

guidance regarding how climate projections might be used for climate change impact analyses by Forest Service units and partner organizations. Decision-makers 

within the Forest Service should ask certain questions about the data and methods 

used to develop projected climate change maps and other decision-support products. 

This document intends to highlight and explain the key concepts that end-users of 

climate projection products should understand in order to appropriately interpret 

them. Recommending particular climate projections for specific regions, questions, or applications is beyond our scope. Such prescriptive guidance must be 

developed on a case-by-case basis. However, we reference a number of regional 

climate research centers that may prove helpful in this regard. 

This document is intended for a general Forest Service audience including planners, resource specialists, researchers, and line officers. We have organized the 

questions into three categories based on the anticipated degree of relevance for 

different sub-audiences. Because the intended audience is so broad, the text may 

not be ideally suited for any one group—likely being too technical in some places 

and too general in others. We strongly encourage readers to not only peruse the 

entire document for perspective, but to explore the many links and references made 

throughout for more information.

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Table of Contents

A. General forest service questions ........................................................................1

1. Why should the Forest Service begin a coordinated dialogue 

about the use of climate projections? .........................................................1

2. How might climate change projections be used in management, 

decision-making, and planning processes? ................................................2

3. What are the components of a climate projection? .....................................3

4. What is the rationale for using downscaled data? ......................................5

5. What are the main approaches for downscaling climate data 

from coarse-scale GCM output? .................................................................7

6. How, where and why is the Forest Service using downscaled climate 

projections? .................................................................................................8

7. How reliable are GCM-based climate projections? .....................................9

8. What are the main sources of uncertainty associated with 

climate projections? ....................................................................................9

9. What are the robust findings with respect to future 

climate projections? .................................................................................10

B. Management Questions ................................................................................... 11

10. What are the advantages and disadvantages of downscaling GCM 

projections? Do these vary by downscaling approach? ............................ 11

11. Is downscaled data always better for my application? .............................. 11

12. Where can I find guidance and information on climate projections 

available for my region? ............................................................................12

13. If more GCMs project a similar result for a given emissions 

scenario, does that indicate greater certainty and/or less error 

about the model’s outcome? .....................................................................13

14. What can I expect from the next generation of climate modeling 

work? Is it better to wait for the next generation of climate scenarios 

or begin to assess impacts with scenarios available at present? .............13

C. Research Questions .........................................................................................14

15. What are the assumptions of statistical downscaling? Of dynamical 

downscaling? ............................................................................................14

16. How do I select a downscaled climate dataset, from which 

climate model(s) and driven by which emissions scenarios? ...................15

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17. Where can I access output from model runs of different GCMs, 

particularly downscaled data? ...................................................................17

18. Where can I find guidance on developing or using downscaled 

climate projections for my research? ........................................................19

D. Literature Cited .................................................................................................20

E. Appendix A (Map of Forest Service’s National Forest System regions)............24

F. Appendix B (Forest Service research using downscaled climate projections) .25

G. Appendix C (Regional assessments using climate projection information) ......27

H. Appendix D (Forest Service downscaled climate data holdings) .....................28

I. Appendix E (Glossary of terms) .......................................................................30

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Acronyms

AR4 IPCC Fourth Assessment Report

BCSD Bias-corrected Statistical Downscaling

GCM General Circulation Model

GHG Greenhouse Gas 

IAM Integrated Assessment Model

IPCC Intergovernmental Panel on Climate Change

RCM Regional Climate Model

TAR IPCC Third Assessment Report

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USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 1

Climate Projections FAQ

Amy E. Daniels, James F. Morrison, Linda A. Joyce, 

Nicholas L. Crookston, Shyh-Chin Chen, and Steven G. McNulty

General Forest Service Questions

1. Why should the Forest Service begin a coordinated dialogue about 

the use of climate projections? 

Every time the Forest Service makes a natural resource management decision, 

e.g., deciding to plant a specific species from a specific seed source, or identifying 

priority restoration actions and locations, assumptions about future climate conditions 

are made. Often, the assumed or implicit climate scenario holds that conditions of 

the past century will continue through this century. Yet, this assumption is widely 

considered to be very unlikely given the projected changes in climate. 

Climate change vulnerability assessments and adaptation planning are greatly 

assisted by evaluating a range of plausible climate futures. These futures are 

represented through climate projections that are based on thoughtfully chosen 

management objectives and the social, ecological, and economic impacts and risk. 

Climate projections are valuable for considering the direction and magnitude of 

potential changes and prioritizing locations for adaptation actions. However, developing—or selecting from already-developed—climate projections involves many 

decisions. It requires transparent documentation of climate projection components, 

and clear communication of uncertainties, among other criteria for judicious use. 

Downscaling coarse climate projections to finer spatial resolution is increasingly 

used to better align the scale of projections with the scale of land management 

processes and decisions. This adds yet another dimension of complexity to impact 

assessments and a further need for a coordinated national dialogue in the Forest 

Service, and with its partners, regarding the opportunities and limitations of using 

such projections. 

Currently no published agency guidance is available to assist Forest Service units 

in determining the climate projection parameters, downscaling methods, and data 

sources that best meet their needs. Guidance would help ensure scientific credibility 

and appropriate use of climate projections within the Forest Service. A coordinated 

dialogue would allow the Forest Service to more effectively engage in on-going 

discussions with partners using climate projections for landscape-level vulnerability 

assessments. Also, a systematic, agency-wide framework for accessing, applying, 

and managing downscaled climate projection data would improve the efficiency 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 2

and consistency of agency investments while enhancing opportunities for long-term 

partnerships with research institutions, other agencies, and contractors. (back)

2. How might climate change projections be used in management, 

decision-making, and planning processes? 

Many management decisions in the Forest Service, such as identifying priority 

restoration actions and locations, are made using historical conditions as a reference. Changing climate may cause significant ecosystem changes regardless of 

management actions to direct the change. Using a historical reference in such cases 

reduces our ability to meet both the agency’s mission and stakeholders’ expectations. Climate scenarios offer a reasonable way to identify and examine the challenges posed by climate change; they provide a meaningful, tangible framework 

for considering management actions against a range of potential climate futures. 

Downscaling climate projections to a spatial resolution relevant to Forest Service 

management units allows decision-makers to better visualize what these different 

futures imply locally and regionally. 

Rather than impossibly attempting to identify the “most accurate” climate scenario, 

it is better to explore the maximum possible range of projected variability through 

the use of multiple climate scenarios. This approach promotes system resilience 

for a range of possible future conditions. Managers are encouraged to work with 

scientists and stakeholders to construct a climate scenario-planning framework in a 

thoughtful way that (a) brackets the upper and lower ends of the range of projected 

variability for a particular variable (e.g., temperature) or process of interest and 

(b) facilitates at least pairwise or multiple comparisons holding all but one climate 

projection component constant (e.g., constant emissions scenario and downscaling 

technique for multiple general circulation models (GCMs), or multiple emissions 

scenarios for a single GCM with a consistent downscaling approach). This facilitates identifying sources and ranges of variability in the model outputs, as well as 

exploring implications of various management options. 

Adaptive management and scenario planning present complementary frameworks 

for decision-making in the face of uncertainty. By considering the management 

options that are appropriate for a number of different scenarios, the Forest Service 

may begin to identify “no regret” strategies that facilitate ecosystem resilience for 

desired functions and services. Adaptation strategies that hedge against impacts 

common to a range of possible future conditions are more robust than traditional 

prescriptive management for a single outcome. By explicitly naming assumptions 

and desired management outcomes, and including monitoring regimes designed to 

track ecosystem trajectories, adaptive management provides a roadmap to navigate 

the many contingencies represented by a suite of future scenarios. Note that the 

spatial and temporal resolution of even the downscaled GCM data may, however, 

still be too coarse a resolution to accurately forecast the probability or frequency 

of extreme events (e.g. flooding and ice storms). (back)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 3

3. What are the components of a climate projection? 

Most climate change impact assessments rely on climate projections developed 

through coordinated international modeling efforts that have supported the Intergovernmental Panel on Climate Change’s (IPCC) role in assessing the science of 

climate change. This process entails a number of components and decision points 

in developing or using a given climate projection, each of which is represented 

as a box or oval in fig. 1. First, socioeconomic storylines are modeled to predict 

emissions that result from different assumptions about population growth, energy 

Figure 1—Conceptual illustration of the components and decision points 

associated with developing a climate projection, as per the IPCC process. 

Acronyms: GCM (general circulation model), RCM (regional climate model) 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 4

use, etc. Each resulting emissions scenario yields a particular trajectory of atmospheric concentrations of greenhouse gases (e.g., carbon dioxide) that make up 

important climate-driving forces. IPCC emissions scenarios have been broadly 

used by modeling centers around the world in the last decade (see Nakicenovic 

and others 2000 for details of emissions scenarios used for the last two IPCC assessments, TAR and AR4). Using these standardized, global emissions scenarios 

as drivers of GCMs has facilitated comparisons among the models’ responses to 

these future emissions (Le Treut and others 2007). Yet note that emissions scenarios 

are all considered equally likely (i.e., there is no built-in or implicit assumptions 

about which path global development will take).

The next component of a climate projection is the general circulation model

(fig. 2). The last IPCC assessment (AR4) rigorously reviewed the latest versions of 

twenty-three different GCMs (Meehl and others 2007). All have been studied and 

characterized in terms of their sensitivity to factors that drive climate, including 

atmospheric chemical composition and other physical parameters. The number of 

Figure 2—Schematic of a general circulation model (GCM) illustrating the horizontal and vertical 

dimensions of the model grid. Physical processes occurring within grid boxes are shown in the inset 

and are represented in the model through parameterization (source: NOAA 2008).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 5

possible parameter settings, emissions scenario and GCM pairings creates a significant number of alternative climate projections for exploring a range of potential 

climate futures. Output from GCMs includes many climate variables for both historical and future time periods for each cell in a grid that covers the globe. These 

direct GCM outputs can be used to derive additional climate variables. Finally, 

coarse GCM projections can also be downscaled to finer resolution, as discussed at 

length in questions below. This entails further decisions on downscaling techniques 

and baseline (observed) climatology.

Though not explored in detail here, other approaches (i.e. independent of the 

described IPCC process) may be used to explore the potential impacts of climate 

change on ecosystems. For example, a constructed suite of hypothetical temperature 

or precipitation surfaces may be input to an ecosystem process model to explore the 

range of outcomes associated with changes in climate variables on some process 

of interest (e.g., net primary productivity or wildland fire behavior). (back)

4. What is the rationale for using downscaled data? 

GCMs are the principal source of future climate projections. They are designed 

to evaluate the behavior of the global climate system and are relatively effective 

at simulating global climate characteristics like global temperature and broad 

circulation patterns. The coarse spatial resolution of GCMs used in AR4 ranges 

from 76 to 350 mi on a grid cell side at the equator (i.e. 5776 to 122,500 mi2

). 

Each modeled grid cell is homogenous—i.e., GCM output for a given variable 

is uniform within the cell). Yet spatial patterns of regional climate are far more 

heterogeneous than suggested by GCM output. The coarse spatial resolution 

severely limits the direct application of GCM output in regional and sub-regional 

analyses and decision-making. This limitation is particularly problematic in 

areas with diverse topography, land cover and drainage patterns, such as mountainous regions or by large lakes (e.g., Great Lakes). For example, projections 

of precipitation, snow dynamics, and runoff at the native GCM resolution are 

of limited use for evaluating regional and sub-regional hydrologic impacts. In 

contrast, the typical range of spatial resolutions achieved through downscaling 

is from 1/16 degree (≈ 3.5 mi over U.S. latitudes) to 1/2 degree (≈ 30 mi over 

the U.S. latitudes), typically constrained by the availability of observational 

data or the gridding model used to spatially interpolate such data. With temporal downscaling, climate projections in the form of monthly averages may 

be downscaled to daily outputs. Increasing spatial resolution increases land 

surface heterogeneity. For example, mean annual temperature is resolved with 

much greater detail with smaller grid cell sizes (fig. 3). 

Downscaling GCM-derived climate projections adds additional decisions, computational steps, and modeling assumptions. Downscaled results are, at best, only 

as realistic as the GCM’s ability to simulate climate dynamics of interest for a given 

scenario of driving forces. If wisely developed and used to address appropriate 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 6

Figure 3—Mean annual temperature (1961-1990) presented at different 

resolutions: 500 km (311 mi) typical of a GCM grid cell; 50 km (31 mi) 

typical of RCM grids; and 10 km (6 mi) and 1 km (0.6 mi), which represents 

some GCM downscaled outputs.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 7

climate change impact questions, downscaled climate projections allow for more 

detailed representations of potential changes associated with a particular climate 

scenario. Also, downscaled data are often better suited as inputs for ecosystem 

models and other important analyses compared with the unaltered spatial resolutions of GCM output, particularly regional and sub-regional impact analyses, 

vulnerability assessments, and adaptation planning. (back)

5. What are the main approaches for downscaling climate data from 

coarse-scale GCM output? 

There are two broad classes of methods for downscaling GCM simulations to 

finer spatial scales: statistical and dynamical.

• Statistical methods range from relatively simple to sophisticated. The simplest is the delta or change factor approach. Techniques in this approach may 

be as simple as applying average GCM-projected change from some future 

period (e.g., 2030-2040 monthly mean) to a finer-resolution interpolated 

grid of observed values from some baseline period (e.g., 1971-2000). The 

most common implementation of the delta method incorporates only changes 

in mean monthly temperature and precipitation and does not include daily 

data, even though these change factors may be applied to daily observed 

values. Another statistical downscaling method widely used in the United 

States known as the “bias-corrected spatial-disaggregation” (BCSD) approach. BCSD combines a bias-correction step that uses a quantile mapping 

technique, with a simple “disaggregation” of the broad scale changes to the 

downscaled grid to produce daily time series over the simulated period. Yet 

other statistical methods rely on coarse GCM outputs (e.g., atmospheric 

pressure fields) as statistical predictors of local climate variables (e.g., precipitation). A statistical method with increasing application is the “weather 

typing” approach based on spatial pattern matching with daily meteorological 

fields from a large library of observed meteorological patterns. Common to 

most statistical downscaling methods are spatial interpolation algorithms 

that transform unevenly distributed point data for observed climate into 

spatially-continuous surfaces. 

• Dynamical downscaling uses a regional climate model (RCM) embedded 

within a GCM to simulate regional climates. RCMs are physical process 

models with much finer spatial resolution than GCMs, typically ranging 

from 7.5 mi to 31 mi. As a result, RCMs effectively simulate the regional 

and subregional effects of topography, land cover, diurnal cycle, lake effects, 

and regional circulation patterns too fine for GCMs to resolve. For example, 

they better capture elevation-dependent precipitation patterns, and the effects 

of snow-albedo feedback on local temperatures (Salathé and others 2008). 

(back)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 8

6. How, where, and why is the Forest Service using downscaled climate 

projections? 

The Forest Service uses downscaled climate projections for both research and 

planning. At the national scale, the 2010 RPA Assessment uses a scenario approach 

to evaluate the future of natural resources on all ownerships of forest and rangelands over the next 50 years in relation to drivers of change such as climate change, 

population growth, and economic development as mandated by the Resources 

Planning Act of 1974. Regionally, Forest Service Regions 1 and 6 (see Appendix 

A for map of Forest Service regions) are using downscaled climate and hydrologic 

scenarios to conduct climate vulnerability assessments. Forest Service Region 9 

used downscaled data from the Wisconsin Initiative on Climate Change Impacts

(WICCI) to evaluate key forest ecosystem vulnerabilities to climate change for the 

Chequamegon-Nicolet National Forest and surrounding landscape (see Swanston 

and others 2011). Specifically, this vulnerability analysis modeled climate change 

effects on tree species dominance and habitat suitability. 

At the level of National Forests & Grasslands, downscaled climate scenarios have 

been used in the Watershed Vulnerability Assessment pilots on 11 units across the 

United States. Downscaled projections are also used to model tree species distributions under a range of anticipated climate futures through species envelope models 

in the northeastern part of the United States (Iverson and others 2010), and to better 

understand in situ forest dynamics like species composition and size through the 

tool Climate-FVS (Crookston and others 2010). TACCIMO is a web-based decision support tool for forest planning applications throughout the United States that 

relies on a relational database framework to integrate four kinds of inputs, including downscaled climate projections, to generate customized reports for assessing 

climate change impacts at the National Forest, state, and county scales. In some 

cases, the Forest Service is also directly engaged in developing downscaled data or 

in contracting its development for particular purposes (in contrast with downloading data already downscaled by others). Downscaled climate projections for the 

2010 RPA Assessment were developed in cooperation with the Canadian Forest 

Service (projections from three emissions scenarios by three GCMs, downscaled 

using a delta method to 1/12 degree resolution, see Coulson and others 2010). Forest Service Regions 1 and 6, together with the Pacific-Northwest Research Station, 

contracted the Climate Impacts Group at the University of Washington to analyze 

a suite of future climate projections and assembled an ensemble of climate models best-suited to simulate the regional climate system. They selected two models 

whose results bracketed the upper and lower bounds of variability projected by 

the ensemble. Finally, they downscaled projections from the ensemble and the two 

bracketing models to 1/16 degree using a delta method and used the projections in 

hydrologic simulations to analyze potential future hydrologic changes (Littell and 

others 2011). 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 9

As of spring 2011 Forest Service units collectively hold over 140 downscaled 

climate projections that vary by emissions scenario, by GCM, by geographic coverage, and by available climate variables (Appendix D). Some of these data were 

expressly downscaled by and/or for the agency, while others were accessed from 

one of the many online repositories mentioned in this FAQ. Forest Service uses 

these data in a number of research and management contexts. See Appendix B for 

research publications using downscaled climate projections. (back)

7. How reliable are GCM-based climate projections? 

The IPCC concluded that GCMs provide a credible range of quantitative estimates of future climate change, particularly at global and continental scales and 

over long time periods (Randall and others 2007). Extensive, rigorous multi-model 

intercomparisons underpin this conclusion. Over the many generations of climate 

models and across a range of emissions scenarios, models unanimously and unambiguously project warming over the next 2 decades in response to increasing 

atmospheric GHG concentrations. 

The scientific credibility of climate models and resulting projections hinges on 

several lines of evidence. First, climate models are consistent with well-understood 

physical processes and physical laws (e.g., conservation of energy and Newton’s laws 

of motion). Second, current-generation climate models demonstrate a significant 

and increasing ability to simulate recent and past climate dynamics (e.g., Reichler 

and Kim 2008). Third, extensive comparisons of multiple models reveal that over 

the past 2 decades different models have converged toward similar results (Reichler 

and Kim 2008). GCM projections include uncertainties and they represent some 

climate elements better than others. For example, confidence in the projections of 

temperatures is greater than for precipitation projections. As with all models, interpreting and applying results appropriately entails understanding models’ strengths 

and limitations. (back)

8. What are the main sources of uncertainty associated with climate 

projections? 

Uncertainty is the degree to which a value is unknown either from a lack of 

information, disagreement about what is known, or because the value may be unknowable (IPCC 2007). Climate projections, partly by design, embody a number 

of uncertainties. Sources include uncertainties about future emissions as driven by 

socioeconomic processes and influenced by currently unpredictable policy choices, 

variability internal to a given GCM’s simulation of weather and climate, variability across GCMs related to parameterization and other model characteristics, and 

uncertainty or error in observed climate data used in downscaling GCM output. 

The relative contributions of these different sources of uncertainty varies by output 

variable, by how far out into the future the projection is carried, by the geographic 

extent of the analysis, and by latitude (Hawkins and Sutton 2009).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 10

Some sources of uncertainty are quantifiable, particularly as climate observation 

networks improve. Others are not, such as what the cumulative global GHG emissions from human activity will be in 2050 or 2100. Nonetheless, uncertainty is not 

the same as incorrect information, nor does it mean we know too little to act. The 

suite of GCMs from AR4 reproduce the range of measured changes in temperature 

over the 20th century (fig. 4), illustrating that GCM-based climate projections 

for given future emissions scenarios are reliable. Decisions are routinely made in 

the context of military operations, monetary policy or financial investments—to 

name a few examples—where levels of uncertainty often exceed those of climate 

projections (Mabey and others 2011). Finally, uncertainty associated with climate 

projections is tempered by the fact that there is very little scientific confidence that 

average climate conditions of the last century will persist into the future (see next 

question). (back)

Figure 4—Global near-surface mean temperature anomalies (relative to the 1901 

to 1950 mean) observed for the 20th century (black) and that simulated by fourteen 

different GCMs driven by natural and anthropogenic factors (yellow). The simulated 

mean is shown in red. Key volcanic eruptions are identified with vertical lines (gray) 

and labeled accordingly (source: AR4 WG1 Ch 8 p. 600).

9. What are the robust findings with respect to future climate 

projections? 

Global GHG emissions will continue to grow, given current policies. For the 

next two decades, a range of climate scenarios projects 0.2 °C warming per decade 

(IPCC 2007a, b) and time series of annual average temperature for North America 

show substantial warming since the middle of the 20th century (Shein 2006). With 

continued warming, the fraction of GHGs remaining in the atmosphere increases. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 11

Even if emissions were stabilized, warming and sea level rise would continue for 

centuries owing to time lags in climate system feedbacks and also because once 

greenhouse gases are emitted, they remain in the atmosphere for decades to centuries (Solomon and others 2009). 

Projected changes for which the AR4 concluded with high or very high confidence 

include the following. 

• Warming is expected to be greatest over land and at high northern latitudes. 

• Precipitation is likely to increase at high latitudes but decrease for subtropical land regions. 

• Area covered by snow and sea ice will contract, while the thaw depth of 

permafrost increases for many regions. 

• Semi-arid and dry regions, particularly at mid-latitudes, will experience decreased water availability due to changes in precipitation, evapotranspiration 

and snow-melt dynamics. 

• At high latitudes and in the tropics, precipitation is likely to increase. 

• Changing patterns of wind, precipitation, and temperature are anticipated 

with projected poleward shifts of extra-tropical storm tracks. 

Building on these climate science findings, the IPCC (2007a) concludes that 

some ecosystems, sectors, and regions are likely to be especially affected. These 

include the ecosystems of the tundra, boreal forest, mountains, Mediterranean-type 

ecosystems, mangroves, salt marshes, coral reefs, and sea-ice biomes. Additional 

areas that are likely to be affected include low-lying coasts, water resources in some 

dry regions at mid-latitudes and in the dry tropics, and areas dependent upon snow 

and ice melt. Increased frequencies and intensities of extreme weather events—like 

heat waves and heavy precipitation—are likely to increase the impacts. (back)

Management Questions

10. What are the advantages and disadvantages of downscaling 

GCM projections? Do these vary by downscaling approach? 

Downscaling the output of GCMs aids with visualizing and understanding potential 

climate futures in relation to local and regional landscapes. Different downscaling 

techniques present unique advantages and disadvantages as outlined in Table 1. 

(back, also see question 15)

11. Is downscaled data always better for my application? 

Downscaling climate projections from the coarse resolution of GCM outputs 

certainly has its advantages (see questions 2, 4, 5). Finer scale climate scenarios

may greatly facilitate a meaningful dialogue about management options under 

different sets of future conditions by allowing managers to visualize those 

conditions in relation to landscape features relevant to their unit and region. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 12

Table 1—Advantages and disadvantages of different approaches of downscaling climate 

projections to finer resolution (from Benestad and others 2008; Fowler and others 

2007; Mearns and others 2003; and Wilby and others 2004). (back)

Advantages Disadvantages

 Statistical (including delta)

Techniques range from exceptionally 

simple to more elaborate, and are flexible 

enough to tailor models for specific 

purposes.

Computationally cheap, allowing for 

consideration of many different emissions 

scenarios and GCM pairings.

The same method can be applied across 

regions or entire globe. This can facilitate 

comparisons across applications or case 

studies.

Relies on observed climate as a basis 

for deriving future climate projections. 

Can locate and assess the strength 

of coupled patterns among climate 

parameters, allowing for analysis of 

historical data as well as results from 

dynamical downscaling. 

Relies on statistically sufficient density of 

observed data that may be unavailable 

for many areas or variables.

Assumes that change factors and model 

bias remain constant over time and 

space, and that coarse-to-fine-scale 

climate relationships under future conditions will remain the same as modeled.

The simplest methods may only provide 

projections at monthly resolution.

Cannot account for feedbacks in regional 

climate systems that change as climate 

changes.

Dynamical

Based on consistent, physical relationships and resolves atmospheric and 

surface processes occurring at subGCM-grid scale.

May better capture changes in interannual and inter-decadal variability, 

and in the magnitude and frequency of 

extreme events.

May be set up to capture regional climate feedbacks by allowing interaction 

between regional processes and the 

overlaying GCM-scale circulation for the 

area modeled.

Models climate system dynamics that are 

physically plausible but not necessarily 

part of historical/observed record.

If using a dynamic land surface, can 

model impacts to regional climate driven 

by regional landscape changes resulting 

from natural processes, anthropogenic 

activities, or even climate change adaptation itself (e.g. vegetation change). 

Computational requirements are often 

prohibitively intensive. Requires hightemporal resolution (e.g., 6-hourly) 

GCM output as input to regional model, 

which is not always archived for GCM 

model runs.

Due to computational demands, typically 

RCMs are driven by only 1 or 2 GCM/

emission scenario simulations, possibly 

under-sampling the range of likely future 

global climates.

Limited number of regional climate 

models available with no model results 

available for many parts of the globe at 

present.

May require further downscaling for 

some purposes.

If using static surface vegetation conditions to interact with overlying atmosphere does not capture regional landscape changes interaction with climate.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 13

Nonetheless, the finer resolution of downscaled data may inappropriately convey 

greater certainty or “accuracy” than the original, coarse GCM outputs, even though 

GCMs are optimized to simulate climate processes at their native resolution (Wilby 

and others 2004). Downscaling methods may actually add uncertainty and error 

that is difficult to quantify. 

Some researchers have explored tradeoffs in error created in the downscaling 

process versus GCM uncertainties at native resolutions (Klausmeyer and Shaw 

2009), demonstrating that it differs by climate variable and that further research 

is needed to understand scale-dependencies.1

 For many applications, knowing the 

sign of change (e.g., increase versus decrease in temperature) is enough to identify 

the potential impacts (e.g., increased heat stress, increased evaporative demand) 

and to define the range of possible management contingencies in the systems of 

interest. 

Finally, in some locations (e.g., flat terrain over large areas) observed spatial 

variability is small for some variables, like air temperature. Downscaling data 

for these regions significantly increases the computational time and data storage 

demands of climate projections used in some ecosystem assessments without any 

appreciable increase in the utility of projected impacts. (back)

12. Where can I find guidance and information on climate projections 

available for my region? 

Regional assessments of projected climatic trends and impacts are rapidly proliferating throughout the United States. One component of these assessments is a 

review of regional climate projections and the rationale for selecting the specific 

climate projections used in the assessment. This discussion may help National Forest 

units determine which climate projections are appropriate for their area. Leading 

sources include NOAA-funded Regional Impact and Science Assessment (“RISA”) 

groups. Also, some states have funded climate impact assessments through universities. More recently, Department of the Interior bureaus have initiated numerous 

regional assessments, and Landscape Conservation Cooperatives, that are compiling 

information on regional climate projections and impacts. In 2009, the U.S. Global 

Change Research Program completed a National Assessment of Climate Change 

Impacts that included regional information on projected climate changes and from 

2004-2009 released a number of synthesis and assessment products. Another periodic national climate assessment is underway currently. Nearly all of these impact 

assessment programs are sources of climate projection information for individual 

regions. Scientists from Forest Service Research Stations are a valuable resource 

1 “Understanding scale-dependencies” refers to understanding the way in which the scale (both 

the spatial and/or temporal resolution and the total extent of area or time period examined) affects 

our understanding of the process or value in question.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 14

for advice on acquiring and using regional climate projection data (see Appendices 

B and D). Appendix C details a number of regional climate impacts assessments 

that may inform planning and management decisions within Forest Service units. 

(back, also see question 17)

13. If more GCMs project a similar result for a given emissions 

scenario, does that indicate greater certainty and/or less error about 

the model’s outcome? 

In some respects convergence in projected outcomes across different climate 

models (holding the emissions scenario constant) may indicate a more refined and 

accurate representation of the underlying physical processes of the climate system. 

Confidence has increased through the advancing generations of GCMs as different 

modeling groups produce accurate simulations of observed climate processes and 

coarse circulation patterns. Indeed, the degree of model spread (the range of projected values across GCMs) is often assumed to provide some estimate of confidence 

for a particular climate system component or region of the world (Knutti 2008). 

However, models may share similar biases because of the way they are developed 

(Pirtle and others 2010) and, for good reason, since all are based on firmly rooted 

knowledge like conservation laws. Model independence across GCMs increases 

for components of the climate system where there is yet no consensus on the best 

approach, such as representation of cloud behavior in GCMs (IPCC 2001). An 

outlier scenario across a number of climate projections may be equally as likely 

or more likely than those representing central tendencies, because it may include 

processes or feedbacks not modeled in other GCMs. Excluding particular models 

for the purpose of understanding how their exclusion affects the range of climate 

projections is useful. In contrast, the indiscriminate exclusion of outlying climate 

values could negatively impact management’s preparedness for extreme events

and outcomes, which is a key goal in the context of land management. (back)

14. What can I expect from the next generation of climate modeling 

work? Is it better to wait for the next generation of climate scenarios 

or begin to assess impacts with scenarios available at present? 

Climate projections for past IPCC assessments were based on “what if” emissions 

scenarios to explore how the climate system might respond in the long term given 

different assumptions about global development (see question 2). One component of 

the next IPCC assessment, however, will include climate predictions out to 2030, as 

opposed to projections. Given that the greenhouse gases driving near-term climate 

change over the next 2 decades have already been released into the atmosphere, 

these predictions will use the current climate as a starting point to assess the actual 

climate system changes. Despite that this near-term “predictive” component of the 

next assessment may sound more certain, results are unlikely to narrow the range 

of projected climate changes. In fact, uncertainty may increase as on-going GCM 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 15

refinements improve the representation of ecological processes and include many 

more feedbacks that past model versions simply omitted. 

Climate change is occurring now and decision makers need not wait for output 

from the next generation of climate models (likely available in 2013) to begin 

adaptation and mitigation planning. The magnitude of climate change and the 

resulting impacts on ecosystems present major challenges that will require creativity and ingenuity to address. While relevant new information and data are being 

produced continuously, very little of the information generated in the past decade 

or so has been shown to be irrelevant (Randall and others 2007). As with most 

resource-management issues, analysts and decision makers must proceed without 

perfect knowledge or unequivocal confidence in available information about future 

conditions regardless of what generation of modeling their climate projections are 

based on (see question 7). (back)

Research Questions

15. What are the implicit assumptions of statistical downscaling? 

Of dynamical downscaling? 

Numerous assumptions are embedded in statistical downscaling, depending on 

the specific method. The delta method assumes changes in long-term averages can 

be applied uniformly to all events. For example, a GCM projection of +0.5 degrees 

in average March temperatures over a defined time period is applied to every observed March temperature in the baseline period by the same +0.5 degrees. Statistical downscaling assumes that GCMs provide a realistic simulation of predictor 

variables (in accordance with assumed socioeconomic driving forces for a given 

projection) and that a tight relationship exists between broad-scale predictors (e.g., 

atmospheric pressure fields) and the finer-scale climate variables they predict (e.g., 

precipitation). Statistical downscaling methods, using transfer functions based on 

comparison of local observations and GCM-scale simulations for the same historic 

period (e.g, 1971-2000), assume these relationships will remain the same over time. 

Numerous assumptions are also embedded in the various interpolation methods 

(not detailed here). 

Current methods of dynamical downscaling assume that large-scale atmospheric 

circulation drives fine-scale events within the RCM by specifying the variables at 

the boundaries of the RCM spatial domain to be those from a GCM. This approach 

is referred to as boundary forcing. Another assumption of the dynamical approach 

is that the many physical and empirical model parameters developed or tuned under 

current climate conditions will remain valid for the future climate. (back, also see 

question 10)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 16

16. How do I select a downscaled climate dataset, from which climate 

model(s) and driven by which emissions scenarios? 

Global simulation results from a suite of 23 GCMs used in the AR4 are archived 

and accessible online2

 (http://www-pcmdi.llnl.gov/ipcc/about_ipcc.php). These 

GCMs have been well-vetted in the scientific literature and share many basic 

features in how they characterize physical relationships of the climate system. 

However, models differ in many key respects including climate sensitivity (i.e. a 

model’s response to doubled atmospheric CO2); parameterization of sub-grid scale 

process (e.g., cloud formation and dissipation); treatment of land surface processes; 

and spatial resolution. Data archived for AR4 models include simulations of 20th

century climate, and select emissions scenarios (typically A1B, A2, and B1).

There is no single “best” downscaled dataset for all applications across regions or 

even within a single region. Exploring the greatest possible range of projected climate change is highly advisable in order to increase the range of impacts considered 

in planning for the future. Yet, in practice, analyzing results from every possible 

combination of climate model and emissions scenario would be time-consuming, 

expensive, and unwieldy (fig. 5). So a range of approaches have been used to select 

subsets of climate projections that maximize the range of projections to the extent 

practical, depending on the questions addressed in impacts studies. 

In selecting models, many analysts consider a GCM’s ability to simulate 20th century 

climatic conditions for the region of interest (e.g., multi-year averages, trends and 

seasonal patterns in precipitation and temperature). Research demonstrates some 

GCMs are better than others in replicating 20th century conditions of particular 

regions (Cai and others 2009). The most appropriate GCMs for the Pacific Northwest are different from those for the Southeast. And although a model’s ability to 

simulate the past is no guarantee of its ability to simulate the future, particularly at 

finer scales, this nonetheless serves as one criterion for distinguishing the relative 

performance of climate models. Also, impact analysts may consider how results 

from individual GCMs for the climate variable of interest compare with a multimodel ensemble mean.

A number of impact studies have based their analyses on a subset of projections 

that were chosen to bracket the full suite’s projected temperature and precipitation 

ranges (CWCB 2010), or use a multi-model mean with two or more bracketing 

GCMs where both approaches attempt to encompass a substantial portion of the 

range of results for the climate variable of interest among all available GCMs 

(Littell and others 2011). While a bracketing approach is helpful in exposing and 

considering some of the uncertainty inherent in GCM simulations, simplifying 

2 For some GCMs, multiple simulations of the same conditions—known as individual “realizations”—are available. The different “realizations” are useful for examining the natural internal 

climate variability represented in the model.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 17

the characterization of individual model projections as strictly “hot,” “dry,” etc. is 

difficult for large regions and particularly for different seasons in the same region. 

While the multi-model ensemble is suggested as the most-optimal approach for 

some applications (Pierce and others 2009), the assumption that a suite of different 

models provides statistically independent information evenly distributed around 

the true state may not be valid (Pennell and Reichler 2010). 

Impact analysts also face choices regarding which emissions scenarios to include 

in their analyses. The differences in cumulative emissions across different emissions 

scenarios do not begin to generate significant differences in projected climate until 

mid-century. So for questions focused on projected changes over the next three to 

four decades, the choice of emissions scenarios to consider is less important than 

the choice of GCMs when interested in the climate part of the projections alone 

(as opposed to also using demographic projections and other components of emissions scenarios for separate impact models as done in the 2010 RPA Assessment, 

(USFS 2012)). After mid-century, cumulative emissions from different scenarios 

and their respective influences on climate begin to diverge. As a result, for longerterm analyses (e.g., out to 2100), emissions scenario selection becomes a more 

important consideration in selecting and interpreting climate scenarios. In sum, 

Figure 5—Scatterplot of change in annual average temperature (°C) and precipitation (%) projected 

by different GCMs for the 2040s (2030-2059) in the Upper Missouri River Basin. Triangles represent 

GCM simulations of the B1 emissions scenario; circles = A1B; and squares = A2. Large bold symbols 

represent multi-model averages for each emissions scenario (source: Littell and other 2011).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 18

when evaluating which GCMs and emissions scenarios to use in developing a 

climate projection for regional impact analyses, it is helpful to consider the relative importance of mid-century and late-century projections to the decisions being 

made. (back)

17. Where can I access output from model runs of different GCMs, 

particularly downscaled data? 

Data from global simulations produced as part of IPCC 4th Assessment Report 

are available online from the Coupled Model Intercomparison Project of the World 

Climate Research Program, known as “CMIP3” data archive (http://www-pcmdi.

llnl.gov/ipcc/about_ipcc.php). This archive containing native-resolution GCM 

data, is intended to support research and advanced analyses, and is unlikely to be 

particularly useful to resource managers. 

• Santa Clara University and the U.S. Bureau of Reclamation (http://gdo-dcp.

ucllnl.org/downscaled_cmip3_projections/dcpInterface.html), in partnership 

with U.S. DOE Lawrence Livermore National Laboratory, has developed a 

public-access archive of statistically downscaled climate projections. The 

online archive includes downscaled versions of 112 climate projections, 

included in the CMIP3 multi-model dataset mentioned above. The BCSD 

downscaling has been applied to support multiple climate change impacts 

assessments on water resources in the western United States. Each downscaled projection dataset includes monthly temperature and precipitation, for 

1950-2099, at 1/8 degree spatial resolution, covering the contiguous United 

States. Conveniently formatted data are freely available at their website, which 

includes functions to support customized data requests (e.g., by variable, 

time period, geographic region). The site also includes data documentation, 

limitations, and a tutorial to orient new users.

• The Nature Conservancy, in collaboration with the University of Washington 

and the University of Southern Mississippi, has created the Climate Wizard, 

(http://www.climatewizard.org/) which enables technical and non-technical 

audiences to easily access online estimates of changes in average temperature 

and precipitation. The climate projections for the United States are the same 

as produced by the Santa Clara/Bureau of Reclamation website above, but 

provide some enhanced abilities for viewing and analyzing data, navigating 

the database, and downloading climate data into ArcGIS. 

• The CASCaDE Project (http://cascade.wr.usgs.gov/data/Task1-climate/

index.shtm) of the USGS maintains a website serving statistically downscaled data, using a constructed analogues method, minimum and maximum 

temperature, plus precipitation data over the conterminous United States. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 19

The website provides access to gridded historical data, simulations from two 

GCMs (GFDL CM2.1 and PCM1) driven by two emissions scenarios (B1 

and A2), downscaled to a 7.5 mi grid using the constructed analog method.

• The Climate Impacts Group at the University of Washington provides downscaled data from a number of AR4 GCMs via three different downscaling 

methods (hybrid delta, transient BCSD, and delta method) for two emissions 

scenarios (A1B and B1) for the Pacific Northwest region. A description of 

the available datasets and variables can be found on the Climate Impacts 

Group website (http://www.hydro.washington.edu/2860/new_users/).

• The Rocky Mountain Research Station Forest Sciences Laboratory (http://

forest.moscowfsl.wsu.edu/climate) in Moscow, Idaho, maintains delta method 

downscaled climate data covering all of North America. The website generates custom gridded or point-based estimates of monthly average, minimum, 

and maximum temperature, plus precipitation for the climate normal period 

of 1961-1990 and for three future 10-year periods: 2030, 2060, and 2090 

according to the projections of these TAR-generation GCM/scenario combinations: CGCM3 from the Canadian Center for Climate Modeling and 

Analysis, scenarios A1B, A2, and B1; HadCM3 is from the Hadley Center 

scenarios A2 and B2; GFDL CM2.1 is from the Geophysical Fluid Dynamics 

Laboratory, scenarios A2 and B1. No explicit grid resolution is associated 

with the data as they are based on spline surface techniques.

• The North American Regional Climate Change Assessment Program (http://

www.narccap.ucar.edu/index.html) makes available dynamically downscaled 

data covering the conterminous United States and most of Canada at 50 

km resolution for multiple pairings of RCMs and GCMs driven by the A2 

emissions scenario. Climate is projected for a mid-range future time period 

(2041-2070) and compared to a current period of 1971 to 2000. 

In addition, to these global and national online databases, regional or state level 

databases are also accessible online, including the following.

• The Scenarios Network for Alaska Planning (SNAP) maintains an online repository of downscaled climate projections covering Alaska. Datasets include 

projected changes in average monthly temperature and total precipitation, 

snow-water equivalent, and other derived data. Data sets are provided for 

five GCMs each driven by three emissions scenarios (B1, A1B, and A2), 

and a multi-model ensemble. The projections are statistically downscaled 

to a 1.2 mi grid using the basic delta method with bias correction. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 20

• Northeast Climate Change Impact Assessment climate data website (http://

www.northeastclimatedata.org/welcome_home.php?userID=38) contains 

downscaled climate projection data from three GCMs, each run with two 

emissions scenarios (A1FI and B1), and two downscaling methods (1/8 

degree gridded data produced via BCSD), and “city-level” downscaled data 

for six large cities in the Northeast based on simple regression analysis. 

• The State of Idaho also has an online repository of downscaled climate 

projection data for the western United States downscaled via modified BCSD 

approach and with a spatial resolution of 2.5 mi (http://inside.uidaho.edu/

index.html). 

• The RPA Scenarios used in the 2010 RPA Assessment used nine projection 

scenarios (A1B, A2 and B2 emissions scenarios, each for three GCMs) and 

a set of historical climate data available at the county or 1/12 degree scale. 

The resources listed above are not comprehensive. Several of the Department of 

Interior’s Landscape Conservation Cooperatives and their Climate Science Centers are actively pursuing development of additional web services for delivering 

downscaled climate data for some areas of the United States. Climate modeling 

centers in the United States (e.g., Geophysical Fluid Dynamics Laboratory and 

the National Center for Atmospheric Research) and around the world provide free 

access to data generated by their GCMs. (back, also see question 12)

18. Where can I find further guidance on developing or using 

downscaled climate projections for my research? 

A number of climate change adaptation guidebooks include sections addressing 

the use of climate models and derived data for climate change impact analyses 

(e.g., Barsugli and others 2009; Peterson and others 2011; Ray and others 2008; 

Stein and others 2011). These guidebooks provide a very approachable starting 

point for exploring how downscaled climate projections might be useful for your 

research. More detailed and technical guidance is available on the use of climate 

scenarios produced through dynamical modeling (Mearns and others 2003) and 

also for statistically downscaled climate projections (Wilby and others 2004). They 

review the nuances of different techniques within the two different approaches, 

along with detailed explanations of their respective assumptions, strengths, and 

limitations. These guidelines also consider the degree of value that is added through 

finer-resolution climate data for different goals and contexts. Pierce and others 

(2009) address the question of selecting a GCM for regional climate change studies, concluding the superiority of multi-model ensemble means over any single 

GCM for many types of regional applications. Knutti and others (2010) discuss the 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 21

challenges of interpreting ensemble means, and Pennell and Reichler (2010) raise 

questions about the effective number of GCMs within the ensemble. Yet, clearly 

using more than one climate projection is absolutely vital for capturing a range of 

plausible futures. 

The spatial scale (resolution and extent), geographic location (particularly latitude), and temporal domain (near-term versus long-term) are all important factors 

to explicitly consider as related to your research questions in order to find and 

interpret guidance on the use of climate projections. You might first consider the 

sensitivity of your research target—the system, species, or process of interest—to 

changes in mean climate conditions and to increasing climate variability. Has a 

particular climate variable, such as relative humidity, been identified as influential with respect to your research target? This step may help identify the climate 

variables and the most relevant driving forces that are most important for your 

study. Driving forces may include different demographic, land use, energy supply 

or macroeconomic assumptions evaluated by different emissions scenarios. 

Unless you are developing your own climate projections (i.e. selecting the 

GCM, emissions scenario, downscaling methods, etc.) you may be either aided or 

constrained by the climate projections already available for your region of interest. Availability of a downscaled variable at a particular temporal resolution key 

to the analysis at hand (e.g., daily temperature maximum) may play a key role 

in the selection of climate projections for practical reasons alone. The climate 

variables, and their derivatives, vary by downscaled dataset. Downscaled climate 

variables with high temporal resolution may be scarce. The database underlying 

the summary chart of downscaled climate data holdings within the Forest Service 

in Appendix D may assist you in locating readily available downscaled climate 

projections appropriate for your criteria. Also, regional climate research centers 

(see question 12) may publish relevant evaluations of the skill-level of different 

GCMs in simulating observed climate features for your study area. (back)

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resources management and adaptation. Boulder, CO: Western Water Assessment.52 pp.

Reichler, T.; Kim, J. 2008. How well do coupled models simulate today’s climate? BAMS 89:303-311.

Savonis, M.J.; Burkett, V.R.; Potter, J.R., eds. 2008. Impacts of climate change and variability on 

transportation systems and infrastructure: Golf Coast Study, Phase I. Synthesis and Assessment 

Product (SAP) 4.7. Washington, DC: U.S. Climate Change Science Program; Subcommittee on 

Global Change Research. 445 p.

Schmidli, J.; Goodess, C.M.; Frei, C.; [and others]. 2007. Statistical and dynamical downscaling 

of precipitation: An evaluation and comparison of scenarios for the European Alps. Journal of 

Geophysical Research. 112, D04105, doi:10.1029/2005JD007026.

Solomon, S.; Plattner, G.; Knutti, R.; Friedlingstein, P. 2009. Irreversible climate change due to 

carbon dioxide emissions. Proceedings of the National Academy of Sciences 106(6): 1704-1709. 

U.S. Forest Service. 2011. Navigating the climate change performance scorecard: A guide for 

national forests and grasslands (version 2, August 2011). Washington, DC: U.S. Department of 

Agriculture, Forest service. 104 p.

U.S. Forest Service. 2012. Future scenarios: A technical document supporting the Forest Service 

2010 RPA Assessment. Gen. Tech. Rep. RMRS-GTR-272. Fort Collins, CO: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain Research Station. 34 p.

Wilby, R.L.; Charles, S.P.; Zorita, E.; [and others]. 2004. Guidelines for use of climate scenarios 

developed from statistical downscaling methods. Geneva, Switzerland: Intergovernmental Panel 

on Climate Change.

(back)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 24

Appendix A: USDA Forest Service Regions. (back)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 25

Appendix B—Forest Service Research Publications using 

Downscaled Climate Projections (back)

Bachelet, D.; Lenihan, J.; Neilson, R.; [and others]. 2005. Simulating the response of natural 

ecosystems and their fire regimes to climatic variability in Alaska. Canadian Journal of Forest 

Research 35: 2244-2257. 

Bachelet, D.; Neilson, R.P.; Hickler, T.; [and others]. 2003. Simulating past and future dynamics of 

natural ecosystems in the United States. Global Biogeochemical Cycles 17(2)1045: 14-1 to 14-21.

Bentz, B.J.; Regniere, J.; Fettig, C.J.; [and others]. 2010. Climate change and bark beetles of the 

western United States and Canada: direct and indirect effects. BioScience 60: 602-613.

Campbell, J.L.; Ollinger, S.V.; Flerchinger, G.N.; [and others]. 2009. Past and projected future 

changes in snowpack and soil front at the Hubbard Brook Experimental Forest, New Hampshire, 

USA. Hydrological Processes 24(17): 2465-2480. 

Rehfeldt, G.E.; Ferguson, D.E.; Crookston, N.J. 2009. Aspen, climate and sudden decline in western 

USA. Forest Ecology and Management 258: 2353-2364.

Gonzalez, P.; Neilson, R.P.; Lenihan, J.M.; Drapek, R.J. 2010. Global patterns in the vulnerability 

of ecosystems to vegetation shifts due to climate change. Global Ecology and Biogeography 19: 

755-768.

Hellmann, J.J.; Nadelhoffer, K.J.; Iverson, L.R.; [and others]. 2010. Climate change impacts on 

terrestrial ecosystems in the Chicago region. Great Lakes Research 36: 74-85.

Iverson, L.; Prasad, A.; Mattews, S. 2008. Potential changes in suitable habitat for 134 tree species 

in northeastern United States. Mitigation and Adaptation Strategies for Global Change.

Iverson, L.R.; Prasad, A.M.; Matthews, S.N.; Peters, M. 2008. Estimating potential habitat for 134 

eastern US tree species under six climate scenarios. Forest Ecology and Management 254: 390-406.

Joyce, L.A. 2007. The Impacts of Climate Change on Forestry. Chapter 14. In: Adams, D.M.; 

Haynes, R.W. Resource and market projections for forest development: Twenty-five years’ experience with the US RPA Timber Assessment. Dordrecht, The Netherlands: Springer: 451-490.

Joyce, L.A.; Price, D.T.; McKenney, D.W.; [and others]. 2011. High resolution interpolation 

of climate scenarios for the conterminous USA and Alaska derived from General Circulation 

Model simulations. Gen. Tech. Rep. RMRS-GTR-263. Fort Collins, CO: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain Research Station.

Kerns, B.K.; Naylor, B.J.; Buonopane, M.; [and others]. 2009. Modeling tamarisk (Tamarix spp.) 

habitat and climate change effects in the Northwestern United States. Invasive Plant Science and 

Management 2(3): 200-215.

Lenihan, J. M.; Bachelet, D.; Neilson, R. P.; Drapek, R. 2008. Simulated response of conterminous 

United States ecosystems to climate change at different levels of fire suppression, CO2, and 

growth response to CO2. Global Planetary Change 64: 16-25. 

Lenihan, J. M.; Bachelet, D.; Neilson, R. P.; Drapek, R. 2008. Response of vegetation distribution, 

ecosystem productivity, and fire to climate change scenarios for California. Climatic Change. 

87 (Suppl 1): S215-S230.

Lenihan, J.M.; Drapek, R.J.; Bachelet, D.; Neilson, R.P. 2003. Climate changes effects on vegetation 

distribution, carbon, and fire in California. Ecological Applications 13(6):1667-1681.

McKelvey, K.S.; Copeland, J.P.; Schwartz, M.K.; [and others]. In press. Predicted effects of climate 

change on wolverine distribution and movement in western North America. Ecological Applications.

McKenzie, D.; Gedalof, Z.M.; Peterson, D.L.; Mote, P. 2004. Climatic change, wildfire, and 

conservation. Conservation Biology 18: 890-902.

Miller, C.; Abatzoglou, J.; Brown, T.; Syphard, A. 2011. Wilderness fire management in a changing 

environment. In: McKenzie, D.; Miller, C.; Falk, D.A., eds. The Landscape Ecology of Fire. 

Springer: 269-294.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 26

Mohan, J.; Cox, R.; Iverson, L. 2009. Northeastern forest composition and carbon dynamics in a 

future, warmer world. Canadian Journal of Forest Research 39: 213-230.

Prasad, A.; Iverson, L.; Matthews, S.; Peter, M. 2009. Atlases of tree and bird species habitats for 

current and future climates. Ecological Restoration 27: 260-263.

Price, D.T.; McKenney, D.W.; Joyce, L.A.; [and others]. 2011. High-resolution interpolation of 

climate scenarios for Canada derived from General Circulation Model simulations. Information 

Report NOR-X-421. Edmonton, Alberta, Canada: Natural Resources Canada, Canadian Forest 

Service, Northern Forestry Centre.

Rehfeldt, G.E.; Ferguson, D.E.; Crookston, N.L. 2009. Aspen, climate, and sudden decline in 

western USA. Forest Ecology and Management 258: 2353-2364.

Rehfeldt, G.E.; Crookston, N.L.; Warwell, M.V.; Evans, J.S. 2006. Empirical analyses of plantclimate relationships for the western United States. International Journal of Plant Science 167(6): 

1123-1150.

Roads, J.; Tripp, P.; Juang, H.; [and others]. 2010. NCEP/ECPC Monthly to Seasonal US Fire 

Danger Forecasts. International Journal of Wildland Fire 19: 399-414.

Rodenhouse, N.L.; Matthews, S.N.; McFarland, K.P.; [and others]. 2008. Potential effects of climate 

change on birds of the northeast. Mitigation and Adaptation Strategies for Global Change 13: 

517-540.

Westerling, A.L.; Turner, M.G.; Smithwick, E.A.H.; [and others]. 2011. Continued warming could 

transform Greater Yellowstone fire regimes by mid-21st Century. Proceedings of the National 

Academy of Sciences 32: 13165-13170.

Salathé, E.P.; Steed, R.; Mass, C.F.; Zahn, P. 2008. A high-resolution climate model for the U.S. 

Pacific Northwest: Mesoscale feedbacks and local responses to climate change. Journal of Climate 

21(21): 5708–5726, doi:10.1175/2008JCLI2090.1.

Shein, K.A., ed. 2006. State of the climate in 2005. Bulletin of the American Meteorological 

Society 87: S1-S102.

Sun, G.; Caldwell, P.; Noormets, A.; [and others]. 2011. Upscaling key ecosystem functions across 

the conterminous united states by a water-centric ecosystem model. Journal of Geophysical 

Research 116: doi:10.1029/2010JG001573 

Swanston, C. 2011. Ecosystem vulnerability assessment and synthesis: a report from the Climate 

Change Response Framework Project in northern Wisconsin. Gen. Tech. Rep. NRS-82. Newtown 

Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 142 p.

Wenger, S.J.; Luce, C.H.; Hamlet, A.F.; [and others]. 2010. Macroscale hydrologic modeling of ecologically relevant flow metrics. Water Resources Research 46: W09513, doi:10.1.1029/2009WR008839.

Wenger, S.J.; Isaak, D.; Luce, C.; [and others]. 2011. Flow regime, temperture, and biotic interactions drive differential declines of trout species under climate change. Proceedings of the National 

Academy of Science (PNAS) 108(34): 14175-14180.

Zacarías-Eslava, Y.; Del Castillo, R.F. 2010. Comunidades vegetales templadas de la Sierra Juárez, 

Oaxaca: pisos altitudinales y sus posibles implicaciones ante el cambio climático. Bol. Soc. Bot. 

Méx. 87:13-28. [In Spanish].

Zhang, C. H. Tian; Wang, Y.; Zeng, T.; Liu, Y. 2010. Predicting response of fuel load to future 

changes in climate and atmospheric composition in the Southern United States. Forest Ecology 

and Management 260(4): 556-564.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 27

Appendix C—Regional assessments using 

climate projection information (back)

Assessment Year

Spatial 

Domain

Spatial 

Resolution

Statistical 

Downscaling 

Method

Regional 

Climate 

Model

# of GCMs/

Emissions 

Scenarios

Multimodel 

ensemble

NE Climate 

Impacts 

Assessment/

Climate 

Change and 

Massachusetts 

Fish and Wildlife

2006/

2010

8 States NE 

U.S.

1/8° 

(~6.5mi)

BCSD CMM5 3/2 N

Climate Change 

in Colorado

2008 36-42°N – 

110-101°W 

1/8° (~7mi) Delta N 22/1 Y (22 

CMIP3 

models)

California 

Climate 

Scenarios

2007 California 1/8° (~7mi) BCSD N 3/2 N

Wisconsin’s 

Changing 

Climate

2011 Wisconsin 5mi N 14/3 Y

Lake Tahoe 

Basin 

Assessment

2010 Lake Tahoe 

Basin

1/8° (~7mi) Constructed 

Analogs 

N 1/2 N

Climate Change 

in Prince 

George

2009 Northcentral 

British 

Columbia

28 mi & 

GCM scale

N CRCM4 1/1, 22/3 N

ChequamegonNicolet 

National Forest 

Ecosystem 

Vulnerability 

Assessment 

and Synthesis

In 

prep.

Northern 

Wisconsin

1/10

GCM scale N N

general 

climate

3/2 (tree 

species 

distribution 

model)

N

Evaluating 

Sustainability 

of Projected 

Water Demands 

Under Future 

Climate Change 

Scenarios

2010 Contiguous 

US

1/8° (~7mi) BCSD N 16/1 Y

Connecting 

Alaska 

Landscapes into 

the Future

2010 Alaska 1.2 mi Delta N 5/3 N

Washington 

Climate 

Change Impact 

Assessment

2010 Washington/

Columbia 

Basin

1/16° 

(~3.5mi)

Delta & 

BCSDmodified

(see 

below)

20/2 Y

Washington 

Climate 

Change Impact 

Assessment

2010 Washington/

Columbia 

Basin

22.4 mi & 

12.4 mi

(see above) CCSM3-

WRF and 

ECHAM5-

WRF

2/1 N
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 28

Appendix D Part I. (back)

2 3 9 3

Other USFS Downscaled GCM Data

by Model + Emissions Scenario (updated 2/28/11)

Group Model Equilibrium

Sensitivity (°C)2

total per 

GCM

national only 

per GCM 

CSIRO_MK2 n/a US

HadCM3 n/a US

CCMA_CGCM2 n/a US

INGV_ECHAM4 n/a HI

Ensemble

10 Model 

Ensemble Mean* n/a PNW & 

N.Rockies(3)

CSIRO_MK3.5 n/a US US(2) US(2)

GISS_AOM n/a HI

CNRM_CM3 n/a US US US

BCCR_BCM2.0 n/a

PNW & 

N.Rockies

Scenario Total 

National Total 2 3 3 3 11

*(BCCR_BCM2.0, CNRM_CM3, CSIRO_MK3.5, MPI_ECHAM5, ECHO_G, UKMO_HADCM3, UKMO_HADGEM1, MIROC3.2_HIRES, MIROC3.2_MEDRES, NCAR_PCM1)

Emissions Scenarios3Downscaled Runs

Pre-AR4 Models

Increasing Cum. Emissions

Unpublished 

Sensitivity

17

1 0

3 3

1 0

5 5

3 0

1 0

1 1

1 1

1 1

B1 B2 A1B A2 A1FI
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 29

Appendix D Part II. (back)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 30

Appendix E: Glossary of Key Terms.

accuracy

A measure of how close a quantity is to its actual, true value. In relation 

to future climate projections, it is typically inappropriate to discuss the 

“accuracy” of climate projections as used for IPCC assessments to date. 

This is because the emissions scenarios that drive such projections vary in 

their assumptions about socioeconomic development; are not assigned a 

degree of likelihood; and by-design, are intended to explore how climate 

might vary along different development storylines.

climate change impacts assessment

A process to identify and evaluate the effects of climate change on natural 

and human systems, as well as the product(s) from that process (adapted 

from Savonis and others 2008).

climate change impacts

The effects of climate change on coupled natural and human systems 

(adapted from Savonis and others 2008).

climate projection 

A quantified response of the climate system, based on GCM simulations, 

to a particular scenario of climate driving forces. Projections are distinguished from predictions in this context since the former are designed 

to explore “what if” scenarios as opposed to intending to predict actual 

values for the future climate. 

climate (change) scenario

A plausible and often simplified representation of the future climate, based 

on an internally consistent set of known principles about the climate system 

and a given set of assumptions about climate driving forces. A ‘climate 

change scenario’ is the difference between a climate scenario and the current climate (adapted from USFS 2011).

emissions scenario

A plausible representation of the future emissions of radiatively active 

substances in the atmosphere, like greenhouse gases and aerosols. These 

scenarios are based on coherent and internally consistent assumptions 

about driving forces such as demographic and socioeconomic development, technological change, etc. To date, the IPCC has developed two 

sets of emissions scenarios: the IS92, used for the Second Assessment, 

and the SRES scenarios used for the TAR and AR4. GCM-based climate 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 31

simulations for the AR5 will be based on “representative concentration 

pathways” (RCPs). Trajectories of emissions concentrations will drive 

the models without regard to the societal and natural forces that result 

in those emissions. In parallel, scenarios will be developed to see what 

socioeconomic development paths result in emissions concentrations corresponding to RCPs. 

ensemble

A group of parallel model simulations used for climate projections. Variation of the results across the GCMs in an ensemble is one approach to 

estimating uncertainty. Ensembles composed of a number of simulations 

from the same GCM but with different starting conditions characterize 

the uncertainty owed to internal climate variability. Ensembles composed 

of many different GCMs characterize uncertainty associated with GCM 

differences.

extreme event

An event such as a flood or drought that is rare within its statistical reference distribution at a particular place. Definitions of “rare” vary, but an 

extreme weather event would normally be as rare as, or rarer than, the 10th 

or 90th percentile. Extreme weather events include floods and droughts 

(adapted from Christensen and others 2007).

general circulation model (GCM)

A numerical representation of the climate system based on the physical, 

chemical, and biological properties of its components, and their interactions and feedback processes. The climate system can be represented by 

models of varying complexity. Coupled atmosphere/ocean/sea-ice general 

circulation models (GCM, also known as AOGCMs) provide a comprehensive representation of the climate system (adapted from Savonis and 

others 2008).

parameterization

Accounts for processes occurring at a finer-scale than a given climate 

model’s grid cell size can represent. Parameterizations are based on empirical relationships and are applied universally to all grid cells. Parameterization schemes are improved by running GCMs many times where 

everything is held constant except small tweaks in the parameter settings 

(adapted from Barsugli et al. 2009). 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-277WWW. 2012 32

regional climate model (RCM)

A model covering typically a continent, or a subregion thereof, capable 

of resolving fine-scale climate processes and effects of land use, topography, etc. In dynamical downscaling, climate variables from a GCM are 

input at the spatial boundaries of an RCM (adapted from Christensen and 

others 2007).

uncertainty

An expression of the degree to which a value (e.g., the future state of the 

climate system) is unknown. Uncertainty can result from lack of information or from disagreement about what is known or even knowable. Uncertainty arises from many sources—from quantifiable errors in the data, to 

ambiguously defined concepts or terminology, or uncertain projections of 

human behavior. Uncertainty can be represented by quantitative measures 

(e.g., a range of values calculated by various GCMs) or by qualitative 

statements (e.g., reflecting the judgment of a team of experts) (adapted 

from USFS 2011).

vulnerability 

The degree to which a system, species or resource is susceptible to, and 

unable to cope with, adverse effects of stressors, including climate variability and extremes. Vulnerability to climate change is a function of the 

character, magnitude, and rate of climate change and variation to which a 

system is exposed, its sensitivity, and its adaptive capacity (adapted from

USFS 2011).
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