Joyce, Linda A.; Price, David T.; Coulson, David P.; McKenney, Daniel W.; Siltanen, R. Martin; 

Papadopol, Pia; Lawrence, Kevin. 2014. Projecting climate change in the United States: 

A technical document supporting the Forest Service RPA 2010 Assessment. Gen. Tech. 

Rep. RMRS-GTR-320. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 85 p.

ABSTRACT: A set of climate change projections for the United States was developed for use in the 2010 

USDA Forest Service RPA Assessment. These climate projections, along with projections for population 

dynamics, economic growth, and land use change in the United States, comprise the RPA scenarios 

and are used in the RPA Assessment to project future renewable resource conditions 50 years into 

the future. This report describes the development of the historical and projected climate data set. The 

climate variables are monthly total precipitation in millimeters (mm), monthly mean daily maximum air 

temperature in degrees Celsius (°C), and monthly mean daily minimum air temperature in degrees 

Celsius (°C). Downscaled climate data were developed for the period 2001-2100 at the 5-arcminute grid 

scale (approximately 9.3 km by 7.1 km grid size at 40 degree N) for the conterminous United States. 

These data were also summarized at the U.S. county level. Computed monthly mean daily potential 

evapotranspiration (mm) and mean grid cell elevation in meters (m) are also included in the data set. 

The scenarios used here from the IPCC Special Report on Emissions Scenarios are A1B, A2, and B2. 

The A1B and A2 scenarios were used to drive three climate models: the Third Generation Coupled 

Global Climate Model, version 3.1, medium resolution; the Climate System Model, Mark 3.5 (T63); and 

the Model for Interdisciplinary Research on Climate, version 3.2, (T42), all used in the Fourth IPCC 

Assessment. The B2 scenario was used to drive three earlier generation climate models: the Second 

Generation Coupled Global Climate Model, version 2, medium resolution; the Climate System Model, 

Mark 2; and the UKMO Hadley Centre Coupled Model, version 3, all used in the IPCC Third Assessment. 

Monthly change factors were developed from global climate model output using the delta method. The 

coarse-resolution change factors were downscaled to a 5-arcminute resolution grid using ANUSPLIN. 

The 30-year mean historical climatology (1961-1990) was developed using the Parameter-elevation 

Regressions on Independent Slopes Model (PRISM) data at 2.5-arcminute resolution and aggregated 

to the 5-arcminute resolution grid. The downscaled change factors were combined with the PRISM 

observed climatology to develop nine future climate projections for the conterminous United States. 

These projection data and the change factor data are available through the U.S. Forest Service data 

archive website (http://www.fs.usda.gov/rds/archive/).

Keywords: Climate scenario, downscaling, PRISM, natural renewable resource assessments, RPA 

Scenarios.

Authors

Linda A. Joyce, U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, 

Fort Collins, Colorado.

David T. Price, Northern Forestry Centre, Natural Resources Canada, Canadian Forest Service, 

Edmonton, Alberta.

David P. Coulson, U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, 

Fort Collins, Colorado.

Daniel W. McKenney, Great Lakes Forestry Centre, Natural Resources Canada, Canadian Forest 

Service, Sault Ste. Marie, Ontario.

R. Martin Siltanen, Northern Forestry Centre, Natural Resources Canada, Canadian Forest Service, 

Edmonton, Alberta.

Pia Papadopol, Great Lakes Forestry Centre, Natural Resources Canada, Canadian Forest Service, 

Sault Ste. Marie, Ontario.

Kevin Lawrence, Great Lakes Forestry Centre, Natural Resources Canada, Canadian Forest Service, 

Sault Ste. Marie, Ontario.
Acknowledgments

We acknowledge the modeling groups, the Program for Climate Model Diagnosis and 

Intercomparison, and the World Climate Research Programme’s (WCRP) Working Group 

on Coupled Modelling for their roles in contributing and willingness to share the WCRP 

Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset through 

the Program for Climate Model Diagnosis and Intercomparison web-based data portal. 

Support of this dataset is provided by the Office of Science, U.S. Department of Energy. 

Further, we greatly appreciate the availability of data provided by: 

The Canadian Centre for Climate Modelling and Analysis

The Australian Commonwealth Scientific and Industrial Research Organisation, particularly M. Collier, M. Dix, and T. Hirst of the Marine and Atmospheric Research Division 

In Japan, the Center for Climate System Research, together with the University of Tokyo, 

the National Institute for Environmental Studies, and the Frontier Research Center for 

Global Change.

The Hadley Centre for Climate Prediction and Research, UK.

We acknowledge the use of PRISM (Parameter-elevation Regressions on Independent 

Slopes Model) climate mapping system, developed by Chris Daly, PRISM group director, 

and colleagues for the characterization of historical climate. We also acknowledge the 

support of the USDA Forest Service and Natural Resources Canada, Canadian Forest 

Service. We greatly appreciate the time and effort by reviewers for technical review: David 

Wear, Michael Bowker, Ashley Askew, Matt Reeves, Jim Morrison, Jeff Lukas, Tom Brown; 

for statistical review, Scott Baggett; for editorial review, Lane Eskew and Loa Collins.

You may order additional copies of this publication by sending your 

mailing information in label form through one of the following 

media. Please specify the publication title and number.

Publishing Services

Telephone (970) 498-1392

FAX (970) 498-1122

E-mail rschneider@fs.fed.us

Web site http://www.fs.fed.us/rmrs

Mailing Address Publications Distribution

Rocky Mountain Research Station

240 West Prospect Road

Fort Collins, CO 80526
Contents

Introduction...................................................................................................1

Linking Population And Economic Drivers With Global Climate.............2

Methods.........................................................................................................6

Selection of Climate Models..........................................................................6

RPA Scenarios......................................................................................6

Climate Models and Output ..................................................................6

Downscaling Global Climate Model Projection Data .....................................9

Downscaling Methods...........................................................................9

Delta Method and Spatial Interpolation of the Change Factors 

using ANUSPLIN ........................................................................12

Development of the RPA Climate Projections for the 

Conterminous United States.......................................................14

Development of the Observed Historical (1961-1990) Climate 

Data Set using PRISM................................................................14

Development of Projections using Change Factors and

Observed Historical Climatology ................................................15

County-Level Summarization..............................................................17

Analysis of Climate Projections ...................................................................17

Spatial and Temporal Patterns of Temperature and 

Precipitation for the Conterminous United States.......................18

Regional Patterns in Annual and Seasonal Climate Projections ........19

North Central

Great

Plains

Intermountain

Pacific

Northwest

Northeast

NORTH

SOUTH

Southeast

Pacific

Southwest

South Central

ROCKY MOUNTAIN

PACIFIC COAST
Results.........................................................................................................21

Mean Temperature and Total Precipitation Projections for the 2060 

Period for the Conterminous United States ....................................21

Mean Temperature and Total Precipitation Projections for the 

2071-2100 Period for the Conterminous United States..................28

Regional Patterns in Annual and Seasonal Climate Projections .................33

Southeast............................................................................................33

Great Plains ........................................................................................38

Colorado .............................................................................................42

Pacific Northwest ................................................................................48

Northeast ............................................................................................52

Developing Composite Indices with the Downscaled Climate 

Projection Data: Aridity ...................................................................56

County-level Projection Data.......................................................................59

Discussion ..................................................................................................60

Model Robustness.......................................................................................60

Climate Sensitivity...............................................................................61

Model Agreement with Observations Globally and Regionally ...........63

Model Agreement with Observations Regionally ................................66

Use of the Climate Projection Data .............................................................67

Change Factor Data............................................................................67

Climate Projection Data ......................................................................68

Availability of Climate Projection Data ................................................69

Summary And Conclusions.......................................................................71

References ..................................................................................................72

Appendix I: Carbon Dioxide Concentrations and Nitrogen 

Emissions associated with the Scenarios ...................................80

Appendix II: CMIP3 Run Numbers for Individual Climate 

Model Projections...........................................................................83

Appendix III: Potential Evaporation Estimation Procedure....................84

Appendix IV: Area-Weighting the Climate Data .......................................85

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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

Introduction _________________________________________

Climate influences the long-term dynamics of forests and rangelands across the United States 

and the production of renewable natural resources from these public and private lands. Human 

communities across the United States are dependent upon ecosystem services from forests 

and rangelands. Such services range from clean water for human consumption to commodity products such as timber to biodiversity of plants and animals. Increasingly, the potential 

effects of climate change have become a concern for resource managers.

The Resource Planning Act (RPA) Assessment produced by the USDA Forest Service provides a snapshot of current U.S. forest and rangeland conditions and trends on all ownerships, 

identifies drivers of change, and projects 50 years into the future (see USDA Forest Service 

2012a). This Assessment is produced every 10 years as required by the Forest and Rangeland 

Renewable Resources Planning Act (RPA) of 1974. In addition, the Act specifically requires 

an analysis of the potential effects of climate change on U.S. forests and rangelands (Joyce 

and Birdsey 2000). The 2010 RPA Assessment uses a scenario-based approach that integrates 

the individual resource analyses with particular emphasis on links to alternative world economic outlooks, population growth, and associated climate change (USDA Forest Service 

2012a,b). Such an approach allows the RPA assessment to analyze a range of possible futures, 

including climate change, for U.S. renewable resources.

Criteria for selecting scenarios to be used in the RPA Assessment specified that the scenarios 

be globally consistent, scientifically credible and well documented, and include key driving 

forces of resource change such as population, economic growth, land use change, energy use, 

and climate (USDA Forest Service 2012a). The scenarios used in Intergovernmental Panel 

on Climate Change (IPCC) Third and Fourth Assessment reports met these criteria, particularly in the areas of documentation and data availability (IPCC 2001a, 2007a). Described 

in the Special Report on Emissions Scenarios (SRES) (Nakicenovic and Swart 2000), these 

scenarios integrate socioeconomic driving forces as well as climate change. Three SRES scenarios (A1B, A2, and B2) were identified that would provide the RPA Assessment with a wide 

range of possible futures (USDA Forest Service 2012a). Socioeconomic and climate data were 

available at the global and macro-regional level from the IPCC scenario-based projections; 

however, RPA resource analyses are typically conducted using U.S. county or finer scale data. 

Procedures used to develop national and sub-national projections of population, economic 

growth, income, bioenergy use, and land use change can be found in Gen. Tech. Rep. RMRSGTR-272 (USDA Forest Service 2012a). Development of the finer-spatial-scale projections of 

climate is the focus of this report.

The requirement of a nationally consistent set of scenario-based climate projections at the 

county spatial scale necessitated a process to retrieve and downscale global climate model 

output to the scale of 5-arcminutes for the conterminous United States and Alaska. This task 

was undertaken through a cooperative effort between the Canadian Forest Service and the 

USDA Forest Service (Price and others 2011a; Joyce and others 2011; this General Technical 

Report). We use the downscaling methods of Price and others (2011a) and Joyce and 

others (2011) and the historical gridded climatology based on PRISM (Parameter-elevation 

Regressions on Independent Slopes Model) to provide a suite of future climate scenarios for 

the conterminous United States for use in the RPA Assessment. In this report, we describe 
2

USDA Forest Service Gen. Tech. Rep. RMRS-GTR-320. 2014

the downscaling approach and development of the climate projection data at the 5-arcminute 

resolution. We present a series of graphics to describe and interpret the climate projections 

for the conterminous United States. These data are publicly available through the U.S. Forest 

Service archive website (http://www.fs.usda.gov/rds/archive/) and are described using internationally accepted standards for metadata documentation (Coulson and Joyce 2010a,b; Coulson 

and others 2010a,b,c,d; Price and others 2011b,c). These climate data are being used in the 

RPA analyses of forest condition, wildlife habitat, water yield/use, recreation participation, 

and effects of natural amenities on rural population migration (e.g., Bowker and others 2012; 

Cordell and others 2011; Foti and others 2012; Greenfield and Nowak 2013; Wear and others 

2013). The intended audiences for this report are those individuals who are interested in modeling the ecological and socio-economic effects of climate change and need climate projection data. These data may also be useful input for other applications exploring the impact of 

climate change on resource management issues.

Linking Population and Economic Drivers With Global 

Climate _____________________________________________

Briefly, the process of constructing alternative world futures undertaken by the IPCC and the 

climate modeling community requires integrating the socioeconomic global driving forces; 

estimating greenhouse gas emissions resulting from these forces as well as the effects of 

the emissions on atmospheric chemistry; and finally determining the effects of that changing atmospheric chemistry on the global climate (Figure 1). Global driving forces include 

Figure 1. Process used by the global climate science community to develop socioeconomic global driving forces and to quantify the effects of those forces to future 

greenhouse gas emissions and future climates for the Intergovernmental Panel on 

Climate Change.
3

Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

demographic shifts, technological changes, energy development, economic interactions, and 

environmental considerations associated with land use changes. Storylines describing trends 

in these future global forces were developed in the Special Report on Emission Scenarios 

(SRES) for the Third IPCC Assessment and were also used in the Fourth IPCC Assessment 

(Nakicenovic and Swart 2000). For example, the A1 storyline describes a future world of very 

rapid economic growth, low population growth, and rapid introduction of new and more efficient technologies (Table 1). Within the A1 storyline, three sub-storylines focus on alternative 

directions of technological change in the energy sector. The A1B storyline reflects a balanced 

future use of fossil fuel and non-fossil energy sources. The A2 storyline describes the future 

world as very heterogeneous, where economic development is regionally oriented and though 

fertility rates vary, global population growth is relatively high. The B2 storyline envisions a 

world where emphasis is on more local solutions to achieve economic, social, and environmental sustainability, and economic growth is intermediate. These storylines do not include 

policies to limit greenhouse gases or implement adaptation strategies. No single storyline is 

considered more or less likely than another, and all include aspects that may be considered 

desirable or undesirable.

Table 1—Summary characteristics of the four storylines developed by the Special Report on Emissions 

(modified from Parry and others [2007]).

SRES

storyline World Economy Population Governance Technology

A1 Market-oriented Fastest per 2050 peak, Strong regional Three sub-storylines: 

 capita growth then decline interactions, income A1FI – fossil intensive

 convergence A1T – non-fossil energy sources

 A1B – balanced across all sources

A2 Differentiated Regionally Continuously Self-reliance with Slowest and most

 oriented; increasing preservation of local fragmented development

 lowest per identities 

 capita growth 

B1 Convergent Service and Same as A1 Global solutions to Clean and resource-efficient

 information economic, social and

 based; lower environmental

 growth than A1 sustainability

B2 Local solutions Intermediate Continuously Local and regional More rapid than A2, less

 growth increasing at solutions to rapid, more diverse than A1/B1

 lower rate environmental

 than A2 protection and social

 equity
4

USDA Forest Service Gen. Tech. Rep. RMRS-GTR-320. 2014

The qualitative storylines were then used to develop global emission scenarios (Nakicenovic 

and Swart 2000). Population and economic trends were quantified and six integrated assessment models (IAMs) were used to estimate the global greenhouse gas emissions that would 

result from these socioeconomic driving forces over the next 100 years. Marker scenarios 

were identified as results from specific IAMs that were illustrative of a particular storyline 

(Figure 2). The integrated assessment models estimated, for each scenario, the total amounts 

(metric tons) of greenhouse gases emitted per year into the atmosphere at 10-year intervals 

(http://www.ipcc-data.org/ddc_co2.html). These emissions are the total amounts of greenhouse gases that would result from future global economic activity and population growth. 

In order to determine the impact of these emission levels on climate, the quantities in total 

metric tons emitted (output from the IAMs) must be converted to atmospheric concentrations 

through time.

Emissions of carbon dioxide (CO2) gas enter the global carbon cycle where carbon is cycled 

through plants, soil, and bedrock within the terrestrial ecosystem, through water, plants, ocean 

bottom within ocean ecosystems, and through the atmosphere. Activities such as deforestation and fossil fuel combustion return CO2 to the atmosphere. Once emitted into the atmosphere, CO2 and other greenhouse gases interact with atmospheric chemistry, the vegetated 

land surface, and other components of the global carbon cycle. Those dynamics determine 

Figure 2. Solid lines are multi-model global averages of 

surface warming (relative to 1980-1999) for the SRES 

scenarios A2, A1B, and B1, shown as continuations of the 

20th century simulation. The orange line is for the experiment where concentrations were held constant at year 

2000 values. To the right of the graph, the vertical colored 

bars indicate the best estimate of surface warming (solid 

black line within each bar) and the likely range assessed 

for the six SRES marker scenarios at 2090-2099 relative 

to 1980-1999. The assessment of the best estimate and 

likely ranges includes results from the Atmosphere-Ocean 

General Circulation Models (AOGCMs) shown in the left 

part of the figure, as well as results from a hierarchy of 

independent models and observational constraints (Figure 

3.2 Synthesis Report AR4, downloaded January 8, 2013).
5

Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

the atmospheric concentration of the gases over time. Two different carbon cycle models 

were used to project future CO2 concentrations based on the SRES scenarios (Figure 1): the 

Integrated Science Assessment Model (ISAM) and the Bern Carbon Cycle model (Bern-CC). 

Though simplified, these carbon cycle models contain ocean and terrestrial ecosystem feedbacks to the atmosphere consistent with more process-based models and allow for uncertainties in climate sensitivity and in ocean and terrestrial responses to CO2 and climate (Prentice 

and others 2001). Atmospheric CO2 concentrations associated with the SRES scenarios A1B, 

A2, and B2—based on output from the Bern-CC and ISAM models—are given in Appendix I. 

Nitrogen oxide (NOx) emissions were also computed in the integrated assessment models 

(http://www.ipcc-data.org/ddc_co2.html) and are given in Appendix I. In contrast to CO2

,

which is a well-mixed gas globally and one atmospheric concentration is reasonably representative of conditions around the entire globe, regional concentrations of nitrogen gases 

vary. Nitrogen gases have been spatially distributed across the global grid for the A2 scenario 

(Lamarque and others 2005).

Global climate models use these projections of atmospheric concentrations of CO2 and other 

greenhouse gases to estimate their effects on global climate (Meehl and others 2007a; see 

also http://www.ipcc-data.org/ddc_co2.html). The published results of these analyses are 

synthesized in IPCC reports (most recently, IPCC 2007a). Storylines influence global emissions (Table 1) and, consequently, the global climate. For example, the B1 scenario (Table 1) 

assumes a future where economic growth is low and there is emphasis on clean and resourceefficient energy technology. The projected global average surface warming based on several 

models using the B1 scenario is 1.8 oC by the 2090-2099 period (relative to 1980-1999, IPCC 

2007a). In contrast, when the high per-capita growth and fossil intensive energy technology 

of the A1FI scenario is analyzed, a surface warming of 4.0 oC is projected for the 2090-2099 

period. The exploration of these scenarios with several climate models shows the range of 

projected future temperatures (Figure 2). Projected global temperatures do not differ appreciably among the emissions scenarios until after 2030 (Figure 2), which indicates that the world 

is already committed to warming of about 1.0 oC, relative to the 1990s, regardless of any 

mitigation efforts. This commitment is caused by the thermal lag of the oceans, which will 

continue to absorb extra heat for several decades because of the additional greenhouse gases 

already present in the atmosphere.

The future climates projected by the climate modeling groups vary among scenarios and 

climate models. All models are based on physical principles, and implementation of those 

principles is nuanced by understandings of the climate modelers and their experience projecting climate dynamics. Climate models depict the global climate using a three-dimensional 

grid over the globe, typically having a horizontal resolution of between 250 and 600 km, 10 

to 20 vertical layers in the atmosphere, and in some cases, as many as 30 layers in the ocean. 

Given the horizontal dimensions of the grid cells, each grid cell typically covers a vast area. 

The results from these models are generated at a scale too coarse for assessing U.S. climate 

change effects in the RPA Assessment, where the spatial scale of analysis is typically the size 

of a U.S. county or smaller. Hence, there is the need to downscale the climate projections to 

the spatial scales used for the RPA Assessment, namely the U.S. counties and the 5-arcminute 

grid (approximately 9.3 km north-south by 7.1 km east-west at 40 degrees N, approximately 

at mid-conterminous United States).
6

USDA Forest Service Gen. Tech. Rep. RMRS-GTR-320. 2014

Methods ____________________________________________

Selection of Climate Models

RPA Scenarios

The requirement of a diverse set of population and economic futures for the RPA Assessment 

led to the initial choice of SRES scenarios A1T, A1FI, A2, and B2 (see Table 1 for the varying 

population and economic assumptions) (USDA Forest Service 2012a). The protocols for 

climate experiments and archival procedures varied between the Third IPCC Assessment 

(TAR) and the Fourth IPCC Assessment (AR4). For the TAR, climate model projection data 

from varying experiments that modeling groups across the world conducted were archived 

through the Data Distribution Center (DDC) of the IPCC. In contrast, the climate model community agreed to structure a consistent series of experiments to explore climate dynamics for 

the AR4 report (Meehl and others 2007a). The archiving process, overseen by the Program 

for Climate Model Diagnosis and Intercomparison (PCMDI), provided a standardization 

of terms and output across models. However, the final set of modeling experiments for the 

AR4 included only the SRES scenarios A1B, A2, and B1 (Meehl and others 2007a). Thus, no 

climate projections for A1T, A1FI, and B2 scenarios were archived through PCMDI (http://

www-pcmdi.llnl.gov/ipcc/data_status_tables.htm). Identifying a priority to use the AR4 model 

results from PCMDI, the RPA process selected the A1B scenario in place of the A1T and A1FI 

scenarios. Projections in the AR4 associated with A2 were available from the PCMDI web 

portal, and we turned to archived projections for the B2 scenario from the TAR at the IPCC 

data portal. Hence, the RPA Scenarios are based on the SRES scenarios of A1B, A2, and B2 

(USDA Forest Service 2012a,b).

Climate Models and Output

For each of the three scenarios (A1B, A2, and B2), output from three climate models was 

used to develop the downscaled projections (Table 2). For scenarios A1B and A2, the same 

three models were selected: the Third Generation Coupled Global Climate Model, version 

3.1, medium resolution [CGCM3.1(T47)]; the Climate System Model, Mark 3.5 [CSIROMK3.5(T63)]; and the Model for Interdisciplinary Research on Climate, version 3.2, medium 

resolution [MIROC3.2(T42)]. For the B2 scenario, the three models selected were the Second 

Generation Coupled Global Climate Model, version 2, medium resolution [CGCM2]; the 

Climate System Model, Mark 2 [CSIRO-MK2]; and the Hadley Centre Coupled Model, 

version 3 [HadCM3] (Table 2). The CGCM and CSIRO models were used in all scenarios, 

however the versions used for the B2 scenario are associated with the TAR, and are separated 

by several years of model development from the versions used the AR4 report (see Discussion 

section for more information on these models).

Scenarios A1B and A2. The climate models were chosen for emissions scenarios A1B 

and A2 based on the availability of projections in the PCMDI Third Coupled Model 

Intercomparison Project (CMIP3) database at the time this study started, and the availability 

of variables needed for the RPA Assessment, and climate assessments being done in Canada (see 

Discussion section for the broader set of scenarios/models available now). Output data (projections from the climate models) were obtained primarily from the CMIP3 data portal, which 
7

Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

offers standardization of format, variable names, units, and other aspects facilitating comparisons among models (Table 3). In addition to output for A1B and A2 projections, climate 

model output for the 20th century simulations (20C3M) were also obtained. The 20th century 

simulations (1961-1990) were used to normalize the global climate projections. Global projections can be biased; combining the normalized values with observed climatological data 

at the scale of interest corrects the bias in the global projections. Variables included monthly 

mean daily minimum air temperature (at 2 m above surface), monthly mean daily maximum 

air temperature (at 2 m above surface), and monthly total precipitation (Table 4). The CMIP3 

catalogue numbers of the individual model runs (also called realizations) associated with the 

AR4 models used in this study are given in Appendix II. This information can be important 

when comparing climate projections used in this study with other climate projections of the 

same models.

Scenario B2. Three climate models using the B2 scenario in the TAR were chosen from the 

suite of climate models that had been downscaled by Price and others (2004) using the same 

approach as for the AR4 scenarios by Price and others (2011a) and this study. The downscaling of these B2 scenarios was part of the VINCERA (Vulnerability and Impacts of North 

American Forests to Climate Change: Ecosystem Response and Adaptation) project (Price 

and others 2004; Price and Scott 2006; Bachelet and others 2008; Lenihan and others 2008). 

Table 2—Climate models used in the 2010 RPA Assessment.

Climate Models

Scenarios Coupled Global Climate Climate System Model Hadley Centre Model Model for Interdisciplinary

Model (CGCM) Research on Climate

 (MIROC)

B2 Second General Climate System Hadley Centre

Coupled Global Climate Model, Mark 2, Coupled Model

Model, version 2, [CSIRO-MK2] version 3

medium resolution [HadCM3]

(T47) [CGCM2]

A1B and A2 Third Generation Climate System Model for Interdisciplinary

Coupled Global Climate Model, Mark 3.5 (T63) Research on Climate, 

Model, version 3.1, [CSIRO-MK3.5 (T63)] version 3.2, medium

medium resolution resolution

(T47) [CGCM3.1(T47)] [MIROC3.2(T42)]

Climate Canadian Centre for Commonwealth Hadley Centre for Japanese Center for 

Modeling Climate Modelling and Scientific and Climate Prediction Climate System Research,

Research Analysis, Canada Industrial Research and Research, UK University of Tokyo; 

Centers Organization National Institute for

(CSIRO), Australia Environmental Studies, and

Frontier Research Center

for Global Change, Japan
8

USDA Forest Service Gen. Tech. Rep. RMRS-GTR-320. 2014 Table 3—Climate model output data for the historical simulation for the 20th century (20C3M) from period 1961-2000 and the scenarios A1B and A2 from period 2001-2100 (used in the Intergovernmental Panel on Climate Change Fourth Assessment report) were obtained from the WCRP Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset and obtained through the Program for Climate Model Diagnosis and Intercomparison (PCMDI) web-based data portal. Climate model version, modeling center Nominal cell size Source of the model data CGCM3.1(T47), Third Generation Coupled Global Climate 3.75 degrees CMIP3 data in PCMDI portal - Majority of the climate Model, version 3.1, medium resolution (T47), developed by latitude by 3.75 model/scenario/variable data were obtained from the CMIP3 the Canadian Centre for Climate Modelling and Analysis degrees longitude data portal. https://esg.llnl.gov:8443/index.jsp http://www.cccma.bc.ec.gc.ca/models/cgcm3.shtml at the equator CCCma data portal for CGCM3 model data (http://www.cccma.bc.ec.gc.ca/data/cgcm3/cgcm3.shtml). [Daily tmin/tmax data as CMIP3 did not have these data for 

the CGCM3 model.]

CSIRO-MK3.5, Climate System Model, Mark 3.5 (T63), 1.875 degrees CMIP3 data in PCMDI portaldeveloped by Commonwealth Scientific and Industrial latitude by 1.865Research Organization (CSIRO), Australia, degrees longitudehttp://www.cmar.csiro.au/e-print/open/gordon_2002a.pdf at the equatorhttp://www-pcmdi.llnl.gov/ipcc/model_documentation/CSIRO-Mk3.5.htmMIROC3.2(T42), Model for Interdisciplinary Research on Climate, 2.79 degrees CMIP3 data in PCMDI portalversion 3.2, medium resolution (T42), developed by the Japanese latitude by 2.81Center for Climate System Research, University of Tokyo; National degrees longitudeInstitute for Environmental Studies, and Frontier Research Center for at the equatorGlobal Change, http://www.ccsr.u-tokyo.ac.jp/kyosei/hasumi/MIROC/tech-repo.pdf
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

Price and others (2004) obtained output data for the 1961-2100 period for three models from 

the IPCC Data Distribution Centre data portal (Table 5). These Third Assessment models are 

several years older than the models used for scenarios A1B and A2 (see Discussion section). 

Historical simulation realizations for the 20th century were also obtained and the 1961-1990 

period was used to normalize the global projections. The data sets included three monthly 

climate variables: monthly mean daily maximum and minimum temperature, and monthly 

total precipitation (Price and others 2004). For CGCM2, multiple ensemble runs had been performed with different initializations, and only results for the second run were used (Price and 

others 2004).

Downscaling Global Climate Model Projection Data

Downscaling Methods

The development of downscaled climate projection data is an active area of research to meet 

the needs of the climate impacts analysis community by providing finer-scale climate change 

projections. Two main approaches to downscaling differ in their complexity (IPCC-TGICA 

2007). The more sophisticated approaches include dynamical and statistical downscaling. 

Table 4—Variables in the climate model data sets obtained from the Program for Climate Model 

Diagnosis and Intercomparison data portal used to create the downscaled climate projections 

in this study. The CMIP3 catalogue numbers of the individual model runs (also called 

realizations) associated with the AR4 models used in this study are given in Appendix II.

Climate modela SRES scenario(s) Monthly variable(s)b Sourcec Time period

CGCM3.1(T47) 20C3Md, A1B, A2 pr CMIP3 1961–2100

CGCM3.1(T47) 20C3M, A1B, A2 tas, tasmin, tasmax CCCma 1961–2100

CSIRO-MK3.5(T63) 20C3M, A1B, A2 tas, tasmin, tasmax, pr CMIP3 1961–2100

MIROC3.2(T42) 20C3M, A1B, A2 tas, tasmin, tasmax, pr CMIP3 1961–2100

a CGCM3.1(T47) = Third Generation Coupled Global Climate Model, version 3.1, medium resolution; CSIROMK3.5(T63) = Commonwealth Scientific and Industrial Research Organisation Climate System Model, Mark 3.5; 

MIROC3.2(T42) = Model for Interdisciplinary Research on Climate, version 3.2, medium resolution.

b Simulated climate variables (as defined by Program for Climate Model Diagnosis and Intercomparison): tas; 

mean 2m air temperature (K); tasmin: mean daily minimum 2m air temperature (K) (Tmin); tasmax: mean daily 

maximum 2m air temperature (K) (Tmax); pr: monthly precipitation (kg m-2 s-1). c The majority of the climate model, scenario, and variable data were downloaded from the WCRP Coupled Model 

Intercomparison Project Phase 3 (CMIP3) at the data portal hosted by the Program for Climate Model Diagnosis 

and Intercomparison (PCMDI) at https://eqg.llnl.gov:83443/index.jsp. This “multi-model data set” is archived by the 

PCMDI project. The major advantage to using CMIP3 data was their standardization of format, variable names, 

units, and other aspects, which facilitated comparison among models. The Canadian Centre for Climate Modelling 

and Analysis (CCCma) web site serves data for CGCM3.1(T47) and other Canadian climate models (http://www.

cccma.bc.ec.gc.ca/data/cgcm3/cgcm3.shtml). Daily Tmin and Tmax data for CGCM3.1(T47) were obtained from this 

source because they were not available from CMIP3. 

d The 20C3M refers to the 20th century model simulations.
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USDA Forest Service Gen. Tech. Rep. RMRS-GTR-320. 2014 Table 5—Climate model output data for the historical simulation for the 20th century (period 1961-2000) and the SRES B2 scenario from the period 2001 to 2100 (used in the Third Assessment report of the Intergovernmental Panel on Climate Change) were obtained from the Intergovernmental Panel on Climate Change data portal. Climate model version, modeling center Nominal cell size Source of the model data CGCM2, Second Generation Coupled Global Climate Model, 3.75 degrees http://www.ipcc-data.org/sres/gcm_data.html version 2, medium resolution (T47), developed by the Canadian latitude by 3.75

Centre for Climate Modelling and Analysis degrees longitudehttp://www.ec.gc.ca/ccmac-cccma/default.asp?lang=En&n=40D6024ECSIRO-MK2, Climate System Model, Mark 2, developed by the 3.2 degrees http://www.ipcc-data.org/sres/csiromk2_info.htmlCommonwealth Scientific and Industrial Research Organisation latitude by 5.6(CSIRO), Australia degrees longitudehttp://www.cmar.csiro.au/e-print/open/hennessy_1998a.html#ccmHadCM3, Hadley Centre Coupled Model version 3, developed by 2.5 degrees http://www.ipcc-data.org/sres/hadcm3_info.htmlthe Hadley Centre for Climate Prediction and Research UK, of latitude by 3.75http://www.metoffice.gov.uk/research/modelling-systems/ degrees of longitudeunified-model/climate-models/hadcm3
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

Dynamical modeling uses a nested modeling approach where a finer resolution climate model 

(typically a regional climate model) is nested within a global climate model. The atmospheric 

processes occurring within the domain of the regional climate model are forced by boundary 

conditions generated by the global climate model at its usual time step. Within the domain of 

the regional climate model, higher-resolution representation of surface topography and more 

detailed parameterization of some processes allow the model to generate physically consistent 

simulations of weather and climate. Christensen and others (2007) cite the main drawback of 

dynamical models as their computation cost, and that in projections of future climate, the parameterization schemes used to represent sub-grid scale processes may be operating outside the 

range for which they were designed. Recently, projections for the conterminous United States 

at the 50-km spatial scale have become available (see North American Regional Climate 

Change Assessment Program, http://www.narccap.ucar.edu/results/rcm3-gfdl-results.html).

In statistical downscaling, a statistical procedure is used to describe climate at a finer spatial 

scale based on global climate information. This relationship is then used to relate the global 

model projection output to the finer spatial scale. This approach requires observational data 

at the spatial scale of interest and over a sufficiently long period in order to develop statistical 

relationships. Christensen and others (2007) identify drawbacks in this approach, notably in 

the assumptions about cross-scale relationships remaining stable in a changing climate.

Simpler methods use global climate model (GCM) output directly (e.g., from the closest 

grid cell node) and spatially interpolate to finer resolution from latitude and longitude coordinates. These methods have been adopted for interpolating both climate observations and 

climate model output to fine spatial resolutions over large regions where it may be impractical to apply statistical downscaling methods or computationally difficult to use dynamical 

downscaling. One such method—the delta or change factor method—has been adopted for 

interpolating climate model output to fine spatial resolutions over large regions (VEMAP 

members 1995; Hamlet and Lettenmaier 1999; Miller and others 2003; Price and others 2004; 

McKenney and others 2006a; Rehfeldt and others 2006; Tabor and Williams 2010; Anandhi 

and others 2011). Global climate model output data are normalized with respect to a historical 

reference period so that bias in the simulated historical values (i.e., compared to observed data 

at the same location) can be removed (Price and others 2004; USDI Bureau of Reclamation 

2010). In this normalization process, every GCM-projected climate data point within the 

geographic region of interest is converted to a change factor (or delta value), relative to a 

particular historical period. For temperature, the change factor is computed as the arithmetic 

difference between the projected monthly temperature variable and the corresponding 30-year 

mean of the simulated historical temperature variable for that month. For precipitation, the 

change factor is the ratio of the projected monthly value to the corresponding 30-year simulated historical mean for that month. These change factors are estimated for each month and 

year in the projection period at the scale of the global model. The change factors are then 

spatially interpolated to the scale of interest. Because climate models typically have very low 

horizontal resolution, their representation of topographic effects on local climate is necessarily poor. For this reason, the normalized and interpolated climate model data (change factors) 

are combined with observed climatological data for the reference period interpolated to the 
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USDA Forest Service Gen. Tech. Rep. RMRS-GTR-320. 2014

same resolution as the change factors. This approach provides a correction for local topographic effects to the downscaled climate projection data. Because historical climate data are 

interpolated to capture the spatial complexity of local climate (e.g. the result of topography), 

a possible drawback to this approach is the assumption that causes of spatial variability in 

past climate will remain unchanged (or will change uniformly) in future climate. In practice, 

such effects are likely to be small compared to the inherent errors and assumptions built into 

the climate model simulations. Ultimately all downscaling approaches have advantages and 

disadvantages that should be recognized and understood by the end user to be sure the results 

suit their specific purposes (see Daniels and others 2012).

Delta Method and Spatial Interpolation of the Change Factors Using ANUSPLIN

Standardized procedures for processing climate model data sets developed by Price and others 

(2004) were used by Price and others (2011a) and Joyce and others (2011) to develop change 

factors with AR4 models for Canada and the United States, respectively. This study uses the 

downscaled change factors developed by Joyce and others (2011) to develop projections for 

the conterminous United States. We briefly describe those procedures here and further detail 

can be found in Price and others (2011a) and Joyce and others (2011).

These procedures were built around interpolation of the climate model output data using 

ANUSPLIN, the thin plate smoothing spline climate interpolation software tool developed 

by Hutchinson and coworkers at the Australian National University (Hutchinson 2010). 

The ANUSPLIN tool was developed for interpolating climate station observations and has 

been used to carry out interpolations of monthly time series data and time series at shorter 

timescales (weeks to days) (Price and others 2000, 2004; McKenney and others 2006b; Price 

and Scott 2006; Rehfeldt 2006; Hutchinson and others 2009; McKenney and others 2011). 

Here the monthly data values from the climate models were treated as records obtained from 

a ‘virtual climate station’ located at the climate model grid-node coordinates. Conversion, 

extraction, and interpolation processes were run on multiple computers, controlled by UNIX 

scripts developed in-house by Price and others (2011a) at the Northern Forestry and at the 

Great Lakes Forestry Centres, Canadian Forest Service. These scripts were edited specifically 

for each climate model, to account for the different spatial resolutions covering the North 

American domain (Tables 3 and 5) and for other differences in the contents of the data files 

(Price and others 2011a). The processing steps were similar for both the AR4 and the TAR 

climate model output data (Table 6).

Once the data were prepared for the North American grid (steps 1 – 5), monthly values of 

daily surface temperature (minimum and maximum) and precipitation were used to calculate 

GCM-simulated 30-year means for the 1961-1990 period using the 20th Century realization (20C3M) at the resolution of each global model (Tables 3 and 5). These monthly mean 

values were then used to compute the monthly change factors in the following manner. For 

maximum and minimum temperature, the change factor was computed as the arithmetic difference between the simulated monthly value for each year in the projection period and the 

corresponding simulated 1961-1990 mean value of the same temperature variable for that 
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

month. For precipitation, the change factor was the ratio of the monthly projected value to the 

simulated 1961-1990 mean for that month. Thus for each grid node at the global resolution 

within the North American domain, there were change factors for each month for monthly 

mean daily maximum temperature, for monthly mean daily minimum temperature and, for 

total monthly precipitation over the 2001 through 2100 period.

The monthly change factors associated with each global grid node were spatially interpolated 

to the 5-arcminute scale. An ANUSPLIN model was generated for each monthly change 

factor variable, which was then used to create gridded data for that monthly variable covering 

the conterminous United States and Alaska at a spatial resolution of 5 arcminutes. Because the 

data were treated as anomalies from the 1961-1990 mean, a fixed signal model, rather than 

a standard optimization model, was used (McKenney and others 2006a). We note there is no 

inherent statistical relationship between these GCM-generated anomalies and the independent 

variables (longitude and latitude). A fixed signal of 60 percent of the data points (climate 

model grid cell values) produced reasonable results (e.g., to avoid singularities [“bulls eyes”] 

in the resultant models). Monthly grids of interpolated change factors were generated in ARC/

INFO ASCII format, with a cell size of 5-arcminute latitude by 5-arcminute longitude, covering the domain from the 168 degree to 52 degree W and from 25 degree to 85 degree N (1,392 

columns by 720 rows, covering all of the continental United States and Canada). This grid 

resolution matches that of many other climate data products previously produced at the Great 

Lakes Forestry Center, Canadian Forest Service (McKenney and others 2011). Additional 

details on the downscaling of the AR4 scenarios (A1B and A2) can be found in Price and 

others (2011a) and Joyce and others (2011) and for the TAR B2 scenario in Price and others 

(2004).

Table 6—Major steps in developing the change factors: downloading, extraction and 

processing of the climate model output data, as described by Price and others (2011a) 

and Joyce and others (2011).

Major step Processes

1 Download global orography data sets for each climate model and create sets of grid cell 

coordinates for elevation, latitude, and longitude files

2 Download climate model global data files for each climate variable and each of the 

desired scenarios.

3 Extract data for each climate variable for North America from the global data file and 

convert to an ASCII format file.

4 Extract data for surface wind components for those climate models with data files 

containing multiple pressure levels and compute estimates of surface wind velocity

5 Prepare the monthly climate model data for input to ANUSPLIN. 

6 Compute vapor pressure from values for specific humidity and sea-level pressure 

(adjusted to grid cell elevation) as simulated by each climate model

7 Spatially interpolate the normalized monthly change factors for each of the six variables, 

all scenarios and all climate models

8 Extract rectangles from the North American grid for the conterminous 48 states and 

Alaska using ARC/INFO.
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Development of the RPA Climate Projections for the Conterminous 

United States

Development of the Observed Historical (1961-1990) Climate Data Set Using 

PRISM

The change factor data generated from the GCM projections needed to be combined with 

gridded observed 30-year climate data to produce physically consistent gridded projections of future climate, corrected both for local topographic effects and for the mean bias 

in the climate model projections. In this manner, projections integrate the spatial variability 

observed in present-day climate at the spatial scale of interest with the spatio-temporal variability simulated by each climate model. In addition, the use of a historical climatology on 

which to base future projections corrects for biases among different climate models and facilitates a direct comparison of their future projections.

The RPA climate projections use a historical climatology based on PRISM data (Daly and 

others 1994), as these historical climate data were used as climate data in the individual RPA 

resources analyses (e.g., Bowker and others 2012; Foti and others 2012; Greenfield and 

Nowak 2013; Wear and others 2013). Several different historical climatologies are available: Kittel and others (2004), Rehfeldt (2006), McKenney and others (2006b,c 2011), and 

DAYMET (see http://www.daymet.org/default.jsp).

PRISM climate mapping system, developed by Christopher Daly, PRISM Group director, is 

a knowledge-based system, continuously updated, that uses point measurements of precipitation, temperature, and other climatic factors to produce continuous grid estimates of monthly, 

yearly, and event-based climatic parameters (Daly and others 1994, 2002; Gibson and others 

2002). Point data, a digital elevation model, and expert knowledge of complex climatic 

extremes, including rain shadows, coastal effects, and temperature inversions are integrated to 

produce the interpolated climate data set (see http://www.prism.oregonstate.edu/).

Historical climate data (monthly mean daily maximum air temperature, monthly mean daily 

minimum air temperature, and monthly total precipitation) were obtained by download from 

ftp://prism.oregonstate.edu/pub/prism/us/grids/. Data are provided in raster files, where each 

file contains 1,405 columns (longitude) and 621 lines (latitude), resulting in 872,505 grid cells 

covering the conterminous United States. Each file represents the month and year of a single 

climate variable. PRISM grid cells are 2.5-arcminutes resolution, referenced by the southwestern corner (left-hand lower corner).

As the change factors were developed at the 5-arcminute scale and PRISM data are at the 

2.5-arcminute scale, we aggregated the PRISM historical climate data to the 5-arcminute 

spatial scale so that these data could be used to construct future climate projections using the 

downscaled change factors. The PRISM grid scale is one half that of the change factor grid 

scale, thus, the area of four PRISM grid cells matches the area of one change factor grid cell. 

The PRISM grid starting point (southwest corner) is offset by 3.75 degrees north and 1.25 

degrees east from the change factor grid. This effectively lines up the longitude/latitude center 

of each change factor grid cell with one PRISM grid cell and portions of eight surrounding 

PRISM grid cells (Figure 3).
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

Using PRISM data, the monthly mean for the 1961 to 1990 period was computed for each 

monthly climate variable: precipitation, maximum temperature, and minimum temperature 

within each PRISM grid cell. The area-weighted mean value for each climate variable was 

calculated for each change factor grid cell using the following formula. One PRISM grid cell 

(p5 in formula; see also Figure 3) overlaps with the center of the change factor grid cell and 

portions of eight surrounding grid cells are added to the estimate.

Mean grid cell value =

(0.25*p1+0.5*p2+0.25*p3+0.5*p4+p5+0.5*p6+0.25*p7+0.5*p8+0.25*p9)/4,

where p1 to p9 are illustrated in Figure 3. In the event an overlapping PRISM grid cell has 

missing data, the change factor climate variable estimate is the weighted average of those 

PRISM grid cells with data. These computations produced a historical climate data set at the 

5-arcminute grid scale and are used as the historical basis for developing the future climate 

projections.

Development of Projections Using Change Factors and Observed Historical 

Climatology

The change factor data at the 5-arcminute spatial scale were combined with the historical climatology data at the same spatial scale. The projected change factor value was multiplied by 

(precipitation) or added to (temperature) the appropriate historical climate variable, resulting 

in climate projections for each climate model and each scenario.

Figure 3. Relationship between the 5-arcminute grid (change factor grid) and the PRISM data grid of 

2.5 arcminute (numbered gray grids). Historical data from PRISM were aggregated from the 2.5-arcminute 

scale to the 5-arcminute scale by matching the grid centers of one 5 arcminute grid with one 2.5 arcminute 

grid and weighting each of the overlapping nine 2.5-arcminute grids to develop the 5-arcminute values. See 

text for individual weighting factors.
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Following the use of potential evapotranspiration (PET) in ecological modeling (Bachelet 

and others 2001, 2003), PET for vegetation was calculated using the mean temperature variable and a modification of Penman’s work by Linacre (1977). Here PET is a function of 

mean monthly temperature, mean monthly temperature adjusted for elevation, elevation, 

latitude, and to replace dew point temperature an adjustment based on the difference between 

the means of the hottest and coldest months. The full calculations are given in Appendix III. 

Potential evapotranspiration is estimated as mm/day. Kingston and others (2009) noted that 

different formulations of PET can result in large differences in PET values. Although this formulation of PET has minimal climate parameter needs, it relies solely on temperature data and 

could result in an overestimate of PET for some areas and, when used in hydrological analyses, could produce overestimates of water demand and underestimates of water yield.

The climate projections were subject to a variety of quality checks to ensure the reasonableness of the data. Given that the data in this project are the work of several developers, we 

describe their quality control and assurance steps as reported in their documentation. The 

global data from the climate models used in the IPCC Fourth Assessment were obtained 

from the Program for Climate Model Diagnosis and Intercomparison (PCMDI) Climate 

Model Intercomparison Project 3 (CMIP3), and for models used in the Third Assessment, the 

Intergovernmental Panel on Climate Change DDC (http://www.ipcc-data.org/sres/gcm_data.

html). Documentation for each climate model is available on the PCMDI website: http://

www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php. Further, the 

web links for each climate model used in this study are given in Tables 3 and 5.

Attribute accuracy for the PRISM data can be found at http://www.ocs.orst.edu/prism/docs/

meta/temp_103yr.htm. The PRISM developers suggest that care should be taken in estimating 

temperature values or precipitation values at any single point because temperature or precipitation estimates for each grid cell (2.5 arcminutes) are an average over the entire area of that 

cell. Thus, point temperature or precipitation can be estimated at a spatial precision no better 

than half the resolution of a single grid cell. In this study, PRISM data were aggregated across 

the original scale of the grid cells to a larger spatial scale and thus did not violate this concern.

Price and others (2011a) document quality control issues encountered in the downscaling 

procedures and the development of the change factors. Data availability was problematic for 

some projections, resulting in some data needing to be retrieved from the climate modeling 

centers (instead of from PCMDI or the IPCC DDC). In addition, missing data for some years 

were addressed (Price and others 2011a; Joyce and others 2011).

During our initial quality checks it was discovered that the CSIRO-MK2 model used for the 

B2 scenario had some abnormally high projected precipitation values. Precipitation in the 

historical record can exceed four times a historical mean. However, these projected values 

seemed to be anomalous as they were associated only with particular years. When these data 

were developed for the VINCERA project (http://www.environment.uwaterloo.ca/research/

vincera/), a filtered data set was produced to correct these data anomalies (Price, personal 

communication). They did so by “capping” the average deltas (ratios) to five (400 percent 

change over the simulated historical data) before interpolating the precipitation change 

factors. We followed this procedure and used their filtered ratios to build the projected precipitation values for the B2 scenario using the CSIRO-MK2 model.
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

Additional quality assurance tests were made in the development of the projection data where 

data from the change factor data sets and the 30-year means were manipulated at different 

spatial scales. In the process of downscaling precipitation, it was discovered that in regions of 

low precipitation, negative change factors (ratios) were sometimes interpolated. As negative 

precipitation values are obviously impossible, these values were set to zero.

For some regions of the United States, the difference between the minimum temperature and 

the maximum temperature can be slight, e.g., northern latitudes or seasonally in winter. It 

is possible that spatial interpolation of observed climate can result in minimum temperature 

values greater than maximum temperature, as they are interpolated separately. This result did 

not occur in the aggregated historical climate data used in this study. However, it was discovered that in some cases, the output from climate models can project greater increases in the 

minimum temperature than the maximum temperature. This difference could be greater than 

the difference between the observed minimum and maximum temperatures. Thus when the 

change factors are imposed on the observed historical climate, the projections could result 

in the values of minimum temperature being greater than maximum temperature values for 

an individual grid cell. This anomaly rarely occurs in all but one of the projections, namely 

the B2 scenario simulated by the CSIRO-MK2 model, especially in winter months and the 

northernmost latitudes of the conterminous United States. No adjustment was made in the 

climate projection data set. If the relationship between minimum and maximum temperature is 

to be included in an analysis with this climate data set, the user should determine whether an 

adjustment is needed. One possibility would be a function where the maximum temperature is 

assigned the maximum value of both temperatures and minimum temperature, the minimum 

value of both temperatures.

County-Level Summarization

For purposes of the RPA Assessment, climate data availability at the county spatial scale is 

critical. We used the U.S. Forest Service Forest Inventory Analysis (FIA) Survey Unit and 

County Coverage as the spatial delineation for counties. An overlay file between the 5-arcminute grid and the county boundaries was developed in ArcGIS 9.2. This ultimately resulted 

in 120,680 grid cells with climate data within the conterminous United States. This resulting 

database file was imported into SAS and merged with the projected climate data.

Once merged, the county means for monthly total precipitation, monthly mean daily 

maximum air temperature, and monthly mean daily minimum air temperature were calculated 

using an area-weighted mean value of the underlying 5-arcminute grid cells within the county. 

With the overlay of the county shape file on the grid shape file, some grid cells are assigned to 

more than one county. During the overlay process, the area of each grid cell falling wholly or 

partially within the county was calculated. These areas were used as weights to calculate the 

county means.

Analysis of Climate Projections

The nine projections (three scenarios associated with each of three climate models) describe 

alternate futures of monthly climate over a 100-year period for three distinct climate variables 
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(i.e., monthly mean daily maximum temperature, monthly mean daily minimum temperature, 

and monthly total precipitation). Because these data will be used in other analyses as input 

for renewable resource models, our analysis focuses on comparing and contrasting the trends 

(spatial and temporal) seen in the scenarios and the nine individual climate model projections 

at the conterminous U.S. scale. And because the data may be of interest to finer-scale analyses, we explore several different types of analyses that can be performed with the climate data 

at regional scales within the United States.

The focus of the RPA Assessment is on projections for the 50-year period from 2011 to 2060. 

Hence we show maps of projected changes at the end of this period, by scenario and model, 

for the 10-year period surrounding the year 2060, 2055-2064, to avoid the issues of the representativeness of a single year. We also show maps of projected change of the last 30-year 

period (2071-2100).

Spatial and Temporal Patterns of Temperature and Precipitation for the 

Conterminous United States

We explore the temporal projections for annual mean daily temperature and annual total precipitation, first by aggregated scenario, and then for all three model projections within each 

scenario. The projections of maximum temperature and minimum temperature are also examined for all nine projections. For the conterminous United States temporal analysis, we areaweight the grid cell values by grid cell areas, thereby accounting for latitudinal distortions in 

grid cell area associated with the underlying map projections.

Climates within the United States are highly varied from the cold climates of alpine regions 

in western United States to the warm and humid climates of southern United States. These 

latitudinal and elevational differences and the east-to-west gradients caused by the synoptic 

weather systems and the Rocky Mountains dominate the displays of the conterminous U.S. 

climate maps. Hence, because the spatial variations in individual and aggregated projections 

are small compared with these strong climatic gradients, we display change in temperature 

and precipitation relative to the 1961-1990 period to reveal the differences among the scenarios and projections by the 2060 period and by the 2071-2100 period. We show changes 

in temperature using degrees Celsius (oC). For precipitation, changes are shown for the 2060 

period in both the units of millimeters and as a percent change, and for the 2071-2100 period 

as percent change only. Percent change in precipitation is computed as the projected precipitation (typically a mean of at least 10 years) minus the historical (1961-1990) precipitation 

mean divided by the historical precipitation mean. The historical period serves as a benchmark to compare changes in projected climate spatially and temporally across the United 

States.

We examine relationships between the area-weighted change in annual mean daily mean 

temperature and the percent change in annual precipitation projected by each climate model 

for the 2060 period and the 2071-2100 period relative to the 1961-1990 period means. These 

scatter plots demonstrate how the three climate models (under each of the three scenarios) 

differ in their projections of climate change for the conterminous United States.
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

Regional Patterns in Annual and Seasonal Climate Projections

The interpretation of the projected changes in climate may be assisted by different types of 

analyses and presentations. While the changes in mean daily temperature are often used to 

quantify the potential impact of climate change, Lobell and others (2007) suggest that it may 

be possible to improve the exploration of climate change impacts by separating the potential 

mean temperature changes into minimum temperature and maximum temperature changes, 

and perhaps by season. Changes in seasonal temperature and precipitation may have a differential effect on vegetation dynamics across a region, particularly in spring when plants are 

initiating growth or during summer when flowering and fruiting occur. We briefly explore 

seasonal changes that may influence the ecological response as an example of the types of 

analyses that can help analysts interpret the change in climate. For the Southeastern regions 

(the states of Virginia, North Carolina, South Carolina, Georgia, and Florida), we explore 

spatial patterns in nine projections of maximum mean daily temperature as compared to projections for minimum mean daily temperature. We focus on summer (the months of June, July, 

and August).

While the spatial display gives a sense of the pattern across the region, we use the frequency 

distributional changes to assess the magnitude of temporal and spatial change. For the 

Northern Great Plains region (the states of North Dakota, South Dakota, and Nebraska), we 

explore the changes in spring temperatures. Warmer temperatures in the spring season potentially project a longer growing season. The occurrence of freezing temperatures is a limiting 

factor in the initiation of spring vegetative growth and changes in the frequency distribution 

of spring temperatures (mean and minimum) could facilitate earlier spring vegetative growth. 

Frequency distributions are constructed for a 30-year period of historical data (1961-1990), 

and a 30-year period as projected by the climate models (2045-2074). The distribution is 

described by the seasonal values associated with individual grid cells for each year within the 

30-year period. For the Southern Great Plains (the states of Oklahoma and Texas), we explore 

the changes in spring and summer temperatures. Precipitation is projected to decline slightly 

or to remain the same in this region. Increasing temperatures could put drought stress on 

vegetation particularly late in the growing season. As with the Northern Great Plains, the frequency distributions are based on the historical period (1961-1990) and the projected period 

(2045-2074). Spring is defined as the months of March, April, and May.

Looking at projected temperatures in the context of the historical record offers a comparison 

that can assist managers and others in planning for climate change. Ray and others (2008) 

compared historical data from a weather station with the interpolated data for grid cells surrounding the local weather station. This analysis allows a comparison of the historical variability and the projected data. Following Ray and others (2008), we use a 50-year historical 

record (1950-1999) for the Lamar weather station located in southeastern Colorado and the 

Lakeview weather station located in eastern Oregon. We aggregate a block of grid cells (five 

grid cells east to west and three grid cells north to south) surrounding the weather station 

(approximately a 35-km by 45-km region) and compute the historical annual precipitation 

and annual mean daily temperature for this region. Projected monthly climatologies are computed for all nine projections for the 20-year period surrounding 2050 (2040-2059), after 

Ray and others (2008). We compare the observed mean and variability with the nine climate 

projections.
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Price and others (2011a) developed a set of comprehensive tables to provide an outlook for 

various regions in Canada. Using their technique, we summarize the projected changes during 

the 21st century for mean daily temperature, mean daily minimum temperature, and total precipitation for the State of Colorado. The 1971-2000 period was selected as baseline because 

this 30-year period represented the most recent 30-year period (at the time of the study) used 

by weather and climate scientists to depict ‘normal.’ The area-weighted mean for each climate 

variable is the mean of the values projected by the three climate models and therefore represents a central estimate or ‘best” guess, assuming that the climate models produce equally 

plausible results. For each variable, the data are organized across the table in three sets of 

five columns. Each set of columns in the tables represents a single emissions scenario (in the 

order A2, A1B, and B2), with the columns containing the means of the three climate model 

projections of monthly values for spring, summer, fall, winter, and the entire year. The rows 

are labeled in the leftmost column according to the period represented. “Baseline 1971–2000” 

refers to the 30-year mean for the period 1971-2000. It is important to distinguish this 30-year 

period from the period 1961–1990, which was used as the reference period for combining scenario data with observed climate normals for 1961–1990. Although any differences between 

the periods 1961–1990 and 1971–2000 are probably small, there is evidence of a general 

warming trend over this entire period that is apparent in many of the graphs shown previously 

(both in the observed temperature records and in the climate model projections). Changes in 

precipitation and temperature are computed in the projected 30-year means relative to 1971-

2000: change by 2001–2030, change by 2031–2060, and change by 2061–2090. For these 

changes over time, a positive value indicates an increase, and a negative value indicates a 

decrease. A “100-year change” is computed and represents the change over the 100 years 

from 2001 to 2100, as determined by fitting a linear relationship to the data. The “100-year 

variability (%)” is the coefficient of variation for the slope of the line. Both of these metrics 

are based on monthly data, thereby not representative of how variability in daily values might 

change in the future.

The Pacific Northwest region (the states of Idaho, Washington, and Oregon) is a climatically 

diverse region; the coastal ranges are wet and cool and the interior is dry and warm. Here we 

explore the changes in precipitation and temperature seasonally and spatially. At the conterminous U.S. scale, we compared the annual mean temperature changes with the annual precipitation changes by scenario and by model and suggested that these graphs could facilitate 

selecting scenarios where a specific range of projected changes in climate (temperature and 

precipitation) is desired (e.g. wettest and warmest). For the Pacific Northwest region, we 

compare the seasonal mean temperature changes with the seasonal precipitation change (as 

percent) for the 2060 period.

In the Northeastern region of the United States (the states of Maine, Vermont, and New 

Hampshire), we display the scenario projections for annual mean daily temperature, based on 

the three model projections within each scenario. These spatial results are then compared and 

contrasted with the nine individual projections of changes in annual mean temperatures.

The historical and projected climate data can be used to develop indices of interest to the user. 

We use the aridity index, developed by the United Nations Environmental Program (UNEP) 

with downscaled climate data from this study. This index is:
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

Aridity Index (AI) = Precipitation/PET

where Precipitation is total annual precipitation (mm) and PET is annual Potential 

Evapotranspiration (see Appendix III for calculations). This aridity index was originally 

developed to identify zones using a common classification:

Zone Aridity Index values

Hyper-arid < 0.05

Arid 0.05 - 0.2

Semi-arid 0.2 – 0.5

Dry Humid 0.5 – 0.65

Humid >0.65

Using annual precipitation and potential evapotranspiration (PET) values from the historical (1971-2000) and the nine projection data sets (2060 period: 2055-2064), we estimate the 

aridity index for each grid cell.

A common metric to assess consensus or agreement in projections is to compute the number 

of projections that show the same result. We are interested in whether there is an agreement in 

the projected changes in aridity. Using the formula above, we estimate the Aridity Index and 

then use the index to classify each grid cell into an aridity class for the historical period and 

using each of the nine model projections for the 2060 period (2055-2064). In terms of changes 

in classification between the historical and the projected period, the three possible cases are: 

1) no change in the classification; 2) the grid cell becomes more humid, e.g., from arid (historical) to semi-arid by 2060; and 3) the grid cell becomes more arid, for example from arid 

(historical) to hyper-arid in 2060. For each grid cell, we count the number of projections that 

agree for each case; no change, increased aridity, and increased humidity. The change can be 

more than one class; for this example, we focus only on whether there is a change in classification, not the degree of the change. The scale of 9 would indicate that all projections agreed 

on the same result, e.g., no change in aridity. We map the aridity zones for the 1961-1990 

period and the agreement in 2060 projected changes of increased aridity for the conterminous 

United States.

Results _____________________________________________

Mean Temperature and Total Precipitation Projections for the 2060 

Period for the Conterminous United States

At the scale of the conterminous United States, annual mean daily temperatures aggregated 

by RPA scenario increase above the range of the entire historical 1940-2000 period; however, 

projected annual precipitation throughout the 21st century nearly always remains within historical variability (Figure 4). The aggregated results show similar increases in temperature 

across the scenarios until 2070 when temperatures for the A2 scenario begin to diverge from 

the A1B and B2 projections. By 2100, the annual mean daily temperature increases from the 

historical (1961-1990) mean of 11.2 oC to about 15.0 oC for the B2 scenario, 15.3 oC for the 

A1B scenario, and 16.3 oC for the A2 scenario. By 2060, the A1B scenario shows the greatest 

increase in mean annual temperature (3.2 oC); however, differences at the conterminous U.S. 

scale are very small among the three scenarios (A2, 3.1oC; B2 2.8 oC). By the end of the 21st
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Figure 4. Observed (1940-2006) and projected (2001-2100) annual mean daily mean temperature (°C) 

(a) and annual precipitation (mm) (b) for conterminous United States and scenarios A1B, A2, and B2. 

Each projection (colors) is the mean of three climate model projections for each scenario. The solid 

black line is the historical annual temperature mean (a) and annual precipitation (b). Horizontal lines 

denote upper and lower range of the historical means.
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

century, the A2 scenario shows the greatest warming (visible in Figure 4). Precipitation projections for all scenarios are highly variable (Figure 4). Given the challenges in measuring and 

modeling precipitation, no real differences over time or by scenario can be drawn from these 

national results for precipitation, in contrast to the temperature projections.

Plots showing the change in annual mean temperature against the change in annual precipitation change have been used to assist in the selection of specific scenarios and projections to 

use in impact analyses. The graphs show the individual model projections as well as each 

scenario mean. From the graph, the relative nature of the individual model projections can be 

seen; for example the model/scenario that is the wettest or warmest in this set of 9 projections 

(Figure 5). The individual model projected changes span a wide range in both precipitation 

(increase of 80 mm to a decrease greater than 100 mm) and temperature (greater than 1.0 oC 

Figure 5. Change in mean annual precipitation (mm) plotted against change in annual 

mean daily mean temperature change (°C) 

for climate model projections (triangles) and 

scenario projections (open circles) for the conterminous United States (a). Area-weighted 

changes (a) are computed as the difference 

between the projected mean for the 2060 

period (2055-2064) and the historical mean 

(1961-1990). Precipitation is shown as percent 

change (b) where the difference is divided by 

the mean for the historical period.
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differences). The warmest and driest projection is the MIROC3.2 model under the A1B scenario. The wettest projection with the smallest increase in temperature is the CSIRO-MK3.5 

model under the A2 scenario. The results for the individual models projecting the B2 scenario 

are less variable than those for either the A1B or the A2 scenario for the 2060 period. We 

also show change in temperature against change in precipitation where change is measured 

as percent change from the historical precipitation (Figure 5b). By the 2060 period, changes 

in precipitation range from a 10 percent increase to nearly a 15 percent decrease in annual 

precipitation.

When viewed as a map of changes, annual mean daily temperature increases at least 1 oC 

by 2060 in every grid cell across the conterminous United States in all models forced by all 

scenarios. The projected changes vary spatially by scenario and by model; relative to historical climates, changes of the same magnitude could have relatively different effects depending upon the historical climate (compare Figures 6 and 7). Along the Rocky Mountains in 

western United States, changes in annual mean temperature are projected to be 2.5 oC and 

greater; grid cells with historical temperatures in the range of –1 oC to 2 oC will see temperatures rising above freezing, as will some grid cells with historical temperatures between 

–3.7 oC to –1.1 oC. Annual mean daily temperatures increase by more than 3 oC in parts of 

the northern and central regions in two of the three models, forced by each scenario, but not 

always the same two models (Figure 7). For the B2 scenario, CGCM2 and CSIRO-MK2 

both show warming in the interior, with CSIRO-MK2 extending this pattern to the eastern 

coast; however HadCM3 shows the greatest warming in western United States. For the A1B 

scenario, the greatest warming is projected for the continental interior according to CSIROMK3.5 and MIROC3.2. With the A2 scenario, the greatest warming in the interior is projected 

by MIROC3.2 and to a lesser degree, CGCM3.1. The CSIRO-MK3.5 projection for the A2 

scenario shows small increases in annual mean daily temperature, compared to the other A2 

projections (Figure 7).

The historical precipitation map shows the gradient of annual precipitation increasing from 

the Great Plains in the mid-continental region to the East Coast of the United States and 

increasing throughout the Southern region (Figure 6). The West Coast, particularly the Pacific 

Northwest region, receives the greatest total annual precipitation in the conterminous United 

States; the Southwest and Intermountain regions (southern California, Arizona, Utah, New 

Mexico, Nevada, and the western parts of Texas) show the lowest historical precipitation 

levels across the United States. Changes in precipitation by 2060 (as measured in millimeters) 

vary among scenarios and among individual climate models (Figure 8). Nevertheless, precipitation is generally projected to increase in the northern regions of the United States and 

decrease across the southern United States (Figure 8). Some of the largest increases in precipitation amounts are projected for the Pacific Northwest where annual precipitation historically has also been the greatest. The A1B and A2 individual model projections show a greater 

spatial similarity than the individual model projections for the B2 scenario. In contrast to the 

A1B and A2 projections, large parts of the conterminous United States see small changes in 

precipitation for the B2 projections (Figure 8). The spatial patterns of increases or decreases 

above 75 mm vary across the conterminous United States by model under the B2 scenario.
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

Figure 6. Historical (1961-1990) annual mean daily mean temperature (°C) (a) and annual precipitation (mm) for the 

conterminous United States based on PRISM climatology.
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Figure 7. Change in annual mean daily mean temperature (°C) by scenario and climate model for the 2060 period (2055-2064) where change is estimated as the difference between projected annual mean daily mean temperature for the 2060 period and annual mean daily mean temperature for the historical period (1961-1990).
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment Figure 8. Change in annual precipitation (mm) by scenario and climate model for the 2060 (2055-2064) period. Change is calculated as the difference between projected mean annual precipitation for the 2060 period and mean annual precipitation for the historical period (1961-1990).
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When precipitation changes are expressed as percent change from the historical annual precipitation, the percent change provides a benchmark comparison to the historical precipitation (1961-1990) for all nine projections across the conterminous United States. While the 

differences projected for precipitation change are large in absolute terms (millimeters as the 

unit), the regional differences when expressed as percent change are more apparent (contrast 

Figure 8 with Figure 9). The large increases in millimeters of annual precipitation seen in 

the Pacific Northwest (CGCM3.1 and CSIRO-MK3.5 projections of A1B and A2, Figure 8) 

represent increases up to 15 percent above historical precipitation. Other parts of the northern region are also projected to see 15 percent and greater increases, even though the absolute change in these areas is smaller than in the Pacific Northwest (compare Figure 8 with 

Figure 9). Across the Southern region of the United States, though projected changes in mm 

were in the largest classes, precipitation as percent ranges from 5 to 40 percent.

Mean Temperature and Total Precipitation Projections for the 2071-

2100 Period for the Conterminous United States

Relationships between changes in annual mean daily temperature and annual precipitation by 

scenario vary between the 2060 period and the 2090 (2085-2094) period (Figures 5 and 10). 

For scenario means, A2 becomes the warmest by 2090, in contrast to A1B in 2060 (reflecting the differences in greenhouse gas [GHG] emissions trajectories assumed for each of these 

scenarios). The wettest scenario in 2090 is projected with A1B in contrast to A2 in 2060. 

Temperature differences among the scenario means increase also compared to 2060, with the 

span between A1B and A2 at 1 oC in 2090, in contrast to less than 0.5 oC in 2060.

While all projections show unidirectional increases in temperature by 2090, precipitation 

changes increase or decrease depending upon the model and scenario (Figure 10). Only 

two models projected annual mean daily temperature increases of 3.5 oC or more in 2060; 

by 2090, seven models project changes greater than 3.5 oC (Figures 5 and 10). Differences 

among the models increase by 2090. The wettest individual model projection is the CSIROMK3.5 for the A1B scenario, in contrast to the A2 CSIRO-MK3.5 in 2060. The driest is 

MIROC3.2 for the A2 scenario in 2090, in contrast to the A1B MIROC3.2 in 2060.

At the scale of the conterminous United States, annual mean daily maximum and minimum temperatures for each of the nine projections increase above the range of the 1940 to 2000 historical 

period (Figure 11); however, projections for annual precipitation mostly remain within historical 

range (Figure 12). The temporal changes in maximum temperatures at the scale of the conterminous United States are greater than for minimum temperatures over the entire projection period. 

By the end of the 21st century, projected maximum temperatures and minimum temperatures are 

largest with the A2 scenario. By 2050, projections for maximum temperature in all scenarios and 

models rise above 19 oC, the upper range of the historical maximum temperature (Figure 11). 

Mean minimum temperature projections exceed the historical maximum of 9 oC by the mid-2040s. 

For minimum temperatures, individual model projections for the A2 scenario cluster together 

very tightly throughout almost the entire period. While the individual model projections may 

make excursions outside of the historical range for precipitation, no consistent divergence among 

the three models is apparent in these results. The MIROC3.2 results for the A2 scenario show a 

decreasing trend in precipitation over time, however this pattern was not repeated by the other two 

models projecting this same scenario (Figure 12c).
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment Figure 9. Change in annual precipitation (percent) by scenario and climate model for 2060 period. Change is computed as the difference of projected mean annual precipi- tation (2055-2064) and historical mean annual precipitation (1961-1990) and divided by the historical mean.
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Figure 10. Change in annual precipitation (mm) plotted against change in annual mean daily mean 

temperature change (°C) for climate model projections (filled triangles) and scenario means (open 

circles) at the conterminous United States. Area-weighted changes (a) are computed as the difference between the projected mean (2085-2094) and the historical mean (1961-1990). Precipitation 

is also shown as percent change (b) where the difference in precipitation is divided by the historical 

mean.
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment Figure 11. Observed (1940-2006) and projected (2001-2100) annual mean daily maximum mean temperature (°C) (a, b, c) and annual mean daily minimum mean tempera- ture (°C) (d, e, f) for conterminous United States under scenarios B2, A1B, and A2, respectively. Each projection (colors) is the mean of three climate model projections for each scenario. The solid black line is the historical maximum (a) or minimum (b) temperature mean. Horizontal black lines denote the upper and lower range of the historical means.
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Figure 12. Observed (1940-2006) and 

projected (2001-2100) mean annual 

precipitation (mm) (a) for conterminous 

United States under scenarios B2, A1B, 

and A2. The solid black line is the 

historical mean annual precipitation. 

Horizontal lines denote the upper and 

lower range of the historical means.
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

By the end of the 21st century, model projections for the A2 scenario show the most warming. 

The minimum increases in annual mean daily temperature exceed 1.5 oC across the United 

States, compared to 1 oC in 2060; the increases do not exceed 5.5 oC anywhere in 2060 but 

rise above this change in the 2071-2100 period (Figures 7 and 13). As with the 2060 projections, the patterns vary by scenario and model. Coastal areas generally see the smallest 

increases in temperature, and the continental interior the largest increases (Figure 13).

By the 2071-2100 period, projected change in precipitation show spatial patterns and ranges 

similar to those in the 2060 period (compare Figure 9 with Figure 14). However for specific areas of the United States, the magnitude of change may be smaller or larger in this 

later period, and in some regions, projected decreases in 2060 are reversed by the end of the 

century. As with the 2060 projections, precipitation in the northern regions of the conterminous United States generally increases. Notably, in the A1B-CSIRO-MK3.5 projection, 

precipitation increases across much of the conterminous United States in the 2090 period in 

contrast to the 2060 period (compare Figure 9e with Figure 14e). In contrast, the CGCM3.1 

model projects less area with increased precipitation for the A1B scenario in 2090 period; but 

greater area of increases in the A2 scenario. All three models project a decrease in precipitation in the Southern United States in the A2 scenario but for different areas across the region. 

Precipitation changes in the B2 scenario are least of all three scenarios for both 2060 and end 

of the 21st century projections. The A2 and A1B scenarios show a more similar spatial pattern 

to each other than with the B2 precipitation projection (Figure 14).

Regional Patterns in Annual and Seasonal Climate Projections

Southeast

Coastal areas in the conterminous United States are projected to have the smallest changes 

in annual mean daily temperatures for the 2060 period, especially the southeastern United 

States (states of Florida, Georgia, North Carolina, South Carolina, and Virginia) (Figure 7). 

Projected changes in annual mean daily temperature ranged from 1oC to 4.5 oC in this region. 

For seven of the individual model projections, annual precipitation was projected to remain 

near historical levels or to increase at most, 15 percent above historical means. For the 

MIROC3.2 projections in A1B and A2, precipitation decreases below 45 percent of historical mean, resulting in the largest relative decreases in precipitation across the United States 

(Figure 9).

Historically, observed summer mean daily maximum temperature ranges from 33 oC in the 

south to 24 oC in the mountainous northwest of the Southeastern region (Figure 15). Cooler 

temperatures in both the maximum and minimum reflect the mountainous topography. 

Observed summer mean daily minimum temperature is about 10 oC cooler than mean daily 

maximum temperature across the region over the historical period 1961-1990. The relatively 

small difference between daily maxima and minima is related to the generally high humidity 

in much of the region.
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Figure 13. Projected changes in annual mean daily mean temperature (°C) by scenario and climate model for the 2071-2100 period. Change is estimated as the difference between the projected annual mean daily mean temperature (2071-2100) and the historical annual mean daily mean temperature (1961-1990 period).
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment Figure 14. Change in mean annual precipitation (percent change) by scenario (B2, A1B, and A2) and climate model for the 2071-2100 period. Change is computed as the difference between projected annual precipitation (2070-2100 period) and historical annual precipitation (1961-1990 period) divided by the historical mean annual precipitation.
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Figure 15. Observed summer mean daily maximum temperature (°C) (a) and summer mean daily minimum temperature (b) for the Southeast region of the United States for the period 1961-1990.
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

The projected changes in summer mean daily maximum and minimum temperatures for the 

2060 period are generally greater than those for the annual means (compare Figure 7 with 

Figure 16). Projected increases by the 2060 period range from 1.1 oC for both minimum and 

maximum temperatures to as much as 7 oC in the case of the maxima. The relative changes 

in minimum temperature versus maximum temperature vary considerably among models 

and scenario. For scenario B2 and the CGCM2 projection, projected increases in summer 

maximum temperature are as much as 2 oC greater than for minimum temperature. The 

MIROC3.2 A1B and A2 project increases in maximum temperature often exceeding those 

for the minimum by more than 3 oC (Figure 16h,i). In other cases, minimum temperature 

increases are projected to be greater than for maximum temperature, e.g., A2 scenario and the 

CSIRO-MK3.5 projection, and A1B scenario for both CGCM3.1 and CSIRO-MK3.5 projections. Generally, where the greatest changes in minimum temperature are projected across the 

region, the greatest changes in maximum temperature are also seen (Figure 16)

Figure 16. Projected change in summer mean daily minimum temperature and maximum temperatue (°C) by the 2060 period (2055-

2064) for the southeastern United States by scenario (B2, A1B, A2) and model.
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Great Plains

The means of temperature and precipitation change mapped for specific periods give a general 

picture of climate change (e.g. for the Southeast, Figure 16); however, variability in those 

changes across a region may be of interest. For the Northern Great Plains region (states of 

North Dakota, South Dakota, Nebraska, and Kansas), mean daily temperatures are projected 

to increase by the 2060 period from 1 oC to 4.5 oC above 1961-1990 means (Figure 7). Across 

the region, annual precipitation changes vary from minimal change to as much as 25 percent 

above historical (Figure 9). The major pulse of precipitation in this region occurs during 

March, April, and May.

Historically, spring mean daily mean temperatures ranged from 14.4 oC to about freezing 

(Figure 17), with colder temperatures reflecting the mountainous terrain of the Black Hills in 

western South Dakota as well as the U.S.-Canadian border. Figure 18 compares the frequency 

distributions of spring mean daily temperature for the historical period (1961-1990) with 

projections from the 2045-2074 period (i.e. centered on 2060) by three different models 

driven with the A1B scenario. These frequency distributions reflect the regional variation 

spatially (all grid cells in the region) and temporally (each year in the 30-year period). 

Figure 17. Spring mean daily mean temperature (°C) for the historical period (1961-1990) for the 

Northern Great Plains region. Spring is defined as the months of March, April, and May.
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

Figure 18. Frequency distribution of spring mean daily mean temperature (°C) in the Northern Great Plains for the historical period (1961-1990) and projected for the 30-year period 

surrounding 2060 (2045-2074) for the A1B scenario and climate models: (a) CGCM3.1, (b) 

CSIRO-MK3.5, and (c) MIROC3.2.
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Though infrequent, spring mean daily temperatures for some grid cells in the historical period 

could be below 0 oC. By 2060, all models for the A1B scenario project a general upward shift 

in the frequency distribution by at least 1.25 oC (Figure 18). Spring mean temperatures are 

projected to stay above zero in seven out of nine projections (not shown), with the CGCM3.1 

model being the exception for the A1B (Figure 18a) and the A2 (not shown). The projected 

mean spring temperatures frequently exceed the warmest historical temperatures (17.5 oC) 

seen in the region.

Spring minimum temperatures are of importance because they can affect the initiation and 

success of vegetative growth. Also, increases in minimum temperatures will affect the occurrence and severity of ‘freeze-thaw’ events, which can have serious impacts when plants are 

at vulnerable stages of development (leaf flushing and flowering). Historically, spring mean 

daily minimum temperatures below freezing (0 oC) were not infrequent and could be as low 

as –9.1 oC (Figure 19). By the 2060 period, all models project fewer occurrences of below 

freezing with the projected lowest temperature (CGCM3.1) about –4 oC. This pattern is also 

common in all models projecting the A2 and the B2 scenario (not shown). Historically, for 

the spring mean minimum temperature, approximately 40 percent of grid cells and years were 

below freezing and by 2060, more than 90 percent of the grid cells and years are projected to 

be above freezing according to the A1B scenario. The upper tail of the frequency distribution 

for the projected spring mean minimum temperature begins to approach the lower tail of the 

historical spring maximum temperatures (not shown).

In the Southern Great Plains (states of Texas and Oklahoma), spring mean daily temperatures 

for the 1961-1990 period were above 11.7 oC with the warmest spring temperatures exceeding 

23 oC along the U.S.-Mexican border (Figure 20). Over this historical period, spring temperatures range from 9.1 oC to 27.5 oC at the scale of the individual grid cell (Figure 21). By the 

2060 period, the frequency distribution of spring mean daily temperature for the A1B scenario 

shifts so that projected spring mean temperatures are never less than 12.6 oC and exceed the 

warmest historical spring temperatures by several degrees in all climate model projections 

(Figure 21).

In the Southern Great Plains, historical spring mean daily mean maximum temperatures range 

from about 17 oC to 35 oC. According to the CGCM3.1 A1B, these spring maximum temperatures are projected to remain above 22 oC by 2060 and to exceed 38 oC (Figure 22a). Summer 

mean daily temperatures historically ranged from about 20 oC to 33 oC and are projected to 

increase to nearly 24 oC in the lower end of the range and to exceed the upper historical range 

by several degrees. Comparison of Figure 22a with Figure 22b shows that spring and summer 

seasonal temperature ranges begin to overlap in the Southern Great Plains by 2060. Projected 

spring mean daily maximum temperatures overlap with and exceed the historical summer 

mean temperatures by as much as 6 oC.
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

Figure 19. Frequency distribution of spring mean daily mean minimum temperature (°C) in 

the Northern Great Plains for historical period (1961-1990) and projected for the 30-year period surrounding 2060 (2045-2074) for the A1B scenario and climate models: (a) CGCM3.1, 

(b) CSIRO-MK3.5, (c) MIROC3.2.
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Figure 20. Spring mean daily mean temperature (°C) for the Southern Great Plains based on PRISM 

climatology for the 1961-1990 period.

Colorado

A comparison of future projections in the context of past observed variability can assist managers and others in planning for climate change. Following the work in Ray and others (2008), 

we calculated the projected mean daily mean temperature and annual total precipitation for 

grid cells within a 35-km by 45-km region surrounding the town of Lamar, Colorado, located 

in eastern Colorado. We compare the nine projections (all scenarios and all models used in 

the current study) for the 2060 period (2045-2074) with the historical climate record (1950-

1999) from the Lamar weather station (Figure 23). The variability in the historical record for 

precipitation is high—the black dashed lines represent the 10th and 90th percentile values of 

all monthly observations in the historic period 1950-1999 (Figure 23a). Simulated monthly 

precipitation data for all nine projections lie completely within the variability of the historical 

record. At the temporal scale of the 20-year moving averages for precipitation (represented 

by vertical black lines), the projections for some months are encompassed by this variability 

or for other months, projections extend beyond this historical variability. Relative to historical precipitation, projected patterns are within the historical ranges. In contrast, projections 

for July and August mean daily temperature are outside of the historical range; for all other 

months, projections are within the historical range. The patterns for July and August are very 

similar to what Ray and others (2008) show for Grand Junction in western Colorado. As Ray 

and others (2008) described for Grand Junction, as the model consensus is above the 90th percentile of the historical record, the implication is that mean temperatures for July and August 

in the Lamar area for the 2060 period could be similar to the five warmest years observed in 

the last 50 years.
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

Figure 21. Frequency distribution of spring mean daily temperature (°C) in the Southern 

Great Plains for the historical period (1961-1990) and projected for the 30-year period surrounding 2060 (2045-2074) for the A1B scenario and climate models: (a) CGCM3.1, (b) 

CSIRO-MK3.5, (c) MIROC3.2.
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Figure 22. Frequency distribution of spring mean daily mean maximum temperature (a) and summer mean daily mean 

temperature (b) in the Southern Great Plains for the historical period (1961-1990) and projected for the 30-year period surrounding 2060 (2045-2074) for the A1B scenario and climate model CGCM3.1.
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

Figure 23. Observed mean annual precipitation (mm) (a) and monthly mean temperature (°C) (b) from the 

Lamar, Colorado, weather station compared with projections for 2060 (2045-2064) from a region (approximately 30 km by 45 km) surrounding Lamar. The observed monthly 50-year mean (solid black lines) and 

10th and 90th percentile values (dashed black lines) are based on all observations over the period 1950-1999. 

Projected mean daily temperature (°C) and annual total precipitation (mm) (solid red lines) are from the nine 

scenario-model projections in this study for grids surrounding Lamar, CO. The black bars on historical precipitation represent the 10th and 90th percentiles of the 20-year moving averages from 1897 to 2010.
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For Colorado, we summarize the seasonal and annual results for all variables for each emissions scenario (i.e., mean of the three climate model projections) by 30-year periods into the 

future (Table 7). By the 2061-2090 period, annual mean daily maximum and minimum temperatures are projected to rise 3.3 to 6.2 oC above those during 1971-2000, depending upon 

the scenario. The changes in annual minimum and maximum temperature in the first 30-year 

period (2001-2030) are similar across scenario and across temperature variable, ranging 

from 0.9 oC to 1.1 oC for minimum temperature and from 1.3 oC to 1.6 oC for maximum. The 

increase in annual minimum temperature is sufficient to raise it slightly above freezing by 

the 2030 period, depending upon the scenario. By 2061-2090, the projected increase over the 

1971-2000 mean is 3.2 oC to 3.8 oC for minimum temperature and 3.4 oC to 4.9 oC for the 

maximum. By that time, minimum temperature is projected to be 3 oC to nearly 4 oC above 

freezing.

Changes by season vary from the annual patterns (Table 7). The historical spring mean daily 

minimum temperature is –1.7 oC. Projections for all scenarios by the 2031-2060 period take 

spring minimum temperature above freezing and by the end of the 21st century, the projected 

mean is at least 1 oC above freezing in all scenarios. Similarly, fall mean minimum temperatures are projected to exceed freezing by the 2001-2030 period, and remain above freezing 

by the end of the 21st century. As noted previously, the seasonal mean daily maximum temperatures increase more than the corresponding minima (Table 7). For example, spring mean 

maximum temperatures rise 3.1 oC to 4.7 oC by 2061-2090, in contrast to an increase of 2.5 oC 

to 3.2 oC for spring mean minimum temperatures. Winter temperature increases are similar 

across all scenarios by 2060-2090. The largest increases in seasonal mean daily maximum 

temperature occur primarily in the summer. By the end of the 21st century, summer maximum 

temperatures are projected to be 3.6 oC to 6.9 oC above the historical means. The 100-year 

change by season varies; the greatest increase (in both maximum and minimum temperature) 

is seen in summer for the A2 scenario, but in fall for the A1B. For the B2 scenario, the greatest increase in minimum temperature is projected for winter, whereas the greatest increase in 

maximum temperature is projected for summer.

Annual and seasonal precipitation amounts for Colorado are projected to decline with all 

emission scenarios, with only the exception of winter showing slight increases (Table 7). By 

2061-2090, annual precipitation is projected to decrease from 10 to 35 mm relative to the 

1971-2000 period, corresponding to a 7 percent decline (Table 7). Historically, most precipitation occurs in spring and summer; these seasons are generally projected to see the greatest 

decline over the 21st century. The interannual variability of seasonal and annual precipitation 

is greater than that of temperature.
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment Table 7—Climate change outlook for Colorado, based on the mean of three climate models within each scenario. Climate variable A2 Emissions scenario A1B Emissions scenario B2 Emissions scenario Mean Daily Tmin (°C) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1971-2000 -1.7 9.5 -0.6 -10.8 -0.9 -1.7 9.5 -0.6 -10.8 -0.9 -1.7 9.5 -0.6 -10.8 -0.9 Change by 2001-2030 0.6 1.2 1.2 0.7 0.9 0.9 1.3 1.2 0.8 1.0 0.8 1.2 1.1 1.4 1.1 Change by 2031-2060 1.6 2.6 2.6 1.9 2.2 2.0 2.6 2.7 1.9 2.3 2.0 2.5 2.3 2.5 2.3 Change by 2061-2090 3.2 4.4 4.3 3.4 3.8 2.9 3.8 3.9 3.3 3.5 2.5 3.3 3.3 3.9 3.2 100-year change 4.6 5.5 5.2 4.9 5.0 3.1 4.3 4.3 4.0 3.9 3.0 3.4 3.4 4.3 3.5 100-year variability (%) 3.9 2.5 3.7 4.9 2.1 5.2 3.2 3.6 5.1 2.7 7.7 4.4 6.3 10.0 4.6 Mean Daily Tmax (°C) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1971-2000 14.3 26.6 15.6 4.1 15.1 14.3 26.6 15.6 4.1 15.1 14.3 26.6 15.6 4.1 15.1 Change by 2001-2030 1.1 1.8 1.7 0.6 1.3 1.6 2.0 1.9 0.9 1.6 1.3 1.8 1.7 0.95 1.4 Change by 2031-2060 2.7 3.5 3.3 1.9 2.9 3.0 3.5 3.5 2.0 3.0 2.7 2.9 2.7 1.8 2.5 Change by 2061-2090 4.7 5.8 5.5 3.5 4.9 4.1 4.9 5.0 3.3 4.3 3.1 3.9 3.8 2.8 3.4 100-year change 6.2 6.9 6.5 5.3 6.2 4.2 5.0 5.2 4.1 4.6 3.1 3.6 3.6 2.9 3.3 100-year variability (%) 4.4 4.8 4.8 4.8 2.8 7.8 6.8 3.8 5.8 4.2 9.8 8.7 7.3 9.3 5.5 Total Precipitation (mm) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1971-2000 139 145 103 77 465 140 145 103 77 465 139 145 103 77 465 Change by 2001-2030 -11 -5 -4 4 -17 -13 -11 -9 0 -33 -15 -9 -7 4 -27 Change by 2031-2060 -22 -9 -3 1 -34 -17 -8 -5 3 -26 -14 0 -3 2 -16 Change by 2061-2090 -19 -16 -11 11 -35 -17 -5 -10 8 -24 -8 -3 -4 4 -10 100-year change -16 -15 -12 10 -33 -7 0 -4 11 -1 17 25 6 2 29 100-year variability (%) -33 -44 -49 44 -36 -86 2169 -129 37 -1350 30 176 94 199 43
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Pacific Northwest

The Pacific Northwest (Washington, Oregon, and Idaho) is a region where precipitation has 

a strong seasonal pattern, with summers typically being much drier across the entire region 

than the other three seasons (Figure 24a,b,c,d). There is also a strong west-to-east precipitation gradient where the coastal and western parts of this region, as well as northern Idaho, 

have much higher precipitation each season than the central and southern parts. Focusing on 

the A1B scenario and the 2060 period, the models project the large decreases in precipitation 

during summer (comparing across Figure 24e,f,g,h). These decreases are projected to occur 

throughout the northern coastal, northwestern interior, and scattered areas in the central interior parts of this region (Figure 24g). For winter, fall, and spring, the majority of the region 

sees anywhere from a slight decrease to an increase of 20 percent above the seasonal mean 

historical (1961-1990) precipitation. The greatest increases generally are in the eastern parts 

of the region in winter and spring and scattered across the region in fall.

Seasonal mean daily minimum temperatures reflect coastal influences, mountainous topography, 

and the continental nature of climate in the eastern interior parts of the region. The coldest 

temperatures in all seasons are found in the eastern mountainous areas (Figure 25a,b,c,d). 

Seasonal minimum temperatures are projected to increase 1.4 oC or more across the region, 

with the greatest increases occurring in winter and summer in the southeastern part of this 

region (Figure 25). Projected increases greater than 3 oC would bring spring and fall mean 

daily minimum temperatures in the eastern parts of the region close to, if not above, freezing 

(Figures 25f and h).

The relative temperature and precipitation changes by projection and scenario are often used to 

identify scenarios for climate impact analysis. We explore these relationships in the Pacific 

Northwest region for each season (Figure 26). As noted earlier, the A1B scenario is the 

warmest scenario for the conterminous United States in the 2060 period. However while that 

trend generally holds for the Pacific Northwest, the seasonal scenario means for A1B, A2, 

and B2 differ by less than 0.5 oC, except for winter where the A1B and A2 scenario means are 

nearly 1.0 oC greater than the B2 mean.

Overall, the greatest warming is projected for summer along with some relatively large 

seasonal declines in precipitation (Figure 26). The scenario mean changes in temperature 

(A1B, A2, and B2) are above 3 oC in summer with decreases of 5 to 18 percent in summer 

precipitation (Figure 26c). Across all scenarios, seven of the individual model projections 

show decreases in summer precipitation. The HadCM3 B2 projected the greatest decrease 

in summer precipitation and the largest temperature increase for summer of all nine projections. Of the seven projections with decreases in summer precipitation, summer temperature 

increases range above 2 oC with several projections exceeding 3.3 oC.
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

Figure 24. Seasonal observed precipitation (mm) based on PRISM climatology for the historical period (1961-1990) 

(a, b, c, and d) and projected change in precipitation (percent) by the 2060 period (2055-2064) for the A1B scenario 

(e, f, g, and h) for the Pacific Northwest region in the United States. Change for precipitation is the mean of changes 

in three climate model projections.
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Figure 25. Seasonal observed minimum temperature (°C) based on PRISM climatology for the historical 

period (1961-1990): (a) winter, (b) spring, and (c) summer, (d) fall, and projected change in minimum 

temperatures (°C) by 2060 period (2055-2064) for the A1B scenario: (e) winter, (f) spring, (g) summer, 

and (h) fall. Change is estimated as the mean of change from the three climate model projections for the 

A1B scenario.
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment Figure 26. For the Pacific Northwest region, percent change in seasonal precipitation plotted against changes in seasonal mean daily temperature (°C) from historical period (1961-1990) to 2060 period (2055-2064) for nine projections (filled triangles) and three scenario means (open circles) for winter (a), spring (b), summer (c), and fall (d) seasons.
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Mean projected temperature increases in winter are close to 3 oC (A2 and A1B), and smaller 

in fall and spring (Figure 26). Winter and fall projections generally resulted in increased precipitation. The largest relative increase in precipitation across all seasons is projected for the 

A1B scenario mean projection for winter at 11 percent above the 1961-1990 mean. Eight of 

the nine models project increases in winter precipitation, with only the HadCM3 B2 projecting a slight decrease. The CSIRO-MK3.5 A1B projection for winter was the greatest seasonal 

increase (20 percent) projected by an individual model across all seasons (Figure 26a). Only 

two models for the B2 scenario projected decreases in fall precipitation.

Seasonally, the individual model projections vary in their relative order for temperature and 

precipitation changes, suggesting caution is needed when identifying which scenario-model 

projection to use in an impact analysis. If a ‘warmest’ projection was the goal for selection, 

the A1B MIROC3.2 projection would be selected for winter and spring, but the B2 HadCM3 

projection generates the largest increases in summer and fall temperature of all nine projections. The wettest projection for winter comes from A1B CSIRO-MK3.5 and the driest is the 

B2 HadCM3 projection; paradoxically both are also the two driest for spring.

We explore the nine projections for a block of grid cells surrounding the town of Lakeview 

in south-central Oregon in the context of the historical variability (Figure 27). Unlike Lamar, 

Colorado (Figure 23) or Grand Junction, Colorado (Ray and others 2008), the 10th and 90th

percentile values of 50-year observations of historical monthly mean temperature and monthly 

total precipitation generally exceeds the projected changes. The historical precipitation 

pattern reflects the general Pacific Northwest pattern, with most of the precipitation coming 

during the fall-winter-spring months, resulting in a relatively dry summer. All nine projections remain mostly above the 20-year moving average ranges (vertical bars) for monthly 

precipitation with a few dropping below the mean during the summer months (Figure 27a). 

This pattern contrasts with that seen for Lamar (Figure 23a). For mean annual temperature, all 

nine projections are above the long-term monthly means for all months (solid black line) but, 

in contrast with the projections for Lamar, Colorado, they still fall within the 50-year range of 

variability (compare Figures 27b and 23b).

Northeast

Historical temperature patterns in the Northeast region (states of Maine, Vermont, and New 

Hampshire) vary spatially with annual means ranging from 1.5 oC at high elevations in the 

center of the region to around 9 oC along the southern coast (Figure 28a). Annual precipitation 

is generally greatest along the coastal areas, but ranges from about 800 mm in the extreme 

north and west to over 2000 mm at locations in the south (Figure 28b).

The spatial patterns in projected mean daily temperature are visually similar across the three 

scenario means (Figure 29a,b,c). By 2060, the northern part of Maine is projected to see 

temperatures in the range of 6.5 oC to 7.5 oC, reflecting a 3 to 4 oC shift above the historical 

ranges of 2.5 oC to 4.5 oC. Historical temperatures seen only in the very southern part of New 

Hampshire (8.5 oC to 9.5 oC) are projected to occur by 2060 as far north as central and coastal 

Maine (Figure 29a,b,c), where 1961-1990 means ranged from 5.5 oC to 6.5 oC.
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

Figure 27. Observed annual precipitation (mm) (a) and monthly mean temperature (°C) (b) from 

the weather station at Lakeview, Oregon, compared with projections for 2060 (2045-2064) of 

annual precipitation (mm) and mean daily temperature (°C) for a 30 by 45 km region surrounding 

Lakeview. Projected monthly mean daily temperature (°C) and annual total precipitation (mm) 

(solid red lines) are from the nine scenario-model projections in this study. The historical monthly 

average (solid black) and 10th and 90th percentile values (dotted black lines) are based on all observations over the 1950-1999 period. The black bars on historical precipitation represent the 10th 

and 90th percentiles of the 20-year moving averages starting from 1897 through 2010.
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Figure 28. Annual mean daily temperature (°C) (a) and mean annual precipitation (mm) (b) for the historical period (1961-1990) 

based on PRISM climatology for the Northeastern region in the United States.
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

Figure 29. Projected annual mean temperatures (°C) for scenarios: B2, A1B and A2 (a, b, c), and change in mean daily 

temperature (°C) from historical period (1961-1990) to the 2060 period (2055-2064) as projected by models for B2 scenario: CGCM2, CSIRO-MK2, HadCM3 (d, g, j); for A1B scenario: CGCM3.1, CSIRO-MK3.5 and MIROC3.2 (e, h, 

k), and for A2 scenario: CGCM3.1, CSIRO-MK3.5, MIROC3.2 (f, i, l) for the Northeastern region in the United States.
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All model projections indicate increases in annual mean daily temperatures of 1.9 oC or more 

(Figure 29d,e,f,g,h,i,j,k,l), in a very consistent spatial pattern, although the magnitudes of 

these increases vary inconsistently among the scenarios. For the B2 scenario, two models 

project changes less than 3 oC in annual mean daily mean temperature (Figure 29d, CGCM2 

and Figure 29j, HadCM3). Conversely, the CSIRO-MK2 model with the B2 scenario (Figure 

29g) projects the greatest increases of all models and scenarios in the Northeastern region. 

Increases in annual mean temperature by 2060 projected by individual models differ somewhat for A1B and A2: CGCM3.1 projects slightly larger increases than CSIRO-MK3.5 when 

forced by the A2 (compare Figure 29f with Figure 29i), while the MIROC3.2 projects markedly greater warming with the A1B (Figure 29k). The change in temperature figures provide a 

clearer picture of climate change in the northeastern region than the figures showing projected 

temperatures where the underlying patterns of historical temperature dominate the patterns 

(Figure 29b versus Figure 29e, h, and k, for example).

We explore the frequency distributions of summer mean daily minimum temperature for all 

models projections for the A1B and A2 scenarios, comparing the historical period (1961-

1990) with the 30 years centered on 2060 (2045-2074) (Figure 30). The six projections are 

very consistent: historical minima range from about 6 oC, but by 2060, the lowest temperatures are projected to exceed nearly 9 oC. At the higher temperature end of the distributions, 

historical summer mean temperatures rarely exceeded 16 oC but these temperatures are 

projected to increase to around 20 oC by 2060. Consistent with other results, the A1B projections for the 2060 period show warmer temperatures overall than the A2 projections (compare 

Figure 30a with Figure 30b). Among the different models, the overlap between the historical 

and the projected is greater for CGCM3.1 and CSIRO-MK3.5 than for MIROC3.2.

Developing Composite Indices with the Downscaled Climate 

Projection Data: Aridity

Much of the western United States is classified as hyper-arid, arid, and semi-arid (Figure 31a) 

for the 1971-2000 period. Areas with higher rainfall, such as the Pacific Northwest region, 

are classified as humid. When the aridity classes are estimated for the 2060 period, relatively 

few grid cells in the West increase in aridity (where increase is defined as the shift into at least 

one more arid class). However there is a major consensus (seven or more projections agree) 

that large areas in the Midwest will increase in aridity (Figure 31b). Areas within the eastern 

United States that are projected to become more arid include the western parts of New York 

State, Pennsylvania, and Ohio, following areas along the Ohio River and to east of where 

the Ohio River meets the Mississippi River, including eastern Arkansas, north and central 

Missouri, and Alabama. The pattern in the western United States reflects the complex topography of this region.
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Figure 30. Frequency distribution of summer mean minimum temperature (oC) across the Northeastern region for the 

historical period (1961-1990) based on PRISM climatology and projected for the 30-year period surrounding 2060 

(2045-2074) by three climate models using the A1B scenario (a) and the A2 scenario (b).
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Figure 31. Aridity in the conterminous United States over the 1971-2000 period based on the UNEP aridity index (a) and 

agreement on projected changes in the aridity index by the 2060 period (2055-2064) where the shift in classification is to a 

more arid zone (could be more than 1 aridity zone). The scale is the number of projections that agree. If all nine agree, the 

grid is coded 9.
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County-Level Projection Data

The gridded climate data (historical, projected) were aggregated to county level to help facilitate interpretations at that scale (Figures 32 and 33). Typically, such data will be valuable for 

county scale analyses, such as economic or recreational analyses where the primary data are 

also available at the county spatial scale. One caution about summarizing county climate data 

to larger spatial scales is that, given that counties vary greatly in size across the conterminous 

United States, these data must be area-weighted if they are to be used to calculate regional or 

conterminous U.S.-scale averages. The gridded data will often be more appropriate to explore 

regional or national climate dynamics.

Figure 32. Observed mean daily temperature (°C) for the historical period (1961-1990) based on PRISM climatology (a) and projections of 

mean daily temperature (°C) based on the A1B scenario for the 2060 decade (2055-2064) at the county spatial scale for conterminous United 

States. The three models projecting the A1B scenario are (b) CGCM3.1, (c) CSIRO-MK3.5, and (d) MIROC3.2
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Figure 33. Observed annual precipitation (mm) for the historical period (1961-1990) based on PRISM climatology (a) and projections of 

mean annual precipitation (mm) based on the A1B scenario for the 2060 decade (2055-2064) at the county spatial scale for conterminous 

United States. The three models projecting the A1B scenario are (b) CGCM3.1, (c) CSIRO-MK3.5, and (d) MIROC3.2

Discussion __________________________________________

Model Robustness

The reliability of individual climate models has been explored by asking many different questions, including: How well do they simulate historical climate or extreme events? Do they 

capture well-known global climate phenomena such as the El Nino-Southern Oscillation 

or transient climate phenomena? How sensitive are the models to changes in atmospheric 

chemistry? How do results from different models compare in projecting large-scale patterns 

of climate change (e.g., vertical patterns (atmospheric) of temperature change, land-sea contrasts)? A number of complicated metrics have been developed to integrate and synthesize 
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

these many individual tests. Two points are important here. First, there is a growing sense of 

the challenge to identify the best model. Gleckler and others (2008) note:

Finally, in spite of the increasing use of metrics in the evaluation of 

models, it is not yet possible to answer the question often posed to 

climate models: “What is the best model?”

Second, Gleckler and others (2008) concluded that the answer to the above question “almost 

certainly will depend on the intended application.” Mote and others (2011) identify this consideration as the first on their list of criteria for selecting climate scenarios:

1) Understand to which aspect of climate your problem or decision 

is most sensitive (e.g., which climate variables, which statistical 

measures of these variables, and at what space and time scales).

We suggest that the reader consider carefully the intended application and how climate affects 

that renewable resource, management strategy, or policy under development. Such information will help identify which climate variables and projections will be most useful.

The RPA climate projections reported here were developed specifically to support U.S. 

national-level renewable resource analyses exploring the influence of drivers of change 

such as population dynamics, economic growth patterns, and climate (U.S. Forest Service 

2012a,b). These climate data are being used as input both for new renewable resource models 

under development and existing climate impact models. Socioeconomic analyses typically use 

county-level data, so this data set is available at the U.S. county scale and at the 5-arcminute 

grid scale. The climate variables identified for this analysis were monthly mean daily values 

of maximum and minimum temperature and monthly total precipitation. We discuss further 

potential uses of these RPA climate projection data in the next section.

Here, we review studies that have contrasted and compared the climate models used in this 

study with observed climate data, and with the dynamics of other climate models over small 

and large regions, over historical periods and with respect to future periods. Because use of 

these data focuses on the United States, we review literature focused on that geographic area 

as we are relying on the availability of tests that are relevant to our situation, e.g., comparisons focused on North America rather than other parts of the globe.

Climate Sensitivity

Climate sensitivity is a metric that has been used to quantify the potential degree of warming 

at the global scale associated with a doubling of atmospheric concentrations of greenhouse 

gases. Using observed climate data and proxy data for periods before the modern instrument record (e.g., ice cores), this metric explores the sensitivity of the global climate to 

past changes in greenhouse gases or other radiative forcings. Controlled experiments can be 

carried out with each climate model to assess the simulated climate sensitivity and to compare 

the sensitivities of different models to changes in the same forcing. Equilibrium climate sensitivity is defined as the equilibrium change in the annual mean global surface temperature following a doubling of the atmospheric equivalent CO2 concentration (IPCC 2007b).
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Randall and others (2007) and Meehl and others (2007b) review the different methods used 

to determine the climate sensitivity: (1) analysis of the historical transient evolution of temperature (observed surface and upper air temperatures, ocean temperatures, proxy data), and 

(2) experiments using current global climate models. Based on these different lines of evidence, Meehl and others (2007b) conclude that the global mean equilibrium warming for a 

doubling of equivalent CO2 concentration, or equilibrium climate sensitivity, is likely to lie 

in the range of 2 oC to 4.5 oC, with a most likely value of about 3 oC. In other words, if GHG 

emissions were to stabilize at a doubling of pre-industrial levels of CO2, then the global mean 

temperature is likely to increase and eventually stabilize at a level about 3 oC above the longterm global average temperature that would have been measured if GHG concentrations had 

been maintained at pre-industrial levels. The range (2 oC to 4.5 oC) reflects the variability in 

the observed data and modeling studies as well as current knowledge about the processes that 

determine global climate.

The Multi-Model Data (MMD) study explored the equilibrium climate sensitivity of 23 

global climate models (Randall and others 2007). The mean climate sensitivity in this 

modeling study was 3.3 oC with a 5 to 95 percent range of 2.1 oC to 4.4 oC. Models with 

greater climate sensitivity project greater warming per CO2 doubling when compared to 

models with a lower sensitivity. Three models used here were included in the MMD study, 

namely CGCM3.1(T47), MIROC3.2(medres), and HadCM3. Their climate sensitivities 

were about and above the mean; CGCM3.1(T47) had a climate sensitivity of 3.4 oC and 

MIROC3.2(medres), 4.0 oC, and HadCM3, 3.3 oC. The CGCM3.1(T47) model and the 

HadCM3 model are close to the 3.3 oC mean of all models analyzed. In this study, we used the 

CSIRO-MK3.5 whereas the MMD study included the earlier CSIRO-MK3.0 with a climate 

sensitivity of 3.1 oC. Climate sensitivity was also discussed in the IPCC’s Third Assessment 

Report by Cubasch and others (2001). They focused on 15 models in active use in the 2001 

IPCC assessment and summarized the equilibrium climate sensitivity as having a mean of 

3.5 oC and a standard deviation of 0.9 oC. The three models used here were included among 

those 15 models, but equilibrium sensitivities were provided only for CSIRO-MK2, 4.3 oC, 

and HadCM3, 3.3 oC.

New studies using observed and proxy data or modeling experiments contribute to our understanding of climate, and climate sensitivity. Randall and others (2007) note that the climate 

sensitivity of some climate models changed when newer versions used in the AR4 were compared to earlier versions of the same GCMs used in the TAR. Increased sensitivity was reported for the more recent UKMO-HadGEM1 compared to the HadCM3 model (used for B2 

scenarios here). Decreased sensitivity was reported for the CSIRO-MK3.0 model compared 

to the CSIRO-MK2 (used for B2 scenarios here) while sensitivity of the CGCM3.1(T47) 

(used here for A1B and A2 scenarios) remained relatively unchanged from the earlier version 

CGCM1. Randall and others (2007) conclude that cloud radiative feedbacks are the primary 

source of inter-model differences in climate sensitivity, although water vapor and surface 

albedo feedbacks are also important. Similarly new analyses of observed and proxy data 

explore further the transient evolution of temperature in response to past events. Mote and 

others (2011) note that “most studies estimate that there is at least a 5 percent chance that 

the sensitivity exceeds 7 oC to 9 oC for a doubling of atmospheric CO2” (see also Hegerl and 

others 2007). No climate model in the CMIP3 archive, where we obtained our projections, 
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represents such a low-likelihood, high-sensitivity future climate. Mote and others (2011) also 

suggest that a “full description of response uncertainty would also involve uncertainties in the 

time-evolving response, and in responses at subglobal scales and of variables other than temperature, which may be proportional to the climate sensitivity, whether on global or regional 

scales.”

We can conclude that the climate models used for the RPA Scenarios have climate sensitivities 

close to or slightly above the mean of the ranges determined for models used in the IPCC’s 

Third and Fourth Assessment Reports. For the B2 scenario, the HadCM3 model falls slightly 

below the mean and the CSIRO-MK2 model above the mean and both within the range 

identified by Cubasch and others (2001). For the A1B and A2 scenarios, the CGCM3.1(T47) 

and the HadCM3 fall on the mean or just above and the MIROC3.2 falls above the mean 

but well within the range identified in Randall and others (2007). The CSIRO-MK2 and 

the MIROC3.2 models clearly have higher sensitivity but are still well within the expected 

ranges. The higher sensitivity of the MIROC3.2(medres) model to increasing greenhouse gas 

emissions caused it to produce the warmest projections for some regions in this study, particularly the southern United States. Price and others (2011) found the results from MIROC3.2 

to be very similar to or even slightly cooler than the U.S. National Center for Atmospheric 

Research (NCAR) CCSM3 model when applied to Canada; Joyce and others (2011) found 

these similarities did not extend to the southern United States, where MIROC3.2 projects distinctly more warming than two other climate models. It is worth noting that MIROC3.2 has 

been shown to perform well according to studies discussed in this section, so it should not be 

dismissed simply because it is more climate-sensitive than most other GCMs applied to North 

America (see next section for further discussion of this point).

Model Agreement with Observations Globally and Regionally

Comparing the dynamics of global climate models with the dynamics of observed climate 

is an important step to determine model confidence (Reichler and Kim 2008), yet what to 

compare has been a much-discussed topic in the literature. Gleckler and others (2008) state 

that “it remains largely unknown what aspects of observed climate must be correctly simulated in order to make reliable predictions of climate change.” In other words, accurate modeling 

of the current mean and variability of one climate variable, for example precipitation, may or 

may not imply that the same model can make reliable temperature or precipitation projections 

of future climate. And while a suite of climate variables can be compared with observed data, 

Gleckler and others (2008) warn against using a single index (composite of many climate 

variables) to gauge model performance. Further, individual models, their different versions, 

different climate variables (precipitation versus zonal wind), historical periods (100 years or 

20 years), and the region of focus (global or individual regions) all influence the ranking of 

performance among climate models (Gleckler and others 2008). For example, when temperature versus sea level pressure is used to establish performance for multiple models, the model 

rankings differ. Models that simulate the 20th century well for the Northern Hemisphere (north 

of the tropics) can have lower skill in the tropics (Gleckler and others 2008). The observed 

climate record also has challenges: a lack of reliable and consistent observation over time 

in some regions of the world; and for some model output variables, atmospheric processes 

occurring at temporal or spatial scales that are not monitored or monitored routinely (Reichler 
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and Kim 2008). Nonetheless, comparisons have been made to satisfy a number of different 

interests. Climate modelers are often interested in comparing the performance of multiple 

models whereas users of climate projection data may be more interested in the performance of 

a small number of carefully selected models applied to a particular region.

Reichler and Kim (2008) quantify agreement between the global results and observations for 

57 models representing three generations of climate models. Using 14 climate variables and 

late 20th century simulations, they develop an aggregated index by first computing a normalized error variance of each climate variable (e.g. air temperature, zonal wind, precipitation, 

specific humidity, etc.) as compared to the observed interannual variance and averaging the 

grid results globally. The final model performance index is developed by taking the mean 

over all climate variables using equal weights. In this index, values greater than 1.0 represent underperforming models (i.e., relative to the mean) and values less than 1.0 represent 

models that agree with observed data better than the mean. Four of the six models used in 

our study were included in the analysis by Reichler and Kim (2008). Three of those models 

have an index less than 1.0, namely HadCM3 (CMIP3 and an earlier CMIP intercomparison 

project), CGCM3.1(T47), and MIROC3.2. In our study, we selected projections from the 

CSIRO-MK3.5 model version, but the Reichler and Kim (2008) study reported only on the 

CSIRO-MK3.0 version, which also achieved an index less than 1.0. Reichler and Kim (2008) 

concluded that the most recent generation of climate models, represented in AR4, would generally simulate present-day climate more realistically than the models associated with IPCC 

TAR or earlier assessments. Models used to simulate response to the B2 scenario in this study 

came from the TAR generation of models; this choice reflected the objective to have a breadth 

of socioeconomic futures in the RPA Assessment. Of these TAR-generation models, Reichler 

and Kim (2008) did not report on the CGCM2, while the HadCM3, previously mentioned, 

achieved an index of less than 1.0, and the CSIRO-MK2, an index of greater than 1.0

Global and regional comparisons were made by Gleckler and others (2008). Here we focus 

on their results for the Northern Hemisphere (north of the tropics). They compared the mean 

values of 26 climate variables from the CMIP3 20th century simulations (20C3M) of 22 

climate models with observed data over the 1980-1999 period. Relative model performance 

was ranked using a normalized root mean square error for each model and the mean of the 

relative errors of each climate output variable for all of the models considered. According to 

their analysis, the HadCM3 model performed better than the typical model for 18 of the 26 

climate variables analyzed, including air temperature at two heights (850 and 200 hectopascals) and precipitation. The CGCM3.1(T47) model performed better for 17 climate variables, 

also including air temperature at two different heights (850 and 200 hectopascals) and precipitation. The MIROC3.2 model performed better than the typical model for 13 climate variables, including temperature at 850 hectopascals and precipitation. The Gleckler and others 

(2008) study reported only on the CSIROMK3 model (CSIRO-MK3.5 was used in our study); 

CSIROMK3 performed better in six climate variables, including precipitation and temperature 

at one height.

While good performance in capturing mean observed values provides some confidence in 

a model, Gleckler and others (2008) suggest that other aspects of climate dynamics may 

also be important to assess. Using two indices, they compare ability of each of 22 models to 
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

capture interannual variability (variability skill) as well as their ability to capture the mean 

annual cycles (climate skill) for the Northern Hemisphere (north of the tropics). The UKMO 

HadCM3, and the CGCM3.1(T47) had relative errors less than the typical model for both 

variability skill and climate skill. The CSIRO-MK3 model performed better than the typical 

model for climate skill but less so for variability skill. Conversely, the MIROC3.2(medres) 

model was less skillful than the typical model in both climate skill and variability skill. Given 

caveats expressed by Gleckler and others (2008) concerning indices and performance, the key 

point in this comparison is that the performance based on annual means does not imply an 

equal skill in capturing climate variability.

Radic and Clarke (2011) focused on North America and the western region of North America, 

for the 1980-1999 period in their comparison of 22 models from the CMIP3 data base. Models 

that performed well over North America also performed well for the western region of North 

America (both United States and Canada). As with other studies, model ranking varied with 

the metric used. For example, while most models simulated the frequencies of daily anomaly 

patterns associated with the 20-year average daily pattern, few models could reproduce 

occurrences of characteristic daily weather patterns in the reference database on a seasonal 

basis. Using the variety of metrics studied, the top five performing models are ECHAM5 

(Max Planck Institute-Ocean Model), MIROC3.2(medres), MIROC3.2 (high resolution), 

CGCM3.1(T47), and CGCM3.1(t63) (Radic and Clarke 2011, Table 5). This list includes two 

of the models used in this study: MIROC3.2(medres) and CGCM3.1(T47).

Regional responses of the 23 models in the MMD study (Randall and others 2007) were 

compared to a global historical observed climate data set (Christensen and others 2007). For 

the western North American region (i.e., western Canada and the western United States), the 

median bias of all models in estimating annual mean temperature was –1.3 oC, i.e., the climate 

models generally estimated annual temperature to be cooler than historical observations. Bias 

of the models for central North America region was –0.5 oC, and for eastern North America, 

–1.2 oC. Bias in the individual climate simulations for the 20th century has been attributed to 

many limitations in the models. Regardless of the causes, this bias was removed (as far as 

possible) in the downscaling process used in our study, by subtracting out the means of the 

climate model estimates for the 1961-1990 period and using historical climate data for the 

same period at the 5-arcminute scale to provide the spatial context when constructing the 

future projections.

We can conclude that the models used to create future climate projections for the AR4 are, in 

general, likely to simulate present climate more accurately than the models used in the TAR 

report. Further, the AR4 models used in this study perform reasonably well when compared 

with many other AR4 models, and several independent studies indicate, depending upon the 

particular metrics used for comparison, that the models used here were in the top performing 

ranks. Of the models used for the B2 scenario in our study, HadCM3 is likely to be a more 

accurate model with respect to historical climate estimations than the CSIRO-MK2 model 

while little information is available to assess the performance of the CGCM2.
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Model Agreement with Observations Regionally

For the specific use of this data in the national analysis of renewable resources, it was critical 

to have a consistent data set at the national level. Hence, the climate projections developed 

here were used for all regions of the United States in the RPA analyses, in contrast to other 

studies using different climate models for different areas of the United States. Users of the 

RPA climate data are likely to be interested in the ability of these models to simulate regional 

climates. Hence, we review a variety of efforts that have compared different models or selected climate models for their analyses based on some criteria.

Focusing on the Pacific Northwest, Mote and Salathe (2009) used a number of metrics 

to compare the performance of 20 climate models hindcasting 20th century climate for an 

area including Washington, Oregon, Idaho, western Montana, and a small slice of adjacent 

states in the United States and British Columbia in Canada. Three of the models used in 

this study were included in the Mote and Salathe comparison: CGCM3.1_t47, MIROC_3.2 

medium resolution, and HADCM (in our terminology CGCM3.1(T47), MIROC3.2(T42), 

and HadCM3 respectively). Mote and Salathe (2009) compared the annual mean difference, 

or bias, between model output and the historical climate data for 1970-1999, and found that 

all models have a cold bias (mean of –1.8 oC) in replicating the historical mean annual temperature. Two models used in this study (CGCM3.1(T47) and MIROC3.2(medres)) showed 

a mean bias very similar to the overall model mean bias. Five of the 20 models had smaller 

biases than the two used here (i.e., they replicated the historical temperature of the region 

more closely). With respect to precipitation, CGCM_t47 (CGCM3.1(T47) in this study) was 

one of the top five models with the smallest bias. All 20 models had a wet bias in the Pacific 

Northwest, ranging from less than 10 mm to more than 60 mm (meaning mean annual precipitation simulated by each model exceeded the observation by any amount within this range). 

Mote and Salathe (2009) also compare the 20th century trend of each model’s annual mean 

temperature for the Pacific Northwest with the observed trend calculated from data from 

the U.S. Historical Climate Network. The observed trend was 0.8 oC over the last 100 years 

compared to 1.1 oC for CGCM3t_47 and only 0.1 oC for MIROC32. Finally, they used a 

Taylor diagram to compare the performance of all 20 models in simulating sea level pressure, 

precipitation and temperature of a larger geographic domain including much of the western 

United States, Canada, and the Pacific Ocean (Figure 6 in Mote and Salathe 2009). Overall, 

CGCM3.1(T47),which was used in this study, ranked the best, and MIROC32, also used in 

this study, ranked 9th out of the 20 models.

For the Northeastern United States, Hayhoe and others (2007) used nine climate models, 

including CGCM3.1 and HadCM3, both used in this study. They also used results from 

MIROC(medres), but no model version number was provided. They analyzed the ability of 

the nine models to reproduce historical climate and concluded that most were able to reproduce 100-year and 30-year trends in annual temperature. Annual temperatures over the 1900-

1999 period have risen 0.08 oC, ±0.01 oC per decade, compared to the nine model ensemble 

average of 0.08 oC, ±0.06 oC per decade. For projections of future temperature trends, interscenario and seasonal differences were consistent across 23 simulations (scenario and model 

combinations): temperature increases were projected to be greater under higher, as compared 

to lower emissions scenarios, with equal or greater increases in summer relative to winter; 

winter precipitation was projected to increase by 10-15 percent and summer precipitation 
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

showed either little change or a decrease. Hayhoe and others (2007) also noted that regional 

processes may be acting to enhance warming trends in the northeastern United States relative 

to the global average in a way not captured by these global-scale models.

Users of climate projection data may also be interested in studies that have explored the performance of the climate projections for specific resource uses. This literature is expanding 

and we point the reader to a few key papers. Brekke and others (2008) evaluated the ability of 

17 climate models from the CMIP3 data base for their credibility in hydroclimatological risk 

assessments. While they identified specific models as more capable of recreating some aspects 

of 20th century climate, when several of 19 metrics were combined (as in an index), all model 

performances were comparable among the 17 models. Anandhi and others (2011) analyzed the 

performance of 19 climate models from the CMIP3 database for their ability to estimate snow 

water equivalent in New York watersheds. They included details on which model version and 

realization were used in the study and categorized the models into three classes, based on skill 

in capturing snow water equivalent. Recognizing the importance of low-frequency variability 

in rainfall in water resource applications, Johnson and others (2011) assessed the ability of 

23 climate models to represent interannual variability and found significant differences in 

performance.

Models used in this study have been used in many studies exploring the effects of climate 

change. Rogers and others (2011) used climate projections from MIROC3.2(medres), 

HadCM3 (both used in this study), and CSIRO-Mk3 forced by the A2 scenario to explore 

the effects of climate change on vegetation in the Oregon-Washington area using the MC1 

dynamic vegetation model. Their representative range of projected future temperatures was 

the rationale for selecting these three climate models. For California, Cayan and others (2006) 

identified three climate models that captured a range of climate sensitivities: NCAR Parallel 

Climate Model (low climate sensitivity), Geophysical Fluids Dynamics Laboratory CM2.1 

(medium sensitivity), U. K. Hadley Centre Climate Model, version 3 (with higher sensitivity). 

Zhu and others (2011) used three climate models in a study of the potential effects of climate 

change on carbon dynamics in the Great Plains, including MIROC3.2(medres) and CGCM3.1, 

though they did not state which resolution; the medium resolution CGCM3.1 model was used 

in this study.

Use of the Climate Projection Data

Change Factor Data

We use the change factor data developed for the conterminous United States by Price and 

others (2011a) and Joyce and others (2011); these data can be found online (http://www.

fs.usda.gov/rds/archive/, Price and others 2011b). Change factors have been developed for 

Canada (Price and others 2011a). Change factors were also developed for Alaska (Joyce and 

others 2011) and can be found online (http://www.fs.usda.gov/rds/archive/, Price and others 

2011c). The historical climate data used in this study were based on the PRISM historical 

gridded climatology (available: http://www.fs.usda.gov/rds/archive/, Coulson and Joyce 

2010a,b). The change factor data have utility to users who have their own historical climatology or wish to use one of the many historical climatologies that are available, such as VEMAP 
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USDA Forest Service Gen. Tech. Rep. RMRS-GTR-320. 2014

(Kittel and others 2004), Rehfeldt (2006), and DAYMET (see http://www.daymet.org/default.

jsp). Price and others (2011a) and Joyce and others (2011) used the North American climatology developed by McKenney and others (2006b, 2011) and the same climate model data 

sources used in the present study to develop projections for Canada and the continental United 

States, including Alaska.

Climate Projection Data

Projections of possible climatic outcomes are not predictions, but should be considered as 

equally likely alternative scenarios of future climate. The process of creating these data begins 

with global climate models shown to give reasonably correct representations of observed 

climate at very coarse scales, as described in the previous section. Bias in each model’s 

ability to simulate 20th century mean values is removed by converting all data to “change 

values” calculated relative to a reference period (taken in the present study as 1961-1990), and 

then downscaled to a 5-arcminute grid. These change values (ratios or differences) are then 

applied to the historical climatology to produce data that capture the spatial characteristics of 

observed climate variables combined with the changes in these variables projected by each 

climate model. Each projection attempts to capture trends that would occur under a given set 

of socioeconomic assumptions (i.e., the IPCC SRES emissions scenarios). The data should not 

be considered as forecasts, but as a single plausible outcome for a given set of assumptions.

With each step in the modeling process and in the downscaling process, assumptions enter 

into the analysis (Meehl and others 2007b; Price and others 2011a). Each climate model has 

an underlying set of assumptions/caveats, e.g., in the parameterizations of “sub-gridscale 

processes” such as cloud dynamics. Further assumptions are captured in their individual 

input data, such as the representation of global soils and vegetation types (Randal and others 

2007). When the climate model simulations are completed, the output data can be viewed as 

estimates of the average of each prognostic variable over a large area. The process of decomposing these “averages” to a finer scale incorporates additional assumptions, while basing 

the work on station data gives a greater sense of realism. Station data are not necessarily the 

“truth” as there are underlying issues with data quality, measurement errors, and missing 

data. Further, station data are not uniformly distributed across a heterogeneous landscape. 

The interpolation used to bring these data to a common grid is itself a form of modeling. It is 

important to recognize these underlying assumptions and limitations when using the data.

While the data are presented at the grid level, these data are modeled and interpolated data 

from coarser grids. As such, they probably should not be viewed as a precise estimate of the 

grid average. Rather the data are best viewed as a point value within the grid, much as station 

data are point-based measurements of many possible outcomes in an area and not necessarily 

the average value for the area. Using these grid level data in models is appropriate; however, 

reporting of trends is only recommended across an aggregation of grid cells.

With these nine model projections presented here, researchers can examine the effects of alternative climate futures in their own assessment methods (or models). Users should also keep in 

mind that each scenario reflects a set of assumptions about global population growth and economic development. Three climate model projections are available for each of the three IPCC 

SRES scenarios. We recommend that each projection be used as separate input to the resource 
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

model(s); such an approach allows the researcher/analyst to capture a range of possible outcomes from the effects of changes in temperature and precipitation as well as from the socioeconomic drivers that determine the projected greenhouse gas emissions levels. Results from 

multiple simulations of the resource model could then be averaged if the analysis warrants it. 

If nine runs of the resource model are prohibitive, selecting those climate scenarios that seem 

the best match/fit to the researcher’s needs (e.g. provide a range of outcomes over the region 

of interest) is prudent. One approach would be to plot all the model projections using a graph, 

such as Figures 5, 10, or 26 for the region of interest, to determine a small subset of the nine 

scenarios that best cover the complete range of possibilities. Another approach would be to 

average the three climate projections within a given emissions scenario, recognizing that such 

averaging will suppress a lot of the simulated year-to-year variability generated by each model 

individually. As seen in Figures 7 and 9 (also Figures 13 and 14), the individual models within 

each scenario can differ greatly in their projected changes. Harding and others (2012) suggest 

that impact analyses relying on one or a few climate scenarios are unacceptably influenced by 

the choice of projections. Hence, if the use of an ensemble (average) of projections is desired, 

Mote and others (2011) recommend using the ensemble to characterize consensus not only 

about the projected mean but also about the range and other aspects of variability. If the analysis also uses the underlying socioeconomic projections, averaging across SRES scenarios is 

not recommended as that could be viewed as averaging different socioeconomic assumptions 

(Manning and others 2010).

Availability of Climate Projection Data

The historical climate data and the downscaled climate projections are available online both 

as gridded and as county level (aggregated grid cells) data (Table 8). Data for four climate 

variables (monthly mean daily minimum temperature, monthly mean daily maximum temperature, monthly total precipitation, and mean daily potential evapotranspiration) are included in 

the gridded and the county level data sets. The gridded climate change factors are also available separately for Alaska and the conterminous United States (Table 8). Metadata documentation following international standards is available for each climate data set.

We provide these data in ASCII text format for the convenience of most users, rather than 

formats commonly used by large-scale modelers, such as netCDF. The projection data 

set for the county spatial scale is available in nine files where each zipped file contains 

the climate projection data (2001-2100) for one scenario (A1B, A2, or B2) as modeled by 

one climate model (GCGM3.1, CSIRO-Mk3.5, MIROC3.2 for scenarios A1B and A2, 

or CGCM2, CSIROMK2 or HadCM3 for scenario B2) for all counties in the conterminous United States. The gridded data set is available in ten individually downloadable 

zipped files, where each file contains the projection data for the specified scenario (A1B, 

A2, B2) as modeled by the three climate models associated with each scenario (GCGM3.1, 

CSIRO-MK3.5, MIROC3.2 for scenarios A1B and A2, or CGCM2, CSIR-MK2 or HadCM3 

for scenario B2) for all grid cells in the conterminous United States. Each of the ten zipped 

files contains three comma-delimited ASCII text files, one for each model and for one decade. 
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USDA Forest Service Gen. Tech. Rep. RMRS-GTR-320. 2014 Table 8—RPA climate change projections, source climate models, and spatial scale at which the downscaled results are available through the RMRS archive website (www.fs.fed.us/rm/data_archive/). Spatial scale Climate data Citation for meta-documentation and weblink 5-arcminute Climate projections using Coulson, David P.; Joyce, Linda A.; Price, David T.; McKenney, Daniel W.; Siltanen, R. Martin; Papadopol, Pia; Lawrence SRES scenarios A1B and Kevin. 2010c. Climate Scenarios for the conterminous United States at the 5 arc minute grid spatial scale using SRES A2 and PRISM climatology scenarios A1B and A2 and PRISM climatology. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky for conterminous United Mountain Research Station. Available: http://dx.doi.org/10.2737/RDS-2010-0017 States 5-arcminute Climate projections using Coulson, David P.; Joyce, Linda A.; Price, David T.; McKenney, Daniel W. 2010d. Climate Scenarios for the SRES scenarios B2 and conterminous United States at the 5 arc minute grid spatial scale using SRES scenario B2 and PRISM climatology. PRISM climatology for Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. conterminous United States Available: http://dx.doi.org/10.2737/RDS-2010-0018 5-arcminute Change factors for SRES Price, David T.; McKenney, Daniel W.; Siltanen, R. Martin; Papadopol, Pia; Lawrence, Kevin; Joyce, Linda A.; Scenarios A1B, A2 for Coulson, David P. 2011c. High resolution interpolation of climate scenario change factors for Alaska derived from AR4 Alaska General Circulation Model simulations. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. Available: http://dx.doi.org/10.2737/RDS-2011-0022

5-arcminute Change factors for SRES Price, David T.; McKenney, Daniel W.; Siltanen, R. Martin; Papadopol, Pia; Lawrence, Kevin; Joyce, Linda A.; 

Scenarios A1B, A2 for the Coulson, David P. 2011b. High resolution interpolation of climate scenario change factors for the conterminous USA

conterminous United States derived from AR4 General Circulation Model simulations. Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. Available: http://dx.doi.org/10.2737/RDS-2011-0023 

5-arcminute Historical climate data Coulson, David P.; Joyce, Linda A. 2010b. Historical climate data (1940-2006) for the conterminous United States at the

(1946-2006) using PRISM 5 arcminute grid spatial scale based on PRISM climatology. Fort Collins, CO: U.S. Department of Agriculture, Forest

climatology for conterminous Service, Rocky Mountain Research Station. Available: http://dx.doi.org/10.2737/RDS-2010-0011

United States

County Climate projections using Coulson, David P.; Joyce, Linda A.; Price, David T.; McKenney, Daniel W.; Siltanen, R. Martin; Papadopol, Pia; 

SRES scenarios A1B and Lawrence, Kevin. 2010a. Climate Scenarios for the conterminous United States at the county spatial scale using SRES

A2 and PRISM climatology scenarios A1B and A2 and PRISM climatology. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky

for conterminous United Mountain Research Station. Available: http://dx.doi.org/10.2737/RDS-2010-0008

States

County Climate projections using Coulson, David P.; Joyce, Linda A.; Price, David T.; McKenney, Daniel W. 2010b. Climate Scenarios for the

SRES scenarios B2 and conterminous United States at the county spatial scale using SRES scenario B2 and PRISM climatology. Fort Collins, 

PRISM climatology for CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 

conterminous United States Available: http://dx.doi.org/10.2737/RDS-2010-0009

County Historical climate data Coulson, David P.; Joyce, Linda A. 2010a. Historical Climate data (1940-2006) for the conterminous United States at

(1940-2006) based on the county spatial scale based on PRISM climatology. Fort Collins, CO: U.S. Department of Agriculture, Forest

PRISM climatology for Service, Rocky Mountain Research Station. Available: http://dx.doi.org/10.2737/RDS-2010-0010

conterminous United States 
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

The individual gridded data files are still large, and as a result, most text reading software, such as Notepad, Wordpad, and Microsoft Word, are unable to open or completely 

open these files for viewing or processing. This is also true for Access and Excel. 

However, Excel 2007 will load the front end of the file for viewing, but as it has a limit 

of 1,048,578 lines of data, only 10 percent of the file will be loaded. Thus, users should 

ensure the software they are planning to use to process the data has the capability to 

handle files of this magnitude (the authors used SAS (Statistical Analysis System)).

Summary and Conclusons _____________________________

This study developed historical climate data and downscaled climate scenario data (derived 

from climate model projections) to be used in concert with population projections, economic 

growth projections, and land use projections in the 2010 RPA Assessment. These data were 

developed for researchers and others who are interested in using climate data to model or 

assess the effects of climate change on natural resources within the conterminous United 

States. The specific applications of this data in the RPA Assessment are in the resource models 

of forest condition, water supply/use projections, wildlife habitat, recreation participation, and 

amenity migration analyses. These data will also be useful input for other applications exploring the impact of climate change on resource management issues in other settings.

In order to provide a broad set of alternative socio-economic futures for the RPA analysis, 

SRES scenarios A1B, A2, and B2 were selected. This study used projections from the following climate models: CGCM3.1(T47), CSIRO-MK3.5(T63) and MIROC3.2 (T42), each forced 

using the A1B and A2 emissions scenarios, as used in the IPCC’s Fourth Assessment. For the 

B2 forcing scenario, we selected simulations generated by the CGCM2, CSIRO-MK2, and 

HadCM3 global climate models, as used in the IPCC’s Third Assessment. These models have 

been used in a number of climate change impact studies and they have been compared with 

other climate models extensively in the literature. Generally, we can conclude from the literature that climate models used in the Fourth IPCC Assessment are likely to simulate present 

climate more accurately than the models used in the Third Assessment. The models used for 

the A1B and A2 scenarios in this study, as evaluated in the literature, perform reasonably well 

for a variety of tests when compared with many other AR4 models, and depending upon the 

metric used for comparison, models used here were among the top performers. Of the models 

used for the B2 scenario in our study, HadCM3 is likely a more accurate model with respect 

to historical climate estimation than the CSIRO-MK2 model. Little information was available 

from published literature to assess the performance of the CGCM2 model. The global climate 

data were downscaled for the conterminous region of the United States to a 5-arcminute grid 

using the delta technique for downscaling. In total, nine individual model projections (three 

scenarios and three models each) were developed.

All model projections forced by each of the three emissions scenarios project increased temperatures across the entire conterminous United States. Results for the A1B and A2 model 

projections are generally similar in magnitude over the next 50 years (i.e., consistent with 

long-term trends projected by other climate models) both regionally and globally. By 2100, 

however, simulations forced with the A2 scenario generally project the greatest warming, 

again consistent with projections of most climate models. On average, models forced by the 
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USDA Forest Service Gen. Tech. Rep. RMRS-GTR-320. 2014

B2 scenario project the least warming by 2060 and 2100. Precipitation projections have less 

consistency among models and among scenarios and at the conterminous U.S. scale, it is difficult to detect a trend over the next 50 or 100 years. However, precipitation at regional scales 

shows some trends; generally projections show increases in precipitation for the northern 

regions.

We explore the use of different types of summarizations and graphical presentations at 

regional scales to show variation in temperature and precipitation projections at the regional 

scale. In the Southeast, both summer maximum and minimum temperatures are projected to 

increase across the region by at least 1.1 oC by mid-21st century and by up to 7 oC in the case 

of maximum temperature. Using the frequency distribution to explore temperature change 

in the Northern Great Plains indicates the spring mean daily minimum temperature will drop 

below 0 oC less frequently in the future. Historically, 40 percent of spring mean minimum 

temperature observations in the Northern Great Plains were below freezing, but by midcentury, more than 90 percent of the grid cell temperatures are projected to be above freezing. 

In the Southern Great Plains by mid-century, projected spring maximum temperatures overlap 

with and exceed the historical summer mean temperatures by as much as 6 oC. Results of a 

point-based comparison for two climate stations (Lamar, Colorado, and Lakeview, Oregon) 

project upward shifts in mean temperatures for all months at both locations; however, only the 

July and August mean temperature projections for the Lamar, Colorado, site are statistically 

outside of the 90th historical percentile. In the Pacific Northwest region, changes in minimum 

temperature across all seasons are projected to be greater than 1.4 oC by mid-century. 

Projected temperature increases in winter and spring brings these seasonal minimum temperatures close to, if not above, freezing in the eastern parts of this region.

The climate projection data are available for all nine projections at the 5-arcminute grid scale 

as well as aggregated to the U.S. county scale. Monthly projection data are available from 

2001 to 2100. The change factor data (“the deltas”) are available for users who may wish to 

apply these factors to their own historical climatology or other available climatologies. These 

data are available online through the U.S. Forest Service Research and Development data 

archive website (http://www.fs.usda.gov/rds/archive/).

References __________________________________________

Anandhi, A.; Frei, A.; Pradhanang, S.M.; Zion, M.S.; Pierson, D.C.; Schneiderman, E.M. 

2011. AR4 climate model performance in simulating snow water equivalent over Catskill 

Mountain watersheds, New York, USA. Hydrological Processes 25: 3302-3311.

Bachelet, D.; Neilson, R.P.; Hickler, T.; Drapek, R.J.; Lenihan, J.M.; Sykes, M.T.; Smith, B.; 

Sitch, S.; Thonicke, K. 2003. Simulating past and future dynamics of natural ecosystems in 

the United States. Global Biogeochemical Cycles 17: 1045. doi:10.1029/2001GB001508, 

2003.

Bachelet, D.; Lenihan, J.; Drapek, R.; Neilson, R.P. 2008. VEMAP vs VINCERA: A DGVM 

sensitivity to differences in climate scenarios. Global and Planetary Change 64: 38-48.
73

Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

Bowker, J.M.; Askew, A.E.; Cordell, H.K.; Betz, C.J.; Zarnoch, S.J.; Seymour, L. 2012. 

Outdoor Recreation Participation in the United States – Projections to 2060: A technical 

document supporting the Forest Service 2010 RPA Assessment. Gen. Tech. Rep. SRS-GTR160. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research 

Station. 34 p.

Brekke, L.D.; Dettinger, M.D.; Maurer, E.P.; Anderson, M. 2008. Significance of model credibility in estimating climate projection distributions for regional hydroclimatological risk 

assessments. Climatic Change 89: 371–394.

Cayan, D.; Luers, A.L.; Hanemann, M.; Franco, G. 2006. Scenarios of climate change in 

California: an overview. CEC-500-2005-1896-SF. California Energy Commission. Online: 

http://www.energy.ca.gov/2005publications/CEC-500-2005-186/CEC-500-2005-186-SF.

PDF (accessed 1-18-2013).

Christensen, J.H.; Hewitson, B.; Busuioc, A.; Chen, A.; Gao, X.; Held, I.; Jones, R.; Kolli, 

R.K.; Kwon, W.-T.; Laprise, R.; Magaña Rueda, V.; Mearns, L.; Menéndez, C.G.; Räisänen, 

J.; Rinke, A.; Sarr, A.; Whetton, P. 2007: Regional climate projections. In: Solomon, S.; 

Qin, D.; Manning, M.; Chen, Z.; Marquis, M.; Averyt, K.B.; Tignor, M.; Miller, H.L. (eds.). 

Climate change 2007: The physical science basis. Contribution of Working Group I to the 

Fourth Assessment Report of the Intergovernmental Panel on Climate Change Cambridge, 

UK: Cambridge University Press: 847-940.

Cordell, H.K.; Heboyan, V.; Santos, F.; Bergstrom, J.C. 2011. Natural amenities and 

rural population migration: a technical document supporting the Forest Service 2010 

RPA Assessment. Gen. Tech. Rep. SRS-GTR-146. Asheville, NC: U.S. Department of 

Agriculture Forest Service, Southern Research Station. 23 p.

Coulson, D.P.; Joyce, L.A.; Price, D.T.; McKenney, D.W.; Siltanen, R.M.; Papadopol, P.; 

Lawrence, K. 2010a. Climate scenarios for the conterminous United States at the county 

spatial scale using SRES scenarios A1B and A2 and PRISM climatology. Fort Collins, CO: 

U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. Online: 

http://dx.doi.org/10.2737/RDS-2010-0008 (accessed 1-18-2013).

Coulson, D.P.; Joyce, L.A.; Price, D.T.; McKenney, D.W. 2010b. Climate scenarios for the 

conterminous United States at the county spatial scale using SRES scenario B2 and PRISM 

climatology. Fort Collins, CO: U.S. Department of Agriculture, USDA Forest Service, 

Rocky Mountain Research Station. Online: http://dx.doi.org/10.2737/RDS-2010-0009 

(accessed 1-18-2013).

Coulson, D.P.; Joyce, L.A.; Price, D.T.; McKenney, D.W.; Siltanen, R.M.; Papadopol, P.; 

Lawrence, K. 2010c. Climate scenarios for the conterminous United States at the 5 arcminute grid spatial scale using SRES scenarios A1B and A2 and PRISM climatology. Fort 

Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station. Online: http://dx.doi.org/10.2737/RDS-2010-0017 (accessed 1-18-2013).

Coulson, D.P.; Joyce, L.A.; Price, D.T.; McKenney, D.W. 2010d. Climate scenarios for the 

conterminous United States at the 5 arcminute grid spatial scale using SRES scenarios B2 

and PRISM climatology. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station. Online: http://dx.doi.org/10.2737/RDS-2010-0018 

(accessed 1-18-2013).

Coulson, D.P.; Joyce, L.A. 2010a. Historical Climate data (1940-2006) for the conterminous 

United States at the county spatial scale based on PRISM climatology. Fort Collins, CO: 

U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. Online: 

http://dx.doi.org/10.2737/RDS-2010-0010 (accessed 1-18-2013).
74

USDA Forest Service Gen. Tech. Rep. RMRS-GTR-320. 2014

Coulson, D.P.; Joyce, L.A. 2010b. Historical climate data (1940-2006) for the conterminous 

United States at the 5 arcminute grid spatial scale based on PRISM climatology. Fort 

Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station. Online: http://dx.doi.org/10.2737/RDS-2010-0011 (accessed 1-18-2013).

Cubasch, U.; Meehl, G.A.; Boer, G.-J.; Stouffer, R.J.; Dix, M.; Noda, A.; Senior, C.A.; 

Raper, S.; Yap, K.S. 2001. Projections of future climate change. In: Houghton, J.T.; Ding, 

Y.; Griggs, D.J.; Noguer, M.; van der Linden, P.J.; Dai, X.; Maskell, K.; Johnson C.A. 

(eds.).Climate change 2001: The scientific basis. Contribution of Working Group I to the 

Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, 

UK: Cambridge University Press: 526-582.

Daly C.; Neilson R.P.; Phillips D.L. 1994. A statistical-topographic model for mapping climatological precipitation over mountainous terrain. Journal of Applied Meteorology 33: 

140–158.

Daly, C.; Gibson, W.P.; Taylor, G.H.; Johnson, G.L.; Pasteris, P. 2002. A knowledge-based 

approach to the statistical mapping of climate. Climate Research 22: 99-113.

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.

Foti, R.; Ramirez, J.A.; Brown, T.C. 2012. Vulnerability of U.S. water supply to shortage: a 

technical document supporting the Forest Service 2010 RPA Assessment. Gen. Tech. Rep. 

RMRS-GTR-295. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 147 p.

Gibson, W.; Daly, C.; Kittel, T.; Nychka, D.; Johns, C.; Rosenbloom, N.; McNab, A.; Taylor, 

G. 2002. Development of a 103-year high-resolution climate data set of the conterminous 

United States. Proceedings of the 13th AMS Conference on Applied Climatology; May 

13-16, 2002; Portland, OR. Boston, MA: American Meteorological Society: 181-183. 

Online: http://ams.confex.com/ams/13ac10av/techprogram/programexpanded_99.htm or 

http://prism.oregonstate.edu/docs/index.phtml.

Gleckler, P.J.; Taylor, K.E.; Doutriaux, C. 2008. Performance metrics for climate models. 

Journal of Geophysical Research: Atmospheres 113: D06104, doi: 10.1029/2007JD008972.

Greenfield, E.J.; Nowak, D.J. 2013. Tree cover and aridity projections to 2060: a technical 

document supporting the Forest Service 2010 RPA Assessment. Gen. Tech. Rep. NRSGTR-125. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern 

Research Station. 35 p.

Hamlet, A.F.; Lettenmaier, D.P. 1999. Effects of climate change on hydrology and water 

resources in the Columbia River Basin. Journal of the American Water Resources 

Association 35: 1597-1623.

Harding, B.L.; Wood, A. W.; Prairie, J.R. 2012. The implications of climate change scenario 

selection for future streamflow projection in the Upper Colorado River Basin Hydrology 

and Earth System Science: 16, 3989–4007.

Hayhoe, K.; Wake, C.P.; Huntington, T.G.; Luo, L.; Schwartz, M.D.; Sheffield, J.; Wood, 

E.; Anderson, B.; Bradbury, J.; DeGaetano, A.; Troy, T.J.; Wolfe, D. 2007. Past and future 

changes in climate and hydrological indicators in the U.S. Northeast. Climate Dynamics 28: 

381-407.
75

Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

Hegerl, G.C.; Zwiers, F.W.; Braconnot, P.; Gillett, N.P.; Luo, Y.; Marengo Orsini, J.A.; 

Nicholls, N.; Penner, J.E.; Stott, P.A. 2007. Understanding and attributing climate change. 

In: Solomon, S.; Qin, D.; Manning, M.; Chen, Z.; Marquis, M.; Averyt, K.B.; Tignor, 

M.; Miller, H.L. (eds.). Climate change 2007: The physical science basis. Contribution 

of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on 

Climate Change. Cambridge, UK: Cambridge University Press: 663-745.

Hutchinson, M.F. 2010. ANUSPLIN Software Version 4.3. Online: http://fennerschool.anu.

edu.au/research/software-datasets/anusplin (accessed 1-18-2013).

Hutchinson, M.F.; McKenney D.W.; Lawrence, K.; Pedlar, J.H.; Hopkinson, R.F.; Milewska, 

E.; Papadopol, P. 2009. Development and testing of Canada-wide interpolated spatial 

models of daily minimum–maximum temperature and precipitation for 1961–2003. Journal 

of Applied Meteorology and Climatology 48: 726–741.

IPCC [Intergovernmental Panel on Climate Change]. 2001a. Climate change 2001: Synthesis 

report. Contribution of Working Groups I, II, and III to the Third Assessment Report of 

the Intergovernmental Panel on Climate Change. Watson, R.T. and the Core Writing Team 

(eds.). Cambridge, UK: Cambridge University Press. 398 p.

IPCC [Intergovernmental Panel on Climate Change]. 2001b. Climate change 2001: The 

scientific basis. Contribution of Working Group I to the Third Assessment Report of the 

Intergovernmental Panel on Climate Change. Houghton, J.T.; Ding, Y.; Griggs, D.J.; 

Noguer, M.; van der Linden, P.J.; Dai, X.; Maskell, K.; Johnson, C.A. (eds.). Cambridge, 

UK: Cambridge University Press. 881 p.

IPCC [Intergovernmental Panel on Climate Change]. 2007a. Climate change 2007: Synthesis 

report. Contribution of Working Groups I, II and III to the Fourth Assessment. Report of 

the Intergovernmental Panel on Climate Change. Core Writing Team, Pachauri, R.K and 

Reisinger, A. (eds.). Geneva, Switzerland: IPCC. 104 p.

IPCC [Intergovernmental Panel on Climate Change]. 2007b. Glossary. In Annex I [A.P.M. 

Baede (ed)]. In: Contribution of Working Group I to the Fourth Assessment Report of the 

Intergovernmental Panel on Climate Change. Solomon, S.; Qin, D.; Manning, M.; Chen, 

Z.; Marquis, M.; Averyt, K.B.; Tignor, M.; Miller, H.L. (eds.). Geneva, Switzerland: IPCC: 

941-954.

IPCC-TGICA [Intergovernmental Panel on Climate Change, Task Group on Data and 

Scenario Support for Impact and Climate Assessment].2007. General guidelines on the use 

of scenario data for climate impact and adaptation assessment. Online: http://www.ipccdata.org/guidelines/TGICA_guidance_sdciaa_v2_final.pdf. 66 p.

Johnson, F.; Westra, S.; Sharma, A. 2011. An assessment of GCM skill in simulating persistence across multiple time scales. Journal of Climate 24: 3609-3623.

Joyce, L.A.; Birdsey, R., tech. eds. 2000. The impact of climate change on America’s forests. 

Gen. Tech. Rep. RMRS-GTR-59. Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 133 p.

Joyce, L.A.; Price, D.T.; McKenney, D.W.; Siltanen, R.M.; Papadopol, P.; Lawrence, K.; 

Coulson, D.P. 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. 87 p.

Kingston, D.G.; Todd, M.C.; Taylor, R.G.; Thompson, J.R.; Arnell, N.W. 2009. Uncertainty in 

the estimation of potential evapotranspiration under climate change. Geophysical Research 

Letters: 36, L20403, doi:10.1029/2009GL040267.
76

USDA Forest Service Gen. Tech. Rep. RMRS-GTR-320. 2014

Kittel, T.G.F.; Rosenbloom, N.A.; Royle, J.A.; Daly, C.; Gibson, W.P.; Fisher, H.H.; Thornton, 

P.; Yates, D.N.; Aulenbach, S.; Kaufman, C.; McKeown, R.; Bachelet, D.; Schimel, D.S.; 

VEMAP2 Participants. 2004. VEMAP Phase 2 bioclimatic database. I. Gridded historical 

(20th century) climate for modeling ecosystem dynamics across the conterminous USA. 

Climate Research 27: 151-170.

Lamarque, J.-F.; Kiehl, J.; Brasseur, G.; Butler, T.; Cameron-Smith, P.; Collins, W.D.; Collins, 

W.J.; Granier, C.; Hauglustaine, D.; Hess, P.; Holland, E.; Horowitz, L.; Lawrence, M.; 

McKenna, D.; Merilees, P.; Prather, M.; Rasch, P.; Rotman, D.; Shindell, D.; Thornton, OP. 

2005. Assessing future nitrogen deposition and carbon cycle feedback using a multi-model 

approach. Part 1: Analysis of nitrogen deposition. Journal of Geophysical Research 110: 

D19303 Online: doi:10.1029/2005JD005825.

Lenihan, J.M.; Bachelet, D.; Neilson, R.P.; Drapek, R.J. 2008. Simulated response of conterminous United States ecosystems to climate change at different levels of fire suppression, 

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

Linacre, E.T. 1977. A simple formula for estimating evaporation rates in various climates 

using temperature data alone. Agricultural Meteorology 18: 409-424.

Lobell, D.B.; Bonfils,C.; Duffy, P.B. 2007. Climate change uncertainty for daily minimum 

and maximum temperatures: A model inter-comparison. Geophysical Research Letters 34: 

L05715. doi:10.1029/2006GL028726

Manning, M.R.; Edmonds, J, E.; Grubler, A.; Hibbard, K.; Joos, F.; Kainuma, M.; Keeling, 

R.F.; Kram, T.; Manning, A.C.; Meinshausen, M.; Moss, R.; Nakicenovic, N.; Riahi, K.; 

Rose, S.K.; Smith, S.; Swart, R.; van Vuuren, D.P. 2010. Misrepresentation of the IPCC 

CO2 emission scenarios. Nature Geoscience 3: 376–377.

McKenney, D.W.; Price, D.T.; Papadapol, P.; Siltanen, R.M; Lawrence, K. 2006a. Highresolution climate change scenarios for North America. Frontline Technical Note 107. Sault 

Ste. Marie, ON, Canada: Canadian Forestry Service, Great Lakes Forestry Centre. 18 p.

McKenney, D.W.; Pedlar, J.H.; Papadopol, P.; Hutchinson, M.F. 2006b. The development of 1901–2000 historical monthly climate models for Canada and the United States. 

Agricultural and Forest Meteorology 138: 69–81.

McKenney, D.W.; Papadapol, P.; Campbell, K.; Lawrence, K.; Hutchinson, M.F. 2006c. 

Spatial models of Canada- and North America-wide 1971/2000 minimum and maximum 

temperature, total precipitation and derived bioclimatic variables. Frontline Technical 

Note 106. Sault Ste. Marie, ON, Canada: Canadian Forestry Service, Great Lakes Forestry 

Centre.

McKenney, D.W.; Hutchinson, M.F.; Papadopol, P.; Lawrence, K.; Pedlar, J.; Campbell, 

K.; Milewska, E.; Hopkinson, R.T.; Price, D.; Owen, T. 2011. Customized spatial climate 

models for North America. Bulletin of the American Meteorology Society 92: 1611-1622. 

doi: 10.1175/BAMS-D-10-3-3132.1.

Meehl, G.A.; Covey, C.; Delworth, T.; Latif, M.; McAvaney, B.; Mitchell, J.F.B.; Stouffer, 

R.J.; Taylor, K.E. 2007a. The WCRP CMIP3 multimodel data set. Bulletin of the American 

Meteorological Society 88: 1383–1394. doi: http://dx.doi.org/10.1175/BAMS-88-9-1383.

Meehl, G.A.; Stocker, T.F.; Collins, W.D.; Friedlingstein, P.; Gaye, A.T.; Gregory, J.M.; Kitoh, 

A.; Knutti, R.; Murphy, J.M.; Noda, A.; Raper, S.C.B.; Watterson, I.G.; Weaver, A.J.; Zhao, 

Z.-C. 2007b: Global Climate Projections. In: Solomon, S.; Qin, D.; Manning, M.; Chen, 

Z.; Marquis, M.; Averyt, K.B.; Tignor, M.; Miller, H.L. (eds.). Climate change 2007: The 

physical science basis. Contribution of Working Group I to the Fourth Assessment Report 

of the Intergovernmental Panel on Climate Change Cambridge, UK: Cambridge University 

Press:ManningMiller: 747-845
77

Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

Miller, N.I.; Bashford, K.E.; Strem, E. 2003. Potential impacts of climate change on 

California hydrology. Journal of the American Water Resources Association 39: 771-784.

Mote, P.W.; Salathé, E.P. 2009. Future climate in the Pacific Northwest. In: The Washington 

Climate Change impacts assessment: Evaluating Washington’s future in a changing climate. 

Seattle, WA: University of Washington, Climate Impacts Group.

Mote, P.; Brekke, L.; Duffy, P.B.; Maurer, E. 2011. Guidelines for Constructing Climate 

Scenarios. EOS 92: 257-264.

Nakicenovic, N.; Swart, R. (eds.). 2000. Special report on emissions scenarios. A Special 

Report of Working Group III of the Intergovernmental Panel on Climate Change. 

Cambridge, UK: Cambridge University Press. 570 p. Online: http://www.ipcc.ch/ipccreports/sres/emission/index.htm.

Parry M.L.; Canziani O.F.; Palutikof J.P.; and others. 2007. Technical summary. In: Parry, 

M.L.; Canziani, O.F.; Palutikof, J.P. van der Linden, P.J.; Hanson, C.E. (eds.). Climate 

Change 2007; Impacts, adaptation and vulnerability. Contribution of Working Group II to 

the Fourth Assessment Report of the Intergovernmental Panel on Climate Change.Parry. 

Cambridge, UK: Cambridge University Press: 23-78.

Prentice, I.C.; Farquhar, G.D.; Fasham, M.J.R.; Goulden, M.L.; Heimann, M.; Jaramillo, V.J.; 

Kheshgi, H.S.; LeQuere, C.; Scholes, R.J.; Wallace, D.W.R. 2001. The carbon cycle and 

atmospheric carbon dioxide. Chapter 3. In: Houghton, J.T.; Ding, Y.; Griggs, D.J.; Noguer, 

M.; van der Linden, P.J.; Dai, X.; Maskell, K.; Johnson C.A. (eds.).Climate change 2001: 

The scientific basis. Contribution of Working Group I to the Third Assessment Report of the 

Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press: 

183-238 p.

Price, D.T.; McKenney, D.W.; Nalder, I.A.; Hutchinson, M.F.; Kestevan, J.L. 2000. A comparison of statistical and thin-plate spline methods for spatial interpolation of Canadian 

monthly mean climate data. Agricultural and Forest Meteorology 101: 81–94.

Price, D.T.; McKenney, D.W.; Papadopol, P.; Logan, T.; Hutchinson, M.F. 2004. High resolution future scenario climate data for North America. In: Proceedings of the 26th Conference 

on Agricultural and Forest Meteorology; August 23-26, 2004; Vancouver, BC. Boston, 

MA: American Meteorological Society: 7.7.1-7.7.13. Online: http://ams.confex.com/ams/

AFAPURBBIO/techprogram/paper_78202.htm.

Price, D.T.; Scott, D. 2006. Project A636: Large scale modelling of Canada’s forest ecosystem responses to climate change. 2nd ed. Final report to Government of Canada: Climate 

change impacts and adaptation program.

Price, D.T.; McKenney, D.W.; Joyce, Linda A.; Siltanen, R.M.; Papadopol, P.; Lawrence, K. 

2011a. High resolution interpolation of climate scenarios for Canada derived from general 

circulation models. Inf. Rep. NOR-X-2011. Edmonton, Alberta, Canada: Canadian Forest 

Service, Northern Forestry Centre, Natural Resources Canada. 135 p.

Price, D.T.; McKenney, D.W.; Siltanen, R.M.; Papadopol, P.; Lawrence, K.; Joyce, L.A.; 

Coulson, D.P. 2011b. High resolution interpolation of climate scenario change factors for 

the conterminous USA derived from AR4 General Circulation Model simulations. Fort 

Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station. Online: http://dx.doi.org/10.2737/RDS-2011-0023.

Price, D.T.; McKenney, D.W.; Siltanen, R.M.; Papadopol, P.; Lawrence, K.; Joyce, L.A.; 

Coulson, D.P. 2011c. High resolution interpolation of climate scenario change factors for 

Alaska derived from AR4 General Circulation Model simulations. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station. Online: 

http://dx.doi.org/10.2737/RDS-2011-0022.
78

USDA Forest Service Gen. Tech. Rep. RMRS-GTR-320. 2014

Price, D.T. 2009. [email to L Joyce]. October 21, 2009. RE: FW: Third IPCC Assessment B2 

scenarios – question. On file at: USDA Forest Service Rocky Mountain Research Station, 

Fort Collins, CO, with author.

Radic, V.; Clarke, G.K.C. 2011. Evaluation of IPCC models’ performance in simulating 

late-twentieth-century climatologies and weather patterns over North America. Journal of 

Climate 24: 5257-5274.

Randall, D.A.,; Wood, R.A.; Bony, S.; Colman, R.; Fichefet, T.; Fyfe, J.; Kattsov, V.; Pitman, 

A.; Shukla, J.; Srinivasan, J.; Stouffer, R.J.; Sumi, A.; Taylor, K.E. 2007. Climate models 

and their evaluation. In: Climate change 2007: The physical science basis. In: Solomon, S.; 

Qin, D.; Manning, M.; Chen, Z.; Marquis, M.; Averyt, K.B.; Tignor, M.; Miller, H.L. (eds.). 

Climate change 2007: The physical science basis. Contribution of Working Group I to the 

Fourth Assessment Report of the Intergovernmental Panel on Climate Change Cambridge, 

UK: Cambridge University Press: 590-662.

Ray, A.J.; Barsugli, J.J.; Averyt, K.B.; and others. 2008. Climate change in Colorado. A synthesis to support water resources management and adaptation. Boulder, CO: CU-NOAA 

Western Water Assessment. 52 p.

Rehfeldt, G.E. 2006. A spline model of climate for the western United States. Gen. Tech. Rep. 

RMRS-GTR-165. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 21 p.

Rehfeldt, G.E.; Crookston, N.L.; Warwell, M.V.; Evans, J.S. 2006. Empirical analyses of 

plant-climate relationships for the western United States. International Journal of Plant 

Science 167: 1123-1150.

Reichler, T.; Kim, J. 2008. How well do coupled models simulate today’s climate? Bulletin of 

the American Meteorology Society 89: 303-311.

Rogers, B.M.; Neilson, R.P.; Drapek, R.; Lenihan, J.M.; Wells, J.R.; Bachelet, D.; Law, B.E. 

2011. Impacts of climate change on fire regimes and carbon stocks of the U.S. Pacific 

Northwest. Journal of Geophysical Research 116: G03037.

Tabor, K.; Williams, J.W. 2010. Globally downscaled climate projections for assessing the 

conservation impacts of climate change. Ecological Applications 20: 554-565.

USDA Forest Service. 2012a. 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.

USDA Forest Service. 2012b. Future of America’s Forest and Rangelands: Forest Service 

2010 Resources Planning Act Assessment. Gen. Tech Rep. WO-87. Washington, DC: U.S. 

Department of Agriculture, Forest Service. 198 p.

USDI Bureau of Reclamation, 2010(draft). Climate change and hydrology scenarios for 

Oklahoma yield studies. Technical Memorandum 86-68210-2010-01 (draft). Prepared by 

Technical Service Center, Water Resources Planning and Operations Support Group Water 

and Environmental Resources Division, Levi Brekke, Tom Pruitt, and Del Smith. 59 p.

VEMAP members [Vegetation/ecosystem modeling and analysis project]. 1995. Comparing 

biogeography and biogeochemistry models in a continental-scale study of terrestrial ecosystem responses to climate change and CO2 doubling. Global Biogeochemical Cycles 9: 

407-437.

Wear, D.N.; Huggett, R.; Li, R.; Perryman, B.; Liu, S. 2013. Forecasts of forest conditions 

in regions of the United States under future scenarios: a technical document supporting 

the Forest Service 2010 RPA Assessment. Gen. Tech. Rep. SRS-GTR-170. Asheville, NC: 

Department of Agriculture, Forest Service, Southern Research Station. 101 p.
79

Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

Zhu, Z.(editor); Bouchard, M.; Butman, D.; Hawbaker, T.; Li, Z.; Liu, J.; Liu, S.; McDonald, 

C.; Reker, R.; Sayler, K.; Sleeter, B.; Sohl, T.; Stackpoole, S.; Wein, A.; Zhu, Z. 2011. 

Baseline and projected future carbon storage and greenhouse-gas fluxes in the Great Plains 

Region of the United States. Professional Paper 1787. Reston, VA: U.S. Geological Survey. 

Online: http://pubs.usgs.gov/pp/1787/.
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Appendix I: Carbon Dioxide Concentrations and Nitrogen 

Emissions Associated with the Scenarios ________________

Development of the storylines and the emission levels for the greenhouse gases is given in 

Nakicenovic and Swart 2000. The anthropogenic emissions for each greenhouse gas are given 

in Nakicenovic and Swart 2000 as well as the Appendix II of Working Group I report (IPCC 

2001b). Two carbon cycle models (ISAM, Bern-CC) were used in the scenario development 

process to compute the CO2 concentrations that develop in the atmosphere as a result of the 

greenhouse gas emission levels from the A1B, A2, and B2 scenarios (Tables I.1, and I.2). 

Table I.1—CO2 concentrations (ppm) used 

by AR4 models as projected by the ISAM 

model reference case. (http://www.ipcc-data.

org/ddc_co2.html).

ISAM model (reference)

Year A1B A2 B2

- - - - - - - - - - - ppm - - - - - - - - - - -

1970 325 325 325

1980 337 337 337

1990 353 353 353

2000 369 369 369

2010 391 390 388

2020 420 417 408

2030 454 451 429

2040 491 490 453

2050 532 532 478

2060 572 580 504

2070 611 635 531

2080 649 698 559

2090 685 771 589

2100 717 856 621

Note: A ‘reference’ case was defined with climate sensitivity 2.5 °C, ocean uptake corresponding to the mean of 

the ocean model results in Chapter 3, Figure 3.10 

(IPCC 2001b), and terrestrial uptake corresponding 

to the mean of the responses of mid-range models, 

LPJ, IBIS and SDGM (Chapter 3, Figure 3.10, IPCC 

2001b). See Chapter 3, Box 3.7 (IPCC 2001b) for 

more details on the ISAM model.
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Table I.2—CO2 concentrations (ppm) used 

by AR4 models as projected by the Bern-CC 

reference case. (http://www.ipcc-data.org/

ddc_co2.html).

Bern-CC model (reference)

Year A1B A2 B2

- - - - - - - - - - - ppm - - - - - - - - - - -

1970 325 325 325

1980 337 337 337

1990 352 352 352

2000 367 367 367

2010 388 386 385

2020 418 414 406

2030 447 444 425

2040 483 481 448

2050 522 522 473

2060 563 568 499

2070 601 620 524

2080 639 682 552

2090 674 754 581

2100 703 836 611

Note: A “reference” case was defined with an average 

ocean uptake for the 1980s of 2.0 PgC/yr. Climate sensitivity was set to 2.5 °C for a doubling of CO2. The Bern-CC 

model was initialized for observed atmospheric CO2 which 

was prescribed for the period 1765 to 1999. The CO2 data 

were smoothed by a spline. Scenario calculations started at 

the beginning of the year 2000. This explains the difference 

in the values given for the years up to 2000. Values shown 

are for the beginning of each year. Annual-mean values are 

generally higher (up to 7ppm) depending on the scenario 

and the year. See Chapter 3, Box 3.7 (IPCC 2001b) for 

more details on the Bern-CC model.

Further information on the carbon cycle models and the development of these concentrations 

is given in IPCC TAR Working Group 1 report (IPCC 2001b) Box 3.7 (http://www.ipcc.ch/

ipccreports/tar/wg1/122.htm). These CO2 concentrations were then used as forcings in various 

General Circulation Models reported in the AR4 (Table I.3). 

Nitrogen oxide (NOx) emissions (Table I.4) were also developed in the SRES process using 

the Integrated Assessment Models (Nakicenovic and Swart 2000). The emission levels are 

given in Nakicenovic and Swart (2000) as well as in Appendix II of Working Group I report 

(IPCC 2001b). The abundances (concentrations) were not developed for NOx (http://www.

ipcc.ch/ipccreports/tar/wg1/525.htm)
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Table I.3—Climate models using future CO2

concentrations output from the Integrated Science 

Assessment Model (ISAM) or the Bern Carbon 

Cycle model (Bern-CC) (from http://www.ipcc-data.

org/ddc_co2.html).

ISAM MRI:CGCM2_3_2

NASA:GISS-AOM, GISS-EH, GISS-ER 

GFDL:CM2 GFDL:CM2_1

Bern-CC BCC:CM1 

BCCR:BCM2 

CNRM:CM3 

INM:CM3 

IPSL:CM4

LASG:FGOALS-G1_0 

NCAR:CCSM3 

NIES:MIROC3_2-HI, MIROC3_2-MED; 

UKMO:HadCM3, HADGEM1

Table I.4—NOx emissions (TgN/yr) (from Working Group I Appendix II, http://www.ipcc.ch/ipccreports/tar/

wg1/525.htm).

Year A1B A1T A1FI A2 B1 B2 A1p A2p B1p B2p IS92a

2000 32.0 32.0 32.0 32.0 32.0 32.0 32.5 32.5 32.5 32.5 37.0

2010 39.3 38.8 39.7 39.2 36.1 36.7 41.0 39.6 34.8 37.6 43.4

2020 46.1 46.4 50.4 50.3 39.9 42.7 48.9 50.7 39.3 43.4 49.8

2030 50.2 55.9 62.8 60.7 42.0 48.9 52.5 60.8 40.7 48.4 55.2

2040 48.9 59.7 77.1 65.9 42.6 53.4 50.9 65.8 44.8 52.8 59.6

2050 47.9 61.0 94.9 71.1 38.8 54.5 49.3 71.5 48.9 53.7 64.0

2060 46.0 59.6 102.1 75.5 34.3 56.1 47.2 75.6 48.9 55.4 67.8

2070 44.2 51.7 108.5 79.8 29.6 56.3 45.1 80.1 48.9 55.6 71.6

2080 42.7 42.8 115.4 87.5 25.7 59.2 43.3 87.3 48.9 58.5 75.4

2090 41.4 34.8 111.5 98.3 22.2 60.9 41.8 97.9 41.2 60.1 79.2

2100 40.2 28.1 109.6 109.2 18.7 61.2 40.3 109.7 33.6 60.4 83.0

Note: NOx is the sum of NO and NO2.
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

Appendix II: CMIP3 Run Numbers for Individual Climate 

Model Projections ____________________________________

Table II.1—Realization (or run) numbers for each climate model and 

greenhouse gas forcing scenario in the CMIP3 catalog that were 

selected for spatial interpolation using ANUSPLIN in this study. 

Model and scenario Run number Time stampa

CGCM3.1(T47)b

20C3M Run 5 reformatted 2005-05-12—22:21:09

A2 Run 5 reformatted 2005-05-12—22:21:09

A1B Run 5 reformatted 2005-05-12—22:21:09

CSIRO-MK3.5(T63)c

20C3M Run 1 2006-09-20—05:09

A2 Run 1 2006-09-20—04:09

A1B Run 1 2006-11-04—10:04

MIROC3.2(T42)d

20C3M Run 3 reformatted 2004-10-14—20:53:37

A2 Run 3 reformatted 2004-12-14—00:22:38

A1B Run 3 reformatted 2004-12-14—00:02:09

aDate and time information extracted from available metadata for the run.

bCGCM3.1(T47) = Third Generation Coupled Global Climate Model, version 3.1, medium 

resolution.

cCSIRO-MK3.5(T63) = Commonwealth Scientific and Industrial Research Organisation 

Climate System Model, Mark 3.5.

dMIROC3.2(T42) = Model for Interdisciplinary Research on Climate, version 3.2, medium 

resolution.
84

USDA Forest Service Gen. Tech. Rep. RMRS-GTR-320. 2014

Appendix III: Potential Evaporation Estimation Procedure___

Potential evapotranspiration (PET) for vegetation was calculated using a modification of 

Penman’s work by Linacre (1977), 

where

PET = [500Tm/(100-A)+15(T-Td)]/(80-T)mm day-1

and

T is the mean monthly temperature in units of degrees Celsius (oC),

Tm is the mean monthly temperature adjusted for elevation,

Td is the mean monthly dew point temperature in units of degrees Celsius, and

A is the latitude in degrees.

Tm = T + 0.0006h,

where

h is the elevation in units of meters.

Linacre (1977) provides an alternate for estimating (T-Td) when dew point data are not 

available. (T-Td) = 0.0023h+0.37T+0.53R+0.35Rann-10.9°C. Rann is the difference between 

the means of the hottest and coldest months. R is mean daily range of temperature.

Rann is calculated from the projected data by the formula Max(T1-T12)-Min(T1-T12). Ti is 

the mean monthly temperature of the month, where T1= Jan, T2=Feb...T12=Dec.

Mean R is estimated from PRISM data using mean monthly of maximum air temperature 

and the minimum air temperature from 1940-2006. This value is used as a surrogate for any 

given year in the future. The R is calculated for each PRISM grid and then a weighted average 

is calculated for the 5-arcminute grids as described above.

	 Note that R = ∑(Dmax – Dmin)/n,

where

R is the monthly average,

Dmax is the daily maximum temperature,

Dmin is the daily minimum temperature,

n is the number of days in the month.

An elevation is obtained from PRISM data, which is available at the 2.5-minute grids 

(1/24 degree) and a weighted mean is calculated for the 5-arcminute grid.

This methodology provides an estimate of future PET using only the temperature and latitude values from the climate projections, elevation, and average mean daily temperature range 

from PRISM data. 
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Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

Appendix IV: Area-Weighting the Climate Data ____________

All regional and conterminous summaries of the climate data were computed using an areaweight calculation. For estimations within the conterminous United States in Geographic 

0.08333 degree, area was calculated for latitude of center of cell in Excel (as per http://eoswebster.sr.unh.edu/data_guides/global_model_dg.jsp#equations) shown here:

Convert to radians:

radians = (90.0-(lat))*3.141593/180.0

Calculate cosines:

cosines = cosine(radians)-cosine(radians+(0.083333333333333*3.141593)/180.0)

Calculate area in square kilometers:

area = 6371221.3*6371221.3*3.141593*cosines/(180/0.0833333333333333)*1.0e-6

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