High Resolution Interpolation of Climate Scenarios 

for the Conterminous USA and Alaska Derived 

from General Circulation Model Simulations

Linda A. Joyce, David T. Price, Daniel W. McKenney, 

R. Martin Siltanen, Pia Papadopol, 

Kevin Lawrence, and David P. Coulson

Published in partnership with the Canadian Forest Service, 

Northern Forestry Centre and Great Lakes Forestry Centre

United States

Department

of Agriculture

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Rocky Mountain

Research Station

General Technical Report

RMRS-GTR-263

December 2011
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Joyce, Linda A.; Price, David T.; McKenney, Daniel W.; Siltanen, R. Martin; Papadopol, Pia; Lawrence, 

Kevin; and Coulson, David 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.

Abstract

Projections of future climate were selected for four well-established general circulation models (GCM) forced 

by each of three greenhouse gas (GHG) emissions scenarios, namely A2, A1B, and B1 from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES). Monthly data for 

the period 1961–2100 were downloaded mainly from the web portal of Third Coupled Model Intercomparison 

Project (Phase 3) of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and subsets of 

data covering North America were extracted. Climate variables included monthly mean daily maximum and 

minimum temperatures, precipitation, incident surface solar radiation, wind speed, and specific humidity. All 

variables were expressed as changes relative to the simulated monthly means for 1961–1990, which corrected 

for GCM bias in reproducing past climate and allowed future projected trends to be compared directly. The 

downscaling procedure used the ANUSPLIN software package to fit a two-dimensional spline function to each 

month’s change data for each climate variable at a spatial resolution of 5 arcminutes (0.0833o) longitude and 

latitude. The A2 emission scenario invariably generated the greatest warming by 2100 and the B1 the least. 

Alaska is projected to undergo the greatest regional increases in temperature and precipitation. Differences 

across the projections were generally greater from the different GHG forcings than those resulting from the 

different GCMs, although the consistency varied spatially. Gridded datasets are publicly available. The downscaled change factors from this study are being used with historical climatology developed from the PRISM 

climate data set to develop the climate projections for the RPA scenarios in the USDA FS RPA assessment. 

A companion report and data set will be issued by Natural Resources Canada (Canadian Forest Service) for 

Canada.

Keywords: climate scenario, GCM, downscaling, interpolation, ANUSPLIN, National Center for Atmospheric 

Research, NCAR, Community Climate System Model, CCSM, Canadian Centre for Climate Modelling and Analysis, CCCma, Coupled Global Climate Model, CGCM, Commonwealth Scientific 

and Industrial Research Organisation, CSIRO, Climate System Model, Centre for Climate System 

Research, CCSR, National Institute for Environmental Studies, NIES, Model for Interdisciplinary 

Research on Climate, MIROC
Authors

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

David T. Price, Natural Resources Canada, Canadian Forest 

Service, Northern Forestry Centre, Edmonton, AB, Canada

Daniel W. McKenney, Natural Resources Canada, Canadian 

Forest Service, Great Lakes Forestry Centre, Sault Ste Marie, 

ON, Canada

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

Pia Papadopol, Natural Resources Canada, Canadian Forest 

Service, Great Lakes Forestry Centre, Sault Ste Marie, ON, 

Canada

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

ON, Canada

David P. Coulson, USDA Forest Service Rocky Mountain 

Research Station, Fort Collins, CO, USA

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 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 U.S. National Center for Atmospheric Research, 

and G Strand of the U.S. University Corporation for Atmospheric Research

•	 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.

This research uses data provided by the Community Climate 

System Model (CCSM) project (www.ccsm.ucar.edu), supported 

by the Directorate for Geosciences of the National Science 

Foundation and the Office of Biological and Environmental 

Research of the U.S. Department of Energy. Any redistribution of CCSM data must include this data acknowledgment 

statement. Similarly, the authors request that any users of the 

scenario data presented in this report also provide appropriate acknowledgments to the respective modeling groups, as 

identified above.

We also acknowledge the support of the USDA Forest Service 

for this research.

We greatly appreciate the time and effort by reviewers: 

Louis Iverson, Nicholas Crookston, Alisa Gallant, Peter Thornton, 

Jorge Ramirez, Robert Bailey, Roger Simmons, and Ray Drapek; 

for editorial review: Lane Eskew; for graphics: Suzy Stephens; 

and for layout: Nancy Chadwick. 

i

Cover photo credits:

The Land Trust for the Little Tennessee River Valley, Macon County, NC., September 2004.

David P. Coulson, USFS. Deer on the White River near Trappers Lake, CO, 2011.

Karen Wattenmaker. Mud slide on the Lowman Road. Boise National Forest, ID, 1996. 

http://www.forestphoto.com/asset-bank/action/viewHome 

C.D. Allen, USGS. View is of the headwaters of Sanchez Canyon in the Jemez Mountains 

on the Santa Fe National Forest, NM, 2011.

The cover figure is the change in annual mean daily minimum temperature (°C) from the 

1961-1990 period to the 2041-2070 period for the A1B scenario. 
Executive Summary

Researchers from the USDA Forest Service and the Canadian 

Forest Service (CFS) collaborated in the production of a suite of 

downscaled climate scenarios covering the continental United 

States and Canada to support the national assessment required 

by the U.S. Forest and Rangelands Renewable Resource 

Planning Act of 1974 (RPA). Each scenario was derived from 

a simulation carried out with a state-of-art general circulation 

model (GCM) for which results were available from the World 

Climate Research Programme’s Coupled Model Intercomparison Project Phase 3 (CMIP3) through the Program for Climate 

Model Diagnosis and Intercomparison. The following four GCMs 

were selected on the basis of data available in 2008 and because 

they were well-recognized within the global GCM community: the 

Third Generation Coupled Global Climate Model, version 3.1, 

medium resolution (CGCM31MR), developed by the Canadian 

Centre for Climate Modelling and Analysis; Mark 3.5 Climate 

System Model (CSIROMK35) developed by the Australian Commonwealth Scientific and Industrial Research Organisation; the 

Model for Interdisciplinary Research on Climate, version 3.2, 

medium resolution (MIROC32MR) developed by the Japanese 

Centre for Climate System Research; and the Community 

Climate Model version 3.0 (NCARCCSM3) developed by the 

U.S. National Center for Atmospheric Research. 

Monthly time-series data were obtained for each GCM, representing both the 20th century (1961–2000) and three scenarios of 

greenhouse gas (GHG) emissions for the 21st century developed 

for the Intergovernmental Panel on Climate Change (IPCC) Third 

and Fourth Assessment Reports, namely A2, A1B, and B1 of the 

Special Report on Emission Scenarios (SRES) (Nakic ́enovic ́ 

and others 2000). When these scenarios are used in global 

models, the potential future global mean surface warming ranges 

from a minimum of 1.8 °C associated with the B1 scenario, 

2.8 °C for the A1B, to 3.4 °C in the A2 for 2090–2100 relative 

to 1980–1999 (IPCC 2007). In each case, data for simulated 

climate variables were downloaded and computer programs 

used to extract and manipulate subsets covering Canada and 

the continental United States (i.e., including Alaska but excluding Hawaii). The climate variables included monthly mean daily 

maximum and minimum temperatures, precipitation, incident 

surface solar radiation, and wind speed. Two other variables, 

monthly mean specific humidity and sea-level barometric pressure, were used to calculate monthly mean vapor pressure as 

a sixth variable. Each monthly value at each GCM grid node 

was normalized either by subtracting (temperature variables) or 

dividing by (other climate variables) the mean of that month’s 

values for the 30-year period 1961–1990. The normalized data 

(or “deltas”) were formatted for input to the ANUSPLIN thin-plate 

spline software of Hutchinson and co-workers at the Australian 

National University (Hutchinson 2010). ANUSPLIN was used to 

fit a unique two-dimensional spline “surface” function to each 

month’s data for each of the six normalized climate variables. 

The fitted spline functions were, in turn, used to create gridded 

data sets for each monthly variable covering North America at 

a spatial resolution of 5 arcminutes (0.0833o) longitude and 

latitude on a geographic projection. Data for Alaska and the 

continental United States were extracted. 

The normalization and interpolation procedures effectively 

removed model biases associated with the individual GCMs 

and allowed direct comparison of the downscaled projections 

for different GHG scenarios and different GCMs. This approach 

is consistent with requirements outlined in U.S. Forest Service 

memorandum (“Draft NEPA Guidance on Consideration of the 

Effects of Climate Change and Greenhouse Gas Emissions,” 

released February 18, 2010, http://www.fs.fed.us/emc/nepa/

climate_change/index.htm). 

The downscaled data were analyzed, partly as a form of 

quality assessment and partly to demonstrate how the data 

can be used for national and regional studies of the impacts 

of climate change. The analysis explored spatial patterns of 

the climate projections across the United States, temporal 

patterns for specific variables at the regional scales, and the 

outlook for each region through tabular summaries of the future 

changes in the six climate variables at the regional scale. In 

the spatial analysis, results for selected variables were plotted as national maps of 30-year means for the three periods 

2011–2040, 2041–2070, and 2071–2100 (or 2071–2099 in the 

case of NCARCCSM3), where the normalized data (deltas) 

were added to (for temperature variables) or multiplied by (for 

other variables) spatially interpolated climatological data for 

the 1961–1990 period. Temporal dynamics of the historical 

observed data (1961-2008) within regions were compared with 

the simulated historical data for each GCM as well as comparing 

future projections across the GCMs by climate variable for the 

2001-2100 period. The conterminous states were divided into 

seven regions, based on the ecoregions (ecoclimatic divisions) 

identified in Bailey’s (1995), with Alaska forming an eighth 

region. Area-weighted ecoregional means of the downscaled 

data were calculated for every monthly value for every climate 

variable in each of the 12 scenarios (four climate models with 

three SRES scenarios), and used to calculate seasonal and 

annual means and totals. These data were summarized by 

region for each climate variable and each GHG forcing scenario 

to create scatterplots comparing projected changes for the 

2050s and 2090s. Summarized data for all four GCMs were 

averaged and further summarized in a comprehensive set of 

tables, to compare results of individual variables across regions 

and scenarios.

ii
The MIROC32MR GCM projected a distinctly warmer and 

drier future under the A2 and A1B forcing scenarios for much of 

the eastern and southern United States, but in other respects 

the maps of projections for temperature and precipitation indicated the GCMs behaved rather similarly. Differences were 

generally too small (e.g., less than 1 °C), given the range of 

each variable at the continental scale, to allow them to be visually discriminated for the same forcing scenario and the same 

30-year period. Accordingly a series of maps of the change 

fields was created. Each map showed the distribution of mean 

temperature increase (°C) or change in precipitation ratio relative 

to 1961–1990 for each of the three 30-year periods according 

to each GCM forced by the A1B emissions scenario. In these 

cases, the range of values was much narrower, which allowed 

a much easier comparison of the climate impacts according to 

each model. 

The generally greater warming detected for MIROC32MR 

was confirmed all the way north into Montana and South 

Dakota, but ranking changes projected by the other three 

GCMs was difficult. For specific states (Nebraska and 

South Dakota), CGCM31MR projected the least warming, 

followed by CSIROMK35 and NCARCCSM3. In the south, 

however, NCARCCSM3 projected the least warming (though 

CGCM31MR was very similar). CSIROMK35 projected generally smaller temperature increases in the northwest, with 

CGCM31MR and NCARCCSM3 projecting progressively 

greater increases. 

For precipitation, the southeastern states were projected to be 

markedly drier according to MIROC32MR compared to all other 

models, with this trend clearly apparent as early as 2010–2040 

in the A1B scenario. In this region, Arkansas provided a ranking 

for precipitation change, with NCARCCSM3 projecting a general 

increase, CGCM31MR a smaller increase, and CSIROMK35 a 

slight decrease, by 2070–2099. NCARCCSM3 also projected 

precipitation declines in the west, particularly in the coastal 

states, and CGCM31MR indicated decreases in the southwest 

but slight increases in Oregon and Washington. CSIROMK35 

projected large increases in precipitation inland, but smaller 

increases were projected by CGCM31MR in Montana, South 

Dakota, Nebraska, and Iowa. All models projected increases 

in precipitation for Alaska, particularly in the north. 

The results of the analysis confirmed that the GCMs 

generally were in agreement, particularly with respect to 

temperature, precipitation, and solar radiation, both in terms 

of their relative responses to the different scenarios, and in 

the magnitudes of the changes projected for the 21st century. 

This is not to say the results agreed in all cases, but the different projections all were often consistent among the four 

models. Further, when historical records of temperature and 

precipitation were compared subjectively to the GCM results 

for the period 1961–2008, the magnitude and periodicity of 

interannual variations, as well as the overall trends in means, 

appeared very similar for all GCMs in all seasons and all 

regions. There was less consistency among the models in 

their projections of changes in interannual variability over 

the 21st century, but the differences were congruent among 

seasons. 

Projections of changes in mean vapor pressure generally 

were consistent with projected increases in mean temperature, and often reflected the associated projected increases in 

seasonal and annual precipitation. Interestingly, increases in 

vapor pressure, particularly in summer, often were matched 

with the slight reductions in solar radiation, consistent with 

a warmer atmosphere holding more water vapor and hence 

creating generally cloudier conditions. 

Wind speed data generally showed agreement among the 

models, projecting little change in either the interannual variability or in mean wind speeds under any of the GHG forcing 

scenarios. (Note this applied only at monthly time scales; 

changes in the distribution of wind speeds at daily or hourly 

time-scales were not investigated.) 

In summary, the suite of 12 climate scenarios provides a range 

of potential future climates for assessing possible effects of 

a changing climate on natural resources, ecosystems, human 

infrastructure and communities. Each should be considered a 

“plausible” scenario for a specific set of assumptions captured 

in each GCM and in each of the emissions scenarios. The 

results are interpolated changes calculated with respect to 

1961-1990 means, and gridded to a common format to facilitate 

handling and comparison among scenarios. The interpolated 

deltas for each monthly variable and grid will be made available 

through the archive website at the USDA FS Rocky Mountain 

Research Station website, as will the climate projections for 

each variable at the monthly time scale and the grid spatial 

scale (http://www.fs.fed.us/rm/data_archive/).

iii
Table of Contents

Executive Summary .........................................................................................................ii

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

Objectives .........................................................................................................................2

Methods.............................................................................................................................2

Review of Spatial Downscaling of Global Climate Simulations ............................. 2

Interpolation of Climate Data Using ANUSPLIN.................................................... 3

Selection of Forcing Scenarios for GHG Emissions.............................................. 6

Use of Historical Climatology...............................................................................12

Analysis of Interpolated Climate Variables ..........................................................13

Calculation of Bioclimatic Indices for the Scenario Data ..................................... 15

Results.............................................................................................................................15

Spatial and Temporal Patterns ............................................................................15

Comparison of Projections of Changes in Temperature and Precipitation .......... 35

Comparison of Simulated Interannual Variability................................................. 38

Projected Climate Trends 2001-2010..................................................................43

Regional Outlook.................................................................................................61

Discussion ......................................................................................................................80

Sources of Error and Uncertainty ........................................................................80

Carbon Dioxide Concentration Scenarios ...........................................................81

Conclusions....................................................................................................................81

References ......................................................................................................................83

Appendix I .......................................................................................................................85

iv
v

Tables

Table 1. General circulation model (GCM) data sets for scenarios from 

the Fourth Assessment Report of the Intergovernmental 

Panel on Climate Change used to create input files for 

interpolation by ANUSPLIN software....................................................... 7

Table 2. Realization (or run) numbers for each general circulation model 

and greenhouse gas forcing scenario in the Coupled Model 

Intercomparison Project (CMIP3) catalog that was selected 

for interpolation using ANUSPLIN software............................................. 8

Table 3. Variables derived from primary climate surfaces. Variables 1-19 

are generated by ANUCLIM (Houlder and others 2000); 

variables 20-29 are generated by SEEDGROW (Mackey 

and others 1966) (modified from McKenney and others 2006b). 

In all cases, the descriptions should be considered estimates 

rather than actual values. ......................................................................16

Table 4. Climate Change Projection Summary for Alaska (mean of 

four general circulation models). ...........................................................64

Table 5. Climate Change Projection Summary for Continental Ecoregion 

(mean of four general circulation models). ............................................ 66

Table 6. Climate Change Projection Summary for Marine Ecoregion 

(mean of four general circulation models). ............................................ 68

Table 7. Climate Change Projection Summary for Mediterranean 

Ecoregion (mean of four general circulation models)............................ 70

Table 8. Climate Change Projection Summary for Prairie Ecoregion 

(mean of four general circulation models). ............................................ 72

Table 9. Climate Change Projection Summary for Subtropical 

Ecoregion (mean of four general circulation models)............................ 74

Table 10. Climate Change Projection Summary for Dry Temperate 

Ecoregion (mean of four general circulation models)............................ 76

Table 11. Climate Change Projection Summary for Dry Subtropical 

Ecoregion (mean of four general circulation models)............................ 78

USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 1

Introduction_______________________

The U.S. Forest and Rangeland Renewable Resources 

Planning Act (RPA) of 1974 requires a comprehensive 

assessment of the state of the U.S. renewable resources 

at 10-year intervals. This Act was amended in 1990 to 

require the U.S. Department of Agriculture to:

1. Assess impacts of climate change on the condition 

of renewable resources on forest and rangelands, 

and

2. Identify rural and urban forestry opportunities to 

mitigate the buildup of atmospheric carbon dioxide.

While previous RPA Assessments had examined the 

impact of climate change on forest production and 

the forest sector (Joyce 2007), the 2010 RPA National 

Renewable Resource Assessment uses an expanded 

framework for exploring the many drivers of change 

that affect natural resource production. In this framework, population, economics and climate projections 

are used to provide a range of future scenarios in which 

to explore future natural resource production. The RPA 

scenarios are based on the emissions scenarios developed 

in the Special Report on Emission Scenarios (SRES) 

(Naki ́cenovi ́c and others 2000) and used in the IPCC 

Third and Fourth Assessments Reports. Specifically, the 

emissions scenarios selected were SRES A2, A1B, and 

B2 (USDA FS, in review). While six emissions scenarios 

were developed in SRES, scenarios A2, A1B, and B1 

have been the focus of model intercomparions studies 

(Meehl and others 2007) and the IPCC report (Randall 

and others 2007). Thus, no climate projections driven by 

the B2 scenario were available as part of the archiving 

efforts associated with the projections used in the Fourth 

Assessment; hence, B2 projection data was obtained from 

models used in the Third Assessment report (Joyce and 

others, in process).

As reported in the Intergovernmental Panel on Climate 

Change (IPCC) Fourth Assessment Report (AR4, e.g., 

http://www.ipcc.ch/ipccreports/ar4-wg1.htm), projections of the future global climate have been developed 

by numerous general circulation model (GCM) research 

groups around the world, using greenhouse gas (GHG) 

forcing scenarios recommended in the SRES (Naki ́cenovi ́c 

and others 2000). We focus here on the development of a 

suite of high resolution climate projections for the North 

American land surface (and enclosed water bodies) using 

a subset of the available AR4 GCM results for the A1B 

and A2 scenarios, with the specific aim of supporting the 

2010 RPA assessment. The downscaled change factors 

from this study are being used to develop the climate 

projections of the RPA scenarions for the USDA FS RPA 

Assessment (USDA FS, in press). Downscaled projections for the AR4 B1 scenario are included in this study. 

We anticipate the products will be of great value to many 

other studies of the impacts of climate change at regional 

to continental scales.

There are several major limitations in using the GCM 

data that are directly available from recognized climate 

data archives, such as the IPCC Data Distribution Centre 

(IPCC-DDC, http://www.ipcc-data.org/) and the Program for Climate Model Diagnosis and Intercomparison 

(PCMDI, http://www-pcmdi.llnl.gov/). These limitations 

include the very coarse spatial resolution inherent in 

GCM output (typically to a separation of 100-400 km 

between grid nodes at mid-latitudes) and the need to 

extract data from huge global data sets for application to 

smaller regions. Further problems include determining 

which scenarios are representative of the range of future 

climates projected for North America and bringing the 

data into a common format to allow easy comparison 

when used for studies assessing the impacts of climate 

change.

High Resolution Interpolation of Climate 

Scenarios for the Conterminous USA and Alaska 

Derived From General Circulation Model Simulations

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

Pia Papadopol, Kevin Lawrence, and David P. Coulson
2 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

This report provides a detailed description of the process to “downscale” the results of four different GCMs, 

operating at a range of spatial resolutions and forced by 

three different emissions scenarios, to a common spatial 

resolution. The downscaling approach described here 

follows that reported by Price and others (2004) and 

McKenney and others (2006c) and uses the ANUSPLIN 

thin-plate smoothing spline-interpolation technique, a 

method developed at the Australian National University 

over the last two decades (Hutchinson 2010). Results of 

this analysis covered Canada, Alaska, and the conterminous 48 states of the United States. Individual grid cells 

measured 5 arcminute (0.0833°) latitude and longitude, 

or approximately 10 km north-to-south.1

 Change factors 

(deltas) for each climate variable are reported as monthly 

departures from simulated monthly means for the 1961–

1990 period. Data for six standard climate variables were 

developed in this way for the period from 1961 to 2100 (or 

2099 in the case of the GCM developed by the National 

Center for Atmospheric Research): monthly mean daily 

maximum and minimum temperature, precipitation, solar 

radiation, vapor pressure, and wind speed. The advantage 

of this approach is that different historical climatologies 

can be used to construct projections of future climate. 

For this report, the historical climatology developed by 

McKenney and others (2007) was used. For the RPA 

scenarios, the historical climatology of PRISM is being 

used to construct climate projection data (e.g., Coulson 

and others 2010).

The data are available for state and other federal agencies, as well as private sector groups (e.g., forest and 

tourism industries, non-governmental organizations, and 

consultants), to use in studies of the effect of climate 

change on systems ranging from wildland management 

to agriculture to human health throughout the continental United States. The projection data sets are available 

at the USDA FS Rocky Mountain Research Station 

archive website [http://www.fs.fed.us/rm/data_archive/

dataaccess/contents_datatype.shtml#MetClimate].

A companion report (Price and others 2011) provides 

a similar analysis for Canada to document the data sets 

available for climate change impacts research in that 

1 Although we refer to the resolution as 10 km, this is a nominal 

dimension. In reality, a 5 arcminute grid cell is an approximate square 

measuring about 9.25 km on a side at the equator. Further, the east-west 

dimension decreases with the cosine of the latitude (i.e., as meridians 

converge toward the poles). 

country. All data sets are freely available for use by researchers and others, on request from the authors at the 

CFS Northern Forestry Centre and Great Lakes Forestry 

Centre.

Objectives ________________________

1. Create a consistent set of climate projections from 

the output generated by four well-established general circulation models for the conterminous United 

States and Alaska forced by SRES A2, A1B, and 

B1greenhouse gas emissions scenarios as defined 

in the IPCC Special Report on Emissions Scenarios 

(Naki ́cenovi ́c and others 2000).

2. Provide results as ASCII text file format 5 arcminute 

(~10 km) resolution geographic grids, each consisting of the change factor (as absolute difference [for 

temperature] and as a ratio [for all other climate 

variables]) for monthly mean daily minimum and 

maximum temperature, precipitation, solar radiation, wind speed, and vapor pressure.

3. Provide results as ASCII text file format 5 arcminute resolution geographic grids, each consisting 

of the monthly value of a single climate variable 

(temperature, precipitation, solar radiation, wind 

speed and vapor pressure).

4. Analyze the data sets generated in Objectives 2and 

3 to demonstrate and interpret similarities and 

differences among the general circulation models 

and emission scenarios for distinct regions of the 

United States and to highlight any problems discovered as a result of the downscaling procedures.

Methods __________________________

Review of Spatial Downscaling of Global 

Climate Simulations

Various approaches to downscaling GCM output differ 

in their complexity. The more sophisticated approaches 

include dynamical and statistical downscaling. Of these, 

dynamical approaches typically use higher resolution 

atmospheric circulation models (generally referred to 

as regional climate models, RCM) that operate over 

a relatively large region bounded by GCM grid cells. 

The atmospheric processes occurring within the RCM 

domain are forced by boundary conditions generated by 

the GCM at its usual time step. Within the RCM domain, 

higher resolution representation of surface topography 

and more detailed parameterization of some processes 

are intended to allow the model to generate physically 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 3

consistent simulations of weather and climate that can 

be validated against observed data. The validated model 

should be able to project more realistic regional climate 

than can be obtained from the GCM alone. In general, 

RCMs are expensive to operate and do not provide 

continuous long-term projections for multiple GCM 

scenarios (but see latest results of the North American 

Regional Climate Change Assessment Program, http://

www.narccap.ucar.edu/results/rcm3-gfdl-results.html).

In contrast to dynamical downscaling, statistical downscaling is based on relationships among multiple local 

scale and larger scale meteorological observations that 

are used to interpret or modify the GCM outputs (e.g., 

Hashmi and others 2009), often by generating a distribution of values for the location of interest. There are three 

broad statistical approaches: weather typing, weather 

generators, and modeling by regression models, the last of 

which are also known as transfer functions (IPCC-TGICA 

2007; Hashmi and others 2009; see also http://www.cics.

uvic.ca/scenarios/index.cgi?More_Info-Downscaling_ 

Background). Weather typing is based on statistical relationships between observed meteorological variables and 

a classification of synoptic weather patterns. The GCMsimulated changes in frequency and spatial distributions 

of these weather patterns are used to project changes in 

the same meteorological variables. Challenges with this 

approach include the known problem of underpredicting 

climate variability to varying degrees, a reflection of the 

choice of variables and predictor domain since only part 

of the regional and local climate variability is related 

to the large-scale climate variations (Fowler and others 

2007). Additionally, the stationary relationship between 

the local observed variables and regional weather patterns has been shown questionable in the observed 

record (Fowler and others 2007), hence it also may not 

hold in the future (e.g., Conway and Jones 1998), which 

is a concern given the expectation of “novel” climates 

(e.g., Williams and others 2007).

Weather generators (e.g., Semenov and Barrow 1997; 

Wilks and Wilby 1999) are used to simulate the occurrence 

of high frequency climatic events, at daily or hourly time 

scales, typically for weekly or monthly climate statistics. 

Hence, such tools are not germane to spatial downscaling 

of GCM projections of monthly climate data, but they 

may be useful in impact studies that make use of data 

that have been spatially downscaled by another method.

Statistical transfer functions represent a range of linear 

or non-linear regression methods (the latter including 

approaches such as artificial neural networks and genetic algorithms) to relate large scale meteorological or 

climatic data (observed or generated by a GCM) to small 

scale climate variables (e.g., Wilby and others 2002, see 

also the bias-corrected spatial disaggregation approach 

[http://gdo-dcp.ucllnl.org/downscaled_cmip3_projections/

dcpInterface.html#Welcome]). While some of these 

techniques are quite powerful, and many provide the 

capacity to estimate daily values (rather than monthly 

means) coupled with physically based predictions of 

changes in variability and the occurrence of extreme 

events, Hashmi and others (2009) noted the following 

limitation:

...there is no universal single statistical downscaling technique that works very well under all 

circumstances. According to the “Guidelines for 

Use of Climate Scenarios Developed from Statistical Downscaling Methods” (IPCC-TGICA 2004), 

the user should carefully select the downscaling 

method according to the nature of problem and 

predictands involved.

Simpler and more transparent methods include using 

the GCM output directly (e.g., from the closest grid 

node) and spatial interpolation to finer resolution from 

latitude and longitude coordinates. Such methods have 

been adopted for interpolating both climate observations 

and GCM scenario output to fine spatial resolutions over 

large regions where it is generally impractical to apply 

statistical downscaling methods. Typically, the GCM 

data are normalized to a historical reference period so 

that bias in the GCM’s estimates of observed values can 

be removed, an approach sometimes referred to as the 

delta method. Because GCMs typically have very low 

horizontal resolution (see Figure 1), their representation 

of topographic effects on local climate is necessarily poor. 

For this reason, the normalized and interpolated GCM 

data (“delta values”) should be combined with observed 

climatological data for the reference period interpolated 

to the same resolution. This approach provides a more 

localized correction to the climate scenario.

Interpolation of Climate Data Using ANUSPLIN

In this study, the monthly data values of GCM output 

were treated as simulated records of a ’virtual climate 

station’ located at GCM grid-node coordinates and were 

used to develop an interpolated surface of the climatology at the spatial scale of interest using the ANUSPLIN 

software developed at the Australian National University by Hutchinson and co-workers (e.g., Hutchinson 

2010). This software has been used widely for estimating 

observed climate data as grids (or at specific locations), 

often, but not always, identified by latitude, longitude, and 
4 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

elevation. The software has been applied in many individual countries (e.g., Hutchinson 1995, 1998a, 1998b), 

including Canada (Price and others 2000; McKenney 

and others 2001, 2004; Hutchinson and others 2009), 

United States (Rehfeldt 2006) and globally (New and 

others 2002; Hijmans and others 2005).

The theory of ANUSPLIN has been described elsewhere (e.g., Hutchinson and Gessler 1994; Hutchinson 

1995, 1998a, 1998b, 2010; Hutchinson and others 

2009), and briefly in McKenney and others (2004), and 

will not be discussed in detail here. Thin-plate splines 

can be described as a multi-dimensional nonparametric 

curve-fitting technique although ANUSPLIN can be 

configured in other ways. For interpolation of climate 

data, the target climate variable generally is modeled as 

a function of various spatially varying dimensions (typically position, as represented by latitude and longitude, 

and topography, as represented by appropriately scaled 

elevation). ANUSPLIN has been applied to many climate 

variables at temporal resolutions ranging from a single 

day, to a month, and extending to long-term mean, such 

as a 30-year monthly normal.

Figure 1. Comparison of grid cell elevations for the four GCMs used in this study from which simulation data were used in the 

downscaling procedures. Note the differences in horizontal resolution, from CGCM31MR (coarsest) to NCARCCSM3 (finest), 

and also the peak elevations achieved in the CSIROMK35 grid are higher than those in the NCARCCSM3 grid because the 

NCARCCSM3 elevation grid is more highly smoothed. For CSIROMK35 and MIROC32MR, some ocean pixels have nonzero values. 

Latitude and longitude values represent the boundaries of the area for the downscaling analysis with each model. CGCM31MR 

= Third Generation Coupled Global Climate Model, version 3.1, medium resolution; CSIROMK35 = Commonwealth Scientific 

and Industrial Research Organisation Climate System Model, Mark 3.5; MIROC32MR = Model for Interdisciplinary Research on 

Climate, version 3.2; NCARCCSM3 = Community Climate System Model, version 3.0.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 5

Price and others (2000) and McKenney and others 

(2001) showed that ANUSPLIN performs extremely well 

for mean values of monthly temperature, precipitation, 

observed over periods of 30 years or more. ANUSPLIN 

also has been successfully used to carry out interpolations 

of monthly time-series data and time series at shorter time 

scales (weeks to days) (McKenney and others 2006b; 

Hutchinson and others 2009). It is recognized, however, 

that the inherently patchy nature of precipitation (both in 

time and space) generally reduces the confidence of precipitation models as compared with temperature models 

(Kang and Ramírez 2010). McKenney and coworkers 

in the CFS have invested considerable effort (in close 

collaboration with Hutchinson at Australian National 

University) to develop high resolution climatologies 

covering the continental United States and Canada (e.g., 

McKenney and others 2006b, 2007; Hutchinson and others 2009).

Price and others (2001, 2004) and Price and Scott 

(2006) were the first to report the use of ANUSPLIN 

as a method of downscaling GCM climate projections. 

The improvements by McKenney and Hutchinson (e.g., 

McKenney and others 2006a, 2006b) were extended to 

interpolate time series of GCM output data as functions 

of grid node latitude and longitude, with treatment of 

the data values simulated for each grid node as if they 

were climate observations at the grid node location. 

Interpolated climate scenarios, derived from GCM data 

produced for the IPCC Third Assessment Report, were 

documented in Price and others (2004) and McKenney 

and others (2006c). A range of climate data products has 

since been developed and made widely available both from 

the CFS Great Lakes Forestry Centre based in Ontario 

and the CFS Northern Forestry Centre in Alberta.

Price and others (2001) carried out several experiments 

to investigate various ANUSPLIN models and settings 

for spatial interpolation of GCM output, in particular 

relating to the use of elevation as a third independent 

variable. Diagnostics produced by ANUSPLIN in those 

tests showed relatively little impact of GCM grid-cell 

elevation on the resulting spline function, likely because 

elevation typically is averaged across an entire GCM grid 

cell such that high mountains are reduced to relatively 

lower-altitude plateaus. Hence the influence of elevation 

on, for example, temperature, precipitation, and wind 

speed currently is not well captured in GCMs. This 

concern will need to be revisited as the spatial resolution of GCMs increases. In the present study, the four 

GCMs generated climate output at scales ranging from 

300–400 km (e.g., Canadian Third Generation Coupled 

Global Climate model, version 3.1, medium resolution, 

CGCM31MR) to 100–150 km (e.g., Community Climate 

System model, version 3.0, of the U.S. National Center 

for Atmospheric Research, NCARCCSM3). For these 

reasons, the interpolations of all GCM results presented 

here use only longitudinal and latitudinal gradients with 

a “fixed signal.” It should be noted that each downscaled 

GCM projection is reported as change factors (i.e., delta 

values) from the means of the data simulated by the 

same GCM for 1961–1990, as a way of normalizing the 

data for any inherent bias between the model and reality 

(i.e., to remove bias in projected means, although not 

necessarily in the projected interannual variation). The 

intention is that these change factors will be combined 

with interpolated normals of observed climate for the 

period 1961–1990—which necessarily should account for 

topographic/elevation effects. In this way, the effects of 

local spatial variability on real climate are captured and 

can be combined with the trends in climate projected by 

the GCMs.

The simulated climate variables to be interpolated in 

this study were monthly means of daily surface temperature (minimum and maximum, denoted Tmin and 

Tmax, respectively), global downward solar radiation, 

wind speed, and monthly precipitation. Monthly mean 

atmospheric vapor pressure was estimated from the 

simulated monthly mean specific humidity and sealevel barometric pressure. The monthly values (including calculated vapor pressure data) were converted to 

monthly change factors with the means of the simulated 

monthly values for the 30-year period 1961–1990 used as 

a reference point. In the case of Tmin and Tmax, the change 

factor was computed as the arithmetic difference between 

the monthly value and the corresponding 30-year mean 

of the same temperature variable for that month. For all 

other variables, the change factor was the ratio of the 

monthly value to mean for that month over the period 

1961–1990.

The change factors were interpolated using the ANUSPLIN software to create time series for the period over 

which the AR4 simulations were carried out (generally 

from 1961 to 2100). An ANUSPLIN model was generated for each monthly variable, which was used to create 

gridded data covering North America at a spatial resolution of 5 arcminutes. It should be noted that the grids 

are simply a convenient expression of the fitted spline 

function mapped over the region of interest. The spline 

functions have been archived and can, in principle, be 

used to estimate monthly values at any location given 

only latitude and longitude.
6 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Selection of Forcing Scenarios for GHG Emissions

The choice of GHG emissions scenarios (used to “force” 

a GCM’s simulation of future climate) was limited to three 

global economic-demographic storylines, as described 

in the SRES (Naki ́cenovi ́c and others 2000) and used 

in the IPCC AR4 reports: A2, A1B, and B1 (see also 

http://www.ippc.ch/publications_and_data/ar4/wg1/en/

contents.html). When these scenarios are used in global 

models, the potential future global mean surface warming ranges from 1.8 °C associated with the B1 scenario, 

2.8 °C for the A1B, and 3.4 °C for the A2 scenario for 

2090–2100 relative to 1980–1999 (IPCC 2007).

In addition, output from each GCM was downloaded 

for an additional scenario, the 20C3M scenario. This 

fourth scenario represents the model’s attempt to simulate historical climate for the 20th century, on the basis 

of known atmospheric forcings (GHG concentrations, 

ozone depletion, aerosols including those caused by 

volcanic eruptions, and variations in solar output). For 

most climate models, 20C3M scenario results were made 

available only for the period 1961–2000, which is sufficient to allow the 1961–1990 period as a reference point 

for normalizing the future scenario data (see below).

For the current project, results were selected from the 

Coupled Model Intercomparison Project Phase 3 (CMIP3) 

data at the Program for Climate Model Diagnosis and 

Intercomparison web portal at https://esg.llnl.gov:8443/

index.jsp,as simulated by each of the following GCMs (see 

Figure 1 for comparison of resolution; note “T” values 

reported in parentheses refer to the triangular truncation 

of the spectral transformations of each model’s horizontal 

spherical harmonic functions; see also Appendix 1.):

•	 CGCM31MR – Third Generation Coupled Global 

Climate Model, version 3.1, medium resolution 

(T47), developed by the Canadian Centre for Climate Modelling and Analysis http://www.cccma.

bc.ec.gc.ca/models/cgcm3.shtml

•	 CSIROMK35 – Climate System Model, Mark 3.5 

(T63), developed by the Commonwealth Scientific and Industrial Research Organisation, 

Australia http://www.cmar.csiro.au/e-print/open/

gordon_2002a.pdf; http://www-pcmdi.llnl.gov/

ipcc/model_documentation/CSIRO-Mk3.5.htm

•	 MIROC32MR – Model for Interdisciplinary 

Research on Climate, version 3.2, medium 

resolution (T42), developed by the Japanese 

Center for Climate System Research, University 

of Tokyo, National Institute for Environmental 

Studies, and Frontier Research Center for Global 

Change http://www.ccsr.u-tokyo.ac.jp/kyosei/

hasumi/MIROC/tech-repo.pdf

•	 NCARCCSM3 – Community Climate System 

Model, version 3.0 (T85), developed by the U.S. 

National Center for Atmospheric Research http://

www.ccsm.ucar.edu/models/ccsm3.0/

The use of these distinct and well-established GCMs 

ensured the downscaled scenarios met recommended 

criteria for selecting and using scenarios for climate 

change impacts studies (IPCC-TGICA 2007), including:

•	 Consistency of global projections and representative of the potential range of future regional 

climate change;

•	 Physically plausible and consistent, because 

multiple climate variables including radiation, 

humidity, and wind speed were to be interpolated 

for each GCM scenario separately and provided 

in each data set;

•	 Applicable for impacts assessment, because the 

downscaled scenario data were reported as change 

factors that can be referenced to locally observed 

climate data.

At the time this work commenced, many climate 

modeling groups were carrying out simulations for the 

various SRES scenarios; however, relatively few of 

these groups had made data sets available for all three 

scenarios. This restricted the choice of GCMs quite severely, and it proved necessary to locate some data from 

sources other than the CMIP3 database for three of the 

four models selected (see Table 1). Furthermore it was 

critically important that when locating simulation results 

from different sources, they be from the same realization 

of each model for each GHG forcing scenario. Table 2 

summarizes the realizations (runs) that were selected for 

each GCM from the CMIP3 database.

Downloading, extraction, and processing GCM data:

Standardized procedures for processing the GCM data 

sets were developed following those reported in Price 

and others (2004). These procedures were built around 

interpolation of GCM output data using ANUSPLIN, 

where the monthly data values were treated as simulated 

records obtained from a “virtual climate station” located 

at the GCM grid node coordinates.

Because the conversion and extraction processes, 

described in the following paragraphs, had to be carried out many times (approximately 40 repetitions for 

each GCM), the processing programs were run on multiple Linux-based computers, controlled by Unix shell 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 7

Table 1. General circulation model (GCM) data sets for scenarios from the Fourth Assessment Report of the Intergovernmental Panel on Climate Change used to create input files for interpolation by ANUSPLIN software.

GCM1 SRES scenario Monthly variables2 Source3 Time period

CGCM31MR 20C3M, A2. A1B, B1 tas,pr, rsds, uas, 

vas, hur, huss, psl CMIP3 1961–2100

CGCM31MR 20C3M, A2, A1B, B1 tasmin, tasmax CCCma 1961–2100

CSIROMK35 20C3M, A2, A1B, B1 tas, tasmin, tasmax, 

pr, rsds, uas, vas, hur, 

huss (except B1), psl CMIP3 1961–2100

CSIROMK35 B1 Huss CSIRO 2001–2100

MIROC32MR 20C3M, A2, A1B, B1 tas, tasmin, tasmax, pr, 

rsds, uas, vas, hur, huss, psl CMIP3 1961–2100

NCARCCSM3 20C3M, A1B, B1 tas, tasmin, tasmax, pr, rsds, 

hur, huss, uas, vas, psl CMIP3 1961–2099

NCARCCSM3 A2 tas, tasmin, tasmax, pr, rsds, 

uas, vas, hur, huss, psl ESG 1961–2099

1 CGCM31MR = Third Generation Coupled Global Climate Model, version 3.1, medium resolution; CSIROMK35 = Commonwealth 

Scientific and Industrial Research Organisation Climate System Model, Mark 3.5; MIROC32MR = Model for Interdisciplinary 

Research on Climate, version 3.2; NCARCCSM3 = Community Climate System Model, version 3.0.

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

tas – mean 2-m air temperature (K)

tasmin – mean daily minimum 2-m air temperature (K)

tasmax – mean daily maximum 2-m air temperature (K)

pr – monthly precipitation (kg m-2 s-1)

rsds – surface downwelling shortwave radiation (W m-2)

uas – zonal wind velocity (m s-1)

vas – meridional wind velocity (m s-1)

hur – relative humidity (%)

huss – surface specific humidity (kg kg-1)

psl – sea level pressure (Pa).

3 Most 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 at httsp://eqg.llnl.gov:83443/index.jsp. This “multi-model data 

set” is archived by the Program for Climate Model Diagnosis and Intercomparison. 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) website serves data for CGCM31MR and other Canadian 

climate models (http://www.cccma.bc.ec.gc.ca/data/cgcm3/cgcm3.shtml). Daily Tmin and Tmax data for CGCM31MR were 

obtained from this source because they were not available from CMIP3. The University Corporation for Atmospheric Research 

(UCAR), Earth System Grid (ESG) data portal (http://www.earthsystemgrid.org) serves data standardized to the specifications 

of the US national Center for Atmospheric Research model. The complete and consistent NCARCCSM3 data set for the A2 

scenario was only available from ESG. CSIRO = Commonwealth Scientific and Industrial Research Organisation.

scripts. These scripts (developed in-house) were edited 

specifically for each GCM, to account for the different 

spatial resolutions covering the North American domain 

(as shown in Figure 1) and for other differences in the 

contents of the data files.

The following major steps were used in preprocessing 

the data for interpolation by ANUSPLIN.

Step 1. Global orography datasets for each GCM were 

downloaded and used to create sets of grid cell coordinates, 

which were stored in three individual comma-separated 

value (CSV) format files: elevs_world.csv, lats_world.

csv, and lons_world.csv. Elevation data contained in 

elevs_world.csv needed to be “naturally oriented” (i.e., 

west-to-east and north-to-south). The global grids for all 
8 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Table 2. Realization (or run) numbers for each general circulation model and 

greenhouse gas forcing scenario in the Coupled Model Intercomparison Project (CMIP3) catalog that was selected for interpolation using 

ANUSPLIN in this study. 

Model and 

scenario Run number Time Stamp1

CGCM31MR2

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

B1 Run 5 reformatted 2005-05-12—22:21:09

CSIROMK353

20C3M Run 1 2006-09-20—05:09

A2 Run 1 2006-09-20—04:09

A1B Run 1 2006-11-04—10:04

B1 Run 1 2006-09-20—04:09

MIROC32MR4

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

B1 Run 3 reformatted 2004-12-14—00:53:41

NCARCCSM35

20C3M b30.030e (Run 5) 2004-10-18—12:38:54 MDT

A2 b30.042e (Run 5) 2004-11-28—15:15:39

A1B b30.040e (Run 5) 2004-12-09—12:52:07 MST

B1 b30.041e (Run 5) 2005-01-26—11:05:29 EST

1 Date and time information extracted from available metadata for the run. MDT = mountain 

daylight time, MST = mountain standard time, EST = eastern standard time. 2 CGCM31MR = Third Generation Coupled Global Climate Model, version 3.1, medium 

resolution. 3 CSIROMK35 = Commonwealth Scientific and Industrial Research Organisation Climate 

System Model, Mark 3.5. 4 MIROC32MR = Model for Interdisciplinary Research on Climate, version 3.2.

5 NCARCCSM3 = Community Climate System Model, version 3.0.

four GCMs were flipped north-to-south (which meant 

that the processing programs had to invert them), with 

the west-most column at 0° longitude. The longitude 

coordinates stored in lons_world.csv were converted 

to their negative equivalent (e.g., 240.0° = 120.0° W = 

-120.0°). Given that the data sets provided global coverage, latitude values for the southern hemisphere stored 

in lats_world.csv were also converted to their negative 

equivalents. These CSV data files were copied to the 

working directory used to extract data for the North 

American rectangle (Figure 1).

Step 2. GCM data files (for each climate variable and 

each of the four scenarios) were downloaded generally 

in NetCDF format. The downloaded files were renamed 

to a systematic format to ensure models and scenarios 

were uniquely identified and to facilitate subsequent 

manipulation.

Step 3. Global data for each climate variable was extracted from the NetCDF files using a script (developed 

in-house) called nc-readvar_to_asg.txt, which then called 

program nc-readvar to convert data for each variable to 

ASCII with GRIB-format headers (a single line at the 

top of the data for each monthly time step, containing 

information about the time step and grid dimension 

information, as described at http://cera-www.dkrz.de/

IPCC_DDC/info/Readme.grbconv). These output files 

were called “ASCII grids,” to reflect the use of ASCII text 

and to refer to the data content organized on a standard 

geographic grid. The files were identified with the same 

names as used for the input NetCDF files but with the 

extension “asg” suffix to replace the extension “nc.”

Step 4. Data for wind speed were extracted for those 

GCMs with data files containing multiple atmospheric 

pressure levels. Further scripts were used to call several 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 9

programs, described below, to extract the required data 

and combine them with the GCM grid node elevations 

to obtain estimates of wind velocity at the surface elevation of the GCM. Scripts called merge_ua.txt, and 

combine_merge_ua.txt were used for the zonal (U) 

component of wind velocity. Scripts called merge_va.txt, 

and combine_merge_va.txt were used for the meridional 

(V) wind velocity component. These programs used to 

extract data and their specific functions were as follows:

nc-readvar Extract data from NetCDF file at 

multiple pressure levels and convert 

to ASCII grids for each month;

cat Concatenate ASCII data from multiple 

months and a single pressure level into 

a single time series;

asg2nc Convert an ASCII time series into a 

NetCDF file containing the time series 

for a single pressure level; and

nc_merge Merge data from multiple pressure 

levels into a single surface (i.e., where 

the grid-cell elevations are sufficiently 

high to penetrate above the bottom 

atmospheric pressure levels in the 

GCM data file). This program has 

switches (--or --inclusive) to allow 

retention of existing surface level 

values as they are merged with data 

from progressively higher atmospheric 

levels.

For all GCMs, the U and V wind velocity component vectors of wind velocity were combined to 

calculate mean wind speed (u) using the hypotenuse 

calculation:

U 2 V 2 u = + [1]

Another script, extract_merged_to_asg.txt, which again 

called nc-readvar, was run to create ASCII files with 

GRIB headers from the merged data.

Step 5.Data for the North American rectangles (including the western part of Greenland, Figure 1) were extracted 

from the global NetCDF data files using a script called 

do_gcm_subsetXXxYY.txt and from the merged global 

ASCII files using a script called do_gcm_subsetXXxYY_

merged.txt, where XX and YY are the longitudinal and 

latitudinal grid dimensions, respectively, of the GCMspecific rectangle. The do_gcm_subsetXXxYY.txt script 

first converted the NetCDF file into ASCII format. Both 

scripts then called the program gcm_subset to extract the 

desired North American spatial subset from the global 

ASCII files. Most GCM grids were “flipped” meaning 

the data are organized with the southern-most grid cells 

at the top, so it was necessary to check for flipping and 

reverse as appropriate. The gcm_subset program defaults 

to handle the flipped grid orientation correctly, but flipping can be suppressed with a -F switch. Output files 

generated by gcm_subset had a suffix added to the input 

file name of the generic format “.XXxYY.subset.” For 

each GCM, gcm_subset also produced the corresponding subsets of the elevs-world.csv, lats-world.csv, and 

lons-world.csv files that were needed to generate the 

grid-node coordinate information when formatting the 

data for input to ANUSPLIN.

Because the spatial domains, grid resolutions, and 

output variables differed among the four GCMs, some 

model-specific details are provided in the following 

paragraphs (see also Figure 1 and Appendix I):

CGCM31MR. The global domain consists of 96 

longitudinal × 48 latitudinal cells, yielding nominal gridcell dimensions of 3.75° longitude by 3.75° latitude at the 

equator. Although the longitudinal angular dimensions are 

constant for all grid cells in common with most GCMs, the 

latitudinal angular dimensions vary with latitude. Within 

this model, generation of a subset for North America 

produced a rectangular grid of 20 cells north-to-south 

and 34 cells east-to-west with northerly and southerly 

boundaries at 83.8789° N and 12.989° N, respectively, 

and westerly and easterly boundaries at 168.75° W and 

45.0° W, respectively. For CGCM31MR, the variables 

tasmax and tasmin (defined in Table 1) were available 

only as daily data. These daily files were downloaded, 

spatial subsets were created, and the data were averaged 

for each month to obtain the monthly mean daily values 

before continuing with Step 6.

CSIROMK35. The global domain consists of 192 

longitudinal × 96 latitudinal cells, yielding a nominal 

grid-cell size of 1.875° longitude × 1.865° latitude at 

the equator. Generation of a subset for North America 

produced a rectangular grid of 39 cells north-to-south 

and 67 cells east-to-west with boundaries at 84.862° N, 

13.9894° N, 168.75° W and 45.0° W, respectively.

MIROC32MR. The global domain consists of 128 

longitudinal cells × 64 latitudinal cells, yielding a nominal grid-cell size of 2.81° longitude × 2.79° latitude at 

the equator. Generation of a subset for North America 

produced a rectangular grid of 26 cells north-to-south 

and 45 cells east-to-west, with boundaries at 85.0965° N, 

15.3484° N, 168.75° W and 45.0° W, respectively.
10 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

NCARCCSM3. The global domain consists of 256 

longitudinal cells × 128 latitudinal cells, yielding a 

nominal grid-cell size of 1.40625° longitude × 1.400768° 

latitude at the equator. Generation of a subset for North 

America produced a rectangular grid of 52 cells northto-south and 84 cells east-to-west, with boundaries at 

86.1415° N, 14.7081° N, 168.75° W and 52.0312° W, 

respectively.

Step 6. The subsets of monthly GCM data grids were 

converted into the columnar format used for input to 

ANUSPLIN: annual data blocks, each comprising fields 

for the latitude, longitude, and elevation of the grid node 

followed by 12 monthly climate values, sorted by lines 

in latitude and longitude order. This procedure was carried out using program gcm_processor, called by a script 

named gcmprocessor_GCM_XXxYY.txt, where “GCM” 

represents the name of the GCM and “XXxYY” represents the longitudinal and latitudinal dimensions (i.e., 

number of grid cells) of its North American rectangle, 

respectively. The main function of gcm_processor was 

to normalize the GCM data in a two-pass procedure. On 

the first pass, gcm_processor was run using the GCM’s 

twentieth century (20C3M) results as input, to calculate 

30-year means for each month during the simulated period 

1961–1990. On the second pass, these calculated means 

were then used to convert the GCM output from projections of absolute values to change factors relative to the 

1961–1990 means. These means were applied to the GCM 

projections (A2, A1B, B1). In the case of temperature variables, the change factors were calculated by subtracting

the means from the monthly values. For all other climate 

variables, the change factors were calculated by dividing

the monthly values by the simulated 1961–1990 means. 

The multi_gcmproc.txt script called multiple instances 

of gcmprocessor_GCM_XXxYY.txt so that data for all 

climate variables for a single GHG emissions scenario 

could be handled in a single process. Specific versions 

of both batch files were created for each GCM and emissions scenario, which also accounted for the period of the 

simulation (1961–2099 for NCARCCSM3; 1961–2100 

for the other three GCMs). Each output file generated 

by multi_gcmproc.txt contained data for a single climate 

variable and a single year, because ANUSPLIN treats 

each month of each year as an independent data set.

Step 7. In the particular case of simulated atmospheric 

humidity, the preferred measure was vapor pressure (denoted e), which required conversion from other humidity 

terms simulated by the GCMs. After much searching, 

complete error-free data sets of simulated specific humidity (denoted Hs) at surface elevation were obtained, 

though data for only 10 of the 12 GCM projections were 

available from the CMIP3 database. One exception was 

the NCARCCSM3 model forced by the A2 emissions 

scenario, for which there were acknowledged errors, 

including the complete absence of data for the 2070s, and 

some very high values occurring every January at several 

locations around the globe (including all grid cells at the 

South Pole and two small groups of adjacent cells in North 

America). The use of surface relative humidity instead of 

Hs to calculate vapor pressure was considered, but these 

data also were also missing for the 2070s decade.

Subsequently, a complete time series of surface Hs 

data was located at the Earth System Grid data portal of 

the University Corporation for Atmospheric Research, 

although this data set also had the problem with extreme 

values at two locations in North America and elsewhere. 

To overcome this problem, a new routine was added to 

gcm_processor, which scanned all of the data in each 

month for values that were excessively high (values of 

650,000 to 750,000 rather than the typical values, on the 

order of 0.001 kg kg-1). Whenever the scanning algorithm 

located a grid cell containing an over-range value, the 

value was replaced by the mean of the values in the 

adjacent grid cells, excluding any that were themselves 

over-range. Because the search algorithm worked from 

northwest to southeast, the means of some grid cells were 

derived from interpolated means in adjacent cells to the 

north and west. Under the circumstances, this seemed 

like a necessary but minor compromise to provide the 

consistent data set needed for the ANUSPLIN interpolation to be carried out successfully.

Precipitation amounts simulated by GCMs are often 

highly correlated with the simulated humidity in the same 

or adjacent grid cells. However, no precipitation data that 

were clearly over range were found in the NCARCCSM3 

data for the months affected by the specific humidity 

problem.

A second exception was the CSIROMK35 when forced 

by the A1B scenario. In this case, surface Hs data were 

unavailable from PCMDI, but were obtained directly from 

the Commonwealth Scientific and Industrial Research 

Organisation, Australia.

Because vapor pressure depends on elevation, sea-level 

pressure data (also simulated by each GCM) were used 

to provide barometric corrections for grid-cell elevation. 

A second custom program, anu_hum, was written to perform the conversion of humidity data extracted for each 

GCM to the format required for input to ANUSPLIN. 

This program read monthly change factors for Hs and 

the corresponding change factors for sea-level pressure, 

recombining these values with the 1961–1990 means 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 11

calculated in Step 6. The appropriate data were used to 

calculate monthly values of e, which were exported to 

new output files, also in ANUSPLIN input format.

Vapor pressure was derived from the values for Hs and 

sea-level pressure simulated by each GCM. Steps for the 

conversion algorithm were as follows:

1. Read specific humidity (Hs, kg kg-1), and sea-level 

pressure (P(0), kPa) for each GCM gridpoint.

2. Adjust sea-level pressure to “surface pressure” at 

the elevation given by the GCM orography data, 

using the equation of Jensen and others (1990):

P(z) = P(0) (1.0 – 0.0065z/293.0)5.26 [2]

where P(z) is the atmospheric pressure (kPa) at elevation z (m).

3. Calculate surface vapor pressure at elevation z, e(z), 

from specific humidity, Hs, and surface pressure, 

P(z), using the following equation:

e(z) = P(z) Hs/[0.622 + Hs(1.0 – 0.622)] [3]

where 0.622 is the ratio of the molecular weights of water 

vapor to air (e.g., Monteith and Unsworth 2008) and e

and P(z) are in kilopascals.

Consideration was given to limiting the calculated 

values of surface vapor pressure to the lesser of the value 

obtained from [3] and saturation at Tmin (or at Tmean) for the 

same time step and GCM grid node, since it is generally 

unlikely that monthly mean e would exceed saturation. 

However, computing saturation at Tmin produced many 

instances where this assumption did not hold. The justifications for not limiting the vapor pressure values were:

(i) Although unlikely, it is possible that diurnal changes 

in e, coupled with the curvilinear response of 

saturation vapor pressure to temperature, would 

cause a situation in which monthly mean e exceeds 

saturation at Tmin or Tmean.

(ii) There are likely to be differences among the GCMs 

in their simulation of variability and trends in vapor 

pressure. Limiting these values to saturation at 

temperatures simulated by each GCM could mask 

some of these differences and cause any comparison 

of the calculated values to be misleading.

(iii) As for precipitation, solar radiation and wind speed 

data, projected changes in monthly mean vapor 

pressure were normalized as ratios of the simulated 

1961–1990 monthly means. These ratios necessarily required that the future and the reference point 

data be computed in the exactly same way; hence 

even if the absolute values simulated by the GCM 

violated the assumption of (e ≤ e*(Tmin), the actual 

vapor pressure data obtained from each downscaled 

scenario would depend on the historical climatology temperature and vapor pressure data that are 

to be combined with the change factors.

(iv) The responsibility for determining whether the 

simulated climate variables are physically consistent should remain with the user of the downscaled 

data. It is safer, and potentially less confusing, for 

users of the data to account for situations where 

vapor pressure exceeds saturation (if needed) than 

it is for them to assume this will never happen.

For these reasons, the final change factors for vapor 

pressure were not arbitrarily limited to saturation at 

monthly Tmin or Tmean.

Step 8. The files of normalized monthly change factors 

for each GCM variable (i.e., four GCMs × three scenarios × six variables, for 72 files in total) were submitted 

to the CFS Great Lakes Forestry Centre for interpolation using ANUSPLIN. At Great Lakes Forestry Centre, 

ANUSPLIN models were developed for each month of 

normalized data, with the data being treated as anomalies 

(deltas) relative to the 1961–1990 means. A fixed signal 

model, rather than a standard optimization model, was 

used because the input data were anomalies, rather than 

actual climate values (McKenney and others 2006c). 

We note there are no inherent statistical relationship 

between these anomalies and the independent variables 

of longitude and latitude. A fixed signal of 60% of the 

data points (GCM grid-cell values) produced reasonable 

results (e.g., avoiding singularities [“bulls eyes”] in the 

resultant climate change scenario models). The LAPGRD 

program (part of the ANUSPLIN package) was used 

to generate the data grids from a 30 arc-second digital 

elevation model of North America. This model was 

constructed by staff at the Great Lakes Forestry Centre 

using the U.S. Geological Survey GTOPO30 digital elevation model coverage for the United States. (http://eros.

usgs.gov/#/Find_Data/Products_and_Data_Available/

GTOPO30_info) and a Canadian digital elevation model 

(see Lawrence and others 2007). Log files containing 

summary statistics were also generated by LAPGRD. 

The monthly grids of interpolated change factors were 

generated in ARC/INFO ASCII format, with a cell size 

of 5 arcminute (300 arc-second) latitude x 5 arcminute 

(300 arc-second) longitude (about 9.25 km2

 at the equator), covering the domain from 168o W to 52o W and 

from 25o N to 85o N (1392 columns x 720 rows). This 
12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

grid resolution matches that of many other climate data 

products previously produced at CFS (McKenney and 

others 2007). The generated monthly files were bundled 

and transmitted back to CFS Northern Forestry Centre 

via FTP for post-processing.

Step 9. “Subset rectangles” were extracted from the 

North American grids for Alaska, the conterminous 48 

States and Canada, by means of macros running in ARC/

INFO and were packaged for final distribution.

The normalization procedures carried out in Step 6 

removed biases in the individual GCMs. That is, any tendency for a GCM model to over or underestimate historical 

climate, defined as the 1961–1990 mean, was removed 

and only the change relative to that period was retained. 

The interpolations carried out in Step 9 allowed direct 

comparison of the downscaled projections for different 

scenarios and different GCMs (which operate at different spatial resolutions). These steps were consistent with 

requirements outlined in a recent USDA Forest Service 

memorandum, “Draft NEPA Guidance on Consideration 

of the Effects of Climate Change and Greenhouse Gas 

Emissions,” which was released on 18 February 2010.

Use of Historical Climatology

The interpolation procedure applied to the GCM output data did not account for topographic effects because 

the representation of surface orography in global-scale 

GCMs is typically is poor (although as Figure 1 shows, 

the horizontal resolution varied substantially among the 

four GCMs). Observed climate normals for the period 

1961–1990 were interpolated to the same grid resolution 

accounting for topographic effects (see McKenney and 

others 2007). The interpolated change factors for each 

GCM can be combined with these or other interpolated 

grid data of observed climate, so that spatial variability 

in future climate attributable to topography is retained 

while the climate-change trends simulated by the GCMs 

are captured. This approach is only considered an approximation of future climate, however, as there may 

be interactions between topography and climate change 

that alter the course of the local projection. Consequently, 

there are many other larger sources of errors in the GCM 

projections that errors associated with this combination 

approach are unlikely to be important (see also the Discussion).

Because the interpolated GCM scenario data are 

consistent with IPCC selection criteria (see http://www.

ipcc-data.org/ddc_scen_selection.html) and have been 

converted to change factors referenced to the 30-year 

monthly means for the simulated 1961–1990 period, they 

can be combined with gridded climate normals for the 

same 30-year period to create “absolute” values for the 

future projections of climate. This approach preserves the 

characteristics of current climate while superimposing the 

climate change signals simulated by each GCM for each 

GHG forcing scenario. A key advantage is that the user 

is free to combine these interpolated scenario data with 

any climatological data set (although of course these data 

should be for an appropriate variable2

 averaged over the 

1961–1990 period). A further advantage is that for change 

factors expressed as ratios (i.e., for climate variables other 

than temperature), the units of the historical climatology 

will always be retained in the combined data.

As previously noted, the CFS has constructed continental scale gridded climatologies derived from climate 

station records collected across Canada and the continental 

United States since 1901. These data, including grids of 

30-year normals and historical monthly models are freely 

available in various formats. In addition, historical 

continent-wide dailey models have been constructed 

(Hutchinson and others 2009). Several other historical climatologies are available for the conterminous 

United States. As part of the VEMAP project (http://

www.cgd.ucar.edu/vemap/) monthly and daily climate 

variables were developed for the 1895-1993 period at 

the 0.5 degree latitude by 0.5 degree longitude grid size 

(Kittel and others 2004). Climate variables include: precipitation, minimum and maximum temperature, total 

incident solar radiation, daylight-period irradiance, vapor 

pressure, and daylight-period relative humidity. PRISM 

data for the 1895 to present time period is available at the 

4 km grid cell size (Daly and others 1994; Gibson and 

others, 2002; see also http://www.prism.oregonstate.edu/) 

and were used with the change factors from this study 

to develop the climate projections for the RPA scenarios 

(USDA Forest Service, in process). Climate variables 

include monthly mean maximum temperature, monthly 

mean minimum temperature and monthly precipitation. 

For the 1971-2000 period, PRISM gridded data of 

the same variables are available at the 800 m grid cell 

(http://www.prism.oregonstate.edu/). Climatologies for 

the United States have also been developed by Rehfeldt 

(2006) and DAYMET (see http://www.daymet.org/default.jsp).

2 Examples of appropriate variables include: radiation expressed 

in W m-2 or MJ m-2 d-1; wind speed in m s-1, miles per hour or knots; 

and precipitation in mm d-1 or inches per month
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 13

Analysis of Interpolated Climate Variables

This section describes the methods used to analyze the 

interpolated GCM data. The objective was to carry out 

a comprehensive survey of the results obtained for the 

Alaska and the conterminous United States, including 

the following aspects:

•	 Compare and contrast the large-scale trends (spatial 

and temporal) seen in the 12 high-resolution GCM 

projections that were produced;

•	 Demonstrate the kinds of analyses that can be 

performed with the data that might be applied to 

specific regions of the United States (other than 

the regions identified for the current project);

•	 Highlight the consistencies and inconsistencies 

among the different GCMs;

•	 Perform quality control on the interpolated data 

products by locating apparent errors; and

•	 Identify problems with GCMs and/or with particular GHG forcing scenarios.

The 12 projections (three scenarios and four GCMs) 

each generated projected changes in monthly climate over 

a 100-year period for six distinct climate variables (i.e., 

maximum temperature, minimum temperature, precipitation, solar radiation, wind speed and vapor pressure). 

We analyzed this data in three different ways: spatial 

and temporal patterns at the scale of the United States, 

interannual variations at the regional scale, and a more 

in-depth look at the future regional patterns.

Spatial and Temporal Patterns

First, spatial variability of three key variables for 

30-year periods (2011–2040, 2041–2070 or 2071–2100) 

was compared and contrasted among the different GCMs 

and GHG emissions scenarios for the conterminous 

United States and Alaska. Initially, maps were developed 

to display the change factors applied to the interpolated 

1961-1990 normals. However, the differences among the 

projections often were very subtle, because the spatial 

variations in projected changes are small compared with 

the strong climatic gradients that exist across the entire 

continent, resulting from latitudinal and elevation differences and from the east-to-west gradients caused by 

synoptic weather systems and the Rocky Mountains.

The approach subsequently adopted was to compare, for 

a single GCM, the 1961-1990 normals with projections 

for each of the 30-year periods for three key variables: 

annual mean daily maximum temperature, annual mean 

daily minimum temperature, and total annual precipitation. The GCM selected was the NCARCCSM3 forced 

by scenario A1B. The temperature maps were derived 

by adding the means of the interpolated change factors 

for each 30-year period to the interpolated 1961-1990 

normals. For precipitation, the projections were derived 

by multiplying the means of the interpolated change factors for each 30-year period by the 1961-1990 normals. 

This analysis was followed by a comparison of the spatial 

variability of the change factors projected by each of the 

four GCMs for the A1B scenario for each of the 30-year 

periods: 2011–2040, 2041–2070 or 2071–2100. The 

change factors in these maps are relative to the 1961-1990 

period. In these maps, all grid-cell means were weighted to 

account for the number of days in each month, including 

leap years, and, hence, can be compared with historical 

30-year climate normals obtained from climate station 

observations. The last spatial comparison was of the 

change factors projected by each of the four GCMs for 

the A2 and B1 scenario for the last period: 2070-2100 

(or 2099 in the case of NCARCCSM3).

Interannual Variations at the Regional Scale

Following the exploration of spatial differences in 

the projected trends, the more detailed analysis of the 

temporal changes involved the computation of seasonal 

and annual values, computed for each of eight regions. 

Data were spatially averaged for Alaska and seven other 

regions of the conterminous 48 states, the latter based 

on groupings of the major ecoclimatic divisions in the 

Bailey (1995) ecoregions classification for the United 

States (Figure 2) (termed “regions” ). These regions 

stratify the conterminous United States in relatively homogenous ecoclimatic divisions, allowing the exploration 

of the effects of future climate change. Alaska was kept 

as a single region to keep the total number of regions 

examined small.

The seasonal and annual absolute data are area-weighted

spatial averages of the monthly aggregated values. The 

area weightings were calculated as ratios for each grid 

cell, where the actual area was expressed relative to the 

mean area of all grid cells in the region, i.e.,

A N

A R

region

i

Ai = [4]

where RAi is the area ratio for grid cell i, N is the number 

of grid cells in the region, Ai is the area of grid cell i and 

Aregion is the total area of the region given by∑

=

N

i

Ai

1

. The 

area Ai was calculated in steradians using the following 

equation:

( ) ( ) 2

2

cos / 2 cos / 2 δ α + δ + α − δ Ai = [5]
14 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

where angle α is the latitude at the grid cell centroid, and 

δ is the dimension of the grid cell (latitude and longitude) 

expressed in radians. The area in steradians can be converted to square kilometers using a value of 6371.2213km 

per radian, assuming the earth is a perfect sphere (Kittel 

and others 1995), but this is unnecessary for [4].

The analysis included a systematic production of 

time-series data for annual and seasonal means of each 

monthly variable in every region for all four GCMs and 

all three scenarios, plus the 20th century simulations and 

observed temperature and precipitation data. Seasons were 

defined as three-month periods: spring (March-May), 

summer (June-August), fall (September-November), 

winter (December-February). Note the winter of 2100 

(2099 for NCARCSM3) would, by this definition, contain only one month, and it was therefore omitted in the 

calculations of 30-year averages.

The first analysis examines the regional relationships 

between the spatially averaged regional change in annual mean daily minimum temperature and the regional 

annual precipitation ratio projected by each GCM for the 

2040-2059 period and the 2080-2099 period, relative to 

the 1961-1990 period. These scatter plots demonstrate 

how the four GCMs, forced by each of the three SRES 

scenarios, differed in their projections of climatic change 

for each ecoregion across two periods into the future. 

These graphs also facilitate selecting scenarios that may 

be representative of the range of projected changes in 

climate (temperature and precipitation) for a particular 

region of the United States. The second analysis examined 

the interannual variation in each climate projection by 

graphing long-term time series of key annual or seasonal 

variables. The regional graphs enable comparison of 

projected trends in means and interannual variability, 

according to each GCM scenario, for a specified variable 

and region.

Climate Scenario Regional Outlook

We also summarized, in a set of comprehensive tables, 

the results for all variables and all scenarios in each of 

the eight regions identified in Figure 2. The time-series 

data were imported into a series of spreadsheets and used 

to generate summary tables providing key information 

about projected changes during the 21st century.

In these tables, the area-weighted mean for each 

climate variable is the mean of the values projected by 

the four GCMs and therefore represents a “best guess,” 

assuming that the GCMs are equally skillful (or equally 

believable). Each table presents results for a total of six 

climate variables (maximum and minimum temperature, 

precipitation, global solar radiation, vapor pressure and 

wind speed) for a single region. For each variable, the 

data are organized across the table, in three sets of five 

columns. Each set of columns represents a single GHG 

emissions scenario (in the order A2, A1B, and B1), with 

the columns containing the means of the four GCM 

Figure 2. Regions of the conterminous United States used in the climate scenario analysis, derived from the Ecoregions map of 

Bailey (1995). The Alaska region was defined by state boundaries rather than ecoclimatic boundaries. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 15

projections of monthly values for spring, summer, fall, 

winter, and the entire year.

The rows of data are labeled in the left-most column 

according to the period represented. “Baseline 1980–2009” 

refers to the 30-year mean for the period 1980–2009. This 

period was selected as the baseline because it represents 

current climate and immediately precedes the three consecutive 30-year periods reported as projections for the 

future (starting with 2010). 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 1980–2009 are probably small, there is evidence of 

a general warming trend over this entire period that is 

apparent in many of graphs shown previously (both 

in the observed temperature records and in the GCM 

projections). Notably, the baseline 1980–2009 mean 

values differed slightly among the three emissions 

scenarios, because data for the nine years from 2001 

to 2009 originated from the different GHG simulations, 

which led to different calculated means.

The rows for “Change by 2010–2039,” “Change by 

2040–2069,” and “Change by 2070–2099” give the mean 

net changes in the projected 30-year means relative to 

1980–2009. In these rows, a positive value indicates an 

increase, and a negative value indicates a decrease. The 

rows for “100-year forcing” and “100-year variability 

(%)” represent the changes in 30-year means and 30-

year standard deviations (SDs), respectively, between 

the periods 1970–1999 and 2070–2099. The changes 

in SD are reported as percentages relative to 100% for 

1970–1999.

Calculation of Bioclimatic Indices for the 

Scenario Data

As an added set of products, 30-year models of mean 

changes in temperature and precipitation were also developed, for the purpose of projecting changes in various 

bioclimatic indicators (see Table 3). This required several additional steps. First, ANUSPLIN surfaces for the 

30-year mean change fields from each GCM scenarios 

were created for the three future periods (2011–2040, 

2041–2070 and 2071–2100). These surfaces were used 

to estimate projected mean changes at North American 

weather stations operating during the period 1961–1990. 

The mean changes were combined, in turn, with the station 

normals for the 1961–1990 period. This allowed for the 

generation of new ANUSPLIN surfaces of projected 

mean values for each future period. For these models, 

trivariate (position and elevation-dependent) splines 

were used. The statistical signals were good because 

there are statistically strong elevational dependencies in 

the 1961–1990 models, which remained in the derived 

ANUSPLIN models of future climate.

With these surfaces it was possible to generate several 

bioclimatic variables (e.g., length of growing season, 

precipitation during the growing season) that are often 

used in modeling in the fields of forestry, agricultural, 

and ecological impacts and hence have greater interest to 

some potential users. The derived variables are possible 

because a daily sequence of temperature and precipitation 

can be generated from the primary monthly surfaces. This 

is done through a Bessel interpolation whereby the daily 

sequence is forced to pass through the monthly means 

in a monotonic form (for details see Mackey and others 

1996). It is important to understand that these data are 

intended to represent mean conditions and that in any 

given year, “noise” would influence the actual daily 

sequence of bioclimatic variables.

It is recognized that some users might desire estimates 

of bioclimatic variables at annual time steps, rather than 

30-year averages. This created an additional challenge 

because of the previously noted caveat concerning the 

greater stochasticity of individual years. After due consideration, we decided to generate another resolution 

of model outputs that would allow the dissemination of 

some of the projected bioclimatic variables at annual 

time steps. Again, users should appreciate that these 

bioclimatic models do not account for stochasticity at 

daily and monthly timescales, but they do retain the 

interannual variations provided in the GCM projections. 

To make the data sets more manageable, these models 

were developed at the slightly coarser resolution of 900 

arc seconds (~30 km).

Results ___________________________

Spatial and Temporal Patterns

Maps showing the trends in annual mean daily Tmax

and Tmin and precipitation are presented in Figures 3, 4 

and 5, respectively. In each figure, the first set of maps 

(a) shows the trends in absolute measures (maximum 

temperature, minimum temperature, and precipitation, 

respectively) according to NCARCCSM3 forced by 

the A1B scenario for three future 30-year periods: 2011–

2040, 2041–2070 or 2071–2100 (or 2071-2099 for 

NCARCCSM3). In each of Figures 3–5, maps b, c, and 

d show the changes relative to the means for 1961–1990, 

for each successive 30-year period, again forced by A1B. 
16 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Table 3. Variables derived from primary climate surfaces. Variables 1-19 are generated by ANUCLIM (Houlder and others 

2000); variables 20-29 are generated by SEEDGROW (Mackey and others 1966)* (modified from McKenney 

and others 2006a). In all cases, the descriptions should be considered estimates rather than actual values.

No. Variable Description

1 Annual mean temperature Annual mean of monthly mean temperatures

2 Mean diurnal temperature range Annual mean of monthly mean daily temperature ranges

3 Isothermality Variable 2 / variable 7

4 Temperature seasonality Standard deviation of monthly mean temperature

 estimates expressed as a percentage of their mean

5 Maximum temperature of warmest period Highest monthly maximum temperature

6 Minimum temperature of coldest period Lowest monthly minimum temperature

7 Annual temperature range Variable 5 – Variable 6

8 Mean temperature of wettest quarter Mean temperature of three consecutive wettest months

9 Mean temperature of driest quarter Mean temperature of three consecutive driest months

10 Mean temperature of warmest quarter Mean temperature of three consecutive warmest months

11 Mean temperature of coldest quarter Mean temperature of three consecutive coldest months

12 Annual precipitation Sum of monthly precipitation values

13 Precipitation of wettest period Precipitation of the wettest month

14 Precipitation of driest period Precipitation of the driest month

15 Precipitation seasonality Standard deviation of monthly precipitation estimates

 expressed as a percentage of their mean 

16 Precipitation of wettest quarter Total precipitation of three wettest months

17 Precipitation of driest quarter Total precipitation of three driest months

18 Precipitation of warmest quarter Total precipitation of three warmest months

19 Precipitation of coldest quarter Total precipitation of three coldest months

20 Start of growing season Date when daily mean temperature first meets or

 exceeds 5 °C for five consecutive days in spring

21 End of growing season Date when daily minimum temperature first falls below

 -2 °C after 1 August 

22 Growing season length Variable 21 – Variable 20

23 Total precipitation in the three months Total precipitation of three months prior to variable 20

 before start of growing season

24 Total growing season precipitation Total precipitation during variable 22

25 Growing degree-days during growing season Total degree days during variable 22, accumulated for

 all days where mean temperature exceeds 5 °C.

26 Annual minimum temperature Annual mean of monthly minimum temperatures

27 Annual maximum temperature Annual mean of monthly maximum temperatures

28 Mean temperature during growing season Mean temperature during variable 22

29 Temperature range during growing season Highest minus lowest temperature during variable 22

*The approach used by Mackey et al. (1996) creates a daily sequence of minimum and maximum temperature and precipitation with 

the values forced monotonically through the monthly values. The resulting values are intended to represent mean conditions only, 

as the weather in any given year would be expected to produce different results, because of interannual variability. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 17

Figure 3a. Maps of annual mean daily maximum temperature derived from climate station records from the period 1961-1990 

and projections according to the Community Climate System Model, version 3.0 (NCARCCSM3), forced by the A1B scenario, for 

2011-2040, 2041-2070, and 2071-2099. The projections were derived by adding the means of the interpolated change factors 

for each 30-year period to the interpolated climate normal data shown in the 1961-1990 normals map (top). 
18 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Figure 3b. Projected changes in annual mean daily maximum temperature for the period 2011–2040, relative to 1961–1990, for 

the A1B scenario according to the four general circulation models used in this study. CGCM31MR = Third Generation Coupled 

Global Climate Model, version 3.1, medium resolution; CSIROMK35 = Commonwealth Scientific and Industrial Research 

Organisation Climate System Model, Mark 3.5; MIROC32MR = Model for Interdisciplinary Research on Climate, version 3.2; 

NCARCCSM3 = Community Climate System Model, version 3.0.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 19

Figure 3c. Projected changes in annual mean daily maximum temperature for the period 2041–2070, relative to 1961–1990, 

for the A1B scenario according to the four general circulation models used in this study.
20 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Figure 3d. Projected changes in annual mean daily maximum temperature for the period 2071-2100 (2071-2099 for 

NCARCCSM3), relative to 1961-1990, for the A1B scenario according to the four general circulation models used in this study.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 21

Figure 3e. Projected changes in annual mean daily maximum temperature for the period 2071-2100 (2071-2099 for 

NCARCCSM3), relative to 1961-1990, for the A2 scenario according to the four general circulation models used in this study.
22 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Figure 3f. Projected changes in annual mean daily maximum temperature for the period 2071-2100 (2071-2099 for 

NCARCCSM3), relative to 1961-1990, for the B1 scenario according to the four general circulation models used in this study.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 23

Figure 4a. Maps of annual mean daily minimum temperature derived from climate station records for the period 1961–1990 

and projections according to the Community Climate System Model, version 3.0 (NCARCCSM3) forced by the A1B scenario, 

for 2011–2040, 2041–2070, and 2071–2099. The projections were derived by adding the means of the interpolated change 

factors for each 30-year period to the interpolated climate normal data shown in the 1961-1990 normals map (top).
24 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Figure 4b. Projected changes in annual mean daily minimum temperature for the period 2011-2040, relative to 1961-1990, 

for the A1B scenario according to the four general circulation models used in this study. CGCM31MR = Third Generation 

Coupled Global Climate Model, version 3.1, medium resolution; CSIROMK35 = Commonwealth Scientific and Industrial 

Research Organisation Climate System Model, Mark 3.5; MIROC32MR = Model for Interdisciplinary Research on Climate, 

version 3.2; NCARCCSM3 = Community Climate System Model, version 3.0.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 25

Figure 4c. Projected changes in annual mean daily minimum temperature for the period 2041-2070, relative to 1961-1990, for 

the A1B scenario according to the four general circulation models used in this study.
26 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Figure 4d. Projected changes in annual mean daily minimum temperature for the period 2071-2100 (2071-2099 for 

NCARCCSM3), relative to 1961-1990, for the A1B scenario according to the four general circulation models used in this study.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 27

Figure 4e. Projected changes in annual mean daily minimum temperature for the period 2071-2100 (2071-2099 for 

NCARCCSM3), relative to 1961-1990, for the A2 scenario according to the four general circulation models used in this study.
28 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Figure 4f. Projected changes in annual mean daily minimum temperature for the period 2071-2100 (2071-2099 for 

NCARCCSM3), relative to 1961-1990, for the B1 scenario according to the four general circulation models used in this study.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 29

Figure 5a. Maps of annual total precipitation derived from climate station records for the period 1961–1990 and projections 

according to the Community Climate System Model, version 3.0 (NCARCCSM3) forced by the A1B scenario, for 2011–2040, 

2041–2070, and 2071–2099. The projections were derived by multiplying the interpolated climate normal data shown in the 

top map by the means of the interpolated change factors for each 30-year period.
30 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Figure 5b. Projected changes in annual total precipitation for the period 2011-2040, expressed as ratios relative 

to the means for 1961-1990, for the A1B scenario, according to the four general circulation models used in this study. 

CGCM31MR = Third Generation Coupled Global Climate Model, version 3.1, medium resolution; CSIROMK35 = 

Commonwealth Scientific and Industrial Research Organisation Climate System Model, Mark 3.5; MIROC32MR = Model 

for Interdisciplinary Research on Climate, version 3.2; NCARCCSM3 = Community Climate System Model, version 3.0.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 31

Figure 5c. Projected changes in annual total precipitation for the period 2041-2070, expressed as ratios relative to the 

means for 1961-1990, for the A1B scenario according to the four general circulation models used in this study.
32 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Figure 5d. Projected changes in annual total precipitation for the period 2071-2100 (2071-2099 for NCARCCSM3), 

expressed as ratios relative to the means for 1961-1990, for the A1B scenario according to the four general circulation 

models used in this study.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 33

Figure 5e. Projected changes in annual total precipitation for the period 2071-2100 (2071-2099 for NCARCCSM3), 

expressed as ratios relative to the means for 1961-1990, for the A2 scenario according to the four general circulation 

models used in this study.
34 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Figure 5f. Projected changes in annual total precipitation for the period 2071-2100 (2071-2099 for NCARCCSM3), 

expressed as ratios relative to the means for 1961-1990, for the B1 scenario according to the four general circulation 

models used in this study.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 35

The final two sets of figures (e, f) show the changes for 

the period 2071–2099, relative to the 1961-1990 period, 

for each of the A2 and B1, which can be compared directly 

to the data for the A1B scenario in (d).

The four GCMs agreed fairly closely in their projections 

of temperature trends, as shown in Figures 3b and 4b for 

the first 30-year period. By the second 30-year period, 

differences between the models became increasingly 

greater for the A1B scenario (Figures 3c and 4c). By 

the 2071-2099 period, not surprisingly the A2 scenario 

projected the greatest warming across the country and 

the B1 the least (Figures 3d, 3e, and 3f, 4d, 4e, and 4f). 

In general, the B1 projection for each period was qualitatively rather similar to the A1B but for the preceding 

30-year period (compare 3f with 3c).

Of the four models, MIROC32MR projected noticeably 

greater warming in annual mean daily maximum temperature than the other three models during the 21st century 

(Figures 3b-f). This pattern was most obvious for Alaska 

and the western states, and by 2099, for the midwestern 

states. For annual mean daily minimum temperature and 

the A1B and A2 scenarios, NCARCCSM3 was often 

within a degree of the projection by MIROC32MR for 

much of the United States. Comparing the minimum 

temperature projections with the maximum temperatures across the models (Figures 3d, 3e, 3f, and 4d, 3e, 

3f), there was generally a greater increase in minimum 

temperature by 2099 for all models within each scenario, 

except MIROC32MR. Here, the projections for maximum 

temperature by MIROC32MR exceeded the projected 

changes in minimum temperature.

Ranking the other three models subjectively was difficult. Within the states of Nebraska and South Dakota, 

there was a noticeable gradation across results for the A2 

scenario in 2070–2099 (Figures 3e and 4e): CGCM31MR 

projected the least warming, followed by CSIROMK35, 

NCARCCSM3 and MIROC32MR. However, the subjective ranking of these three models was reversed for 

southern Texas, where NCARCCSM3 projected the least 

warming (though CGCM31MR is very similar for Tmin). 

In the A1B and the A2 scenario, CSIROMK35 projected 

generally smaller temperature increases in the northwestern states, notably Oregon and Washington, with CGCM31MR and NCARCCSM3 projecting progressively 

greater increases. Patterns changed for B1; CGCM31MR 

projected the least change in the northwestern states.

For annual precipitation, increases and decreases were 

across the United States (Figure 5). For the first 30-year 

period, CGCM31MR and MIROC32MR both established 

a drier pattern in the southwest and southern regions, 

respectively (Figure 5b) and this pattern intensified in the 

second and third 30-year period (Figures 5c and 5d). The 

states across the South became markedly drier by 2099 

according to MIROC32MR compared to other models, 

with this trend apparent as early as 2011–2040 in the 

A1B scenario (Figure 5b). In contrast, NCARCCSM3 

projected increases in precipitation from the southwest 

to the north of midwestern region in the first 30-year 

period (Figure 5b) and this intensified in the second 30-

year period (Figure 5c). Within Alaska, all models and 

all scenarios projected increases in annual precipitation, 

particularly in the north (Figure 5 d, e, f).

The spatial patterns of precipitation change as projected within each model by 2099 were similar across the 

scenarios A1B and A2 (Figures 5d and 5e). For B1, the 

patterns of increase and decrease in precipitation were 

similar by model to A1B and A2, but less in magnitude. 

For example, MIROC32MR showed large areas of the 

southern United States with declines in precipitation for 

both A1B and A2 (Figures 5d and 5e); the pattern for B1 

still showed declines in the southern United States but of 

less intensity. Similarly, NCARCCSM3 showed greater 

increases in precipitation in the midwestern states for 

both the A1B and A2, relative to the increases seen in B1.

Within each scenario, the geographic pattern of precipitation change varied by model. NCARCCSM3 showed 

major decreases throughout the western coastal states, 

while CGCM31MR indicated decreases in the southwestern 

states (for example, Figure 5d). CSIROMK35 projected 

large increases inland, notably in Nevada and Utah, and 

also in northern Montana and North Dakota; CGCM31MR 

projected increases in Montana, South Dakota, Nebraska, and 

Iowa. For the B1 scenario across all models, precipitation 

remained the same or a slight increase; declines were seen in 

the southwest (CGCM31MR), central south (MIROC32M) 

and Pacific Northwest (NCARCCSM3).

Comparison of Projections of Changes in 

Temperature and Precipitation

The four GCMs, forced by each of the three SRES scenarios, differ in their projections of climate change for each 

ecoregion. The scatter-plots presented in Figure6 show mean 

changes in annual mean daily minimum temperature and 

precipitation ratio for 20-year periods centered on 2050 and 

2090 (i.e., approximately 40 and 80 years from present day), 

referenced to the 1961–1990 period. Plots such as these, 

have been used when selecting specific GCM scenarios to 

be representative of the range of projected changes in climate (temperature and precipitation) for a particular region. 

The plots show some expected trends and a few surprising 

differences.
36 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Figure 6. Scatter plots showing the changes in annual mean daily minimum temperature (x-axis) and annual precipitation ratio 

(y-axis) projected by each general circulation models, as forced by each greenhouse gas emissions scenario (A1B, B1, A2), relative 

to means for 1961–1990. Open symbols represent mean changes for 2040–2059, and closed symbols represent mean changes 

for 2080–2099. Each scatter plot shows area-weighted means for a specific region of the continental United States: (a) Alaska, 

(b) Mediterranean ecoregion, (c) Marine ecoregion, and (d) Prairie ecoregion. Note the change in the y-axis range of precipitation 

ratios between panels (a) and (b) compared to (c) and (d). 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 37

Figure 6 (cont). Scatter plots showing the changes in annual mean daily minimum temperature (x-axis) and annual precipitation 

ratio (y-axis) projected by each general circulation model, as forced by each greenhouse gas emissions scenario (A1B, B1, A2), 

relative to means for 1961–1990. Open symbols represent mean changes for 2040–2059, and closed symbols represent mean 

changes for 2080–2099. Each scatter plot shows area-weighted means for a specific region of the continental United States: (e) 

Continental ecoregion, (f) Dry Temperate ecoregion, (g) Dry Subtropical ecoregion, and (h) Subtropical ecoregion.
38 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

In general the projected warming was greater for the 

2090s than the 2050s, but with increasing divergence 

among the GCMs. In the 2060s, projected changes 

clumped along the precipitation axis with temperature 

changes ranging from 1.5 to 3 oC across all of the regions 

but precipitation changes ranging from decreases greater 

than 20 percent to increases of 20 percent. In the 2090s, 

a more scattered picture of the changes in temperature 

and precipitation developed and the results varied greatly 

across the regions. In Alaska, precipitation projections 

suggested increasing precipitation and a widening of the 

projected temperature from a range of 2 to nearly 5 oC in 

the 2060s to 3 to 8 oC in the 2090s, with the NCARCCSM3 

model projecting the greatest warming in Alaska. In the 

Mediterranean, Prairie, Dry Subtropical, and Subtropical, 

precipitation ratios widened and temperature increased 

from the 2060s to the 2090s, in contrast to the Marine 

region where the precipitation ratios across the models 

remained similar but temperature increased in 2090. By 

the 2090, the models forced by the A2 scenario almost 

invariably projected a greater warming than the projections associated with A1B, which in turn produced greater 

warming than the projections associated with B1.

Consistent with expectations, the models all projected 

the greatest warming for high latitudes (Alaska, Figure 

6a) and the least warming for the narrow West Coast 

ecoregions (Figures 6b and 6c). Though MIROC32MR 

generally projected the greatest warming, notably in the 

southern and mid-continental regions (Figures 6d–h), it 

was not always the warmest projection in each region. 

NCARCCSM3 projected similar increases for Alaska and 

on the West Coast, both NCARCCSM3 and CGCM31MR 

projected slightly greater warming than MIROC32MR. 

CSIROMK35 generally projected the least warming.

There was much less agreement among the models on 

projected changes in precipitation, and the trends varied 

greatly with location. In the Mediterranean ecoregion (Figure 6b), CSIROMK35 projected precipitation increases 

of 30 to 40% (A1B and A2 scenarios) by 2090, greater 

than projected for the Marine ecoregion. NCARCCSM3 

projected decreases of 13% with the A1B, but only 8% 

with the A2 in the Marine region (Figure 6b). In these 

coastal regions MIROC32MR generally projected decreases similar to those projected by CGCM31MR in the 

Mediterranean region and slight increases in the Marine 

ecoregion.

MIROCMR32 projected the greatest increase in precipitation for Alaska (with the A2 forcing, Figure 6a), but in 

the southern and midcontinental regions (Figures 6d–h), 

and particularly in the Subtropical ecoregions (Figures 

6g, 6h), the model projected large decreases where other 

models either projected no change or small increases. 

Among other models, in the southern and mid-continental 

regions, the NCARCCSM3 and CSIROMK35 generally 

projected increases in precipitation, and the CGCM31MR 

was most conservative, projecting modest increases in 

most regions and slight decreases in the Dry Subtropical 

ecoregion (Figure 6h).

Comparison of Simulated Interannual 

Variability

The following discussion attempts to sample all the 

available data by comparing some results obtained with 

each model and each GHG scenario for each ecoregion, 

climate variable, and season. The selection of graphs 

representative of the different regions and seasons, and 

are used to highlight some specific strengths and weaknesses, where differences among the results may indicate 

problems with particular variables or GCMs.

The leftmost panels of Figures 7 and 8 (together with 

those of graphs a, b, and c in Figures 11–14) show how 

the different GCMs were able to capture observed interannual variability in seasonal and annual temperature and 

precipitation. In each figure, observed data for 1961-2008 

(black line) are superimposed on the results of the GCM 

simulations, comprising the 20C3M scenario (historical 

simulated) for the period of 1961-2000 (1961-1999 for 

NCARCCSM3) and the three future emissions scenarios 

for the 2001-2008 (2000-2008 for NCARCCSM3)

Although there clearly were some important differences, 

the amplitudes of the variations around the simulated 

means for the 1961-2008 period were generally comparable to the amplitudes of the interpolated observed data 

(shown in black), and the differences among seasons were 

captured well. This result was also seen in the differences 

in patterns of variability between Tmax and Tmin, seen 

both in summer (Figures 7a, 7b and 13a, 13b) and winter 

(Figures 7c, 7d and 14a, 14b). However, some GCMs 

tended to exaggerate the observed variability (notably 

NCARCCSM3 for the Marine ecoregion in winter seen 

in Figures 14a, 14b).

The quality of agreement in amplitude of variation 

was poorer for precipitation, both between the simulated 

historical and the observed interpolated data and among 

models. Figures 8a–d show CSIROMK35 generally exaggerated observed variability year round in the Prairie 

ecoregion. Figure 11c shows NCARCCSM3 exaggerated 

the annual variability for the Dry Temperate ecoregion, 

but MIROC32MR appeared to underestimate variability. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 39

Conversely, in the Subtropical ecoregion (Figure 12c), 

MIROC32MR greatly overestimated observed variability in summer. In the Dry Subtropical during fall, 

NCARCCSM3 and CSIROMK35 both overestimated 

variability (Figure 13c), but in the Marine ecoregion during winter (when mean precipitation amounts were large 

and interannual variability was correspondingly large), 

all the GCMs seemed to generate variability similar to 

the observed interpolated data (Figure 14c).

The results overall suggested considerable consensus 

among the models, particularly regarding simulation of 

the interannual variability of observed temperature means, 

but even to some extent with the observed precipitation 

data. Hence, the GCMs appeared to capture many of the 

observed characteristics of these climate variables, which 

in turn suggests that the future scenarios can be treated 

as plausible projections of future climate, as determined 

by different scenarios of future GHG emissions.

Figure 7a, b. Projections of spatially averaged summer mean daily maximum (a, upper) and minimum temperature (b, 

lower) (°C) for Alaska for four greenhouse gas forcing scenarios, relative to interpolated observed data for 1961–2008. 

The simulated historical data for the period 1961-2000 (20C3M scenario for each general circulation model) and the 

observed data are shown only in the leftmost panels, but are common to all three future projections. CGCM31MR = Third 

Generation Coupled Global Climate Model, version 3.1, medium resolution; CSIROMK35 = Commonwealth Scientific 

and Industrial Research Organisation Climate System Model, Mark 3.5; MIROC32MR = Model for Interdisciplinary 

Research on Climate, version 3.2 medium resolution; NCARCCSM3 = Community Climate System Model, version 3.0.
40 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Figure 7c, d. Projections of spatially averaged winter mean daily maximum (c, upper) and minimum temperature 

(d, lower) (°C) for Alaska for four greenhouse gas forcing scenarios, relative to interpolated observed data 

for 1961–2008. The simulated historical data for the 1961-2000 period (20C3M scenario for each general 

circulation model) and the observed data are shown only in the leftmost panels, but are common to all three 

future projections.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 41

Figure 8a, b. Projections of spatially averaged spring (a, upper) and summer (b, lower) total seasonal 

precipitation (mm) for the Prairie region for four greenhouse gas forcing scenarios, relative to interpolated 

observed data for 1961–2008. The simulated historical data for the period 1961-2000 (20C3M scenario for 

each general circulation model) and observed data are shown only in the leftmost panels, but are common 

to all three future projections. CGCM31MR = Third Generation Coupled Global Climate Model, version 3.1, 

medium resolution; CSIROMK35 = Commonwealth Scientific and Industrial Research Organisation Climate 

System Model, Mark 3.5; MIROC32MR = Model for Interdisciplinary Research on Climate, version 3.2 medium 

resolution; NCARCCSM3 = Community Climate System Model, version 3.0.
42 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Figure 8c, d. Projections of spatially averaged fall (c, upper) and winter (d, lower) total seasonal precipitation 

(mm) for the Prairie region for four greenhouse gas forcing scenarios, relative to interpolated observed data for 

1961–2008. The simulated historical data for the 1961-2000 period (20C3M scenario for each general circulation 

model) and observed data are shown only in the leftmost panels, but are common to all three future projections.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 43

Projected Climate Trends 2001–2100

Alaska was projected to experience the largest increases 

in temperature within the continental United States over 

the next 90 years (Figure 6a). Summer temperatures (both 

daily minima and maxima) were projected to increase by 

3–5 °C (Figures 7a and 7b), while the increases for winter 

temperatures were larger, in the range 4–8 °C (Figures 

7c and 7d). Clearly, there was some divergence among 

the models, with MIROC32MR and NCARCCSM3 both 

projected greater increases (winter daily minima by as 

much as 12 °C and maxima by up to 10 °C by 2100 with 

the A2 scenario) than the CGCM31MR and CSIROMK35 

projections, which were still in the range 4–6 °C and 

4–7 °C, for winter Tmax and Tmin, respectively, by 2100. 

Variation in summer projections was less across all 

scenarios and models than the winter projections, likely 

reflecting the corresponding variations in the seasonal 

historical temperatures. Projections for winter mean daily 

maximum remain within the historical range of variability 

well into the 21st century (Figure 7c).

With few exceptions, all four models showed good 

agreement in the projected changes in means and interannual variation in precipitation in the Prairie region up to 

2100 (Figure 8). The NCARCCSM3, and in particular 

the CSIROMK35, generally projected greater extremes. 

MIROC32MR was the only GCM to show a distinct 

pattern of decreasing precipitation across all scenarios, 

particularly in spring and summer with the A2 forcing 

scenario (Figure 8a and b), a trend that recurred in other 

regions (notably for annual precipitation in the Dry Temperate region [Figure 11c] and summer precipitation in 

the Subtropical region [Figure 12c]). Winter precipitation 

showed little to no change (Figure 8d) whereas spring 

precipitation showed an increase in the mean of 9 mm to 

23 mm by 2079-2100 (Table 8). When projections for all 

four models within each scenario were averaged for the 

Prairie region, the general pattern was for a slight increase 

in annual precipitation of 14 to 42 mm by 2070–2099, 

mainly in spring. There was little obvious dependence on 

the GHG forcing scenario as the precipitation projections 

for all models in each season encompassed each other 

(Figure 8).

Vapor pressure projections for the Mediterranean 

region provide a good comparison of the results for this 

variable from different GCMs found throughout the 

downscaled data set (Figure 9). All four models within 

all three future scenarios projected steady increasing 

values for vapor pressure reflecting the projected trends 

in temperature (Figures 3d, 3e, and 3f). Although individual model projections within each season tracked each 

other; projections tended to be the highest by 2100 for 

the A2 scenario. Vapor pressure increased the greatest in 

summer and the least in winter.

Projections by the four models of changes in summer 

and winter solar radiation for the Continental region 

were very similar in their projected amplitudes of interannual variability, particularly in summer (Figure 10a), 

when it is almost impossible to rank them. For winter, 

NCARCCSM3 produced the greatest extremes and 

CGCM31MR the smallest, with MIROC32MR tended 

higher than the mean and CSIROMK35 tended lower 

(Figure 10b). The substantial interannual variability obscures changes that can be seen in the 10-year moving 

averages of the same data (Figure 10c and 10d), such as 

the pattern of increasing summer radiation with CGCM31MR and MIROC32MR, but little change projected 

by NCARCCSM3 and CSIROMK35. These trends were 

strongest with the A2 scenario, which has the highest 

emission levels by 2100, but barely detectable with B1, 

the lowest emission levels. Three models projected a trend 

of decreasing winter radiation (seen in Figure 10d), with 

the A2 resulting in the largest declines; MIROC32MR 

exceptionally projected an initial increase followed by 

steady decline back to initial levels in all three future 

scenarios.

Projections by individual model for mean daily minimum temperature for the Dry Temperature region tracked 

each other closely within each scenario and show the influence of the GHG forcing in that the greatest temperature 

increases were associated with A2 and the least with B1 

(Figure 11a and 11b, similar patterns seen in Figure 6f). 

For maximum temperature, the individual model projections separated early in the 21st century with the MIROC 

projection increasing above all other models in all three 

future scenarios (Figure 11a). The comparison of the 

simulated with the observed precipitation data showed 

that NCARCCSM3 exhibited high variability and trended 

above the other model projections (Figure 11c). When 

annual precipitation means of the 1980-2009 period were 

compared with the 2070-2100 period, precipitation was 

projected to increase for the B1 scenario (7.4 mm) and 

the A2 scenario (2.7 mm), and to decrease for the A1B 

scenario (7.7 mm). Obviously there was great variation 

over the 21st century within each projection.

Projections by the four models of changes in annual 

solar radiation for the Dry Temperate region were very 

similar in their projected amplitudes of interannual 

variability (Figure 11d). MIROC32MR tended higher 

than the mean but the projections within each scenario 

encompassed each other. When the model results are 

averaged by scenario, summer radiation declines only 
44 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

slightly and to the same extent in all scenarios. As with 

the Mediterranean region (Figure 9), vapor pressure for 

the Dry Temperate region increases but annually only 

a small amount (Figure 11e). Scenario A2 shows the 

greatest increase, with the MIROC32 model showing 

the smallest changes. Wind speed for the Dry Temperate region showed little change by scenario. This pattern 

was seen consistently in all other regions, in the means 

or variability, and no consistent sensitivity to the level of 

GHG. Hence, there do not appear to be large or consistent 

differences among the four GCMs with respect to wind 

speed.

Similar to the annual temperature changes in Dry 

Temperate region (Figure 11a), the summer mean daily 

maximum and minimum temperatures for the Subtropical 

region projected by individual models showed the influence of the GHG forcing with the warmest temperatures 

at the end of the 21st occurring in the A2 scenario, then 

A1B and finally B1 (Figure 12). The summer mean daily 

minimum temperatures for the Subtropical region projected by individual models within each scenario tracked 

each other closely (Figure 12b); but greater variation of 

the model projections within each of the scenarios was 

seen for the mean daily maximum temperature, with the 

MIROC projection rising above all of the other model 

projections in the A2 and A1B scenarios (Figure 12a). The 

Subtropical region encompasses the southeastern parts 

of the United States and annual precipitation projection 

varied by model and scenario, with the CGCM31MR 

and the CSIROMK35 projecting little change across all 

scenarios; but the MIROC32MR projecting large declines 

in annual precipitation when the 2071-2100 period is 

compared to the 1961-1990 period (Figures 5d, 5e, and 

5f) Projections for summer precipitation declined in the 

MIROC32MR projections for scenarios A2 and A1B, 

but changed minimally in the other models (Figure 12c). 

When averaged across models by scenarios, summer 

precipitation declined for the A2 and A1B scenarios, 

showing a slight increase in the B1 scenario (Table 9). A 

major influence in these composite projections was the 

MIROC projection for A2 and A1B (Figure 12c). These 

precipitation patterns were also seen in the summer vapor 

pressure (Figure 12e). Wind speed projections for the 

Subtropical region, as with other regions, showed very 

little sensitivity to model or to scenario (Figure 12f).

Fall mean daily maximum and minimum temperatures 

for the dry Subtropical region increased in all scenarios, 

with the largest increases in the A2 scenario and the smallest changes in the B1 scenario (Figure 13a). For maximum 

temperature, the MIROC32MR projections tended to be 

the highest; however for minimum temperature, the projections encompassed one another within each scenario. 

Projections for fall precipitation had high variability, 

reflecting the historical variability (Figure 13c). This 

region spans from central Texas west to the California 

border and when compared to the 1961-1990 period, the 

spatial patterns of annual precipitation projections for 

2070-2100 varied greatly within the region by model, more 

than by scenario. For example, NCARCCSM3 projected 

little change to some increases in precipitation across all 

scenarios, with great spatial variability (Figures 5d, 5e, 

5f). MIRCO32MR consistently projected declines across 

the region. Projections for fall solar radiation showed 

little sensitivity to scenario, similar to other regions, 

but were more variable, than annual solar radiation for 

the Dry Temperate region (Figure 11d) or summer solar 

radiation for the Subtropical region (Figure 12c). Vapor 

pressure projections for the fall season showed sensitivity 

to scenario; however wind speed showed little sensitivity 

to scenario or model.

Winter projections for the Marine region reflected 

general patterns seen in other regions, winter maximum 

and minimum temperatures were sensitive to the scenario, 

precipitation projections had great variability, and little 

changes were seen in solar radiation or wind speed (Figure 

14). NCARCCSM3 projected much greater interannual 

variability, particularly for minimum temperature in 

winter. Increases in winter temperature were greater for 

minimum temperature than maximum, a pattern seen in 

the Alaska region (Figure 7c and 7d).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 45

Figure 9a, b. Projections of spatially averaged spring (a, upper) and summer (b, lower) mean vapor pressure 

(kPa) for the Mediterranean region for four greenhouse gas forcing scenarios. The simulated historical data for 

the 1960-2000 period (20C3M scenario for each general circulation model) are shown only in the leftmost panels, 

but are common to all three future projections. CGCM31MR = Third Generation Coupled Global Climate Model, 

version 3.1, medium resolution; CSIROMK35 = Commonwealth Scientific and Industrial Research Organisation 

Climate System Model, Mark 3.5; MIROC32MR = Model for Interdisciplinary Research on Climate, version 3.2 

medium resolution; NCARCCSM3 = Community Climate System Model, version 3.0.
46 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Figure 9c, d. Projections of spatially averaged fall (c, upper) and winter (d, lower) mean vapor pressure (kPa) 

for the Mediterranean region for four greenhouse gas forcing scenarios. The simulated historical data for the 

1960-2000 period (20C3M scenario for each GCM) are shown only in the leftmost panels, but are common to 

all three future projections.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 47

Figure 10a, b. Projections of spatially averaged summer (a, upper) and winter (b, lower) mean daily global 

solar radiation (MJ m-2 d-1) for the Continental region for four greenhouse gas forcing scenarios. The simulated 

historical data for the 1960-2000 period (20C3M scenario for each general circulation model) are shown only in 

the leftmost panels, but are common to all three future projections. CGCM31MR = Third Generation Coupled 

Global Climate Model, version 3.1, medium resolution; CSIROMK35 = Commonwealth Scientific and Industrial 

Research Organisation Climate System Model, Mark 3.5; MIROC32MR = Model for Interdisciplinary Research 

on Climate, version 3.2 medium resolution; NCARCCSM3 = Community Climate System Model, version 3.0.
48 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Figure 10c, d. Projections of spatially averaged summer (c, upper) and winter (d, lower) mean daily global 

solar radiation (MJ m-2 d-1) for the Continental region for four greenhouse gas forcing scenarios. Annual data 

have been smoothed with a 10-year moving average. The simulated historical data for the 1960-2000 period 

(20C3M scenario for each general circulation model) are shown only in the leftmost panels, but are common 

to all three future projections.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 49

Figure 11a, b. Projections of spatially averaged annual mean daily maximum (a, upper) and minimum 

(b, lower) temperature (°C) for the Dry Temperate region for four greenhouse gas forcing scenarios, relative 

to interpolated observed data for 1961–2008. The simulated historical data for the 1960-2000 period (20C3M 

scenario for each general circulation model) and observed data are shown only in the leftmost panels, but 

are common to all three future projections. CGCM31MR = Third Generation Coupled Global Climate Model, 

version 3.1, medium resolution; CSIROMK35 = Commonwealth Scientific and Industrial Research Organisation 

Climate System Model, Mark 3.5; MIROC32MR = Model for Interdisciplinary Research on Climate, version 3.2; 

NCARCCSM3 = Community Climate System Model, version 3.0.
50 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Figure 11c, d. Projections of spatially averaged annual total precipitation (mm) (c, upper) and daily global 

solar radiation (d, lower) (MJ m-2 d-1) for the Dry Temperate region, for four greenhouse gas forcing scenarios. 

Precipitation projections are also compared with the interpolated observed data for 1961-2008. The simulated 

historical data for the 1960-2000 period (20C3M scenario for each general circulation model) and observed 

data (precipitation only) are shown only in the leftmost panels, but are common to all three future projections.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 51

Figure 11e, f. Projections of spatially averaged annual mean vapor pressure (kPa) (e, upper) and wind 

speed (m s-1) (f, lower) for the Dry Temperate region, for four greenhouse forcing scenarios. The simulated 

historical data for the 1960-2000 period (20C3M scenario for each GCM) are shown only in the leftmost 

panels, but are common to all three future projections.
52 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Figure 12a, b. Projections of spatially averaged summer (June, July, August) mean daily maximum and minimum 

temperature (°C) for the Subtropical region for four greenhouse forcing scenarios, relative to interpolated 

observed data for 1961–2008. The simulated historical data for the 1960-2000 period (20C3M scenario for 

each GCM) and observed data are shown only in the leftmost panels, but are common to all three future 

projections. CGCM31MR = Third Generation Coupled Global Climate Model, version 3.1, medium resolution; 

CSIROMK35 = Commonwealth Scientific and Industrial Research Organisation Climate System Model, Mark 

3.5; MIROC32MR = Model for Interdisciplinary Research on Climate, version 3.2; NCARCCSM3 = Community 

Climate System Model, version 3.0.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 53

Figure 12c, d. Projections of spatially averaged summer (June, July, August) total precipitation (mm) and 

daily global solar radiation (MJ m-2 d-1) for the Subtropical region, for four greenhouse forcing scenarios. 

Precipitation projections are also compared to interpolated observed data for 1961–2008. The simulated 

historical data for the 1960-2000 period (20C3M scenario for each GCM) and observed data are shown 

only in the leftmost panels, but are common to all three future projections.
54 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Figure 12e, f. Projections of spatially averaged summer (June, July, August) mean vapor pressure (kPa) (e, 

upper) and wind speed (m s-1) (f, lower) for the Subtropical region, for four greenhouse gas forcing scenarios. 

The simulated historical data for the 1960-2000 period (20C3M scenario for each GCM) are shown only in the 

leftmost panels, but are common to all three future projections.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 55

Figure 13a, b. Projections of spatially averaged fall (September, October, November) mean daily maximum 

(a, upper) and minimum (b, lower) temperature (°C) for the Dry Subtropical region for four greenhouse gas 

forcing scenarios, relative to interpolated observed data for 1961–2008. The simulated historical data for the 

1960-2000 period (20C3M scenario for each GCM) and observed data are shown only in the leftmost panels, 

but are common to all three future projections. CGCM31MR = Third Generation Coupled Global Climate Model, 

version 3.1, medium resolution; CSIROMK35 = Commonwealth Scientific and Industrial Research Organisation 

Climate System Model, Mark 3.5; MIROC32MR = Model for Interdisciplinary Research on Climate, version 3.2; 

NCARCCSM3 = Community Climate System Model, version 3.0.
56 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Figure 13c, d. Projections of spatially averaged fall (September, October, November) total precipitation (mm) (c, 

upper) and daily global solar radiation (MJ m-2 d-1) (d, lower) for the Dry Subtropical region, for four greenhouse 

gas forcing scenarios. The simulated historical precipitation projections (20C3M scenario) are compared to 

interpolated observed data for 1961–2008. The simulated historical data for the 1960-2000 period (20C3M 

scenario for each GCM) and observed data are shown only in the leftmost panels, but are common to all three 

future projections
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 57

Figure 13e, f. Projections of spatially averaged fall (September, October, November) mean vapor pressure 

(kPa) (e, upper) and wind speed (m s-1) (f, lower) for the Dry Subtropical region, for four greenhouse gas forcing 

scenarios. The simulated historical data for the 1960-2000 period (20C3M scenario for each GCM) are shown 

only in the leftmost panels, but are common to all three future projections.
58 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Figure 14a, b. Projections of spatially averaged winter (December, January, February) mean daily maximum 

(a, upper) and minimum (b, lower) temperature (°C) for the Marine region for four greenhouse gas forcing 

scenarios, compared to interpolated observed data for 1961–2008. The simulated historical data for the 1960-

2000 period (20C3M scenario for each GCM) and observed data are shown only in the leftmost panels, but 

are common to all three future projections. CGCM31MR = Third Generation Coupled Global Climate Model, 

version 3.1, medium resolution; CSIROMK35 = Commonwealth Scientific and Industrial Research Organisation 

Climate System Model, Mark 3.5; MIROC32MR = Model for Interdisciplinary Research on Climate, version 3.2; 

NCARCCSM3 = Community Climate System Model, version 3.0.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 59

Figure 14c, d. Projections of spatially averaged winter (December, January February) total precipitation (mm) 

(c, upper) and daily global solar radiation (MJ m-2 d-1) (d, lower) for the Marine region, for four greenhouse 

gas forcing scenarios. The simulated historical data for precipitation (20C3M scenario for each GCM) are also 

compared to interpolated observed data for 1961–2008. The simulated historical data for the 1960-2000 period 

(20C3M) and observed data are shown only in the leftmost panels, but are common to all three future projections.
60 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Figure 14e, f. Projections of spatially averaged winter (December, January, February) mean vapor pressure (kPa) 

(e, upper) and wind speed (m s-1) (f, lower) for the Marine region, for four greenhouse gas forcing scenarios. 

The simulated historical data for the 1960-2000 period (20C3M scenario for each GCM) are shown only in the 

leftmost panels, but are common to all three future projections.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 61

Regional Outlook

The data shown in Tables 4–11 deserve careful study; 

some key results are summarized that could be interpreted 

as a “national climate outlook” for the United States, 

somewhat analogous to a national weather forecast based 

on multiple sources of information. When ranges for 

future projections are reported in the following paragraphs, they generally range from the minimum change 

(obtained with the B1 scenario) to the maximum change 

(obtained from the A2 scenario), expressed as a change 

from “present-day,” circa. 2000. It also is important to 

note use of the word “will” is merely a convenient simplification; the outlook presented here was generated only by a 

suite of imperfect models projecting an uncertain future.

Alaska

Projected temperature increases for Alaska were the 

largest of those for the eight regions. Annual mean Tmin

was projected to increase by about 3.0 to 5.0 oC and Tmax

by 2.5 to 4.0 oC by 2100 (Table 4). Winter Tmin increased 

the most (4.0–7.5 oC), and spring/summer Tmax the least 

(2.5–4.0 oC). Interannual variability was projected to 

decline in all seasons except for summer, where increases 

of up to 26% occurred with the most extreme A2 emissions scenario. Precipitation was projected to increase 

year-round by 10–20%, accompanied by increases in 

interannual variability, particularly in spring and summer. Related to these increases in precipitation, mean 

vapor pressure was projected to increase by 10 to 25% in 

general (more in spring and summer), and solar radiation 

declined by 6 to 10% year round. Mean wind speeds were 

projected to increase very slightly, while they became 

less variable, particularly in fall and winter.

Continental Ecoregion

Projected increases in Tmin and Tmax were relatively 

consistent year round, ranging from 2.0 to 4.5 oC by 2099 

(Table 5). Interannual variability, particularly for Tmin, 

was projected to increase under the A2 by 20 to 30%, but 

projected changes for A1B and B1 were smaller and often 

negative. Annual precipitation was projected to increase 

by 7 to 8%, mostly in spring, when increases were as much 

as 10% according to the A1B scenario. Interannual variation also was projected to increase, particularly in winter 

and particularly with the A2 forcing scenario. Summer 

and fall radiation levels were projected to increase by 1 

to 2%, balanced by proportionately larger decreases in 

winter radiation, for very little net change annually, but 

interannual variation was projected to decrease yearround. Vapor pressure was strongly influenced by the 

projected increases in temperature under each forcing 

scenario, with increases of 10 to 25% year-round, reflecting also the projected increases in annual precipitation. 

Interannual variability in vapor pressure was projected to 

increase quite dramatically according to the A2, compared 

to relatively little change with the B1. Changes in vapor 

pressure variability were projected to be greatest in spring, 

but the patterns for other seasons were inconsistent. Mean 

wind speeds were projected to increase, by as much as 

5% year-round with the A2. Wind speed variability was 

projected to decline slightly in general, but all scenarios 

indicated greater variability during fall.

Marine Ecoregion

This region was projected to warm by 2.5 to 3.5 oC, 

by 2070-2099 (Table 6). Projected changes in Tmin and 

Tmax were very similar; although it appeared summer 

maxima increased more than the annual mean (in the range 

2.5–4.2 oC). Projected changes in interannual variability 

were inconsistent, with greater variability expected for 

summer under A2, but smaller increases or reductions 

otherwise. Much of the increased variability was traced 

to the contribution of the NCARCCSM3 model, which 

projected much greater interannual variability, particularly 

for Tmin in winter, than did the other models, although all 

four GCMs agreed quite strongly in the general trends 

(Figure 14b). Precipitation was projected to increase by 

5–8% annually, mainly in winter and fall, with smaller 

decreases projected for spring and summer. Summer 

precipitation in this region already was very low (about 

120 mm), so the projected decreases, on the order of 10 

to 20%, could be ecologically very significant. Interannual variability of precipitation was projected to increase 

slightly with A1B and A2. The CSIROMK35 model was 

markedly more variable in its projections of future summer precipitation.

Consistent with these projections of reduced summer 

precipitation, solar radiation during spring and summer 

was projected to increase by as much as 5% under A1B 

and A2, but decrease in winter. Also consistent with 

reduced summer precipitation, interannual variability in 

solar radiation was projected to decrease by 5 to 10%, 

particularly in summer and fall. Vapor pressure also was 

projected to increase, particularly in summer and fall, 

with increases by 2070-2099 in the range 10 to 20% compared to the present day. Changes in variability of vapor 

pressure were less consistent, showing large increases, 

particularly in summer and fall for A2. Projections for 

wind speed showed slight increases on average, except 

for general decreases in spring. There was a suggestion 

of decreasing variability on average, also particularly in 

spring.
62 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Mediterranean Ecoregion

The models projected rather similar increases in Tmin

and Tmax throughout the year (MIROC32MR tended to 

be slightly higher, and CSIROMK35 slightly lower than 

the mean for Tmax and NCARCCSM3 somewhat higher 

for summer Tmin), with annual mean increases of 0.8 to 

0.9 oC by 2010–2039 gradually diverging to a range of 

about 2.0 to 3.5 oC by 2070–2099 (Table 7). Summer was 

projected to warm the most, in the range of 2.2 to 4.0 oC 

toward the end of the century. Winter and spring were 

projected to increase the least. Interannual variability 

was noticeably greater with NCARCCSM3 than with 

other models during winter, and all models suggested 

fall Tmin was particularly variable. Projected changes in 

temperature variability, however, did not show consistent 

patterns: with the A2 there was a general increase (by 

50% annually), but the other scenarios produced much 

smaller increases, including decreases with the A1B.

Precipitation was projected to increase in winter with all 

scenarios, while other seasons showed slight decreases, 

suggesting little change overall in annual amounts. Interannual variability of summer precipitation decreased 

by 20 to 30%, but there were no clear trends for other 

seasons. Annual precipitation variability increased by 

15 to 20% according to the models for the A2 and B1 

scenarios. Solar radiation in spring was projected to 

increase marginally, with general decreases in other seasons and a slight reduction in interannual variability. 

Vapor pressure was projected to increase with temperature, particularly in summer and fall, but the projected 

changes in variability were inconsistent among the three 

emissions scenarios. Mean wind speeds showed little 

projected change and small, but inconsistent, reductions 

in interannual variability.

Prairie Ecoregion

Projections for Tmin and Tmax generally were similar 

throughout the year, with annual averages increasing 2 to 

3 oC in the B1 scenario, 3 to 4 oC with the A1B and 4 to 

5 oC with the A2, by 2070–2099 (Table 8). Increases projected by MIROC32MR were higher than the average, 

particularly for Tmax, and projections by NCARCCSM3 

were lower, with a tendency to more extreme interannual 

variation, particularly for Tmin. Interannual variability was 

projected to increase by 15 to 40% in most seasons according to the A2, but less consistent and smaller changes 

occurred with the other emissions scenarios. Annual 

precipitation was projected to increase by 3 to 5% by 

2070-2099, particularly in spring, with little change in 

interannual variability. MIROC32MR projected a general 

decrease in precipitation, particularly in the second half 

of the 21st century and particularly in summer that corresponded with projections for the Subtropical and Dry 

Subtropical regions and, to some extent, the Continental 

region. Over the same period solar radiation was projected 

to change little, but there were suggestions of decreases 

in spring that would be consistent with the increased 

precipitation. Vapor pressure was projected to increase 

with temperature by around 13% under B1, to 20 to 

25% under A2. Wind speeds were generally projected 

to increase slightly, particularly in spring and summer, 

while projections for variability showed slight increases 

in summer, but small and inconsistent changes in other 

seasons.

Subtropical Ecoregion

Tmin was projected to increase quite steadily, rising 2 to 

4 oC by 2070–2099, with spring, summer, and fall tending to warm more than fall and winter. The pattern was 

very similar for Tmax, with comparable or even slightly 

larger increases (Table 9) (though it should be noted this 

trend was the result of the very large increases projected 

by MIROC32MR shown in Figure 12b). Inter-annual 

variation in temperature was projected to increase year 

round with the A2, but trends in temperature variability 

with A1B and B1 were generally much smaller or even 

negative. Mean precipitation was projected to decline, 

particularly under the A2, but this again was traced to 

MIROC32MR which projected major decreases, particularly in summer, although the other three models 

projected relatively little change (Figure 12c). Overall 

the projected annual precipitation change was about 5% 

decrease with A2 and 1 to 2% increase with A1B and B1, 

with no clear seasonality. Solar radiation was projected to 

increase slightly in all scenarios and all seasons (except 

B1 spring), but interannual variability generally declined. 

Consistent with the projected temperature increases, mean 

vapor pressure also increased, by about 18% with A2, 

compared to 15% with A1B and 10% with B1 (Figure 

12e). Mean wind speed was projected to increase slightly 

under all scenarios, with consistent agreement that the 

general trend will be reduced interannual variation (see 

also Figure 12f), particularly under the A2 emissions 

scenario.

Dry Temperate Ecoregion

Temperatures were projected to increase by 2.5 to 4.5oC 

by 2070-2099, with little difference in the annual mean 

increase for both Tmin and Tmax (Table 10). Seasonally, 

summer temperatures were projected to increase by as 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 63

much as 5 oC with the A2, and spring temperatures increased by 2 to 4 oC. Interannual variability in temperature 

was projected to increase dramatically in summer with 

the A2, particularly as simulated by MIROC32MR and 

NCARCCSM3. The MIROC32MR also projected lower 

summer precipitation than the model mean for all three 

GHG scenarios, consistent with the Subtropical region. 

Summer precipitation variability was markedly higher 

with the NCARCCSM3 and CSIROMK35, but projected 

means showed very little change from present-day either 

in amounts or interannual variation. Similarly there 

was little projected change in solar radiation annually, 

though winter and spring were consistently projected to 

have decreases of 1 to 2% as summer shows balancing 

increases of about 1%. Projected changes in interannual 

variability of solar radiation were inconsistent in sign, 

but generally small.

Depending on the GHG scenario, vapor pressure 

was projected to increase by 15 to 25% annually by 

2070–2099, with larger proportionate increases occurring in winter (20–30%). Interannual variability in 

vapor pressure was projected to increase dramatically 

with the A2 (Figure 11e), but much less with A1B 

and B1. Wind speed projections showed inconsistent 

changes both seasonally and annually, with a suggestion of general increases in variability in summer and 

decreases in fall and winter.

Dry Subtropical Ecoregion

Projected temperature increases in this region (Table 

11)were similar to those for the Subtropical ecoregion 

(Table 9), ranging from 2.5 to 4.5 C for Tmin and slightly 

less for Tmax. Increases in summer/fall maxima were somewhat greater than the annual averages, with MIROC32MR 

higher than other models particularly in summer and winter. Temperature variability was projected to increase by 

25 to 40% with the A2 scenario. In comparison, the A1B 

and B1 showed very little overall change in variability 

(Figure 13a, b). Precipitation in this region was projected 

to change very little year round (about 5% decrease 

annually with A2, but no substantial change with A1B 

and B1), although there was a suggestion that summer 

precipitation might increase slightly under all scenarios. 

Given the relatively low present-day annual precipitation 

(~400 mm) and the high evaporative demand, even small 

changes may be significant ecologically and for land 

management. Solar radiation was projected to increase 

slightly, but decrease in summer, presumably related to 

the general changes in precipitation. Conversely, vapor 

pressure was projected to increase by 12 to 20% correlated with the projected warming, with less consistent 

changes in seasonal and annual variability. Wind speeds 

were projected to change little with the B1, but A1B and 

A2 led to progressively larger increases, particularly in 

summer and fall. 
64 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 Table 4. Climate change projection summary for Alaska (mean of four general circulation models). Climate Variable - - - - - - - A2 Emissions Scenario - - - - - - - - - - - - -A1B Emissions Scenario- - - - - - - - - - - - -B1 Emissions Scenario- - - - - - Mean Daily 

Tmin (°C) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year

Baseline 1980-2009 -10.07 5.67 -7.90 -21.04 -8.30 -10.14 5.61 -7.86 -20.91 -8.29 -10.28 5.66 -7.90 -21.10 -8.38

Change by 2010-2039 1.22 0.81 1.44 1.70 1.25 1.11 0.84 1.05 1.55 1.07 1.39 0.69 1.08 1.40 1.12

Change by 2040-2069 2.51 2.02 2.65 4.10 2.77 2.81 1.90 2.86 4.31 2.92 2.35 1.58 2.19 2.91 2.23

Change by 2070-2099 4.66 3.68 4.84 7.43 5.10 3.95 3.07 4.14 5.92 4.24 3.04 2.03 2.82 3.79 2.90

100-year forcing 5.26 4.06 5.22 8.10 5.66 4.47 3.41 4.55 6.73 4.81 3.43 2.41 3.19 4.41 3.38

100-year variability (%) -6.67 26.40 -32.08 -28.65 -18.51 -12.80 2.65 -29.50 -27.25 -21.84 1.74 4.97 -28.92 -13.95 -16.08

Mean Daily 

Tmax (°C) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year

Baseline 1980-2009 -0.01 15.22 -0.32 -12.65 0.59 -0.06 15.19 -0.28 -12.51 0.61 -0.17 15.28 -0.31 -12.69 0.54

Change by 2010-2039 0.87 0.78 1.18 1.10 0.95 0.73 0.76 0.88 0.93 0.76 0.98 0.62 0.90 0.85 0.82

Change by 2040-2069 1.76 1.96 2.18 2.90 2.16 2.02 1.75 2.37 3.10 2.27 1.69 1.49 1.80 1.96 1.72

Change by 2070-2099 3.42 3.63 4.08 5.54 4.12 2.93 2.99 3.48 4.43 3.43 2.22 1.95 2.38 2.73 2.31

100-year forcing 3.82 3.98 4.40 5.94 4.55 3.28 3.32 3.84 4.97 3.88 2.46 2.37 2.71 3.10 2.68

100-year variability (%) -5.75 25.05 -28.33 -30.34 -19.79 -13.56 4.36 -24.39 -27.35 -22.97 -2.16 10.86 -28.33 -13.03 -20.85

Total Precipitation (mm) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year

Baseline 1980-2009 85 178 168 109 540 85 180 167 111 543 86 176 167 109 537

Change by 2010-2039 6 8 8 6 28 6 7 5 5 22 5 12 8 4 30

Change by 2040-2069 13 16 17 13 58 12 21 19 10 63 8 17 12 7 45

Change by 2070-2099 23 29 36 24 112 18 27 25 19 87 13 18 19 12 62

100-year forcing 25 32 38 26 121 20 30 27 23 100 16 18 20 15 69

100-year variability (%) 26.12 32.02 18.46 6.62 11.05 23.81 18.66 -1.29 9.44 5.18 8.40 27.97 3.27 8.61 7.47

Tables 4–11. Summaries of future projections for each climate variable by ecoregion. These data are area-weighted means obtained for each climate variable according to each climate scenario. That is, they are the means of the four values projected by each GCM and therefore represent a “best estimate,” assuming that all GCMs are equally skillful (or equally plausible). Each two-page table contains results for six climate variables for a single ecoregion derived from the classification of Bailey (1995) within the conterminous 48 states, while the Alaska region comprises the state of Alaska in entirety. Each table is split into two parts: (a) maximum and minimum temperature and precipitation and (b) global solar radiation incident on a horizontal surface, vapor pressure, and windspeed. 

Some further notes on how to interpret these tables: For each variable, data are organized across the page, in three sets of five columns. Each set is for a single GHG 

emissions scenario, in the order A2, A1B, B1, with each column containing the averages of the four GCM projections of monthly values for spring, summer, fall, and winter, and for the entire year, respectively. The lines of data are labeled in the left-most column, as follows: 

The 30-year period (1980-2009) was selected as a baseline for comparison of model results because it represents current climate and immediately precedes the three 

consecutive 30-year periods reported as projections for the future (starting with 2010). It is important to distinguish this from the 1961–1990 period used as the baseline for combining the scenario data with observed climate normals. Although the changes between the periods of 1961–1990 and of 1980–2009 are likely to be small, there is evidence of a general warming trend over the 1961 through 2009 period (IPCC 2007). This is visible in many of the graphs (both in the historical trends and in most of the GCM projections). The 1980–2009 baseline mean values differ slightly among the three emissions scenarios because data for the nine years 2001–2009 originate from the different GHG scenario simulations and, hence, lead to different calculated means. The following three lines, labeled Change by 2010–2039, Change by 2040–2069, and Change by 2070–2099, respectively, give the average net changes in the projected 30-year means relative to 1980–2009. A positive value indicates an increase, and a negative value a decrease. The units of these changes are indicated with the variable name in the line directly above. In particular, note that precipitation changes are reported in millimeters relative to baseline (1980-2009). Following the mean changes for the three 30-year periods are two lines labeled 100-year forcing and 100-year variability (%). Here we are looking at the changes over 100 years, comparing two 30-year periods, 1970-1999 and 2070-2099. The 100-year forcing is the changes in the 30-year means, and the 100-year variability is the change in the 30-year standard deviations (S.D.). The change in S.D. is reported as a percentage relative to 100% for 1970–1999. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 65 Table 4. Continued Climate Variable - - - - - - - A2 Emissions Scenario - - - - - - - - - - - - -A1B Emissions Scenario- - - - - - - - - - - - -B1 Emissions Scenario- - - - - - Mean Daily Global Solar Radiation (MJ m-2) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 12.80 16.55 4.03 1.28 8.66 12.87 16.74 4.00 1.28 8.72 12.88 16.79 4.04 1.29 8.75 Change by 2010-2039 -0.45 -0.21 -0.20 -0.04 -0.22 -0.47 -0.58 -0.09 -0.04 -0.29 -0.43 -0.44 -0.19 -0.04 -0.27 Change by 2040-2069 -1.04 -0.51 -0.34 -0.11 -0.50 -1.13 -0.96 -0.38 -0.10 -0.64 -0.89 -0.57 -0.27 -0.07 -0.45 Change by 2070-2099 -1.79 -0.41 -0.58 -0.18 -0.74 -1.53 -0.56 -0.42 -0.16 -0.67 -1.12 -0.45 -0.37 -0.10 -0.51 100-year forcing -2.07 -0.70 -0.63 -0.20 -0.90 -1.74 -0.66 -0.49 -0.18 -0.77 -1.33 -0.50 -0.40 -0.12 -0.59 100-year variability (%) 3.68 24.29 -27.19 -29.72 14.76 -5.06 14.97 -20.92 -23.27 20.13 5.51 23.93 -21.85 -11.15 24.49 Mean Vapor Pressure (kPa) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 0.349 1.009 0.434 0.135 0.482 0.347 1.005 0.436 0.137 0.482 0.345 1.009 0.433 0.135 0.481 Change by 2010-2039 0.022 0.058 0.035 0.013 0.031 0.025 0.059 0.027 0.011 0.030 0.026 0.048 0.031 0.011 0.029 Change by 2040-2069 0.052 0.151 0.072 0.031 0.076 0.057 0.139 0.079 0.033 0.077 0.046 0.114 0.059 0.022 0.060 Change by 2070-2099 0.102 0.282 0.142 0.061 0.146 0.086 0.231 0.119 0.048 0.121 0.063 0.149 0.080 0.030 0.080 100-year forcing 0.113 0.308 0.151 0.066 0.159 0.096 0.254 0.130 0.054 0.133 0.070 0.176 0.088 0.034 0.092 100-year variability (%) 39.64 59.90 14.88 3.14 37.82 21.96 25.48 12.56 -3.16 14.65 37.40 26.32 -7.93 5.45 10.829 Mean Windspeed (m s-1) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 4.42 3.16 3.52 4.24 3.85 4.40 3.14 3.53 4.28 3.85 4.43 3.09 3.52 4.25 3.83 Change by 2010-2039 0.09 0.14 -0.05 -0.08 0.01 0.12 0.24 -0.11 -0.12 0.01 0.09 0.14 -0.07 -0.05 0.02 Change by 2040-2069 -0.08 0.20 -0.18 0.03 -0.02 -0.09 0.29 -0.10 0.06 0.02 0.05 0.20 -0.02 0.07 0.07 Change by 2070-2099 -0.05 0.28 -0.16 0.09 0.03 0.00 0.19 -0.09 0.09 0.03 0.03 0.30 -0.08 -0.02 0.05 100-year forcing 0.00 0.28 -0.19 0.08 0.04 0.04 0.17 -0.12 0.12 0.06 0.10 0.23 -0.12 -0.03 0.05 100-year variability (%) 6.44 0.15 -8.35 -17.54 -10.41 10.95 3.35 -4.32 -5.17 -8.88 4.61 15.60 -11.70 -7.69 -8.79
66 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 Table 5. Climate change projection summary for Continental Ecoregion (mean of four general circulation models). Climate Variable - - - - - - - A2 Emissions Scenario - - - - - - - - - - - - -A1B Emissions Scenario- - - - - - - - - - - - -B1 Emissions Scenario- - - - - - Mean Daily Tmin (°C) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 2.75 14.80 5.11 -8.45 3.54 2.79 14.89 5.12 -8.50 3.56 2.91 14.89 5.18 -8.32 3.64 Change by 2010-2039 1.00 1.08 1.16 1.10 1.10 1.13 1.18 1.31 1.43 1.28 0.61 0.93 0.91 0.74 0.82 Change by 2040-2069 2.34 2.54 2.63 2.63 2.52 2.40 2.45 2.60 2.76 2.55 1.67 1.64 1.78 1.92 1.77 Change by 2070-2099 3.87 4.29 4.35 4.28 4.21 3.23 3.29 3.39 3.91 3.46 1.99 2.20 2.21 2.39 2.21 100-year forcing 4.07 4.50 4.67 4.71 4.48 3.48 3.60 3.72 4.29 3.76 2.35 2.50 2.60 2.95 2.59 100-year variability (%) 17.22 17.80 25.65 -6.08 26.31 -17.73 -10.29 4.27 -10.09 -7.63 -11.76 -21.56 6.12 -15.03 -15.21 Mean Daily Tmax (°C) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 15.51 27.60 17.15 2.21 15.60 15.55 27.78 17.18 2.16 15.66 15.68 27.83 17.21 2.31 15.74 Change by 2010-2039 1.06 1.17 1.16 0.77 1.05 1.14 1.24 1.41 1.07 1.23 0.68 1.01 1.06 0.44 0.82 Change by 2040-2069 2.36 2.80 2.80 2.13 2.52 2.49 2.51 2.65 2.24 2.47 1.59 1.46 1.84 1.53 1.62 Change by 2070-2099 4.01 4.55 4.50 3.69 4.20 3.25 3.39 3.49 3.34 3.37 1.94 2.23 2.39 1.99 2.15 100-year forcing 4.21 4.65 4.85 3.99 4.41 3.49 3.67 3.86 3.59 3.64 2.30 2.56 2.79 2.39 2.50 100-year variability (%) 23.32 13.30 30.83 20.34 40.54 -11.59 1.92 5.01 10.74 5.43 2.18 -5.47 4.24 2.37 -1.64 Total Precipitation (mm) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 288 311 252 203 1054 289 304 253 204 1050 287 300 254 203 1044 Change by 2010-2039 9 -1 6 5 17 11 3 7 3 26 7 1 -1 2 12 Change by 2040-2069 18 -11 4 16 28 11 7 8 8 35 23 23 11 8 65 Change by 2070-2099 18 -5 18 23 55 28 10 6 23 70 29 10 2 15 58 100-year forcing 25 7 18 24 74 36 16 8 25 85 35 12 5 15 67 100-year variability (%) 15.89 4.81 17.79 27.27 19.52 0.60 -2.87 0.24 34.85 -3.04 5.66 -6.56 -3.41 23.17 1.56
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 67 Table 5. Continued Climate Variable - - - - - - - A2 Emissions Scenario - - - - - - - - - - - - - A1B Emissions Scenario - - - - - - - - - - - - - B1 Emissions Scenario - - - - - - Mean Daily Global Solar Radiation (MJ m-2) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 17.28 21.44 11.12 7.54 14.35 17.25 21.51 11.13 7.52 14.36 17.27 21.55 11.10 7.50 14.36 Change by 2010-2039 0.00 0.19 0.00 -0.12 0.02 0.03 0.17 0.08 -0.08 0.04 0.07 0.13 0.12 -0.02 0.07 Change by 2040-2069 -0.05 0.31 0.15 -0.25 0.04 0.05 0.27 0.10 -0.25 0.05 -0.09 0.15 0.12 -0.13 0.01 Change by 2070-2099 0.06 0.35 0.16 -0.48 0.02 -0.02 0.30 0.18 -0.44 0.00 -0.09 0.24 0.24 -0.17 0.05 100-year forcing 0.10 0.35 0.21 -0.48 0.05 -0.01 0.37 0.25 -0.45 0.04 -0.07 0.35 0.28 -0.21 0.09 100-year variability (%) 3.71 -9.87 -6.27 -9.73 -7.86 -14.85 -8.46 -9.60 -11.10 -25.85 -6.44 -9.74 -18.92 -9.96 -18.55 Mean Vapor Pressure (kPa) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 0.86 1.93 1.07 0.37 1.06 0.86 1.93 1.07 0.37 1.06 0.87 1.92 1.07 0.38 1.06 Change by 2010-2039 0.058 0.110 0.074 0.024 0.066 0.063 0.127 0.084 0.030 0.076 0.039 0.092 0.054 0.014 0.050 Change by 2040-2069 0.131 0.222 0.172 0.061 0.146 0.126 0.252 0.181 0.063 0.155 0.097 0.211 0.136 0.045 0.123 Change by 2070-2099 0.203 0.403 0.296 0.107 0.252 0.184 0.353 0.238 0.098 0.218 0.116 0.242 0.145 0.055 0.140 100-year forcing 0.217 0.446 0.313 0.116 0.273 0.200 0.389 0.257 0.106 0.238 0.134 0.275 0.166 0.066 0.160 100-year variability (%) 47.46 26.72 23.77 38.97 56.39 16.81 5.42 16.50 20.76 14.93 17.76 -16.11 9.26 16.31 -4.96 Mean Windspeed (m s-1) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 4.07 3.15 3.52 4.15 3.72 4.04 3.20 3.54 4.15 3.73 4.05 3.13 3.55 4.19 3.73 Change by 2010-2039 0.24 -0.06 0.01 0.14 0.08 0.00 0.02 -0.07 0.11 0.02 0.00 -0.02 -0.06 -0.05 -0.03 Change by 2040-2069 0.23 0.01 -0.02 0.26 0.13 0.14 -0.07 -0.06 0.11 0.04 0.12 0.02 -0.03 0.11 0.06 Change by 2070-2099 0.41 0.01 -0.01 0.28 0.18 0.23 -0.05 -0.05 0.13 0.07 0.05 0.02 -0.10 0.09 0.02 100-year forcing 0.44 0.00 0.01 0.33 0.19 0.23 0.00 -0.01 0.20 0.10 0.06 0.00 -0.04 0.18 0.05 100-year variability (%) 5.64 -8.38 23.57 5.51 -0.24 -1.18 -2.16 8.19 2.38 -6.17 -0.12 -1.59 14.14 6.17 5.96
68 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 Table 6. Climate change projection summary for Marine Ecoregion (mean of four general circulation models). Climate Variable - - - - - - - A2 Emissions Scenario - - - - - - - - - - - - - A1B Emissions Scenario - - - - - - - - - - - - - B1 Emissions Scenario - - - - - - Mean Daily Tmin (°C) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 0.92 8.04 2.96 -2.50 2.35 0.90 8.01 2.98 -2.47 2.34 0.83 7.95 2.89 -2.58 2.27 Change by 2010-2039 0.58 0.90 0.78 0.80 0.78 0.91 1.05 0.77 1.01 0.95 1.02 0.83 0.75 0.95 0.88 Change by 2040-2069 1.67 2.20 1.96 2.13 1.97 1.83 2.36 2.07 2.16 2.11 1.35 1.74 1.61 1.80 1.62 Change by 2070-2099 2.90 3.80 3.33 3.29 3.34 2.40 3.26 3.04 2.82 2.88 1.79 2.26 2.17 2.23 2.11 100-year forcing 3.09 4.08 3.58 3.66 3.60 2.57 3.51 3.31 3.21 3.14 1.89 2.44 2.35 2.51 2.30 100-year variability (%) -8.90 32.60 20.15 -8.35 21.21 -8.42 -6.10 -5.23 -2.92 -1.44 -14.26 -7.54 5.55 -14.49 -9.16 Mean Daily Tmax (°C) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 12.10 22.52 14.36 5.25 13.55 12.04 22.40 14.31 5.31 13.50 11.98 22.39 14.13 5.17 13.41 Change by 2010-2039 0.59 1.09 0.64 0.66 0.76 1.06 1.16 0.64 0.75 0.91 1.11 0.92 0.80 0.78 0.90 Change by 2040-2069 1.76 2.42 1.82 1.81 1.94 1.92 2.73 1.96 1.75 2.10 1.49 1.87 1.56 1.45 1.59 Change by 2070-2099 3.03 4.18 3.15 2.78 3.30 2.44 3.79 2.92 2.32 2.87 2.04 2.56 2.28 1.88 2.19 100-year forcing 3.27 4.53 3.45 3.08 3.59 2.62 4.01 3.16 2.67 3.11 2.16 2.78 2.34 2.09 2.35 100-year variability (%) 12.33 9.55 14.27 6.72 15.20 8.18 -10.67 3.70 18.57 -2.28 1.01 -1.72 5.21 -5.08 -3.92 Total Precipitation (mm) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 344 117 418 656 1532 346 123 421 653 1539 348 120 425 664 1555 Change by 2010-2039 1 -13 22 24 37 -4 -3 22 42 66 -16 -7 15 -1 -5 Change by 2040-2069 -8 -14 37 43 54 -3 -19 43 70 91 -13 -10 35 44 56 Change by 2070-2099 -8 -19 47 71 95 1 -27 52 88 117 -13 -9 41 49 71 100-year forcing -13 -21 52 71 88 -3 -24 60 86 118 -14 -8 53 58 87 100-year variability (%) 12.70 1.94 15.74 2.05 8.47 14.73 -10.91 7.28 -0.78 8.07 9.75 6.24 6.08 6.14 -2.26
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 69 Table 6. Continued Climate Variable - - - - - - - A2 Emissions Scenario - - - - - - - - - - - - - A1B Emissions Scenario - - - - - - - - - - - - - B1 Emissions Scenario - - - - - - Mean Daily Global Solar Radiation (MJ m-2) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 14.95 22.48 9.63 4.63 12.92 14.87 22.32 9.56 4.68 12.86 14.90 22.39 9.48 4.61 12.84 Change by 2010-2039 0.09 0.34 -0.15 -0.04 0.06 0.30 0.32 -0.12 -0.19 0.07 0.23 0.28 0.06 0.02 0.14 Change by 2040-2069 0.28 0.52 -0.11 -0.15 0.14 0.26 0.76 -0.08 -0.20 0.18 0.33 0.46 -0.02 -0.15 0.16 Change by 2070-2099 0.26 0.80 -0.03 -0.23 0.20 0.18 1.06 0.00 -0.32 0.23 0.51 0.70 0.13 -0.19 0.29 100-year forcing 0.33 0.95 0.03 -0.25 0.27 0.18 1.05 -0.02 -0.29 0.24 0.54 0.75 0.03 -0.23 0.27 100-year variability (%) 1.32 -6.53 -14.09 -6.57 -6.29 1.29 -20.49 -8.37 -0.69 -8.06 0.62 -6.37 -13.09 9.13 -8.22 Mean Vapor Pressure (kPa) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 0.768 1.202 0.954 0.627 0.887 0.765 1.205 0.954 0.627 0.887 0.762 1.199 0.953 0.623 0.884 Change by 2010-2039 0.028 0.056 0.044 0.031 0.040 0.046 0.073 0.053 0.042 0.054 0.049 0.051 0.047 0.035 0.045 Change by 2040-2069 0.082 0.156 0.128 0.089 0.113 0.090 0.148 0.129 0.091 0.115 0.069 0.122 0.097 0.072 0.090 Change by 2070-2099 0.148 0.277 0.224 0.144 0.198 0.123 0.230 0.200 0.124 0.169 0.090 0.156 0.130 0.095 0.118 100-year forcing 0.157 0.293 0.237 0.157 0.211 0.130 0.249 0.213 0.138 0.182 0.094 0.169 0.142 0.104 0.127 100-year variability (%) 5.95 63.26 45.20 27.77 70.73 4.65 28.06 16.73 27.36 35.58 2.31 8.17 16.08 7.85 11.98 Mean Windspeed (m s-1) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 3.41 3.30 2.99 3.36 3.26 3.45 3.27 3.01 3.34 3.27 3.43 3.29 3.00 3.36 3.27 Change by 2010-2039 -0.01 0.07 0.10 0.07 0.06 -0.13 0.03 0.04 0.18 0.04 -0.13 0.03 0.01 0.10 0.00 Change by 2040-2069 -0.04 0.11 0.12 0.19 0.09 -0.05 0.15 0.13 0.17 0.09 -0.18 0.04 0.09 0.22 0.04 Change by 2070-2099 -0.02 0.18 0.06 0.16 0.10 -0.08 0.12 0.11 0.22 0.09 -0.24 0.05 0.07 0.15 0.01 100-year forcing -0.08 0.22 0.07 0.19 0.10 -0.10 0.14 0.14 0.22 0.09 -0.27 0.09 0.09 0.18 0.02 100-year variability (%) -13.33 -0.40 -7.64 -1.30 -8.49 -10.30 -7.08 -6.16 9.04 -6.53 -3.42 4.55 0.26 0.81 -8.98
70 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 Table 7. Climate change projection summary for Mediterranean Ecoregion (mean of four general circulation models). Climate Variable - - - - - - - A2 Emissions Scenario - - - - - - - - - - - - - A1B Emissions Scenario - - - - - - - - - - - - - B1 Emissions Scenario - - - - - - Mean Daily Tmin (°C) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 3.59 11.31 6.08 0.01 5.24 3.57 11.26 6.04 -0.03 5.20 3.52 11.21 5.96 -0.12 5.14 Change by 2010-2039 0.62 1.00 0.75 0.59 0.76 0.90 1.12 0.90 0.85 0.95 0.89 0.91 0.87 0.82 0.87 Change by 2040-2069 1.68 2.28 2.08 1.74 1.94 1.79 2.43 2.09 1.87 2.05 1.34 1.75 1.62 1.51 1.55 Change by 2070-2099 3.05 3.91 3.52 2.92 3.36 2.39 3.36 3.10 2.51 2.84 1.71 2.24 2.20 1.98 2.03 100-year forcing 3.21 4.21 3.83 3.22 3.61 2.54 3.61 3.37 2.77 3.06 1.80 2.44 2.38 2.15 2.19 100-year variability (%) 20.68 39.80 21.98 18.11 53.18 12.31 -7.38 -11.97 18.65 13.81 -4.67 5.48 1.47 1.05 6.63 Mean Daily Tmax (°C) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 17.86 28.91 21.36 11.67 19.94 17.89 28.85 21.32 11.70 19.93 17.80 28.82 21.15 11.60 19.83 Change by 2010-2039 0.77 1.12 0.70 0.61 0.81 1.12 1.13 0.90 0.79 0.99 0.90 0.89 1.05 0.69 0.88 Change by 2040-2069 2.02 2.40 2.13 1.80 2.08 2.01 2.49 2.09 1.76 2.10 1.43 1.74 1.58 1.39 1.54 Change by 2070-2099 3.58 3.93 3.47 2.85 3.47 2.49 3.42 3.08 2.38 2.85 1.80 2.19 2.24 1.87 2.04 100-year forcing 3.81 4.26 3.76 3.13 3.74 2.75 3.69 3.33 2.68 3.11 1.97 2.42 2.32 2.08 2.20 100-year variability (%) 17.49 25.15 13.21 7.38 30.78 8.20 -7.48 -6.90 26.74 6.41 3.43 2.47 -10.60 8.49 -2.04 Total Precipitation (mm) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 178 32 167 347 723 179 32 170 338 718 179 33 170 339 721 Change by 2010-2039 -5 -8 -12 11 -10 -17 -3 0 -1 -16 -4 -7 -8 19 -1 Change by 2040-2069 -16 -11 -15 3 -40 -14 -5 -7 35 6 0 -8 -5 22 8 Change by 2070-2099 -27 -9 -4 42 2 1 -10 -17 42 16 -2 -4 0 45 36 100-year forcing -35 -9 -2 49 -1 -6 -11 -12 40 8 -9 -4 5 44 31 100-year variability (%) 6.52 -24.72 4.73 15.79 18.80 10.17 -36.36 -20.30 -1.51 3.59 -8.54 -18.26 6.34 18.92 20.68
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 71 Table 7. Continued Climate Variable - - - - - - - A2 Emissions Scenario - - - - - - - - - - - - - A1B Emissions Scenario - - - - - - - - - - - - - B1 Emissions Scenario - - - - - - Mean Daily Global Solar Radiation (MJ m-2) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 19.82 25.93 14.06 8.39 17.05 19.82 25.93 14.03 8.46 17.06 19.79 25.93 14.02 8.44 17.04 Change by 2010-2039 0.21 0.06 -0.02 0.03 0.06 0.29 -0.03 0.03 -0.04 0.06 0.22 0.00 0.10 -0.10 0.05 Change by 2040-2069 0.38 0.00 0.02 0.03 0.11 0.36 -0.04 0.03 -0.16 0.05 0.24 -0.03 -0.04 -0.13 0.02 Change by 2070-2099 0.65 -0.15 -0.08 -0.11 0.08 0.20 -0.02 0.03 -0.17 0.01 0.25 -0.09 -0.01 -0.20 -0.01 100-year forcing 0.76 -0.13 -0.08 -0.13 0.11 0.31 0.00 -0.01 -0.12 0.05 0.32 -0.06 -0.06 -0.17 0.01 100-year variability (%) -3.62 -5.80 -8.29 -6.23 0.98 -3.97 -14.48 -15.00 -18.50 -3.47 -1.70 -1.97 -11.20 7.53 0.81 Mean Vapor Pressure (kPa) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 0.934 1.473 1.173 0.802 1.095 0.931 1.470 1.165 0.798 1.090 0.929 1.465 1.165 0.794 1.088 Change by 2010-2039 0.031 0.081 0.041 0.029 0.047 0.043 0.083 0.054 0.049 0.058 0.044 0.071 0.042 0.040 0.049 Change by 2040-2069 0.083 0.184 0.128 0.090 0.121 0.099 0.208 0.143 0.104 0.139 0.078 0.151 0.119 0.081 0.107 Change by 2070-2099 0.153 0.334 0.246 0.162 0.225 0.141 0.288 0.232 0.141 0.201 0.101 0.202 0.155 0.110 0.142 100-year forcing 0.162 0.358 0.271 0.178 0.242 0.147 0.309 0.248 0.153 0.214 0.105 0.218 0.172 0.118 0.153 100-year variability (%) 7.53 65.67 35.32 37.31 54.33 13.94 13.81 7.57 -1.39 16.95 11.24 18.85 10.73 8.83 13.86 Mean Windspeed (m s-1) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 3.62 3.63 2.81 2.90 3.24 3.64 3.64 2.83 2.86 3.24 3.64 3.65 2.83 2.84 3.24 Change by 2010-2039 0.00 0.04 0.09 -0.11 0.01 -0.05 0.00 0.03 -0.02 0.00 -0.16 -0.01 0.02 0.00 -0.03 Change by 2040-2069 0.08 0.13 0.08 -0.02 0.06 -0.06 0.02 0.07 0.01 0.01 0.03 0.01 -0.01 0.09 0.03 Change by 2070-2099 0.07 0.16 0.09 -0.04 0.07 0.03 0.03 -0.02 -0.01 0.00 -0.08 -0.03 -0.02 0.05 -0.01 100-year forcing 0.06 0.18 0.08 0.00 0.08 0.03 0.05 -0.01 -0.02 0.01 -0.08 0.01 0.00 0.03 -0.01 100-year variability (%) -6.53 -0.16 0.18 2.66 -14.11 -0.11 0.99 0.17 10.37 9.61 -12.33 2.53 -6.83 5.55 -12.01
72 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 Table 8. Climate change projection summary for Prairie Ecoregion (mean of four general circulation models). Climate Variable - - - - - - - A2 Emissions Scenario - - - - - - - - - - - - - A1B Emissions Scenario - - - - - - - - - - - - - B1 Emissions Scenario - - - - - - Mean Daily Tmin (°C) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 6.22 18.13 7.53 -6.28 6.38 6.25 18.27 7.55 -6.36 6.42 6.29 18.30 7.53 -6.27 6.44 Change by 2010-2039 0.87 1.22 1.12 0.96 1.06 1.14 1.36 1.31 1.32 1.30 0.69 1.05 1.07 0.76 0.91 Change by 2040-2069 2.17 2.68 2.77 2.34 2.48 2.27 2.54 2.65 2.50 2.49 1.50 1.63 1.90 1.80 1.72 Change by 2070-2099 3.66 4.57 4.55 3.89 4.18 2.98 3.49 3.62 3.55 3.41 1.80 2.21 2.40 2.40 2.21 100-year forcing 3.80 4.78 4.91 4.38 4.46 3.17 3.83 4.00 3.96 3.73 2.02 2.59 2.76 2.90 2.55 100-year variability (%) 27.94 34.75 35.07 -7.48 36.13 -6.49 13.35 1.76 -8.01 -5.36 1.08 -2.32 10.48 -14.87 -9.71 Mean Daily Tmax (°C) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 18.92 30.95 20.34 5.09 18.81 18.98 31.20 20.37 5.04 18.89 19.04 31.28 20.26 5.09 18.90 Change by 2010-2039 1.00 1.50 1.22 0.82 1.15 1.14 1.52 1.55 1.16 1.36 0.74 1.17 1.35 0.66 1.00 Change by 2040-2069 2.22 2.91 2.92 2.21 2.56 2.43 2.61 2.76 2.45 2.56 1.45 1.48 2.20 1.72 1.73 Change by 2070-2099 3.90 4.79 4.71 3.85 4.33 3.19 3.60 3.88 3.49 3.54 1.82 2.17 2.77 2.31 2.27 100-year forcing 4.04 4.91 5.07 4.24 4.56 3.39 3.97 4.28 3.82 3.85 2.07 2.61 3.05 2.69 2.59 100-year variability (%) 15.26 20.78 27.28 14.83 39.52 -5.79 6.84 1.28 11.84 3.29 0.81 1.86 7.11 3.47 4.46 Total Precipitation (mm) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 261 284 228 120 891 258 273 228 121 881 260 271 231 120 881 Change by 2010-2039 3 -6 5 1 4 10 -4 -3 2 5 12 -3 -9 -1 1 Change by 2040-2069 21 -13 8 -3 15 13 10 4 -7 20 22 24 -1 0 46 Change by 2070-2099 9 -5 9 0 14 25 15 3 -1 42 23 17 -7 -2 33 100-year forcing 15 7 9 2 33 28 17 4 2 50 28 17 -3 0 42 100-year variability (%) -2.42 10.70 5.68 12.84 2.53 0.93 15.03 -1.33 13.92 -10.87 4.57 8.35 -6.56 3.75 4.10
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 73 Table 8. Continued Climate Variable - - - - - - - A2 Emissions Scenario - - - - - - - - - - - - - A1B Emissions Scenario - - - - - - - - - - - - - B1 Emissions Scenario - - - - - - Mean Daily Global Solar Radiation (MJ m-2) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 18.34 22.88 12.61 8.87 15.67 18.33 22.93 12.60 8.88 15.68 18.33 22.94 12.54 8.85 15.67 Change by 2010-2039 -0.04 0.10 -0.01 -0.05 0.00 -0.08 0.12 0.12 -0.05 0.03 -0.05 0.07 0.16 -0.02 0.04 Change by 2040-2069 -0.19 0.07 0.04 -0.10 -0.04 -0.04 0.10 0.08 -0.11 0.01 -0.14 0.01 0.18 -0.06 0.00 Change by 2070-2099 -0.09 0.06 0.09 -0.31 -0.06 -0.10 0.07 0.17 -0.26 -0.03 -0.18 0.04 0.28 -0.09 0.01 100-year forcing -0.06 0.06 0.13 -0.30 -0.04 -0.09 0.12 0.20 -0.24 0.00 -0.17 0.11 0.25 -0.10 0.02 100-year variability (%) -9.81 -6.54 -16.19 -12.25 -9.02 -14.08 -7.75 -21.69 -9.25 -25.36 -5.87 -7.11 -21.35 -8.32 -11.53 Mean Vapor Pressure (kPa) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 1.094 2.203 1.238 0.469 1.251 1.095 2.182 1.239 0.470 1.246 1.093 2.179 1.244 0.469 1.246 Change by 2010-2039 0.055 0.100 0.075 0.023 0.063 0.075 0.104 0.075 0.033 0.072 0.057 0.096 0.049 0.019 0.056 Change by 2040-2069 0.152 0.216 0.177 0.062 0.151 0.134 0.245 0.175 0.059 0.153 0.114 0.222 0.126 0.047 0.127 Change by 2070-2099 0.220 0.375 0.295 0.109 0.250 0.193 0.346 0.243 0.097 0.220 0.128 0.250 0.127 0.061 0.142 100-year forcing 0.234 0.421 0.313 0.119 0.271 0.208 0.372 0.262 0.109 0.237 0.140 0.272 0.151 0.072 0.159 100-year variability (%) 25.88 28.85 4.28 30.42 35.09 8.26 14.22 15.36 2.20 5.90 15.73 5.14 7.29 10.39 11.990 Mean Windspeed (m s-1) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 4.75 3.85 4.06 4.53 4.30 4.71 3.86 4.10 4.53 4.29 4.72 3.84 4.11 4.56 4.30 Change by 2010-2039 0.27 0.03 0.17 0.07 0.13 0.08 0.17 0.00 0.05 0.08 0.05 0.12 0.07 -0.05 0.05 Change by 2040-2069 0.34 0.17 0.14 0.18 0.20 0.23 0.14 0.19 0.10 0.17 0.18 0.11 0.07 0.12 0.13 Change by 2070-2099 0.55 0.38 0.32 0.20 0.37 0.29 0.15 0.18 0.07 0.18 0.11 0.21 -0.03 0.04 0.08 100-year forcing 0.58 0.41 0.34 0.23 0.39 0.27 0.19 0.23 0.11 0.20 0.10 0.23 0.03 0.10 0.11 100-year variability (%) 4.48 17.44 8.04 5.69 5.12 -11.18 12.89 -0.38 -4.47 -11.19 2.24 21.88 13.55 -0.45 9.81
74 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 Table 9. Climate change projection summary for Subtropical Ecoregion (mean of four general circulation models). Climate Variable - - - - - - - A2 Emissions Scenario - - - - - - - - - - - - - A1B Emissions Scenario - - - - - - - - - - - - - B1 Emissions Scenario - - - - - - Mean Daily Tmin (°C) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 11.06 20.39 12.18 2.30 11.47 11.11 20.46 12.20 2.23 11.49 11.10 20.47 12.21 2.30 11.51 Change by 2010-2039 0.89 1.05 1.06 0.57 0.90 0.99 1.05 1.28 0.81 1.04 0.64 0.92 0.93 0.38 0.74 Change by 2040-2069 2.01 2.31 2.47 1.51 2.08 1.95 2.21 2.39 1.57 2.03 1.49 1.45 1.73 1.05 1.44 Change by 2070-2099 3.23 3.92 4.15 2.74 3.52 2.73 3.04 3.15 2.53 2.87 1.79 1.97 2.14 1.43 1.85 100-year forcing 3.39 4.10 4.44 3.00 3.73 2.95 3.29 3.46 2.73 3.10 1.99 2.24 2.47 1.70 2.10 100-year variability (%) 22.75 36.98 32.24 26.36 53.39 -1.67 2.43 7.16 3.85 9.85 4.22 -0.45 11.09 -0.13 3.11 Mean Daily Tmax (°C) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 24.49 32.39 25.32 14.77 24.23 24.58 32.56 25.40 14.71 24.31 24.61 32.64 25.43 14.77 24.35 Change by 2010-2039 0.97 1.20 1.06 0.54 0.95 0.96 1.06 1.20 0.69 0.98 0.57 0.99 0.89 0.33 0.71 Change by 2040-2069 2.06 2.72 2.51 1.51 2.20 2.06 2.32 2.30 1.62 2.07 1.32 1.26 1.57 1.06 1.31 Change by 2070-2099 3.40 4.42 4.16 2.85 3.72 2.68 3.22 3.17 2.58 2.91 1.65 1.92 2.13 1.50 1.81 100-year forcing 3.55 4.47 4.42 3.07 3.87 2.91 3.43 3.51 2.74 3.14 1.91 2.22 2.50 1.72 2.08 100-year variability (%) 20.75 9.47 37.75 15.99 37.14 -5.84 -2.47 7.12 1.34 -3.13 8.53 8.96 8.86 -3.31 6.17 Total Precipitation (mm) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 354 376 287 334 1351 346 368 281 335 1329 347 363 280 334 1324 Change by 2010-2039 -16 -17 6 -3 -28 -4 -11 20 -5 2 10 -10 8 -8 4 Change by 2040-2069 -10 -44 5 -2 -49 -3 -16 12 -13 -20 16 10 20 -2 45 Change by 2070-2099 -13 -51 6 -28 -84 26 -13 5 -10 11 26 -7 17 -5 33 100-year forcing -1 -42 8 -26 -61 30 -12 1 -7 13 30 -11 12 -4 29 100-year variability (%) 12.34 -16.95 -5.07 7.01 -3.34 1.82 -6.78 3.28 16.61 -13.07 22.02 2.78 1.17 19.33 -0.66
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 75 Table 9. Continued Climate Variable - - - - - - - A2 Emissions Scenario - - - - - - - - - - - - - A1B Emissions Scenario - - - - - - - - - - - - - B1 Emissions Scenario - - - - - - Mean Daily Global Solar Radiation (MJ m-2) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 19.36 21.94 14.38 10.19 16.47 19.37 22.02 14.42 10.17 16.50 19.44 22.03 14.45 10.14 16.52 Change by 2010-2039 0.07 0.18 0.04 0.07 0.09 0.09 0.09 -0.03 0.11 0.06 -0.07 0.16 0.01 0.17 0.06 Change by 2040-2069 0.01 0.28 0.12 0.17 0.14 0.21 0.11 0.07 0.24 0.16 -0.10 0.01 0.01 0.22 0.03 Change by 2070-2099 0.16 0.33 0.19 0.22 0.22 0.05 0.21 0.21 0.16 0.15 -0.13 0.11 0.14 0.30 0.10 100-year forcing 0.16 0.29 0.21 0.26 0.23 0.06 0.25 0.27 0.19 0.19 -0.05 0.15 0.23 0.30 0.16 100-year variability (%) 6.71 -19.87 -12.33 -17.43 -6.42 -13.49 -21.47 -2.09 -9.24 -24.03 5.84 1.10 -8.52 -9.85 -6.12 Mean Vapor Pressure (kPa) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 1.474 2.629 1.672 0.825 1.649 1.473 2.619 1.672 0.824 1.647 1.469 2.617 1.671 0.828 1.645 Change by 2010-2039 0.071 0.103 0.092 0.030 0.074 0.085 0.120 0.116 0.039 0.090 0.069 0.088 0.083 0.015 0.065 Change by 2040-2069 0.176 0.221 0.217 0.075 0.173 0.158 0.247 0.219 0.073 0.174 0.144 0.205 0.175 0.051 0.145 Change by 2070-2099 0.266 0.372 0.356 0.138 0.283 0.253 0.352 0.282 0.125 0.254 0.172 0.250 0.190 0.065 0.170 100-year forcing 0.287 0.417 0.378 0.150 0.307 0.273 0.386 0.304 0.136 0.275 0.189 0.283 0.211 0.079 0.190 100-year variability (%) 39.20 25.70 25.95 29.56 61.96 15.04 3.64 20.35 17.93 19.95 21.78 6.77 8.91 11.03 19.647 Mean Windspeed (m s-1) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 3.74 2.77 3.11 3.68 3.33 3.74 2.79 3.11 3.68 3.33 3.78 2.78 3.11 3.69 3.34 Change by 2010-2039 0.13 0.06 0.03 0.01 0.06 0.08 0.05 -0.02 -0.05 0.02 -0.10 0.04 0.00 -0.04 -0.02 Change by 2040-2069 0.17 0.13 0.07 0.02 0.09 0.11 0.09 0.07 0.05 0.08 0.07 0.12 -0.04 0.08 0.06 Change by 2070-2099 0.26 0.24 0.10 0.13 0.18 0.09 0.12 0.01 0.02 0.06 -0.01 0.09 -0.02 0.05 0.03 100-year forcing 0.27 0.24 0.15 0.19 0.21 0.11 0.14 0.06 0.09 0.10 0.05 0.10 0.03 0.13 0.08 100-year variability (%) -6.32 -20.45 1.09 -2.86 -27.61 -8.37 -11.92 6.57 -3.94 -14.94 -2.69 4.19 5.37 -5.46 -20.64
76 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 Table 10. Climate change projection summary for Dry Temperate Ecoregion (mean of four general circulation models). Climate Variable - - - - - - - A2 Emissions Scenario - - - - - - - - - - - - - A1B Emissions Scenario - - - - - - - - - - - - - B1 Emissions Scenario - - - - - - Mean Daily Tmin (°C) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 -1.13 10.30 0.20 -10.59 -0.32 -1.08 10.30 0.21 -10.60 -0.31 -1.14 10.33 0.11 -10.71 -0.36 Change by 2010-2039 0.74 1.20 0.96 0.98 1.00 1.25 1.39 1.08 1.33 1.28 1.04 1.06 1.03 0.99 1.04 Change by 2040-2069 2.27 2.77 2.53 2.59 2.52 2.36 2.81 2.49 2.65 2.58 1.66 1.86 1.89 2.14 1.89 Change by 2070-2099 3.88 4.63 4.16 4.14 4.22 3.07 3.80 3.58 3.62 3.52 2.12 2.46 2.53 2.84 2.49 100-year forcing 4.02 4.91 4.48 4.61 4.51 3.25 4.08 3.91 4.08 3.82 2.26 2.77 2.77 3.20 2.74 100-year variability (%) 1.14 76.08 30.36 0.44 47.74 -6.32 20.16 -14.51 0.17 -1.45 -21.79 7.72 -5.54 -5.79 -9.36 Mean Daily Tmax (°C) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 13.90 28.01 16.22 2.39 15.12 13.96 28.00 16.18 2.40 15.12 13.94 28.08 16.05 2.34 15.08 Change by 2010-2039 0.86 1.45 0.86 0.73 1.00 1.42 1.66 1.20 1.02 1.34 1.07 1.26 1.23 0.74 1.09 Change by 2040-2069 2.51 3.06 2.62 2.10 2.56 2.56 3.04 2.56 2.14 2.58 1.70 1.95 1.90 1.65 1.81 Change by 2070-2099 4.31 4.88 4.32 3.50 4.27 3.27 4.12 3.81 3.00 3.56 2.18 2.52 2.71 2.28 2.43 100-year forcing 4.48 5.23 4.73 3.85 4.57 3.51 4.46 4.18 3.36 3.86 2.39 2.94 2.94 2.58 2.70 100-year variability (%) 8.13 38.52 18.37 19.16 44.31 -3.83 5.19 -10.41 23.07 1.43 -13.36 8.34 -12.25 8.52 3.47 Total Precipitation (mm) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 124 135 91 75 425 124 135 92 75 425 124 135 92 75 425 Change by 2010-2039 4 1 4 3 12 4 -2 2 2 6 6 0 -1 2 7 Change by 2040-2069 7 -2 2 6 12 6 5 4 7 22 10 5 4 6 25 Change by 2070-2099 6 3 3 12 24 10 0 2 11 23 10 6 0 8 24 100-year forcing 7 3 3 13 26 11 1 2 12 25 10 7 1 8 26 100-year variability (%) 10.52 4.74 12.62 13.86 2.72 -8.31 -2.21 5.72 4.77 -7.73 9.32 4.54 -1.26 20.03 7.40
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 77 Table 10. Continued Climate Variable - - - - - - - A2 Emissions Scenario - - - - - - - - - - - - - A1B Emissions Scenario - - - - - - - - - - - - - B1 Emissions Scenario - - - - - - Mean Daily Global Solar Radiation (MJ m-2) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 19.07 24.37 12.72 8.03 16.05 19.05 24.34 12.70 8.04 16.04 19.09 24.36 12.67 8.06 16.04 Change by 2010-2039 -0.08 0.16 -0.12 -0.13 -0.05 -0.08 0.16 0.01 -0.20 -0.03 -0.18 0.13 0.03 -0.12 -0.04 Change by 2040-2069 -0.15 0.19 -0.07 -0.35 -0.09 -0.21 0.21 -0.09 -0.39 -0.12 -0.24 0.15 -0.11 -0.31 -0.12 Change by 2070-2099 -0.23 0.15 -0.09 -0.61 -0.20 -0.37 0.25 -0.06 -0.56 -0.19 -0.37 0.11 0.00 -0.42 -0.17 100-year forcing -0.21 0.24 -0.05 -0.67 -0.17 -0.36 0.30 -0.05 -0.60 -0.18 -0.34 0.18 -0.02 -0.45 -0.16 100-year variability (%) -5.92 -7.60 -1.13 9.13 -4.91 -9.92 -11.01 -6.19 -3.68 -11.23 9.41 -2.49 -15.41 16.89 4.30 Mean Vapor Pressure (kPa) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 0.693 1.361 0.795 0.376 0.806 0.695 1.360 0.794 0.375 0.806 0.691 1.357 0.791 0.373 0.803 Change by 2010-2039 0.031 0.095 0.045 0.023 0.049 0.048 0.075 0.043 0.033 0.050 0.046 0.078 0.041 0.024 0.047 Change by 2040-2069 0.093 0.174 0.112 0.064 0.110 0.092 0.190 0.114 0.067 0.116 0.076 0.155 0.098 0.053 0.096 Change by 2070-2099 0.154 0.303 0.193 0.111 0.191 0.131 0.265 0.173 0.095 0.166 0.095 0.194 0.116 0.072 0.119 100-year forcing 0.162 0.326 0.207 0.121 0.204 0.141 0.287 0.186 0.105 0.179 0.100 0.213 0.126 0.078 0.129 100-year variability (%) 7.40 70.45 60.86 36.83 60.65 0.12 28.34 23.56 20.04 19.55 7.07 24.35 30.44 18.72 25.574 Mean Windspeed (m s-1) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 4.39 3.83 3.70 3.88 3.95 4.36 3.84 3.71 3.85 3.94 4.35 3.87 3.75 3.87 3.96 Change by 2010-2039 0.04 0.02 0.09 -0.01 0.04 -0.15 0.08 0.09 0.01 0.02 -0.14 0.03 0.02 -0.07 -0.03 Change by 2040-2069 0.01 0.13 0.06 0.02 0.05 -0.09 0.02 0.11 -0.02 0.01 -0.06 -0.06 0.02 0.03 -0.02 Change by 2070-2099 -0.01 0.20 0.07 -0.05 0.05 0.00 -0.01 0.06 0.03 0.03 -0.18 -0.07 -0.02 -0.02 -0.07 100-year forcing 0.02 0.19 0.04 -0.03 0.05 0.00 -0.01 0.05 0.02 0.02 -0.19 -0.03 0.01 -0.01 -0.06 100-year variability (%) 0.42 36.79 -18.66 -5.85 -16.26 -8.78 6.47 -22.23 -8.06 -20.87 5.37 9.38 -11.43 -3.67 -2.17
78 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 Table 11. Climate change projection summary for Dry Subtropical Ecoregion (mean of four general circulation models). Climate Variable - - - - - - - A2 Emissions Scenario - - - - - - - - - - - - - A1B Emissions Scenario - - - - - - - - - - - - - B1 Emissions Scenario - - - - - - Mean Daily Tmin (°C) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 6.94 17.70 8.85 -1.52 7.98 6.99 17.78 8.81 -1.57 7.99 6.93 17.75 8.80 -1.68 7.94 Change by 2010-2039 0.83 1.19 0.94 0.61 0.91 1.10 1.20 1.18 0.93 1.12 0.83 1.05 1.00 0.76 0.92 Change by 2040-2069 2.12 2.67 2.55 1.75 2.27 2.10 2.47 2.48 1.85 2.23 1.53 1.74 1.84 1.39 1.62 Change by 2070-2099 3.66 4.31 4.31 3.12 3.86 2.86 3.51 3.61 2.73 3.18 1.88 2.17 2.35 1.96 2.09 100-year forcing 3.80 4.58 4.70 3.47 4.13 3.04 3.86 3.96 3.02 3.46 2.00 2.49 2.70 2.14 2.33 100-year variability (%) 34.37 49.86 24.38 26.23 80.63 4.26 19.20 -14.66 6.79 8.89 -3.43 3.24 -13.65 5.61 -6.87 Mean Daily Tmax (°C) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 24.05 33.81 24.90 13.90 24.15 24.25 33.96 24.90 13.95 24.25 24.21 33.96 24.80 13.81 24.18 Change by 2010-2039 1.14 1.33 0.99 0.80 1.08 1.22 1.18 1.39 0.93 1.20 0.64 1.05 1.25 0.72 0.92 Change by 2040-2069 2.74 2.83 2.70 2.29 2.63 2.51 2.43 2.69 2.33 2.49 1.59 1.70 2.01 1.63 1.74 Change by 2070-2099 4.61 4.38 4.75 3.85 4.41 3.33 3.51 3.92 3.24 3.51 2.13 1.99 2.76 2.33 2.31 100-year forcing 4.76 4.61 5.14 4.15 4.66 3.69 3.88 4.31 3.59 3.85 2.44 2.36 3.04 2.54 2.59 100-year variability (%) 8.05 13.41 20.84 23.85 33.54 -6.35 12.56 0.72 27.68 3.83 -0.55 6.34 -12.71 14.65 -9.14 Total Precipitation (mm) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 85 144 115 69 412 82 139 114 67 402 82 141 116 67 405 Change by 2010-2039 -4 -1 -2 0 -6 2 9 -5 2 9 7 -3 -6 4 2 Change by 2040-2069 -4 -5 -1 -6 -15 2 17 -5 -6 8 7 13 -2 0 18 Change by 2070-2099 -10 2 -13 -6 -26 1 10 -7 -5 -1 -1 17 -12 -2 3 100-year forcing -7 5 -12 -4 -18 1 8 -7 -5 -3 -1 18 -10 -2 5 100-year variability (%) 0.61 10.10 -6.59 -3.52 -11.55 18.17 10.79 0.53 -3.28 2.40 21.77 27.13 -6.18 0.04 1.83
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 79 Table 11. Continued Climate Variable - - - - - - - A2 Emissions Scenario - - - - - - - - - - - - - A1B Emissions Scenario - - - - - - - - - - - - - B1 Emissions Scenario - - - - - - Mean Daily Global Solar Radiation (MJ m-2) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 22.80 25.57 16.45 12.03 19.22 22.92 25.61 16.46 12.09 19.27 22.89 25.59 16.41 12.04 19.23 Change by 2010-2039 0.10 -0.06 0.04 0.02 0.02 -0.01 -0.10 0.16 -0.12 -0.01 -0.19 -0.04 0.19 -0.09 -0.03 Change by 2040-2069 0.17 -0.13 0.14 0.10 0.07 0.04 -0.19 0.20 0.01 0.02 -0.13 -0.09 0.15 0.00 -0.01 Change by 2070-2099 0.31 -0.24 0.31 -0.06 0.08 0.04 -0.25 0.25 -0.07 -0.01 0.00 -0.19 0.29 -0.04 0.02 100-year forcing 0.30 -0.27 0.31 -0.05 0.07 0.14 -0.24 0.25 0.01 0.04 0.09 -0.20 0.25 -0.01 0.03 100-year variability (%) -19.79 -8.15 -8.29 -7.33 -14.80 -11.30 2.74 -10.13 -7.34 -15.68 -2.50 4.46 -13.54 -1.64 -8.70 Mean Vapor Pressure (kPa) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 0.949 1.809 1.232 0.656 1.165 0.941 1.808 1.226 0.654 1.158 0.939 1.791 1.231 0.650 1.155 Change by 2010-2039 0.030 0.064 0.063 0.018 0.047 0.057 0.105 0.057 0.043 0.065 0.055 0.067 0.043 0.033 0.055 Change by 2040-2069 0.098 0.131 0.149 0.062 0.120 0.098 0.242 0.141 0.062 0.135 0.092 0.119 0.110 0.053 0.107 Change by 2070-2099 0.149 0.235 0.246 0.118 0.210 0.140 0.314 0.218 0.104 0.194 0.096 0.175 0.121 0.075 0.133 100-year forcing 0.160 0.255 0.272 0.132 0.230 0.143 0.332 0.239 0.117 0.207 0.097 0.176 0.146 0.083 0.144 100-year variability (%) 13.35 23.66 25.70 33.67 28.45 19.99 71.91 17.51 5.02 4.13 10.71 42.25 19.10 6.03 16.593 Mean Windspeed (m s-1) Spring Summer Fall Winter Year Spring Summer Fall Winter Year Spring Summer Fall Winter Year Baseline 1980-2009 4.45 3.88 3.57 3.58 3.88 4.48 3.90 3.59 3.56 3.89 4.51 3.91 3.64 3.60 3.91 Change by 2010-2039 0.16 0.14 0.07 0.00 0.09 0.13 0.11 0.03 -0.12 0.04 -0.08 0.08 -0.01 -0.07 -0.02 Change by 2040-2069 0.26 0.28 0.11 -0.04 0.15 0.04 0.17 0.11 -0.04 0.07 0.11 0.10 -0.02 0.01 0.05 Change by 2070-2099 0.24 0.45 0.18 -0.13 0.18 0.16 0.16 0.07 -0.09 0.07 -0.02 0.08 -0.06 -0.08 -0.02 100-year forcing 0.17 0.45 0.21 -0.18 0.16 0.13 0.19 0.11 -0.14 0.07 -0.02 0.12 0.02 -0.11 0.01 100-year variability (%) 0.01 15.30 2.26 8.23 1.68 -2.91 15.98 12.22 5.77 12.30 6.84 24.09 9.07 21.39 17.37
80 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

Discussion ________________________

Interpolated climate scenarios derived from state-ofthe-art GCMs provide an effective and reasonable means 

for comparing standardized climate projections in assessments of the impacts of climate change at high spatial 

resolution. The suite of 12 climate scenarios presented 

here were downscaled from simulations carried out 

with four well-established GCMs, each forced by three 

GHG emissions scenarios developed for the IPCC: the 

A2 scenario, which provides the strongest GHG forcing; 

the B1 scenario, derived from a storyline with significant 

GHG mitigation, which provides the weakest forcing; and 

the A1B scenario, derived from an intermediate forcing 

scenario, which provides intermediate projections. The 

downscaled climate scenarios were developed in support 

of large-scale assessments of vulnerability to climate 

change, for the United States, the USDA FS RPA Assessment, and for Canada, as a contribution to the Canadian 

Council of Forest Ministers Climate Change Task Force. 

In both projects, a key objective was to follow recommendations on the selection and use of climate scenario 

data from the IPCC’s AR4 (see http://www.ipcc-data.

org/ddc_scen_selection.html). Each downscaled climate 

scenario comprised data for six monthly climate variables, 

reported as differences from or ratios in relation to mean 

values for 1961–1990. When combined with observed 

interpolated climatologies (data for 1961–1990 normals), 

these data sets should provide a solid basis for exploring the potential effects of climate change anywhere in 

Alaska, the conterminous United States and Canada.

Producing such a comprehensive data set requires an 

assessment of the quality and consistency of the data. 

With that in mind, an analysis was carried out to highlight 

both the consistencies and discrepancies among different 

GCMs and their differential responses to the three GHG 

forcing scenarios. To make this analysis more informative, the land area of the continental United States was 

divided into eight regions, comprising the entire state of 

Alaska and seven ecoclimatic divisions identified in the 

classification of Bailey (1995).

The results across models generally were highly 

consistent, particularly with respect to temperature and 

precipitation, both in terms of each model’s response to 

the different forcing scenarios, and in agreement among 

the four models. This is not to say the models agreed 

completely in all cases, but in general, the different 

projections seemed plausible, and those from any single 

model rarely looked completely inconsistent with results 

from the other models. However, the divergence among 

model projections generally increased further into the 

future, and with increasing GHG forcing (i.e., from B1 

to A1B to A2). Hence the uncertainty in projections of 

future climate must inevitably increase as the projected 

change from present-day conditions increases (consistent 

with many other assessments of climate scenarios).

Subjective comparisons for the period 1961–2008, for 

which both observed and modeled monthly temperature 

and precipitation data were available, strongly suggested 

the magnitude and periodicity of interannual variations 

produced by all four GCMs were consistent with observations in all seasons and all regions. There was less 

consistency among the models in their projections of 

changes in interannual variability over the 21st century, 

but time-dependent changes in amplitude and frequency 

generally were similar among models and seasonal differences appeared consistent.

Sources of Error and Uncertainty

Users of the data should keep in mind that general circulation models (GCM) provide imperfect but reasonably 

correct representations of observed climate, considered as 

variables averaged over large areas with little representation of the effects of surface topography. Furthermore, the 

global projections of future climate created by GCMs are 

based on an understanding of current global trends, such 

as eccentricities in the earth’s orbit and observed rates 

of increase of atmospheric GHG--and the assumption 

that these trends will change only in predictable ways. 

For example, GCMs cannot capture future stochastic 

events, such as major volcanic eruptions, which could 

alter future climate at any time. Hence, the downscaled 

scenarios reported here should not be considered accurate 

predictions of future climate.

For each step in the process used for downscaling the 

data, there are areas of concern affecting the reliability 

of these projections. The concerns begin with the assumptions that underlie each GCM, which represent the 

physical and chemical processes occurring in the global 

atmosphere, in the oceans and on the land surface. These 

assumptions govern each GCM’s responses to the SRES 

forcing scenarios, which are themselves based on a set of 

socio-economic assumptions that are unlikely to occur 

exactly as stated in reality. In addition, documentation 

for and availability of GCM output data were sometimes 

incomplete. For example, as discussed in the Methods 

section, humidity data generated by the NCARCCSM3 

model forced by the A2 emissions scenario for the 2070s 

were missing from the PCMDI data portal and needed to 

be obtained elsewhere. Of these substitute data, humidity values for certain grid cells during the 2070s were 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 81

completely out of range and required correction. The 

latter problem was fixed in more recent simulations, but 

the metadata did not document why it occurred or how 

it was resolved.

Any process of downscaling GCM grid-level “averages” 

to a finer scale incorporates further assumptions (see the 

section ‘Review of Spatial Downscaling of Global Climate 

Simulations” at the beginning of the Methods section). 

In the present study, GCM outputs were normalized 

and converted to change factors (relative to 1961–1990 

simulated means) and downscaled by interpolation to a 5 

arcminute grid. Clearly these downscaled change factors 

cannot be more accurate than the data from which they 

were derived. The interpolated change factors were applied to historical climatology for 1961–1990 to produce 

data that more closely resemble climatic observations.

Basing projections of future climate on interpolated 

weather station data provides a realistic context within 

which to assess the GCM results; however, station data also 

are subject to problems with data quality, measurement 

errors, and missing values. Further, observing stations 

are not uniformly distributed across a heterogeneous land 

surface. For example, there are relatively few stations 

to monitor climate at high elevations, resulting in larger 

errors in interpolated data in remote mountain regions. 

Hence, any baseline value is only an estimate of “truth.” 

The implication is that the scenarios of future climate 

reported here must not be considered forecasts, but as a 

range of plausible futures. In particular, the influences 

of unpredictable climatic drivers, such as future volcanic 

eruptions, cannot be included in the GCM simulations, 

and the representation of biospheric feedbacks on GHG 

forcing, such as increased occurrence of forest fires, and 

accelerated oxidation of peatland soils, are represented 

very simplistically, if at all.

Carbon Dioxide Concentration Scenarios

The question might be posed: “Which projections of 

increases in atmospheric carbon dioxide (CO2) concentration do the downscaled scenarios reported here represent?” 

This is not as easy to answer as it may seem because, 

although the original NetCDF files downloaded from 

PCMDI and other sites provide comprehensive metadata, 

most did not explicitly state whether the GHG forcing 

follows the ISAM or Bern-CC trajectories.

The individual scenarios of future climate developed 

by GCM modeling groups for the IPCC AR4 result from 

projections of how the world will change over the 21st

century. As mentioned in the Methods section, the assumptions supporting these projections were documented 

by Naki ́cenovi ́c and others (2000) in the IPCC Special 

Report on Emissions (SRES) developed by Working 

Group III as part of the TAR. Two modeling groups 

developed separate global carbon cycle models (with 

components of GCMs, ocean carbon models, and dynamic 

vegetation models, including representations of historical 

land-use change effects, and climate change feedbacks 

on ocean and terrestrial processes) known as Bern-CC 

and ISAM. (See http://www.ipcc-data.org/ddc_co2.

html and http://www.ipcc.ch/ipccreports/tar/wg1/122.

htm#box37 for an overview of these models and their 

underlying assumptions.) These two models were used to 

project future global emissions of CO2 and other GHGs 

for six SRES scenarios (four “marker scenarios” and two 

additional “illustrative scenarios”). The projected range 

of CO2 concentrations for 2100 is from 550 to 970 ppm 

according to ISAM and from 540 to 960 ppm according 

to Bern-CC, which implies close agreement between the 

two modeling approaches. Of course, as mentioned above, 

none of these scenarios can be considered an accurate 

prediction of what may actually happen. In principle, all 

six scenarios, and underlying storylines of population 

growth and economic and technological development 

from which the concentration projections are derived, 

could be considered equally likely—depending on one’s 

views of these possible paths for global society.

For all four GCMs considered in this report, however, the 

metadata refer to the “720 ppm stabilization experiment” 

and “550 ppm stabilization experiment” for the A1B and 

B1 projections, respectively. This implies closer agreement with the ISAM trajectories for A1B and B1, which 

reach 717 and 549 ppm in 2100, respectively, compared 

to 703 and 540 ppm according to Bern-CC. However, in 

the specific case of CGCM31MR, the metadata for A1B 

(see Appendix 1) state: “The CO2 concentrations are from 

the Bern-CC model.” Stabilization concentrations are not 

given in the metadata for the A2 projections—because, 

according to the A2 scenario, GHG emissions are assumed 

to continue growing and stabilization in 2100 is unlikely. 

In general, we recommend the ISAM trajectories be used 

where CO2 concentration data are needed for an impacts 

assessment, though in practice either data set is likely to 

be acceptable.

Conclusions _______________________

The data sets are valuable for use in models and assessment frameworks that need climate variables as inputs. 

Twelve model/scenario combinations are presented to 

offer a variety of futures for assessing possible effects 

of a changing climate on natural resources, ecosystems, 
82 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

human infrastructure, and communities. These data 

sets capture the trends of a projected climate and each 

should be considered a plausible outcome under a set of 

assumptions that we encourage the user to understand. 

Data are presented as grids and may be used in models 

to determine effects at scales similar to the grid-cell size. 

Reporting of spatial and temporal trends is meaningful 

across an aggregation of grid cells. For example, the 

timing of extreme values simulated for any given grid 

cell should be considered as a general indicator of future 

possibilities in that region, but not as a precise forecast 

of extreme events at that specific location.

Given the less predictable nature of precipitation and 

the known limitations in the capacity of GCMs to simulate 

precipitation patterns accurately, the agreement among 

the four GCMs in their simulations of observed data 

(when expressed at seasonal scales) was surprisingly 

good. Further, projections of future precipitation were 

generally similar among models and forcing scenarios, 

though MIROC32MR was a notable exception for the 

southern states, where it projected a future climate with 

substantially lower precipitation than the other GCMs. 

Consistency among model projections for temperature 

and precipitation probably also explained the good general 

agreement among models in projections of small changes 

in solar radiation, which typically were inversely related 

with the projected trends in precipitation. This is expected, 

because trends in simulated precipitation are related to 

vapor condensation as cloud and, hence, inversely related 

to solar radiation arriving at the earth’s surface.

The different GCMs generally were similar in their 

projections of future changes in vapor pressure, although 

the MIROC32MR often showed relatively little change 

compared with other models, particularly during summer in the south. This difference is consistent with that 

model’s projections of reduced summer precipitation in 

the same regions.

There was general agreement among the models that 

changes in mean wind speeds will be relatively small, 

but this result varied regionally and seasonally. There 

was some evidence that greater warming (i.e., as would 

result from the A1B or A2 emissions scenarios) would 

cause greater increases in wind speed in regions and 

seasons where some sensitivity occurs. In general, projected changes in interannual variability of wind speeds 

were small and the relationships were inconsistent across 

regions, seasons, and GHG forcing scenarios.

The climate projection data are available through the 

web for analysts who wish to use the data as input to climate change impact models, or for summaries at regional 

and national scales. The data are archived at http://www.

fs.fed.us/rm/data_archive/.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 83

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USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 85

Appendix I ______________________________________________________________

Examples of metadata embedded in the GCM NetCDF files downloaded from the data portal of the Program for 

Climate Model Diagnosis and Intercomparison (PCMDI) Climate Model Intercomparison Project 3 (CMIP3). 

These metadata were obtained directly from the data files used to create the input data for interpolation by 

ANUSPLIN (using ncdump –h <fname>), and hence can be considered sample documentation of the GCM 

simulation results used to create all of the climate scenario products described in this report.

NCAR-CCSM3.0

:table_id = “Table A1” ;

:title = “model output prepared for IPCC AR4” ;

:institution = “NCAR (National Center for Atmospheric \n”,

 “Research, Boulder, CO, USA)” ;

:source = “CCSM3.0, version beta19 (2004): \n”,

 “atmosphere: CAM3.0, T85L26;\n”,

 “ocean : POP1.4.3 (modified), gx1v3\n”,

 “sea ice : CSIM5.0, T85;\n”,

 “land : CLM3.0, gx1v3” ;

:contact = “ccsm@ucar.edu” ;

:project_id = “IPCC Fourth Assessment” ;

:Conventions = “CF-1.0” ;

:references = “Collins, W.D., and others, 2005:\n”,

 “ The Community Climate System Model, Version 3\n”,

 “ Journal of Climate\n”,

 “ \n”,

 “ Main website: http://www.ccsm.ucar.edu” ;

:acknowledgment = “ Any use of CCSM data should acknowledge the contribution\n”,

 “ of the CCSM project and CCSM sponsor agencies with the \n”,

 “ following citation:\n”,

 “ \’This research uses data provided by the Community Climate\n”,

 “ System Model project (www.ccsm.ucar.edu), supported by the\n”,

 “ Directorate for Geosciences of the National Science Foundation\n”,

 “ and the Office of Biological and Environmental Research of\n”,

 “ the U.S. Department of Energy.\’\n”,

 “In addition, the words \’Community Climate System Model\’ and\n”,

 “ \’CCSM\’ should be included as metadata for webpages referencing\n”,

 “ work using CCSM data or as keywords provided to journal or book\n”,

 “publishers of your manuscripts.\n”,

 “Users of CCSM data accept the responsibility of emailing\n”,

 “ citations of publications of research using CCSM data to\n”,

 “ ccsm@ucar.edu.\n”,

 “Any redistribution of CCSM data must include this data\n”,

 “ acknowledgement statement.” ;

:realization = 5 ;

:experiment_id = “720 ppm stabilization experiment (SRES A1B)” ;

:history = “Created from CCSM3 case b30.040e\n”,

 “ by strandwg@ucar.edu\n”,

 “ on Thu Dec 9 12:52:07 MST 2004\n”,

 “ \n”,

 “ For all data, added IPCC requested metadata” ;

:comment = “This simulation was initiated from year 2000 of \n”,

 “ CCSM3 model run b30.030e and executed on \n”,

 “ hardware bluesky.ucar.edu. The input external forcings are\n”,

 “ozone forcing: A1B.ozone.128x64_L18_1991-2100_c040528.nc\n”,

 “aerosol optics : AerosolOptics_c040105.nc\n”,
86 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011

 “aerosol MMR : AerosolMass_V_128x256_clim_c031022.nc\n”,

 “carbon scaling : carbonscaling_A1B_1990-2100_c040609.nc\n”,

 “solar forcing: Fixed at 1366.5 W m-2\n”,

 “GHGs : ghg_ipcc_A1B_1870-2100_c040521.nc\n”,

 “GHG loss rates : noaamisc.r8.nc\n”,

 “volcanic forcing : none\n”,

 “DMS emissions: DMS_emissions_128x256_clim_c040122.nc\n”,

 “oxidants : oxid_128x256_L26_clim_c040112.nc\n”,

 “SOx emissions: SOx_emissions_A1B_128x256_L2_1990-2100_c040608.nc\n”,

 “ Physical constants used for derived data:\n»,

 “ Lv (latent heat of evaporation): 2.501e6 J kg-1\n»,

 “ Lf (latent heat of fusion ): 3.337e5 J kg-1\n»,

 “ r[h2o] (density of water ): 1000 kg m-3\n»,

 “ g2kg (grams to kilograms ): 1000 g kg-1\n»,

 “ \n»,

 “ Integrations were performed by NCAR and CRIEPI with support\n»,

 “ and facilities provided by NSF, DOE, MEXT and ESC/JAMSTEC.» ;

MIROC3.2mr

:title = “CCSR/NIES/FRCGC model output prepared for IPCC Fourth Assessment climate of the 

20th Century experiment (20C3M)” 

institution = “CCSR/NIES/FRCGC (Center for Climate System Research, Tokyo, Japan / National 

Institute for 

Environmental Studies, Ibaraki, Japan / Frontier Research Center for Global Change, Kanagawa, 

Japan)”

:source = “MIROC3.2 (2004): atmosphere: AGCM (AGCM5.7b, T42 L20); ocean & sea ice: COCO 

(COCO3.3, 256x192 L44); land: MATSIRO (T42)” ;

:contact = “Toru Nozawa (nozawa@nies.go.jp)”

:project_id = “IPCC Fourth Assessment” ;

:table_id = “Table A1 (8 October 2004)” ;

:experiment_id = “climate of the 20th Century experiment (20C3M)” ;

:realization = 3 ;

:cmor_version = 0.96f ;

:Conventions = “CF-1.0” ;

:history = “output from MIROC3.2 At 20:53:37 on 10/14/2004, CMOR rewrote data to comply with 

CF standards and IPCC Fourth Assessment requirements” ;

:references = “K-1 Coupled GCM Description (K-1 Technical Report No.1) in preparation” ;

:comment = “This run was initiated after 300-year spin-up of the coupled model from an 

arbitrary chosen initial condition (a snapshot result of a previous version of the model). The 

preceding spinup was forced by fixed external conditions for the year 1850, including solar 

and volcanic forcings, GHGs concentration, various aerosols emissions and land use, while all 

those conditions were changed according to historical data during the 20C in the course of 

this run.” ;

CGCM3.1mr

:title = “CCCma model output prepared for IPCC Fourth Assessment 720 ppm stabilization 

experiment (SRES A1B)” ;

:institution = “CCCma (Canadian Centre for Climate Modelling and Analysis, Victoria, BC, 

Canada)” ;

:source = “CGCM3.1 (2004): atmosphere: AGCM3 (GCM13d, T47L31); ocean: CCCMA 

(OGCM3.1,192x96L29)” ;

:contact = “Greg Flato (Greg.Flato@ec.gc.ca)” ;

:project_id = “IPCC Fourth Assessment” ;
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-263. 2011 87

:table_id = “Table A1 (17 November 2004)” ;

:experiment_id = “720 ppm stabilization experiment (SRES A1B)” ;

:realization = 5 ;

:cmor_version = 0.96f ;

:Conventions = “CF-1.0” ;

:history = “ At 20:30:10 on 06/07/2005, CMOR rewrote data to comply with CF standards and 

IPCC Fourth Assessment requirements” ;

:comment = “This model run continues from the end of the 20th century simulation with GHG and 

aerosol loadings for the IPCC SRES A1B scenario as tabulated in the IPCC Third Assessment 

Report, Appendix II. The CO2 concentrations are from the Bern-CC model (Contribution of 

Working Group I to the Third Assesment Report of IPCC, p808) and the aerosol loadings are 

from O. Boucher, Laboratoire d\’Optique Atmospherique, France. For years 2101-2300, all 

GHG concentrations and the aerosol loading are held constant at the values obtained by 

extrapolation to year 2101.” ;

CSIRO Mk 3.5

:title = “CSIRO model output prepared for IPCC Fourth Assessment” ;

:institution = “CSIRO (CSIRO Atmospheric Research, Melbourne, Australia)” ;

:source = “CSIRO Mk3.5d (2005): atmosphere: spectral (T63L18); ocean: MOM2.2 (1.875x0.925L31)” 

;

:contact = “Mark Collier (Mark.Collier@csiro.au), Martin Dix (Martin.Dix@csiro.au), Tony Hirst 

(Tony.Hirst@csiro.au)” ;

:project_id = “IPCC Fourth Assessment” ;

:experiment_id = “720 ppm stabilization experiment (SRES A1B)” ;

:realization = 1 ;

:Conventions = “CF-1.0” ;

:references = “Model described by Gordon and others The CSIRO Mk3 Climate System Model, 2002, 

www.dar.csiro.au/publications/gordon_2002a.pdf” ;

:comment = “SRES A1B experiment with CSIRO Mk 3.5d model, starting from year 2000 (model year 

300) of 20C3M experiment. Radiative forcings held constant from year 2100.” ;

:history = “Date/Time stamp=year:2006:month:04:day:11:hour:06:minute:04:second:59:UTC. 

Processed from model output using tcl-nap version 8.4.” ;

:table_id = “Table A1a” ;




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