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The 2020 RPA Assessment includes climate change as a driver affecting natural resources on forests and rangelands in the United States. This publication describes the process used to select the scenarios, climate models, and climate projections that will be used to project renewable resource conditions 50 years into the future. Downscaled climate data selected are the MACAv2-METDATA developed by Abatzoglou and others at the University of Idaho. The dataset covers the conterminous United States at a grid size of approximately 4 km (1/24 degree) on a side. The two selected scenarios are the Representative Concentration Pathways (RCPs) 4.5 and 8.5. Three criteria were used to select the climate models: (1) identification and elimination of poor performing models based on historical climate projections, (2) restriction of selection to only one model from a modeling institution, and (3) selection of a climate model that could provide projections for both the RCP 4.5 and RCP 8.5 scenarios. Climate models and projections were selected to capture a range of future climates at the conterminous scale: least warm projection, hottest projection, driest projection, and wettest projection, and one projection that reflected the middle of these ranges. The core model projections to be used in the 2020 RPA Assessment under RCP 4.5 and RCP 8.5 are: Least Warm-MRI-CGCM3; Hot-HadGEM2-ES; Dry-IPSL-CM5A-MR; Wet-CNRM-CM5; Middle-NorESM1-M. Future climates at mid-century (2041-2070) are summarized for the conterminous United States. The data are available at the USDA Forest Service Research and Development Data Archive.

We developed a methodology to index statistical changes in the variance of ecological measures over time. While ecological indicators are used to assess ecosystem health, vulnerability, risk and damage to ecosystems, their primary focus has been on changes to the mean of the ecological state or process. Little work has been done on incorporating variability into ecological indices. The methodology developed here is based on the Modified Levene's test of variance and a moving block where an initial time period (block) of ecosystem behavior is compared to the moving block. This allows for the detection of not only shifts in variance but also the magnitude of the shift on a continuous basis. Our results compared well with the benchmarked results from the Centered Cumulative Sum of Squares Algorithm (CUSUM) for detection of variance changes in fixed time series. The output from this methodology is a continuous stream of parameters (significant variance shift, magnitude of the shift, and the direction of the variance shift) suitable for indexing variance or integrating into an index measuring ecosystem change. Results suggest that the block interval widths should be at least 50 and that a smoothing factor of five be used to avoid false positives. We used modeled vegetation carbon output to analyze the utility of the methodology, illustrating that different model assumptions and CO2 regimes affect the variability of the ecological response. The degree of risk resource managers may want to explore can be altered by choices in the block length, the length of the string used to smooth variance shifts, the assumption that the initial period has constant variance and the alpha levels used to determine statistical significance.

A set of climate change projections for the United States was developed for use in the 2010 USDA Forest Service RPA Assessment. These climate projections, along with projections for population dynamics, economic growth, and land use change in the United States, comprise the RPA scenarios and are used in the RPA Assessment to project future renewable resource conditions 50 years into the future. This report describes the development of the historical and projected climate data set. The climate variables are monthly total precipitation in millimeters (mm), monthly mean daily maximum air temperature in degrees Celsius (°C), and monthly mean daily minimum air temperature in degrees Celsius (°C). Downscaled climate data were developed for the period 2001-2100 at the 5-arcminute grid scale (approximately 9.3 km by 7.1 km grid size at 40 degree N) for the conterminous United States. These data were also summarized at the U.S. county level. Computed monthly mean daily potential evapotranspiration (mm) and mean grid cell elevation in meters (m) are also included in the data set. The scenarios used here from the IPCC Special Report on Emissions Scenarios are A1B, A2, and B2. The A1B and A2 scenarios were used to drive three climate models: the Third Generation Coupled Global Climate Model, version 3.1, medium resolution; the Climate System Model, Mark 3.5 (T63); and the Model for Interdisciplinary Research on Climate, version 3.2, (T42), all used in the Fourth IPCC Assessment. The B2 scenario was used to drive three earlier generation climate models: the Second Generation Coupled Global Climate Model, version 2, medium resolution; the Climate System Model, Mark 2; and the UKMO Hadley Centre Coupled Model, version 3, all used in the IPCC Third Assessment. Monthly change factors were developed from global climate model output using the delta method. The coarse-resolution change factors were downscaled to a 5-arcminute resolution grid using ANUSPLIN. The 30-year mean historical climatology (1961-1990) was developed using the Parameter-elevation Regressions on Independent Slopes Model (PRISM) data at 2.5-arcminute resolution and aggregated to the 5-arcminute resolution grid. The downscaled change factors were combined with the PRISM observed climatology to develop nine future climate projections for the conterminous United States. These projection data and the change factor data are available through the U.S. Forest Service data archive website (http://www.fs.usda.gov/rds/archive/).

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.Data for this publication: Conterminous US (http://dx.doi.org/10.2737/RDS-2011-0023) and Alaska (http://dx.doi.org/10.2737/RDS-2011-0022)