BLM REA COP 2014 ensemble pet1530.img

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Refer to methods document for tables and figures:http://eemaps.consbio.webfactional.com/static/Docs/Climate_and_impacts_analysis_methods.pdfClimate Data Selection: Criteria set forth by the Utah Division of Natural Resources (UT-DWR) and Utah State office of the Bureau of Land Management (UT-BLM) specified that the time periods used in the analysis be consistent with those used in the Colorado Plateau Rapid Ecological Assessment Report (Bryce et al., 2012), the spatial resolution for the study area be downscaled from 4 km x 4 km pixel resolution to 1 km x 1 km, that data used for future climate projections be obtained from phase 5 of the Coupled Model Intercomparison Project (CMIP5; (Taylor et al., 2012), and that the study area be [...]

Summary

Refer to methods document for tables and figures:http://eemaps.consbio.webfactional.com/static/Docs/Climate_and_impacts_analysis_methods.pdfClimate Data Selection: Criteria set forth by the Utah Division of Natural Resources (UT-DWR) and Utah State office of the Bureau of Land Management (UT-BLM) specified that the time periods used in the analysis be consistent with those used in the Colorado Plateau Rapid Ecological Assessment Report (Bryce et al., 2012), the spatial resolution for the study area be downscaled from 4 km x 4 km pixel resolution to 1 km x 1 km, that data used for future climate projections be obtained from phase 5 of the Coupled Model Intercomparison Project (CMIP5; (Taylor et al., 2012), and that the study area be expanded to include the entire State of Utah as well as the Colorado Plateau (Figure 1).Climate data used to represent the historical period (1968- 1999) were obtained from the LT71m PRISM 30 arc-second spatial climate dataset for the Conterminous United States (Daly et al., 2008). We evaluated ten of the 34 CMIP5 General Circulation Models (GCMs) that have been shown to reproduce several observed climate metrics such as the Pacific Decadal Oscillation (PDO) and El Niño Southern Oscillation (ENSO) over the study area (Wang et al., 2011; Wang et al., 2014). Four models (Table 1) were selected that captured the full range of projected change for both annual average temperature and annual precipitation (Figure 2) under the representative concentration pathway 8.5 (RCP8.5; (Meinshausen et al., 2011; van Vuuren et al., 2011). We then obtained downscaled climate projections for the selected GCMs from the NASA Earth Exchange (NEX) U.S. Downscaled Climate Projections (NEX US-DCP30) dataset (Thrasher et al., 2013)for the entire spatial extent of the study area and for the 2015-2030 and 2045-2060 time periods. The multi-model ensemble mean of the four downscaled climate models was calculated for each of the climate variables discussed below.Description of Climate Model Datasets: The LT71m PRISM dataset is monthly long-term time series consisting of monthly-modeled values for precipitation (rain + melted snow) and maximum, minimum, and mean temperatures. The LT71m dataset uses only station networks that have at least some stations with =20 years of observed data. The data were derived using the climatologically-aided interpolation (CAI) method using the 1971-2000 monthly climatologies (Daly et al., 2008). The NEX US-DCP30 dataset represent climate projection output from 34 GCMs that have be statistically downscaled to the 30 arc-second spatial resolution using the Bias-Correction Spatial Disaggregation (BCSD) method (Maurer and Hidalgo, 2008). The bias in monthly temperature and precipitation output from the GCMs is corrected by comparing the GCM output against the “observational”data from the PRISM dataset. The data are then downscaled to the finer 30 arc-second grid using the complex spatial interpolation method during the “spatial-disaggregation”step (Thrasher et al., 2013).Climate Data Extraction: All PRISM data used for the historical period were converted from the ESRI ASCII raster format (ESRI, 2014)to the Network Common Data Form (NetCDF; (Rew et al., 1997; UNIDATA, 2015)). The origin of the PRSIM data (Table 2) had to be altered to conform to the NEX US-DCP30 data. We discovered that when the NEX data were processed the left lower corners of the PRISM data were used as the center coordinates for the origin of the grids in the NEX NetCDF files (Table 2). Therefore, the PRISM grids, which indicate the origin of the grid, were adjusted by one-half grid cell (0.004166666667 decimal degrees) so that the two datasets would be spatially aligned and consistent with the way the NEX data set was processed.Once all of the data were in the NetCDF format and spatially aligned, the base climate variables, maximum average monthly temperature (tmax), minimum average monthly temperature (tmin), and average monthly precipitation (prec) were extracted from the both CONUS datasets for the area of interest (Figure 1). The multi-model ensemble was then created for each base climate variable by taking the un-weighted mean of the projections from the four GCMs. Calculation of Climate Variables: The base climate variables (tmax, tmin, and prec) for each of the time periods were obtained by first calculating the annual averages for the temperature values and the annual total precipitation. Then the mean value was then calculated for each time period.Two derived climate variables, potential evapotranspiration (PET) and aridity, were also calculated for the historical and future scenarios. PET was calculated using the 1985 version of the Hargreaves potential evaporation equation(Hargreaves and Allen, 2003):PET= 0.0023 x 0.408RA x (Tavg + 17.8) x TD 0.5(1)where RAis extraterrestrial radiation expressed in (MJ m-2d-1), Tavg(oC) is average daily temperature, and TD(oC) is the temperature range. The constant 0.408 is the inverse of latent heat flux of vaporization at 20 oC, is used to convert extraterrestrial radiation units from MJ m-2d-1 to mm d-1. RAwas calculated using the r.sun (Šúri and Hofierka, 2004)routine in the GRASS geographic information system (GRASS, 2015)For this study aridity was defined as P/PETwhere Pis annual precipitation and PETis defined by equation 1. The percentage change in aridity was calculated by the following form (Feng and Fu, 2013):All climate variables for the historic and the five future projections (four climate models and the ensemble) were calculated from the NetCDF files in the NCAR Command Language (NCL) software program (NCL, 2014)and are listed in Table 3.

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