Projected Change

The values in the projected change table represent the projected change from the historical period 1971-2000 averaged across the specified protected area and the selected time period (2016-2045 or 2046-2075). These values are based on the ensemble average of nine models (CanESM2, CCSM4, HadGEM2-CC, HadGEM2-ES, IPSL-CM5A-MR, NorESM1-M, BCC-CSM1-1-M, MIROC5, CSIRO-Mk3.6.0) under representative concentration pathway 8.5 (RCP8.5).

= Projected Change in Maximum Temperature
= Projected Change in Minimum Temperature
= Projected Change in Precipitation

Detailed view

Clicking on a protected area name in the projected change table will display detailed information for that protected area in a panel on the right, including a point chart that captures climate projections for individual models and a soil sensitivity profile. The point chart shows the historical climate conditions for the period (1971-2000) within the selected area, as well as the modeled projections for the two future time periods. Model averages (ensembles) are shown in red.

Clicking any point in the chart will display the corresponding dataset in the map.

The table above the point chart provides a quick snapshot of the changes that are projected to occur within the selected area according to the model averages (ensembles).

Derivation of Climate and Soil Variables

The information below provides an overview of the process used to create the climate and soil datasets for the Landscape Climate Dashboard.

Climate Data Selection

Climate data used to represent the historical period (1971- 2000) were obtained from the LT71m PRISM 30 arc-second spatial climate dataset for the Conterminous United States (Daly et al. 2008). In addition we selected nine General Circulation Models (GCMs) or Earth System Models (ESMs) to represent future climate projections from the twenty-seven climate modeling centers participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5; Taylor et al. 2012). These models were chosen based on evaluations of their ability to simulate historical climate conditions globally and over the western United States (Rupp et al. 2013, Sheffield et al., 2013).

The nine GCM/ESMs capture a wide range of projected change for both annual average temperature and annual precipitation under the representative concentration pathway 8.5 (RCP8.5; Meinshausen et al. 2011; van Vuuren et al. 2011). RCP8.5 is a highly energy-intensive scenario that results from high population growth and a moderate rate of technology development without establishment of climate change policies.

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 western United States. Two thirty-year time series, 2016-2045 and 2046-2075, were extracted to represent projected future climates. A multi-model ensemble mean of the nine downscaled climate models was calculated for each of the climate variables discussed below for each thirty-year period.

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 & 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). We discovered that when the NEX data were processed by Thrasher, et al. (2013) the left lower corners of the PRISM data were used as the center coordinates for processing the NEX NetCDF files. Therefore, the origin of the NEX US-DCP30 data had to be altered to conform to the PRISM data. The NEX grids were adjusted by one-half grid cell (0.004166666667 decimal degrees) so that the two datasets would be spatially aligned and consistent.

Once all of the data were in the NetCDF format and spatially aligned, the 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 spatial extent of the western United States. The multi-model ensemble was then created for each base climate variable by taking the un-weighted mean of the projections from the nine GCM/EMSs.

Calculation of Climate Variables

The climate variables (tmax, tmin, and prec) for each of the time periods (1971-2000, 2016-2045, and 2046-2075) were obtained by first calculating the annual averages for the temperature values and the annual total precipitation. Then the mean value was calculated for each time period.

All climate variables for the historic and the ten future projections (nine climate models and the ensemble) were calculated from the NetCDF files in the NCAR Command Language (NCL) software program (NCL 2014).

The delta values for the future periods for tmin and tmax were calculated by subtracting historical values from future values. However, the delta values for the future periods for precipitation and aridity are calculated as percent change from the historical (((future-historical) / historical) * 100).

Soil Data for EEMS Analysis

Soil data for this analysis were obtained from the CONUS Multi-Layer Soil Characteristics dataset (Miller & White 1998) and the STATSGO soil database (Soil Survey Staff 2015).

Processing of Soil Data

All soil variables were downloaded at the scale of the conterminous United States and processed in ESRI ArcInfo workstation (ESRI 2014). The polygon data were converted to a raster dataset with a cell size of 0.0083333333 decimal degrees. The data were then clipped to the geographic extent of the study area and exported in the NetCDF format.

Post-Processing

Each dataset used in the Climate Dashboard was projected to California Teale Albers (NAD83) using a cubic convolution resampling method in ArcGIS 10.3. The zonal means for each dataset were subsequently calculated for each of the reporting units and loaded into a PostGIS database. If a protected area was too small to allow for the calculation of the zonal mean, the climate data were extracted directly from the centroid of the protected area.

Potential Climate Impact

EEMS model of potential climate impacts generated using data from STATSGO soils data and climate model results. Results from the Site Sensitivity and Climate Exposure models contribute equally to the results of the Potential Climate Impact model. As with the Climate Exposure Model, the Climate Impacts Model was run for each climate future (full results available on Data Basin). The results from the run with ensemble climate data are used in the Climate Console.

View or Download this dataset on Data Basin (2016-2045)
View or Download this dataset on Data Basin (2046-2075)

Vegetation:

Vegetation data were simulated with the Dynamic Global Vegetation Model MC2 (http://bit.ly/2a9TrKD) at 30 arc-secs. The model was run from 1895-2010 using PRISM historical data, and from 2011-2099 using the bias-corrected spatially downscaled (BCSD) climate futures (Thrasher et al. 2013) for 9 global climate models (because vapor pressure/relative humidity was missing for CMCC-CMS available results, it could not be used by MC2 which requires this input). All climate futures were run with the RCP8.5 emission scenario. The results were run without fire suppression.

MC2 vegetation types were aggregated into broader categories listed below:

0:undefined
1:taiga/tundra
2:conifer forest
3:mixed forest
4:broadleaf forest
5:shrubland/woodland/savanna
6:grassland
7:arid land
8:annual agriculture
9:perennial agriculture
10:developed/mined

The mode of the vegetation cover was calculated for each decade starting in 2011 (2011-2020, 2021-2030, etc). The final decade (2091-2099) ends in 2099 since most climate models were missing data for 2100. 'Year' on the chart corresponds to the start of each decade.

The version of MC2 that was used here uses the Natural Resources Conservation Service (NRCS) runoff curve method ( http://bit.ly/2aawHpu)

Postprocessing of the data involved projecting to California Teale Albers (NAD83) and performing an area tabulation with the protected areas database using ArcGIS 10.3.