Detailed, high-resolution maps and GIS-ready datasets are now publicly available for download. They depict the probability of rain versus snow in the western U.S. under both historic and future (projected) climate (see research summary here). The detailed methods, data, and findings of the research are described in the publication: Klos, P. Z., T. E. Link, and J. T. Abatzoglou (2014), Extent of the rain-snow transition zone in the western U.S. under historic and projected climate, Geophys. Res. Lett., 41, doi:10.1002/2014GL060500.
Monthly Maps and Datasets
Monthly Map Description: The current and future extent of the strongly rain-dominated (blue), strongly snow- dominated (white), and rain-snow mix (pink to red) areas within the western U.S. based on wet-day mean temperature. Future extents are based upon the RCP8.5 scenario using a 20-model global climate model (GCM) mean. (ΔT ranging from ~1.5 to ~4°C spatially).
Monthly Datasets: GIS-ready monthly probability data of climatic snow-likelihood (ASCII Format).
1979-2012 climate period (historic) by numeric month (i.e. 1 = January): hist1 hist2 hist3 hist4 hist5 hist6 hist7 hist8 hist9 hist10 hist11 hist12
2035-2065 climate period (projected under the RCP 8.5 emissions scenario) by numeric month (i.e. 1 = January): rcp85-1 rcp85-2 rcp85-3 rcp85-4 rcp85-5 rcp85-6 rcp85-7 rcp85-8 rcp85-9 rcp85-10 rcp85-11 rcp85-12
Data Description: Data values indicate the probability of snow likelihood for each 4km pixel across the western U.S. Due to a tangential relationship in the Dai (2008) equation, values do not reach 0 or 1. For use/display, a threshold of 0.97 for 100% snow is suggested, as is a minimum threshold of 0.03 for 0% snow (i.e. 100% rain). The file is in ASCII format, with header rows indicating the spatial reference and resolution in lat/lon degrees.
Datum: NAD 1983
How to import the data in an ESRI GIS framework:
Step 1: Download dataset and rename the file extension to .txt from .doc (.doc is required for WordPress import, apologies for this inconvenience).
Step 2: Open ESRI ArcMap and choose the ‘ASCII to Raster Conversion’ tool from the Toolbox. Convert your .txt file to a raster file. All the appropriate header information is included in the ASCII file, so this should be automatically found by ArcMap. Make sure to select ‘FLOAT’ values for the output raster.
Step 3: Open your new raster dataset and reclassify the pixels appropriately based on the above data description. You will need to specify the datum as NAD 1983.
Winter-aggregated Maps, Datasets, and Regional Summaries
Winter-aggregated Map Description: Current extent of strongly snow-dominated (white and light gray), strongly rain-dominated (blue), and mixed phase (pink to red) winter precipitation regimes based on the mean wet-day winter temperature (1979–2012 Climate Period, December–February (DJF)mean) and the encroachment (light gray) of the mixed-phase rain-snow transition zone into previously 100% snow-dominated areas. Inset of Yosemite National Park to display spatial resolution. Locations of selected experimental sites include: Boulder Creek Critical Zone Observatory (BCCZO), Beaver Creek Experimental Watershed (BCEW), Dry Creek E. W. (DCEW), Fraser Experimental Forest (FEF), H. J. Andrews E. F. (HJA), Jemez River Basin C. Z. O. (JRBCZO), Little Bear River WATERS testbed (LBR), Mica Creek E. W. (MCEW), Priest River E. F. (PREF), Reynolds Creek E. W., C. Z. O., and WATERS testbed (RCEW), Santa Catalina C. Z. O. (SCCZO), Sevilleta Research Site (SEV), Sheep Range Meteorological Transect (ShRMT), Snake Range M. T. (SnRMT), Southern Sierra C. Z. O. (SSCZO), and Tenderfoot Creek E. F. (TCEF).
Winter-aggregated Datasets: GIS-ready probability data of climatic snow-likelihood (ASCII Format) using a December, January, February (DJF) mean.
1979-2012 climate period (historic): histDJF
2035-2065 climate period (projected under the RCP 8.5 emissions scenario): rcp85-DJF
Data Description: Data values indicate the probability of snow likelihood for each 4km pixel across the western U.S. Due to a tangential relationship in the Dai (2008) equation, values do not reach 0 or 1. For use/display, a threshold of 0.97 for 100% snow is suggested, as is a minimum threshold of 0.03 for 0% snow (i.e. 100% rain). The file is in ASCII format, with header rows indicating the spatial reference and resolution in lat/lon degrees.
How to import the data in an ESRI GIS framework:
Step 1: Download dataset and rename the file extension to .txt from .doc (.doc is required for WordPress import, apologies for this inconvenience).
Step 2: Open ESRI ArcMap and choose the ‘ASCII to Raster Conversion’ tool from the Toolbox. Convert your .txt file to a raster file. All the appropriate header information is included in the ASCII file, so this should be automatically found by ArcMap. Make sure to select ‘FLOAT’ values for the output raster.
Step 3: Open your new raster dataset and reclassify the pixels appropriately based on the above data description.
Winter-aggregated Regional Summary: Changes in Wintertime Precipitation Phase by Region; Based on mean snow-only and rain-only areal extent for the winter months (DJF) between historic (late20C) and projected (mid21C) climate; ranked by greatest percent loss by mid21C in the percent of late20C snow-only areal extent.
| Region | Snow-only extent in late20C (%) | Change in snow-only extent by mid21C (%) | Rain-only extent in late20C (%) | Change in rain-only extent by mid21C (%) |
| US EPA Level-III Ecoregions | ||||
| 15 Northern Rockies | 56 | -56 | 0 | 3 |
| 77 North Cascades | 48 | -48 | 1 | 18 |
| 11 Blue Mountains | 27 | -27 | 0 | 29 |
| 80 Northern Basin and Range | 18 | -18 | 0 | 23 |
| 20 Colorado Plateaus | 18 | -18 | 6 | 50 |
| 04 Cascades | 6 | -6 | 17 | 42 |
| 13 Central Basin and Range | 6 | -6 | 2 | 66 |
| 09 Eastern Cascades Slopes and Foothills | 5 | -5 | 0 | 27 |
| 10 Columbia Plateau | 3 | -3 | 0 | 65 |
| 23 Arizona/New Mexico Mountains | 1 | -1 | 36 | 54 |
| 12 Snake River Plain | 44 | -42 | 0 | 29 |
| 16 Idaho Batholith | 88 | -79 | 0 | 1 |
| 19 Wasatch and Uinta Mountains | 68 | -60 | 0 | 8 |
| 18 Wyoming Basin | 44 | -37 | 0 | 0 |
| 05 Sierra Nevada | 19 | -14 | 26 | 26 |
| 41 Canadian Rockies | 100 | -67 | 0 | 0 |
| 22 Arizona/New Mexico Plateau | 3 | -2 | 20 | 71 |
| 17 Middle Rockies | 82 | -46 | 0 | 0 |
| 21 Southern Rockies | 69 | -34 | 0 | 3 |
| 08 Southern California Mountains | 0 | 0 | 77 | 16 |
| 14 Mojave Basin and Range | 0 | 0 | 95 | 4 |
| 01 Coast Range | 0 | 0 | 88 | 11 |
| 02 Puget Lowland | 0 | 0 | 95 | 5 |
| 03 Willamette Valley | 0 | 0 | 99 | 1 |
| 06 Southern and Central California Chaparral and Oak Woodlands | 0 | 0 | 99 | 1 |
| 07 Central California Valley | 0 | 0 | 100 | 0 |
| 78 Klamath Mountains | 0 | 0 | 65 | 30 |
| 79 Madrean Archipelago | 0 | 0 | 99 | 1 |
| 81 Sonoran Basin and Range | 0 | 0 | 100 | 0 |
| USGS HUC-4 Watersheds | ||||
| MIDDLE SNAKE | 29 | -29 | 0 | 23 |
| GREAT SALT LAKE | 16 | -16 | 0 | 56 |
| YAKIMA | 16 | -16 | 0 | 25 |
| ESCALANTE DESERT SEVIER LAKE | 14 | -14 | 0 | 70 |
| BLACK ROCK DESERT HUMBOLDT | 14 | -14 | 0 | 49 |
| OREGON CLOSED BASINS | 13 | -13 | 0 | 26 |
| UPPER COLORADO DIRTY DEVIL | 12 | -12 | 10 | 64 |
| PUGET SOUND | 7 | -7 | 40 | 18 |
| MIDDLE COLUMBIA | 6 | -6 | 0 | 54 |
| CENTRAL LAHONTAN | 5 | -5 | 1 | 75 |
| UPPER CANADIAN | 4 | -4 | 30 | 18 |
| CENTRAL NEVADA DESERT BASINS | 4 | -4 | 11 | 53 |
| SALT | 1 | -1 | 59 | 31 |
| SACRAMENTO | 1 | -1 | 54 | 26 |
| LOWER COLORADO LAKE MEAD | 1 | -1 | 59 | 35 |
| NORTH LAHONTAN | 1 | -1 | 0 | 50 |
| KLAMATH NORTHERN CALIFORNIA COASTAL | 1 | -1 | 46 | 27 |
| WILLAMETTE | 1 | -1 | 64 | 25 |
| UPPER PECOS | 1 | -1 | 71 | 23 |
| BEAR | 79 | -76 | 0 | 3 |
| RIO GRANDE ELEPHANT BUTTE | 12 | -11 | 22 | 51 |
| UPPER COLORADO DOLORES | 17 | -15 | 0 | 54 |
| LOWER SNAKE | 50 | -44 | 0 | 25 |
| LOWER GREEN | 55 | -48 | 0 | 15 |
| WHITE YAMPA | 81 | -69 | 0 | 0 |
| POWDER TONGUE | 36 | -29 | 0 | 0 |
| SAN JOAQUIN | 10 | -8 | 74 | 8 |
| MISSOURI MUSSELSHELL | 35 | -27 | 0 | 0 |
| MISSOURI MARIAS | 44 | -33 | 0 | 0 |
| UPPER SNAKE | 74 | -55 | 0 | 8 |
| LOWER YELLOWSTONE | 24 | -17 | 0 | 0 |
| SAN JUAN | 10 | -7 | 1 | 77 |
| LOWER COLUMBIA | 3 | -2 | 45 | 34 |
| NORTHERN MOJAVE MONO LAKE | 3 | -2 | 79 | 11 |
| MISSOURI HEADWATERS | 69 | -44 | 0 | 0 |
| TULARE BUENA VISTA LAKES | 10 | -6 | 76 | 7 |
| GREAT DIVIDE UPPER GREEN | 52 | -31 | 0 | 0 |
| BIG HORN | 40 | -23 | 0 | 0 |
| UPPER YELLOWSTONE | 62 | -32 | 0 | 0 |
| RIO GRANDE HEADWATERS | 65 | -29 | 0 | 0 |
| COLORADO HEADWATERS | 73 | -30 | 0 | 11 |
| GUNNISON | 68 | -26 | 0 | 13 |
| OREGON WASHINGTON COASTAL | 0 | 0 | 79 | 15 |
| LITTLE COLORADO | 0 | 0 | 13 | 83 |
| SOUTHERN CALIFORNIA COASTAL | 0 | 0 | 85 | 3 |
| SOUTHERN MOJAVE SALTON SEA | 0 | 0 | 95 | 1 |
| CENTRAL CALIFORNIA COASTAL | 0 | 0 | 97 | 1 |
| LOWER COLORADO | 0 | 0 | 82 | 3 |
| LOWER GILA | 0 | 0 | 99 | 1 |
| MIDDLE GILA | 0 | 0 | 96 | 1 |
| SAN FRANCISCO BAY | 0 | 0 | 98 | 0 |
| UPPER GILA | 0 | 0 | 72 | 27 |
Citation for all data:
Acknowledgments:
The authors provide thanks to the Oregon State Climate Group for creating and providing free online access to the PRISM climate data. Financial support was provided by the National Science Foundation’s IGERT Program (Award 0903479) and by the National Science Foundation’s CBET Program (Award 0854553).
Zion Klos, Ph.D. 