The PRISM Climate Group gathers climate observations from a wide range of monitoring networks, applies sophisticated quality control measures, and develops spatial climate datasets to reveal short- and long-term climate patterns. The resulting datasets incorporate a variety of modeling techniques and are available at multiple spatial/temporal resolutions, covering the period from 1895 to the present.

This data set consists of PRSIM mean air temperature climatologies for Alaska in GeoTIFF format. The files in this data set are available from the PRISM Climate Group as text files but have been processed into GeoTIFFs. These are monthly climatologies with a resolution of 771m. Units are degrees Celsius. There are multiple climatological periods currently available through PRISM, but only one is currently available through SNAP in this dataset: 1971-2000.

Spatially distributed monthly and annual temperature. Each file represents 1 month of 1 year for the period 1895-1997. Distribution of the point measurements to a spatial grid was accomplished using the PRISM model, developed by Christopher Daly, Director, The PRISM Climate Group, Oregon State University. Care should be taken in estimating temperature values at any single point on the map. Temperature estimated for each grid cell is an average over the entire area of that cell; thus, point temperature can be estimated at a spatial precision no better than half the resolution of a cell. For example, the temperature data were distributed at a resolution of approximately 4km. Therefore, point temperature can be estimated at a spatial precision no better than 2km. However, the overall distribution of temperature features is thought to be accurate. For further information, the online PRISM homepage can be found at URL:http://prism.oregonstate.edu. Further information on the current state of this project can be found at URL:ftp://ftp.ncdc.noaa.gov/pub/data/prism100

Monthly 30-year "normal" dataset covering the conterminous U.S., including the Russian River watershed, averaged over the climatological period 1981-2010. Contains spatially gridded average monthly and average annual precipitation, maximum temperature, and minimum temperature at 800m grid cell resolution. Distribution of the point measurements to the spatial grid was accomplished using the PRISM model, developed and applied by Dr. Christopher Daly of the PRISM Climate Group at Oregon State University. This dataset was heavily peer reviewed, and is available free-of-charge on the PRISM website. The dataset was downloaded from the PRISM website in 2019

The U.S. Geological Survey (USGS) computed rasters of pre-solved values for the watersheds draining to the pixel delineation point representing the watershed's mean maximum and minimum January temperature from PRISM 1981-2010 4km data (resampled to 30m resolution). These values, which cover the conterminous United States, will be served in the National StreamStats Fire-Hydrology application to describe delineated watersheds ( https://streamstats.usgs.gov/ ). The StreamStats application provides access to spatial analysis tools that are useful for water-resources planning and management, and for engineering and design purposes. The map-based user interface can be used to delineate drainage areas, to retrieve basin characteristics, to estimate flow statistics, and more.

The Gridded Surface Meteorological dataset provides high spatial resolution (~4-km) daily surface fields of temperature, precipitation, winds, humidity and radiation across the contiguous United States from 1979. The dataset blends the high resolution spatial data from PRISM with the high temporal resolution data from the National Land Data Assimilation System (NLDAS) to produce spatially and temporally continuous fields that lend themselves to additional land surface modeling. This dataset contains provisional products that are replaced with updated versions when the complete source data become available. Products can be distinguished by the value of the 'status' property. At first, assets are ingested with status='early'. After several days, they are replaced by assets with status='provisional'. After about 2 months, they are replaced by the final assets with status='permanent'.

These tabular data sets represent mean monthly temperature (degrees Celsius) data from 800 meter resolution PRISM for the years 2016 and 2017 compiled for two spatial components of the NHDPlus version 2.1 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. This dataset can be linked to the NHDPlus version 2 data suite by the unique identifier COMID. The source data for mean monthly temperature (degrees Celsius) from 800 meter resolution resolution PRISM data was produced by the PRISM Group at Oregon State University. Units are degrees degrees Celsius. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values are computed using two methods, 1) divergence-routed and 2) total cumulative drainage area. Both approaches use a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the metrics derived from the reach catchment scale. (Schwarz and Wieczorek, 2018).

These tabular data sets represent mean 30 year (1971-2000) monthly temperature (degrees Celsius) data from 800 meter resolution PRISM for two spatial components of the NHDPlus version 2.1 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. This dataset can be linked to the NHDPlus version 2 data suite by the unique identifier COMID. The source data for mean monthly temperature (degrees Celsius) from 800 meter resolution resolution PRISM data was produced by the PRISM Group at Oregon State University. Units are degrees degrees Celsius. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values are computed using two methods, 1) divergence-routed and 2) total cumulative drainage area. Both approaches use a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the metrics derived from the reach catchment scale. (Schwarz and Wieczorek, 2018).

Spatially distributed monthly and annual average maximum/minimum/dew point temperature. Each file represents 1 month of 1 year for the period January 1997 to the present. Distribution of the point measurements to a spatial grid was accomplished using the PRISM model, developed by Christopher Daly, Director, The PRISM Climate Group, Oregon State University. Care should be taken in estimating temperature values at any single point on the map. Temperature estimated for each grid cell is an average over the entire area of that cell; thus, point temperature can be estimated at a spatial precision no better than half the resolution of a cell. For example, the temperature data were distributed at a resolution of approximately 4km. Therefore, point temperature can be estimated at a spatial precision no better than 2km. However, the overall distribution of temperature features is thought to be accurate. For further information, the online PRISM homepage can be found at URL:http://prism.oregonstate.edu.

This dataset provides gridded meteorological forcing data (specifically, daily precipitation and daily mean air temperature). The dataset has been generated by downscaling the Parameter-elevation Regressions on Independent Slopes Model (PRISM) dataset from a spatial resolution of 800 m to 400 m. The time period is 2008-2019, and the mapped area is East Taylor subbasin in Upper Colorado. The data are in the form of NetCDF files, arranged by year. Compared to the PRISM dataset, the dates of the downscaled dataset are shifted backwards by one (e.g., downscaled data for May 25 corresponds to PRISM data for May 26). This temporal shifting makes the meteorological forcing correspond more closely to the prescribed date. The NetCDF format is a standard raster format that can be read using any Geographic Information System (GIS) software; plenty of modules exist in popular scripting languages (such as Python, R and Matlab) that can also be used to read NetCDF files. East_Taylor_Tavg_PRISM400 corresponds to mean air temperature. East_Taylor_Precip_PRISM400.zip corresponds to precipitation. The proprietary PRISM data (800 m resolution) were purchased with funding from the Watershed Function Scientific Focus Area supported by U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under award no. DE-AC02-05CH11231. Dataset update on March 9, 2022: Uploaded new data files that are identical to the earlier files but are now in the NetCDF format, arranged by year. Earlier, the files were in the GeoTiff format, arranged by date.

Using PRISM data, temperature zones were created. Below is the original metadata from OSU.Monthly 30-year "normal" dataset covering the conterminous U.S., averaged over the climatological period 1981-2010. Contains spatially gridded average annual mean temperature at 800m grid cell resolution. Distribution of the point measurements to the spatial grid was accomplished using the PRISM model, developed and applied by Dr. Christopher Daly of the PRISM Climate Group at Oregon State University. This dataset was heavily peer reviewed, and is available free-of-charge on the PRISM website.

This is a dataset download, not a document. The Open button will start the download.This data layer is an element of the Oregon GIS Framework. Monthly 30-year "normal" dataset covering Oregon, averaged over the climatological period 1991-2020. Contains spatially gridded average daily maximum temperature at 800m grid cell resolution. Distribution of the point measurements to the spatial grid was accomplished using the PRISM model, developed and applied by Dr. Christopher Daly of the PRISM Climate Group at Oregon State University. This dataset is available free-of-charge on the PRISM website.

MethodsStudy area: Our initial study area included the entire globe. We began with a seamless grid of cells with a resolution of 0.5 degrees (i.e., ~50 km at the equator). Next, we created polylines representing coastlines using SRTM (Shuttle Radar Topographic Mission) v4.1 global digital elevation model data at a resolution of 250 m (Reuter et al. 2007). We used these coastline polylines to identify and retain cells that intersected the coast. We excluded 192,227 cells that did not intersect the coast. To avoid cells with minimal potential coastal wetland habitat, we used the coastline data to remove an additional 1,056 coastal cells that contained less than or equal to 5% coverage of land. We also removed 176 cells which did not have suitable climate data; most of these cells were removed because they either did not have minimum air temperature data or they had unrealistic low or high minimum air temperature data relative to their neighboring cells. Collectively, these steps produced a grid (hereafter, study grid) that contained a total of 4,908 cells at a resolution of 0.5 degrees. Biogeographic zone and range limit assignmentsFor biogeographic zone and range limit-specific analyses, we assigned various identification codes to each study grid cell. Biogeographic zone assignments included either Atlantic East Pacific (AEP) or Indo West Pacific (IWP) (sensu Duke et al. 1998). Range limits, defined as areas where mangroves abruptly become absent from coastlines, were assigned individually using a combination of climate data, mangrove presence data, and descriptions in the literature. We conducted a literature review to develop hypotheses regarding the climatic and non-climatic factors that control each range limit (Table 1). We created polygons for 14 focal range limits (Fig. 2), and used these polygons to assign study grid cells to a particular range limit. All range limits spanned a mangrove presence-absence transition. For range limits that were expected to be controlled, at least in part, by winter temperatures, we created polygons that spanned the cold-to-hot transition zone. Where possible, this zone extended from a minimum temperature of -20 °C (cold) up to a maximum temperature of 20 °C (hot). However, due to various constraints, most of these transitions covered smaller temperature gradients. For range limits that were expected to be controlled, at least in part, by precipitation, we created polygons that spanned the wet-to-dry transition zone, as determined via the mean annual precipitation data.Climate dataPrior studies in North America have identified the importance of using air temperature extremes in mangrove distribution and abundance models (Osland et al. 2013, Cavanaugh et al. 2014). For all cells within the study grid, we sought to identify the absolute coldest daily air temperature that occurred across a recent multi-decadal period. Although monthly-based mean minimum air temperature data are readily available, daily minimum air temperature data have historically been more difficult to obtain at the global scale (Donat et al. 2013). Due to the absence of a consistent and seamless global dataset of daily air temperature minima, we used a combination of three different gridded daily minimum air temperature data sources. For cells in the United States, we used 2.5-arcminute resolution data created by the PRISM Climate Group (Oregon State University; http://prism.oregonstate.edu) (Daly et al. 2008), for the period extending from 1981-2010. For all continental cells outside of the United States (i.e., coastal cell connected to large bodies of land on all continents except for the United States), we used 1-degree resolution data created by Sheffield et al. (2006), for the same time period. For most islands, we used 0.5-degree resolution data created by Maurer et al. (2009), for the period extending from 1971-2000. From these three data sources, we created a minimum temperature (MINT) data set for the study grid cells to represent the absolute coldest air temperature that occurred across a recent three to four decade period, depending upon the source. For each study grid cell, we also obtained 30-second resolution mean annual precipitation (MAP) data from the WorldClim Global Climate Data (Hijmans et al. 2005), for the period extending from 1950-2000. We also obtained 5-arcminute resolution global gridded mean annual sea surface temperature data from a dataset produced by UNEP-WCMC (2015), for the period extending from 2009-2013. In addition to the gridded climate data, we obtained station-based air temperature data. For 13 of the 14 focal range limits, we identified a proximate station with a long-term record of daily air temperatures. For each of these stations, we obtained daily minimum air temperature data for the 30-year period extending from 1981-2010. From these data, we calculated: (1) the absolute coldest temperature during the 30-year record (MINT); (2) the annual minimum temperature (i.e., the coldest temperature of each year); and (3) annual mean winter minimum temperature (i.e., the mean of the daily minima for the coldest quarter of each year). Mangrove dataTo determine mangrove presence, we used two global mangrove distribution data sources (Spalding et al. 2010, Giri et al. 2011), and assigned a binary code to each study grid cell denoting presence or absence. For most of the world, mangrove presence was assigned to a cell only when both of these sources deemed that mangroves were present. For Myanmar, however, the two mangrove distribution sources were not in agreement, and the Giri et al. (2011) data were deemed more reliable and used to assign mangrove presence for those cells. The two sources were also not in agreement for the coasts of Gabon, Congo, and the Cabinda Province of Angola, and the Spalding et al. (2011) data were deemed more reliable and used to assign mangrove presence for those cells. To determine mangrove species richness within each cell, we used data produced by Polidoro et al. (2010). For each cell where mangroves were deemed to be present, we used the sum of the species-specific mangrove distributional range data to determine the total number of mangrove species potentially present within a cell. To determine mangrove abundance within each cell, we used the 30-m resolution global mangrove distribution data produced by Giri et al. (2011).

This data layer is an element of the Oregon GIS Framework. Monthly 30-year "normal" dataset covering Oregon, averaged over the climatological period 1991-2020. Contains spatially gridded average daily mean temperature at 800m grid cell resolution. Distribution of the point measurements to the spatial grid was accomplished using the PRISM model, developed and applied by Dr. Christopher Daly of the PRISM Climate Group at Oregon State University. This dataset is available free-of-charge on the PRISM website.

Dataset Description This dataset contains aggregated meteorological variables for U.S. counties and ZIP Code Tabulation Areas (ZCTAs) derived from the gridMET dataset. The gridMET product combines high-resolution spatial climate data (e.g., temperature, precipitation, humidity) from the PRISM Climate Group with daily temporal attributes and additional meteorological variables from the NLDAS-2 regional reanalysis dataset. The final product includes daily meteorological data at approximately 4km x 4km spatial resolution across the contiguous United States. This dataset has been processed to provide spatial (ZCTA, County) and temporal (daily, yearly) aggregations for broader climate analysis. This dataset was created to support climate and public health research by providing ready-to-use, high-resolution meteorological data aggregated at county and ZCTA levels. This allows for efficient linking with health and socio-demographic data to explore the impacts of climate on public health. Contributors: Harvard T.H. Chan School of Public Health, NSAPH (National Studies on Air Pollution and Health) The data is organized by geographic unit (County and ZCTA) and temporal scale (daily, yearly). The dataset is structured to facilitate the computation of climate exposure variables for health impact studies. A data processing pipeline was used to generate this dataset.

The Weather Generator Gridded Data consists of two products:

[1] statistically perturbed gridded 100-year historic daily weather data including precipitation [in mm], and detrended maximum and minimum temperature in degrees Celsius, and

[2] stochastically generated and statistically perturbed gridded 1000-year daily weather data including precipitation [in mm], maximum temperature [in degrees Celsius], and minimum temperature in degrees Celsius.

The base climate of this dataset is a combination of historically observed gridded data including Livneh Unsplit 1915-2018 (Pierce et. al. 2021), Livneh 1915-2015 (Livneh et. al. 2013) and PRISM 2016-2018 (PRISM Climate Group, 2014). Daily precipitation is from Livneh Unsplit 1915-2018, daily temperature is from Livneh 2013 spanning 1915-2015 and was extended to 2018 with daily 4km PRISM that was rescaled to the Livneh grid resolution (1/16 deg). The Livneh temperature was bias corrected by month to the corresponding monthly PRISM climate over the same period. Baseline temperature was then detrended by month over the entire time series based on the average monthly temperature from 1991-2020. Statistical perturbations and stochastic generation of the time series were performed by the Weather Generator (Najibi et al. 2024a and Najibi et al. 2024b).

The repository consists of 30 climate perturbation scenarios that range from -25 to +25 % change in mean precipitation, and from 0 to +5 degrees Celsius change in mean temperature. Changes in thermodynamics represent scaling of precipitation during extreme events by a scaling factor per degree Celsius increase in mean temperature and consists primarily of 7%/degree-Celsius with 14%/degree-Celsius as sensitivity perturbations. Further insight for thermodynamic scaling can be found in full report linked below or in Najibi et al. 2024a and Najibi et al. 2024b.

The data presented here was created by the Weather Generator which was developed by Dr. Scott Steinschneider and Dr. Nasser Najibi (Cornell University). If a separate weather generator product is desired apart from this gridded climate dataset, the weather generator code can be adopted to suit the specific needs of the user. The weather generator code and supporting information can be found here: https://github.com/nassernajibi/WGEN-v2.0/tree/main. The full report for the model and performance can be found here: https://water.ca.gov/-/media/DWR-Website/Web-Pages/Programs/All-Programs/Climate-Change-Program/Resources-for-Water-Managers/Files/WGENCalifornia_Final_Report_final_20230808.pdf

Yearly topographically modified effective energy and mass transfer (EEMT-topo) (MJ m−2 yr−1) was calculated for the Valles Caldera, upper part of the Jemez River basin by summing the 12 monthly values. Effective energy and mass flux varies seasonally, especially in the desert southwestern United States where contemporary climate includes a bimodal precipitation distribution that concentrates in winter (rain or snow depending on elevation) and summer monsoon periods. This seasonality of EEMT-topo can be estimated by calculating monthly values using topographic variations of solar radiation, temperature, precipitation, evapotranspiration and surface wetting as described by Rasmussen et al. (2015). The following datasets were used to compute EEMT-topo: the precipitation climatology (1981-2010) data from the PRISM Climate Group at Oregon State Universityat an 800-m spatial resolution; the Jemez River Basin 2010 LiDARbased DEM dataset was up-scaled to 10 m DEM; the local meteorological data (Temperature, RH, Wind Speed and Pressure) downloaded for the Valles Caldera National Preserve Climate Stationsfrom 2003 to 2012; 2011 National Agriculture Imagery Program (NAIP) multispectral (4-band) images for the Valles Caldera downloaded from the USGS Seamless Data Distribution; and MODIS Albedo 16-Day L3 Global 500m data (MCD43A3) obtained from theLand Processes Distributed Active Archive Center (LP DAAC).

30-year "normal" dataset covering the conterminous U.S., averaged over the climatological period 1991-2020. Contains spatially gridded average daily mean temperature at 800m grid cell resolution. Distribution of the point measurements to the spatial grid was accomplished using the PRISM model, developed and applied by Dr. Christopher Daly of the PRISM Climate Group at Oregon State University. PRISM is an analytical model that uses point data and an underlying grid such as a digital elevation model (DEM) or a 30 yr climatological average to generate gridded estimates of monthly or annual precipitation and temperature (as well as other climatic parameters). PRISM is well suited to regions with mountainous terrain, because it incorporates a conceptual framework that addresses the spatial scale and pattern of orographic processes. Grids were modeled on a monthly basis. Annual grids were produced by averaging (temperature, vapor pressure, vapor pressure deficit, and solar radiation) or summing (precipitation) the monthly grids. These gridded normals supersede the 1991-2020 normals released in December 2022. Revisions were made to a limited number of grid cells along the US West Coast to align with the recently-developed daily normals for maximum, minimum and mean temperature, mean dew point, and maximum and minimum vapor pressure deficit. No revisions were made to the precipitation normals.

Mean monthly maximum and minimum air temperature spatial grids (1971-2000), adjusted for the effects of solar radiation and sky view factors, Andrews Experimental Forest. Maps were created using PRISM (Parameter-elevation Regressions on Independent Slopes Model), developed by Dr. Christopher Daly at Oregon State University’s PRISM Climate Group in 2010 (prism.oregonstate.edu). Grids were exported into ASCII format from GRASS GIS software; values are in degrees C x 100. Spatial resolution is 50 meters. Two sets of temperature values are available: (1) values derived from an interpolation of point station temperature values accounting for elevation; and (2) values from (1), adjusted for effects of solar radiation exposure and sky view factors. Radiation exposure and sky view factors were calculated from a two-stream solar radiation model that accounts for elevation, slope, aspect, and shading from adjacent pixels on a 50-m digital elevation model. Temperature data were obtained from selected benchmark and reference stand climate stations within the HJ Andrews, as well as National Weather Service Cooperative (COOP) and USDA NRCS Snow Telemetry (SNOTEL) stations in the vicinity. Due to the sparseness of the station data outside the Andrews, values outside the Andrews are considered to have high uncertainty. Temperature values assume an open site with no canopy cover, so are not appropriate for describing temperatures within the forest canopy. See MS033 for radiation grids used to make the radiation adjustments.

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