Grasslands in the northern Great Plains are important ecosystems that support local economies, tribal communities, livestock grazing, diverse plant and animal communities, and large-scale migrations of big game ungulates, grassland birds, and waterfowl. Climate change and variability impact how people and animals live on and interact with grasslands, and can bring more frequent droughts, fires, or new plant species that make managing these landscapes challenging. Understanding how climate change and variability will impact grassland ecosystems and their management in the 21st century first requires a synthesis of what is known across all of these scales and a gap analysis to identify key areas to focus future research. Researchers have addressed this need by conducting a series of synthesis efforts to  (1) identify and describe known management questions and information needs of grasslands managers; (2) assess the state-of-the-science on climate change and variability in the northern Great Plains region; (3) describe ecological responses to climate variability and change across the grasslands, including tipping points, changing fire patterns, spreading invasive species, changing species distributions, habitat fragmentation, and other changes in ecological communities.  This project supports resource managers by providing them with the scientific information needed to make best-practice management decisions about northern Great Plains grasslands and will foster relationships with the conservation and management organizations that will utilize this science to make decisions about public lands.

The US Global Change Research Program website, including pages hosting the Fifth National Climate Assessment (NCA5) and previous National Climate Assessments, is currently unavailable. However, this tool is still operational and contains links to PDFs of the assessment in the "Sources" section. In addition, you can find links to the entire NCA5 or select chapters here.

This workflow describes the process to visualize and quantify future climate change uncertainty, select plausible and divergent climate scenarios, extract quantitative summaries for a large suite of climate and hydrological metrics for those scenarios, and use various tools available in Climate Toolbox to visualize spatial or temporal trends for the selected scenarios

This app quantifies relationships, based on linear regression, between 'observed' grasslands productivity and different climate variables (that includes precipitation, potential evapotranspiration (PET), PET minus precipitation) for a point location in the US Great Plains, and apply those relationships to project grasslands productivity into the future under different climate scenarios. Grassland productivity is quantified as Aboveground Net Primary Productivity (ANPP) which is the total aboveground biomass production during the growing season. For more information on how it is estimated, please refer to Harmann et al. (2020; full citation provided below). This application allows a user to:   Quickly acquire annual time series of 'observed' historical ANPP and climate data for a point location as .csv Select different timescales for climate variables to assess the relationship between ANPP and climate Quantify and visualize the relationship between 'observed' historical ANPP and different climate variables Quantify, visualize and download (data as .csv) annual time series of future projections of climate and ANPP Estimate and visualize projected mean ANPP for a selected future time period and compare it to the historical mean.

Potential Evapotranspiration (PET) is a measure of the atmospheric evaporative demand (i.e., atmospheric thirst). Anomalously high PET often corresponds with increased water stress in the plants and wildfire risk. The goal of this app is to quantify and visualize the extremes in PET at different timescales (weeks to months) that have occurred in the recent historical period (1981-2020) for any location within the Contiguous United States (CONUS). This application allows a user to: Quickly acquire PET data for a single point location as an nc file Quantify, visualize and ability to download mean PET for a user selected timescale as number of days Quantify, visualize and download number of extreme events per year for a user selected threshold and timescale

Standardized Precipitation Index (SPI) quantifies standardized departures in precipitation at different timescales. For more information on SPI go to https://climatedataguide.ucar.edu/climate-data/standardized-precipitation-index-spi. The primary objective of this app is to quantify and visualize the time series of SPI at different timescales (1-month to 12-month) projected into the future under different climate scenarios for a point location within the Contiguous United States (CONUS). This app also provides observed historical time series of SPI based on the training data, gridMET, which is used in the development of the MACAv2-METDATA downscaling climate projections data that is considered in this application. This application allows a user to quantify, visualize and download SPI for a user-selected month and timescale (1 month - 12 month) for the (i) observed period (1979-2020) and (ii) future climate scenario (1950-2099) available from 40 downscaled projections from MACAv2-METDATA datasets that considers both RCP 4.5 and RCP 8.5 emission scenarios.

Evaporative Demand Drought Index (EDDI) quantifies standardized departures in Potential Evapotranspiration (PET) at different timescales. For more information on EDDI go to https://psl.noaa.gov/eddi/. The primary objective of this app is to quantify and visualize the time series of EDDI at different timescales (1-month to 12-month) projected into the future under different climate scenarios for a point location within the Contiguous United States (CONUS). This app also provides observed historical time series of EDDI based on the training data, gridMET, which is used in the development of the MACAv2-METDATA downscaling climate projections data that is considered in this application. This application allows a user to quantify, visualize and download EDDI for a user-selected month and timescale (1 month - 12 month) for the (i) observed period (1979-2020) and (ii) future climate scenario (1950-2099) available from 40 downscaled projections from MACAv2-METDATA datasets that considers both RCP 4.5 and RCP 8.5 emission scenarios.

The primary objective of this app is to quantify daily rainfall and snowfall amounts from total precipitation and visualize the time series of both quantities at daily and user-defined seasonal timescales into the future under different climate scenarios for a point location within the Contiguous United States (CONUS). This app also provides an observed historical time series of snowfall and rainfall data, based on gridMET, which is used in the development of the MACAv2-METDATA downscaled climate projections data considered in this application. This application allows a user to quantify, visualize and download snowfall and rainfall daily data as well as seasonal data for a user-selected season ranging from previous year's January through current December with water year as default (previous October through current September) from (i) observations (1980-2020) and (ii) future climate projections (1950-2099) available from 40 downscaled climate projections from MACAv2-METDATA datasets which include both RCP 4.5 and RCP 8.5 emission scenarios.

Vapour-pressure deficit (VPD) is the difference between the amount of actual moisture in the air and the amount of moisture the air can hold when it is saturated. VPD is a measure of the atmospheric evaporative demand (i.e., atmospheric thirst). Anomalously high VPD often corresponds with increased water stress in the plants and wildfire risk. The goal of this app is to quantify and visualize the extremes in VPD at different timescales (weeks to months) that have occurred in the recent historical period (1981-2020) and projected in future under various climate scenarios for any location within the Contiguous United States (CONUS). This application allows a user to:   Quickly acquire VPD data for a single point location as an nc file Quantify, visualize and ability to download mean VPD for a user selected timescale as number of days Quantify, visualize and download number of extreme events per year for a user selected threshold and timescale

Standardized Precipitation Evapotranspiration Index (SPEI) quantifies standardized departures in the difference between precipitation and potential evapotranspiration (PET) at different timescales. For more information on SPEI go to https://spei.csic.es/home.html. The primary objective of this app is to quantify and visualize the time series of SPEI at different timescales (1-month to 12-month) projected into the future under different climate scenarios for a point location within the Contiguous United States (CONUS). This app also provides observed historical time series of SPEI based on the training data, gridMET, which is used in the development of the MACAv2-METDATA downscaling climate projections data that is considered in this application. This application allows a user to quantify, visualize and download SPEI for a user-selected month and timescale (1 month - 12 month) for the (i) observed period (1979-2020) and (ii) future climate scenario (1950-2099) available from 40 downscaled projections from MACAv2-METDATA datasets that considers both RCP 4.5 and RCP 8.5 emission scenarios.