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.

Forest Stress Drought Index (FDSI) measures dryness or drought stress across the water year by integrating anomalies in cold season (November - March) precipitation and warm season vapor pressure deficit (August - October of previous year and May - July of current year). The formulation of FDSI incorporates calibration using tree ring data in US west (Williams et al., 2013; https://www.nature.com/articles/nclimate1693 ). This index is particularly appropriate for the western US ecosystems but could also be relevant for other ecosystems where the cold season provides a large majority of annual precipitation. FDSI is found to be a sensitive metric at identifying extreme drought years such as 2002, 2012 and 2018 across Colorado for example. FDSI could also be forward projected in time, using projections of climate drivers, to assess changing severity of yearly droughts and to quantify reoccurrence frequency of an extreme historical drought in a future period. The primary objective of this app is to quantify and visualize the time series of FDSI 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 FDSI 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 FDSI for the (i) observed period (1980-2020) and (ii) future climate scenario (1951-2099) available from 40 downscaled projections from MACAv2-METDATA datasets that considers both RCP 4.5 and RCP 8.5 emission scenarios.

This application performs a systematic space-time analysis of Integrated Vapor Transport (IVT) over the entire world for the user-selected period and season. This user-friendly application allows to examine the different and complex patterns of moisture transport globally. Navigate through various tabs to explore composites, trends, and examine anomalies and climatologies of IVT. With the capability to customize parameters such as the selected season, period of the record, and significance levels, this app allows users to gain valuable insights into the trends and dynamics of climatic phenomena.

This application identifies spatially-cont iguous precipitation clusters (where each cluster represents a region that has a congruent precipitation characteristic) for a user-defined season and examines the nature of large-scale drivers (atmos pheric circulation and moisture transport; Ocean Sea Surface Temperatures) that influences seasonal precipitation for that cluster. It performs a systematic space-time analysis of user-selected seasonal precipitation over the Contiguous United States (CONUS) for the user-selected historical time-period by employing Partition Around Medoid (PAM) clustering technique proposed by Bracken et al. 2015 to identify clusters that are contiguous in space. This user-friendly application allows for examining the relationships between seasonal rainfall, sea surface temperature (SST), and Integrated Vapor Transport (IVT). The app allows for navigating through various options to explore the clustering pattern, correlate SST with precipitation clusters, and examine anomalies and climatologies of IVT. With the capability to customize parameters such as seasonality, period of the record, and significance levels, this app allows users to gain valuable insights into large-scale ocean-atmospheric processes influencing regional precipitation in CONUS.

Drought Index Portal (DrIP) was developed to display, compare, and extract time series for various indicators of drought in the contiguous United States. The Host Team collaborated with CIRES IT to host the tool under the colorado.edu domain with updates and maintenance carried out by CIRES IT.