Downscaling in the context of climate models refers to the process of taking global or large-scale climate model outputs and refining them to provide higher-resolution information for a smaller, specific area or region. The purpose is to make predictions or projections more relevant and applicable to local scales, where climate impacts on society and the environment often occur.
Downscaling helps in bridging the gap between the coarse resolution of global climate models and the fine-scale information needed for impact assessments, adaptation planning, and mitigation strategies.
WY-Adapt uses the LOCA statistical downscaling method.
This visualization uses a color gradient on the map to represent varying levels of a selected variable. For each year period on the timeline, darker shades signify higher values of the variable in question, while lighter shades indicate lower values. To illustrate, if you select a temperature-related variable, counties or HUCs depicted in darker tones will have higher temperatures compared to those represented in lighter tones. Thus, the color intensity serves as a direct indicator of the magnitude or intensity of the chosen variable.
This visualization is interconnected with the chart. As you hover over the chart, the shading on the map will adjust—darkening or lightening—to match the specific year your cursor is highlighting.
Visualize climate projections from 1980 to 2100 across multiple variables. Select months, seasons, or view annually to assess precipitation and temperature changes against the 1980-2010 baseline
A climate projection is a simulation of Earth's climate in future decades, given assumptions about future greenhouse gas concentrations in the atmosphere and how earth systems interact and react to those greenhouse gas concentrations. Climate projections are generated using global climate models (GCMs) and are sometimes downscaled to produce data at fine spatial scales. Climate projections are not weather projections; they cannot tell us what the weather will be like on any given day or in a year in the future. Instead, climate projections tell us what conditions in the future are going to be like on average over long time periods, often expressed as 30-year periods called climate normals.
The Shared Socioeconomic Pathways (SSPs) present five potential global socioeconomic scenarios up to 2100, considering factors like population, economic growth, and technological development. These scenarios envision how the world might progress without new climate policies beyond those already adopted by countries (excluding commitments to enact new policies in the future):
In its 6th report, the IPCC pairs these SSPs with the Representative Concentration Pathways (RCPs) from the 5th report to predict possible climate outcomes. Each SSP links a socioeconomic narrative with a specific warming level.
WY-Adapt uses the SSP370 scenario, combining SSP3's "Rocky Road" socioeconomic projections with a 7.0 watts per square meter increase in radiative forcing by 2100, a 7 w/m² rise since 1750.
SSP3: Regional Rivalry – A Rocky Road (High challenges to mitigation and adaptation)
A resurgent nationalism, concerns about competitiveness and security, and regional conflicts push countries to increasingly focus on domestic or, at most, regional issues. Policies shift over time to become increasingly oriented toward national and regional security issues. Countries focus on achieving energy and food security goals within their own regions at the expense of broader-based development. Investments in education and technological development decline. Economic development is slow, consumption is material-intensive, and inequalities persist or worsen over time. Population growth is low in industrialized and high in developing countries. A low international priority for addressing environmental concerns leads to strong environmental degradation in some regions.
Counties are currently limited to the project area, i.e., the colored area on the map.
Grid cells are the smallest unit displayed given the down-scaled climate model data available.
HUCs represent nested watersheds. They can be viewed at four levels: HUC 4 (the largest in WY-Adapt) through HUC 12 (the smallest). For instance, one HUC 4 watershed encompasses multiple HUC 8, HUC 10, and even more HUC 12 watersheds.
WY-Adapt currently categorizes data spatially into counties, hydrologic units, or HUCs, and grid cells.
These HUCs represent nested watersheds. They can be viewed at four levels: HUC 4 (the largest in WY-Adapt) through HUC 12 (the smallest). For instance, one HUC 4 watershed encompasses multiple HUC 8 and even more HUC 12 watersheds.
A user interface component that allows users to select the temporal granularity for data analysis or visualization. The dropdown offers three options:
This control enables users to customize the time period over which data is summarized or displayed, making it easier to analyze trends at different levels of detail.
Annual average maximum temperature is the average of the hottest (maximum) temperatures for every day in a year. We calculated these data for each model by selecting each day’s highest projected temperature and then averaging those daily highs across the entire year. In WY-Adapt, we define the year as Water Year (October 1st - September 30th).
Annual average minimum temperature is the average of the coldest (minimum) temperatures for every day in a year. We calculated these data for each model by selecting each day’s lowest projected temperature and then averaging those daily lows across the entire year. In WY-Adapt, we define the year as Water Year (October 1st - September 30th).
Annual average precipitation is the amount of total precipitation in the form of rain or snow over the course of a year.
The global climate models (GCMs) used by WY-Adapt provide average 24-hour precipitation rates. We transformed these daily averages from each model into inches of precipitation per day. By summing these daily amounts, we calculate the annual average precipitation. WY-Adapt typically presents these annual values for water years, spanning October 1st - September 30th.
Annual average temperature represents the average of daily temperatures over a year. We calculate this by first determining the daily average temperature for each day, then averaging those values across the entire year. In WY-Adapt, a year is defined as the Water Year, which runs from October 1st to September 30th.
WY-Adapt currently has four climate variable options: max/mean/min temperature and precipitation. All variables are presented as annual averages and are averaged over a water year (October 1st - September 30th).
The 30-year average is a rolling mean, calculated by averaging the values over a 30-year period. For each year, the mean is based on the preceding 30 years, then shifts forward by one year. For example, the 1997 value represents the mean from 1981 to 2011, while the next rolling mean is calculated for 1982 to 2012.
Precipitation encompasses all forms of water, whether in liquid or solid state, that descend from the atmosphere and make contact with the Earth's surface. This includes phenomena such as rain, snow, sleet, and hail. It's important to differentiate precipitation from dew, frost, and rime. While all are related to moisture, dew, frost, and rime are not classified as precipitation because they form directly from water vapor in the air condensing or freezing onto surfaces, rather than falling from the atmosphere.
Navigate to the Dashboards section and select Multi-Variable Climate Dashboard. The map will load, centered on a default location, showcasing average temperature projections based on selected time frames.
Upon loading, the map visualizes the average temperature for the selected month or season, compared to the 1980-2010 mean baseline. The display is color-coded to indicate areas where temperatures are projected to rise or fall.
Tailor the information displayed on the map and chart by adjusting the following settings:
The interactive chart displays a comparative analysis of precipitation, minimum temperature, maximum temperature, and average temperature, all referenced against the 1980-2010 baseline.
This dashboard provides a comprehensive tool for visualizing and analyzing projected climate data over time, focusing on key variables such as temperature and precipitation. Customize your view to explore data for different times of the year and visualize how each variable compares to historical averages.