WY-Adapt | Snowpack

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Snowpack

Explore projected changes in monthly Snow Water Equivalent

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Overview Glossary

Future Projections

Future Projections Summary

The average depicted here is based on a long-term, 30-year period. To ensure a fair comparison, this average should only be compared with other averages that are also derived from the same 30-year duration. This allows for a meaningful evaluation of how a particular variable has changed over time.

In terms of the range around this average, it illustrates the variability within the Global Climate Models (GCMs) used. The highest and lowest points within this range are determined by the maximum and minimum averages produced by individual GCMs. Meanwhile, the central average that is presented reflects the mean value of the ensemble. This provides a comprehensive picture, accounting for both the consensus value (average) and the extent of model variability (range).

These values are future projections. Therefore, they take into account the SSP370 scenario.

Modeled Historical

Modeled Historical Summary

These values are part of the modeled historical dataset meaning that the focus is on recreating, as accurately as possible, the observed climate values. Therefore, future scenarios are not included in the model.

Other Terms

Climate Forcing

Radiative forcing, often called climate forcing, gauges Earth's energy balance. It compares the energy from the sun that reaches Earth to the energy Earth emits back into space. Ideally, these energy amounts should be equal, but various forcings can upset this balance. Some factors, like volcanic eruptions or Earth's orbital changes, are natural. Others, such as increased atmospheric CO2 or altered land use, result from human actions.

In climate research, forcings are denoted in watts per square meter, using 1750—a time roughly marking the start of the industrial revolution—as a reference. This allows scientists to assess how Earth's energy balance has changed since then. Positive forcings indicate the Earth retains more energy than it emits, causing warming. Conversely, negative forcings show Earth emits more energy than it absorbs, leading to cooling.

The IPCC, in its reports, combines forcings with socioeconomic predictions to forecast possible climate futures. Their recent report introduced Shared Socioeconomic Pathways (SSPs) to depict potential climate trajectories. Each SSP merges a socioeconomic story with a specific warming level, described by its forcing, to outline five potential Earth futures.

The WY-Adapt scenario is based on SSP370, which blends two parts: SSP3, a moderate socioeconomic forecast, and a projected forcing of 7.0 watts per square meter by 2100. This means a 7 w/m² increase since 1750.

Climate Projection

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.

Shared Socioeconomic Pathways (SSPs)

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):

SSP1: Sustainability (Taking the Green Road)
SSP2: Middle of the Road
SSP3: Regional Rivalry (A Rocky Road)
SSP4: Inequality (A Road divided)
SSP5: Fossil-fueled Development (Taking the Highway)

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.

Modeled Historical

30-Year AVG

30-Year Range

Future Projections

30-Year AVG

30-Year Range

Future Projections

30-Year AVG

30-Year Range

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Controls Glossary

Select a spatial element

County/Grid Cell/HUC

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.

Spatial Element

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.

Select Climate Variable

Annual Average Maximum Temperature

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

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

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

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.

Select GCM

Global Climate Model (GCM)

A global climate model (GCM) is a mathematical model that represents the processes and interactions that drive the Earth’s climate. Also known as general circulation models, GCMs incorporate the atmosphere, oceans, lands, and ice cover to generate potential future climate conditions.

Wy-Adapt uses 15 different GCMs. Each model is slightly different, having been created by different groups around the world. Thus, the results from all of the GCMs show a range of varying climate projections. WY-Adapt mathematically combines all 15 GCMs to create an ensemble projection for past and future climate. In Wy-Adapt, all data shown in the Annual Future Climate app are products of GCMs. We present observed data in the current conditions app.

Hot and Smoky Scenario

Hot & Smoky Scenario is an ensemble combining the following Global Climate Models (GCMs): cesm2:r11i1p1f1, giss-e2-1-g:r1i1p1f2, taiesm1:r1i1p1f1, cesm2-le:1071 and cesm2-le:1111.

Shrinking Snowpack Scenario

Shrinking Snowpack Scenario is an ensemble combining the following Global Climate Models (GCMs): ec-earth3:r1i1p1f1, giss-e2-1-g:r1i1p1f2, miroc6:r1i1p1f1, mpi-esm1-2-lr:r7i1p1f1, taiesm1:r1i1p1f1, cesm2-le:1111, cesm2-le:1191.

Warm Refuge Scenario

Warm Refuge Scenario is an ensemble combining the following Global Climate Models (GCMs): canesm5:r1i1p2f1, ec-earth3-veg:r1i1p1f1, fgoals-g3:r1i1p1f1, mpi-esm1-2-hr:r3i1p1f1, mpi-esm1-2-hr:r7i1p1f1, cesm2-le:1011, cesm2-le:1131.

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Map Glossary

Data

Downscaling

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.

Timeline Visualization

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.

Climate Layer

Temperature

100

Time Series

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Chart Glossary

Chart Series

30 Year Average

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.

Ensemble

The 'ensemble' refers to a combined output of all GCMs which averages the results from all these nine models for each year. By averaging the data from multiple models, the ensemble approach aims to provide a more robust and representative forecast, reducing the uncertainties that might arise from any single model.

Other Terms

Percentiles

In WyAdapt, the P10 to P90 and P25 to P75 reflects the range of most (10-90% and 25-75%) of the values from all nine GCMs for a given year. This range indicates the spread between the highest and lowest outputs of the GCMs for that year: the upper limit of the range is the highest GCM value, and the lower limit is the lowest. A smaller range (or narrower gray area) suggests that the GCMs are closely aligned, which increases our confidence in that year's projection. Conversely, a broader range could mean diverse GCM outputs or the presence of an outlier. To see the individual outputs and determine the cause of the variation, select all the GCMs from the drop-down menu.