Climate Risk & Resilience Portal
Climate Scenarios
For completeness, we include text below from (Burdi and Branham 2023)
Climate scenarios are the set of conditions used to represent estimates of future greenhouse gas (GHG) concentrations in the atmosphere. Climate models then evaluate how these GHG concentrations affect future (projected) climate.
The data in ClimRR include model results from two future climate scenarios, called Representative Concentration Pathways (RCPs):
- RCP4.5: in this scenario, human GHG emissions peak around 2040, then decline
- RCP8.5: in this scenario, human GHG emissions continue to rise throughout the 21-st century
Each RCP is modeled over a mid-century period (2045—2054) and end-of-century-period (2081 to 2094). A historical period (1995—2004) is also modeled using GHG concentrations during this period.
Downscaled Climate Models
A global climate model (GCM) is a complex mathematical representation of the major climate system components (atmosphere, land surface, ocean, and sea ice) and their interactions.
These models project climatic conditions at frequent intervals over long periods of time (e.g., every 3 hours for the next 50—100 years), often with the purpose of evaluating how one or more GHG scenarios will impact future climate.
Most GCMs project patterns at relatively coarse spatial resolutions, using grid cells ranging from 100km to 200km.
The climate data presented in this portal has been downscaled to a higher spatial resolution (12km) to fill a growing need for risk analysis and resilience planning at the local level.
We use dynamical downscaling, which applies the outputs of a GCM as inputs to a separate, high-resolution regional climate model.
Dynamical downscaling accounts for the physical processes and natural features of a region, as well as the complex interaction between these elements and global dynamics under a climate scenario.
Argonne’s dynamical downscaling uses the Weather Research and Forecasting (WRF) model, which is a regional weather model for North America developed by the National Center for Atmospheric Research.
Scientists at Argonne dynamically downscaled three different GCMs, including:
CCSM: The Community Climate System Model (Version 4) is a coupled global climate model developed by the University Corporation for Atmospheric Research with funding from the National Science Foundation, the Department of Energy, and the National Aeronautics and Space Administration. It is comprised of atmospheric, land surface, and sea ice sub-models that run simultaneously with a central coupler component.
GFDL: The Geophysical Fluid Dynamics Laboratory at the National Oceanic and Atmospheric Administration developed the Earth System Model Version 2G (note: the general convention, which we use, is to use the Laboratory’s abbreviation to identify this model). It includes an atmospheric circulation model and an oceanic circulation model, and takes into account land, sea ice, and iceberg dynamics.
HadGEM: The United Kingdom’s Met Office developed the Hadley Global Environment Model 2—Earth System. It is used for both operational weather forecasting and climate research, and includes coupled atmosphere‐ocean analysis and an earth system component that includes dynamic vegetation, ocean biology, and atmospheric chemistry.
Ensemble Means
All data layers in ClimRR represent a climate variable along with its associated time period and climate scenario (e.g. mid-century RCP4.5). Each time period comprises one decade’s worth of information:
- historical: (1995 — 2004)
- mid-century: (2045 — 2054)
- end-of-century: (2085 — 2094)
For each scenario, the WRF model is run with each of the three GCM outputs, producing three individual decades of weather data for each scenario.
In other words, 30 years of downscaled climate data is produced for each decadal scenario.
By using the outputs from three different GCMs, rather than a single model, Argonne’s climate projections better account for the internal uncertainty associated with any single model.
Each year’s worth of data includes weather outputs for every 3 hours, or 8 modeled outputs per day.
While this allows for a high degree of granularity in assessing future climate models, there are many different ways to analyze this data; however, there are several important common methodologies share across all variables presented in this portal.
Most variables are presented as annual or seasonal averages of daily observations, yet each annual / seasonal average draws upon all three different climate model runs for that scenario and the ten years of data produced by each model.
Each variable (e.g. total_annual_precipitation) for a given scenario (e.g. Mid-century RCP4.5) is produced by calculating an individual estimate for each of the 30 years of climate data associated with that scenario, and then taking the average of 30 estimates.
This result is what we term the ensemble mean.
Metadata
The links below direct to the REST service of the gridded data. Metadata, descriptions, and field names were last updated on 11/7/2022.
- Temperature Minimum – Annual
- Temperature Minimum – Seasonal
- Temperature Maximum – Annual
- Temperature Maximum – Seasonal
- Precipitation – Annual Total
- Precipitation None – Annual Average
- Wind Speed – Annual Average
- Cooling Degree Days – Annual Total
- Heating Degree Days – Annual Total
The definitions for each of these terms can be found here.
References
Citation
@online{foreman2023,
author = {Foreman, Sam},
title = {Energy {Justice} {Analysis} of {Climate} {Data} with
{ClimRR}},
date = {2023-07-18},
url = {https://saforem2.github.io/climate-analysis},
langid = {en}
}