Climate models are systems of differential equations based on the basic laws of physics, fluid motion, and chemistry. To "run" a model, scientists divide the planet into a 3-dimensional grid, apply the basic equations, and evaluate the results. Atmospheric models calculate winds, heat transfer, radiation, relative humidity, and surface hydrology within each grid and evaluate interactions with neighboring points. (source: NOAA)
SNAP evaluates, chooses, and downscales the best performing models for the North
A Global Climate Model (GCM) is a type of General Circulation Model that focuses on projections of climate change by simulating how Earth’s physical processes respond to increasing greenhouse gas concentrations.
There are over 20 global climate modeling centers that produce more than 60 GCM versions, all attempting to approximate the global climate.
We use a methodical, statistically based protocol to assess which GCMs best replicate historical climate patterns in Alaska and the Arctic so that we can provide the best models and data for our subregion of the globe.
SNAP evaluated the performance of 15 GCMs used in the Coupled Model Intercomparison Project 3 (CMIP3) and 22 GCMs used in the Coupled Model Intercomparison Project 5 (CMIP5). For several regions across Alaska and the Arctic, we calculated the degree to which each model’s output aligned with 1958–2001 climate data for precipitation, sea level pressure, and surface air temperature.
The AR5 model evaluation is described in the article: Downscaling of climate model output for Alaskan stakeholders (Walsh et al. 2018).
This paper has a companion web application that summarizes key results of SNAP's GCM statistical evaluation and model selection. It evaluates historical climate model runs over several geographic domains with an Alaska and Arctic focus. It then ranks GCM performance based on minimum error with respect to a European Re-Analysis (ERA-40) baseline data set using several error metrics. Go to the AR5 Model Selection Web Application
Model selection, downscaling, and data offerings
Our analysis showed that the best-performing models over the larger domains also tended to be those that performed best over Alaska.
We then selected the 5 best-performing CMIP3 and CMIP5 models for downscaling (see table below). We offer downscaled projections for each model, as well as for the average of all 5 models.
The AR5/CMIP5 climate model evaluation application developed by SNAP summarizes key results of SNAP's general circulation model (GCM) statistical evaluation and model selection. The evaluation is of historical climate model runs over several geographic domains with an Alaska and Arctic focus. GCM performance is ranked based on minimum error with respect to a European Re-Analysis (ERA-40) baseline data set using several error metrics.
This application supplements the paper Downscaling of climate model output for Alaskan stakeholders (Walsh et al. 2018), which includes additional context and details.
|CMIP3/AR4||Canadian Centre for Climate Modelling and Analysis||General Circulation Model version 3.1 - t47||CCCMA-CGCM3.1(T47)|
|CMIP3/AR4||Max Planck Institute for Meteorology||European Centre Hamburg Model 5||MPI-ECHAM5/MPI-OM|
|CMIP3/AR4||Geophysical Fluid Dynamics Laboratory||Coupled Climate Model 2.1||GFDL-CM2.1|
|CMIP3/AR4||UK Met Office - Hadley Centre||Coupled Model 3.0||UKMO-HadCM3|
|CMIP3/AR4||Center for Climate System Research||Model for Interdisciplinary Research on Climate||JAMSTEC-MIROC3.2(medres)|
|CMIP3/AR4||5-model average||Calculated as the mean of the above 5 models||5modelavg|
|CMIP5/AR5||National Center for Atmospheric Research||Community Earth System Model 4||NCAR-CCSM4|
|CMIP5/AR5||NOAA Geophysical Fluid Dynamics Laboratory||Coupled Model 3.0||GFDL-CM3|
|CMIP5/AR5||NASA Goddard Institute for Space Studies||ModelE/Russell||GISS-E2-R|
|CMIP5/AR5||Institut Pierre-Simon Laplace||IPSL Coupled Model v5A||IPSL-CM5A-LR|
|CMIP5/AR5||Meteorological Research Institute||Coupled General Circulation Model v3.0||MRI-CGCM3|
|CMIP5/AR5||5-model average||Calculated as the mean of the above 5 models||5modelavg|
We acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for CMIP, and we thank all the climate modeling groups for producing and making available their model output. For CMIP the US Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and leads development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.