Climate models can provide useful information for climate adaptation planning. GLISA is committed to evaluating climate models to determine which models offer the most credible information for the Great Lakes region. This page is dedicated to providing detailed summaries and findings of our model evaluation work. Climate model information can be combined with other important sources of knowledge, data, and information to help practitioners prepare for future climate challenges and opportunities. One approach is through the use of scenario planning. Read more about GLISA’s scenario planning approach or view GLISA’s past projects that demonstrate how climate information can be integrated into adaptation efforts.
GLISA performs ongoing evaluation of global and regional climate models to determine which ones best represent the climate of the Great Lakes region so we can deliver the highest quality information to our stakeholders. Many climate models do not provide credible information for the Great Lakes region, because they poorly represent the Great Lakes and lake-land-atmosphere dynamics. However, there is a small set of models designed specifically for our region and new models coming online soon. GLISA’s Great Lakes Ensemble project tracks progress made in regional climate modeling by evaluating the representation of lakes and important lake-land-atmosphere processes and climate model biases. We also assess data processing techniques (i.e., downscaling and bias correction) and develop guidance for practitioners to use when choosing and/or using climate projections in their work. This page is dedicated to providing detailed summaries and findings of our model evaluation work.
To date, GLISA has evaluated models from the following ensembles:
Climate Model Guidance
GLISA develops guidance for users choosing and/or using climate projections in their work. With the help of our Scientific Advisory Committee, GLISA published a set of model evaluation criteria to use when selecting climate models for the Great Lakes region.
Climate Model Report Cards
GLISA is developing a suite of climate model report cards that provide technical information about how specific climate models are constructed, including details about their land, lake, and atmospheric components. Report cards will be available spring 2021!
Climate Model Biases
GLISA evaluates climate model biases to better understand errors in the way the models represent our physical climate system. Our research focuses on seasonal precipitation and temperature biases and lake-effect precipitation biases. We also provide guidance on bias and bias correction.
- Seasonal temperature and precipitation bias overview
- Lake-effect precipitation biases
- White Paper: Overview and Guidance on Bias and Bias Correction
Evaluation of Lakes in Climate Models
GLISA evaluates the representation of the Great Lakes in climate models, because poor lake simulations compromise the quality of the information coming from the models. Many models do not include large lakes or lake-land-atmosphere feedbacks, or sometimes the Great Lakes are represented as a wet land type. GLISA looks for climate models that include 1-dimensional or 3-dimensional lake simulations that aim to better represent the influence of the lakes on regional climate.
- Large Lakes in Climate Models: A Great Lakes Case Study on the Usability of CMIP (a GLISA publication describing how the Great Lakes are represented in CMIP5 GCMs and why GCMs typically do not offer usable information for regional planning)
- The role of meteorological processes in the description of uncertainty for climate change decision-making (a GLISA publication describing the importance of looking beyond model biases and investigating the representation of regional climate processes in GCMs)
- Table summary of CMIP5 Great Lakes representations (see right)
- Detailed GLISA report on the treatment of lakes in CMIP5
- Lake representations for specific models are summarized in our Climate Model Report Cards
- The role of meteorological processes in the description of uncertainty for climate change decision-making