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Student Project: Regional Climate Adaptation Indicators

This page is part of the project: Great Lakes Climate Ensemble

Project Description:

This semester long student project will advance the work of the Great Lakes Ensemble by starting to match end-user climate information needs with the types of model output that are required to inform those needs.  This project will start by analyzing GLISA's past project white papers to identify what climate indicators (variables, indices, and/or thresholds) were important to the questions and problems being addressed.  Projects will be assigned topics (i.e., water-related, agricultural, urban, etc) and within those topics the indicators of importance will be collected.  The main goal is to have a list of indicators and descriptions of each indicator for every topic.  Further review from peer-reviewed literature will also be included.  In the final phase of this project, the student will investigate and match each indicator to the climate model output(s) that are necessary for calculating the indicator.  This will inform a set of products for the Ensemble to deliver.           

Project Deliverables:

  1. A page in the Ensemble's project space on that organizes project topics and relevant indicators (including narrative description and analytical description of how the indicator is calculated) used by practitioners
  2. An example calculation and presentation of an indicator for CMIP5 models over the Great Lakes region.
    1. Summer Days (annual number of days where tmax > 25C) will be calculated for each available CMIP5 model and experiment (historical and RCPs)
    2. Regional maps of the mean number of Summer Days for the historical period (to use as reference) 1976-2005 for a select number of models
    3. Comparison of historical observations to model historical period to identify bias;
    4. Time series of 9-year running means of Great Lakes annual number of Summer Days for length of model record


  1. Review GLISA white papers and record any climate variables/indices/thresholds that are mentioned, a short definition including why they are important, the reference of the paper, and assign an adaptation topic. Information will be collected in a table here.
  2. If specific white papers are more qualitative in nature and do not provide quantitative metrics, review their references and perform additional literature review to search for metrics that can be calculated from model output (if available).
  3. Perform analysis for a selected metric (Summer Days) and create visuals for communicating information about how the metric is projected to change over time