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Taking climate model evaluation to the next level

  • Author(s): Eyring, Veronika
  • Cox, Peter M
  • Flato, Gregory M
  • Gleckler, Peter J
  • Abramowitz, Gab
  • Caldwell, Peter
  • Collins, William D
  • Gier, Bettina K
  • Hall, Alex D
  • Hoffman, Forrest M
  • Hurtt, George C
  • Jahn, Alexandra
  • Jones, Chris D
  • Klein, Stephen A
  • Krasting, John P
  • Kwiatkowski, Lester
  • Lorenz, Ruth
  • Maloney, Eric
  • Meehl, Gerald A
  • Pendergrass, Angeline G
  • Pincus, Robert
  • Ruane, Alex C
  • Russell, Joellen L
  • Sanderson, Benjamin M
  • Santer, Benjamin D
  • Sherwood, Steven C
  • Simpson, Isla R
  • Stouffer, Ronald J
  • Williamson, Mark S
  • et al.
Abstract

© 2019, Springer Nature Limited. Earth system models are complex and represent a large number of processes, resulting in a persistent spread across climate projections for a given future scenario. Owing to different model performances against observations and the lack of independence among models, there is now evidence that giving equal weight to each available model projection is suboptimal. This Perspective discusses newly developed tools that facilitate a more rapid and comprehensive evaluation of model simulations with observations, process-based emergent constraints that are a promising way to focus evaluation on the observations most relevant to climate projections, and advanced methods for model weighting. These approaches are needed to distil the most credible information on regional climate changes, impacts, and risks for stakeholders and policy-makers.

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