14 Jul 2017
14 Jul 2017
Assessing NARCCAP climate model effects using spatial confidence regions
Joshua P. French1, Seth McGinnis2, and Armin Schwartzman3
Joshua P. French et al.
Joshua P. French1, Seth McGinnis2, and Armin Schwartzman3
- 1Department of Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO 80204, USA
- 2Institute for Mathematics Applied to Geosciences, National Center for Atmospheric Research, Boulder, CO 80307, USA
- 3Division of Biostatistics, University of California, San Diego, La Jolla, CA 92093, USA
- 1Department of Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO 80204, USA
- 2Institute for Mathematics Applied to Geosciences, National Center for Atmospheric Research, Boulder, CO 80307, USA
- 3Division of Biostatistics, University of California, San Diego, La Jolla, CA 92093, USA
Correspondence: Joshua P. French (joshua.french@ucdenver.edu)
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Received: 05 Oct 2016 – Revised: 15 May 2017 – Accepted: 07 Jun 2017 – Published: 14 Jul 2017
Abstract. We assess similarities and differences between model effects for the North American Regional Climate Change Assessment Program (NARCCAP) climate models using varying classes of linear regression models. Specifically, we consider how the average temperature effect differs for the various global and regional climate model combinations, including assessment of possible interaction between the effects of global and regional climate models. We use both pointwise and simultaneous inference procedures to identify regions where global and regional climate model effects differ. We also show conclusively that results from pointwise inference are misleading, and that accounting for multiple comparisons is important for making proper inference.
