Data from: A Spatial Dependent Model for Climate Emulation

For studying impacts and policy issues related to climate change, it is often critical to be able to forecast the future climate for a range of forcing scenarios. Complex climate models can be used to study climate change, but they are expensive to run, and thus, can only be used to investigate a limited number of scenarios. For some climate summaries, it is possible to develop a statistical emulator of the climate model that accurately and quickly reproduces the climate model output. Training such an emulator based on a small number of model runs can be challenging, especially when emulating at a fine spatial resolution. This work considers developing such an emulator for a specific climate model, CCSM3, as a function of the past trajectory of atmospheric CO₂ concentrations. We propose a new approach to fitting an emulator for annual temperature at the pixel level of the climate model by combining a spatially varying coefficient model and an infinite distributed lag model. The approach can capture the annual mean temperature at grid-cell level of climate model output in transient climates based on model runs from just a single CO₂ trajectory. We apply the approach to annual temperature emulation over North America and Africa, and show that the resulting emulator predicts annual temperature quite well and that the emulator can be fit in a computationally efficient manner. We show that the emulator outperforms procedures that do not take account of the spatial structure.