14 February 2018

A Basis Set for Exploration of Sensitivity to Prescribed Ocean Conditions for Estimating Human Contributions to Extreme Weather in CAM5.1-1degree

Generating a large dataset for exploring the role of human activities on extreme weather and using it to improve experiment design.


We describe simulation of the CAM5.1-1degree model of the atmosphere run under observed influences from both human and natural sources and under observed influences from natural sources only, following an international experiment protocol.  We use the unusual length and number of these simulations to determine what aspects of the experiment design are most important for the robustness of typical studies examining the role of human activities on extreme weather.


This research produced a new resource for examining the role of human activities on extreme weather, and it used that resource to further develop the production of future resources.


This paper presents two contributions for research into better understanding the role of anthropogenic warming in extreme weather. The first contribution is the generation of a large number of multi-decadal simulations using a medium-resolution atmospheric climate model, CAM5.1-1degree, under two scenarios of historical climate following the protocols of the C20C+ Detection and Attribution project: the one we have experienced (All-Hist), and one that might have been experienced in the absence of human interference with the climate system (Nat-Hist). These simulations are specifically designed for understanding extreme weather and atmospheric variability in the context of anthropogenic climate change.

The second contribution takes advantage of the duration and size of these simulations in order to identify features of variability in the prescribed ocean conditions that may strongly influence calculated estimates of the role of anthropogenic emissions on extreme weather frequency (event attribution). There is a large amount of uncertainty in how much anthropogenic emissions should warm regional ocean surface temperatures, yet contributions to the C20C+ Detection and Attribution project and similar efforts so far use only one or a limited number of possible estimates of the ocean warming attributable to anthropogenic emissions when generating their Nat-Hist simulations. Thus, the importance of the uncertainty in regional attributable warming estimates to the results of event attribution studies is poorly understood. The identification of features of the anomalous ocean state that seem to strongly influence event attribution estimates should, therefore, be able to serve as a basis set for effective sampling of other plausible attributable warming patterns. The identification performed in this paper examines monthly temperature and precipitation output from the CAM5.1-1degree simulations averaged over 237 land regions, and compares interannual anomalous variations in the ratio between the frequencies of extremes in the All-Hist and Nat-Hist simulations against variations in ocean temperatures. 

William D. Collins
Lawrence Berkeley National Laboratory (LBNL)