Exploring global surface temperature pattern scaling methodologies and assumptions from a CMIP5 model ensemble
Abstract. Pattern scaling is used to explore the uncertainty in future forcing scenarios. Of the possible techniques used for pattern scaling, the two most prominent are the delta and least squared regression methods. Both methods assume that local climate changes scale with globally averaged temperature increase, allowing for spatial patterns to be generated for multiple models for any future emission scenario. We explore this assumption by using different time periods and scenarios, and examine the differences and the statistical significance between patterns generated by each method. Regardless of epoch chosen, the relationship between globally averaged temperature increase and local temperature are similar. Temperature patterns generated by the linear regression method show a better fit to global mean temperature change than the delta method. Differences in pat terns between methods and epochs are largest in high latitudes (60 90 degrees N/S) Error terms in the least squared regression method are higher in lower forcing scenarios, and global mean temperature sensitivity is higher. These patterns will be used to examine feedbacks and uncertainty in the climate system.