Increasing the detectability of external influence on precipitation by correcting feature location in GCMs
Journal of Geophysical Research: Atmospheres
American Geophysical Union
This is the final version of the article. Available from the American Geophysical Union via the DOI in this record.
©2014. American Geophysical Union. All Rights Reserved. Understanding how precipitation varies as the climate changes is essential to determining the true impact of global warming. This is a difficult task not only due to the large internal variability observed in precipitation but also because of a limited historical record and large biases in simulations of precipitation by general circulation models (GCMs). Here we make use of a technique that spatially and seasonally transforms GCM fields to reduce location biases and investigate the potential of this bias correction to study historical changes. We use two versions of this bias correction - one that conserves intensities and another that conserves integrated precipitation over transformed areas. Focussing on multimodel ensemble means, we find that both versions reduce RMS error in the historical trend by approximately 11% relative to the Global Precipitation Climatology Project (GPCP) data set. By regressing GCMs' historical simulations of precipitation onto radiative forcings, we decompose these simulations into anthropogenic and natural time series. We then perform a simple detection and attribution study to investigate the impact of reducing location biases on detectability. A multiple ordinary least squares regression of GPCP onto the anthropogenic and natural time series, with the assumptions made, finds anthropogenic detectability only when spatial corrections are applied. The result is the same regardless of which form of conservation is used and without reducing the dimensionality of the fields beyond taking zonal means. While "detectability" is dependent both on the exact methodology and the confidence required, this nevertheless demonstrates the potential benefits of correcting location biases in GCMs when studying historical precipitation, especially in cases where a signal was previously undetectable.
We acknowledge the World Climate Research Programme’s Working Group on coupled modeling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. This paper benefited greatly from the Oxford Advanced Research Computing department, and we are especially grateful for invaluable assistance from Albert Solernou. A.A.L.L., W.J.I., F.H.L., C.H., and M.J. were supported by NERC under contract NE/I00680X/1 (HYDRA). M.R.A. also received support from the NOAA/DOE IDAG project.
Journal of Geophysical Research: Atmospheres, 2014, Vol. 119, pp. 12466 - 12478