dc.contributor.author | Levy, AAL | |
dc.contributor.author | Jenkinson, M | |
dc.contributor.author | Ingram, W | |
dc.contributor.author | Lambert, FH | |
dc.contributor.author | Huntingford, C | |
dc.contributor.author | Allen, M | |
dc.date.accessioned | 2016-04-11T13:59:40Z | |
dc.date.issued | 2014-11-18 | |
dc.description.abstract | ©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. | en_GB |
dc.description.sponsorship | 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. | en_GB |
dc.identifier.citation | Journal of Geophysical Research: Atmospheres, 2014, Vol. 119, pp. 12466 - 12478 | en_GB |
dc.identifier.doi | 10.1002/2014JD022358 | |
dc.identifier.uri | http://hdl.handle.net/10871/21056 | |
dc.language.iso | en | en_GB |
dc.publisher | American Geophysical Union | en_GB |
dc.rights | This is the final version of the article. Available from the American Geophysical Union via the DOI in this record. | en_GB |
dc.title | Increasing the detectability of external influence on precipitation by correcting feature location in GCMs | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2016-04-11T13:59:40Z | |
dc.identifier.issn | 2169-897X | |
dc.identifier.journal | Journal of Geophysical Research: Atmospheres | en_GB |