New publication: Correcting feature location in GCMs aids the detectability of external influence on precipitation
Adam A. L. Levy, Mark Jenkinson, William Ingram, F. Hugo Lambert, Chris Huntingford and Myles Allen (2014) Correcting feature location in GCMs aids the detectability of external influence on precipitation Journal of Geophysical Research: Atmospheres DOI: 10.1002/2014JD022358
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 multi-model 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) dataset. By regressing GCMs’ historical simulations of precipitation onto radiative forcings, wedecompose 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 ofGPCP 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 dependant 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.