Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

To the extent that deficiencies in GCM simulations of precipitation are due to persistent errors of location and timing, correcting the spatial and seasonal distribution of features would provide a physically based improvement in inter-model agreement on future changes. We use a tool for the analysis of medical images to warp the precipitation climatologies of 14 General Circulation Models (GCMs) closer to a reanalysis of observations, rather than adjusting intensities locally as in conventional bias correction techniques. These warps are then applied to the same GCMs' simulated changes in mean climate under a CO2 quadrupling experiment. We find that the warping process not only makes GCMs' historical climatologies more closely resemble reanalysis but also reduces the disagreement between the models' response to this external forcing. Developing a tool that is tailored for the specific requirements of climate fields may provide further improvement, particularly in combination with local bias correction techniques. © 2013. American Geophysical Union. All Rights Reserved.

Original publication




Journal article


Geophysical Research Letters

Publication Date





354 - 358