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The Modeling of Low-Frequency Rainfall Variability

Reference
Rocheta, E., Sugiyanto, M., Johnson, F., Evans, J. and Sharma, A. 2014. How well do general circulation models represent low-frequency rainfall variability? Water Resources Research 59: 2108-2123.
In the words of Rocheta et al. (2014), "simulating hydrological variables under enhanced greenhouse gas emissions using general circulation models (GCMs) is essential for hydrological impact assessment (Fowler et al., 2007; Giorgi et al., 2001; Ines and Hansen, 2006)." But they say that "GCM precipitation simulations are less robust than other GCM fields, such as temperature," noting that "precipitation simulations [1] fail to replicate some characteristics of observed twentieth-century precipitation (Goddard et al., 2001; Perkins et al., 2007; Sun et al., 2006), [2] differ substantially across GCMs for future simulations (Johnson and Sharma, 2009), and [3] fail to adequately capture important precipitation characteristics such as persistence (Johnson et al., 2011)." As their contribution to the topic, Rocheta et al.'s analysis "presents a performance metric, the aggregated persistence score (APS), which is used to assess the reliability of GCMs in simulating low-frequency rainfall variability," where "the APS identifies regions where GCMs poorly represent the amount of variability seen in the observed precipitation."

Based on their analysis, the five Australian researchers report "it was found that there were (1) large spatial variations in the skill of GCMs to capture observed rainfall persistence, (2) widespread under-simulation of rainfall persistence characteristics in GCMs, and (3) substantial improvement in rainfall persistence," but only "after applying bias correction." Thus, as has been found to be the case in so many other studies of the integrity of state-of-the-art climate models, the findings of Rocheta et al. clearly indicate that current GCMs are not yet up to the task of reliably representing low-frequency rainfall variability.

Additional References
Fowler, H.J., Blenkinsop, J.S. and Tebaldi, C. 2007. Linking climate change modelling to impacts studies: Recent advances in downscaling techniques for hydrological modelling. International Journal of Climatology 27: 1547-1578.

Giorgi, F., Whetton, P.H., Jones, R.G., Christensen, J.H., Mearns, L.O., Hewitson, B., VonStorch, H., Francisco, R. and Jack, C. 2001. Emerging patterns of simulated regional climatic changes for the 21st century due to anthropogenic forcings. Geophysical Research Letters 28: 3317-3320.

Goddard, L., Mason, S.J., Zebiak, S.E., Ropelewski, C.F., Basher, R. and Cane, M.A. 2001. Current approaches to seasonal-to-interannual climate predictions. International Journal of Climatology 21: 1111-1152.

Ines, A.V.M. and Hansen, J.W. 2006. Bias correction of daily GCM rainfall for crop simulation studies. Agricultural and Forest Meteorology 138: 44-53.

Johnson, F. and Sharma, A. 2009. Measurement of GCM skill in predicting variables relevant for hydroclimatological assessments. Journal of Climate 22: 4373-4382.

Johnson, F., Westra, S., Sharma, A. and Pitman, A.J. 2011. An assessment of GCM skill in simulating persistence across multiple time scales. Journal of Climate 24: 3609-3623.

Perkins, S.E., Pitman, A.J., Holbrook, N.J. and McAneney, J. 2007. Evaluation of the AR4 climate models' simulated daily maximum temperature, minimum temperature, and precipitation over Australia using probability density functions. Journal of Climate 20: 4356-4376.

Sun, Y., Solomon, S., Dai, A. and Portmann, R.W. 2006. How often does it rain? Journal of Climate 19: 916-934.

Archived 6 August 2014