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The Non-Stationary Precipitation Biases of Current Climate Models

Paper Reviewed
Chen, J., Brissette, F.P. and Lucas-Picher, P. 2015. Assessing the limits of bias-correcting climate model outputs for climate change impact studies. Journal of Geophysical Research Atmospheres 120: 1123-1136.

Chen et al. (2015) begin their study by noting "climate model outputs are generally considered too biased to be used as direct inputs in environmental models for climate change impact studies," citing in this regard the studies of Sharma et al. (2007), Christensen et al. (2008), Maraun et al. (2010) and Chen et al. (2013a). And in previous attempts to overcome this problem, they say "several bias correction methods have been developed and used in hundreds of climate change impact studies," citing as examples the studies of Mpelasoka and Chiew (2009), Johnson and Sharma (2011), Teutschbein and Seibert (2012), Theme▀l et al. (2011) and Chen et al. (2013b).

The approach taken by Chen et al. (2015) in addressing this particular subject was to test the bias stationarity of several climate model outputs over Canada and the contiguous Unites States by comparing model outputs with corresponding observations over two 20-year periods (1961-1980 and 1981-2000)," because, as they continue, "all bias correction approaches ranging from simple scaling to sophisticated distribution mapping are based on an assumption that climate model biases are stationary over time," citing Hewitson and Crane (2006), Piani et al. (2010), Maraun (2012) and Maurer et al. (2013). But is this truly the case?

In the study the three researchers conducted to explore this simple but important question, they found that such was not the case, and that "the typical 10 to 20% projected precipitation change in many impact studies ... is possibly of the same magnitude as the uncertainty error brought in by the assumption of bias stationarity," which leaves this particular field of scientific endeavor (climate modeling) in a world of hurt.

Chen, J., Brissette, F.P., Chaumont, D. and Braun, M. 2013a. Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America. Water Resources Research 49: 4187-4205.

Chen, J., Brissette, F.P., Chaumont, D. and Braun, M. 2013b. Performance and uncertainty evaluation of empirical downscaling methods in quantifying the climate change impacts on hydrology over two North America river basins. Journal of Hydrology 479: 200-214.

Christensen, J.H., Boberg, F., Christensen, O.B. and Lucas-Picher, P. 2008. On the need for bias correction of regional climate change projections of temperature and precipitation. Geophysical Research Letters 35: 10.1029/2008GL035694.

Hewitson, B.C. and Crane, R.G. 2006. Consensus between GCM climate change projections with empirical downscaling: Precipitation downscaling over South Africa. International Journal of Climatology 26: 1315-1337.

Johnson, F. and Sharma, A. 2011. Accounting for interannual variability: A comparison of options for water resources climate change impact assessments. Water Resources Research 47: 10.1029/2010WR009272.

Maraun, D. 2012. Nonstationarities of regional climate model biases in European seasonal mean temperature and precipitation sums. Geophysical Research Letters 39: 10.1029/2012GL051210.

Maraun, D., F. Wetterhall, F., Ireson, A.M., Chandler, R.E., Kendon, E.J., Widmann, M., Brienen, S., Rust, H.W., Sauter, T., Theme▀l, M., Venema, V.K.C., Chun, K.P., Goodess, C.M., Jones, R.G., Onof, C., Vrac, M. and Thiele-Eich, I. 2010. Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user. Reviews of Geophysics 48: 10.1029/2009RG000314.

Maurer, E.P., Das, T. and Cayan, D.R. 2013. Errors in climate model daily precipitation and temperature output: Time invariance and implications for bias correction. Hydrology and Earth Systems Science 17: 2147-2159.

Mpelasoka, F.S. and Chiew, F.H.S. 2009. Influence of rainfall scenario construction methods on runoff projections. Journal of Hydrometeorology 10: 1168-1183.

Piani, C., Haerter, J.O. and Coppola, E. 2010. Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and Applied Climatology 99: 187-192.

Sharma, D., Das Gupta, A. and Babel, M.S. 2007. Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand. Hydrology and Earth System Sciences 11: 1373-1390.

Teutschbein, C. and Seibert, J. 2012. Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. Journal of Hydrology 456-457: 12-29.

Theme▀l, M.J., Gobiet, A. and Leuprecht, A. 2011. Empirical statistical downscaling and error correction of daily precipitation from regional climate models. International Journal of Climatology 31: 1530-1544.

Posted 19 June 2015