A Comparison of CMIP3 Simulations of Precipitation over North America with Observations: Daily Statistics and Circulation Features Accompanying Extreme Events

Abstract

Climate model simulations of daily precipitation statistics from the third phase of the Coupled Model Intercomparison Project (CMIP3) were evaluated against precipitation observations from North America over the period 1979-99. The evaluation revealed that the models underestimate the intensity of heavy and extreme precipitation along the Pacific coast, southeastern United States, and southern Mexico, and these biases are robust among the models. The models also overestimate the intensity of light precipitation events over much of North America, resulting in fairly realistic mean precipitation in many places. In contrast, heavy precipitation is simulated realistically over northern and eastern Canada, as is the seasonal cycle of heavy precipitation over a majority of North America. An evaluation of the simulated atmospheric dynamics and thermodynamics associated with extreme precipitation events was also conducted using the North American Regional Reanalysis (NARR). The models were found to capture the large-scale physical mechanisms that generate extreme precipitation realistically, although they tend to overestimate the strength of the associated atmospheric circulation features. This suggests that climate model deficiencies such as insufficient spatial resolution, inadequate representation of convective precipitation, and overly smoothed topography may be more important for biases in simulated heavy precipitation than errors in the large-scale circulation during extreme events.

The role of meteorological processes in the description of uncertainty for climate change decision-making

“…The GCMs simulate precipitation amounts closest to the Maurer et al observations during October through April (cool season). This is evident by the models' relatively tight and even distribution of EP curves around the Maurer et al observations which is consistent with previous findings (Deangelis et al 2013). Model bias during the cool season increases for higher precipitation amounts, which means the GCMs have more difficulty capturing years with high precipitation.…”

Evaluation of daily precipitation statistics and monsoon onset/retreat over western Sahel in multiple data sets

“…Gutowski et al 2003;Iorio et al 2004;DeAngelis et al 2013), as are also some reanalyses (e.g. MERRA, ERA-Interim and CR20).…”

“…These daily-precipitation indices have been used widely to analyse extremes in observations (e.g., Frich et al 2002;Alexander et al 2006;Costa and Soares 2009;Donat et al 2013), in historical simulations and climate-change projections (e.g., Alexander and Arblaster 2009;DeAngelis et al 2013;Sillmann et al 2013a, b) and in Regional Climate Models (RCMs) simulations (e.g., Gao et al 2002;Im et al 2011;Sylla et al 2012;Roy et al 2012). The occurrence and duration indices are typically used in impact studies where fixed thresholds are related to local extremes, while percentile indices are generally employed in climatechange detection studies because they permit to compare changes in the same parts of the precipitation distribution over large regions (Costa and Soares 2009).…”

Trend in frequency of extreme precipitation events over Ontario from ensembles of multiple GCMs

“…The changes in the structure of global and regional atmospheric circulation caused by the warmer climate may affect the frequency of extreme precipitation events as well (Thomas 2009). Some studies show the increase in frequency of precipitation events in cold season is associated with increased atmospheric moisture, increased moisture convergence, and a poleward shift in mid-latitude cyclones activity (Christensen et al 2013;Grise and Polvani 2014). A possible reason of increase precipitation events over southern Ontario in warm season is the poleward movement of tropical cyclones as global warming continues (Jien and Gough 2013;Shawn et al 2009).…”