By Melissa Gervais
Two articles recently published in the July edition of journal of Climate (Gervais et al. 2014a, 2014b) examine the representation of precipitation in observations and Global Climate Models (GCMs). In the first article, the impact of station density on errors in the production of gridded precipitation data is explored. An experiment was conducted in which station data in the United States were gridded repeatedly with subsequently fewer input stations. Two distinct error responses to loss of station density were found to characterize the western and the eastern United States. These were attributed to the relative spatial homogeneity of precipitation in these regions. As the station density decreases, the influence of stations farther from the analysis point increases, and therefore, if the distributions are inhomogeneous in space, the analysis point is influenced by stations with very different precipitation distributions. Upper and lower bounds on these errors distributions across the entire United States can be used to estimate the range of errors associated with a given station density.
Figure 1: Percent climatological error of JA and JF extreme precipitation (1979–2003) in the Rockies and Gulf regions, for all HRES grid boxes in a region and removal steps, as a function of station density (number of stations per 0.9° × 1.25° box). The colour of the symbols represents the concentration of climatological error points within 1% error bins, for a given station density.
The second article looks at the ability of a GCM to represent the full distribution of precipitation in North America. As part of this analysis, the upper and lower bounds of errors determined in Gervais et al. 2014a were applied to a gridded precipitation dataset including both the contiguous US and Canada. Due to the low density of station observations in much of Canada, the range of errors due to station density are very large, on the order of 50% for extreme precipitation.
Figure 2: (a),(c) Upper and (b),(d) lower bound on the percent bias in climatological annual (a),(b) median and (c),(d) extreme precipitation for the NAAP data using an experimentally derived relationship between upper and lower errors bounds and station density found in Gervais et al. (2014). Note that the colour scales are reversed between the upper and lower bound maps such that the magnitude of the colour schemes are identical but in opposing directions.
For more details please see:
Gervais, Melissa, L. Bruno Tremblay, John R. Gyakum, Eyad Atallah, 2014: Representing Extremes in a Daily Gridded Precipitation Analysis over the United States: Impacts of Station Density, Resolution, and Gridding Methods. J. Climate, 27, 5201–5218.
Gervais, Melissa, John R. Gyakum, Eyad Atallah, L. Bruno Tremblay, Richard B. Neale, 2014: How Well Are the Distribution and Extreme Values of Daily Precipitation over North America Represented in the Community Climate System Model? A Comparison to Reanalysis, Satellite, and Gridded Station Data. J. Climate, 27, 5219–5239.
-Melissa Gervais, CanSISE Graduate Student