Monitoring Precipitation over the Arctic Terrestrial Drainage System: Data Requirements, Shortcomings, and Applications of Atmospheric Reanalysis

Abstract

An effort is under way aimed at historical analysis and monitoring of the pan-Arctic terrestrial drainage system. A key element is the provision of gridded precipitation time series that can be readily updated. This has proven to be a daunting task. Except for a few areas, the station network is sparse, with large measurement biases due to poor catch efficiency of solid precipitation. The variety of gauges used by different countries along with different reporting practices introduces further uncertainty. Since about 1990, there has been serious degradation of the monitoring network due to station closure and a trend toward automation in Canada. Station data are used to compile monthly gridded time series for the 30-yr period 1960?89 at a cell resolution of 175 km. The station network is generally sufficient to estimate the mean and standard deviation of precipitation at this scale (hence the statistical distributions). However, as the interpolation procedures must typically draw from stations well outside of the grid box bounds, grid box time series are poorly represented. Accurately capturing time series requires typically four stations per 175-km cell, but only 38% of cells contain even a single station. Precipitation updates at about a 1-month time lag can be obtained by using the observed precipitation distributions to rescale precipitation forecasts from the NCEP-1 reanalysis via a nonparametric probability transform. While recognizing inaccuracies in the observed time series, cross-validated correlation analyses indicate that the rescaled NCEP-1 forecasts have considerable skill in some parts of the Arctic drainage, but perform poorly over large regions. Treating climatology as a first guess with replacement by rescaled NCEP-1 values in areas of demonstrated skill yields a marginally useful monitoring product on the scale of large watersheds. Further improvements are realized by assimilating data from a limited array of station updates via a simple replacement strategy, and by including aerological estimates of precipitation less evapotranspiration (P ? ET) within the initial rescaling procedure. Doing a better job requires better observations and an improved atmospheric model. The new ERA-40 reanalysis may fill the latter need.