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Spatial-Scale Characteristics of Precipitation Simulated by Regional Climate Models and the Implications for Hydrological Modeling

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  • 1 Danish Meteorological Institute, Copenhagen, Denmark
  • | 2 National Center for Atmospheric Research, Boulder, Colorado
  • | 3 Geological Survey of Denmark and Greenland (GEUS), Copenhagen, Denmark
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Abstract

Precipitation simulated by regional climate models (RCMs) is generally biased with respect to observations, especially at the local scale of a few tens of kilometers. This study investigates how well two different RCMs are able to reproduce the spatial correlation patterns of observed summer precipitation for the central United States. On local scales, gridded precipitation observations and simulated precipitation are compared for the period of the 1987 First International Satellite Land Surface Climatological Project Field Experiment (FIFE) campaign. The results show that spatial correlation length scales on the order of 130 km are found in both observed data and RCM simulations. When simulations and observations are aggregated to different grid sizes, the pattern correlation significantly decreases when the aggregation length is less than roughly 100 km. Furthermore, the intermodel standard deviation between simulations with different domains or resolutions increases for aggregation lengths below ~130 km. Below this length scale there is a high level of randomness in the precise location of precipitation events. Conversely, spatial correlation values increase above this length scale, reflecting larger predictive certainty of the RCMs at larger scales. The findings on aggregated grid scales are shown to be largely independent of the underlying RCMs grid resolutions but not of the overall size of RCM domain. With regard to hydrological modeling applications, these findings indicate that precipitation extracted from the present RCM simulations at a catchment scale below the intermodel standard deviation length cannot be expected to accurately match observations.

Current affiliation: DTU Climate Centre, Roskilde, Denmark.

Corresponding author address: S. H. Rasmussen, Danish Meteorological Institute, Lyngbyvej 100, DK-2100 Copenhagen, Denmark. E-mail: sohr@dmi.dk

Abstract

Precipitation simulated by regional climate models (RCMs) is generally biased with respect to observations, especially at the local scale of a few tens of kilometers. This study investigates how well two different RCMs are able to reproduce the spatial correlation patterns of observed summer precipitation for the central United States. On local scales, gridded precipitation observations and simulated precipitation are compared for the period of the 1987 First International Satellite Land Surface Climatological Project Field Experiment (FIFE) campaign. The results show that spatial correlation length scales on the order of 130 km are found in both observed data and RCM simulations. When simulations and observations are aggregated to different grid sizes, the pattern correlation significantly decreases when the aggregation length is less than roughly 100 km. Furthermore, the intermodel standard deviation between simulations with different domains or resolutions increases for aggregation lengths below ~130 km. Below this length scale there is a high level of randomness in the precise location of precipitation events. Conversely, spatial correlation values increase above this length scale, reflecting larger predictive certainty of the RCMs at larger scales. The findings on aggregated grid scales are shown to be largely independent of the underlying RCMs grid resolutions but not of the overall size of RCM domain. With regard to hydrological modeling applications, these findings indicate that precipitation extracted from the present RCM simulations at a catchment scale below the intermodel standard deviation length cannot be expected to accurately match observations.

Current affiliation: DTU Climate Centre, Roskilde, Denmark.

Corresponding author address: S. H. Rasmussen, Danish Meteorological Institute, Lyngbyvej 100, DK-2100 Copenhagen, Denmark. E-mail: sohr@dmi.dk
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