Spatiotemporal Climate Model Validation - Case Studies for MM5 over Northwestern Canada and Alaska
Dates
Year
2007
Citation
Herzfeld, Ute C., Drobot, Sheldon, Wu, Wanli, Fowler, Charles, and Maslanik, James, 2007, Spatiotemporal Climate Model Validation - Case Studies for MM5 over Northwestern Canada and Alaska: Earth Interactions, v. 11, no. 20, p. 1-23.
Summary
The Western Arctic Linkage Experiment (WALE) is aimed at understanding the role of high-latitude terrestrial ecosystems in the response of the Arctic system to global change through collection and comparison of climate datasets and model results. In this paper, a spatiotemporal approach is taken to compare and validate model results from the fifth-generation Pennsylvania State University?National Center for Atmospheric Research Mesoscale Model (MM5) with commonly used analysis and reanalysis datasets for monthly averages of temperature and precipitation in 1992?2000 and for a study area at 55°?65°N, 160°?110°W in northwestern Canada and Alaska. Objectives include a quantitative assessment of similarity between datasets and climate [...]
Summary
The Western Arctic Linkage Experiment (WALE) is aimed at understanding the role of high-latitude terrestrial ecosystems in the response of the Arctic system to global change through collection and comparison of climate datasets and model results. In this paper, a spatiotemporal approach is taken to compare and validate model results from the fifth-generation Pennsylvania State University?National Center for Atmospheric Research Mesoscale Model (MM5) with commonly used analysis and reanalysis datasets for monthly averages of temperature and precipitation in 1992?2000 and for a study area at 55°?65°N, 160°?110°W in northwestern Canada and Alaska. Objectives include a quantitative assessment of similarity between datasets and climate model fields, and identification of geographic areas and seasons that are problematic in modeling, with potential causes that may aid in model improvement. These are achieved by application of algebraic similarity mapping, a simple yet effective method for synoptic analysis of many (here, 45) different spatial datasets, maps, and models. Results indicate a dependence of model?data similarity on seasonality, on climate variable, and on geographic location. In summary, 1) similarity of data and models is better for temperature than for precipitation; and 2) modeling of summer precipitation fields, and to a lesser extent, temperature fields, appears more problematic than that of winter fields. The geographic distribution of areas with best and worst agreement shifts throughout the year, with generally better agreement between maps and models in the northeastern and northern inland areas than in topographically complex and near-coastal areas. The study contributes to an understanding of the geographic complexity of the Arctic system and modeling its diverse climate.; Abstract The Western Arctic Linkage Experiment (WALE) is aimed at understanding the role of high-latitude terrestrial ecosystems in the response of the Arctic system to global change through collection and comparison of climate datasets and model results. In this paper, a spatiotemporal approach is taken to compare and validate model results from the fifth-generation Pennsylvania State University?National Center for Atmospheric Research Mesoscale Model (MM5) with commonly used analysis and reanalysis datasets for monthly averages of temperature and precipitation in 1992?2000 and for a study area at 55°?65°N, 160°?110°W in northwestern Canada and Alaska. Objectives include a quantitative assessment of similarity between datasets and climate model fields, and identification of geographic areas and seasons that are problematic in modeling, with potential causes that may aid in model improvement. These are achieved by application of algebraic similarity mapping, a simple yet effective method for synoptic analysis of many (here, 45) different spatial datasets, maps, and models. Results indicate a dependence of model?data similarity on seasonality, on climate variable, and on geographic location. In summary, 1) similarity of data and models is better for temperature than for precipitation; and 2) modeling of summer precipitation fields, and to a lesser extent, temperature fields, appears more problematic than that of winter fields. The geographic distribution of areas with best and worst agreement shifts throughout the year, with generally better agreement between maps and models in the northeastern and northern inland areas than in topographically complex and near-coastal areas. The study contributes to an understanding of the geographic complexity of the Arctic system and modeling its diverse climate.
