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Valentina Radić and Garry K. C. Clarke

Abstract

The authors analyze the performance of 22 Intergovernmental Panel on Climate Change (IPCC) global climate models (GCMs) over all of North America and its western subregion using several different evaluation metrics. They assess the model skill in simulating climatologies of several climate variables and the skill in simulating the daily synoptic patterns. The evaluation is performed by comparing the model output with the North American Regional Reanalysis (NARR) over the period 1980–99. One set of metrics, based on root-mean-square errors and variance ratios, compares modeled versus the NARR mean annual cycle and interannual variability. Based on these measures the three top performing models are the ECHAM5–Max Planck Institute Ocean Model (MPI-OM), the third climate configuration of the Met Office Unified Model (HadCM3), and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled General Circulation Model, version 3.1 [CGCM3.1(T47)]. Models that perform well over all North America also perform well over its western subregion. However, the model ranking is sensitive to the choice of climate variable. For another evaluation measure the method of self-organizing maps was applied to classify the characteristic daily patterns of sea level pressure over the region. The evaluation consists of correlating the frequencies of these patterns, as generated in GCMs, with the frequencies in the NARR over the baseline period. Most of the models are successful in simulating the frequencies of daily anomaly patterns from the 20-yr-average daily pattern. However, very few GCMs are able to reproduce the occurrences of characteristic daily weather patterns in the NARR on seasonal basis over the baseline period. In terms of relative performance, the three top performing models are the Meteorological Research Institute (MRI) CGCM2.3.2, ECHAM5–MPI-OM, and the Model for Interdisciplinary Research on Climate 3.2, high-resolution version [MIROC3.2(hires)]. The model skill in simulating daily synoptic patterns is not strongly linked to the skill in simulating the climatologies of selected variables. Despite the large scatter of model performance across all the metrics, some models consistently rank high [e.g., ECHAM5–MPI-OM and MIROC3.2(medres)]. Likewise, some models consistently rank low [e.g., the Community Climate System Model, version 3 (CCSM3) and the Goddard Institute for Space Studies Model E-R (GISS-ER)] independently of the evaluation measures, domain size, and climate variable of interest.

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Garry K. C. Clarke, Faron S. Anslow, Alexander H. Jarosch, Valentina Radić, Brian Menounos, Tobias Bolch, and Etienne Berthier

Abstract

A method is described to estimate the thickness of glacier ice using information derived from the measured ice extent, surface topography, surface mass balance, and rate of thinning or thickening of the ice column. Shear stress beneath an ice column is assumed to be simply related to ice thickness and surface slope, as for an inclined slab, but this calculation is cast as a linear optimization problem so that a smoothness regularization can be applied. Assignment of bed stress is based on the flow law for ice and a mass balance calculation but must be preceded by delineation of the ice flow drainage basin. Validation of the method is accomplished by comparing thickness estimates to the known thickness generated by a numerical ice dynamics model. Once validated, the method is used to estimate the subglacial topography for all glaciers in western Canada that lie south of 60°N. Adding the present ice volume of each glacier gives the estimated total volume as 2320 km3, equivalent to 5.8 mm of sea level rise. Taking the glaciated area as 26 590 km2 gives the average glacier thickness as 87.2 m. A detailed error analysis indicates that systematic errors are likely to increase the estimated sea level rise and when random errors are included the combined result is 6.3 ± 0.6 mm or, expressed as ice volume, 2530 ± 220 km3.

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