Assessing climate model simulations of storm track variability
Xiaoming Xia, SUNY, Stony Brook, NY; and E. K. M. Chang
With increasing greenhouse gases, it is generally accepted that global temperature will increase in the foreseeable future. However, how that impacts regional climate is still not entirely clear. Regional climate impacts, especially during the cool season in the mid-latitudes, depend critically on how the storm tracks change. Several recent studies have suggested that the mid-latitude storm tracks are predicted to shift poleward based on analyses of the IPCC AR4 experiments. Can we trust these model simulations in terms of this poleward shift of storm tracks? Before we go further into the exploration of the real mechanism behind such a response under global warming, it is important to assess the performance of IPCC AR4 models.
Therefore in this study the quality of IPCC AR4 20th century climate experiments has been investigated. If they are good, we could have more confidence in the prediction of the climate models. Most previous attempts to assess the quality of climate model simulations focus on validating the simulation of the mean flow. In this study we will examine the variability of the storm tracks instead of just the storm track climatology.
Empirical orthogonal function (EOF) analysis has been performed to examine the principle modes of month-to-month variability of the wintertime storm tracks and mean flow for NCEP/NCAR and ECMWF reanalysis data and IPCC AR4 models output. The analysis is based on 120-month long IPCC models output and 86-month NCEP/NCAR reanalysis data for the Northern Hemisphere winter (December-January-February). Storm track variables are evaluated in terms of monthly mean 24-hour filtered momentum flux, heat flux and meridional wind variance produced from daily data. The mean flow is identified by upper troposphere streamfunction monthly mean anomalies. In addition to the analysis of individual field by EOF analysis, the relationship between storm tracks and mean flow found in reanalysis and model output is investigated by applying singular value decomposition (SVD) analysis on the covariance matrix between a storm track and a mean flow field.
In this study, we use Taylor diagram, which provides a concise statistical summary of how well patterns match each other in terms of their correlation, their root-mean-square difference, and the ratio of their variances, to demonstrate the statistical comparison between the model outputs and reanalysis data. All the analyses suggest that most of the IPCC AR4 models capture the mean flow and storm tracks variability quite well, including the co-variability between the two fields,. The uncertainties of model simulations are consistent with the uncertainty of reanalysis data before satellite (before 1979), compared with the reference defined as NCEP reanalysis after satellite. We also compare these IPCC model outputs with simulations from a model used in the AR3 assessment and find significant improvements.
Session 12A, Seasonal-interannual variability II
Thursday, 15 January 2009, 8:45 AM-9:45 AM, Room 129A
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