Doubled CO2 precipitation changes for the Susquehanna basin: Down-scaling from the GENESIS general circulation model

Robert G. Crane, Bruce C. Hewitson

Research output: Contribution to journalArticle

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Artificial neural nets are used in an empirical down-scaling procedure to derive daily subgrid-scale precipitation from general circulation model (GCM) geopotential height and specific humidity data. The neural net-based transfer functions are developed using a 2° × 2.5° gridded data assimilation product from the Goddard Space Flight Center, applied to a 4 × 4 matrix of grid-cells centred on the Susquehanna river basin. The down-scaled precipitation is a close match to the observed data (temporal correlations at individual grid-points range from 0.6 to 0.84). Doubled CO2 climate change scenarios are produced by applying the same transfer functions to the geopotential height and specific humidity fields from 1 × CO2 and 2 × CO2 simulations of version II of the GENESIS climate model. The analysis indicates a 32 per cent increase in spring and summer rainfall over the basin, resulting from changes in both moisture availability and the orientation of the storm track over the region. The down-scaled precipitation increases, derived from the change in the GCM's circulation and humidity fields, are considerably larger than the change in the model's actual computed precipitation.

Original languageEnglish (US)
Pages (from-to)65-76
Number of pages12
JournalInternational Journal of Climatology
Issue number1
Publication statusPublished - Jan 1 1998


All Science Journal Classification (ASJC) codes

  • Atmospheric Science

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