A statistical modeling approach for the simulation of local paleoclimatic proxy records using general circulation model output

Abstract

A statistical modeling approach is proposed for the simulation of local paleoclimatic proxy records using general circulation model (GCM) output. A method for model‐consistent statistical downscaling to local weather conditions is developed which can be used as input for process‐based proxy models in order to investigate to what extent climate variability obtained from proxy data can be represented by a GCM, and whether, for example, the response of glaciers to climatic change can be reproduced. Downscaling is based on a multiple linear forward regression model using daily sets of operational weather station data and large‐scale predictors at various pressure levels obtained from reanalyses of the European Centre for Medium‐Range Weather Forecasts. Composition and relative impact of predictors vary significantly for individual stations within the area of investigation. Owing to a strong dependence on individual synoptic‐scale patterns, daily data give the highest performance which can be further increased by developing seasonal‐specific relationships. The model is applied to a long integration of a GCM coupled to a mixed layer ocean (ECHAM4/MLO) simulating present‐day and preindustrial climate variability. Patterns of variability are realistically simulated compared to observed station data within an area of Norway for the period 1868–1993.