Data-model comparison using fuzzy logic in paleoclimatology

Until now, most paleoclimate model-data comparisons have been limited to si mple statistical evaluation and simple map comparisons. We have applied a n ew method, based on fuzzy logic, to the comparison of 17 model simulations of the mid-Holocene (6 ka BP) climate with reconstruction of three bioclima tic parameters (mean temperature of the coldest month, MTCO, growing degree -days above 5 degrees C, GDD5, precipitation minus evapotranspiration, P - E) from pollen and lake-status data over Europe. With this method, no assum ption is made about the distribution of the signal and on its error, and bo th the error bars related to data and to model simulations are taken into a ccount. Data are taken at the drilling sites (not using a gridded interpola tion of proxy data) and a varying domain size of comparison enables us to m ake the best common resolution between observed and simulated maps. For eac h parameter and each model, we compute a Hagaman distance which gives an ob jective measure of the goodness of fit between model and data. The results show that there is no systematic order for the three climatic parameters be tween models. None of the models is able to satisfactorily reproduce the th ree pollen-derived data. There is larger dispersion in the results for MTCO and P - E than for GDD5. There is also no systematic relationship between model resolution and the Hagaman distance, except for P - E. The more local character of P - E has little chance to be reproduced by a low resolution model, which can explain the inverse relationship between model resolution and Hagaman distance. The results also reveal that most of the models are b etter at predicting 6 ka climate than the modern climate.