Using high-resolution simulated climate projections in forest process-based modelling

Abstract

Forest management decisions often rely on forest growth process based models. These models require climate data at a time-scale and a time-frame that is frequently not available in the area of interest. With the purpose of evaluating the use of modelled climate as a replacement for observational data, we compared the performance (efficiency, precision and bias) of a forest growth process based model (3-PG) when the inputs of the observational climate data were replaced by modelled climate data. Based on previous research, we focused on two promising regional climate models: 1) the Regional Atmospheric Climate Model (RACMO) and 2) the Weather Research and Forecast Modelling System and Program (WRF).Results suggest that when using simulated climate data there are minor losses of performance in the forest growth model predictions with a general growth overestimation, with RACMO providing the best results. A deeper analysis suggests that improving the temperature accuracy of the model will reduce the overestimation of the predictions.The use of simulated climate data with RACMO and WRF is therefore recommended when observed climate is scarce or inexistent. The use of these datasets can certainly widen the usage of forest growth process based models, improving the support for decision-making in forest management, especially when considering climate change, one of the cornerstones for which modelled climate is developed.