| description abstract | Projected climatic warming has direct implications for future disturbance regimes, particularly fire-dominated ecosystems at high latitudes, where climate warming is expected to be most dramatic. It is important to ascertain the potential range of climate change impacts on terrestrial ecosystems, which is relevant to making projections of the response of the Earth system and to decisions by policymakers and land managers. Computer simulation models that explicitly model climate?fire relationships represent an important research tool for understanding and projecting future relationships. Retrospective model analyses of ecological models are important for evaluating how to effectively couple ecological models of fire dynamics with climate system models. This paper uses a transient landscape-level model of vegetation dynamics, Alaskan Frame-based Ecosystem Code (ALFRESCO), to evaluate the influence of different driving datasets of climate on simulation results. Our analysis included the use of climate data based on first-order weather station observations from the Climate Research Unit (CRU), a statistical reanalysis from the NCEP?NCAR reanalysis project (NCEP), and the fifth-generation Pennsylvania State University?NCAR Mesoscale Model (MM5). Model simulations of annual area burned for Alaska and western Canada were compared to historical fire activity (1950?2000). ALFRESCO was only able to generate reasonable simulation results when driven by the CRU climate data. Simulations driven by the NCEP and MM5 climate data produced almost no annual area burned because of substantially colder and wetter growing seasons (May?September) in comparison with the CRU climate data. The results of this study identify the importance of conducting retrospective analyses prior to coupling ecological models of fire dynamics with climate system models. The authors? suggestion is to develop coupling methodologies that involve the use of anomalies from future climate model simulations to alter the climate data of more trusted historical climate datasets. | |
