Author Description

"Carbon moves through the atmosphere, through the oceans, onto land, and into ecosystems. This cycling has a large effect on climate - changing geographic patterns of rainfall and the frequency of extreme weather - and is altered as the use of fossil fuels adds carbon to the cycle. The dynamics of this global carbon cycling are largely predicted over broad spatial scales and long periods of time by Earth system models. This book addresses the crucial question of how to assess, evaluate, and estimate the potential impact of the additional carbon to the land carbon cycle. The contributors describe a set of new approaches to land carbon cycle modeling for better exploring ecological questions regarding changes in carbon cycling; employing data assimilation techniques for model improvement; doing real- or near-time ecological forecasting for decision support; combining newly available machine learning techniques with process-based models to improve prediction of land carbon cycle under climate change. This new edition includes 7 new chapters on machine learning and its applications to carbon cycle research (5 chapters). on principles underlying carbon dioxide removal from the atmosphere (1 chapter), a contemporary active research and management issue, and on community infrastructure for ecological forecasting"--