PREDICTED PHOTOSYNTHESIS RATES AND FIRE INCIDENCE FOR THE AMAZON RAINFOREST

Abstract

Tropical rainforests cycle vast quantities of water and carbon with the atmosphere. Photosynthesis moves carbon dioxide from the atmosphere to terrestrial storage, while fires reverse the exchange. Both processes affect the hydrologic cycle in rainforests, altering not only local humidity but also clouds that transport the consequences downwind. These complex interactions are why most earth system models (ESMs) include modules that simulate photosynthesis and fire. Accuracy of these modules’ equations affects the quality of climate predictions, and also predictions of the processes themselves in response to climate disruption. This dissertation assesses some aspects of the accuracy of the predictions and, for fire, suggests new and more accurate options. The studies focus on evergreen broadleaf forests in South America, which broadly overlap the Amazon River watershed. Chapter 2 provides equations that predict historic carbon monoxide emissions from Amazon rainforest fires for 2003–2018, which could be implemented within ESMs’ current structures. We also include equations to convert emissions to burned area. Chapter 3 quantifies the relative impact of deforestation and climate on fire for the Amazon rainforest. Both fire emissions and burned area are more abundant in approximate proportion to the prevalence of compromised or absent tree canopies. Changing climate exacerbates fire incidence also in proportion to forest openings, while meteorologic variability alone has little independent predictive power. Chapter 4 focuses on Amazon rainforest gross primary productivity (GPP), whose estimated rates differ by a factor of 2 across a suite of statistical and process models. Only one model's mean GPP falls within a 99% confidence interval for mean GPP at six eddy covariance tower sites. The model-to-data comparison focuses on a trade-off inherent to deterministic models between accurate simulation of variability and accurate responsiveness to drivers.