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|CSDMS meeting abstract presentation=Montane Cloud Forests (MCFs) are globally relevant ecological zones that spend the majority of their growing season in cloud and fog. Prior eco-physiological studies have demonstrated that MCFs are incredibly efficient at assimilating CO2 during photosynthesis. This increased efficiency is attributed to how plants in these ecosystems operate within their unique microclimates. Specifically, MCF trees maintain high photosynthesis rates under fog and low cloud conditions. While this has been observed and quantified in lab and field experiments, current sub-models of plant-atmosphere interactions within Earth systems models (ESMs) cannot recreate enhanced levels of gas exchange measured in ecophysiology studies. This lack of understanding leads to high uncertainty in ESM estimates of evapotranspiration and carbon assimilation rates for MCF ecosystems. It is critical to improve our estimates of MCF hydrologic and photosynthetic processes as these ecosystems are vulnerable to drought and microclimatic conditions are likely to be altered by climate change. This talk will explore the gaps in our process-based understanding of water, energy, and carbon budgets for MCFs, how these gaps lead to uncertainties in ESMs at different spatial and temporal scales, and how we can address these gaps in future work. | |CSDMS meeting abstract presentation=Montane Cloud Forests (MCFs) are globally relevant ecological zones that spend the majority of their growing season in cloud and fog. Prior eco-physiological studies have demonstrated that MCFs are incredibly efficient at assimilating CO2 during photosynthesis. This increased efficiency is attributed to how plants in these ecosystems operate within their unique microclimates. Specifically, MCF trees maintain high photosynthesis rates under fog and low cloud conditions. While this has been observed and quantified in lab and field experiments, current sub-models of plant-atmosphere interactions within Earth systems models (ESMs) cannot recreate enhanced levels of gas exchange measured in ecophysiology studies. This lack of understanding leads to high uncertainty in ESM estimates of evapotranspiration and carbon assimilation rates for MCF ecosystems. It is critical to improve our estimates of MCF hydrologic and photosynthetic processes as these ecosystems are vulnerable to drought and microclimatic conditions are likely to be altered by climate change. This talk will explore the gaps in our process-based understanding of water, energy, and carbon budgets for MCFs, how these gaps lead to uncertainties in ESMs at different spatial and temporal scales, and how we can address these gaps in future work. | ||
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Latest revision as of 09:03, 22 May 2022
CSDMS 2022: Environmental Extremes and Earthscape Evolution
Modeling Eco-hydrologic Processes Across Scales in Montane Cloud Forests
Abstract
Montane Cloud Forests (MCFs) are globally relevant ecological zones that spend the majority of their growing season in cloud and fog. Prior eco-physiological studies have demonstrated that MCFs are incredibly efficient at assimilating CO2 during photosynthesis. This increased efficiency is attributed to how plants in these ecosystems operate within their unique microclimates. Specifically, MCF trees maintain high photosynthesis rates under fog and low cloud conditions. While this has been observed and quantified in lab and field experiments, current sub-models of plant-atmosphere interactions within Earth systems models (ESMs) cannot recreate enhanced levels of gas exchange measured in ecophysiology studies. This lack of understanding leads to high uncertainty in ESM estimates of evapotranspiration and carbon assimilation rates for MCF ecosystems. It is critical to improve our estimates of MCF hydrologic and photosynthetic processes as these ecosystems are vulnerable to drought and microclimatic conditions are likely to be altered by climate change. This talk will explore the gaps in our process-based understanding of water, energy, and carbon budgets for MCFs, how these gaps lead to uncertainties in ESMs at different spatial and temporal scales, and how we can address these gaps in future work.
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