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Michael Ryan

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Tropical forests contain > 50% of the world’s known species (Heywood 1995), 55% of global forest biomass (Pan et al. 2011), and exchange more carbon (C), water and energy with the atmosphere than any other ecosystem type (e.g., Saugier et al. 2001). Despite their importance, there is more uncertainty associated with predictions of how tropical forests will respond to warming than for any other biome (Randerson et al. 2009). This uncertainty is of global concern due to the large quantity of C cycled by these forests and the high potential for biodiversity loss. Given the importance of tropical forests, decision makers and land managers around the globe need increased predictive capacity regarding how tropical forests...
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Abstract For more accurate projections of both the global carbon (C) cycle and the changing climate, a critical current need is to improve the representation of tropical forests in Earth system models. Tropical forests exchange more C, energy, and water with the atmosphere than any other class of land ecosystems. Further, tropical-forest C cycling is likely responding to the rapid global warming, intensifying water stress, and increasing atmospheric CO2 levels. Projections of the future C balance of the tropics vary widely among global models. A current effort of the modeling community, the ILAMB (International Land Model Benchmarking) project, is to compile robust observations that can be used to improve the accuracy...
Categories: Publication; Types: Citation
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Summary Changes in tropical forest carbon sink strength during El Niño Southern Oscillation (ENSO) events can indicate future behavior under climate change. Previous studies revealed ˜6 Mg C ha-1 yr-1 lower net ecosystem production (NEP) during ENSO year 1998 compared with non-ENSO year 2000 in a Costa Rican tropical rainforest. We explored environmental drivers of this change and examined the contributions of ecosystem respiration (RE) and gross primary production (GPP) to this weakened carbon sink. For 1998-2000, we estimated RE using chamber-based respiration measurements, and we estimated GPP in two ways: using (1) the canopy process model MAESTRA, and (2) combined eddy covariance and chamber respiration data....
Categories: Publication; Types: Citation
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