Through a combination of fossil fuel emissions, land use change, and other anthropogenic activities, mankind has dramatically altered global biogeochemical cycles, leading to an unprecedented era of rapid environmental change. To anticipate how the carbon and water cycles will change in the future, and inform decisions about how to adapt and mitigate these changes, we need a better understanding of the inherent predictability of these cycles. To begin to address this challenge I designed, implemented, and analyzed a 35-day iterative forecasting workflow using Harvard Forest as an initial testbed. A key aim of this forecast is to understand the predictability of leaf area index (LAI), net ecosystem exchange (NEE), and latent heat flux (LE), which I assess in terms of how forecast uncertainty changes as a function of forecast lead time, and how the predictability of LAI, NEE and LE is impacted by the assimilation of MODIS LAI observations. I used four metrics of uncertainty (root mean square error, bias, continuous ranked probability score, and mean absolute error) to evaluate the forecast performance. Uncertainty in LAI, LE, and NEE was not positively correlated with forecast lead time. The inclusion of MODIS LAI observations improved predictability of NEE and LE, but had the greatest impact on LAI (~50% uncertainty reduction). Carbon stores (LAI as a proxy for leaf carbon) were more predictable than terrestrial fluxes (NEE, LE).
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/49334 |
| Date | 25 September 2024 |
| Creators | Helgeson, Alexis Rose |
| Contributors | Dietze, Michael |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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