Assimilation of Global Satellite Leaf Area Estimates Reduces Modeled Global Carbon Uptake and Energy Loss by Terrestrial Ecosystems. Issue 8 (11th August 2022)

• Record Type: Journal Article
• Authors: Fox, Andrew M.; Huo, Xueli; Hoar, Timothy J.; Dashti, Hamid; Smith, William K.; MacBean, Natasha; Anderson, Jeffrey L.; Roby, Matthew; Moore, David J. P.
• Is Part Of: Journal of geophysical research. Volume 127:Issue 8(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-08-11
• Subjects: carbon cycle -- data assimilation -- community land model -- leaf area index; Geobiology -- Periodicals; Biogeochemistry -- Periodicals; Biotic communities -- Periodicals; Geophysics -- Periodicals; 577.14
• DOI: 10.1029/2022JG006830
• Languages: English
• ISSNs: 2169-8953

Abstract

Abstract: Carbon, water and energy exchange between the land and atmosphere controls how ecosystems either accelerate or ameliorate the effect of climate change. However, evaluating improvements to processes controlling carbon cycling, water use and energy exchange in global land surface models (LSMs) remains challenging in part because of persistent model errors in estimating leaf area. Here we evaluate the changes in global carbon, water and energy exchange brought about when a LSM prognostic estimates of leaf area are made consistent with estimates from satellites. This approach achieves two aims; first to quantify the effect of ignoring errors in leaf area index (LAI) on land‐atmosphere fluxes and second, to evaluate how closely this LSM replicates fluxes with and without an LAI constraint. We implemented an ensemble Kalman filter with spatiotemporal adaptive inflation to more closely match community land model (CLM5.0) estimates of leaf area to those from the Global Inventory Modeling and Mapping Studies leaf area index (LAI3g) product. We then evaluate the model's estimates of gross primary productivity (GPP) and latent heat flux (LE) against well established global estimates of these fluxes. We find that the model is biased high by 27% relative to the LAI3g product. Moreover, the effect of bias in LAI is substantial for GPP (18%) and LE (6%) and likely to confound efforts to refine processes controlling these fluxes. This data assimilation approach serves as a method to evaluate the efficacy of refinements to flux processes until the processes controlling the dynamics of LAI are better resolved in LSMs. Plain Language Summary: The climate system is influenced by the plants that grow on land. Leaves exchange carbon and water with the atmosphere and absorb and reflect energy. Over the whole globe it is difficult to predict when and how many leaves emerge and drop. Our global models disagree with each other and have errors and biases because of this. We forced a land surface model to agree with the leaf area estimated from satellite observations over an 11‐year period. When we did this, we found that carbon uptake and water loss went down on average. However the reductions did not occur everywhere equally; some regions saw much larger (nearly 50%) decreases in carbon and water exchange. Adjusting leaf area index (LAI) to match satellites did not lead to uniform improvements in forecasts of LAI on different vegetation types. It is likely that this model, and similar models, contain compensating errors in processes governing gross primary productivity (GPP) and leaf turnover. While we used only one estimate of the Earth's leaf area, there are several other estimates available. Using these and other datasets to bound the model estimates will likely improve estimates of the current carbon cycle and lead to better forecasts. Key Points: Assimilating satellite derived observations of leaf area on average reduced Community Land Model estimates of Leaf Area Index This reduced global estimates of gross primary production by 18% and latent heat flux by 6%, improving fit to independent data sets The persistence in improvements of model forecasts was highly dependent on plant functional type, enabling discovery of model errors