Abstract
The ability to monitor, understand, and predict the dynamics of the terrestrial carbon cycle requires the capacity to robustly and coherently synthesize multiple streams of information that each provide partial information about different pools and fluxes. In this study, we introduce a new terrestrial carbon cycle data assimilation system, built on the PEcAn model-data eco-informatics system, and its application for the development of a proof-of-concept carbon "reanalysis" product that harmonizes carbon pools (leaf, wood, soil) and fluxes (GPP, Ra, Rh, NEE) across the contiguous United States from 1986–2019. We first calibrated this system against plant trait and flux tower Net Ecosystem Exchange (NEE) using a novel emulated hierarchical Bayesian approach. Next, we extended the Tobit-Wishart Ensemble Filter (TWEnF) State Data Assimilation (SDA) framework, a generalization of the common Ensemble Kalman Filter which accounts for censored data and provides a fully Bayesian estimate of model process error, to a regional-scale system with a calibrated localization. Combined with additional workflows for propagating parameter, initial condition, and driver uncertainty, this represents the most complete and robust uncertainty accounting available for terrestrial carbon models. Our initial reanalysis was run on an irregular grid of ~500 points selected using a stratified sampling method to efficiently capture environmental heterogeneity. Remotely sensed observations of aboveground biomass (Landsat LandTrendr) and LAI (MODIS MOD15) were sequentially assimilated into the SIPNET model. Reanalysis soil carbon, which was indirectly constrained based on modeled covariances, showed general agreement with SoilGrids, an independent soil carbon data product. Reanalysis NEE, which was constrained based on posterior ensemble weights, also showed good agreement with eddy flux tower NEE and reduced RMSE compared to the calibrated forecast. Ultimately, PEcAn's carbon cycle reanalysis provides a scalable framework for harmonizing multiple data constraints and providing a uniform synthetic platform for carbon monitoring, reporting, and verification (MRV) and accelerating terrestrial carbon cycle research.
Highlights
Accurate assessment of both biogenic carbon stocks and exchanges between the biosphere and atmosphere is crucial for a more complete carbon monitoring, reporting, and verification (MRV) framework, as well as understanding climate-carbon feedbacks. quantifying different components of the carbon cycle to identify whether different regions/landscapes are C sinks or sources has been a major challenge for earth scientists (Williams et al, 2005) as the sink strength of the terrestrial biosphere 25 is more variable than the ocean (Battle et al, 2000)
We report on our initial proof-of-concept reanalysis, which was constrained by MODIS leaf area index (LAI) and LandTrendr Aboveground Biomass (AGB)
Because 320 Net Ecosystem Exchange (NEE) is the difference between Gross Primary productivity (GPP) and total ecosystem respiration we expected that parameters that primarily regulate modeled GPP and respiration would be the most sensitive with respect to NEE
Summary
Accurate assessment of both biogenic carbon stocks and exchanges between the biosphere and atmosphere is crucial for a more complete carbon monitoring, reporting, and verification (MRV) framework, as well as understanding climate-carbon feedbacks. quantifying different components of the carbon cycle to identify whether different regions/landscapes are C sinks or sources has been a major challenge for earth scientists (Williams et al, 2005) as the sink strength of the terrestrial biosphere 25 is more variable than the ocean (Battle et al, 2000). Other products are based on calibrating remote sensing measurements against in-situ data, such as leaf area index (Liu et al, 2018) and aboveground biomass (Myneni et al, 2001), or quantify properties that correlate strongly with carbon stocks or changes in those stocks, such as land use/land cover (Schillaci et al, 2017) and disturbance (Liu et al, 2011). These data products describe particular processes or features of the terrestrial carbon cycle at various spatial scales and have been essential to 35 improving our understating of plant and soil processes. Often times, the full potential of these data sets, as well as their combination, is not fully exploited (Montzka et al, 2012)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
