Preprints
https://doi.org/10.5194/gmd-2021-236
https://doi.org/10.5194/gmd-2021-236

Submitted as: development and technical paper 22 Oct 2021

Submitted as: development and technical paper | 22 Oct 2021

Review status: this preprint is currently under review for the journal GMD.

A novel model–data fusion approach to terrestrial carbon cycle reanalysis across the contiguous U.S using SIPNET and PEcAn state data assimilation system v. 1.7.2

Hamze Dokoohaki1, Bailey D. Morrison2, Ann Raiho3, Shawn P. Serbin2, and Michael Dietze4 Hamze Dokoohaki et al.
  • 1University of Illinois at Urbana-Champaign, Crop Science Department, Urbana-Champaign, IL, USA
  • 2Brookhaven National Laboratory, Environmental and Climate Sciences Department, Upton, NY, USA
  • 3Colorado State University, Fort Collins, CO, USA
  • 4Boston University, Earth and Environment Department, Boston, MA, USA

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.

Hamze Dokoohaki et al.

Status: open (until 17 Dec 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Hamze Dokoohaki et al.

Hamze Dokoohaki et al.

Viewed

Total article views: 430 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
330 96 4 430 2 2
  • HTML: 330
  • PDF: 96
  • XML: 4
  • Total: 430
  • BibTeX: 2
  • EndNote: 2
Views and downloads (calculated since 22 Oct 2021)
Cumulative views and downloads (calculated since 22 Oct 2021)

Viewed (geographical distribution)

Total article views: 403 (including HTML, PDF, and XML) Thereof 403 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 04 Dec 2021
Download
Short summary
We present 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. Here, we build on a decade of work on uncertainty propagation to generate the most complete and robust uncertainty accounting available to date.