Development of an open-source regional data assimilation system in PEcAn v. 1.7.2: application to carbon cycle reanalysis across the contiguous US using SIPNET
Abstract
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 efficientlymore »
- Authors:
- Publication Date:
- Research Org.:
- Brookhaven National Laboratory (BNL), Upton, NY (United States)
- Sponsoring Org.:
- USDOE; National Aeronautics and Space Administration (NASA)
- OSTI Identifier:
- 1864062
- Alternate Identifier(s):
- OSTI ID: 1869268
- Report Number(s):
- BNL-223004-2022-JAAM
Journal ID: ISSN 1991-9603
- Grant/Contract Number:
- SC0012704; 80NSSC17K0711
- Resource Type:
- Published Article
- Journal Name:
- Geoscientific Model Development (Online)
- Additional Journal Information:
- Journal Name: Geoscientific Model Development (Online) Journal Volume: 15 Journal Issue: 8; Journal ID: ISSN 1991-9603
- Publisher:
- Copernicus GmbH
- Country of Publication:
- Germany
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES
Citation Formats
Dokoohaki, Hamze, Morrison, Bailey D., Raiho, Ann, Serbin, Shawn P., Zarada, Katie, Dramko, Luke, and Dietze, Michael. Development of an open-source regional data assimilation system in PEcAn v. 1.7.2: application to carbon cycle reanalysis across the contiguous US using SIPNET. Germany: N. p., 2022.
Web. doi:10.5194/gmd-15-3233-2022.
Dokoohaki, Hamze, Morrison, Bailey D., Raiho, Ann, Serbin, Shawn P., Zarada, Katie, Dramko, Luke, & Dietze, Michael. Development of an open-source regional data assimilation system in PEcAn v. 1.7.2: application to carbon cycle reanalysis across the contiguous US using SIPNET. Germany. https://doi.org/10.5194/gmd-15-3233-2022
Dokoohaki, Hamze, Morrison, Bailey D., Raiho, Ann, Serbin, Shawn P., Zarada, Katie, Dramko, Luke, and Dietze, Michael. Wed .
"Development of an open-source regional data assimilation system in PEcAn v. 1.7.2: application to carbon cycle reanalysis across the contiguous US using SIPNET". Germany. https://doi.org/10.5194/gmd-15-3233-2022.
@article{osti_1864062,
title = {Development of an open-source regional data assimilation system in PEcAn v. 1.7.2: application to carbon cycle reanalysis across the contiguous US using SIPNET},
author = {Dokoohaki, Hamze and Morrison, Bailey D. and Raiho, Ann and Serbin, Shawn P. and Zarada, Katie and Dramko, Luke and Dietze, Michael},
abstractNote = {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 leaf area index (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 root mean square error (RMSE) compared to the calibrated forecast. Ultimately, PEcAn's new open-source regional data assimilation framework provides a scalable workflow for harmonizing multiple data constraints and providing a uniform synthetic platform for carbon monitoring, reporting, and verification (MRV) as well as accelerating terrestrial carbon cycle research.},
doi = {10.5194/gmd-15-3233-2022},
journal = {Geoscientific Model Development (Online)},
number = 8,
volume = 15,
place = {Germany},
year = {Wed Apr 20 00:00:00 EDT 2022},
month = {Wed Apr 20 00:00:00 EDT 2022}
}
https://doi.org/10.5194/gmd-15-3233-2022
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