Supplementary material to "Three decades of simulated global terrestrial carbon fluxes from a data assimilation system confronted to different periods of observations"

Abstract. A Land Data Assimilation System (LDAS) able to ingest surface soil moisture (SSM) and Leaf Area Index (LAI) observations is tested at local scale to increase prediction accuracy for water and carbon fluxes. The ISBA-A-gs Land Surface Model (LSM) is used together with LAI and the soil water content observations of a grassland at the SMOSREX experimental site in southwestern France for a seven-year period (2001–2007). Three configurations corresponding to contrasted model errors are considered: (1) best case (BC) simulation with locally observed atmospheric variables and model parameters, and locally observed SSM and LAI used in the assimilation, (2) same as (1) but with the precipitation forcing set to zero, (3) real case (RC) simulation with atmospheric variables and model parameters derived from regional atmospheric analyses and from climatological soil and vegetation properties, respectively. In configuration (3) two SSM time series are considered: the observed SSM using Thetaprobes, and SSM derived from the LEWIS L-band radiometer located 15 m above the ground. Performance of the LDAS is examined in the three configurations described above with either one variable (either SSM or LAI) or two variables (both SSM and LAI) assimilated. The joint assimilation of SSM and LAI has a positive impact on the carbon, water, and heat fluxes. It represents a greater impact than assimilating one variable (either LAI or SSM). Moreover, the LDAS is able to counterbalance large errors in the precipitation forcing given as input to the model.

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Supplementary material to "Evaluation of terrestrial pan-Arctic carbon cycling using a data-assimilation system"

10.5194/esd-2018-19-supplement ◽

2018 ◽

Author(s):

Efrén López-Blanco ◽

Jean-François Exbrayat ◽

Magnus Lund ◽

Torben R. Christensen ◽

Mikkel P. Tamstorf ◽

...

Keyword(s):

Data Assimilation ◽

Carbon Cycling ◽

Supplementary Material ◽

Data Assimilation System ◽

Assimilation System

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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

10.5194/gmd-2021-236 ◽

2021 ◽

Author(s):

Hamze Dokoohaki ◽

Bailey D. Morrison ◽

Ann Raiho ◽

Shawn P. Serbin ◽

Michael Dietze

Keyword(s):

Data Assimilation ◽

Carbon Cycle ◽

Soil Carbon ◽

Model Data ◽

Terrestrial Carbon ◽

State Data ◽

Flux Tower ◽

Terrestrial Carbon Cycle ◽

Data Assimilation System ◽

Assimilation System

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.

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Investigating the usefulness of satellite-derived fluorescence data in inferring gross primary productivity within the carbon cycle data assimilation system

Biogeosciences ◽

10.5194/bg-12-4067-2015 ◽

2015 ◽

Vol 12 (13) ◽

pp. 4067-4084 ◽

Cited By ~ 50

Author(s):

E. N. Koffi ◽

P. J. Rayner ◽

A. J. Norton ◽

C. Frankenberg ◽

M. Scholze

Keyword(s):

Data Assimilation ◽

Carbon Cycle ◽

Primary Productivity ◽

Carbon Fluxes ◽

Gross Primary Productivity ◽

Sensitivity Tests ◽

Parameter Values ◽

Data Assimilation System ◽

Scope Model ◽

Assimilation System

Abstract. Simulations of carbon fluxes with terrestrial biosphere models still exhibit significant uncertainties, in part due to the uncertainty in model parameter values. With the advent of satellite measurements of solar induced chlorophyll fluorescence (SIF), there exists a novel pathway for constraining simulated carbon fluxes and parameter values. We investigate the utility of SIF in constraining gross primary productivity (GPP). As a first test we assess whether SIF simulations are sensitive to important parameters in a biosphere model. SIF measurements at the wavelength of 755 nm are simulated by the Carbon-Cycle Data Assimilation System (CCDAS) which has been augmented by the fluorescence component of the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model. Idealized sensitivity tests of the SCOPE model stand-alone indicate strong sensitivity of GPP to the carboxylation capacity (Vcmax) and of SIF to the chlorophyll AB content (Cab) and incoming short wave radiation. Low sensitivity is found for SIF to Vcmax, however the relationship is subtle, with increased sensitivity under high radiation conditions and lower Vcmax ranges. CCDAS simulates well the patterns of satellite-measured SIF suggesting the combined model is capable of ingesting the data. CCDAS supports the idealized sensitivity tests of SCOPE, with SIF exhibiting sensitivity to Cab and incoming radiation, both of which are treated as perfectly known in previous CCDAS versions. These results demonstrate the need for careful consideration of Cab and incoming radiation when interpreting SIF and the limitations of utilizing SIF to constrain Vcmax in the present set-up in the CCDAS system.

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