Estimation of Asian and Global Carbon Fluxes Using Maximum Likelihood Ensemble Filter (MLEF)

2017 ◽  
Author(s):  
K.M.P. Perera ◽  
◽  
R.S. Lokupitiya ◽  
D. Zupanski ◽  
A.S. Denning ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Carbon Fluxes ◽  
Maximum Likelihood Ensemble Filter ◽  
Global Carbon

Estimation of Global Scale Carbon Fluxes Using Maximum Likelihood Ensemble Filter

2020 ◽  
Author(s):  
Manjula Perera ◽  
Ravindra Lokupitiya ◽  
Scott Denning ◽  
Prabir K. Patra ◽  
Dusanka Zupanski ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Carbon Fluxes ◽  
Global Scale ◽  
Maximum Likelihood Ensemble Filter

Maximum Likelihood Ensemble Filter: Theoretical Aspects

2005 ◽  
Vol 133 (6) ◽  
pp. 1710-1726 ◽  
Author(s):  
Milija Zupanski
Keyword(s):  
Maximum Likelihood ◽  
Data Assimilation ◽  
Cost Function ◽  
Ensemble Data Assimilation ◽  
Error Covariance ◽  
Ensemble Data ◽  
Analysis Error ◽  
Nonlinear Observation ◽  
The Cost ◽  
Maximum Likelihood Ensemble Filter

Abstract A new ensemble-based data assimilation method, named the maximum likelihood ensemble filter (MLEF), is presented. The analysis solution maximizes the likelihood of the posterior probability distribution, obtained by minimization of a cost function that depends on a general nonlinear observation operator. The MLEF belongs to the class of deterministic ensemble filters, since no perturbed observations are employed. As in variational and ensemble data assimilation methods, the cost function is derived using a Gaussian probability density function framework. Like other ensemble data assimilation algorithms, the MLEF produces an estimate of the analysis uncertainty (e.g., analysis error covariance). In addition to the common use of ensembles in calculation of the forecast error covariance, the ensembles in MLEF are exploited to efficiently calculate the Hessian preconditioning and the gradient of the cost function. A sufficient number of iterative minimization steps is 2–3, because of superior Hessian preconditioning. The MLEF method is well suited for use with highly nonlinear observation operators, for a small additional computational cost of minimization. The consistent treatment of nonlinear observation operators through optimization is an advantage of the MLEF over other ensemble data assimilation algorithms. The cost of MLEF is comparable to the cost of existing ensemble Kalman filter algorithms. The method is directly applicable to most complex forecast models and observation operators. In this paper, the MLEF method is applied to data assimilation with the one-dimensional Korteweg–de Vries–Burgers equation. The tested observation operator is quadratic, in order to make the assimilation problem more challenging. The results illustrate the stability of the MLEF performance, as well as the benefit of the cost function minimization. The improvement is noted in terms of the rms error, as well as the analysis error covariance. The statistics of innovation vectors (observation minus forecast) also indicate a stable performance of the MLEF algorithm. Additional experiments suggest the amplified benefit of targeted observations in ensemble data assimilation.

scholarly journals A global CO<sub>2</sub> flux dataset (2015–2019) inferred from OCO-2 retrievals using the Tan-Tracker inversion system

2021 ◽  
Author(s):  
Zhe Jin ◽  
Xiangjun Tian ◽  
Rui Han ◽  
Yu Fu ◽  
Xin Li ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Carbon Cycle ◽  
Carbon Flux ◽  
Carbon Budget ◽  
Terrestrial Ecosystem ◽  
Carbon Fluxes ◽  
Global Carbon Cycle ◽  
Ocean Carbon ◽  
Global Carbon ◽  
Regional Fluxes

Abstract. Accurate assessment of the various sources and sinks of carbon dioxide (CO2), especially terrestrial ecosystem and ocean fluxes with high uncertainties, is important for understanding of the global carbon cycle, supporting the formulation of climate policies, and projecting future climate change. Satellite retrievals of the column-averaged dry air mole fractions of CO2 (XCO2) are being widely used to improve carbon flux estimation due to their broad spatial coverage. However, there is no consensus on the robust estimates of regional fluxes. In this study, we present a global and regional resolved terrestrial ecosystem carbon flux (NEE) and ocean carbon flux dataset for 2015–2019. The dataset was generated using the Tan-Tracker inversion system by assimilating Observing Carbon Observatory 2 (OCO-2) column CO2 retrievals. The posterior NEE and ocean carbon fluxes were comprehensively validated by comparing posterior simulated CO2 concentrations with OCO-2 independent retrievals and Total Carbon Column Observing Network (TCCON) measurements. The validation showed that posterior carbon fluxes significantly improved the modelling of atmospheric CO2 concentrations, with global mean biases of 0.33 ppm against OCO-2 retrievals and 0.12 ppm against TCCON measurements. We described the characteristics of the dataset at global, regional, and Tibetan Plateau scales in terms of the carbon budget, annual and seasonal variations, and spatial distribution. The posterior 5-year annual mean global atmospheric CO2 growth rate was 5.35 PgC yr−1, which was within the uncertainty of the Global Carbon Budget 2020 estimate (5.49 PgC yr−1). The posterior annual mean NEE and ocean carbon fluxes were −4.07 and −3.33 PgC yr−1, respectively. Regional fluxes were analysed based on TransCom partitioning. All 11 land regions acted as carbon sinks, except for Tropical South America, which was almost neutral. The strongest carbon sinks were located in Boreal Asia, followed by Temperate Asia and North Africa. The entire Tibetan Plateau ecosystem was estimated as a carbon sink, taking up −49.52 TgC yr−1 on average, with the strongest sink occurring in eastern alpine meadows. These results indicate that our dataset captures surface carbon fluxes well and provides insight into the global carbon cycle. The dataset can be accessed at https://doi.org/10.11888/Meteoro.tpdc.271317 (Jin et al., 2021).

scholarly journals A Global Carbon Assimilation System using a modified EnKF assimilation method

2014 ◽  
Vol 7 (5) ◽  
pp. 6519-6547
Author(s):  
S. Zhang ◽  
X. Zheng ◽  
Z. Chen ◽  
B. Dan ◽  
J. M. Chen ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Carbon Assimilation ◽  
Terrestrial Ecosystem ◽  
Carbon Fluxes ◽  
Ecosystem Model ◽  
Atmospheric Co2 ◽  
Forecast Errors ◽  
Global Carbon ◽  
Assimilation System

Abstract. A Global Carbon Assimilation System based on Ensemble Kalman filter (GCAS-EK) is developed for assimilating atmospheric CO2 abundance data into an ecosystem model to simultaneously estimate the surface carbon fluxes and atmospheric CO2 distribution. This assimilation approach is based on the ensemble Kalman filter (EnKF), but with several new developments, including using analysis states to iteratively estimate ensemble forecast errors, and a maximum likelihood estimation of the inflation factors of the forecast and observation errors. The proposed assimilation approach is tested in observing system simulation experiments and then used to estimate the terrestrial ecosystem carbon fluxes and atmospheric CO2 distributions from 2002 to 2008. The results showed that this assimilation approach can effectively reduce the biases and uncertainties of the carbon fluxes simulated by the ecosystem model.

scholarly journals A Maximum Likelihood Ensemble Filter via a Modified Cholesky Decomposition for Non-Gaussian Data Assimilation

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 877 ◽  
Author(s):  
Elias David Nino-Ruiz ◽  
Alfonso Mancilla-Herrera ◽  
Santiago Lopez-Restrepo ◽  
Olga Quintero-Montoya
Keyword(s):  
Maximum Likelihood ◽  
General Circulation ◽  
Circulation Model ◽  
Cholesky Decomposition ◽  
Practical Implementation ◽  
Square Root ◽  
Background Error ◽  
Modified Cholesky Decomposition ◽  
Highly Nonlinear ◽  
Maximum Likelihood Ensemble Filter

This paper proposes an efficient and practical implementation of the Maximum Likelihood Ensemble Filter via a Modified Cholesky decomposition (MLEF-MC). The method works as follows: via an ensemble of model realizations, a well-conditioned and full-rank square-root approximation of the background error covariance matrix is obtained. This square-root approximation serves as a control space onto which analysis increments can be computed. These are calculated via Line-Search (LS) optimization. We theoretically prove the convergence of the MLEF-MC. Experimental simulations were performed using an Atmospheric General Circulation Model (AT-GCM) and a highly nonlinear observation operator. The results reveal that the proposed method can obtain posterior error estimates within reasonable accuracies in terms of ℓ − 2 error norms. Furthermore, our analysis estimates are similar to those of the MLEF with large ensemble sizes and full observational networks.

scholarly journals Regional CO<sub>2</sub> fluxes from 2010 to 2015 inferred from GOSAT XCO<sub>2</sub> retrievals using a new version of the Global Carbon Assimilation System

2021 ◽  
Vol 21 (3) ◽  
pp. 1963-1985
Author(s):  
Fei Jiang ◽  
Hengmao Wang ◽  
Jing M. Chen ◽  
Weimin Ju ◽  
Xiangjun Tian ◽  
...  
Keyword(s):  
Carbon Assimilation ◽  
Carbon Fluxes ◽  
Atmospheric Co2 ◽  
Interannual Variations ◽  
Carbon Sinks ◽  
Area Index ◽  
Spatial Coverage ◽  
The Impact ◽  
Global Carbon ◽  
Assimilation System

Abstract. Satellite retrievals of the column-averaged dry air mole fractions of CO2 (XCO2) could help to improve carbon flux estimation due to their good spatial coverage. In this study, in order to assimilate the GOSAT (Greenhouse Gases Observing Satellite) XCO2 retrievals, the Global Carbon Assimilation System (GCAS) is upgraded with new assimilation algorithms, procedures, a localization scheme, and a higher assimilation parameter resolution. This upgraded system is referred to as GCASv2. Based on this new system, the global terrestrial ecosystem (BIO) and ocean (OCN) carbon fluxes from 1 May 2009 to 31 December 2015 are constrained using the GOSAT ACOS (Atmospheric CO2 Observations from Space) XCO2 retrievals (Version 7.3). The posterior carbon fluxes from 2010 to 2015 are independently evaluated using CO2 observations from 52 surface flask sites. The results show that the posterior carbon fluxes could significantly improve the modeling of atmospheric CO2 concentrations, with global mean bias decreases from a prior value of 1.6 ± 1.8 ppm to −0.5 ± 1.8 ppm. The uncertainty reduction (UR) of the global BIO flux is 17 %, and the highest monthly regional UR could reach 51 %. Globally, the mean annual BIO and OCN carbon sinks and their interannual variations inferred in this study are very close to the estimates of CarbonTracker 2017 (CT2017) during the study period, and the inferred mean atmospheric CO2 growth rate and its interannual changes are also very close to the observations. Regionally, over the northern lands, the strongest carbon sinks are seen in temperate North America, followed by Europe, boreal Asia, and temperate Asia; in the tropics, there are strong sinks in tropical South America and tropical Asia, but a very weak sink in Africa. This pattern is significantly different from the estimates of CT2017, but the estimated carbon sinks for each continent and some key regions like boreal Asia and the Amazon are comparable or within the range of previous bottom-up estimates. The inversion also changes the interannual variations in carbon fluxes in most TransCom land regions, which have a better relationship with the changes in severe drought area (SDA) or leaf area index (LAI), or are more consistent with previous estimates for the impact of drought. These results suggest that the GCASv2 system works well with the GOSAT XCO2 retrievals and shows good performance with respect to estimating the surface carbon fluxes; meanwhile, our results also indicate that the GOSAT XCO2 retrievals could help to better understand the interannual variations in regional carbon fluxes.

scholarly journals Photoheterotrophy by aerobic anoxygenic bacteria modulates carbon fluxes in a freshwater lake

2021 ◽  
Author(s):  
Kasia Piwosz ◽  
Cristian Villena-Alemany ◽  
Izabela Mujakić
Keyword(s):  
Primary Production ◽  
Net Primary Production ◽  
Carbon Fluxes ◽  
Photochemical Reactions ◽  
Freshwater Lake ◽  
Infrared Light ◽  
Community Respiration ◽  
New Paradigm ◽  
Heterotrophic Microorganisms ◽  
Global Carbon

AbstractLakes are a significant component of the global carbon cycle. Respiration exceeds net primary production in most freshwater lakes, making them a source of CO2 to the atmosphere. Driven by heterotrophic microorganisms, respiration is assumed to be unaffected by light, thus it is measured in the dark. However, photoheterotrophs, such as aerobic anoxygenic photoheterotrophic (AAP) bacteria that produce ATP via photochemical reactions, substantially reduce respiration in the light. They are an abundant and active component of bacterioplankton, but their photoheterotrophic contribution to microbial community metabolism remains unquantified. We showed that the community respiration rate in a freshwater lake was reduced by 15.2% (95% confidence interval (CI): 6.6–23.8%) in infrared light that is usable by AAP bacteria but not by primary producers. Moreover, significantly higher assimilation rates of glucose (18.1%; 7.8–28.4%), pyruvate (9.5%; 4.2–14.8%), and leucine (5.9%; 0.1–11.6%) were measured in infrared light. At the ecosystem scale, the amount of CO2 from respiration unbalanced by net primary production was by 3.69 × 109 g CO2 lower over these two sampling seasons when measured in the infrared light. Our results demonstrate that dark measurements of microbial activity significantly bias the carbon fluxes, providing a new paradigm for their quantification in aquatic environments.

scholarly journals A global carbon assimilation system using a modified ensemble Kalman filter

2015 ◽  
Vol 8 (3) ◽  
pp. 805-816 ◽  
Author(s):  
S. Zhang ◽  
X. Zheng ◽  
J. M. Chen ◽  
Z. Chen ◽  
B. Dan ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Carbon Assimilation ◽  
Terrestrial Ecosystem ◽  
Carbon Fluxes ◽  
Ecosystem Model ◽  
Atmospheric Co2 ◽  
Forecast Errors ◽  
Global Carbon ◽  
Assimilation System

Abstract. A Global Carbon Assimilation System based on the ensemble Kalman filter (GCAS-EK) is developed for assimilating atmospheric CO2 data into an ecosystem model to simultaneously estimate the surface carbon fluxes and atmospheric CO2 distribution. This assimilation approach is similar to CarbonTracker, but with several new developments, including inclusion of atmospheric CO2 concentration in state vectors, using the ensemble Kalman filter (EnKF) with 1-week assimilation windows, using analysis states to iteratively estimate ensemble forecast errors, and a maximum likelihood estimation of the inflation factors of the forecast and observation errors. The proposed assimilation approach is used to estimate the terrestrial ecosystem carbon fluxes and atmospheric CO2 distributions from 2002 to 2008. The results show that this assimilation approach can effectively reduce the biases and uncertainties of the carbon fluxes simulated by the ecosystem model.

scholarly journals Are small mountainous tropical watersheds of oceanic islands important for carbon export?

2012 ◽  
Vol 9 (6) ◽  
pp. 7117-7163 ◽  
Author(s):  
E. Lloret ◽  
C. Dessert ◽  
E. Lajeunesse ◽  
O. Crispi ◽  
L. Pastor ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Water Level ◽  
Carbon Fluxes ◽  
Oceanic Islands ◽  
Carbon Export ◽  
Extreme Floods ◽  
Dissolved Carbon ◽  
Small Watersheds ◽  
Tropical Watersheds ◽  
Global Carbon

Abstract. In the tropic, the small watersheds are affected by intense meteorological events playing an important role on the erosion of soils and therefore on the associated organic carbon fluxes. We studied the geochemistry of three small watersheds around the Basse-Terre volcanic Island (FWI) during a four years period, by measuring DOC, POC and DIC concentrations. The mean annual yields ranged 8.1–15.8 t C km−2 yr−1, 1.9–8.6 t C km−2 yr−1 and 8.1–25.5 t C km−2 yr−1 for DIC, DOC and POC, respectively. Floods and extreme floods represent 45 to 70 % of the annual DOC flux, and more than 80 % of the annual POC flux. The DIC flux occurs essentially during the low water level, only 43 % of the annual DIC flux is exported during floods. The distribution of the dissolved carbon between the inorganic and the organic fraction is correlated to the hydrodynamic of rivers. During low water level and floods, the dissolved carbon is exported under the inorganic form (DIC/DOC = 2.6 ± 2.1), while during extreme floods, the dissolved carbon transported is mostly organic (DIC/DOC = 0.7 ± 0.2). The residence time of the organic carbon in Guadeloupean soils may vary from 381 to 1000 yr, and is linked to the intensity of meteorological events than the frequency of meteorological events. Looking at the global carbon mass balance, the total export of organic carbon coming from small tropical and volcanic mountainous rivers is estimated about 2.0–8.9 Tg C yr−1 for DOC and about 8.4–26.5 Tg C yr−1 for POC, emphasizing that these carbon fluxes are significant and should be included in global carbon budgets.

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