scholarly journals A joint data assimilation system (Tan-Tracker) to simultaneously estimate surface CO<sub>2</sub> fluxes and 3-D atmospheric CO<sub>2</sub> concentrations from observations

2013 ◽  
Vol 13 (9) ◽  
pp. 24755-24784 ◽  
Author(s):  
X. Tian ◽  
Z. Xie ◽  
Y. Liu ◽  
Z. Cai ◽  
Y. Fu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Dynamical Model ◽  
Model Parameters ◽  
Co2 Concentrations ◽  
Atmospheric Observations ◽  
Atmospheric Transport Model ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. To quantitatively estimate CO2 surface fluxes (CFs) from atmospheric observations, a joint data assimilation system ("Tan-Tracker") is developed by incorporating a joint data assimilation framework into the GEOS-Chem atmospheric transport model. In Tan-Tracker, we choose an identity operator as the CF dynamical model to describe the CFs' evolution, which constitutes an augmented dynamical model together with the GEOS-Chem atmospheric transport model. In this case, the large-scale vector made up of CFs and CO2 concentrations is taken as the prognostic variable for the augmented dynamical model. And thus both CO2 concentrations and CFs are jointly assimilated by using the atmospheric observations (e.g., the in-situ observations or satellite measurements). In contrast, in the traditional joint data assimilation frameworks, CFs are usually treated as the model parameters and form a state-parameter augmented vector jointly with CO2 concentrations. The absence of a CF dynamical model will certainly result in a large waste of observed information since any useful information for CFs' improvement achieved by the current data assimilation procedure could not be used in the next assimilation cycle. Observing system simulation experiments (OSSEs) are carefully designed to evaluate the Tan-Tracker system in comparison to its simplified version (referred to as TT-S) with only CFs taken as the prognostic variables. It is found that our Tan-Tracker system is capable of outperforming TT-S with higher assimilation precision for both CO2 concentrations and CO2 fluxes, mainly due to the simultaneous assimilation of CO2 concentrations and CFs in our Tan-Tracker data assimilation system.

scholarly journals A joint data assimilation system (Tan-Tracker) to simultaneously estimate surface CO<sub>2</sub> fluxes and 3-D atmospheric CO<sub>2</sub> concentrations from observations

2014 ◽  
Vol 14 (23) ◽  
pp. 13281-13293 ◽  
Author(s):  
X. Tian ◽  
Z. Xie ◽  
Y. Liu ◽  
Z. Cai ◽  
Y. Fu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
Surface Fluxes ◽  
Atmospheric Co2 ◽  
Simultaneous Estimation ◽  
Co2 Fluxes ◽  
Co2 Concentrations ◽  
Atmospheric Co2 Concentrations ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We have developed a novel framework ("Tan-Tracker") for assimilating observations of atmospheric CO2 concentrations, based on the POD-based (proper orthogonal decomposition) ensemble four-dimensional variational data assimilation method (PODEn4DVar). The high flexibility and the high computational efficiency of the PODEn4DVar approach allow us to include both the atmospheric CO2 concentrations and the surface CO2 fluxes as part of the large state vector to be simultaneously estimated from assimilation of atmospheric CO2 observations. Compared to most modern top-down flux inversion approaches, where only surface fluxes are considered as control variables, one major advantage of our joint data assimilation system is that, in principle, no assumption on perfect transport models is needed. In addition, the possibility for Tan-Tracker to use a complete dynamic model to consistently describe the time evolution of CO2 surface fluxes (CFs) and the atmospheric CO2 concentrations represents a better use of observation information for recycling the analyses at each assimilation step in order to improve the forecasts for the following assimilations. An experimental Tan-Tracker system has been built based on a complete augmented dynamical model, where (1) the surface atmosphere CO2 exchanges are prescribed by using a persistent forecasting model for the scaling factors of the first-guess net CO2 surface fluxes and (2) the atmospheric CO2 transport is simulated by using the GEOS-Chem three-dimensional global chemistry transport model. Observing system simulation experiments (OSSEs) for assimilating synthetic in situ observations of surface CO2 concentrations are carefully designed to evaluate the effectiveness of the Tan-Tracker system. In particular, detailed comparisons are made with its simplified version (referred to as TT-S) with only CFs taken as the prognostic variables. It is found that our Tan-Tracker system is capable of outperforming TT-S with higher assimilation precision for both CO2 concentrations and CO2 fluxes, mainly due to the simultaneous estimation of CO2 concentrations and CFs in our Tan-Tracker data assimilation system. A experiment for assimilating the real dry-air column CO2 retrievals (XCO2) from the Japanese Greenhouse Gases Observation Satellite (GOSAT) further demonstrates its potential wide applications.

scholarly journals Global methane emission estimates for 2000–2012 from CarbonTracker Europe-CH<sub>4</sub> v1.0

2017 ◽  
Vol 10 (3) ◽  
pp. 1261-1289 ◽  
Author(s):  
Aki Tsuruta ◽  
Tuula Aalto ◽  
Leif Backman ◽  
Janne Hakkarainen ◽  
Ingrid T. van der Laan-Luijkx ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Vertical Mixing ◽  
The United States ◽  
Total Carbon ◽  
Ch4 Emissions ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We present a global distribution of surface methane (CH4) emission estimates for 2000–2012 derived using the CarbonTracker Europe-CH4 (CTE-CH4) data assimilation system. In CTE-CH4, anthropogenic and biospheric CH4 emissions are simultaneously estimated based on constraints of global atmospheric in situ CH4 observations. The system was configured to either estimate only anthropogenic or biospheric sources per region, or to estimate both categories simultaneously. The latter increased the number of optimizable parameters from 62 to 78. In addition, the differences between two numerical schemes available to perform turbulent vertical mixing in the atmospheric transport model TM5 were examined. Together, the system configurations encompass important axes of uncertainty in inversions and allow us to examine the robustness of the flux estimates. The posterior emission estimates are further evaluated by comparing simulated atmospheric CH4 to surface in situ observations, vertical profiles of CH4 made by aircraft, remotely sensed dry-air total column-averaged mole fraction (XCH4) from the Total Carbon Column Observing Network (TCCON), and XCH4 from the Greenhouse gases Observing Satellite (GOSAT). The evaluation with non-assimilated observations shows that posterior XCH4 is better matched with the retrievals when the vertical mixing scheme with faster interhemispheric exchange is used. Estimated posterior mean total global emissions during 2000–2012 are 516 ± 51 Tg CH4 yr−1, with an increase of 18 Tg CH4 yr−1 from 2000–2006 to 2007–2012. The increase is mainly driven by an increase in emissions from South American temperate, Asian temperate and Asian tropical TransCom regions. In addition, the increase is hardly sensitive to different model configurations ( <  2 Tg CH4 yr−1 difference), and much smaller than suggested by EDGAR v4.2 FT2010 inventory (33 Tg CH4 yr−1), which was used for prior anthropogenic emission estimates. The result is in good agreement with other published estimates from inverse modelling studies (16–20 Tg CH4 yr−1). However, this study could not conclusively separate a small trend in biospheric emissions (−5 to +6.9 Tg CH4 yr−1) from the much larger trend in anthropogenic emissions (15–27 Tg CH4 yr−1). Finally, we find that the global and North American CH4 balance could be closed over this time period without the previously suggested need to strongly increase anthropogenic CH4 emissions in the United States. With further developments, especially on the treatment of the atmospheric CH4 sink, we expect the data assimilation system presented here will be able to contribute to the ongoing interpretation of changes in this important greenhouse gas budget.

scholarly journals CTDAS-Lagrange v1.0: A high-resolution data assimilation system for regional carbon dioxide observations

2017 ◽  
Author(s):  
Wei He ◽  
Ivar R. van der Velde ◽  
Arlyn E. Andrews ◽  
Colm Sweeney ◽  
John Miller ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Transport Model ◽  
Lateral Boundary ◽  
Lateral Boundary Conditions ◽  
Sensitivity Tests ◽  
Lagrange System ◽  
Data Assimilation System ◽  
Flux Adjustment ◽  
Assimilation System

Abstract. We have implemented a regional carbon dioxide data assimilation system based on the CarbonTracker Data Assimilation Shell (CTDAS) and a high-resolution Lagrangian transport model, the Stochastic Time-Inverted Lagrangian Transport model driven by the Weather Forecast and Research meteorological fields (WRF-STILT). With this system, named as CTDAS‑Lagrange, we simultaneously optimize terrestrial biosphere fluxes and four parameters that adjust the lateral boundary conditions (BCs) against CO2 observations from the NOAA ESRL North America tall tower and aircraft Programmable Flask Packages (PFPs) sampling program. Least-squares optimization is performed with a time-stepping ensemble Kalman smoother, over a time window of 10 days and assimilating sequentially a time series of observations. Because the WRF-STILT footprints are pre-computed, it is computationally efficient to run the CTDAS-Lagrange system. To estimate the uncertainties of the optimized fluxes from the system, we performed sensitivity tests with various a priori biosphere fluxes (SiBCASA, SiB3, CT2013B) and BCs (optimized mole fraction fields from CT2013B and CTE2014, and an empirical data set derived from aircraft observations), as well as with a variety of choices on the ways that fluxes are adjusted (additive or multiplicative), covariance length scales, biosphere flux covariances, BC parameter uncertainties, and model-data mismatches. In pseudo-data experiments, we show that in our implementation the additive flux adjustment method is more flexible in optimizing NEE than the multiplicative flux adjustment method, and that the CTDAS-Lagrange system has the ability to correct for the potential biases in the lateral boundary conditions and to resolve large biases in the prior biosphere fluxes. Using real observations, we have derived a range of estimates for the optimized carbon fluxes from a series of sensitivity tests, which places the North American carbon sink for the year 2010 in a range from −0.92 to −1.26 PgC/yr. This is comparable to the TM5-based estimates of CarbonTracker (version CT2016, −0.91 ± 1.10 PgC/yr) and CarbonTracker Europe (version CTE2016, −0.91 ± 0.31 PgC/yr). We conclude that CTDAS-Lagrange can offer a versatile and computationally attractive alternative to these global systems for regional estimates of carbon fluxes, which can take advantage of high-resolution Lagrangian footprints that are increasingly easy to obtain.

scholarly journals The CarbonTracker Data Assimilation System for CO<sub>2</sub> and <i>δ</i><sup>13</sup>C (CTDAS-C13 v1.0): retrieving information on land–atmosphere exchange processes

2018 ◽  
Vol 11 (1) ◽  
pp. 283-304 ◽  
Author(s):  
Ivar R. van der Velde ◽  
John B. Miller ◽  
Michiel K. van der Molen ◽  
Pieter P. Tans ◽  
Bruce H. Vaughn ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Carbon Balance ◽  
Modular Design ◽  
Transport Model ◽  
Co2 Exchange ◽  
Severe Drought ◽  
Terrestrial Biosphere ◽  
Global Carbon ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. To improve our understanding of the global carbon balance and its representation in terrestrial biosphere models, we present here a first dual-species application of the CarbonTracker Data Assimilation System (CTDAS). The system's modular design allows for assimilating multiple atmospheric trace gases simultaneously to infer exchange fluxes at the Earth surface. In the prototype discussed here, we interpret signals recorded in observed carbon dioxide (CO2) along with observed ratios of its stable isotopologues 13CO2∕12CO2 (δ13C). The latter is in particular a valuable tracer to untangle CO2 exchange from land and oceans. Potentially, it can also be used as a proxy for continent-wide drought stress in plants, largely because the ratio of 13CO2 and 12CO2 molecules removed from the atmosphere by plants is dependent on moisture conditions.The dual-species CTDAS system varies the net exchange fluxes of both 13CO2 and CO2 in ocean and terrestrial biosphere models to create an ensemble of 13CO2 and CO2 fluxes that propagates through an atmospheric transport model. Based on differences between observed and simulated 13CO2 and CO2 mole fractions (and thus δ13C) our Bayesian minimization approach solves for weekly adjustments to both net fluxes and isotopic terrestrial discrimination that minimizes the difference between observed and estimated mole fractions.With this system, we are able to estimate changes in terrestrial δ13C exchange on seasonal and continental scales in the Northern Hemisphere where the observational network is most dense. Our results indicate a decrease in stomatal conductance on a continent-wide scale during a severe drought. These changes could only be detected after applying combined atmospheric CO2 and δ13C constraints as done in this work. The additional constraints on surface CO2 exchange from δ13C observations neither affected the estimated carbon fluxes nor compromised our ability to match observed CO2 variations. The prototype presented here can be of great benefit not only to study the global carbon balance but also to potentially function as a data-driven diagnostic to assess multiple leaf-level exchange parameterizations in carbon-climate models that influence the CO2, water, isotope, and energy balance.

scholarly journals Global NO<sub>x</sub> emission estimates derived from an assimilation of OMI tropospheric NO<sub>2</sub> columns

2011 ◽  
Vol 11 (12) ◽  
pp. 31523-31583 ◽  
Author(s):  
K. Miyazaki ◽  
H. J. Eskes ◽  
K. Sudo
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
A Priori ◽  
Eastern China ◽  
Nox Emissions ◽  
Chemical Transport Model ◽  
Background Error ◽  
Tropospheric No2 ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. A data assimilation system has been developed to estimate global nitrogen oxides (NOx) emissions using OMI tropospheric NO2 columns (DOMINO product) and a global chemical transport model (CTM), CHASER. The data assimilation system, based on an ensemble Kalman filter approach, was applied to optimize daily NOx emissions with a horizontal resolution of 2.8° during the years 2005 and 2006. The background error covariance estimated from the ensemble CTM forecasts explicitly represents non-direct relationships between the emissions and tropospheric columns caused by atmospheric transport and chemical processes. In comparison to the a priori emissions based on bottom-up inventories, the optimized emissions were higher over Eastern China, the Eastern United States, Southern Africa, and Central-Western Europe, suggesting that the anthropogenic emissions are mostly underestimated in the inventories. In addition, the seasonality of the estimated emissions differed from that of the a priori emission over several biomass burning regions, with a large increase over Southeast Asia in April and over South America in October. The data assimilation results were validated against independent data: SCIAMACHY tropospheric NO2 columns and vertical NO2 profiles obtained from aircraft and lidar measurements. The emission correction greatly improved the agreement between the simulated and observed NO2 fields; this implies that the data assimilation system efficiently derives NOx emissions from concentration observations. We also demonstrated that biases in the satellite retrieval and model settings used in the data assimilation largely affect the magnitude of estimated emissions. These dependences should be carefully considered for better understanding NOx sources from top-down approaches.

scholarly journals A multi-species data assimilation system to retrieve information on land-atmosphere exchange processes

2017 ◽  
Author(s):  
Ivar R. van der Velde ◽  
John B. Miller ◽  
Michiel K. van der Molen ◽  
Pieter P. Tans ◽  
Bruce H. Vaughn ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Carbon Balance ◽  
Modular Design ◽  
Transport Model ◽  
Co2 Exchange ◽  
Severe Drought ◽  
Terrestrial Biosphere ◽  
Global Carbon ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. To improve our understanding of the global carbon balance and its representation in terrestrial biosphere models we present here a first multi-species application of the CarbonTracker Data Assimilation System (CTDAS). The system's modular design allows for assimilating multiple atmospheric trace gases simultaneously to infer exchange fluxes at the Earth surface. In the prototype discussed here we interpret signals recorded in observed carbon dioxide (CO2) along with observed ratios of its stable isotopologues 13CO2/12CO2 (δ13C). The latter is in particular a valuable tracer to untangle CO2 exchange from land and oceans. Potentially, it can also be used as a proxy for continent-wide drought stress in plants, largely because the ratio of 13CO2 and 12CO2 molecules removed from the atmosphere by plants is dependent on moisture conditions. The multi-species CTDAS system varies the net exchange fluxes of both 13CO2 and CO2 in ocean and terrestrial biosphere models to create an ensemble of 13CO2 and CO2 fluxes that propagates through an atmospheric transport model. Based on differences between observed and simulated 13CO2 and CO2 mole fractions (and thus δ13C) our Bayesian minimization approach solves for weekly adjustments to both net fluxes and isotopic terrestrial discrimination that minimizes the difference between observed and estimated mole fractions. With this system we are able to estimate changes in terrestrial δ13C exchange on seasonal and continental scales in the Northern hemisphere where the observational network is most dense. Our results indicate a decrease in stomatal conductance on a continent-wide scale during a severe drought. These changes could only be detected after applying combined atmospheric CO2 and δ13C constraints as done in this work. The additional constraints on surface CO2 exchange from δ13C observations neither affected the estimated carbon fluxes, nor compromised our ability to match observed CO2 variations. The prototype presented here can be of great benefit not only to study the global carbon balance but potentially also to function as a data driven diagnostic to assess multiple leaf-level exchange parameterizations in carbon-climate models that influence the CO2, water, isotope, and energy balance.

scholarly journals Four-dimensional variational data assimilation for inverse modelling of atmospheric methane emissions: method and comparison with synthesis inversion

2008 ◽  
Vol 8 (21) ◽  
pp. 6341-6353 ◽  
Author(s):  
J. F. Meirink ◽  
P. Bergamaschi ◽  
M. C. Krol
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
Time Window ◽  
Methane Emissions ◽  
Satellite Observations ◽  
Inverse Modelling ◽  
Model Parameters ◽  
Full Potential ◽  
Atmospheric Methane ◽  
Assimilation System

Abstract. A four-dimensional variational (4D-Var) data assimilation system for inverse modelling of atmospheric methane emissions is presented. The system is based on the TM5 atmospheric transport model. It can be used for assimilating large volumes of measurements, in particular satellite observations and quasi-continuous in-situ observations, and at the same time it enables the optimization of a large number of model parameters, specifically grid-scale emission rates. Furthermore, the variational method allows to estimate uncertainties in posterior emissions. Here, the system is applied to optimize monthly methane emissions over a 1-year time window on the basis of surface observations from the NOAA-ESRL network. The results are rigorously compared with an analogous inversion by Bergamaschi et al. (2007), which was based on the traditional synthesis approach. The posterior emissions as well as their uncertainties obtained in both inversions show a high degree of consistency. At the same time we illustrate the advantage of 4D-Var in reducing aggregation errors by optimizing emissions at the grid scale of the transport model. The full potential of the assimilation system is exploited in Meirink et al. (2008), who use satellite observations of column-averaged methane mixing ratios to optimize emissions at high spatial resolution, taking advantage of the zooming capability of the TM5 model.

scholarly journals Intraseasonal SST–precipitation relationship in a coupled reanalysis experiment using the MRI coupled atmosphere–ocean data assimilation system

2021 ◽  
Author(s):  
Chiaki Kobayashi ◽  
Yosuke Fujii ◽  
Ichiro Ishikawa
Keyword(s):  
Data Assimilation ◽  
Surface Temperature ◽  
Heat Propagation ◽  
Observation Data ◽  
Near Surface ◽  
Ocean Data Assimilation ◽  
Meteorological Research Institute ◽  
Atmospheric Observations ◽  
Data Assimilation System ◽  
Assimilation System

AbstractTo evaluate the atmosphere–ocean coupled data assimilation system developed at the Meteorological Research Institute, the lead-lag relation between the intraseasonal variations (with a time scale of 20–100 days) in precipitation and sea surface temperature (SST) is examined in the tropics. It is shown that the relationship over the tropical western Pacific in the coupled reanalysis experiment (CDA) follows the observed relationship more closely than that in the uncoupled reanalysis experiment (UCPL). However, the lead-lag correlations with the observed SST are almost identical between precipitations in CDA and UCPL, indicating that the atmospheric component is strongly constrained by atmospheric observations and hardly affected by the SSTs as boundary conditions. Better representation of the SST–precipitation relationship in CDA is, thus, mostly due to the SST variation modified by the model physics. Comparison with additional reanalysis experiments using coupled and uncoupled systems that assimilate only in-situ observations without satellite observations suggests that the coupled model's physics complements the relatively weak observation constraints and reduces the degradation of the SST–precipitation relationship. Additional analysis for CDA suggests that the warming-to-cooling (cooling-to-warming) transition of the surface net flux, which is in phase with precipitation, is delayed from the positive (negative) peak of SST due to downward heat propagation in the ocean. Comparison of the oceanic near-surface temperature field with observation data indicates that the downward propagation of heat signals is too fast in CDA, resulting in smaller lags of transitions of the net heat flux and precipitation behind SST peaks.

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