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 A global coupled Eulerian-Lagrangian model and 1 × 1 km CO<sub>2</sub> surface flux dataset for high-resolution atmospheric CO<sub>2</sub> transport simulations

2011 ◽  
Vol 4 (3) ◽  
pp. 2047-2080 ◽  
Author(s):  
A. Ganshin ◽  
T. Oda ◽  
M. Saito ◽  
S. Maksyutov ◽  
V. Valsala ◽  
...  
Keyword(s):  
High Resolution ◽  
High Frequency ◽  
Transport Model ◽  
Dispersion Model ◽  
Particle Dispersion ◽  
Surface Fluxes ◽  
Atmospheric Co2 ◽  
Lagrangian Model ◽  
Co2 Concentrations ◽  
Atmospheric Co2 Concentrations

Abstract. We designed a method to simulate atmospheric CO2 concentrations at several continuous observation sites around the globe using surface fluxes at a very high spatial resolution. The simulations presented in this study were performed using a Lagrangian particle dispersion model coupled to a global atmospheric tracer transport model with prescribed global surface CO2 flux maps at a 1 × 1 km resolution. The surface fluxes used in the simulations were prepared by assembling the individual components of terrestrial, oceanic and fossil fuel CO2 fluxes. This experimental setup (i.e., a transport model running at a medium resolution, coupled to a high-resolution Lagrangian particle dispersion model together with global surface fluxes at a very high resolution), which was designed to represent high-frequency variations in atmospheric CO2 concentration, has not been reported at a global scale previously. Two sensitivity experiments were performed: (a) using the global transport model without coupling to the Lagrangian dispersion model, and (b) using the coupled model with a reduced resolution of surface fluxes, in order to evaluate the performance of Eulerian-Lagrangian coupling and the role of high-resolution fluxes in simulating high-frequency variations in atmospheric CO2 concentrations. A correlation analysis between observed and simulated atmospheric CO2 concentrations at selected locations revealed that the inclusion of both Eulerian-Lagrangian coupling and high-resolution fluxes improves the high-frequency simulations of the model. The results highlight the potential of a coupled Eulerian-Lagrangian model in simulating high-frequency atmospheric CO2 concentrations at many locations worldwide. The model performs well in representing observations of atmospheric CO2 concentrations at high spatial and temporal resolutions, especially for coastal sites and sites located close to sources of large anthropogenic emissions. While this study focused on simulations of CO2 concentrations, the model could be used for other atmospheric compounds with known estimated emissions.

scholarly journals A global coupled Eulerian-Lagrangian model and 1 × 1 km CO<sub>2</sub> surface flux dataset for high-resolution atmospheric CO<sub>2</sub> transport simulations

2012 ◽  
Vol 5 (1) ◽  
pp. 231-243 ◽  
Author(s):  
A. Ganshin ◽  
T. Oda ◽  
M. Saito ◽  
S. Maksyutov ◽  
V. Valsala ◽  
...  
Keyword(s):  
High Resolution ◽  
High Frequency ◽  
Transport Model ◽  
Dispersion Model ◽  
Particle Dispersion ◽  
Surface Fluxes ◽  
Atmospheric Co2 ◽  
Lagrangian Model ◽  
Co2 Concentrations ◽  
Atmospheric Co2 Concentrations

Abstract. We designed a method to simulate atmospheric CO2 concentrations at several continuous observation sites around the globe using surface fluxes at a very high spatial resolution. The simulations presented in this study were performed using the Global Eulerian-Lagrangian Coupled Atmospheric model (GELCA), comprising a Lagrangian particle dispersion model coupled to a global atmospheric tracer transport model with prescribed global surface CO2 flux maps at a 1 × 1 km resolution. The surface fluxes used in the simulations were prepared by assembling the individual components of terrestrial, oceanic and fossil fuel CO2 fluxes. This experimental setup (i.e. a transport model running at a medium resolution, coupled to a high-resolution Lagrangian particle dispersion model together with global surface fluxes at a very high resolution), which was designed to represent high-frequency variations in atmospheric CO2 concentration, has not been reported at a global scale previously. Two sensitivity experiments were performed: (a) using the global transport model without coupling to the Lagrangian dispersion model, and (b) using the coupled model with a reduced resolution of surface fluxes, in order to evaluate the performance of Eulerian-Lagrangian coupling and the role of high-resolution fluxes in simulating high-frequency variations in atmospheric CO2 concentrations. A correlation analysis between observed and simulated atmospheric CO2 concentrations at selected locations revealed that the inclusion of both Eulerian-Lagrangian coupling and high-resolution fluxes improves the high-frequency simulations of the model. The results highlight the potential of a coupled Eulerian-Lagrangian model in simulating high-frequency atmospheric CO2 concentrations at many locations worldwide. The model performs well in representing observations of atmospheric CO2 concentrations at high spatial and temporal resolutions, especially for coastal sites and sites located close to sources of large anthropogenic emissions. While this study focused on simulations of CO2 concentrations, the model could be used for other atmospheric compounds with known estimated emissions.

scholarly journals Three decades of simulated global terrestrial carbon fluxes from a data assimilation system confronted with different periods of observations

2019 ◽  
Vol 16 (15) ◽  
pp. 3009-3032 ◽  
Author(s):  
Karel Castro-Morales ◽  
Gregor Schürmann ◽  
Christoph Köstler ◽  
Christian Rödenbeck ◽  
Martin Heimann ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Atmospheric Co2 ◽  
Term Trend ◽  
Atmospheric Co2 Concentrations ◽  
Long Term Trend ◽  
Global Carbon ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. During the last decade, carbon cycle data assimilation systems (CCDAS) have focused on improving the simulation of seasonal and mean global carbon fluxes over a few years by simultaneous assimilation of multiple data streams. However, the ability of a CCDAS to predict longer-term trends and variability of the global carbon cycle and the constraint provided by the observations have not yet been assessed. Here, we evaluate two near-decade-long assimilation experiments of the Max Planck Institute – Carbon Cycle Data Assimilation System (MPI-CCDAS v1) using spaceborne estimates of the fraction of absorbed photosynthetic active radiation (FAPAR) and atmospheric CO2 concentrations from the global network of flask measurement sites from either 1982 to 1990 or 1990 to 2000. We contrast these simulations with independent observations from the period 1982–2010, as well as a third MPI-CCDAS assimilation run using data from the full 1982–2010 period, and an atmospheric inversion covering the same data and time. With 30 years of data, MPI-CCDAS is capable of representing land uptake to a sufficient degree to make it compatible with the atmospheric CO2 record. The long-term trend and seasonal amplitude of atmospheric CO2 concentrations at station level over the period 1982 to 2010 is considerably improved after assimilating only the first decade (1982–1990) of observations. After 15–19 years of prognostic simulation, the simulated CO2 mixing ratio in 2007–2010 diverges by only 2±1.3 ppm from the observations, the atmospheric inversion, and the MPI-CCDAS assimilation run using observations from the full period. The long-term trend, phenological seasonality, and interannual variability (IAV) of FAPAR in the Northern Hemisphere over the last 1 to 2 decades after the assimilation were also improved. Despite imperfections in the representation of the IAV in atmospheric CO2, model–data fusion for a decade of data can already contribute to the prognostic capacity of land carbon cycle models at relevant timescales.

scholarly journals Three decades of simulated global terrestrial carbon fluxes from a data assimilation system confronted to different periods of observations

2019 ◽  
Author(s):  
Karel Castro-Morales ◽  
Gregor Schürmann ◽  
Christoph Köstler ◽  
Christian Rödenbeck ◽  
Martin Heimann ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Gross Primary Production ◽  
Atmospheric Co2 ◽  
Model Data ◽  
Term Trend ◽  
Atmospheric Co2 Concentrations ◽  
Long Term Trend ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. This paper presents global land carbon fluxes for the period 1982–2010 (gross primary production, GPP, and net ecosystem exchange, NEE) estimated with the Max Planck Institute – Carbon Cycle Data Assimilation System (MPI-CCDAS v1). The primary aim of this work is to analyze the performance of the MPI-CCDAS when it is confronted with three different time periods for data assimilation (DA), and thereby to assess its prognostic capability. To this extend we assimilated nearly three decades (1982–2010) of space borne measurements of the fraction of absorbed photosynthetic active radiation (FAPAR) and atmospheric CO2 concentrations from the global network of flask and in situ measurements. Both data sets were incorporated with different assimilation windows covering the periods 1982–1990, 1990–2000 and 1982–2010. The assimilation results show a considerable improvement in the long-term trend and seasonality of FAPAR in the Northern Hemisphere, as well as in the long term trend and seasonal amplitude of the atmospheric CO2 concentrations when compared to the observations in sites globally distributed. After the assimilation, the global net land-atmosphere CO2 exchange (NEE) was −1.2 PgC yr−1, in agreement with independent estimates, while gross primary production (GPP; 92.5 PgC yr−1) was somewhat below the magnitude of independent estimates. The NEE in boreal eastern regions (Northeast Asia) increased on average by −0.13 PgC yr−1, which translated into an intensification of the carbon uptake in those regions by nearly 30 % than the contribution to the global annual average in the model before the assimilation. Our results demonstrate that using information only over a decade already yielded a large fraction of the overall model improvement, in particular for the simulation of phenological seasonality, its interannual variability (IAV) and long-term trend. Adding longer than decadal data did only lead to very moderate improvements in the long-term trend of the FAPAR simulated by the model, which may be attributed to the small model-data mismatch at the long timescales compared to the significantly larger observational signal and model-data mismatch error at seasonal cycle time scale. Decadal data also significantly improved the seasonality, IAV and long-term simulated trend in atmospheric CO2. Importantly, when running the MPI-CCDAS v1 with 30 years of data, the results remained in line with observations throughout this period, suggesting that the model can represent land uptake to a sufficient degree to make it compatible with the atmospheric CO2 record. Using data from 1982 to 1990 in the assimilation yielded only a difference to the observations of 2 ± 1.3 ppm for the period 15 to 19 years after the end of the assimilation. This suggests that despite imperfections in the representation of IAV, model-data fusion can increase the prognostic capacity of land carbon cycle models at relevant time-scales.

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 Constraining a land surface model with multiple observations by application of the MPI-Carbon Cycle Data Assimilation System

2016 ◽  
Author(s):  
G. J. Schürmann ◽  
T. Kaminski ◽  
C. Köstler ◽  
N. Carvalhais ◽  
M. Voßbeck ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Land Surface ◽  
Data Stream ◽  
Atmospheric Co2 ◽  
Surface Model ◽  
Terrestrial Biosphere ◽  
Multiple Observations ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We describe the Max Planck Institute Carbon Cycle Data Assimilation System (MPI-CCDAS) built around the tangent-linear version of the land surface scheme of the MPI-Earth System Model v1 (JSBACH). The simulated terrestrial biosphere processes (phenology and carbon balance) were constrained by observations of the fraction of photosynthetically active radiation (TIP-FAPAR product) and by observations of atmospheric CO2 at a global set of monitoring stations for the years 2005–2009. The system successfully, and computationally efficiently, improved average foliar area and northern extra-tropical seasonality of foliar area when constrained by TIP-FAPAR. Global net and gross carbon fluxes were improved when constrained by atmospheric CO2, although the system tended to underestimate tropical productivity. Assimilating both data streams jointly allowed the MPI-CCDAS to match both observations (TIP-FAPAR and atmospheric CO2) equally well as the single data stream assimilation cases, therefore overall increasing the appropriateness of the resultant parameter values and biosphere dynamics. Our study thus highlights the role of the TIP-FAPAR product in stabilising the underdetermined atmospheric inversion problem and demonstrates the value of multiple-data stream assimilation for the simulation of terrestrial biosphere dynamics. The constraint on regional gross and net CO2 flux patterns is limited through the parametrisation of the biosphere model. We expect improvement on that aspect through a refined initialisation strategy and inclusion of further biosphere observations as constraints.

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.

Sources and Sinks of Atmospheric CO2

1992 ◽  
Vol 40 (5) ◽  
pp. 407 ◽  
Author(s):  
JA Taylor ◽  
J Lloyd
Keyword(s):  
Climate Change ◽  
Biomass Burning ◽  
Transport Model ◽  
Atmospheric Co2 ◽  
Tropical Rainforests ◽  
Co2 Uptake ◽  
Tracer Transport ◽  
Intergovernmental Panel ◽  
Co2 Concentrations ◽  
Atmospheric Co2 Concentrations

The biosphere plays an important role in determining the sources, sinks, levels and rates of change of atmospheric CO2 concentrations. Significant uncertainties remain in estimates of the fluxes of CO2 from biomass burning and deforestation, and uptake and storage of CO2 by the biosphere arising from increased atmospheric CO2 concentrations. Calculation of probable rates of carbon sequestration for the major ecosystem complexes and global 3-D tracer transport model runs indicate the possibility that a significant net CO2 uptake (> 1 Pg C yr-1), a CO2 'fertilisation effect', may be occurring in tropical rainforests, effectively accounting for much of the 'missing sink'. This sink may currently balance much of the CO2 added to the atmosphere from deforestation and biomass burning. Interestingly, CO2 released from biomass burning may itself be playing an important role in enhanced carbon storage by tropical rainforests. This has important implications for predicting future CO2 concentrations. If tropical rainforest destruction continues then much of the CO2 stored as a result of the CO2 'fertilisation effect' will be rereleased to the atmosphere and much of the 'missing sink' will disappear. These effects have not been considered in the IPCC (Intergovernmental Panel on Climate Change) projections of future atmospheric CO2 concentrations. Predictions which take account of the combined effects of deforestation, the return of carbon previously stored through the CO2 'fertilisation effect' and the loss of a large proportion of the 'missing sink' as a result of deforestation, would result in much higher predicted concentrations and rates of increase of atmospheric CO2 and, as a consequence, accelerated rates of climate change.

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.

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