Carbon Cycle Data Assimilation System Research Articles

On the capability of Monte Carlo and adjoint inversion techniques to derive posterior parameter uncertainties in terrestrial ecosystem models

Terrestrial ecosystem models (TEMs) contain the coupling of many biogeochemical processes with a large number of parameters involved. In many cases those parameters are highly uncertain. In order to reduce those uncertainties, parameter estimation methods can be applied, which allow the model to be constrained against observations. We compare the performance and results of two such parameter estimation techniques ‐ the Metropolis algorithm (MA) which is a Markov Chain Monte Carlo (MCMC) method and the adjoint approach as it is used in the Carbon Cycle Data Assimilation System (CCDAS). Both techniques are applied here to derive the posterior probability density function (PDF) for 19 parameters of the Biosphere Energy Transfer and Hydrology (BETHY) scheme. We also use the MA to sample the posterior parameter distribution from the adjoint inversion. This allows us to assess if the commonly made assumption in variational data assimilation, that everything is normally distributed, holds. The comparison of the posterior parameter PDF derived by both methods shows that in most cases an approximation of the PDF by a normal distribution as used by the adjoint approach is a valid assumption. The results also indicate that the global minimum has been identified by both methods for the given set up. However, the adjoint approach outperforms the MA by several orders of magnitude in terms of computational time. Both methods show good agreement in the PDF of estimated net carbon fluxes for the decades of the 1980s and 1990s.

Consistent assimilation of MERIS FAPAR and atmospheric CO<sub>2</sub> into a terrestrial vegetation model and interactive mission benefit analysis

Abstract. The terrestrial biosphere is currently a strong sink for anthropogenic CO2 emissions. Through the radiative properties of CO2, the strength of this sink has a direct influence on the radiative budget of the global climate system. The accurate assessment of this sink and its evolution under a changing climate is, hence, paramount for any efficient management strategies of the terrestrial carbon sink to avoid dangerous climate change. Unfortunately, simulations of carbon and water fluxes with terrestrial biosphere models exhibit large uncertainties. A considerable fraction of this uncertainty reflects uncertainty in the parameter values of the process formulations within the models. This paper describes the systematic calibration of the process parameters of a terrestrial biosphere model against two observational data streams: remotely sensed FAPAR (fraction of absorbed photosynthetically active radiation) provided by the MERIS (ESA's Medium Resolution Imaging Spectrometer) sensor and in situ measurements of atmospheric CO2 provided by the GLOBALVIEW flask sampling network. We use the Carbon Cycle Data Assimilation System (CCDAS) to systematically calibrate some 70 parameters of the terrestrial BETHY (Biosphere Energy Transfer Hydrology) model. The simultaneous assimilation of all observations provides parameter estimates and uncertainty ranges that are consistent with the observational information. In a subsequent step these parameter uncertainties are propagated through the model to uncertainty ranges for predicted carbon fluxes. We demonstrate the consistent assimilation at global scale, where the global MERIS FAPAR product and atmospheric CO2 are used simultaneously. The assimilation improves the match to independent observations. We quantify how MERIS data improve the accuracy of the current and future (net and gross) carbon flux estimates (within and beyond the assimilation period). We further demonstrate the use of an interactive mission benefit analysis tool built around CCDAS to support the design of future space missions. We find that, for long-term averages, the benefit of FAPAR data is most pronounced for hydrological quantities, and moderate for quantities related to carbon fluxes from ecosystems. The benefit for hydrological quantities is highest for semi-arid tropical or sub-tropical regions. Length of mission or sensor resolution is of minor importance.

Atmospheric constraints on gross primary productivity and net ecosystem productivity: Results from a carbon‐cycle data assimilation system

This paper combines an atmospheric transport model and a terrestrial ecosystem model to estimate gross primary productivity (GPP) and net ecosystem productivity (NEP) of the land biosphere. Using atmospheric CO2observations in a Carbon Cycle Data Assimilation System (CCDAS) we estimate a terrestrial global GPP of 146 ± 19 GtC/yr. However, the current observing network cannot distinguish this best estimate from a different assimilation experiment yielding a terrestrial global GPP of 117 GtC/yr. Spatial estimates of GPP agree with data‐driven estimates in the extratropics but are overestimated in the poorly observed tropics. The uncertainty analysis of previous studies was extended by using two atmospheric transport models and different CO2 observing networks. We find that estimates of GPP and NEP are less sensitive to these choices than the form of the prior probability for model parameters. NEP is also found to be significantly sensitive to the transport model and this sensitivity is not greatly reduced compared to direct atmospheric transport inversions, which optimize NEP directly.

Advance in Dynamic Global Vegetation Models

The interaction between vegetation and climate is a complicated process.Many vegetation models were developed to understand the mechanism of interacting between vegetation and climate and assess the effects of climate change on vegetation.Furthermore,vegetation models have progressed from Static Vegetation Models(SVMs) to Dynamic Global Vegetation Models(DGVMs).DGVMs include the dynamic biogeochemical models and dynamic biogeophysical models,which mainly simulate the vegetation physiological processes,the vegetation dynamics,the vegetation phenophase and the nutrient cycling.The widely used DGVMs in the world include LPJ,IBIS,VECODE,TRIFFID and so on.The present research on DGVMs mainly focuses on four points:① the improvement of the accuracy of DGVMs;② the comparison among different models;③ the integrated model of DGVMs and Climate Models;④ the research on the Carbon Cycle Data Assimilation System(CCDAS).

Development of an ensemble-adjoint optimization approach to derive uncertainties in net carbon fluxes

Abstract. Accurate modelling of the carbon cycle strongly depends on the parametrization of its underlying processes. The Carbon Cycle Data Assimilation System (CCDAS) can be used as an estimator algorithm to derive posterior parameter values and uncertainties for the Biosphere Energy Transfer and Hydrology scheme (BETHY). However, the simultaneous optimization of all process parameters can be challenging, due to the complexity and non-linearity of the BETHY model. Therefore, we propose a new concept that uses ensemble runs and the adjoint optimization approach of CCDAS to derive the full probability density function (PDF) for posterior soil carbon parameters and the net carbon flux at the global scale. This method allows us to optimize only those parameters that can be constrained best by atmospheric carbon dioxide (CO2) data. The prior uncertainties of the remaining parameters are included in a consistent way through ensemble runs, but are not constrained by data. The final PDF for the optimized parameters and the net carbon flux are then derived by superimposing the individual PDFs for each ensemble member. We find that the optimization with CCDAS converges much faster, due to the smaller number of processes involved. Faster convergence also gives us much increased confidence that we find the global minimum in the reduced parameter space.

Investigating spatial differentiation of model parameters in a carbon cycle data assimilation system

Better estimates of the net exchange of CO(2) between the atmosphere and the terrestrial biosphere are urgently needed to improve predictions of future CO(2) levels in the atmosphere. The carbon cycle data assimilation system (CCDAS) offers the capability of inversion, while it is at the same time based on a process model that can be used independent of observational data. CCDAS allows the assimilation of atmospheric CO(2) concentrations into the terrestrial biosphere model BETHY, constraining its process parameters via an adjoint approach. Here, we investigate the effect of spatial differentiation of a universal carbon balance parameter of BETHY on posterior net CO(2) fluxes and their uncertainties. The parameter, beta, determines the characteristics of the slowly decomposing soil carbon pool and represents processes that are difficult to model explicitly. Two cases are studied with an assimilation period of 1979 to 2003. In the base case, there is a separate beta for each plant functional type (PFT). In the regionalization case, beta is differentiated not only by PFT, but also according to each of 11 large continental regions as used by the TransCom project. We find that the choice of spatial differentiation has a profound impact not only on the posterior (optimized) fluxes and their uncertainties, but even more so on the spatial covariance of the uncertainties. Differences are most pronounced in tropical regions, where observations are sparse. While regionalization leads to an improved fit to the observations by about 20% compared to the base case, we notice large spatial variations in the posterior net CO(2) flux on a grid cell level. The results illustrate the need for universal process formulations in global-scale atmospheric CO(2) inversion studies, at least as long as the observational network is too sparse to resolve spatial fluctuations at the regional scale. (Less)

Constraining predictions of the carbon cycle using data

We use a carbon-cycle data assimilation system to estimate the terrestrial biospheric CO(2) flux until 2090. The terrestrial sink increases rapidly and the increase is stronger in the presence of climate change. Using a linearized model, we calculate the uncertainty in the flux owing to uncertainty in model parameters. The uncertainty is large and is dominated by the impact of soil moisture on heterotrophic respiration. We show that this uncertainty can be greatly reduced by constraining the model parameters with two decades of atmospheric measurements.

Carbon cycle data assimilation with a generic phenology model

Photosynthesis by terrestrial plants is the main driver of the global carbon cycle, and the presence of actively photosynthesizing vegetation can now be observed from space. However, challenges remain when translating remotely sensed data into carbon fluxes. One reason is that the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), which documents the presence of photosynthetically active vegetation, relates more directly to leaf development and leaf phenology than to photosynthetic rates. Here, we present a new approach for linking FAPAR and vegetation‐to‐atmosphere carbon fluxes through variational data assimilation. The scheme extends the Carbon Cycle Data Assimilation System (CCDAS) by a newly developed, globally applicable and generic leaf phenology model, which includes both temperature and water‐driven leaf development. CCDAS is run for seven sites, six of them included in the FLUXNET network. Optimization is carried out simultaneously for all sites against 20 months of daily FAPAR from the Medium Resolution Imaging Spectrometer on board the European Space Agency's ENVISAT platform. Fourteen parameters related to phenology and 24 related to photosynthesis are optimized simultaneously, and their posterior uncertainties are computed. We find that with one parameter set for all sites, the model is able to reproduce the observed FAPAR spanning boreal, temperate, humid‐tropical, and semiarid climates. Assimilation of FAPAR has led to reduced uncertainty (by >10%) of 10 of the 38 parameters, including one parameter related to photosynthesis, and a moderate reduction in net primary productivity uncertainty. The approach can easily be extended to regional or global studies and to the assimilation of further remotely sensed data.

Quantifying the benefit of A-SCOPE data for reducing uncertainties in terrestrial carbon fluxes in CCDAS

ESA’s Earth Explorer candidate mission A-SCOPE aims at observing CO2 from space with an active LIDAR instrument. This study employs quantitative network design techniques to investigate the benefit of A-SCOPE observations in a Carbon Cycle Data Assimilation System. The system links the observations to the terrestrial vegetation model BETHY via the fine resolution version of the atmospheric transport model TM3. In the modelling process chain the observations are used to reduce uncertainties in the values of BETHY’s process parameters, and then the uncertainty in the process parameters is mapped forward to uncertainties in both in long-term net carbon flux and net primary productivity over three regions. A-SCOPE yields considerably better reductions in posterior uncertainties than the ground-based GLOBALVIEW station network. This is true for assimilating monthly mean values and instantaneous values, and it is true for two potential vertical weighting functions. The strength of the constraint through A-SCOPE observations is high over the range of observational uncertainties.

Prognosing terrestrial carbon cycling with uncertainties: Results from a Carbon Cycle Data Assimilation System (CCDAS)

This article was submitted without an abstract, please refer to the full-text PDF file.

Testing the use of adjoints for parameter estimation in a simple GCM on climate time-scales

The paper presents a feasibility study for a Climate Prediction Data Assimilation System following the methodological approach of the Carbon Cycle Data Assimilation System (CCDAS). The usefulness of accurate gradient information for estimating process parameters of the spectral atmospheric circulation model PUMA on climate time-scales is investigated. Pseudo observations of the long-term mean surface temperature are generated by the model itself. The gradient of the model-data misfit computed by the tangent linear version of the model provides a good approximation for integration periods of 10 days arid one year. In an identical twin experiment the correct parameter values can be retrieved by variational assimilation of the pseudo observations for an integration period of 10 days. For an integration period of 100 days this worked after adding pseudo observations of the seasonality of the surface temperature. (Less)

Propagating uncertainty through prognostic carbon cycle data assimilation system simulations

One of the major advantages of carbon cycle data assimilation is the possibility to estimate carbon fluxes with uncertainties in a prognostic mode, that is beyond the time period of carbon dioxide (CO2) observations. The carbon cycle data assimilation system is built around the Biosphere Energy Transfer Hydrology Scheme (BETHY) model, coupled to the atmospheric transport model TM2. It uses about 2 decades of observations of the atmospheric carbon dioxide concentration from a global network to constrain 57 process parameters via an adjoint approach. The model's Hessian matrix of second derivatives provides uncertainty estimates for the optimized process parameters that are consistent with the assumed uncertainties in the observations and the model. With those estimated parameter values, the model can predict the response of the terrestrial biosphere to prescribed climate forcing beyond the assimilation period. We develop a methodological framework that is able to propagate parameter uncertainties through such a prognostic simulation and provide uncertainty estimates for the simulation results. We demonstrate the concept for a 4‐year hindcast simulation from 2000 to 2003 following a 21‐year assimilation period from 1979 to 1999. We discuss prognostic uncertainties for surface fluxes and atmospheric carbon dioxide.

Two decades of terrestrial carbon fluxes from a carbon cycle data assimilation system (CCDAS)

This paper presents the space‐time distribution of terrestrial carbon fluxes for the period 1979–1999 generated by a terrestrial carbon cycle data assimilation system (CCDAS). CCDAS is based around the Biosphere Energy Transfer Hydrology model. We assimilate satellite observations of photosynthetically active radiation and atmospheric CO2 concentration observations in a two‐step process. The control variables for the assimilation are the parameters of the carbon cycle model. The optimized model produces a moderate fit to the seasonal cycle of atmospheric CO2 concentration and a good fit to its interannual variability. Long‐term mean fluxes show large uptakes over the northern midlatitudes and uptakes over tropical continents partly offsetting the prescribed efflux due to land use change. Interannual variability is dominated by the tropics. On interannual timescales the controlling process is net primary productivity (NPP) while for decadal changes the main driver is changes in soil respiration. An adjoint sensitivity analysis reveals that uncertainty in long‐term storage efficiency of soil carbon is the largest contributor to uncertainty in net flux.