大气中CO2含量的增加速率已经超过了自然界所能吸收的速度,并逐步影响到全球气候变暖。利用模型模拟分析已经成为一个重要的工具用以深入对碳循环的理解。本文使用2008~2010年的生物模型SiB3(Simple Biosphere version 3)与优化后的CT20...大气中CO2含量的增加速率已经超过了自然界所能吸收的速度,并逐步影响到全球气候变暖。利用模型模拟分析已经成为一个重要的工具用以深入对碳循环的理解。本文使用2008~2010年的生物模型SiB3(Simple Biosphere version 3)与优化后的CT2016(Carbon Tracker 2016)陆地生态系统碳通量驱动GEOS-Chem大气化学传输模型模拟全球CO2浓度。通过分析模拟CO2浓度的空间分布与季节变化,加深对全球碳源汇分布特点的理解,探究陆地生态系统碳通量不确定性对模拟结果的影响,进而认识陆地生态系统碳通量反演精度提升的重要性。SiB3与优化后的CT2016陆地生态系统碳通量都具有明显的季节变化,但在欧洲地区碳源汇的表现相反,其全球总量与空间分布也存在极大的不确定性。模拟CO2浓度结果表明:在人为活动较少地区,陆地生态系统碳通量对近地面CO2浓度空间分布起主导作用,尤其在南半球和欧洲地区模拟浓度有明显差异,且两种模拟结果的季节差异依赖于陆地生态系统碳通量的季节变化。将模拟结果与9个观测站点资料进行对比,以期选用合适的陆地生态系统碳通量来提升GEOS-Chem模拟CO2浓度的精度。实验结果表明:两种模拟结果均能较好的模拟CO2浓度的季节变化及其峰谷值,但CT2016模拟的CO2浓度在多数站点处更接近观测资料,模拟准确性更高。展开更多
The Ensemble Kalman Filter (EnKF) is well known and widely used in land data assimilation for its high precision and simple operation. The land surface models used as the forecast operator in a land data assimilation ...The Ensemble Kalman Filter (EnKF) is well known and widely used in land data assimilation for its high precision and simple operation. The land surface models used as the forecast operator in a land data assimilation system are usually designed to consider the model subgrid-heterogeneity and soil water thawing and freezing. To neglect their effects could lead to some errors in soil moisture assimilation. The dual EnKF method is employed in soil moisture data assimilation to build a soil moisture data as- similation framework based on the NCAR Community Land Model version 2.0 (CLM 2.0) in considera- tion of the effects of the model subgrid-heterogeneity and soil water thawing and freezing: Liquid volumetric soil moisture content in a given fraction is assimilated through the state filter process, while solid volumetric soil moisture content in the same fraction and solid/liquid volumetric soil moisture in the other fractions are optimized by the parameter filter. Preliminary experiments show that this dual EnKF-based assimilation framework can assimilate soil moisture more effectively and precisely than the usual EnKF-based assimilation framework without considering the model subgrid-scale heteroge- neity and soil water thawing and freezing. With the improvement of soil moisture simulation, the soil temperature-simulated precision can be also improved to some extent.展开更多
Freeze-thaw processes in soils,including changes in frost and thaw fronts(FTFs),are important physical processes.The movement of FTFs affects soil hydrothermal characteristics,as well as energy and water exchanges bet...Freeze-thaw processes in soils,including changes in frost and thaw fronts(FTFs),are important physical processes.The movement of FTFs affects soil hydrothermal characteristics,as well as energy and water exchanges between the land surface and the atmosphere and hydrothermal processes in the land surface.This paper reduces the issue of soil freezing and thawing to a multiple moving-boundary problem and develops a soil water and heat transfer model which considers the effects of FTF on soil hydrothermal processes.A local adaptive variable-grid method is used to discretize the model.Sensitivity tests based on the hierarchical structure of the Community Land Model(CLM)show that multiple FTFs can be continuously tracked,which overcomes the difficulties of isotherms that cannot simultaneously simulate multiple FTFs in the same soil layer.The local adaptive variable-grid method is stable and offers computational efficiency several times greater than the high-resolution case.The simulated FTF depths,soil temperatures,and soil moisture values fit well with the observed data,which further demonstrates the potential application of this simulation to the land-surface process model.展开更多
The proper orthogonal decomposition (POD) method is used to construct a set of basis functions for spanning the ensemble of data in a certain least squares optimal sense. Compared with the singular value decomposition...The proper orthogonal decomposition (POD) method is used to construct a set of basis functions for spanning the ensemble of data in a certain least squares optimal sense. Compared with the singular value decomposition (SVD), the POD basis functions can capture more energy in the forecast ensemble space and can represent its spatial structure and temporal evolution more effectively. After the analysis variables are expressed by a truncated expansion of the POD basis vectors in the ensemble space, the control variables appear explicitly in the cost function, so that the adjoint model, which is used to de-rive the gradient of the cost function with respect to the control variables, is no longer needed. The application of this new technique significantly simplifies the data assimilation process. Several as-similation experiments show that this POD-based explicit four-dimensional variational data assimila-tion method performs much better than the usual ensemble Kalman filter method on both enhancing the assimilation precision and reducing the computation cost. It is also better than the SVD-based ex-plicit four-dimensional assimilation method, especially when the forecast model is not perfect and the forecast error comes from both the noise of the initial filed and the uncertainty of the forecast model.展开更多
Seasonal and interannual changes in the Earth's gravity field are mainly due to mass exchange among the atmosphere,ocean,and continental water sources.The terrestrial water storage changes,detected as gravity chan...Seasonal and interannual changes in the Earth's gravity field are mainly due to mass exchange among the atmosphere,ocean,and continental water sources.The terrestrial water storage changes,detected as gravity changes by the Gravity Recovery and Climate Experiment(GRACE) satellites,are mainly caused by precipitation,evapotranspiration,river transportation and downward infiltration processes.In this study,a land data assimilation system LDAS-G was developed to assimilate the GRACE terrestrial water storage(TWS) data into the Community Land Model(CLM3.5) using the POD-based ensemble four-dimensional variational assimilation method PODEn4 DVar,disaggregating the GRACE large-scale terrestrial water storage changes vertically and in time,and placing constraints on the simulation of vertical hydrological variables to improve land surface hydrological simulations.The ideal experiments conducted at a single point and assimilation experiments carried out over China by the LDAS-G data assimilation system showed that the system developed in this study improved the simulation of land surface hydrological variables,indicating the potential of GRACE data assimilation in large-scale land surface hydrological research and applications.展开更多
Bayesian model averaging(BMA) is a recently proposed statistical method for calibrating forecast ensembles from numerical weather models.However,successful implementation of BMA requires accurate estimates of the weig...Bayesian model averaging(BMA) is a recently proposed statistical method for calibrating forecast ensembles from numerical weather models.However,successful implementation of BMA requires accurate estimates of the weights and variances of the individual competing models in the ensemble.Two methods,namely the Expectation-Maximization(EM) and the Markov Chain Monte Carlo(MCMC) algorithms,are widely used for BMA model training.Both methods have their own respective strengths and weaknesses.In this paper,we first modify the BMA log-likelihood function with the aim of removing the addi-tional limitation that requires that the BMA weights add to one,and then use a limited memory quasi-Newtonian algorithm for solving the nonlinear optimization problem,thereby formulating a new approach for BMA(referred to as BMA-BFGS).Several groups of multi-model soil moisture simulation experiments from three land surface models show that the performance of BMA-BFGS is similar to the MCMC method in terms of simulation accuracy,and that both are superior to the EM algo-rithm.On the other hand,the computational cost of the BMA-BFGS algorithm is substantially less than for MCMC and is al-most equivalent to that for EM.展开更多
The concepts of sample sphere radius and sample density are proposed in this paper to help illustrate that different vector transformations result in diverse sample density with the same sample ensemble, which finally...The concepts of sample sphere radius and sample density are proposed in this paper to help illustrate that different vector transformations result in diverse sample density with the same sample ensemble, which finally affects their assimilation performance. Several numerical experiments using a onedimensional (1-D) soil water equation and synthetic observations are conducted to evaluate this new theory in land data assimilation.展开更多
基金partially supported by the National Key Research and Development Program of China [grant number 2016YFA0600203]the National Natural Science Foundation of China [grant number 41575100]
基金supported by the National Key Research and Development Program of China [grant number2016YFA0600203]the National Natural Science Foundation of China [grant number 41575100]the Key Research Program of Frontier Sciences,Chinese Academy of Sciences[grant number QYZDY-SSW-DQC012]
基金the National Natural Science Foundation of China (Grant No. 40705035)the Knowledge Innovation Project of Chinese Academy of Sciences (Grant Nos. KZCX2-YW-217 and KZCX2-YW-126-2)the National Basic Research Program of China (Grant No.2005CB321704)
文摘The Ensemble Kalman Filter (EnKF) is well known and widely used in land data assimilation for its high precision and simple operation. The land surface models used as the forecast operator in a land data assimilation system are usually designed to consider the model subgrid-heterogeneity and soil water thawing and freezing. To neglect their effects could lead to some errors in soil moisture assimilation. The dual EnKF method is employed in soil moisture data assimilation to build a soil moisture data as- similation framework based on the NCAR Community Land Model version 2.0 (CLM 2.0) in considera- tion of the effects of the model subgrid-heterogeneity and soil water thawing and freezing: Liquid volumetric soil moisture content in a given fraction is assimilated through the state filter process, while solid volumetric soil moisture content in the same fraction and solid/liquid volumetric soil moisture in the other fractions are optimized by the parameter filter. Preliminary experiments show that this dual EnKF-based assimilation framework can assimilate soil moisture more effectively and precisely than the usual EnKF-based assimilation framework without considering the model subgrid-scale heteroge- neity and soil water thawing and freezing. With the improvement of soil moisture simulation, the soil temperature-simulated precision can be also improved to some extent.
基金supported by the National Natural Science Foundation of China(Grant No.91125016)National Basic Research Program of China(Grants Nos.2010CB951001,2010CB428403)
文摘Freeze-thaw processes in soils,including changes in frost and thaw fronts(FTFs),are important physical processes.The movement of FTFs affects soil hydrothermal characteristics,as well as energy and water exchanges between the land surface and the atmosphere and hydrothermal processes in the land surface.This paper reduces the issue of soil freezing and thawing to a multiple moving-boundary problem and develops a soil water and heat transfer model which considers the effects of FTF on soil hydrothermal processes.A local adaptive variable-grid method is used to discretize the model.Sensitivity tests based on the hierarchical structure of the Community Land Model(CLM)show that multiple FTFs can be continuously tracked,which overcomes the difficulties of isotherms that cannot simultaneously simulate multiple FTFs in the same soil layer.The local adaptive variable-grid method is stable and offers computational efficiency several times greater than the high-resolution case.The simulated FTF depths,soil temperatures,and soil moisture values fit well with the observed data,which further demonstrates the potential application of this simulation to the land-surface process model.
基金Supported by National Natural Science Foundation of China (Grant No. 40705035)National High Technology Research and Development Program of China (Grant No. 2007AA12Z144)+1 种基金Knowledge Innovation Project of Chinese Academy of Sciences (Grant Nos. KZCX2-YW-217 and KZCX2-YW-126-2)National Basic Research Program of China (Grant No. 2005CB321704)
文摘The proper orthogonal decomposition (POD) method is used to construct a set of basis functions for spanning the ensemble of data in a certain least squares optimal sense. Compared with the singular value decomposition (SVD), the POD basis functions can capture more energy in the forecast ensemble space and can represent its spatial structure and temporal evolution more effectively. After the analysis variables are expressed by a truncated expansion of the POD basis vectors in the ensemble space, the control variables appear explicitly in the cost function, so that the adjoint model, which is used to de-rive the gradient of the cost function with respect to the control variables, is no longer needed. The application of this new technique significantly simplifies the data assimilation process. Several as-similation experiments show that this POD-based explicit four-dimensional variational data assimila-tion method performs much better than the usual ensemble Kalman filter method on both enhancing the assimilation precision and reducing the computation cost. It is also better than the SVD-based ex-plicit four-dimensional assimilation method, especially when the forecast model is not perfect and the forecast error comes from both the noise of the initial filed and the uncertainty of the forecast model.
基金supported by the National Natural Science Foundation of China(Grant Nos.41075062,91125016)the National Basic Research Program of China(Grants Nos.2010CB951001,2010CB428403)
文摘Seasonal and interannual changes in the Earth's gravity field are mainly due to mass exchange among the atmosphere,ocean,and continental water sources.The terrestrial water storage changes,detected as gravity changes by the Gravity Recovery and Climate Experiment(GRACE) satellites,are mainly caused by precipitation,evapotranspiration,river transportation and downward infiltration processes.In this study,a land data assimilation system LDAS-G was developed to assimilate the GRACE terrestrial water storage(TWS) data into the Community Land Model(CLM3.5) using the POD-based ensemble four-dimensional variational assimilation method PODEn4 DVar,disaggregating the GRACE large-scale terrestrial water storage changes vertically and in time,and placing constraints on the simulation of vertical hydrological variables to improve land surface hydrological simulations.The ideal experiments conducted at a single point and assimilation experiments carried out over China by the LDAS-G data assimilation system showed that the system developed in this study improved the simulation of land surface hydrological variables,indicating the potential of GRACE data assimilation in large-scale land surface hydrological research and applications.
基金supported by National Basic Research Program of China (Grant No. 2010CB428403)National Natural Science Foundation of China (Grant No.41075076)Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No.KZCX2-EW-QN207)
文摘Bayesian model averaging(BMA) is a recently proposed statistical method for calibrating forecast ensembles from numerical weather models.However,successful implementation of BMA requires accurate estimates of the weights and variances of the individual competing models in the ensemble.Two methods,namely the Expectation-Maximization(EM) and the Markov Chain Monte Carlo(MCMC) algorithms,are widely used for BMA model training.Both methods have their own respective strengths and weaknesses.In this paper,we first modify the BMA log-likelihood function with the aim of removing the addi-tional limitation that requires that the BMA weights add to one,and then use a limited memory quasi-Newtonian algorithm for solving the nonlinear optimization problem,thereby formulating a new approach for BMA(referred to as BMA-BFGS).Several groups of multi-model soil moisture simulation experiments from three land surface models show that the performance of BMA-BFGS is similar to the MCMC method in terms of simulation accuracy,and that both are superior to the EM algo-rithm.On the other hand,the computational cost of the BMA-BFGS algorithm is substantially less than for MCMC and is al-most equivalent to that for EM.
基金Supported by National Natural Science Foundation of China (Grant No. 40705035)National High Technology Research and Development Program of China (Grant Nos. 2007AA12Z144 and 2009AA12Z129)+1 种基金Chinese COPES Project (Grant No. GYHY200706005) the National Basic Research Program (Grant Nos. 2009CB421407 and 2005CB321703)
文摘The concepts of sample sphere radius and sample density are proposed in this paper to help illustrate that different vector transformations result in diverse sample density with the same sample ensemble, which finally affects their assimilation performance. Several numerical experiments using a onedimensional (1-D) soil water equation and synthetic observations are conducted to evaluate this new theory in land data assimilation.