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The terrestrial carbon cycle is an important component of global biogeochemical cycling and is closely related to human well-being and sustainable development. However, large uncertainties exist in carbon cycle simulations and observations. Model-data fusion is a powerful technique that combines models and observational data to minimize the uncertainties in terrestrial carbon cycle estimation. In this paper, we comprehensively overview the sources and characteristics of the uncertainties in terrestrial carbon cycle models and observations. We present the mathematical principles of two model-data fusion methods, i.e., data assimilation and parameter estimation, both of which essentially achieve the optimal fusion of a model with observational data while considering the respective errors in the model and in the observations. Based upon reviewing the progress in carbon cycle models and observation techniques in recent years, we have highlighted the major challenges in terrestrial carbon cycle model-data fusion research, such as the “equifinality” of models, the identifiability of model parameters, the estimation of representativeness errors in surface fluxes and remote sensing observations, the potential role of the posterior probability distribution of parameters obtained from sensitivity analysis in determining the error covariance matrixes of the models, and opportunities that emerge by assimilating new remote sensing observations, such as solar-induced chlorophyll fluorescence. It is also noted that the synthesis of multisource observations into a coherent carbon data assimilation system is by no means an easy task, yet a breakthrough in this bottleneck is a prerequisite for the development of a new generation of global carbon data assimilation systems. This article also highlights the importance of carbon cycle data assimilation systems to generate reliable and physically consistent terrestrial carbon cycle reanalysis data products with high spatial resolution and long-term time series. These products are critical to the accurate estimation of carbon cycles at the global and regional scales and will help future carbon management strategies meet the goals of carbon neutrality.

Lakes in the permafrost zone have been proposed to serve as key outlets for methane and carbon dioxide emissions. However, there has been no geological record of the hydrological and biogeochemical responses of lakes throughout the thawing of surrounding permafrost. We use multiple biomarker and isotopic proxies to reconstruct hydrological and biogeo-chemical changes in Lake Wudalianchi in northeastern China during regional thawing of the permafrost. We show permafrost thawing, as indicated by lignin degradation, initiated rapid lake water freshening as a result of the opening of groundwater conduits, and negative organic δ13C excursion due to increased inorganic and organic carbon fluxes. These hydro-logical changes were followed, with an ∼5–7 yr delay, by abrupt and persistent increases in microbial lake methanotrophy and methanogenesis, indicating enhanced anaerobic organic decomposition and methane emissions from lakes as permafrost thaws. Our data provide a detailed assessment of the processes involved during permafrost thaw, and highlight the importance of lakes in ventilating greenhouse gases to the atmosphere.

Agricultural ecosystems are important for regulating terrestrial hydrological and carbon cycles. Hydrological and carbon processes in agricultural ecosystem models are complex due to interactions between parameters. It is therefore crucial to identify parameter sensitivity before a process-based model is applied for simulations and predictions of water, energy and carbon fluxes in agricultural ecosystems. In this study, we investigated the sensitivity and equifinality of the CoupModel parameters in modeling an arid agricultural ecosystem in northwestern China. In total, 27 model parameters were analyzed using a global parameters sensitivity analysis approach and a combination of multiple in situ and remotely sensed data sets. Among the five major model processes, we found that the energy balance process account for much of the importance in the model, followed by soil hydrology, plant growth, soil heat, and soil carbon processes. Meanwhile, parameters from the plant growth process exhibited higher equifinalities than other processes. We found that net ecosystem exchange (NEE) is controlled by soil heat, soil hydrology and energy balance processes, which is mainly due to a high equifinality (0.91) between the parameters g max (maximal stomatal conductance) and V cmax (maximal carboxylation rate). The equifinalities between different parameters result in a trade-off in model performance metrics (i.e. determination coefficient R 2 and mean error ME) in the water, energy and carbon balance simulations. We revealed that daytime and yearly accumulated eddy fluxes (sensible heat H s , latent heat LE and NEE) can constrain the model parameters better. Remotely sensed data were also promising as additional constraints on soil water contents and energy fluxes. This study introduced a systematic global parameter sensitivity analysis approach together with the equifinality identification in an ecosystem model. The approach proposed here is applicable to other studies and the equifinalities detected in this study can be important implications for modelling arid agricultural ecosystems. Additional exploration on remotely sensed data in constraining the model from different aspects are highly recommended in modeling agricultural ecosystems.

Gross Primary Production (GPP) is the sum of carbon absorbed by plant canopy. It is a key measurement of carbon mass flux in carbon cycle studies. Remote sensing based light use efficiency model is a widely used method to estimate regional GPP. In this study, MODIS-PSN was used to estimate GPP in Heihe River Basin. In order to better the model accuracy, maximum light use efficiency ( e 0) in MODIS-PSN is estimated using local observed carbon flux data and meteorological data. After adjustment of parameter e 0, MODIS-PSN can correctly estimate GPP for major vegetation type in the Heihe River Basin. Then, yearly GPP over Heihe River Basin was estimated. The results indicated that about 1.4*10 13 g carbon enter terrestrial ecosystem through vegetation photosynthesis in the Heihe River Basin one year. In contrast, there is just 5.73*10 13 g carbon enter terrestrial ecosystem according to the standard MODIS GPP product, which is greatly underestimated GPP in the Heihe River Bain.