During the summer of 2022, a record-breaking heatwave-drought (DH2022) hit southern China, especially in the middle and lower reaches of the Yangtze River basin (MLYR). However, its impact on regional carbon budget remains unclear. Here, we assessed the response of regional terrestrial carbon fluxes to DH2022 using Global Carbon Assimilation System (GCASv2) by assimilating OCO-2 XCO2 retrievals. Our results show that compared to 2015-2021, MLYR experienced a clear reduction in carbon uptake by 45.8 TgC (44.1%) during July-September in 2022. However, the total annual carbon sink in southern China increased by 42.8 TgC. This is due to a warm and wet spring, leading to an increase in carbon sink by 50.8 TgC and greatly compensating for the carbon losses. Our study provides a comprehensive assessment of land carbon dynamics in southern China under the influence of DH2022, enhancing understanding of the impacts of climate extremes on the regional carbon cycle.

The terrestrial ecosystems of Qinghai-Tibet Plateau (QTP) are highly sensitive to climate change, yet the magnitude and distribution of the carbon budget for QTP remain quite uncertain. Here, utilizing long short-term memory networks (LSTM), in conjunction with available eddy covariance flux data from recent extensive observation campaigns, multiple satellite land surface data, and observation-based environmental data (e.g., soil organic carbon, SOC), we revisit the regional carbon budget estimation over the QTP with a special focus on investigating the impacts of considering memory effect and incorporating SOC. Our estimate points the QTP region to a mean carbon sink of 20.89 Tg C yr-1 during 2003–2018. Spatially, the major sinks distribute in the western and northern QTP dominated by alpine steppes, while major sources in the eastern QTP dominated by alpine meadows. During the study period, the regional sink declines at the rate of 0.0003 Tg C yr-2, which is primarily contributed by the reduced carbon sink of alpine steppes and the increased carbon source of alpine meadows. We found that considering memory effect and incorporating SOC are critical for estimating the regional carbon budget for QTP. Without considering memory effect leads to a huge carbon source of 161.10 Tg C yr-1, with unreasonable seasonal and interannual variation of carbon budgets. Without incorporating SOC leads to a larger estimated carbon sink (61.94 Tg C yr-1), with clearly overestimated sink in steppes ecosystems and underestimated source in meadows ecosystems. Our study provides new insights into the carbon budget estimation for the QTP region.

Accurate estimation of regional-scale terrestrial carbon budgets is of great importance but remains challenging. With particular advantages, the Long Short-Term Memory (LSTM) networks method show potential in improving regional carbon budget upscaling estimations. Here, based on LSTM, we upscale regional net ecosystem carbon exchange (NEE) with available flux tower measurements and satellite land surface observations in North America. With well-established ecosystem-specific LSTMs, we produced monthly NEE at a spatial resolution of 0.1°×0.1° over 2001–2021 (labeled as CROSS2023). Our estimate pointed the largest carbon sink to the Midwest Corn-Belt area during peak growing seasons and to the Southeast on an annual basis, agreeing with empirical knowledges. Moreover, the estimated seasonal variations of NEE by CROSS2023 coincided well with those by atmospheric inversions, i.e., the ensemble mean of Orbiting Carbon Observatory-2 Model Intercomparison Project (OCO-2 v10 MIP; r = 0.95, p < 0.001) and CarbonTracker2022 (CT2022) (r = 0.97, p < 0.001). The mean annual NEE was estimated at -1.27 ± 0.12 Pg C yr-1, aligning more closely with the inversions (-0.70 to -0.63 Pg C yr-1) than with existing upscaling estimates (-3.30 to -1.81 Pg C yr-1). In addition, our estimate plausibly captured the NEE spatial anomalies caused by all the recent extreme drought and flood events. We further confirmed that considering memory effects was critical for better indicating interannual variability and spatial anomalies of NEE induced by climate extremes. This study provides an improved bottom-up estimation of North American NEE, largely narrowing the gap with top-down inversions.

Remotely sensed solar-induced fluorescence (SIF) has emerged as a novel approach for terrestrial vegetation monitoring. The in situ continuous optical remote sensing tool in conjunction with concurrent eddy covariance (EC) flux measurements provides a new opportunity to advance terrestrial ecosystem science. Here we introduce a network of ground-based SIF observations at flux tower sites across the mainland China referred as ChinaSpec. Until now, it consists of 15 tower sites including 5 cropland sites, 4 grassland sites, 4 forest sites and 2 wetland sites. At each of these sites, an automated spectroscopy system was deployed to collect continuous super-high resolution spectra for high-frequency SIF retrievals in synergy with EC flux measurements. The goal of ChinaSpec is to provide ground SIF measurements and promote the collaborations between optical remote sensing and EC flux communities in China. We present here the details of instrument specifications, data collection and processing procedures, data sharing and utilization protocols, and future plans. Furthermore, we show the examples how ground SIF observations can be used to track vegetation photosynthesis from diurnal to seasonal scales, to assist in the validation of fluorescence models and satellite SIF products (e.g., from OCO-2, TanSat and TROPOMI) with the measurements from these sites since 2016. This network of SIF observations could improve our understanding of the controls on the biosphere-atmosphere carbon exchange and enable the improvement of carbon flux predictions. This SIF network will also help integrate ground SIF measurements with EC flux networks which will advance ecosystem and carbon cycle researches globally.

Remotely sensed solar-induced fluorescence (SIF) has emerged as a novel and powerful approach for terrestrial vegetation monitoring. Continuous measurements of SIF in synergy with concurrent eddy covariance (EC) flux measurements can provide a new opportunity to advance terrestrial ecosystem science. Here we introduce a network of ground-based continuous SIF observations at flux tower sites across the mainland China referred to as ChinaSpec. The network consists of sixteen tower sites including 6 cropland sites, 4 grassland sites, 4 forest sites and 2 wetland sites. An automated SIF system was deployed at each of these sites to collect continuous high resolution spectra for high-frequency SIF retrievals in synergy with EC flux measurements. The goal of ChinaSpec is to provide long-term ground-based SIF measurements and promote the collaborations between optical remote sensing and EC flux observation communities in China. We present here the details of instrument specifications, data collection and processing procedures, data sharing and utilization protocols, and future plans. Furthermore, we show the examples how ground-based SIF observations can be used to track vegetation photosynthesis from diurnal to seasonal scales, and to assist in the validation of fluorescence models and satellite SIF products (e.g., from OCO-2 and TROPOMI) with the measurements from these sites since 2016. This network of SIF observations could improve our understanding of the controls on the biosphere-atmosphere carbon exchange and enable the improvement of carbon flux predictions. It will also help integrate ground-based SIF measurements with EC flux networks which will advance ecosystem and carbon cycle researches globally.

Satellite XCO2 retrievals could improve the estimates of surface carbon fluxes, but it remains unknow on what scales these estimates are robust. Here, we use the time-dependent Bayesian synthesis top-down method and prior net ecosystem exchanges (NEEs) from 12 terrestrial biosphere models (TBMs) to infer the monthly carbon fluxes of 51 land regions with constraints by GOSAT XCO2 retrievals. We find that the uncertainty (standard deviation of 12 TBMs) reduction rates (URR) decrease significantly at decreasing spatial scales. On the continental-scale, the mean URR is about 60%, and the annual and seasonal cycle estimates of NEE are rather robust. The evaluation shows that the posterior CO2 concentrations are significantly improved at the continental scale. Our study suggests that the GOSAT XCO2 can only promise a robust continental-scale NEE estimate, and improving the XCO2 accuracy is an effective way to achieve robust estimates on smaller scales under current spatial coverage.