Search

Accurate quantification of regional terrestrial fluxes is essential for improving our knowledge of the carbon sequestration potential of ecosystems, ecosystem functioning, and emission reduction demand in the context of climate change mitigation. However, the quantification is challenging owing to methodological and observational constraints, especially for regions with severe gaps in the ground-based observational network, like India. This study examines the potential of recent satellite missions, such as TROPOMI and OCO-2 providing retrievals of Solar-Induced chlorophyll Fluorescence (SIF) to improve terrestrial biosphere CO2 flux estimates over India. Here, we present high-resolution estimates of Gross Primary Productivity (GPP) and Net Ecosystem Exchange (NEE) over India on a 0.1°×0.1° grid at a temporal resolution of 1 hour from 2012 to 2020. These products can be used for various applications such as those related to carbon cycle (e.g., inverse modelling of CO2), benchmarking terrestrial biosphere models over the region, and understanding ecosystem responses to climate change. We follow a satellite-based diagnostic data-driven approach using a biosphere model, namely the Vegetation Photosynthesis and Respiration Model (VPRM) simulating both GPP and NEE, based on light use efficiency and satellite observations of the near-infrared radiance of vegetation (NIRv). We calibrate the standard VPRM GPP estimates using SIF-GPP relationship and investigate the model performance by comparing the simulations with eddy-covariance flux tower measurements. Our best model predictions are with a mean bias error (MBE) = 2.4 µmol m-2 s-1, root mean squared error (RMSE) = 3.8 µmol m-2 s-1 and squared correlation coefficient (R²) = 0.56 when evaluating with observations at a monthly scale over the period from 2012 to 2018. The observed seasonal anomalies in NEE and GPP range from -4.9 to 8.0 µmol m-2 s-1 and -7.0 to 17.0 µmol m-2 s-1, respectively, and are well captured by our model. The model simulations are highly correlated with observations during 2018, the only common year when both EC and SIF observations are available, with R² values of 0.68 and 0.74 for NEE and GPP, respectively. Incorporating the SIF signals in the vegetation model improves model performance in capturing the seasonality and magnitudes of GPP, thereby improving the estimates of NEE. We show the influence of soil temperature and soil moisture on ecosystem respiration and refined the VPRM's ecosystem respiration calculation to better constrain the fluxes, resulting in simulations closer to the observations. Ecosystem respiration fluxes are less well constrained than ecosystem productivity fluxes due to the limited observations. Based on satellite observations and the refined model, the annual NEE and GPP estimates range from -0.38 Pg C yr-1 to -0.53 Pg C yr-1 (land C sink) and 3.39 Pg C yr-1 to 3.88 Pg C yr-1, respectively over India for the years from 2012 to 2020. The biospheric flux distribution over the region is found to be associated with ecosystem heterogeneity, and variations in precipitation, and soil characteristics at a regional scale. Overall, our results show that the satellite-based SIF data products can potentially inform the ecosystem-scale vegetation responses across biomes over India. Future improvements in the terrestrial biosphere CO2 flux estimates over India can be attained through the carbon cycle data assimilation with the availability of both flux and mixing ratio observations of CO2. [ABSTRACT FROM AUTHOR]

Satellite XCO2 retrievals could improve the estimates of surface carbon fluxes, but it remains unknown 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 (uncertainty reduction rate (URR)) decrease significantly at decreasing spatial scales. On the continental‐scale, the mean URR is about 57%, 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. Plain Language Summary: Remotesensing‐based CO2 measurement can improve the estimates of surface carbon fluxes due to its relatively well global coverage, but it remains unknown on what spatial scales the satellite observation could provide a robust estimate. Here, net ecosystem exchanges (NEEs) from 12 terrestrial biosphere models (TBMs) of 51 land regions for the period of 2011–2014 are constrained using GOSAT XCO2 retrievals, and the uncertainty (standard deviation of 12 TBMs) reduction rates (uncertainty reduction rate (URR)) at different spatial scales are analyzed. We find that (a) from the whole globe to the mean of 51 regions, the URR decreases from 95% to 16%. (b) On the continental‐scale, the mean URR is about 57%, and the annual NEEs in Asia, N. America, Europe, S. America, Africa, and Australia are estimated to be −2.07 ± 0.25, −0.85 ± 0.09, −0.76 ± 0.21, −0.36 ± 0.17, −0.36 ± 0.30, and −0.15 ± 0.12 PgC yr−1, respectively. 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 satellite observing capacity. Key Points: Terrestrial carbon sinks estimated based on GOSAT XCO2 and net ecosystem exchanges (NEE) from 12 terrestrial biosphere models using atmospheric inverse methodThe uncertainty reduction rates decrease significantly at decreasing spatial scalesThe GOSAT XCO2 can only promise a robust continental‐scale NEE estimate [ABSTRACT FROM AUTHOR]

As the focus of climate policy shifts from pledges to implementation, there is a growing need to track progress on climate change mitigation at the country level, particularly for the land-use sector. Despite new tools and models providing unprecedented monitoring opportunities, striking differences remain in estimations of anthropogenic land-use CO 2 fluxes between, on the one hand, the national greenhouse gas inventories (NGHGIs) used to assess compliance with national climate targets under the Paris Agreement and, on the other hand, the Global Carbon Budget and Intergovernmental Panel on Climate Change (IPCC) assessment reports, both based on global bookkeeping models (BMs). Recent studies have shown that these differences are mainly due to inconsistent definitions of anthropogenic CO 2 fluxes in managed forests. Countries assume larger areas of forest to be managed than BMs do, due to a broader definition of managed land in NGHGIs. Additionally, the fraction of the land sink caused by indirect effects of human-induced environmental change (e.g. fertilisation effect on vegetation growth due to increased atmospheric CO 2 concentration) on managed lands is treated as non-anthropogenic by BMs but as anthropogenic in most NGHGIs. We implement an approach that adds the CO 2 sink caused by environmental change in countries' managed forests (estimated by 16 dynamic global vegetation models, DGVMs) to the land-use fluxes from three BMs. This sum is conceptually more comparable to NGHGIs and is thus expected to be quantitatively more similar. Our analysis uses updated and more comprehensive data from NGHGIs than previous studies and provides model results at a greater level of disaggregation in terms of regions, countries and land categories (i.e. forest land, deforestation, organic soils, other land uses). Our results confirm a large difference (6.7 GtCO 2 yr -1) in global land-use CO 2 fluxes between the ensemble mean of the BMs, which estimate a source of 4.8 GtCO 2 yr -1 for the period 2000–2020, and NGHGIs, which estimate a sink of -1.9 GtCO 2 yr -1 in the same period. Most of the gap is found on forest land (3.5 GtCO 2 yr -1), with differences also for deforestation (2.4 GtCO 2 yr -1), for fluxes from other land uses (1.0 GtCO 2 yr -1) and to a lesser extent for fluxes from organic soils (0.2 GtCO 2 yr -1). By adding the DGVM ensemble mean sink arising from environmental change in managed forests (-6.4 GtCO 2 yr -1) to BM estimates, the gap between BMs and NGHGIs becomes substantially smaller both globally (residual gap: 0.3 GtCO 2 yr -1) and in most regions and countries. However, some discrepancies remain and deserve further investigation. For example, the BMs generally provide higher emissions from deforestation than NGHGIs and, when adjusted with the sink in managed forests estimated by DGVMs, yield a sink that is often greater than NGHGIs. In summary, this study provides a blueprint for harmonising the estimations of anthropogenic land-use fluxes, allowing for detailed comparisons between global models and national inventories at global, regional and country levels. This is crucial to increase confidence in land-use emissions estimates, support investments in land-based mitigation strategies and assess the countries' collective progress under the Global Stocktake of the Paris Agreement. Data from this study are openly available online via the Zenodo portal (Grassi et al., 2023) at 10.5281/zenodo.7650360. [ABSTRACT FROM AUTHOR]

Vegetation greening has been widely occurring on the Chinese Loess Plateau, and the contributions of human land-use management have been well-understood. However, the influences of climatic change and CO2 fertilization on reported vegetation variations remain difficult to determine. Therefore, we quantified the impacts of multiple factors on vegetation changes for the Chinese Loess Plateau from 2000 to 2019 by integrating satellite-based leaf area index (LAI) and simulated LAI from dynamic global vegetation models. More than 96% of the vegetated areas of the Loess Plateau exhibited greening trends, with an annually averaged satellite-based LAI rate of 0.037 ± 0.006 m2 m−2 a−1 (P < 0.01). Human land-use management and environmental change have jointly accelerated vegetation growth, explaining 54% and 46% of the overall greening trend, respectively. CO2 fertilization and climate change explain 55% and 45% of the greening trend due to environmental change, respectively; solar radiation and precipitation were the main driving factors for climate-induced vegetation greenness (P < 0.05). Spatially, the eastern part of the Loess Plateau was dominated by CO2 fertilization, while the western part was mainly affected by climate change. Furthermore, solar radiation was the key limiting factor affecting LAI variations in the relatively humid area, while precipitation was the major influencing factor in relatively arid areas. This study highlights the important roles that climate change and CO2 fertilization have played in vegetation greenness in recent decades of the Loess Plateau, despite strong influences of anthropogenic footprint. [ABSTRACT FROM AUTHOR]