| 1. |
- He, Wei, et al.
(författare)
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China's Terrestrial Carbon Sink Over 2010–2015 Constrained by Satellite Observations of Atmospheric CO2 and Land Surface Variables
- 2022
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Ingår i: Journal of Geophysical Research: Biogeosciences. - 2169-8953. ; 127:2
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Tidskriftsartikel (refereegranskat)abstract
- The magnitude and distribution of China's terrestrial carbon sink remain uncertain due to insufficient constraints at large scales, whereby satellite data offer great potential for reducing the uncertainty. Here, we present two carbon sink estimates for China constrained either by satellite CO2 column concentrations (XCO2) within the Global Carbon Assimilation System or by remotely sensed soil moisture and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) in addition to in situ CO2 observations within the Carbon Cycle Data Assimilation System. They point to a moderate size of carbon sinks of 0.34 ± 0.14 (mean ± unc.) and 0.43 ± 0.09 PgC/yr during 2010–2015, which are supported by an inventory-based estimate for forest and soil carbon sink (0.26 PgC/yr) and fall in the range of contemporary ensemble atmospheric inversions (0.25–0.48 PgC/yr). They also agree reasonably well on interannual variations, which reflect the carbon sink anomalies induced by regional droughts in southwest China. Furthermore, their spatial distributions are broadly consistent that of the forest inventory-based estimate, indicating that the largest carbon sinks locate in central and eastern China. Their estimates for forest carbon sink coincide fairly well with the inventory-based estimate across different regions, especially when aggregated to the north and south of China. Although enhanced recently by afforestation, China's carbon sink was also significantly weakened by regional droughts, which were often not fully represented in previous in situ CO2-based inversions due to insufficient observations. Our results suggest that satellite-based atmospheric CO2 and land surface observations are vital in characterizing terrestrial net carbon fluxes at regional scales.
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| 2. |
- Lu, Haibo, et al.
(författare)
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Comparing machine learning-derived global estimates of soil respiration and its components with those from terrestrial ecosystem models
- 2021
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Ingår i: Environmental Research Letters. - : IOP Publishing. - 1748-9318 .- 1748-9326. ; 16:5
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Tidskriftsartikel (refereegranskat)abstract
- The CO2 efflux from soil (soil respiration (SR)) is one of the largest fluxes in the global carbon (C) cycle and its response to climate change could strongly influence future atmospheric CO2 concentrations. Still, a large divergence of global SR estimates and its autotrophic (AR) and heterotrophic (HR) components exists among process based terrestrial ecosystem models. Therefore, alternatively derived global benchmark values are warranted for constraining the various ecosystem model output. In this study, we developed models based on the global soil respiration database (version 5.0), using the random forest (RF) method to generate the global benchmark distribution of total SR and its components. Benchmark values were then compared with the output of ten different global terrestrial ecosystem models. Our observationally derived global mean annual benchmark rates were 85.5 ± 40.4 (SD) Pg C yr-1 for SR, 50.3 ± 25.0 (SD) Pg C yr-1 for HR and 35.2 Pg C yr-1 for AR during 1982-2012, respectively. Evaluating against the observations, the RF models showed better performance in both of SR and HR simulations than all investigated terrestrial ecosystem models. Large divergences in simulating SR and its components were observed among the terrestrial ecosystem models. The estimated global SR and HR by the ecosystem models ranged from 61.4 to 91.7 Pg C yr-1 and 39.8 to 61.7 Pg C yr-1, respectively. The most discrepancy lays in the estimation of AR, the difference (12.0-42.3 Pg C yr-1) of estimates among the ecosystem models was up to 3.5 times. The contribution of AR to SR highly varied among the ecosystem models ranging from 18% to 48%, which differed with the estimate by RF (41%). This study generated global SR and its components (HR and AR) fluxes, which are useful benchmarks to constrain the performance of terrestrial ecosystem models.
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| 3. |
- Wang, Meirong, et al.
(författare)
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Recent recovery of the boreal spring sensible heating over the Tibetan Plateau will continue in CMIP6 future projections
- 2019
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Ingår i: Environmental Research Letters. - : IOP Publishing. - 1748-9326. ; 14:12, s. 1-9
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Tidskriftsartikel (refereegranskat)abstract
- The spring sensible heating (SH) over the Tibetan Plateau (TP) serves as a huge 'air pump', significantly influencing the Asian summer monsoon, has experienced a decreasing trend. However, it remains unclear whether this decline will continue. Therefore, we here examine the long-term trends of spring SH over the central and eastern TP (CETP) based on a meteorological station-based calculated SH dataset, and CMIP6 multi-model simulations. These two sources confirmed the previous finding that the SH peaks in May. Further, we find that the declining SH was replaced by a fast recovery after approximate 2000 in the station-based SH. This is to some extent verified by the historical simulations of CMIP6 models. Importantly, CMIP6 future projections suggest that this increasing trend will continue, and get stronger with higher radiative forcing from SSP126 to SSP585. Mechanism analysis indicates that the previous decreasing trend in SH was mainly caused by the decline of 10 m wind speed, while the recent and future increasing trend results from the rising ground-air temperature difference. We suggest that this increasing trend of spring SH over the CETP may serve as an alternative driver for the enhancement of the East Asian summer monsoon in the future.
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| 4. |
- Wu, Mousong, et al.
(författare)
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Regional Responses of Vegetation Productivity to the Two Phases of ENSO
- 2024
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Ingår i: GEOPHYSICAL RESEARCH LETTERS. - 0094-8276 .- 1944-8007. ; 51:8
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Tidskriftsartikel (refereegranskat)abstract
- The two phases of El-Nino-Southern Oscillation (ENSO) influence both regional and global terrestrial vegetation productivity on inter-annual scales. However, the major drivers for the regional vegetation productivity and their controlling strengths during different phases of ENSO remain unclear. We herein disentangled the impacts of two phases of ENSO on regional carbon cycle using multiple data sets. We found that soil moisture predominantly accounts for similar to 40% of the variability in regional vegetation productivity during ENSO events. Our results showed that the satellite-derived vegetation productivity proxies, gross primary productivity from data-driven models (FLUXCOM) and observation-constrained ecosystem model (Carbon Cycle Data Assimilation System) generally agree in depicting the contribution of soil moisture and air temperature in modulating regional vegetation productivity. However, the ensemble of weakly constrained ecosystem models exhibits non-negligible discrepancies in the roles of vapor pressure deficit and radiation over extra-tropics. This study highlights the significance of water in regulating regional vegetation productivity during ENSO. ENSO, a significant climate phenomenon, profoundly influences ecosystem productivity from regional to global scales. Yet, accurately pinpointing the attributions of different climate factors that control Gross Primary Productivity (GPP) during ENSO events remains challenging, partly due to large uncertainties in existing GPP data. Through extensive analysis using various data sets, we found that soil moisture plays a dominant role in controlling GPP across most continents. This implies that current terrestrial biosphere models might underestimate the importance of soil moisture in driving productivity during ENSO events. Addressing this gap in understanding is crucial for refining and properly constraining the related processes in terrestrial biosphere models. Soil moisture predominantly governs vegetation productivity changes during El-Nino-Southern Oscillation (ENSO) events Satellite-derived vegetation indices consistently support the predominant influence of water on vegetation productivity ENSO triggers varying effects on vegetation productivity across different regions, with distinct impacts during El Nino and La Nina
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| 5. |
- Xing, Xiuli, et al.
(författare)
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Modeling China's terrestrial ecosystem gross primary productivity with BEPS model : Parameter sensitivity analysis and model calibration
- 2023
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Ingår i: Agricultural and Forest Meteorology. - 0168-1923. ; 343
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Tidskriftsartikel (refereegranskat)abstract
- Terrestrial ecosystems are the largest sink for carbon, and their ecosystem gross primary productivity (GPP) regulates variations in atmospheric carbon dioxide (CO2) concentrations. Current process-based ecosystem models used for estimating GPP are subject to large uncertainties due to poorly constrained parameter values. In this study, we implemented a global sensitivity analysis (GSA) on parameters in the Boreal Ecosystem Productivity Simulator (BEPS) considering the parameters’ second-order impacts. We also applied the generalized likelihood estimation (GLUE) method, which is flexible for a multi-parameter calibration, to optimize the GPP simulation by BEPS for 10 sites covering 7 plant functional types (PFT) over China. Our optimized results significantly reduced the uncertainty of the simulated GPP over all the sites by 17 % to 82 % and showed that the GPP is sensitive to not only the photosynthesis-related parameters but also the parameters related to the soil water uptake as well as to the energy balance. The optimized GPP across South China showed that the mix forest, shrub, and grass have a higher GPP and are more controlled by the soil water availability. This study showed that the GLUE method together with the GSA scheme could constrain the ecosystem model well when simulating GPP across multiple ecosystems and provide a reasonable estimate of the spatial and temporal distribution of the ecosystem GPP over China. We call for more observations from more sites, as well as data on plant traits, to be collected in China in order to better constrain ecosystem carbon cycle modeling and understand its response to climate change.
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| 6. |
- Xing, Xiuli, et al.
(författare)
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Soil Moisture Assimilation Improves Terrestrial Biosphere Model GPP Responses to Sub-Annual Drought at Continental Scale
- 2023
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 15:3
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Tidskriftsartikel (refereegranskat)abstract
- Due to the substantial gross exchange fluxes with the atmosphere, the terrestrial carbon cycle plays a significant role in the global carbon budget. Drought commonly affects terrestrial carbon absorption negatively. Terrestrial biosphere models exhibit significant uncertainties in capturing the carbon flux response to drought, which have an impact on estimates of the global carbon budget. Through plant physiological processes, soil moisture tightly regulates the carbon cycle in the environment. Therefore, accurate observations of soil moisture may enhance the modeling of carbon fluxes in a model–data fusion framework. We employ the Carbon Cycle Data Assimilation System (CCDAS) to assimilate 36-year satellite-derived surface soil moisture observations in combination with flask samples of atmospheric CO2 concentrations. We find that, compared to the default model, the performance of optimized net ecosystem productivity (NEP) and gross primary productivity (GPP) has increased with the RMSEs reduced by 1.62 gC/m2/month and 10.84 gC/m2/month, which indicates the added value of the ESA-CCI soil moisture observations as a constraint on the terrestrial carbon cycle. Additionally, the combination of soil moisture and CO2 concentration in this study improves the representation of inter-annual variability of terrestrial carbon fluxes as well as the atmospheric CO2 growth rate. We thereby investigate the ability of the optimized GPP in responding to drought by comparing continentally aggregated GPP with the drought index. The assimilation of surface soil moisture has been shown to efficiently capture the influences of the sub-annual (≤9 months drought durations) and large-scale (e.g., regional to continental scales) droughts on GPP. This study highlights the significant potential of satellite soil moisture for constraining inter-annual models of the terrestrial biosphere’s carbon cycle and for illustrating how GPP responds to drought at a continental scale.
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