Change trend and attribution analysis of leaf area index in the East African Plateau from 1982 to 2020.

The ecosystems on the East African Plateau are crucial for maintaining the biodiversity, water resource balance, and ecological equilibrium of the African continent. However, the spatiotemporal variations of vegetation and the driving factors remain unclear. We analyzed leaf area index (LAI) change trends in the East African Plateau based on the GIMMS LAI4g dataset and further conducted attribution analysis combining temperature and precipitation data, as well as 10 Dynamic Global Vegetation Models (DGVMs) in TRNEDY v9. The results showed that LAI of the East African Plateau had a modest change trend from 1982 to 1999 (2.5×10-3 m2·m-2·a-1), but significantly increased from 2000 to 2020 (5.2×10-3 m2·m-2·a-1), which was 2.1 times faster than that during 1982-1999. Temperature and precipitation had weak correlations with LAI from 1982 to 1999, but showed significant correlations from 2000 to 2020. The DGVMs demonstrated consistent attribution results, with temperature and precipitation contributing significantly more to the LAI variations from 2000 to 2020 compared to the period from 1982 to 1999. The results highlighted the key role of climate change in driving vegetation greening on the East African Plateau during 2000-2020, which could provide important evidence for ecological conservation and sustainable development strategies in the region.

Emergent constraints on historical and future global gross primary productivity.

Terrestrial gross primary productivity (GPP) is the largest carbon flux in the global carbon cycle and plays a crucial role in terrestrial carbon sequestration. However, historical and future global GPP estimates still vary markedly. In this study, we reduced uncertainties in global GPP estimates by employing an innovative emergent constraint method on remote sensing-based GPP datasets (RS-GPP), using ground-based estimates of GPP from flux towers as the observational constraint. Using this approach, the global GPP in 2001-2014 was estimated to be 126.8 ± 6.4 PgC year-1, compared to the original RS-GPP ensemble mean of 120.9 ± 10.6 PgC year-1, which reduced the uncertainty range by 39.6%. Independent space- and time-based (different latitudinal zones, different vegetation types, and individual year) constraints further confirmed the robustness of the global GPP estimate. Building on these insights, we extended our constraints to project global GPP estimates in 2081-2100 under various Shared Socioeconomic Pathway (SSP) scenarios: SSP126 (140.6 ± 9.3 PgC year-1), SSP245 (153.5 ± 13.4 PgC year-1), SSP370 (170.7 ± 16.9 PgC year-1), and SSP585 (194.1 ± 23.2 PgC year-1). These findings have important implications for understanding and projecting climate change, helping to develop more effective climate policies and carbon reduction strategies.

CMIP6 ESMs overestimate greening and the photosynthesis trends in Dryland East Asia.

The Dryland East Asia (DEA) is one of the largest inland arid regions, and vegetation is very sensitive to climate change. The complex environment in DEA with defects of modeling construction make it difficult to simulate and predict changes in vegetation structure and productivity. Here, we use the emergent constraint (EC) method to constrain the future interannual leaf area index (LAI) and gross primary productivity (GPP) trends in DEA, under four scenarios of the latest Sixth Coupled Model Intercomparison Project (CMIP6) model ensemble. LAI and GPP increase in all scenarios in the near term (2015-2050), with continued growth in SSP370 and SSP585 and stasis in SSP126 and SSP245 in the far term (2051-2100). However, after building effective EC relationships, the constrained increasing trends of LAI (GPP) are reduced by 43.5 %-53.9 % (30.5 %-50.0 %) compared with the uncertainties of the original ensemble, which are reduced by 10.0 %-45.7 % (4.6 %-34.3 %). We also extend the EC in moving windows and grid cells, further strengthening the robustness of the constraints, especially by illustrating spatial sources of these emergent relationships. Overestimations of LAI and GPP trends suggest that current CMIP6 models may be insufficient to capture the complex relationships between climate change and vegetation dynamics in DEA; however, these models can be adjusted based on established emergent relationships.

Constrained CMIP6 projections indicate less warming and a slower increase in water availability across Asia.

Climate projections are essential for decision-making but contain non-negligible uncertainty. To reduce projection uncertainty over Asia, where half the world's population resides, we develop emergent constraint relationships between simulated temperature (1970-2014) and precipitation (2015-2100) growth rates using 27 CMIP6 models under four Shared Socioeconomic Pathways. Here we show that, with uncertainty successfully narrowed by 12.1-31.0%, constrained future precipitation growth rates are 0.39 ± 0.18 mm year-1 (29.36 mm °C-1, SSP126), 0.70 ± 0.22 mm year-1 (20.03 mm °C-1, SSP245), 1.10 ± 0.33 mm year-1 (17.96 mm °C-1, SSP370) and 1.42 ± 0.35 mm year-1 (17.28 mm °C-1, SSP585), indicating overestimates of 6.0-14.0% by the raw CMIP6 models. Accordingly, future temperature and total evaporation growth rates are also overestimated by 3.4-11.6% and -2.1-13.0%, respectively. The slower warming implies a lower snow cover loss rate by 10.5-40.2%. Overall, we find the projected increase in future water availability is overestimated by CMIP6 over Asia.

The Impact of Cropland Abandonment of Post-Soviet Countries on the Terrestrial Carbon Cycle Based on Optimizing the Cropland Distribution Map.

Land use and cover changes (LUCC) have a fundamental impact on the terrestrial carbon cycle. The abandonment of cropland as a result of the collapse of the Soviet Union offers a typical case of the conversion from cropland to natural vegetation, which could have a significant effect on the terrestrial carbon cycle. Due to the inaccuracy of LUCC records, the corresponding impact on the terrestrial carbon cycle has not been well quantified. In this study, we estimated the carbon flux using the Vegetation-Global-Atmosphere-Soil (VEGAS) model over the region of Russia, Belarus and Ukraine during 1990-2017. We first optimized the LUCC input data by adjusting the Food and Agriculture Organization (FAO) data by Russian statistical data and redistributing the spatiotemporal input data from the Historical Database of the Global Environment (HYDE) to the original model. Between 1990 and 2017, the area of cropland abandonment was estimated to be 36.82 Mha, compared to 11.67 Mha estimated by FAO. At the same time, the carbon uptake from the atmosphere to the biosphere was 9.23 GtC (vs fixed cropland 8.24 and HYDE 8.25 GtC) during 1990-2017, which means by optimizing the cropland distribution data, the total carbon absorption during the abandonment process increased by 0.99 GtC. Meanwhile, the growth of the vegetation carbon pool was significantly higher than that of the soil carbon pool. Therefore, we further highlight the importance of accurate cropland distribution data in terrestrial carbon cycle simulation.

Spatial and temporal variations of land use and vegetation cover in Southwest China from 2000 to 2015.

Southwestern China is an important ecologically resource area and ecologically fragile area in China, which plays an important role in the national project of "Clear Waters and Green Mountains". Based on land use data set with a 1 km spatial resolution and combined with land use transfer matrix, we analyzed the characteristics and driving forces of land use change in Southwest China from 2000 to 2015. Based on the MODIS remote sensing index, we calculated the vegetation coverage in Southwest China using the dimidiate pixel model, and analyzed the changes of the normalized vegetation index (NDVI) and vegetation coverage. Results showed that the main land types were woodland, cropland and grassland. The built-up land area increased by 5874 km2(55.8%), the cropland area decreased by 6211 km2, and grassland decreased by 2099 km2. From 2000 to 2015, the area that had been changed to built-up land was the largest, mainly from cropland (contributed 68.2%), woodland (contributed 19.2%) and grassland (contributed 13.1%). The transformed areas were mostly close to urban area. The area and rate for the transformation of cropland were 7079 km2 and 2.2% respectively, accounting for 46.0% of all the transferred out areas. Most of the woodland were transformed from grassland (61.8%), mainly distributed in central and southern Guizhou and western Yunnan. Both NDVI and vegetation coverage were significantly increased, indicating that the whole region was greening. NDVI of both natural vegetation and cropland increased significantly, while the NDVI of areas with expanded build-up land decreased. Therefore, natural vegetation and cropland dominated the vegetation change in this region. Results of the resi-dual analysis showed that both climate change and human activities contributed significantly to the greening trend.

Occurrence and importance of anaerobic ammonium-oxidising bacteria in vegetable soils.

The quantitative importance of anaerobic ammonium oxidation (anammox) has been described in paddy fields, while the presence and importance of anammox in subsurface soil from vegetable fields have not been determined yet. Here, we investigated the occurrence and activity of anammox bacteria in five different types of vegetable fields located in Jiangsu Province, China. Stable isotope experiments confirmed the anammox activity in the examined soils, with the potential rates of 2.1 and 23.2 nmol N2 g(-1) dry soil day(-1), and the anammox accounted for 5.9-20.5% of total soil dinitrogen gas production. It is estimated that a total loss of 7.1-78.2 g N m(-2) year(-1) could be linked to the anammox process in the examined vegetable fields. Phylogenetic analyses showed that multiple co-occurring anammox genera were present in the examined soils, including Candidatus Brocadia, Candidatus Kuenenia, Candidatus Anammoxoglobus and Candidatus Jettenia, and Candidatus Brocadia appeared to be the most common anammox genus. Quantitative PCR further confirmed the presence of anammox bacteria in the examined soils, with the abundance varying from 2.8 × 10(5) to 3.0 × 10(6) copies g(-1) dry soil. Correlation analyses suggested that the soil ammonium concentration had significant influence on the activity and abundance of anammox bacteria in the examined soils. The results of our study showed the presence of diverse anammox bacteria and indicated that the anammox process could serve as an important nitrogen loss pathway in vegetable fields.