Science China Earth Sciences最新文献

“Double ITCZ” is a common precipitation bias over the tropical Pacific in current climate models and Earth system models, but the reasons for its formation are still worth exploring and discussing. In this study, we adopted the second version of Chinese Academy of Sciences Earth System Model (CAS-ESM2), by comparing a set of sea surface temperature (SST) bias correction experiments over the tropical Pacific, to explore the possible mechanism of SST bias on inducing the “Double ITCZ” from the perspective of the climatic annual mean bias in the coupled model. We revealed that the simulated climatic annual mean SST bias over the tropical Pacific can affect the bias of latent heat flux through the saturation specific humidity, and the bias of latent heat flux can further affect that of vertical velocity of humid air by the condensation release mechanism, and finally modulate the simulated bias in precipitation. Furthermore, through the inter-comparison between different experiments, it is found that the source of Double ITCZ might mainly come from the annual mean SST bias over the tropical western Pacific through the proposed air-sea coupled process of “SST-saturated specific humidity-latent heat flux-vertical velocity-precipitation”, indicating a possible way on reducing the couple biases in models over the tropical Pacific to improve the accuracy of CAS-ESM2 for climate simulation.

Plants require a number of essential elements in different proportions for ensuring their growth and development. The elemental concentrations in leaves reflect the functions and adaptations of plants under specific environmental conditions. However, less is known about how the spectrum of leaf elements associated with resource acquisition, photosynthesis and growth regulates forest biomass along broad elevational gradients. We examined the influence of leaf element distribution and diversity on forest biomass by analyzing ten elements (C, N, P, K, Ca, Mg, Zn, Fe, Cu, and Mn) in tree communities situated every 100 meters along an extensive elevation gradient, ranging from the tropical forest (80 meters above sea level) to the alpine treeline (4200 meters above sea level) in the Kangchenjunga Landscape in eastern Nepal Himalayas. We calculated community-weighted averages (reflecting dominant traits governing biomass, i.e., mass-ratio effect) and functional divergence (reflecting increased trait variety, i.e., complementarity effect) for leaf elements in a total of 1,859 trees representing 116 species. An increasing mass-ratio effect and decreasing complementarity in leaf elements enhance forest biomass accumulation. A combination of elements together with elevation explains biomass (52.2% of the variance) better than individual elemental trait diversity (0.05% to 21% of the variance). Elevation modulates trait diversity among plant species in biomass accumulation. Complementarity promotes biomass at lower elevations, but reduces biomass at higher elevations, demonstrating an interaction between elevation and complementarity. The interaction between elevation and mass-ratio effect produces heterogeneous effects on biomass along the elevation gradient. Our research indicates that biomass accumulation can be disproportionately affected by elevation due to interactions among trait diversities across vegetation zones. While higher trait variation enhances the adaptation of species to environmental changes, it reduces biomass accumulation, especially at higher elevations.

Northeast China is an important base for grain production, dominated by rain-fed agriculture that relies on green water. However, in the context of global climate change, rising regional temperatures, changing precipitation patterns, and increasing drought frequency pose threats and challenges to agricultural green water security. This study provides a detailed assessment of the spatiotemporal characteristics and development trends of green water security risks in the Northeast region under the base period (2001–2020) and the future (2031–2090) climate change scenarios (SSP245 and SSP585) using the green water scarcity (GWS) index based on raster-scale crop spatial distribution data, Delta downscaling bias-corrected ERA5 data, and CMIP6 multimodal data. During the base period, the green water risk-free zone for dry crops is mainly distributed in the center and east of the Northeast region (72.4% of the total area), the low-risk zone is primarily located in the center (14.0%), and the medium-risk (8.3%) and high-risk (5.3%) zones are mostly in the west. Under SSP245 and SSP585 future climate change scenarios, the green water security risk shows an overall expansion from the west to the center and east, with the low-risk zone increasing to 21.6% and 23.8%, the medium-risk zone increasing to 16.0% and 17.9%, and the high-risk zone increasing to 6.9% and 6.8%, respectively. Considering dry crops with GWS greater than 0.1 as in need of irrigation, the irrigated area increases from 27.6% (base period) to 44.5% (SSP245) and 48.6% (SSP585), with corresponding increases in irrigation water requirement (IWR) of 4.64 and 5.92 billion m³, respectively, which further exacerbates conflicts between supply and demand of agricultural water resources. In response to agricultural green water security risks, coping strategies such as evapotranspiration (ET)-based water resource management for dry crops and deficit irrigation are proposed. The results of this study can provide scientific basis and decision support for the development of Northeast irrigated agriculture and the construction planning of the national water network.

Cement is a widely used construction material globally. Its manufacturing contributes to anthropogenic CO₂ emissions significantly. However, its alkaline compounds can absorb CO₂ from the surrounding environment and engage in a carbonation reaction, thereby functioning as a carbon sink. As a major cement producer and consumer, China has an important responsibility to rigorously investigate and accurately account for cement carbon uptake. This study presents a comprehensive analytical model of cement carbon uptake from China, revealing a substantial increase in carbon uptake from 1930 to 2021, peaking at 426.77 MtCO₂ (95% Confidence Interval: 317.67–874.33 Mt CO₂) in 2021. The uptake accounts for 8.10% to 45.40% of China’s annual land sink and 2.51% to 4.54% of the global land sink. The cumulative carbon uptake by cement is approximately 7.06 Gt CO₂ (95% CI: 5.22–9.44 Gt CO₂) during this period, offsetting 50.7% of the total emissions (13.91 Gt CO₂, 95% CI: 12.44–17.00 Gt CO₂) from the cement industry. Notably, cement mortar contributed to most absorption (65.64%). From a life cycle perspective, the service stage of cement materials is the period where the largest CO₂ sink is formed, accounting for 90.03% of the total. Therefore, the potential for carbon sequestration in cement materials and their waste is enormous. Additionally, the model improves the accuracy of cement carbon accounting, supporting both China and global carbon neutrality assessments. Thus, it is crucial for China to achieve its carbon neutrality goals sooner by prioritizing the environmental benefits of cement materials and wastes, and accelerating the development and commercialization of CO₂ sequestration technologies for cement and its by-products.

Reflectance spectroscopy is rapid, inexpensive, and non-destructive and can provide important information about the mineralogy of rocks and sediments. We measured the reflectance spectroscopy of Miocene red clay deposits on the northeastern margin of the Tibetan Plateau, with the aim of developing a rapid methodology for detecting paleoclimatic changes. We obtained visible/near-infrared (VNIR) and short-wave infrared (SWIR) spectroscopy data from the red clay in the Jianzha Basin, and analyzed their relationship with independent paleoclimatic records, including mineral contents and environmental magnetic parameters. The results show that the VNIR parameters, including D500, D900, R500, and R900 (where D and R represent the depth and reflectance of the absorption peaks around 500 and 900 nm, respectively) are temperature-sensitive and correlated with the magnetic susceptibility, frequency-dependent magnetic susceptibility, and the marine δ¹⁸O record. The results of frequency-domain analysis of the VNIR parameters show that they reflect climate change on orbital timescales. SWIR parameters, such as AS1400, D1400/D1900 and D1900 (where AS represents the asymmetry of the absorption peaks around 1400 nm), are correlated with the illite and montmorillonite content, and they are sensitive to the weathering intensity. The spectral parameters of the eolian red clay in the Jianzha Basin reflect regional climatic changes caused by the uplift of the Tibetan Plateau at ∼8.5 Ma and global climatic cooling at ∼7.2 Ma, and thus they are applicable as both regional and global paleoenvironmental indicators.

As a natural disaster, extreme precipitation is among the most destructive and influential, but predicting its occurrence and evolution accurately is very challenging because of its rarity and uniqueness. Taking the example of the “21·7” extreme precipitation event (17–21 July 2021) in Henan Province, this study explores the potential of using physics-guided machine learning to improve the accuracy of forecasting the intensity and location of extreme precipitation. Three physics-guided ways of embedding physical features, fusing physical model forecasts and revised loss function are used, i.e., (1) analyzing the anomalous circulation and thermodynamical factors, (2) analyzing the multi-model forecast bias and the associated underlying reasons for it, and (3) using professional forecasting knowledge to design the loss function, and the corresponding results are used as input for machine learning to improve the forecasting accuracy. The results indicate that by learning the relationship between anomalous physical features and heavy precipitation, the forecasting of precipitation intensity is improved significantly, but the location is rarely adjusted and more false alarms appear. Possible reasons for this are as follows. The anomalous features used here mainly contain information about large-scale systems and factors which are consistent with the model precipitation deviation; moreover, the samples of extreme precipitation are sparse and so the algorithm used here is simple. However, by combining “good and different” multi models with machine learning, the advantages of each model are extracted and then the location of the precipitation center in the forecast is improved significantly. Therefore, by combining the appropriate anomalous features with multi-model fusion, an integrated improvement of the forecast of the rainfall intensity and location is achieved. Overall, this study is a novel exploration to improve the refined forecasting of heavy precipitation with extreme intensity and high variability, and provides a reference for the deep fusion of physics and artificial intelligence methods to improve intense rain forecast.

To improve soil carbon sequestration capacity, the full soil carbon cycle process needs to be understood and quantified. It is essential to evaluate whether water erosion acts as a net source or sink of atmospheric CO₂ at the basin scale, which encompasses the entire hydrological process. This study introduced an approach that combined a spatially distributed sediment delivery model and biogeochemical model to estimate the lateral and vertical carbon fluxes by water erosion at the basin scale. Applying this coupling model to the Dongting Lake Basin, the results showed that the annual average amount of soil erosion during 1980–2020 was 1.33×10⁸ t, displaying a decreasing trend followed by a slight increase. Only 12% of the soil organic carbon displacement was ultimately lost in the riverine systems, and the rest was deposited downhill within the basin. The average lateral soil organic carbon loss induced by erosion was 8.86×10¹¹ g C in 1980 and 1.50×10¹¹ g C in 2020, with a decline rate of 83%. A net land sink for atmospheric CO₂ of 5.54×10¹¹ g C a⁻¹ occurred during erosion, primarily through sediment burial and dynamic replacement. However, ecological restoration projects and tillage practice policies are still significant in reducing erosion, which could improve the capacity of the carbon sink for recovery beyond the rate of horizontal carbon removal. Moreover, our model enables the spatial explicit simulation of erosion-induced carbon fluxes using cost-effective and easily accessible input data across large spatial scales and long timeframes. Consequently, it offers a valuable tool for predicting the interactions between carbon dynamics, land use changes, and future climate.

Abstract

The Qinghai-Tibetan Plateau experienced a unique geological evolution during the Jurassic, driven by the termination of the Palaeotethys and the reduction of the Neotethys. The Indian Plate separated from the northern margin of Gondwana and drifted northward from the Southern Hemisphere. Given that the timing of strata serves as the basis for reconstructing geological history, the present work aimed to develop a new multiple stratigraphic and chronologic framework for the Jurassic strata of the Qinghai-Tibetan Plateau region via a synthesis of the material on lithostratigraphy, palaeontology, iso-radiometric dating, magnetostratigraphy, and other techniques with an emphasis on recent progress and findings. The new framework included the Jurassic System from the four major subdivisions of the plateau: the Baryan Har, Qiangtang, Lhasa-Gandise, and Southern Xizang (Himalaya). Ultimately, a more complete, refined biostratigraphic sequence was proposed, comprising the most common fossils in the plateau and those that are stratigraphically significant for the Jurassic stratigraphy, including ammonites, bivalves, brachiopods, foraminifera, radiolarians, and dinoflagellate cysts for the marine strata, and pollen and spores, and charophytes for the terrestrial sediments. This biostratigraphic framework was correlated with the Jurassic international standard zonation of the Geological Time Scale 2020 via standard or representative species or genera of ammonites. Based on this framework, we constructed a lateral correlation of the Jurassic strata between different basins of the plateau. The palaeontologic correlation in the present work shows that the Lhasa-Gandise Block had a closer relationship with the Qiangtang Block than with the Southern Xizang Himalaya during the Jurassic Period. Meanwhile, the Lhasa-Gandise Block and Qiangtang Block shared similar marine fauna features of the north marginal East Tethys. This contrasts the opinion suggesting that the Yarlung Zangbo Tethys was a small back-arc basin. A combination of stratigraphical, palaeontological, and sedimentological analyses implies that the Bangong Co-Nujiang Tethys may have begun rifting in the Late Triassic, evolving to the birth at the late Early Jurassic with the formation of ocean crust. However, this resulted in failure after it grew into the climax at the end of the Middle Jurassic when the Qiangtang Block began subducting under the Lhasa-Gandise Block. In the Early Cretaceous, the two blocks finally merged.