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This paper aims to update our understanding of the carbon cycle in the Himalayas, the most intense collisional orogeny globally, by providing new insight into its impact on Cenozoic climate cooling through the use of isotopic variations in both organic and inorganic carbon and an isotopic mass balance model. Our results from 20 selected hot springs show that the relative contributions of dissolved carbon from the mantle, metamorphic decarbonization, aqueous dissolution, and soil organic matter are approximately 2%, 82%, 6%, and 10%, respectively. Approximately 87% ± 5% of CO2 generated in the deep crust precipitates as calcite, while approximately 5.5% ± 1% of this carbon is converted to biomass through microbial chemosynthesis at depths less than 2 km. Our random forests approach yielded a metamorphic carbon flux from the entire Himalayan orogenic belt of approximately 2.7 ∼ 4.5 × 1012 mol/yr. The minor CO2 released into the atmosphere (2.5 ∼ 4.2 × 1011 mol/yr) is comparable to the carbon consumption driven by Himalayan weathering. This paper provides new insights into deep carbon cycling, notably that approximately 93% of deeply sourced carbon is trapped in the shallow crust, rendering orogenic processes carbon neutral and possibly acting as one of the major triggers of long‐term climate cooling in the Cenozoic. Plain Language Summary: Large‐scale deep metamorphic decarbonization and CO2 release into the atmosphere during continental collision orogenesis worldwide have had significant impacts on Cenozoic global climate change. However, the carbon sources and sinks and the mechanisms underlying their impacts on climate change are not fully understood. This paper reports the chemistry of aqueous solutions, carbon isotopic compositions of dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC), and helium and carbon isotopic compositions of free gases from hot springs in southern Tibet. The carbon isotope fractionation model between gas and aqueous solutions revealed that the carbon released from depth underwent six different stages. Based on the random forests machine learning algorithm, we estimated the metamorphic carbon flux across the Himalayan orogenic belt. Furthermore, only a small fraction (7%) of deep carbon is eventually released into the atmosphere, and this volume is the same magnitude as the amount of carbon absorbed by silicate weathering on the Tibetan Plateau. This paper emphasizes that the carbon sequestration process in the upper crust may have resulted in nearly carbon‐neutral geological bodies in the Himalayan orogenic belt, thus playing a critical role in Cenozoic climate cooling. Key Points: Approximately 87% of deeply sourced carbon precipitates as calcite, while approximately 5.5% is sequestered as biomass in the shallow crustThe carbon flux across the Himalayan orogenic belt was estimated by the random forests approachCrustal thickening has attenuated long‐term deep carbon emissions from the Himalayas [ABSTRACT FROM AUTHOR]

Changes in patterns of meteorological parameters, like precipitations, temperature, wind, etc., are causing significant increases in various extreme events. And these extreme events, i.e., floods, heatwaves, hurricanes, droughts, etc., lead to a shortage of water resources, crop failures, wildfires, and economic losses. However, Global Circulation Models (GCMs) are considered the most important tools for quantifying climate change. Therefore, we selected 20 different GCMs of precipitation in our research, as the frequency of extreme events, like drought and flood, is highly related to changes in precipitation patterns. However, this research introduced a new weighting scheme — MCFSAWS-Ensemble: Monte Carlo Feature Selection Adaptive Weighting Scheme to Ensemble multiple GCMs, whereas, Monte Carlo Feature Selection (MCFS) is one of the most popular algorithms for discovering important variables. However, the proposed weighting scheme (MCFSAWS-Ensemble) is mainly based on two sources. Initially, it evaluates the prior performance of each GCM model to define their relative importance using MCFS. Then, it computes value by value difference between the observed and simulated model. In addition, the application of this paper is based on the monthly time series data of precipitation in the Tibet Plateau region of China. In addition, we used twenty GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to analyze the implications of the MCFSAWS-Ensemble. Further, we compared the performance of the MCFSAWS-Ensemble scheme with Simple Model Averaging (SMA) through Mean Average Error (MAE) and correlation statistics. The results of this research indicate that the proposed weighting scheme (MCFSAWS-Ensemble) is more accurate than the SMA approach. Consequently, we recommend the use of advanced machine learning algorithms such as MCFS for making accurate multi-model ensembles. [ABSTRACT FROM AUTHOR]

The growth history of the Tibetan Plateau provides a valuable natural laboratory to understand tectonic processes of the India–Asia collision and their impact on and interactions with Asian and global climate change. However, both Tibetan Plateau growth and Asian paleoenvironments are generally poorly documented in pre-Pliocene times and reflect limited temporal coverage for different parts of the plateau. In this paper, the 238 m thick Cenozoic sediments in the Hongzhuang section of the Xunhua Basin were tested and analyzed via paleomagnetic and environmental magnetic methods. The formation age was determined, and the evolution history of the regional climate environment was analyzed. The magnetostratigraphy study shows that the sediments record a continuous sequence of geomagnetic polarity changes from C5ACn to C10r, which spans an interval of approximately 30~14.3 Ma from the early Oligocene to the middle Miocene. The magnetic susceptibility of the Hongzhuang section is basically similar to the deep-sea oxygen isotope fluctuation, indicating that the monsoon climate change indicated by the magnetic susceptibility is affected by global temperature. It is worth noting that at ~27 Ma and ~15 Ma, there is a negative correlation between magnetic susceptibility and deep-sea oxygen isotope, and magnetic susceptibility lags behind the increase in deep-sea oxygen isotope. Combined with the change in the sedimentary rate curve, we explain the asynchrony between the magnetic susceptibility and the deep-sea oxygen isotope around ~27 Ma and ~15 Ma. As the uplift of the plateau leads to the enhancement of the East Asian summer monsoon, the soil formation in the region is strengthened, resulting in an increase in magnetic susceptibility. At the same time, the rapid uplift of the plateau caused the erosion of the surrounding mountains to strengthen, and the input of near-source materials may promote the increase in magnetic susceptibility. [ABSTRACT FROM AUTHOR]

The alpine grassland ecosystem accounts for 53% of the Qinghai-Tibet Plateau (QTP) area, which is an important ecological protection barrier, but fragile and highly vulnerable to climate change. Therefore, continuous monitoring of the aboveground biomass (AGB) of grassland is necessary. Although many studies have mapped the spatial distribution of AGB over the QTP, the results vary widely due to the limited ground samples and mismatches with satellite pixel scales. This paper proposed a new algorithm using unmanned aerial vehicles (UAVs) as a bridge to re-estimate the grassland AGB over 20 the QTP from 2000 to 2019. The innovations were as follows: 1) In the aspect of ground data collection, the spatial scale matching among the traditional ground quadrat sampling, UAV photos, and MODIS pixels was fully considered. From 2015 to 2019, 906 pairs of ground-UAV sample data at the quadrat scale and 2,602 sets of UAV data matching the MODIS pixel scale were collected. A total of more than 37,000 UAV photos were captured at the height of 20 meters. Therefore, the ground validation samples was sufficient and scale matched. 2) In terms of model construction, the traditional quadrat scale 25 (0.25m2) was successfully upscaled to the MODIS pixel scale (6,2500 m2) based on the random forest method and stepwise upscaling scheme. Compared with previous studies, the scale matching of independent and dependent variables was realized, effectively reducing the impact of scale mismatch. At the pixel scale, the AGB value estimated by UAV had a more linear correlation with the MODIS vegetation indices than the traditional sampling method. The multi-year independent crossvalidation results showed that the constructed pixel scale AGB estimation had good robustness, with an average R2 of 0.83 30 and RMSE of 34.13 g/m2. Our dataset provides an important input parameter for a comprehensive understanding of the QTP in the process of global climate change. [ABSTRACT FROM AUTHOR]

Mountain glaciers are one of the most sensitive indicators of climate change. Reconstructing past glacier extents provides valuable insights into the climate change for specific regions where instrumental records are not enough and even absent. In the paper we mapped the outlines for 691 Little Ice Age (LIA) glaciers in terms of prominent moraines beyond contemporary glaciers in the Tanggula Mountains, central Tibetan Plateau (TP). Based on these glacier outlines, we reconstructed the ice thicknesses and surfaces for the LIA glaciers in the region. Accordingly, we calculated the equilibrium-line altitudes (ELAs) for contemporary and LIA glaciers. By comparing the ELAs between the contemporary and LIA glaciers, we estimated the LIA summer temperature decreases in different subregions of the Tanggula Mountains. Since the LIA, the ELA have risen by 35.6 ± 18.8 m, 26.1 ± 17.6 m, and 63.1 ± 38.6 m in the respective western, central, and eastern subregions of the Tanggula Mountains. Assuming no change in precipitation, the LIA summer temperature would decrease by 0.22 ± 0.12 °C, 0.14 ± 0.09 °C, and 0.34 ± 0.21 °C in the three subregions, respectively. Moreover, glaciers in the western, central, and eastern subregions have lost 13.3%, 23.9%, and 42.8% of their area, 15.4%, 25.3%, and 39.6% of their length, and 20.8%, 39.0%, and 61.0% of their volume, respectively. By compiling the LIA ELA data from previous studies, we also estimated the LIA summer temperature changes for other regions of the TP. The LIA ELA and summer temperature changes relative to the present showed a decreasing trend from the edges towards the interior on the TP. • LIA glaciers and summer climate were reconstructed at the Tanggula Mountains. • LIA summers were 0.22 ± 0.12 °C, 0.14 ± 0.09 °C, and 0.34 ± 0.21 °C cooler than present in three subregions. • Summer temperature change had a decreasing trend from the edges towards the interior on the Tibetan Plateau. [ABSTRACT FROM AUTHOR]