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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]

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]