The Tibetan Plateau (TP) has experienced accelerated warming in recent decades, especially in winter. However, a comprehensive quantitative study of its long-term warming processes during daytime and nighttime is lacking. This study quantifies the different processes driving the acceleration of winter daytime and nighttime warming over the TP during 1961-2022 using surface energy budget analysis. The results show that the surface warming over the TP is mainly controlled by two processes: (a) a decrease in snow cover leading to a decrease in albedo and an increase in net downward shortwave radiation (snow-albedo feedback), and (b) a warming in tropospheric temperature (850 - 200 hPa) leading to an increase in downward longwave radiation (air warming-longwave radiation effect). The latter has a greater impact on the spatial distribution of warming than the former, and both factors jointly influence the elevation dependent warming pattern. Snow-albedo feedback is the primary factor in daytime warming over the monsoon region, contributing to about 59% of the simulated warming trend. In contrast, nighttime warming over the monsoon region and daytime/nighttime warming in the westerly region are primarily caused by the air warming-longwave radiation effect, contributing up to 67% of the simulated warming trend. The trend in the near-surface temperature mirrors that of the surface temperature, and the same process can explain changes in both. However, there are some differences: an increase in sensible heat flux is driven by a rise in the ground-atmosphere temperature difference. The increase in latent heat flux is associated with enhanced evaporation due to increased soil temperature and is also controlled by soil moisture. Both of these processes regulate the temperature difference between ground and near-surface atmosphere.

Globally, land subsidence (LS) often adversely impacts infrastructure, humans, and the environment. As climate change intensifies the terrestrial hydrologic cycle and severity of climate extremes, the interplay among extremes (e.g., floods, droughts, wildfires, etc.), LS, and their effects must be better understood since LS can alter the impacts of extreme events, and extreme events can drive LS. Furthermore, several processes causing subsidence (e.g., ice-rich permafrost degradation, oxidation of organic matter) have been shown to also release greenhouse gases, accelerating climate change. Our review aims to synthesize these complex relationships, including human activities contributing to LS, and to identify the causes and rates of subsidence across diverse landscapes. We primarily focus on the era of synthetic aperture radar (SAR), which has significantly contributed to advancements in our understanding of ground deformations around the world. Ultimately, we identify gaps and opportunities to aid LS monitoring, mitigation, and adaptation strategies and guide interdisciplinary efforts to further our process-based understanding of subsidence and associated climate feedbacks. We highlight the need to incorporate the interplay of extreme events, LS, and human activities into models, risk and vulnerability assessments, and management practices to develop improved mitigation and adaptation strategies as the global climate warms. Without consideration of such interplay and/or feedback loops, we may underestimate the enhancement of climate change and acceleration of LS across many regions, leaving communities unprepared for their ramifications. Proactive and interdisciplinary efforts should be leveraged to develop strategies and policies that mitigate or reverse anthropogenic LS and climate change impacts.

Brown carbon (BrC) has been recognized as an important light-absorbing carbonaceous aerosol, yet understanding of its influence on regional climate and air quality has been lacking, mainly due to the ignorance of regional coupled meteorology-chemistry models. Besides, assumptions about its emissions in previous explorations might cause large uncertainties in estimates. Here, we implemented a BrC module into the WRF-Chem model that considers source-dependent absorption and avoids uncertainties caused by assumptions about emission intensities. To our best knowledge, we made the first effort to consider BrC in a regional coupled model. We then applied the developed model to explore the impacts of BrC absorption on radiative forcing, regional climate, and air quality in East Asia. We found notable increases in aerosol absorption optical depth (AAOD) in areas with high OC concentrations. The most intense forcing of BrC absorption occurs in autumn over Southeast Asia, and values could reach around 4 W m(-2). The intensified atmospheric absorption modified surface energy balance, resulting in subsequent declines in surface temperature, heat flux, boundary layer height, and turbulence exchanging rates. These changes in meteorological variables additionally modified near-surface dispersion and photochemical conditions, leading to changes of PM2.5 and O-3 concentrations. These findings indicate that BrC could exert important influence in specific regions and time periods. A more in-depth understanding could be achieved later with the developed model.

Since the 5th Assessment Report of the Intergovernmental Panel on Climate Change (AR5) an extended concept of the energetic analysis of climate change including forcings, feedbacks and adjustment processes has become widely adopted. Adjustments are defined as processes that occur in response to the introduction of a climate forcing agent, but that are independent of global-mean surface temperature changes. Most considered are the adjustments that impact the Earth energy budget and strengthen or weaken the instantaneous radiative forcing due to the forcing agent. Some adjustment mechanisms also impact other aspects of climate not related to the Earth radiation budget. Since AR5 and a following description by Sherwood et al. (2015, ), much research on adjustments has been performed and is reviewed here. We classify the adjustment mechanisms into six main categories, and discuss methods of quantifying these adjustments in terms of their potentials, shortcomings and practicality. We furthermore describe aspects of adjustments that act beyond the energetic framework, and we propose new ideas to observe adjustments or to make use of observations to constrain their representation in models. Altogether, the problem of adjustments is now on a robust scientific footing, and better quantification and observational constraint is possible. This allows for improvements in understanding and quantifying climate change. Climate change is driven by perturbations to the atmospheric composition, to land use, or by changes of incoming solar radiation. It can be understood energetically by quantifying the perturbation to the Earth energy budget-the instantaneous radiative forcing-and the response of the climate system to this perturbation. This response can be split into feedbacks-mechanisms that act in response to global-mean surface temperature changes-and other processes that act independently of the global-mean surface temperature change. These latter processes are called adjustments. There is also a category of climate-relevant adjustments that is not directly related to the energy budget. This review documents the improved classification, understanding, constraint, and quantification of adjustments. A clearer picture of adjustments allows to better understand and quantify climate change. Adjustments impact the Earth energy budget, but also circulation, precipitation and atmospheric structure Adjustments are classified into six different mechanisms and act at time scales ranging from seconds to multiple years Observational constraints can inform on some aspects of adjustments

Global climate warming is accelerating permafrost degradation. The large amounts of soil organic matter in permafrost-affected soils are prone to increased microbial decomposition in a warming climate. Along with permafrost degradation, changes to the soil microbiome play a crucial role in enhancing our understanding and in predicting the feedback of permafrost carbon. In this article, we review the current state of knowledge of carbon-cycling microbial ecology in permafrost regions. Microbiomes in degrading permafrost exhibit variations across spatial and temporal scales. Among the short-term, rapid degradation scenarios, thermokarst lakes have distinct biogeochemical conditions promoting emission of greenhouse gases. Additionally, extreme climatic events can trigger drastic changes in microbial consortia and activity. Notably, environmental conditions appear to exert a dominant influence on microbial assembly in permafrost ecosystems. Furthermore, as the global climate is closely connected to various permafrost regions, it will be crucial to extend our understanding beyond local scales, for example by conducting comparative and integrative studies between Arctic permafrost and alpine permafrost on the Qinghai-Tibet Plateau at global and continental scales. These comparative studies will enhance our understanding of microbial functioning in degrading permafrost ecosystems and help inform effective strategies for managing and mitigating the impacts of climate change on permafrost regions.

Quantifying permafrost carbon feedback (PCF) is a critical step in conveying the significance of permafrost carbon emissions to decision-makers and stakeholders and achieving sustainable development goals. Simply assuming a rapid reduction in permafrost area may be an overaggressive approach. This study revisited PCF by incorporating relatively clear permafrost physics into the Dynamic Integrated model of Climate and the Economy. The results show that the total carbon released from permafrost regions in 2100 is 30.5 GtC, which is accompanied by an additional atmospheric warming of 0.038 degrees C, much lower than previous studies. This study provides a potential perspective to scrutinize the climate feedback and related economic impacts due to permafrost thawing. We may need to pay more attention to carbon processes during nongrowing seasons and sudden changes in permafrost.

Positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impact land-atmosphere interactions, disrupt the global carbon cycle, and accelerate climate change. The widespread distribution of thawing permafrost is causing a cascade of geophysical and biochemical disturbances with global impacts. Currently, few earth system models account for permafrost carbon feedback (PCF) mechanisms. This research study integrates artificial intelligence (AI) tools and information derived from field-scale surveys across the tundra and boreal landscapes in Alaska. We identify and interpret the permafrost carbon cycling links and feedback sensitivities with GeoCryoAI, a hybridized multimodal deep learning (DL) architecture of stacked convolutionally layered, memory-encoded recurrent neural networks (NN). This framework integrates in-situ measurements and flux tower observations for teacher forcing and model training. Preliminary experiments to quantify, validate, and forecast permafrost degradation and carbon efflux across Alaska demonstrate the fidelity of this data-driven architecture. More specifically, GeoCryoAI logs the ecological memory and effectively learns covariate dynamics while demonstrating an aptitude to simulate and forecast PCF dynamics-active layer thickness (ALT), carbon dioxide flux (CO2), and methane flux (CH4)-with high precision and minimal loss (i.e. ALTRMSE: 1.327 cm [1969-2022]; CO2 RMSE: 0.697 mu molCO2m-2s-1 [2003-2021]; CH4 RMSE: 0.715 nmolCH4m-2s-1 [2011-2022]). ALT variability is a sensitive harbinger of change, a unique signal characterizing the PCF, and our model is the first characterization of these dynamics across space and time.

Permafrost degradation is altering biogeochemical processes throughout the Arctic. Thaw-induced changes in organic matter transformations and mineral weathering reactions are impacting fluxes of inorganic carbon (IC) and alkalinity (ALK) in Arctic rivers. However, the net impact of these changing fluxes on the concentration of carbon dioxide in the atmosphere (pCO(2)) is relatively unconstrained. Resolving this uncertainty is important as thaw-driven changes in the fluxes of IC and ALK could produce feedbacks in the global carbon cycle. Enhanced production of sulfuric acid through sulfide oxidation is particularly poorly quantified despite its potential to remove ALK from the ocean-atmosphere system and increase pCO(2), producing a positive feedback leading to more warming and permafrost degradation. In this work, we quantified weathering in the Koyukuk River, a major tributary of the Yukon River draining discontinuous permafrost in central Alaska, based on water and sediment samples collected near the village of Huslia in summer 2018. Using measurements of major ion abundances and sulfate (SO42-) sulfur (S-34/S-32) and oxygen (O-18/O-16) isotope ratios, we employed the MEANDIR inversion model to quantify the relative importance of a suite of weathering processes and their net impact on pCO(2). Calculations found that approximately 80% of SO42- in mainstem samples derived from sulfide oxidation with the remainder from evaporite dissolution. Moreover, S-34/S-32 ratios, C-13/C-12 ratios of dissolved IC, and sulfur X-ray absorption spectra of mainstem, secondary channel, and floodplain pore fluid and sediment samples revealed modest degrees of microbial sulfate reduction within the floodplain. Weathering fluxes of ALK and IC result in lower values of pCO(2) over timescales shorter than carbonate compensation (similar to 10(4) yr) and, for mainstem samples, higher values of pCO(2) over timescales longer than carbonate compensation but shorter than the residence time of marine SO42- (similar to 10(7) yr). Furthermore, the absolute concentrations of SO42- and Mg2+ in the Koyukuk River, as well as the ratios of SO42- and Mg2+ to other dissolved weathering products, have increased over the past 50 years. Through analogy to similar trends in the Yukon River, we interpret these changes as reflecting enhanced sulfide oxidation due to ongoing exposure of previously frozen sediment and changes in the contributions of shallow and deep flow paths to the active channel. Overall, these findings confirm that sulfide oxidation is a substantial outcome of permafrost degradation and that the sulfur cycle responds to permafrost thaw with a timescale-dependent feedback on warming.

Projected future changes in snow cover patterns associated with global warming in cold zone ecosystems could affect soil biochemical cycling. However, the effects of snow cover changes on soil available carbon, nitrogen and enzyme activities and their potential response mechanisms have not been clarified. Therefore, from November 2021 to April 2022, this study conducted a snow depth manipulation test of four treatments in the northeast black soil region, and divided the test period into five stages to measure soil temperature and humidity, microbial biomass, enzyme activity, and available carbon and nitrogen. The results showed that the decrease of snow cover increased the freeze-thaw cycle frequency and freezing temperature of soil, but decreased the soil water content. Soil total organic carbon and inorganic nitrogen contents were increased in early and deep snow periods, while snow treatment was on the contrary. Due to the release of soluble nutrients caused by frequent freeze-thaw processes, Soil soluble organic carbon and Soil soluble organic nitrogen contents increased with the decrease of snow depth in deep snow period, snowmelt period and subsequent early crop growth period. Snow treatment increased soil microbial carbon and nitrogen content in early winter and early spring because snow provided heat insulation. Soil enzyme activities increased with the increase of snow cover. Compared with the control, soil urease activities and sucrase activities increased by 18.5 % and 11.5 % under snow treatment, and decreased by 23.2 % and 10.8 % under snow reduction treatment. In addition, soil soluble organic matter was a controlling factor for soil microbial biomass and enzyme activity throughout winter. The direct effect of soil soluble organic carbon and nitrogen on soil enzymes will make soil enzymes participate in the cyclic transformation process of available carbon, thus forming a closed loop of mutual feedback between soil available carbon and nitrogen and enzymes. These results demonstrated that the changes of snow cover in the future will have certain effects on soil carbon and nitrogen cycles and enzyme activities and hence biogeochemical cycling in terrestrial system of earth.

This study examines the Arctic surface air temperature response to regional aerosol emissions reductions using three fully coupled chemistry-climate models: National Center for Atmospheric Research-Community Earth System Model version 1, Geophysical Fluid Dynamics Laboratory-Coupled Climate Model version 3 (GFDL-CM3) and Goddard Institute for Space Studies-ModelE version 2. Each of these models was used to perform a series of aerosol perturbation experiments, in which emissions of different aerosol types (sulfate, black carbon (BC), and organic carbon) in different northern mid-latitude source regions, and of biomass burning aerosol over South America and Africa, were substantially reduced or eliminated. We find that the Arctic warms in nearly every experiment, the only exceptions being the U.S. and Europe BC experiments in GFDL-CM3 in which there is a weak and insignificant cooling. The Arctic warming is generally larger than the global mean warming (i.e. Arctic amplification occurs), particularly during non-summer months. The models agree that changes in the poleward atmospheric moisture transport are the most important factor explaining the spread in Arctic warming across experiments: the largest warming tends to coincide with the largest increases in moisture transport into the Arctic. In contrast, there is an inconsistent relationship (correlation) across experiments between the local radiative forcing over the Arctic and the simulated Arctic warming, with this relationship being positive in one model (GFDL-CM3) and negative in the other two. Our results thus highlight the prominent role of poleward energy transport in driving Arctic warming and amplification, and suggest that the relative importance of poleward energy transport and local forcing/feedbacks is likely to be model dependent.