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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. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) is an area highly sensitive to climate change and is warming faster than the global average. The TP temperature change has a significant impact on the local ecological environment and the downstream weather and climate. The TP will undoubtedly warm in the future, but the warming extent is uncertain. Using the Coupled Model Inter‐comparison Project Phase 6 multi‐model ensemble, we found that models simulating smaller TP temperature increases in recent decades tend to project weaker warming in the future. This relationship is driven by the simulation of snowmelt response to greenhouse gas increases, as snow‐related albedo feedback dominates the TP temperature changes in both historical and future periods. Based on a two‐step emergent constraint approach, the rectified TP warming magnitude increases by about 0.3°C compared to the unconstrained result under both the medium and high emission scenarios, and the inter‐model uncertainty is reduced by about 60%. Plain Language Summary: The Tibetan Plateau (TP), known as "the third pole of the world," is the highest terrain on Earth. It has experienced a rapid increase in surface temperature over the past few decades, which has significantly impacted the local ecological environment and downstream weather and climate. Therefore, how the surface temperature of the TP will change in the future is of concern to both the scientific community and society at large. Although a future warming trend over the TP is evident in model projections, the extent of this warming remains highly uncertain. Based on a multi‐model ensemble, we found that the projected temperature change over the TP during the boreal winter is significantly influenced by the simulation of TP temperature changes in recent decades, which is related to model sensitivities in simulating the snowmelt rate in response to greenhouse gas increases. Removing the model bias using observations would further increase the projected future warming over the TP, meaning that humanity will face greater challenges. Key Points: A two‐step emergent constraint (EC) technique is developed for projections of the Tibetan Plateau (TP) warming in the boreal winterThe EC arises from model sensitivities in simulating snow cover change in response to greenhouse gas increasesConstrained results effectively reduce inter‐model uncertainty and show stronger warming over the TP than the original results [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) has experienced an accelerated wintertime warming in recent decades under global warming, but consensus on its causes has not yet been reached. This study quantifies the processes of the warming through analyzing surface temperature budget and surface energy balance. It is found that increased diabatic heating (73%) and warm advection (27%) by an anomalous anticyclone southeast of TP are two primary processes determining the surface air warming. The former is caused by a significant increase of the TP skin temperature which warms the near surface atmosphere through increasing upward surface sensible heat flux. The land surface warming is attributed to increased absorbed radiation fluxes in which three processes are identified to be major contributors. While outside forcing which is primarily due to increased anthropogenic emissions of greenhouse gases contributes to the warming by 25% through increasing downward longwave radiation, two types of local positive feedbacks which are triggered by the land surface warming are found to contribute to most of the warming. One is the snow-albedo feedback which accounts for 47% of the surface warming by increasing surface absorption of incident solar radiation. The other is the moisture process feedback which accounts for 28% of the surface warming. The surface warming which works with increased soil water due to increased precipitation in the preceding seasons tends to promote surface evaporation and moisten the atmosphere aloft over the eastern TP, which, in turn, tends to increase downward longwave radiation and cause a further surface warming. [ABSTRACT FROM AUTHOR]

Various studies reported an elevation dependent precipitation and temperature changes in mountainous regions of the world including the Himalayas. Various mechanisms are proposed to link the possible dependence of the precipitation and temperature on elevation with other variables, including, long- and short-wave radiation, albedo, clouds, humidity, etc. In the present study changes and trends of precipitation and temperature at different elevation ranges in the Indian Himalayan region (IHR) is assessed. Observations and modelling fields during the period 1970–2099 are used. Modelling simulations from the Coordinated Regional Climate Downscaling Experiment-South Asia experiments (CORDEX-SA) suites are considered. In addition, four seasons—winter (Dec, Jan, Feb: DJF), pre-monsoon (Mar, Apr, May: MAM), monsoon (Jun, Jul, Aug, Sep: JJAS) and post-monsoon (Oct, Nov: ON)—are considered to detect the possible seasonal response of elevation dependency. Firstly, precipitation and temperature fields, separately, as well as the diurnal temperature range (DTR) are assessed. Following, their long-term trends are investigated, if varying, at different elevational ranges in the IHR. To explain plausible physical mechanisms due to elevation dependency, trend of other variables viz., surface downward longwave radiation (DLR), total cloud faction, soil moisture, near surface specific humidity, surface snow melt and surface albedo, etc. are investigated. Results point towards an decreased (increased) precipitation in higher (lower) elevation. And amplified warming signals at higher elevations (above 3000 m), both in daytime and nighttime temperatures, during all seasons except the monsoon, are noticed. Increased DLR trends at higher elevation are also simulated well by the model and are likely the main elevation dependent driver in the IHR. [ABSTRACT FROM AUTHOR]

To examine the effect of the snow‐albedo feedback (SAF) on the current surface climate across terrestrial regions at mid–high latitudes, we perform a series of idealized sensitivity experiments using an atmospheric general circulation model (AGCM), in which the snow albedo is assumed to be considerably smaller than that in the control experiment. When the snow albedo is reduced, the fall and spring surface air temperatures (SATs) in northern Eurasia are considerably higher than those in the control experiment. Particularly, the spring SAT at terrestrial high latitudes rises by more than 10°C, due to low snow albedo. Despite the small surface snow albedo during both fall and spring, the fall temperature difference between the two experiments is smaller than that during spring. The feedback with the hydrological effect, which accelerates snowmelt, is either nonexistent or weak during periods of seasonally low air temperatures. This result indicates that the hydrological effect significantly influences the SAF, and different magnitudes of the hydrological effect including cloud differences are a possible source of SAF uncertainty between models. The radiative albedo effect also impacts the evolution of the snow area in central Asia and eastern Europe through changes in lateral water vapour transport. Furthermore, the experiments indicate that the cooling effect of snow on land is necessary for the strength of the Siberian High and the pattern of Arctic Oscillation, suggesting that snow cover may also significantly extend the duration of Arctic Oscillation effects. [ABSTRACT FROM AUTHOR]

I extend the reduced Greenland ice sheet (GIS) model-module of DICE-GIS (Nordhaus, Proc Natl Acad Sci 25(116):12261–12269, 2019) by integrating snow-albedo feedback (SAF) and potential tipping of the ice sheet into the resuming DICE-GIS SAF model. The increasing global temperature no longer only results in the melting of the GIS, but also in albedo loss, which in turn impacts the strength of the SAF. As a consequence, global warming and the melting of the ice accelerate. The social cost of carbon (SCC) increases because the economic damages are not only related to intensified sea level rise, but also to accelerated global temperature rise. Accounting for the SAF raises the SCC from 37 to 41 $/t CO2 in 2020, an increase of 11%. The temperature increase is the key channel through which the SAF impacts the SCC. The long-term volume of the GIS decreases by 2%, while the additional inclusion of tipping reduces it further by up to 35%. [ABSTRACT FROM AUTHOR]

Analyzing a long simulation and a set of seasonal reforecasts of the Climate Forecast System version 2 (CFSv2), this study demonstrates a large model cold bias in the deep soil layer (100–200 cm) over most of North America continent during summer due to weaker seasonal change in summer. The summer subsurface temperature (SUBT) cold bias influences the land surface temperature (LST) during the summer and subsequent seasons in different ways over different geographical regions in North America: West of the Rocky Mountains, the SUBT's effect on LST is largely overruled by the stronger upstream marine influence from the Pacific. Over the central Great Plains, however, it is a major cause for severe cold LST bias during summer in the model simulation and reforecasts. As a result, model underestimates sensible heat flux into the atmosphere but overestimates latent heat flux. The latter may contribute to an excessive summer rainfall in the region. Over the northeast region, the SUBT cold bias persists to August and September, which causes an additional surface cooling in the fall and helps to bring LST to the freezing point early. This sets up the stage for a prolonged snow‐albedo feedback. In particular, the model long simulation that passes through previous summer and fall demonstrates longer persistence of snow cover over the northeast region than reforecasts initialized in late winter and spring do. A cold bias of the water temperature in the North Atlantic seems also to play a role to prolong cold bias in the northeast region. Key Points: Model depicts large cold bias at deep subsurface temperature (SUBT) during summer over North America due to weaker seasonal change in summerWest of Rocky Mountains, SUBT effect on land surface temperature is largely overruled by stronger upstream marine influence from the PacificOver the central Great Plains, SUBT is a major cause for severe cold LST bias during summer in simulation and reforecasts [ABSTRACT FROM AUTHOR]

The National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) has a large cold bias in the model's deep soil temperature during summer. This study explores the potential triggering effect of that bias on excessive Eurasian snow cover in early winter. Snow cover appears erroneously early in the fall, especially in western Eurasia, in long simulations with CFSv2. The seasonal transition may be too early because the model land surface temperature (LST) reaches its freezing point earlier than observed, so that new snow cannot melt. This process initiates snow‐albedo feedback too early. The early cooling of LST is partially influenced by a seasonal resurfacing of the cold bias in the deep soil layer. From winter to early spring, a cold bias prevails in LST and upper soil temperature as snow cover remains. During this season, the temperature in the deep soil is generally warmer than in the upper soil and has relatively little bias. From spring to summer, the cold bias in the upper soil becomes smaller as it warms up in response to solar heating. On the other hand, the deep soil temperature has a noticeably smaller seasonal change than observed, resulting in a severe cold bias during summer. As the solar radiation declines quickly in early fall, the cold deep soil temperature causes additional cooling in the upper soil layer and helps to bring LST to the freezing point early in the western Eurasia, which leads to enhanced bias in the snow properties. Key Points: Model overestimates SCF up to 60% in western Eurasia during October because LST reaches its freezing point there earlier than observedThe Model deep soil temperature has a noticeably smaller seasonal change than observed, resulting in a severe cold bias during summerCold deep soil temperature causes additional cooling in upper soil layer and helps to bring LST to freezing point early over there [ABSTRACT FROM AUTHOR]

This study demonstrates the potential impact of land and atmospheric initial conditions (ICs) on the mean state of land surface temperature (SKT) and mid-troposphere circulation over Eurasia and North America (NA) using the Climate Forecast System version-2 (CFSv2) ensemble seasonal reforecasts for 1958–2014. The land ICs and atmosphere ICs have assembled from several different data sources before and after 1979 in January initialized reforecasts (JIR). Therefore, two-time periods have defined, earlier period (1958–1978) and later period (1994–2014). The climatological difference between JIR P58-78 and P94-14 depicts enhanced cold SKT and sub-surface temperature at 0–10 cm over northern Eurasia and northeast region of NA from May to June. Model overestimates snow cover fraction from May to June in JIR P58-78 than JIR P94-14, which leads to excessive upwards-shortwave radiation in the northern latitudes. Due to cold temperature from the surface to mid-troposphere, model depicts lower geopotential heights in mid-troposphere, which leads to increase westerly winds at mid-troposphere over northern latitudes from May to June. The climatological differences between earlier and later periods of JIR from May to June are similar to differences between reforecasts initialized in winter and spring as reported by some recent studies. The climatological difference between JIR P58-78 and P94-14 depicts enhanced cooling from surface to 600 hPa over northern latitudes in January. As lead months increase, the magnitude of cold temperature decreases gradually from February to March. Most prominent centers of geopotential height from surface to mid-troposphere over northern latitude in climatological differences between earlier and later periods of JIR in January vanish in February. The different sources of ICs and the nature of land conditions before and after 1979 are main causes of large differences in JIR from May to June over northern latitudes.

Several simulations of the surface climate and energy balance of Vatnajökull ice cap, Iceland, are used to estimate the glacier runoff for the period 1980⁻2015 and the sensitivity of runoff to the spring conditions (e.g., snow thickness). The simulations are calculated using the snow pack scheme from the regional climate model HIRHAM5, forced with incoming mass and energy fluxes from the numerical weather prediction model HARMONIE-AROME. The modeled runoff is compared to available observations from two outlet glaciers to assess the quality of the simulations. To test the sensitivity of the runoff to spring conditions, simulations are repeated for the spring conditions of each of the years 1980⁻2015, followed by the weather of all summers in the same period. We find that for the whole ice cap, the variability in runoff as a function of varying spring conditions was on average 31% of the variability due to changing summer weather. However, some outlet glaciers are very sensitive to the amount of snow in the spring, as e.g., the variation in runoff from Brúarjökull due to changing spring conditions was on average 50% of the variability due to varying summer weather.

The effect of the snow-albedo feedback (SAF) on 2m temperatures and their future changes in the European Alps is investigated in the ENSEMBLES regional climate models (RCMs) with a focus on the spring season. A total of 14 re-analysis-driven RCM experiments covering the period 1961-2000 and 10 GCM-driven transient climate change projections for 1950-2099 are analysed. A positive springtime SAF is found in all RCMs, but the range of the diagnosed SAF is large. Results are compared against an observation-based SAF estimate. For some RCMs, values very close to this estimate are found; other models show a considerable overestimation of the SAF. Net shortwave radiation has the largest influence of all components of the energy balance on the diagnosed SAF and can partly explain its spatial variability. Model deficiencies in reproducing 2m temperatures above snow and ice and associated cold temperature biases at high elevations seem to contribute to a SAF overestimation in several RCMs. The diagnosed SAF in the observational period strongly influences the estimated SAF contribution to twenty first century temperature changes in the European Alps. This contribution is subject to a clear elevation dependency that is governed by the elevation-dependent change in the number of snow days. Elevations of maximum SAF contribution range from 1500 to 2000 m in spring and are found above 2000 m in summer. Here, a SAF contribution to the total simulated temperature change between 0 and 0.5 °C until 2099 (multi-model mean in spring: 0.26 °C) or 0 and 14 % (multi-model mean in spring: 8 %) is obtained for models showing a realistic SAF. These numbers represent a well-funded but only approximate estimate of the SAF contribution to future warming, and a remaining contribution of model-specific SAF misrepresentations cannot be ruled out. [ABSTRACT FROM AUTHOR]

Snow-albedo feedback (SAF) is examined in 25 climate change simulations participating in the Coupled Model Intercomparison Project version 5 (CMIP5). SAF behavior is compared to the feedback's behavior in the previous (CMIP3) generation of global models. SAF strength exhibits a fivefold spread across CMIP5 models, ranging from 0.03 to 0.16 W m K (ensemble-mean = 0.08 W m K). This accounts for much of the spread in 21st century warming of Northern Hemisphere land masses, and is very similar to the spread found in CMIP3 models. As with the CMIP3 models, there is a high degree of correspondence between the magnitudes of seasonal cycle and climate change versions of the feedback. Here we also show that their geographical footprint is similar. The ensemble-mean SAF strength is close to an observed estimate of the real climate's seasonal cycle feedback strength. SAF strength is strongly correlated with the climatological surface albedo when the ground is covered by snow. The inter-model variation in this quantity is surprisingly large, ranging from 0.39 to 0.75. Models with large surface albedo when these regions are snow-covered will also have a large surface albedo contrast between snow-covered and snow-free regions, and therefore a correspondingly large SAF. Widely-varying treatments of vegetation masking of snow-covered surfaces are probably responsible for the spread in surface albedo where snow occurs, and the persistent spread in SAF in global climate models. [ABSTRACT FROM AUTHOR]

Recent years (i.e., 2007–2014) have exhibited large declines in snow cover extent (SCE) in the Northern Hemisphere (NH), marked by earlier snowmelt in the springtime. In Northern latitudes, the snow-albedo feedback (SAF) is most pronounced in the spring and may be contributing to these decreasing trends in SCE. Rising surface air temperatures and changes in precipitation patterns could also vary the declining trends in SCE depending on latitude and elevation. Previous trend analyses of NH SCE are extended here to cover the period 1 October 1971 to 30 September 2014 using observed data from the National Oceanic and Atmospheric Administration snow chart climate data record. Trends in snow coverage (significant when p

Understanding the current regional climate is of great importance for better estimating future climate change and assessing its influence on water resources and ecosystems. The Yellow River provides important water resources for industrial and domestic activities. However, climate simulations over the Yellow River Basin (YRB) have rarely been attempted. In this study, downscaled mean climate over the upper and middle reaches of the YRB using the REgional MOdel (REMO) at 0.22° spatial resolution under the Coordinated Regional Downscaling Experiment (CORDEX), driven by ERA-Interim reanalysis, was evaluated by comparison against one gridded observational dataset (CN05.1) for the period 1982–2016. Two reanalysis datasets (ERA5 and ERA-Interim) were used to reveal possible causes for the differences between the simulations and observations. REMO reproduced the spatial patterns of mean temperature satisfactorily despite some evident deviations. REMO had a predominantly mixed bias (a cold bias for the upper reach and a warm bias for the rest) in all four seasons. Due to the effects of differential snow cover, temperature biases were elevation-dependent, especially in winter months. REMO broadly reproduced the spatial variability of precipitation with a wet bias, which could be largely attributed to anomalous moisture flux transport. In terms of interannual variability, the observational data showed significant warming across all four seasons, while REMO simulated weaker warming trends. Precipitation trends were positive in all seasons except in summer, but REMO failed to capture the trends in winter and spring. The observed elevation-dependent warming (EDW) was reproduced by REMO, except in spring. The EDW was likely to be explained by the snow-albedo feedback, owing to the apparent decrease in snow cover at high elevations. • REMO reproduces spatial pattern of temperature reasonably well, but roughly captures spatial variability of precipitation. • The performance of REMO in representing interannual variability of climate is evaluated. • Causes for the REMO biases for temperature and precipitation are investigated. • REMO reproduces the elevation-dependent warming, which is likely to be explained by the decrease in snow cover. [ABSTRACT FROM AUTHOR]

Emergent constraints use relationships between future and current climate states to constrain projections of climate response. Here we introduce a statistical, hierarchical emergent constraint (HEC) framework in order to link future and current climates with observations. Under Gaussian assumptions, the mean and variance of the future state are shown analytically to be a function of the signal‐to‐noise ratio between current climate uncertainty and observation error and the correlation between future and current climate states. We apply the HEC to the climate change, snow‐albedo feedback, which is related to the seasonal cycle in the Northern Hemisphere. We obtain a snow‐albedo feedback prediction interval of (−1.25,−0.58)%/K. The critical dependence on signal‐to‐noise ratio and correlation shows that neglecting these terms can lead to bias and underestimated uncertainty in constrained projections. The flexibility of using HEC under general assumptions throughout the Earth system is discussed. Plain Language Summary: Reducing the uncertainty in climate projections has been one of the signature challenges in Earth science because simulated future climate states cannot be directly falsified. We propose a hierarchical statistical framework that formally relates projections of future climate to present‐day climate and observations. We show that the future‐climate estimate is driven by the correlation between future and present climate variability and the signal‐to‐noise ratio obtained from observations and present climate. This framework is applied to a future northern hemispheric climate projection that is influenced by the snow‐albedo feedback, which is an amplification of temperature due to reduced snow extent as a consequence of anthropogenic CO2 emissions. We show that the climate change snow‐albedo temperature sensitivity ranges from (−1.25,−0.58)%/K. The flexibility of this approach can be applied more broadly to constrain climate projections across the Earth system. Key Points: A hierarchical emergent constraints (HEC) framework for climate projections is introducedHEC depends on the signal‐to‐noise ratio between climate and observational uncertaintyUsing HEC, the snow‐albedo feedback prediction interval is found to be (−1.25,−0.58)%/K [ABSTRACT FROM AUTHOR]

The Asian dust cycle has significant effects on the climate and environment, while its spatiotemporal variability and change mechanisms are not yet completely understood. Reanalysis data from the Modern‐Era Retrospective Analysis for Research and Applications, version 2 (MERRA2), data set are used to explore the spatiotemporal distribution of the East Asian dust cycle and possible reasons for the interannual variations. Based on the empirical orthogonal function analysis, the dominant mode of dust emissions from the East Asian deserts in the dust season (spring) shows that the Gobi Desert contributes most of the interannual variance of dust emissions in East Asia. The patterns of the regional circulation, temperature, and radiation are analyzed by regressing these variables against the principal component time series of the first empirical orthogonal function mode. The results show that the enhanced dust emissions are associated with a cyclonic circulation anomaly and cooling in the lower and middle troposphere over Central Siberia. The cooling is attributed to local snow‐albedo and cloud‐albedo feedbacks. The surface cooling is conducive to maintain the snow cover, whereas the cooling in the middle troposphere is associated with the increase of the relative humidity and cloud cover. The increased snow and cloud cover reflect more shortwave radiation, tending to maintain or amplify the surface cooling. It is also found that the negative phase of the Arctic Oscillation in winter initiates the surface cooling in the next spring and results in positive snow‐albedo and cloud feedbacks in Central Siberia, eventually enhancing the East Asian dust cycle. Key Points: The enhanced East Asian dust emission is associated with a cyclonic wind anomaly induced by Central Siberian mid‐lower troposphere coolingThe Central Siberian cooling is attributed to local snow‐albedo and cloud‐albedo feedbacksWinter Arctic Oscillation leads to changes in surface temperature in Siberia and induces positive snow‐albedo and cloud feedbacks in spring [ABSTRACT FROM AUTHOR]

Several simulations of the surface climate and energy balance of Vatnajökull ice cap, Iceland, are used to estimate the glacier runoff for the period 1980–2015 and the sensitivity of runoff to the spring conditions (e.g., snow thickness). The simulations are calculated using the snow pack scheme from the regional climate model HIRHAM5, forced with incoming mass and energy fluxes from the numerical weather prediction model HARMONIE-AROME. The modeled runoff is compared to available observations from two outlet glaciers to assess the quality of the simulations. To test the sensitivity of the runoff to spring conditions, simulations are repeated for the spring conditions of each of the years 1980–2015, followed by the weather of all summers in the same period. We find that for the whole ice cap, the variability in runoff as a function of varying spring conditions was on average 31% of the variability due to changing summer weather. However, some outlet glaciers are very sensitive to the amount of snow in the spring, as e.g., the variation in runoff from Brúarjökull due to changing spring conditions was on average 50% of the variability due to varying summer weather. [ABSTRACT FROM AUTHOR]