Surface Air Temperature Research Articles

Surface warming in Svalbard may have led to increases in highly active ice-nucleating particles

The roles of Arctic aerosols as ice-nucleating particles remain poorly understood, even though their effects on cloud microphysics are crucial for assessing the climate sensitivity of Arctic mixed-phase clouds and predicting their response to Arctic warming. Here we present a full-year record of ice-nucleating particle concentrations over Svalbard, where surface warming has been anomalously faster than the Arctic average. While the variation of ice-nucleating particles active at around −30 °C was relatively small, those active at higher temperatures (i.e., highly active ice-nucleating particles) tended to increase exponentially with rising surface air temperatures when the surface air temperatures rose above 0 °C and snow/ice-free barren and vegetated areas appeared in Svalbard. The aerosol population relevant to their increase was largely characterized by dust and biological organic materials that likely originated from local/regional terrestrial sources. Our results suggest that highly active ice-nucleating particles could be actively released from Arctic natural sources in response to surface warming.

Internal Variability Dominated the Extreme Cold Wave Over North America in December 2022

AbstractIn December 2022, North America experienced an unprecedented extreme cold event. However, the underlying physical mechanisms of this cold wave, and the extent to which it is driven by internal variability or external forcing, are not fully understood. Using ERA5 reanalysis data and the HadGEM3‐A‐N216 attribution simulations, we identified internal variability as the main cause, contributing −5.14 K to surface air temperature (SAT) anomalies in North America. External forcing slightly mitigated the cold by 0.42 K. An internally generated wave train from the North Pacific, influenced in combination by Pacific‐North American (PNA) and North Pacific Oscillation (NPO) teleconnection patterns, initiated this intense cyclonic event, contributing −2.18 K and −2.12 K to SAT anomalies, respectively. La Niña‐like sea surface temperature anomalies amplified this wave train and resultant cold wave. Additionally, excessive snow cover in the previous November also intensified the December cold anomalies by enhancing surface albedo and reducing solar radiation.

Skillful Long-Lead Seasonal Predictions in the Summertime Northern Hemisphere Midlatitudes

Abstract In contrast to boreal winter when extratropical seasonal predictions benefit greatly from ENSO-related teleconnections, our understanding of forecast skill and sources of predictability in summer is limited. Based on 40 years of hindcasts of the Canadian Seasonal to Interannual Prediction System, version 3 (CanSIPSv3), this study shows that predictions for the Northern Hemisphere summer surface air temperature are skillful more than 6 months in advance in several midlatitude regions, including eastern Europe–Middle East, central Siberia–Mongolia–North China, and the western United States. These midlatitude regions of statistically significant predictive skill appear to be connected to each other through an upper-tropospheric circumglobal wave train. Although a large part of the forecast skill for the surface air temperature and 500-hPa geopotential height is attributable to the linear trend associated with global warming, there is significant long-lead seasonal forecast skill related to interannual variability. Two additional idealized hindcast experiments are performed to help shed light on sources of the long-lead forecast skill using one of the CanSIPSv3 models and its uncoupled version. It is found that tropical ENSO-related sea surface temperature (SST) anomalies contribute to the forecast skill in the western United States, while land surface conditions in winter, including snow cover and soil moisture, in the Siberian and western U.S. regions have a delayed or long-lasting impact on the atmosphere, which leads to summer forecast skill in these regions. This implies that improving land surface initial conditions and model representation of land surface processes is crucial for the further development of a seasonal forecasting system. Significance Statement Useful seasonal predictions in the boreal summer midlatitude regions are of great value. In this study, we show that predictions for the boreal summer season are skillful more than 6 months in advance in several midlatitude regions, including eastern Europe–Middle East, central Siberia–Mongolia–North China, and the western United States. The forecast skill in these regions is associated with a circumglobal teleconnection atmospheric circulation pattern. Sources of the long-lead forecast skill include the global warming–related trend and anomalies in the ocean and land surface initial conditions. It is found that the wintertime snow cover and soil moisture in the Siberian and western U.S. regions have a delayed or long-lasting impact on the atmosphere, which leads to summer forecast skill.

Contribution of land-atmosphere coupling in 2022 CONUS compound drought-heatwave events and implications for forecasting

Severe compound drought-heatwave events were observed over three regions of the Contiguous United States (CONUS), Northwest (NW), Great Plains (GP), and Northeast (NE) regions, during July and August 2022. In this study, we have found that the developments of these drought-heatwave events were shaped by different land-atmosphere coupling behaviors which are associated with water and energy limitation regimes in these regions. In the NW and GP regions, the surface soil moisture (SM) and evapotranspiration (ET) were coupled through water-limited processes. Heatwaves in these two regions were affected by the decrease of ET and the available SM due to the precipitation deficit. This type of land-atmosphere coupling was especially prominent in the GP. In the NE region, the heatwave governed ET through the increase of potential ET (PET) based on energy-limited coupling, which played a crucial role in the development of drought.The impacts of the different land-atmosphere coupling behaviors on the predictability of the 13-km Geophysical Fluid Dynamics Laboratory (GFDL) System for High-resolution prediction on Earth-to-Local Domains (SHiELD) were also investigated by checking its 10-day forecasts during the same period. The analysis was particularly focused on the GP and NE regions, where different land-atmosphere coupling behaviors were observed. The model's warm bias in the GP region was associated with the overestimated net radiation, and the bias was further amplified through the water-limited coupling. In the NE region, the PET-related variables, including surface air temperature, influenced the predictability of drought onset by limiting ET through the energy-limited coupling. Based on our findings, this study highlights the crucial role of land-atmosphere coupling behaviors and provides a scientific strategy for enhancing the model predictability of compound drought-heatwaves.

Characterizing the local and global climatic factors associated with vegetation dynamics in the karst region of southwest China

Understanding the relationship between vegetation and climatic drivers is essential for assessing terrestrial ecosystem patterns and managing future vegetation dynamics. This study examines the effects of local climatic factors and remote large-scale ocean–atmosphere circulations from the Pacific, Atlantic, and Arctic Oceans, as well as the East Asian and Indian summer monsoons, on the spatiotemporal variability of the Normalized Difference Vegetation Index (NDVI) in the karst region of southwest China (KRSC) using Mann-Kendall test, Sen’s slope, cross-correlation, and wavelet analysis. We observed a significant increase in NDVI over karst and non-karst regions from 1981 to 2019, with a notable abrupt shift from 2001 onwards, underscoring the importance of understanding the underlying drivers. The significant correlation and coherence of surface air (TMP) and soil temperatures (ST) with NDVI, especially when analyzed using wavelet methods, indicate their crucial role in vegetation dynamics. Additionally, the broad coherence patterns of AMO and WHWP with NDVI at annual and decadal cycles suggest that ocean–atmosphere interactions also play a significant part. At interannual periodicities, most large-scale indices displayed significant coherence with NDVI. These findings highlight the complexity of NDVI variability, which is better explained by the integration of multiple local and global factors rather than by single variables. The integrated local–global drivers, particularly TMP-ST-AMO-NP-WHWP and PCP-SM-AMO-NP-WHWP with mean coherence of 0.90 and 0.89, respectively, showed the highest mean coherence, emphasizing the need for a multifaceted approach in understanding vegetation changes rather than a single local variable or atmospheric circulation index. These findings have significant implications for policymakers, aiding in better planning and policy formulations considering climate change and atmospheric variability.

Microclimate analysis of Ganga riverfront in Patna: spatial form perspective

ABSTRACT The investigation in this research scrutinizes the influence of spatial form factors on thermal comfort along the Ganga riverfront trail during the spring season. Understanding how spatial form factors impact local climate parameters and PET values is crucial for optimizing thermal comfort in urban waterfronts. The research addresses the need to improve thermal comfort in rapidly urbanizing areas through strategic urban design. Data were collected from eight measurement points along the riverfront, analyzing Air Temperature (Ta), Relative Humidity (RH), Wind Velocity (v), Surface Temperature (Ts), Global Radiation (G) and Mean Radiant Temperature (Tmrt). The study employs a combination of field measurements and statistical analysis to assess the correlation between spatial form factors and thermal comfort indicators. Findings reveal that lower building height (Hb) values are associated with more favorable thermal comfort conditions, while higher Hb negatively impacts thermal comfort. The study also highlights that building spacing (ADb) influences thermal comfort, with findings contrasting those from composite climates, suggesting a need for context-specific design strategies. The research underscores the significant role of Ta and v in shaping Physiological Equivalent Temperature (PET), aligning with previous studies in diverse urban contexts. The results emphasize the critical impact of spatial form on microclimate and thermal comfort, informing urban design and planning to enhance livability along the Ganga riverfront. This study contributes to the field by providing empirical evidence and practical insights for developing thermally comfortable walking trails.

Biennial variability of boreal spring surface air temperature over India

In this study, we report significant biennial variability or oscillation (BO) in the boreal spring (March–May) Surface Air Temperature (SAT) over India and unravelled the causative mechanisms. The positive phase of the BO exhibit significant seasonal warming over India, whereas seasonal cooling is observed during the negative phase of BO. Heat wave days are more during the positive phase of BO compared to negative or neutral phase. The positive (negative) phase of BO is generally coherent with the central (eastern) Pacific warming (cooling) years. The anomalous low-level divergence associated with low-level anticyclonic circulation induces less cloudiness and intense surface solar radiation ovr India during the positive phase, favouring surface warming. The evolution of some positive and/or negative phases of BO without any large scale forcing from the equatorial Pacific suggested the possibility of alternate pathways. The strong anomalous upper-level (at 200 hPa) anticyclonic circulation provoked by mid-latitude Rossby waves is found contributing to the positive phase, thereby highlighting the role of dominant mid-latitude pathways in the biennial SAT variability in addition to El Niño forcing. The sinking motion associated with persistent high, and the associated adiabatic compression also supported surface heating during the positive phase of BO. On the other hand, the mid-latitude Rossby wave induced upper-level cyclonic circulation is found contributing to the negative phase. The sinking motion associated with persistent high, and the associated adiabatic compression also supported surface heating during the positive phase of BO. In contrast, negative soil temperature anomalies and high latent heat flux release to the atmosphere supported surface cooling during the negative phase.

Sampling Error of Mean and Trend of Nighttime Air Temperature at Weather Stations Over China Using Satellite Data as Proxy

AbstractMeteorological observations of surface air temperature have provided fundamental data for climate change detection and attribution. However, the weather stations are unevenly distributed, and are still very sparse in remote regions. The possible sampling error is well known, but not well quantified because we are lack of the adequate and regularly distributed measurements. The high resolution of satellite land surface temperature retrieval during night time provide a nice proxy for near surface temperature as both temperatures controlled by surface longwave radiative cooling and the nocturnal temperature inversion depress land‐atmosphere turbulent exchange. The sampling error of mean value and trend were assessed by comparing station point measurements (pixel of ∼0.01°) with grid (1°) mean and national mean from 2001 to 2021. This method permits us to make the first assessment of under‐sampling error and spatial representative error on both national mean and trend of air temperature during nighttime collected at ∼2,400 weather stations over China. The sampling error in national mean temperature is more than 3°C. The under‐sampling error due to lack of observation explains two thirds and the spatial representative error due to the difference between station and grid/regional mean elevation contribute the other one third. The sampling error in trend account for one third of the national mean trend. The urban heat island effect associated with urbanization around the weather stations (spatial representative error) can explain four fifths of the sampling error in trend, which is consistent with existing studies based on air temperature collected at paired weather station.

Is home where the heat is? comparing residence-based with mobility-based measures of heat exposure in San Diego, California.

Heat can vary spatially within an urban area. Individual-level heat exposure may thus depend on an individual's day-to-day travel patterns (also called mobility patterns or activity space), yet heat exposure is commonly measured based on place of residence. In this study, we compared measures assessing exposure to two heat indicators using place of residence with those defined considering participants' day-to-day mobility patterns. Participants (n = 599; aged 35-80 years old [mean =59 years]) from San Diego County, California wore a GPS device to measure their day-to-day travel over 14-day intervals between 2014-10-17 and 2017-10-06. We measured exposure to two heat indicators (land-surface temperature [LST] and air temperature) using an approach considering their mobility patterns and an approach considering only their place of residence. We compared participant mean and maximum exposure values from each method for each indicator. The overall mobility-based mean LST exposure (34.7 °C) was almost equivalent to the corresponding residence-based mean (34.8 °C; mean difference in means = -0.09 °C). Similarly, the mean difference between the overall mobility-based mean air temperature exposure (19.2 °C) and the corresponding residence-based mean (19.2 °C) was negligible (-0.02 °C). Meaningful differences emerged, however, when comparing maximums, particularly for LST. The mean mobility-based maximum LST was 40.3 °C compared with a mean residence-based maximum of 35.8 °C, a difference of 4.51 °C. The difference in maximums was considerably smaller for air temperature (mean = 0.40 °C; SD = 1.41 °C) but nevertheless greater than the corresponding difference in means. As the climate warms, assessment of heat exposure both at and away from home is important for understanding its health impacts. We compared two approaches to estimate exposure to two heat measures (land surface temperature and air temperature). The first approach only considered exposure at home, and the second considered day-to-day travel. Considering the average exposure estimated by each approach, the results were almost identical. Considering the maximum exposure experienced (specific definition in text), the differences between the two approaches were more considerable, especially for land surface temperature.

The cooperative effects of November Arctic sea ice and Eurasian snow cover on the Eurasian surface air temperature in January–February

AbstractDue to their significant influence on large‐scale atmospheric circulation and climate anomalies, the variability of Arctic sea ice and Eurasian snow cover during late autumn and their combined effects have garnered increasing attention. This study aims to investigate the physical mechanism underlying the covariation among the Barents‐Kara Seas (BKS) sea ice concentration (SIC), Eurasian snow cover extent (SCE) and the ensuing winter Eurasian surface air temperature (SAT). The statistics results of singular value decomposition suggest a significant linkage between the decreased BKS SIC, zonal “negative–positive” dipole SCE anomalies over Eurasia in November and cold Eurasian SAT in January–February (JF). Observational diagnosis analyses about the meridional moisture, heat transport and surface heat flux demonstrate that subpolar Eurasian anticyclonic circulation plays a crucial role in connecting the predominant modes of SIC and SCE. Furthermore, the BKS SIC and Eurasian SCE anomalies can jointly excite upward‐propagating planetary waves into the stratosphere, while simultaneously reducing the subpolar meridional temperature gradient. This results in westerly wind deceleration and favours the continuous planetary wave propagation. Consequently, the stratospheric polar vortex is significantly weakened, along with negative Northern Annular Mode anomalies propagating downward from the stratosphere to troposphere. Negative‐phase Arctic Oscillation anomalies correspondingly develop during JF, resulting in widespread cold anomalies over the Eurasian continent. These results are further confirmed by numerical sensitivity experiments from the Community Atmosphere Model forced by the above mentioned SIC and SCE anomalies. The empirical hindcast model analyses further suggest that the prediction skill of JF Eurasian SAT is enhanced when both the November BKS SIC and Eurasian SCE signals are considered.

Global assessment of climatic responses to ozone–vegetation interactions

Abstract. The coupling between surface ozone (O3) and vegetation significantly influences the regional to global climate. O3 uptake by plant stomata inhibits the photosynthetic rate and stomatal conductance, impacting evapotranspiration through land surface ecosystems. Using a climate–vegetation–chemistry coupled model (the NASA GISS ModelE2 coupled with the Yale Interactive terrestrial Biosphere, or ModelE2-YIBs), we assess the global climatic responses to O3–vegetation interactions during the boreal summer of the present day (2005–2014). High O3 pollution reduces stomatal conductance, resulting in warmer and drier conditions worldwide. The most significant responses are found in the eastern US and eastern China, where the surface air temperature increases by +0.33 ± 0.87 and +0.56 ± 0.38 °C, respectively. These temperature increases are accompanied by decreased latent heat and increased sensible heat in both regions. The O3–vegetation interaction also affects atmospheric pollutants. The surface maximum daily 8 h average O3 concentrations increase by +1.46 ± 3.02 ppbv in eastern China and +1.15 ± 1.77 ppbv in the eastern US due to the O3-induced inhibition of stomatal uptake. With reduced atmospheric stability following a warmer climate, increased cloud cover but decreased relative humidity jointly reduce aerosol optical depth by −0.06 ± 0.01 (−14.67 ± 12.15 %) over eastern China. This study suggests that vegetation feedback should be considered for a more accurate assessment of climatic perturbations caused by tropospheric O3.

Significant influence of winter Pacific-North American pattern on spring vegetation in mid-high latitude Asia

Abstract Given that the vegetation over mid-high latitude Asia (MHA) has been more variable in recent years, it is necessary to better understand the physical causes of vegetation variations in this region. Based on the normalized difference vegetation index (NDVI), this study reveals a close linkage of the variability of spring (April–May) vegetation in MHA to the winter (December–January–February) Pacific-North American (PNA) pattern. When the winter PNA pattern lies in the positive phase, the NDVI tends to decrease in most parts of the MHA region during the following spring. Further analysis suggests that the lagged influence of winter PNA on spring atmospheric circulations and hence the vegetation in MHA is accomplished by the stratospheric pathway. The positive PNA phase can enhance the upward transport of wave energy into the stratosphere over the high latitudes in winter through the linear constructive interference of zonal wavenumber 1 (WN1), consequently leading to a weaker polar vortex in the stratosphere during February-March. Subsequently, the weakened polar vortex signal propagates downward from the stratosphere to the troposphere, inducing the negative Arctic Oscillation-like circulation with an anomalous cyclonic circulation dominating the MHA region in spring. The anomalous cyclonic circulation further cools the surface air temperature in MHA via modulating downward solar radiation and temperature advection, resulting in a decrease of spring NDVI in situ.

Differentiated influences of anomalous subtropical high on extreme persistent precipitation and heatwave events in the Yangtze River Valley

AbstractIn the summers of 2020 and 2022, the Western Pacific Subtropical High (WPSH) intensified extremely and extended westward. However, in summer 2020, the Yangtze River Valley (YRV) witnessed record‐breaking floods, while in 2022, an unprecedented and prolonged heatwave occurred. Distinctly, these two extreme events were caused by different effects of the WPSH: one is enhancement of the transportation of water vapor and the other is adiabatic heating caused by the descending airflow. In June–July 2020, the stable extension of the WPSH ridge line to South China directed a southwesterly airflow along its northwest flank, leading to sustained precipitation in the YRV. Additionally, the midlatitude circulation pattern featured two troughs and two ridges. Such a circulation configuration, combined with the strong and westward WPSH, enabled the continuous southward intrusion of cold air and northward transport of warm moist air, converging over the YRV, and thus influenced extreme persistent precipitation. In contrast, the WPSH covered the YRV almost entirely during summer 2022. Under this influence, the clear‐sky condition and descending airflow through adiabatic warming directly resulted in the heatwave. In addition, local land–atmosphere feedback was crucial in its development and persistence. The soil moisture deficit induced by high temperatures increased the sensible heat flux between the soil and atmosphere upward, further enhanced the surface air temperature and strengthened the heat dry condition.

Changes in the spatiotemporal distribution of the timing and duration of the soil freeze–thaw status from 1979 to 2018 over the Tibetan Plateau

AbstractChanges in the soil freeze–thaw status will inevitably affect the thermal conditions and properties of the Tibetan Plateau (TP), thereby affecting its upper atmosphere, and further afield in East Asia and even globally. In this study, using the soil temperature simulated by the Community Land Model Version 5.0 (CLM5.0), the timing and duration of the soil freeze–thaw status were divided into freeze start‐date, freeze end‐date and freeze duration. Then, using linear trend estimation, correlation analysis and other methods, the changes in the spatiotemporal distribution of the timing and duration of the soil freeze–thaw status from 1979 to 2018 over the TP were analysed, and the relationships between them and surface temperature, altitude and latitude were analysed. The results obtained were as follows: (1) The soil temperature simulated by CLM5.0 can reasonably reproduce the seasonal changes in multilayer soil temperature, and correlated well with observations. Simulated by CLM5.0 of the time and duration of the soil freeze–thaw status also correlated well with observations. (2) The spatial distribution of the soil freeze–thaw status is characterized by a trend of delayed freezing, advanced thawing and shortened freeze duration from northwest to southeast over the TP. From 1979 to 2018, the freeze start‐date postponed by 7.3 days and became delayed at a rate of 1.9 days per decade, while the freeze end‐date advanced by 6.4 days at a rate of 1.7 days per decade, and the freeze duration shortened by 13.7 days at a rate of 3.6 days per decade. The timing and duration of the soil freeze–thaw status vary across different regions of the TP. The freeze start‐date in all areas of the TP has been delayed in the past 39 years. Except for the subcold zone and arid regions of the TP, the freeze end‐date has occurred earlier and the freeze duration has shortened, with the most significant changes in the subcold zone and humid regions, while the freeze end‐date has advanced at a rate of 3.6 days per decade and the freeze duration has shortened at a rate of 6.3 days per decade. (3) The timing and duration of the soil freeze–thaw status are significantly correlated with surface air temperature, elevation and latitude, exceeding the 99% confidence level. The correlation between the timing and duration of the soil freeze–thaw status and surface temperature is strongest, followed by altitude, and correlation with latitude is weaker. The correlation between surface air temperature and the timing and duration of the soil freeze–thaw status in the western TP is stronger than that in the eastern TP. The rate of change in the soil freeze–thaw status increases with altitude to 3000 m above sea level, while this rate decreases with elevation above 3000 m. The rate of change in the soil freeze–thaw status is greatest at 29°N, while the rate of delay in the freeze start‐date is minimal at 33°N and the rates of advancement in the freeze end‐date and shortening of the freeze duration are minimal at 35°N.

Growth Simulation of Lyophyllum decastes and Coprinus comatus and Their Influencing Factors in a Forested Catchment

Wild edible mushrooms are an important food source globally and have a crucial role in forest ecosystems. However, there is limited research on the growth characteristics and the contribution of agronomic traits to biomass, and the environmental factors affecting mushroom growth are limited. This study was conducted in the Qilian Mountains, China, and focused on investigating the growth patterns and agronomic traits of Lyophyllum decastes and Coprinus comatus. The results revealed that the growth of these mushrooms followed a logical growth curve. By calculating the model parameters, we obtained the maximum daily growth of height (PH), pileus diameter (PD), and cluster perimeter (CP) of L. decastes on the 5th, 7th, and 7th days, respectively, with values of 0.55 cm d−1, 0.54 cm d−1, and 4.54 cm d−1, respectively. However, the maximum daily growth of PH, pileus length (PL), and PD of the C. comatus appeared on the 3rd day, 2nd day, and 2nd day of the observation, respectively. This study identified near-surface relative humidity, air relative humidity, and rainfall as the primary factors influencing mushroom growth, as indicated by Pearson’s correlation analysis, redundancy analysis (RDA), and multiple linear and stepwise regression. Additionally, land surface temperature and air temperature were also identified as important factors affecting mushroom growth. By utilizing random forest and stepwise regression analysis, this study identified PH and stipe diameter (SD) as the most crucial agronomic traits affecting mushroom biomass. Overall, this study offers insights for industrial mushroom cultivation and basic fungal research.

Климатические изменения температуры воздуха западной части российской Арк­тики в 1940—2099 гг. по данным ERA5 и моделям CMIP6

The study deals with the climatic changes in surface air temperature (SAT) in the western part of the Russian Arctic (60—80° N, 30—90° E). To analyze the changes in SAT that occurred over the period 1940—2023, we use data from the ERA5 reanalysis and the results of the Historical model experiment CMIP6. Future changes in SAT until the end of the 21st century we consider based on the results of SSP experiments of CMIP6 models. An increase in the average SAT of the studied region by 2—4°C is shown from approximately the mid-1970s to 2023. Moreover, this increase in SAT is most noticeable in the White and Kara Seas, as well as in the north and east of the Barents Sea. CMIP6 models based on different greenhouse gas emission scenarios produce markedly different forecasts for the increase in SAT for the region under study until the end of the 21st century. Thus, depending on the scenario, the average increase in SAT in the region under study by the end of the 21st century can range from 2—4°C to 6—10°, with stronger growth of SAT in the north of the region under study. At the same time, with little dependence on the future scenario, CMIP6 models predict that in the next 30 years the average SAT of the western part of the Russian Arctic will increase by approximately 2—3°C, and in the north of the region under study its increase may be more than 3°C, and in south — about 2°C. Thus, the difference in average SAT between the north and south of the region under study will decrease throughout the 21st century under any of the SSP scenarios considered.

Influence of the Atlantic and Pacific Multidecadal Variability on Arctic Sea Ice in Pacemaker Simulations during 1920–2013

Abstract The Atlantic multidecadal variability (AMV) and Pacific multidecadal variability (PMV) can influence Arctic sea ice and modulate its trend, but to what extent the AMV and PMV can affect Arctic sea ice and which processes are dominant are not well understood. Here, we analyze the Community Earth System Model, version 1, idealized and time-varying pacemaker ensemble simulations to investigate these issues. These experiments show that the sea ice concentration varies mainly over the marginal Arctic Ocean, while the sea ice thickness variations occur over the entire Arctic Ocean. The internal components of AMV and PMV can enhance or weaken the decadal sea ice loss rates over the marginal Arctic Ocean by more than 50%. The AMV- or PMV-induced anomalous atmospheric energy transport and downward longwave radiation related to low clouds (thermodynamical processes) and sea ice motion (dynamical processes) contribute to the Arctic surface air temperature and sea ice concentration and thickness changes. Anomalous oceanic heat flux is mainly a response to rather than a cause of sea ice variations. The dynamic processes contribute to the winter Arctic sea ice variations as much as the thermodynamic processes, but they contribute less (more) to the summer Arctic sea ice variability than the thermodynamic processes over the marginal Arctic Ocean (parts of the central Arctic Ocean). Sea ice loss enhances air–sea heat fluxes, which cause oceanic heat convergence and warm near-surface air and the lower troposphere, which in turn melt more sea ice.

Accelerated warming of High Mountain Asia predicted at multiple years ahead

High-Mountain Asia (HMA) is an important source of freshwater since it holds the largest reservoir of frozen water outside the polar regions. HMA feeds ten great rivers, ultimately supporting more than 2 billion people. However, the threat of accelerated glacier melt, which is a consequence of unprecedented global warming since the early 1950s, threatens water resources in the surrounding countries. Accurate predictions of the near-term temperature change and synergistic mass loss of glaciers are essential but challenging to implement because of the impacts of internal climate variability. Here, based on large ensembles of state-of-the-art decadal climate prediction experiments, we provide evidence that the internally generated surface air temperature variations in HMA can be predicted multiple years in advance, and the model initialization has robust added value to the decadal prediction skill. Real-time decadal forecasts suggest that the HMA will experience accelerated warming in 2025–2032, where the surface warming will increase by 0.98 °C (0.67 to 1.33 °C; 5 % to 95 % range) relative to the reference period 1991–2020, which is equivalent to 1.75 times the observed warming during 2016–2023. The decadal predictability originates from extratropical Pacific decadal variability modes, which modulate the convective heating in the tropical Pacific and influence HMA via the eastward-propagating atmospheric Kelvin waves. Accelerated warming in the coming decade will likely increase the shrinkage of the glacier volume over the HMA by 1.4 %. This change poses unprecedented challenges, including potential water scarcity, ecosystem disruption, and increased risk of natural disasters, to HMA and surrounding regions.

Evaluation of Subseasonal Forecast Skill for Northern Hemisphere Winter Snow Cover

Abstract Accurate subseasonal forecasts for snow cover have significant socioeconomic value. This paper evaluates subseasonal forecasts for winter snow cover in the Northern Hemisphere as predicted by three numerical models: the Model for Prediction Across Scales–Atmosphere (MPAS-A), the China Meteorological Administration (CMA) model, and the European Centre for Medium-Range Weather Forecasts (ECMWF) model. While these models can generally simulate the spatial distribution of winter snow cover climatology and subseasonal variability, they tend to underestimate both the climatology and the intensity of subseasonal variability. Compared to persistence forecasts, these models demonstrate skill in subseasonal snow cover forecasting. Notably, the ECMWF model outperforms the MPAS and CMA models. The sensitivity of the surface air temperature subseasonal forecast skill to the predicted snow cover was also investigated using the MPAS. The results show that for forecasts with lead times of 1–2 weeks, the predicted snow cover contributes to the temperature forecasting skill. However, for forecasts with lead times of 3–4 weeks, the predicted snow cover does not enhance the temperature forecasting skill. Furthermore, part of the errors in temperature forecasts can be attributed to inaccuracies in snow cover forecasts with lead times of 2 weeks or more. These findings suggest that refining snow cover parameterization schemes and effectively exploiting predictability from snow cover can enhance the skill of subseasonal atmospheric forecasts. Significance Statement Snow cover is a crucial variable in hydrometeorology. Subseasonal forecasting, which involves predicting snow cover anomalies 1–4 weeks in advance, has socioeconomic value. We conducted an evaluation of the subseasonal forecasts for Northern Hemisphere winter snow cover produced by three numerical models. This evaluation provides insights into the accuracy and reliability of these models, which could contribute to their enhancement. Furthermore, we examined the impact of the predicted snow cover on the skill of surface air temperature subseasonal forecasts. The results suggest that improvements in snow cover modeling and forecasting can lead to more accurate subseasonal atmospheric forecasts. Therefore, future efforts to refine snow cover parameterization schemes suitable for subseasonal forecasting are promising and worthwhile.

Unveiling the Evolution of Extreme Rainfall Storm Structure Across Space and Time in a Warming Climate

AbstractClimate change induces significant changes in storm characteristics, particularly for short‐duration extreme storms (heavy rain features), impacting their intensity and spatio‐temporal distribution. Although alterations in precipitation intensity are well documented, past studies examining changes in spatio‐temporal distribution of storms (storm rainrates) were region‐specific and focused on isolated aspects of change in space or time, eluding a comprehensive understanding of the precise nature and extent of these changes. Bridging this gap, this study introduces a novel grid‐based measure of storm homogeneity, “spatio‐temporal homogeneity” metric and investigates the global patterns of change in combined spatio‐temporal characteristics of extreme storms. Analyzing the 30 min × 0.1° × 0.1° resolution Global Precipitation Measurements, the study finds that extreme storms are shrinking in both space and time due to rising surface air temperatures, predominantly in tropics. In contrast, temperate regions experience expanded extreme storms with increasing temperatures. The study also identifies a global trend toward more front‐loading in storms with rising temperatures, driven by a substantial increase in tropics and southern temperate regions. Conversely, storms in northern temperate regions become slightly more rear‐loaded as temperature increases. Furthermore, the study finds that characteristics of short–duration storms (6–12 hr) are more sensitive to temperature changes. Overall, this study contributes valuable insights into the global spatio‐temporal changes of extreme storms, highlighting regions most susceptible to alterations in storm patterns due to climate change. These findings are essential for developing effective adaptation strategies and flood management practices to cope with the changing nature of extreme storms in a warming climate.