Surface Air Research Articles

Influences of Stratospheric Arctic Vortex on Surface Air Temperature Over Asia

AbstractThe influence of northern polar vortex in the stratosphere (SPV) in December‐January on Asia's surface air temperature (SAT) in February has been examined using reanalysis data sets and a barotropic model. An out‐of‐phase interannual linkage between the SPV in December‐January and SAT in February during 1979–2022 has been observed, that is, a strong (weak) SPV corresponds to a cooling (warming) over Asia. Approximately 25% of the SAT over Asia in February can be explained by the SPV in December‐January. This relationship between the SPV and SAT is independent of the Arctic Oscillation. The influence of the SPV on SAT over Asia cannot be solely explained by radiative processes, but is instead related to circulation anomalies in the troposphere. A stronger SPV tends to result in negative geopotential height anomalies with cyclonic circulation over Asia. The SPV‐related geopotential height over Asia is accompanied by a weakened teleconnection pattern between the North Atlantic and Asia, with three centers from the northeastern Atlantic‐eastern Europe‐Asia, and fewer stationary waves propagated from North Atlantic into Asia. These anomalous circulation patterns and anomalous northerly wind over Central Asia in February are beneficial to the colder air transportation from the higher latitudes to Asia, facilitating a surface cooling over Asia. Our results shed light on the interannual linkage between SPV and SAT over Asia, suggesting that the SPV in December‐January could be considered as a new predicator of SAT in February over Asia.

Patterns and Trends in Northern Hemisphere River Ice Phenology from 2000 to 2021

River ice plays a crucial role in hydrology, ecosystems, and socio-economic systems in mid-to-high latitude regions. However, there is a notable gap in the analysis of patterns and trends in river ice phenologies and their influence factors at the river level. In this study, we developed a practical method to identify river ice phenologies by leveraging Landsat observations from 2000 to 2021, and established a comprehensive dataset encompassing 7970 river segments across the entire Northern Hemisphere. Our results revealed that the mean freeze-up date, break-up date, and ice duration for these river segments were October 24, May 4, and 192.1 days, respectively. Notably, 56% of the river segments exhibited a warming trend, with the average freeze-up date delayed by 2.7 days per decade, break-up date advanced by 2.5 days per decade, and ice duration shortened by 5.5 days per decade—nearly five times the documented decrease rate over the past 150 years. Furthermore, high-latitude polar regions and the Qinghai-Tibet Plateau demonstrated the most pronounced decreases in ice duration, exceeding 7.0 days per decade on average. We observed a substantial relative contribution, reaching up to 70%, of surface air temperature (SAT) in middle and low latitude regions characterized by relatively high winter temperatures. However, as winter temperatures decrease, the relative influence of SAT diminishes, while the contributions of precipitation, solar radiation, and residuals increase. Our dataset provided essential baseline for further understanding the fundamental physical processes within river systems, offering crucial insights for river management, hydrological disaster risk control, climate change research, and ecosystem conservation efforts.

Impact of late winter sea surface temperature seesaw between equatorial central Pacific and Philippine Sea on early spring vegetation over the low-latitude highlands of China

In this study, the impact of late winter (January–February) sea surface temperature (SST) seesaw between equatorial central Pacific and Philippine Sea on early spring (March–April) vegetation interannual variation over the low-latitude highlands of China (CLLH) is investigated. The positive phase of late winter seesaw SST pattern, characterized by warmer (colder)-than-normal SSTs in the equatorial central Pacific (Philippine Sea), favors early spring CLLH vegetation growth. From the local perspective, the positive phase of late winter SST seesaw results in stronger (higher)-than-normal precipitation (surface air temperature) in the west (east) of the CLLH, these abnormal signals may be recorded by land surface and may persist to early spring, and this wetter(warmer)-than-normal land surface condition in the west (east) of the CLLH favors local vegetation growth. For the atmospheric circulation anomalies, the positive phase of late winter seesaw SST pattern contributes to an anomalous anticyclone over Philippine Sea in lower troposphere. On one hand, anomalous northerly winds on the eastern flank of this anomalous cyclone advect off-equatorial dry and cold air to the Maritime Continent and cause precipitation reduction there, consequently an anomalous anticyclone over the tropical north Indian Ocean in lower troposphere is excited via a Gill-type Rossby wave atmospheric response. This anomalous anticyclone leads to enhanced upward motion and increased precipitation in the west of the CLLH resultantly. On the other hand, southerly wind anomalies being the western flank of the anomalous anticyclone advect warm air to the east of the CLLH, result in increased surface air temperature there.

Reduced Arctic Ocean CO2 uptake due to coastal permafrost erosion

Arctic coastal permafrost erosion is projected to increase by a factor of 2–3 by 2100. However, organic matter fluxes from the coastal permafrost into the ocean have not been considered in Earth system models so far. Here we represent coastal permafrost erosion in an Earth system model and perform simulations with varying permafrost organic matter properties, such as sinking fraction and nutrient content. We find that coastal erosion reduces the Arctic Ocean CO2 uptake from the atmosphere in all simulations: by 4.6–13.2 TgC yr−1 by 2100, which is ~7–14% of the Inner Arctic Ocean uptake. We show that coastal permafrost erosion exerts a positive biogeochemical feedback on climate, increasing atmospheric CO2 by 1–2 TgC yr−1 per °C of increase in global surface air temperature. Our work will allow coastal permafrost erosion to be considered in future climate change assessments.

Urbanization Effect on Regional Thermal Environment and Its Mechanisms in Arid Zone Cities: A Case Study of Urumqi

Urumqi is located in the arid region of northwestern China, known for being one of the most delicate ecological environments and an area susceptible to climate change. The urbanization of Urumqi has progressed rapidly, yet there is a lack of research on the urbanization effect (UE) in Urumqi in terms of the regional climate. This study investigates the UE of Urumqi (urban built-up area) on the regional thermal environment and its mechanisms for the first time, based on the WRF (Weather Research and Forecasting) model (combined with the Urban Canopy Model, UCM) simulation data of 10 consecutive years (2012–2021). The results show that the UE on surface temperature (Ts) and air temperature at 2 m (T2m) is strong (weak) during the night (daytime) in all seasons, and the UE on these is largest (smallest) in spring (winter). In addition, the maximum UE on both Ts and T2m is present over southern Urumqi in winter, whereas the maximum UE is identified over the northern Urumqi in other seasons. The maximum UE on Ts occurred in northwestern Urumqi at 18 LST (Local Standard Time, i.e., UTC+6) in autumn (reaching 5.2 °C), and the maximum UE on T2m occurred in northern Urumqi at 4 LST in summer (reaching 2.6 °C). Urbanization showed a weak cooling effect during daytime in summer and winter, reflecting the unique characteristics of the UE in arid regions, which are different from those in humid regions. The maximum cooling of Ts occurred in northern Urumqi at 11 LST in summer (reaching −0.4 °C), while that of T2m occurred at 10 LST in northern and northwestern Urumqi in winter (reaching −0.25 °C), and the cooling effect lasted for a longer period of time in summer than in winter. The UE of Urumqi causes the increase of Ts mainly through the influence of net short-wave radiation and geothermal flux and causes the increase of T2m through the influence of sensible heat flux and net long-wave radiation. The UE on the land surface energy balance in Urumqi can be used to explain the seasonal variation and spatial differences of the UEs on the regional thermal environment and the underlying mechanism.

Antarctic Warm Extremes Across Seasons and Their Response to Advection

AbstractAntarctic warm extremes impact the cryosphere, with very warm extremes driving surface melt on ice shelves. Here, we analyze temperatures exceeding the 90th percentile and the associated circulation patterns and radiation anomalies. ERA5 reanalysis data show positive geopotential height anomalies related to the occurrence of warm extremes. The highest temperature during warm extremes appears on the western periphery of high‐pressure systems, consistent with anticyclonic advection. Temperature anomalies during warm extremes are strongest in winter due to the transport of warm and moist air and a strong meridional temperature gradient. In summer, the weak meridional gradients of top‐of‐atmosphere downward solar radiation flux and surface air temperature contribute to weak temperature anomalies. Warm extremes are associated with positive longwave radiation anomalies in all seasons, but with negative shortwave radiation anomalies at the surface except during polar night. These relationships are verified by station observations. Our results confirm that Antarctic warm extremes are mostly driven by meridional advection of warm air, and suggest that these warm air masses are predominantly moist and cloudy.

Elucidating the boundary layer turbulence dissipation rate using high-resolution measurements from a radar wind profiler network over the Tibetan Plateau

Abstract. The planetary boundary layer (PBL) over the Tibetan Plateau (TP) exerts a significant influence on regional and global climate, while its vertical structures of turbulence and evolution features remain poorly understood, largely due to the scarcity of observations. This study examines the vertical profile of and daytime variation in the turbulence dissipation rate (ε) in the PBL and free troposphere over the TP using the high-resolution (6 min and 120 m) measurements from a radar wind profiler (RWP) network, combined with hourly data from ERA5 during the period from 1 September 2022 to 31 October 2023. Observational analyses show that the magnitude of ε below 3 km under all-sky conditions exhibits a large spatial discrepancy over the six RWP stations over the TP. Particularly, the values of ε at Minfeng and Jiuquan over the northern TP and at Dingri (alternately Tingri) over the southern TP are roughly an order of magnitude greater than those at Lijiang, Ganzi (alternately Garzê), and Hongyuan over the eastern TP. This could be partially attributed to the difference in land cover across the six RWP stations. In terms of the diurnal variation, ε rapidly intensifies from 09:00 local standard time (LST) to 14:00 LST and then gradually levels off in the late afternoon. Under clear-sky conditions, both ε and the planetary boundary layer height (zi) are greater compared with cloudy-sky conditions, which could be due to the cooling effect of clouds, which reduces the solar irradiation reaching the surface. In the lower PBL (0.3 ≤ z/zi ≤ 0.5), where z is the height above ground level, the dominant influential factor in the development of turbulence is the surface–air temperature difference (Ts−Ta). By comparison, in the upper PBL (0.6 ≤ z/zi ≤ 1.0), both Ts−Ta and vertical wind shear (VWS) affect the development of turbulence. Above the PBL (1.0 < z/zi ≤ 2.0), the shear production resulting from VWS dominates the variation in turbulence. Under cloudy-sky conditions, the reduced Ts−Ta and weakened surface sensible heat flux tend to inhibit the turbulent motion in the PBL. On the other hand, the strong VWS induced by clouds enhances the turbulence above the PBL. The findings obtained here underscore the importance of the RWP network in revealing the fine-scale structures of the PBL over the TP and gaining new insight into the PBL evolution.

Paleoclimate data provide constraints on climate models' large-scale response to past CO2 changes

The paleoclimate record provides a test-bed in which climate models can be evaluated under conditions of substantial CO2 change; however, these data are typically under-used in the process of model development and evaluation. Here, we use a set of metrics based on paleoclimate proxy observations to evaluate climate models under three past time periods. We find that the latest CMIP6/PMIP4 ensemble mean does a remarkably good job of simulating the global mean surface air temperatures of these past periods, and is improved on CMIP5/PMIP3, implying that the modern climate sensitivity of the CMIP6/PMIP4 model ensemble mean is consistent with the paleoclimate record. However, some models, in particular those with very high or very low climate sensitivity, simulate paleo temperatures that are outside the uncertainty range of the paleo proxy temperature data; in this regard, the paleo data can provide a more stringent constraint than data from the historical record. There is also consistency between models and data in terms of polar amplification, with amplification increasing with increasing global mean temperature across all three time periods. The work highlights the benefits of using the paleoclimate record in the model development and evaluation cycle, in particular for screening models with too-high or too-low climate sensitivity across a range of CO2 concentrations.

Predicting significant wave height in the South China Sea using the SAC-ConvLSTM model

IntroductionThe precise forecasting of Significant wave height(SWH) is vital to ensure the safety and efficiency of aquatic activities such as ocean engineering, shipping, and fishing.MethodsThis paper proposes a deep learning model named SAC-ConvLSTM to perform 24-hour prediction with the SWH in the South China Sea. The long-term prediction capability of the model is enhanced by using the attention mechanism and context vectors. The prediction ability of the model is evaluated by mean absolute error (MAE), root mean square error (RMSE), mean square error (MSE), and Pearson correlation coefficient (PCC).ResultsThe experimental results show that the optimal input sequence length for the model is 12. Starting from 12 hours, the SAC-ConvLSTM model consistently outperforms other models in predictive performance. For the 24-hour prediction, this model achieves RMSE, MAE, and PCC values of 0.2117 m, 0.1083 m, and 0.9630, respectively. In addition, the introduction of wind can improve the accuracy of wave prediction. The SAC-ConvLSTM model also has good prediction performance compared to the ConvLSTM model during extreme weather, especially in coastal areas.DiscussionThis paper presents a 24-hour prediction of SWH in the South China Sea. Through comparative validation, the SAC-ConvLSTM model outperforms other models. The inclusion of wind data enhances the model's predictive capability. This model also performs well under extreme weather conditions. In physical oceanography, variables related to SWH include not only wind but also other factors such as mean wave period and sea surface air pressure. In the future, additional variables can be incorporated to further improve the model's predictive performance.

Unraveling climate trends in the mediterranean: a hybrid machine learning and statistical approach

This study presents a comprehensive spatiotemporal analysis of sea surface temperatures (SST) and surface air temperatures (TAS) across 15 Mediterranean coastal stations, leveraging centennial-scale data to analyze regional climate dynamics. The modeling framework integrates three sequential phases: data preprocessing, statistical analysis, and advanced machine learning techniques, creating a robust analytical pipeline. The data preprocessing phase harmonizes diverse datasets, addresses missing values, and applies transformations to ensure analytical consistency. The statistical modeling employs the Pettitt test for change point detection and linear trend analysis to unveil underlying patterns. The machine learning phase utilizes K-means clustering for climate regime classification and implements tailored Convolutional Neural Networks (CNNs) for cluster-specific future climate anomaly projections. Results unveil a marked anthropogenic climate signal, with contemporary observations consistently surpassing historical baselines. Breakpoint analyses and linear trend assessments reveal heterogeneous climatic shifts, with pronounced warming in the northern Mediterranean. Notably, Nice and Ajaccio exhibit the highest SST increases (0.0119 and 0.0113 °C/decade, respectively), contrasting with more modest trends in Alexandria (0.0052 °C/decade) and Antalya (0.0047 °C/decade) in the eastern Mediterranean. The application of clustering and CNN projections provides granular insights into differential warming trajectories. By 2050, cooler northwestern Mediterranean zones are projected to experience dramatic SST anomalies of approximately 3 °C above the average, with corresponding TAS increases of 2.5 °C. In contrast, warmer eastern and southern regions display more subdued warming patterns, with projected SST and TAS increases of 1.5–2.5 °C by mid-century. This research’s importance is highlighted by its potential to inform tailored adaptation strategies and contribute to the theoretical understanding of climate dynamics, advancing climate modeling and analysis efforts.

About Height of the Surface Air Layer by Sodar Data

Average empirical estimations of the surface air layer height in Moscow have been received by the data of long-term acoustic remote sensing of the atmosphere using MODOS Doppler sodar of METEK (Germany) production. Based on the assumption that the average conditions are close to neutral stratification, this height, as the top of the quasi-linear section of the average long-term wind velocity profile in semi-logarithmic coordinates, is 40–60 m. The wind rotation height, i. e. the height of intersection of day and night wind profiles is on average 95 meters per year. The roughness length in conditions of loose but high urban development in vicinity of Moscow State University in Moscow is 5 m. According to the criterion of the constant wind direction in the surface air layer, its height appears in the average monthly wind direction profiles over the “dead zone” of the sodar (40 m) in approximately one case out of three and usually amounts to 60 m, less often 80 or 100 m. In the rest cases, it is apparently masked by “dead zone”. The average height of the surface layer according to this approach is probably a little less than 50 m, which is close to the estimate obtained from the logarithmic distribution of wind velocity with height in this layer. The daily course of the surface air layer height is noted by the largest values in the afternoon (80–100 m in summer under conditions of prevailing unstable stratification and 60–80 m in winter) and the smallest ones (less than 40 m) in the late evening and at night in summer and from evening to noon in winter.

Assessing the Impacts of Falling Ice Radiative Effects on the Seasonal Variation of Land Surface Properties

AbstractThe impacts of falling ice radiative effects (FIREs) on land‐atmosphere feedback processes were examined, with a focus on the fidelity of land surface properties and their variability as inferred by global climate models (GCMs). We conducted a pair of sensitivity experiments using the National Center for Atmospheric Research (NCAR) Community Earth System Model Version 1 (CESM1) in fully coupled modes with FIREs turned on and off. This allowed us to investigate the seasonal response of land surface properties to changes in radiation fluxes and land surface temperature (LST) associated with FIREs across global land areas. Our findings indicate that during boreal winter, excluding FIREs results in less surface downward longwave and net flux (∼5–15 Wm−2), leading to a colder land surface (∼2–4 K) and air temperatures (∼1–4 K) at mid‐ and high latitudes. Consequently, the surface frozen soil layer and snow cover persist through spring, delaying snowmelt and thawing until summer. This delay reduces liquid soil moisture, thereby suppressing vegetation productivity in subsequent seasons. Conversely, tropical regions, exhibit contrasting responses, with a warmer land surface (∼0.5 K) and warmer air temperatures (∼0.1–0.5 K) due to increased surface downward shortwave and net flux (∼2–10 Wm−2). This enhancement in radiation fosters increased vegetation productivity throughout the seasonal cycle. These findings illustrate a local response of land surface properties to changes in the surface energy balance and LST, highlighting the significant role that FIREs play in land surface modeling within GCMs.

Can urban shrinkage contribute to mitigating surface air temperature warming?

In recent years, urban areas are increasingly experiencing intense warming. Although urbanization is an important driver of warming in urban environments, it remains unclear whether depopulation trends in shrinking cities mitigate this warming effect. Here, we explored the relationship between shrinking cities and observed warming in China. Of the 356 Chinese cities, 95 were identified as shrinking between 2000 and 2010. We categorized 2419 observation stations into three groups—rural, shrinking, and non-shrinking—and generated a surface air temperature (SAT) anomaly series for each group from 1961 to 2014. Temperature differences between the shrinking and non-shrinking urban stations were investigated. Using segmented generalized least squares regression, the spatiotemporal temperature patterns across the mean (Tmean), maximum (Tmax), and minimum temperatures (Tmin), diurnal temperature range (DTR) indicators, and across seasons—spring, summer, autumn, and winter—were explored. Results revealed a cooling effect in shrinking cities, with decadal decreases of 0.042 °C (–0.078 to –0.005 °C), 0.083 °C (–0.126 to –0.039 °C), and 0.029 °C (–0.062 to –0.005 °C) in regional Tmean, Tmax, and Tmin anomalies, respectively. Moreover, pronounced seasonality was identified in this phenomenon—the cooling effect was most notable for Tmean in spring and Tmax in autumn, less significant in summer, and negligible in winter. These results suggested that the population decline in shrinking cities could alleviate regional warming, having implications that could influence urban planning and climate mitigation policies.

A long-term Doppler wind LiDAR study of heavy pollution episodes in western Yangtze River Delta region, China

Previous studies are primarily based on surface air pollution for a single event, leaving research on the temporal characteristics of aerosols and wind fields in the atmospheric boundary layer (ABL) and their associations unclear due to a lack of long-term vertical profile data. This study was the first to conduct long-term Doppler wind LiDAR measurements from Sep. 2019 to Aug. 2022 in Hefei in western Yangtze River Delta (YRD) region, China. The spatiotemporal characteristics of retrieved aerosol backscatter coefficient (β) and wind profiles were analyzed from the perspective of long-term statistics and typical heavy pollution episodes (HPEs). The seasonal profile of β showed a peak in the near-surface layer meanwhile the overall β profile (<0.5 km) was the highest in winter but lowest in summer. Combined with ground-based meteorological and air quality observations, 12 HPEs were identified and classified into dust-related events and fog-haze episodes. The results showed a consistency between hourly variations in PM10 (particulate matter smaller than 10 μm) concentration and β retrieved at 300 m during a high PM10 concentration (>150 μg/m3) period. Different roles of horizontal wind at different altitudes and its associated time delay effect on surface PM10 pollution were evaluated based on correlation coefficients (Ф) and air pollution diffusion conditions. Significant positive Ф values were noticed below 0.5 km during the entire dust-related EP4 and EP6, indicating that the higher wind speeds could exacerbate PM10 pollution. In contrast, large negative Ф values meant the removal of PM10 pollutants by strong winds below 0.8 km during 24 h after peak in EP3. Combining with backward trajectory analysis and meteorological condition, notable transboundary pollution with positive contributions from upper winds (>1.5 km) was discovered in dust-related EP1, EP4, EP7, EP9, and EP10. Time delay effect (1-h/2-h lag) of upper winds (>0.8 km) on surface PM10 pollution was explored in EP5, EP6, EP7, EP8, and EP9. In spring dust-related HPEs, surface PM10 pollution was mostly contributed by long-range transport of aerosol particles at higher altitudes (>1.5 km) from the northwest direction, driven by the Mongolian cyclone and the cold front system. Transboundary aerosols originated from the northern part of Anhui province and the YRD region in the middle altitudes (∼0.5 km) was the main contributor to fog-haze HPEs. The findings demonstrated the ability of the real-time Doppler wind LiDAR system to monitor transboundary air pollution and provided a scientific reference for policy makers.

Understanding the multifaceted influence of urbanization, spectral indices, and air pollutants on land surface temperature variability in Hyderabad, India

Hyderabad, similar to many cities worldwide, has experienced accelerated urbanization, leading to increased surface temperature and air pollution. The present study investigated the relationship between land surface temperature (LST) and various air pollutants, urbanization, surface indices, and meteorological parameters, which are associated with Sustainable Development Goals (SDGs), especially Goal 13 (Climate Action) and Goal 11 (Sustainable Cities and Communities). The effect of air pollution on LST across seasons was also examined for five years (2018–2022). LST images collected from MODIS satellite. Data on air pollutants and meteorological parameters were collected from air quality monitoring stations. The results showed that PM2.5 exceeded the WHO guidelines. A weak or moderate relationship was observed between LST and individual air pollutants. However, the association significantly improved when all air pollutants were considered simultaneously. The relationship improved even further when additional parameters such as Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), road line density (RLD), and global population density (GPD), were also included. The resulting relationship was very strong (R2 > 0.80) during the summer and winter seasons. The study identified that NH3 had a prominent influence on LST during winter, while PM2.5 and NH3 exhibited a higher influence during summer. The research findings emphasized that LST variations were influenced by both air pollutants and meteorological conditions, along with urbanization factors represented by various parameters and indices. The research underscores the importance of considering these findings in future studies. It suggests the need to explore efficient approaches to address the combined problem of rising LST and concentration of air pollutants in urban areas.

Distinguishing Impacts on Winter Temperatures in Northern Mid–High-Latitude Continents during Multiyear and Single-Year La Niña Events: A Modeling Study

Abstract Utilizing a 2200-yr CESM1 preindustrial simulation, this study examines the influence of property distinctions between single-year (SY) and multiyear (MY) La Niñas on their respective impacts on winter surface air temperatures across mid–high-latitude continents in the model, focusing on specific teleconnection mechanisms. Distinct impacts were identified in four continent sectors: North America, Europe, Western Siberia (W-Siberia), and Eastern Siberia (E-Siberia). The typical impacts of simulated SY La Niña events are featured with anomalous warming over Europe and W&amp;E-Siberia and anomalous cooling over North America. Simulated MY La Niña events reduce the typical anomalous cooling over North America and the typical anomalous warming over W&amp;E-Siberia but intensify the typical anomalous warming over Europe. The distinct impacts of simulated MY La Niñas are more prominent during their first winter than during the second winter, except over W-Siberia, where the distinct impact is more pronounced during the second winter. These overall distinct impacts in the CESM1 simulation can be attributed to the varying sensitivities of these continent sectors to the differences between MY and SY La Niñas in their intensity, location, and induced sea surface temperature anomalies in the Atlantic Ocean. These property differences were linked to the distinct climate impacts through the Pacific North America, North Atlantic Oscillation, Indian Ocean–induced wave train, and tropical North Atlantic–induced wave train mechanisms. The modeling results are then validated against observations from 1900 to 2022 to identify disparities in the CESM1 simulation.

Constraining future surface air temperature change on the Tibetan Plateau

Abstract The rapid warming of the Tibetan Plateau (TP) in recent decades has led to severe consequences, including the melting of glaciers and snow cover, which further accelerates warming. Accurately projecting the magnitude of future warming is crucial for effective climate change adaptation. However, the projection of future temperature change is model dependent. In this study, we demonstrate a significant correlation between the historical intermodel warming trend and future temperature change, suggesting this relationship could be used to calibrate the best estimate of projections and reduce the uncertainty by observations. For a high emission scenario, the constraint helps to narrow down the uncertainty range of annual and summer temperature change on the western TP by up to 2 °C and 4 °C, respectively, in the end of this century. The most substantial calibrated increase of future change is in winter up to 2 °C, followed by autumn with an increase of up to about 1 °C. Discrepancies of historical warming trend among different observation datasets expose the largest impact on the constrained best estimate compared with emergent relationship derived from different climate models and historical periods.&amp;#xD;

Causes of the extreme cold event in December 2023 on Eastern China

Abstract An extreme cold event outbreaks in Eastern China (EC) in December 16–22, 2023. Its maximum intensity (−8.30 °C) and duration (7 days) are in the second place in December during 1980–2023. In Early Stage (December 6–10), surface air temperature (SAT) anomalies reach the highest at 6.77 °C, exceeding mean value by two standard deviations. The variation of SAT anomalies (differences of SAT anomalies between the last day and the first day for a given period) is 0.60 °C. In Development Stage (December 11–15), SAT anomalies begin to decline but remain positive. In Outbreak Stage (December 16–22), the variation of SAT anomalies reaches a minimum of −3.17 °C, reflecting the cooling of EC. From December 1, cold air gradually gathers in Siberia under the influence of Arctic high moving southward. Cold air is locked in Siberia due to negative anomalies of geopotiential height (GH) and the westerlies anomalies between 40°–50°N. On December 11, these negative GH anomalies begin to move southeastward, and the westerlies anomalies weaken to a easterlies. From December 16–22, EC experiences an extreme cold event due to the southward of Arctic high and the eastward of Ural and Okhotsk high. On the basis of the zonal wind index (ZI) phase changes from negative to positive and the jet stream moves southeastward, the strong (weak) jet stream is spotted to block (promote) the southward of cold air. Linear regression shows that negative Arctic Oscillation (AO) conducts to the concentration of cold air in Siberia. Positive Siberia High (SH) pushes cold air to EC. SAT anomalies decrease by 2.29 °C in EC with the increase of 1 unit for SH. In empirical orthogonal function (EOF) analysis, EOF1 (28.07%) is characterized by warm Arctic and cold Siberia (WA-CS), which reflects the effect of SH on the occurrence of extreme cold events.

Impact of surface ultraviolet radiation intensity on hospital admissions for ischemic and hemorrhagic stroke: A large-scale database study using distributed lag nonlinear analysis, 2015-2022, in Harbin, China

ObjectivesOur aim is to evaluate the impact of surface ultraviolet radiation intensity on hospital admissions for stroke and to compare the correlation and differences among different subtypes of strokes. Materials and methodsWe collected daily data on surface ultraviolet radiation intensity, temperature, air pollution, and hospital admissions for stroke in Harbin from 2015 to 2022. Using a distributed lag non-linear model, we determined the correlation between daily surface ultraviolet radiation intensity and the stroke admission rate. Relative risks (RR) with 95% confidence intervals (CI) and attributable fractions (AF) with 95% CI were calculated based on stroke subtypes, gender, and age groups. ResultsA total of 132,952 hospitalized stroke cases (including hemorrhagic and ischemic strokes) were included in the study. We assessed the non-linear effects of ultraviolet intensity on hospitalized patients with ischemic and hemorrhagic strokes. Compared to the maximum morbidity benchmark ultraviolet intensity (19.2 × 10^5 for ischemic stroke and 20.25 for hemorrhagic stroke), over the 0-10 day lag period, the RR for extreme low radiation (1st percentile) was 0.86 (95% CI: 0.77, 0.96), and the RR for extreme high radiation (99th percentile) was 0.86 (95% CI: 0.77, 0.96). In summary, -4.842% (95% CI: -7.721%, -2.167%) and -1.668% (95% CI: -3.061%, -0.33%) of ischemic strokes were attributed to extreme low radiation intensity with a lag of 0 to 10 days and extreme high radiation intensity with a lag of 0 to 5 days, respectively. The reduction in stroke hospitalization rates due to low or high ultraviolet intensity was more pronounced in females and younger individuals compared to males and older individuals. None of the mentioned ultraviolet intensity intensities and lag days had a statistically significant impact on hemorrhagic stroke. ConclusionsOur study fundamentally suggests that both lower and higher levels of surface ultraviolet radiation intensity in Harbin, China, contribute to a reduced incidence of ischemic stroke, with this effect lasting approximately 10 days. This finding holds significant potential for public health and clinical relevance.

Spatial and temporal characteristic of PM2.5 and influence factors in the Yellow River Basin.

The Yellow River Basin has been instrumental in advancing ecological preservation and fostering national high-quality development. However, since the advent of China's reform and opening-up policies, the basin has faced severe environmental pollution issues. This study leverages remote sensing data from 1998 to 2019. As per the "Basin Scope and Its Historical Changes" published by the Yellow River Conservancy Commission of the Ministry of Water Resources, the Yellow River Basin is categorized into upstream, midstream, and downstream regions for analysis of their spatial and temporal distribution traits using spatial autocorrelation methods. Additionally, we employed probes to study the effects of 10 factors, including mean surface temperature and air pressure, on PM2.5. The study findings reveal that (1) the annual average concentration of PM2.5 in the Yellow River Basin exhibited a fluctuating trend from 1998 to 2019, initially increasing, then decreasing, followed by another increase before ultimately declining. (2) The air quality in the Yellow River Basin is relatively poor, making it challenging for large-scale areas with low PM2.5 levels to occur. (3) The PM2.5 concentration in the Yellow River Basin exhibits distinct high and low-value concentration areas indicative of air pollution. Low-value areas are predominantly found in the sparsely populated central and southwestern plateau regions of Inner Mongolia, characterized by a better ecological environment. In contrast, high-value areas are prevalent in the inland areas of Northwest China, with poorer natural conditions, as well as densely populated zones with high energy demand and a relatively developed economy. (4) The overall population density in the Yellow River Basin, as well as in the upstream, midstream, and downstream regions, serves as a primary driving factor. (5) The primary drivers in the middle reaches and the entire Yellow River Basin remain consistent, whereas those in the upper and lower reaches have shifted. In the upstream, air pressure emerges as a primary driver of PM2.5, while in the downstream, NDVI and precipitation become the main influencing factors.