Search Results (376)

Atmospheric warming results in increase in temperatures for the mean, the coldest, and the hottest day of the year, season, or month. Global warming leads to a large increase in the atmospheric water vapor content and to changes in the hydrological cycle, which include an intensification of precipitation extremes. Using the GISS-E2.1 climate model, we present the future changes in the coldest and hottest daily temperatures as well as in extreme precipitation indices (under four main Shared Socioeconomic Pathways (SSPs)). The increase in the wet-day precipitation ranges between 6% and 15% per 1 °C global surface temperature warming. Scaling of the 95th percentile versus the total precipitation showed that the sensitivity for the extreme precipitation to the warming is about 10 times stronger than that for the mean total precipitation. For six precipitation extreme indices (Total Precipitation, R95p, RX5day, R10mm, SDII, and CDD), the histograms of probability density functions become flatter, with reduced peaks and increased spread for the global mean compared to the historical period of 1850–2014. The mean values shift to the right end (toward larger precipitation and intensity). The higher the GHG emission of the SSP scenario, the more significant the increase in the index change. We found an intensification of precipitation over the globe but large uncertainties remained regionally and at different scales, especially for extremes. Over land, there is a strong increase in precipitation for the wettest day in all seasons over the mid and high latitudes of the Northern Hemisphere. There is an enlargement of the drying patterns in the subtropics including over large regions around Mediterranean, southern Africa, and western Eurasia. For the continental averages, the reduction in total precipitation was found for South America, Europe, Africa, and Australia, and there is an increase in total precipitation over North America, Asia, and the continental Russian Arctic. Over the continental Russian Arctic, there is an increase in all precipitation extremes and a consistent decrease in CDD for all SSP scenarios, with the maximum increase of more than 90% for R95p and R10 mm observed under SSP5–8.5. Full article

Stable oxygen isotopes in tree rings (δ¹⁸O) serve as important proxies for climate change and offer unique advantages for climate reconstruction in arid and semi-arid regions. We established an annual δ¹⁸O chronology spanning 1964–2023 using Juniperus excelsa tree-ring samples collected from the Alborz Mountains in Iran. We analyzed relationships between δ¹⁸O and key climate variables: precipitation, temperature, Palmer Drought Severity Index (PDSI), vapor pressure (VP), and potential evapotranspiration (PET). Correlation analysis reveals that tree-ring δ¹⁸O is highly sensitive to hydroclimatic variations. Tree-ring cellulose δ¹⁸O shows significant negative correlations with annual total precipitation and spring PDSI, and significant positive correlations with spring temperature (particularly maximum temperature), April VP, and spring PET. The strongest correlation occurs with spring PET. These results indicate that δ¹⁸O responds strongly to the balance between springtime moisture supply (precipitation and soil moisture) and atmospheric evaporative demand (temperature, VP, and PET), reflecting an integrated signal of both regional moisture availability and energy input. The pronounced response of δ¹⁸O to spring evaporative conditions highlights its potential for capturing high-resolution changes in spring climatic conditions. Our δ¹⁸O series remained stable from the 1960s to the 1990s, but showed greater interannual variability after 2000, likely linked to regional warming and climate instability. A comparison with the δ¹⁸O variations from the eastern Alborz Mountains indicates that, despite some differences in magnitude, δ¹⁸O records from the western and eastern Alborz Mountains show broadly similar variability patterns. On a larger climatic scale, δ¹⁸O correlates significantly and positively with the Niño 3.4 index but shows no significant correlation with the Arctic Oscillation (AO) or the North Atlantic Oscillation (NAO). This suggests that ENSO-driven interannual variability in the tropical Pacific plays a key role in regulating regional hydroclimatic processes. This study confirms the strong potential of tree-ring oxygen isotopes from the Alborz Mountains for reconstructing hydroclimatic conditions and high-frequency climate variability. Full article

This study examines the complex interactions among soil moisture, evaporation, extreme weather events, and lightning, and their influence on fire activity across the extratropical and Pan-Arctic regions. Leveraging reanalysis and remote-sensing datasets from 2000 to 2020, we applied cross-correlation analysis, a modified Mann–Kendall trend test, and assessments of interannual variability to key variables including soil moisture, fire frequency and risk, evaporation, and lightning. Results indicate a significant increase in dry days (up to 40%) and heatwave events across Central Eurasia and Siberia (up to 50%) and Alaska (25%), when compared to the 1980–2000 baseline. Upward trends have been detected in evaporation across most of North America, consistent with soil moisture trends, while much of Eurasia exhibits declining soil moisture. Fire danger shows a strong positive correlation with evaporation north of 60° N (r ≈ 0.7, p ≤ 0.005), but a negative correlation in regions south of this latitude. These findings suggest that in mid-latitude ecosystems, fire activity is not solely driven by water stress or atmospheric dryness, highlighting the importance of region-specific surface–atmosphere interactions in shaping fire regimes. In North America, most fires occur in temperate grasslands, savannas, and shrublands (47%), whereas in Eurasia, approximately 55% of fires are concentrated in forests/taiga and temperate open biomes. The analysis also highlights that lightning-related fires are more prevalent in Eastern Europe and Southeastern Asia. In contrast, Western North America exhibits high fire incidence in temperate conifer forests despite relatively low lightning activity, indicating a dominant role of anthropogenic ignition. These findings underscore the importance of understanding land–atmosphere interactions in assessing fire risk. Integrating surface conditions, climate extremes, and ignition sources into fire prediction models is crucial for developing more effective wildfire prevention and management strategies. Full article

The observations of atmospheric, oceanic, and sea ice data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition were used to analyze the influence of snow redistribution and melt-pond processes on the evolution of sea ice thickness (SIT) in 2019 and 2020. To mitigate the effect of missing atmospheric observations from the time of the expedition, we used ERA5 atmospheric reanalysis along the MOSAiC drift trajectory to force the single-column sea ice model Icepack. SIT simulations from six combinations of two melt-pond schemes and three snow-redistribution configurations of Icepack were compared with observations and analyzed to investigate the sources of model–observation discrepancies. The three snow-redistribution configurations are the bulk scheme, the snwITDrdg scheme, and one simulation conducted without snow redistribution. The bulk scheme describes snow loss from level ice to leads and open water, and snwITDrdg describes wind-driven snow redistribution and compaction. The two melt-pond schemes are the TOPO scheme and the LVL scheme, which differ in the distribution of melt water. The results show that Icepack without snow redistribution simulates excessive snow–ice formation, resulting in an SIT thicker than that observed in spring. Applying snow-redistribution schemes in Icepack reduces snow–ice formation while enhancing the congelation rate. The bulk snow-redistribution scheme improves the SIT simulation for winter and spring, while the bias is large in simulations using the snwITDrdg scheme. During the summer, Icepack underestimates the sea ice surface albedo, resulting in an underestimation of SIT at the end of simulation. The simulations using the TOPO scheme are characterized by a more realistic melt-pond evolution compared to those using the LVL scheme, resulting in a smaller bias in SIT simulation. Full article

Arctic sea ice variations exhibit relatively strong statistical associations with precipitation variability over northeastern and southern China. Using Arctic Ocean reanalysis data from the EU Copernicus Project, this study examines the time-lagged statistical relationships between winter Arctic sea ice conditions and subsequent spring precipitation variability over China through wavelet analysis and Granger causality tests. Singular value decomposition (SVD) identifies the Barents, Kara, East Siberian, and Chukchi Seas as key regions exhibiting strong associations with spring precipitation anomalies. Increased winter sea ice in the East Siberian and Chukchi Seas generates positive geopotential height anomalies over the Arctic and negative anomalies over Northeast Asia, adjusting upper-level jet streams and influencing precipitation patterns in Northeast China. Conversely, increased sea ice in the Barents–Kara Seas leads to persistent negative geopotential height anomalies simultaneously occurring over both the Arctic and South China regions, enhancing southern jet stream activity and intensifying warm-moist airflow at the 850 hPa level, thus favoring precipitation in southern China. Compared to considering only climate factors such as the Pacific Decadal Oscillation (PDO), El Niño–Southern Oscillation (ENSO), and Arctic Oscillation (AO), the inclusion of Arctic sea ice significantly enhances the influence of multiple climate factors on precipitation variability in China. Full article

The planetary boundary layer height (PBLH) in coastal Arctic regions is influenced by sea breeze circulation. However, the specific mechanisms through which sea breeze affects PBLH evolution remain insufficiently explored. This study uses meteorological data, micro-pulse lidar (MPL) data, and sounding profiles from 2014 to 2021 to investigate the annual and polar day PBLH evolution driven by sea breezes in the Barrow region of Alaska, as well as the specific mechanisms. The results show that sea breeze events significantly suppress PBLH, especially during the polar day, when prolonged solar radiation intensifies the thermal contrast between land and ocean. The cold, moist sea breeze stabilizes the atmospheric conditions, reducing net radiation and sensible heat flux. All these factors inhibit turbulent mixing and PBLH development. Lidar and sounding analyses further reveal that PBLH is lower during sea breeze events compared to non-sea-breeze conditions, with the peak of its probability density distribution occurring at a lower PBLH range. The variable importance in projection (VIP) analysis identifies relative humidity (VIP = 1.95) and temperature (VIP = 1.1) as the primary factors controlling PBLH, highlighting the influence of atmospheric stability in regulating PBLH. These findings emphasize the crucial role of sea breeze in modulating PBL dynamics in the Arctic, with significant implications for improving climate models and studies on pollutant dispersion in polar regions. Full article

Sea surface temperature (SST) is considered a strong indicator of climate change, being an essential parameter for long-range weather and climate forecasting. Another important indicator of climate change is sea level (SL), which has a longer history of systematic instrumental observations. This paper aims to examine the relationships between low-latitude variations in ocean characteristics (SST and SL) and surface air temperature (SAT) anomalies in the Arctic and mid-latitudes, and discuss the possibility of using SST and SL as predictors to forecast seasonal SAT anomalies. Archives of meteorological observations, atmospheric and oceanic reanalyses, and long-term series of tide gauge data on SL were used in this study. An analysis of relationships between seasonal SAT in different mid-to-high latitude regions and SST made it possible to identify areas in the ocean that have the greatest influence on SAT patterns. The most commonly identified area is located in the tropical North Atlantic. Another area was found in the Indo-Pacific warm pool. The predictive potential of the relationships identified between ocean characteristics (SST and SL) and SAT will be used to build deep learning models aimed at predicting climate variability in mid-to-high latitudes. Full article

Dust emissions significantly impact the radiation balance, ecosystems, human health, and global climate change through long-range transport. However, their spatiotemporal characteristics and driving mechanisms in East Asia remain poorly understood. This study integrates multi-source reanalysis and remote sensing data (1980–2023) to analyze dust emissions across East Asian source regions using statistical methods and SHapley Additive exPlanations (SHAP) interpretability. The results show significant spatial and seasonal variations, with peak emissions occurring in spring (March–May). The Taklamakan Desert (S4) accounts for 38.1% of total emissions and is the largest source region. Meteorological factors are the main drivers (49.4–68.8% contribution), while climate indices contribute the least (2.9–8.0%). Wind speed is the most critical factor driving dust emissions, showing a significant positive correlation and interacting with 850 hPa geopotential height and boundary layer height. The driving factors of dust emissions vary across regions. In Mongolia (S1), dust emissions are mainly influenced by wind speed and atmospheric circulation, while in S4, near-surface meteorological conditions play a dominant role. In the Tsaidam Basin and Kumutage Desert (S5), as well as the Badain Jaran, Tengger, and Ulan Buh Deserts (S6), dust emissions are primarily driven by wind speed and boundary layer height, with atmospheric circulation also playing a certain role. Relative humidity shows a significant negative correlation with dust emissions in S5 and S6, while snowmelt and soil temperature have significant impacts on S4 and S5. The negative phases of the Arctic Oscillation and North Atlantic Oscillation enhance cold air activity and wind speed, significantly promoting dust emissions in S1 and S6. This study quantifies the mechanisms of dust emissions in East Asia and offers scientific support for improving climate models and developing disaster mitigation strategies. Full article

Spring frosts were examined using 1981–2023 meteorological data from five weather stations located in areas with a significant share of agricultural crops, especially orchards. Days with frost were determined from air temperature data at 5 and 200 cm above ground level from the onset of the growing season. Atmospheric circulation conditions during frosts were analyzed using the HYSPLIT model (backward trajectory clusters) and empirical orthogonal functions (EOFs). EOFs were used to identify pressure distributions at the 850 hPa level that favor the occurrence of spring frosts. In the first half of spring, frosts were mainly associated with high-pressure systems centered over Poland and the advection of air masses from the northwest, west, and east. Meanwhile in the second part of spring, they occurred during the advection of Arctic air masses from the north and northeast. Changes in the pressure distribution and backward trajectories in the first and second parts of spring indicate a change in the thermal properties of air masses over eastern Europe with increasing day length and angle of sunlight incidence. Furthermore, a decrease in the frequency of spring frosts has been observed in the study area since the 1990s. Full article

Xinjiang, an arid area located in the central part of the Eurasian continent with high evaporation and low precipitation, experiences frequent droughts. This study builds on previous research by incorporating five key ocean-atmosphere oscillations and using the one-month SPEI as a meteorological drought indicator. Monthly time series of precipitation and temperature from 53 meteorological stations are utilized to calculate the monthly SPEI time series, and the seasonal Kendall test analyzes trends. Despite increased precipitation, the drought conditions in Xinjiang worsened due to increased temperatures, especially in the south, during 1961–2017. The 53 monthly SPEI time series are clustered using the agglomerative hierarchical method, basically reflecting Xinjiang’s topographical and climatic diversity. However, classical correlation methods show a weak or negligible overall correlation between the SPEI and large-scale ocean-atmosphere oscillators. Therefore, the partial wavelet coherence (PWC) method was used to detect the scale-specific correlations. Both bivariate wavelet coherence (BWC) and PWC detected significant correlations between the SPEI and the ocean-atmosphere oscillators at some specific time scales. Our analyses indicate that southern Xinjiang droughts are more influenced by Pacific or Indian Ocean oscillators, while northern droughts are affected by Atlantic or Arctic climate variations. Full article

Variations in stratospheric atmospheric circulation significantly impact tropospheric weather and climate. Understanding these variations not only aids in better prediction of tropospheric weather and climate but also provides guidance for the development and flight trajectories of stratospheric aircraft. Our understanding of the stratosphere has made remarkable progress over the past 100 years. However, we still lack a comprehensive perspective on large-scale patterns in stratospheric circulation, as the stratosphere is a typical complex system. To address this gap, we employed the eigen microstate approach (EMA) to revisit the characteristics of zonal wind from 70–10 hPa from 1980 to 2022, based on ERA5 reanalysis data. Our analysis focused on the three leading modes, corresponding to variations in the strength of the quasi-biennial oscillation (QBO) and the stratospheric atmospheric circulations in the Arctic and Antarctic, respectively. After filtering out high-frequency components from the temporal evolutions of these modes, a significant 11-year cycle was observed in the Antarctic stratospheric atmospheric circulation mode, potentially linked to the 11-year solar cycle. In contrast, the Arctic stratospheric atmospheric circulation mode showed a 5–6-year cycle without evidence of an 11-year periodicity. This difference is likely due to the timing of polar vortex breakdowns: the Antarctic polar vortex breaks up later, experiencing its greatest variability in late spring and early summer, making it more susceptible to solar radiation effects, unlike the Arctic polar vortex, which peaks in winter and early spring. The fourth mode exhibits characteristics of a Southern Hemisphere dipole and shows a significant correlation with the Antarctic stratospheric atmospheric circulation mode, leading it by about two months. We designed a linear prediction model that successfully demonstrated its predictive capability for the Antarctic polar vortex. Full article

Numerical simulation studies of the dispersion of dimethyl sulfide (DMS) in the air have increased over the last two decades in parallel with the interest in understanding its role as a precursor of non-sea salt aerosols in the lower to middle levels of the troposphere. Here, we review recent numerical modeling studies that have included DMS emissions, their atmospheric oxidation mechanism, and their subsequent impacts on air quality at regional and global scales. In addition, we discuss the available methods for estimating sea–air DMS fluxes, including parameterizations and climatological datasets, as well as their integration into air quality models. At the regional level, modeling studies focus on the Northern Hemisphere, presenting a large gap in Antarctica, Africa, and the Atlantic coast of South America, whereas at the global scale, modeling studies tend to focus more on polar regions, especially the Arctic. Future studies must consider updated climatologies and parameterizations for more realistic results and the reduction in biases in numerical simulations analysis. Full article

The Arctic Ocean has a uniquely complex system associated with tightly coupled ocean–ice–atmosphere–land interactions. The Arctic Ocean is considered to be highly susceptible to global climate change, with the potential for dramatic environmental impacts at both regional and global scales, and its spatial differences particularly have been exacerbated. A comprehensive understanding of Arctic Ocean environmental responses to climate change thus requires classifying the Arctic Ocean into subregions that describe spatial homogeneity of the clusters and heterogeneity between clusters based on ocean physical properties and implementing the regional-scale analysis. In this study, utilizing the long-term optimum interpolation sea surface temperature (SST) datasets for the period 1982–2023, which is one of the essential indicators of physical processes, we applied the K-means clustering algorithm to generate subregions of the Arctic Ocean, reflecting distinct physical characteristics. Using the variance ratio criterion, the optimal number of subregions for spatial clustering was 12. Employing methods such as information mapping and pairwise multi-comparison analysis, we found that the 12 subregions of the Arctic Ocean well represent spatial heterogeneity and homogeneity of physical properties, including sea ice concentration, surface ocean currents, SST, and sea surface salinity. Spatial patterns in SST changes also matched well with the boundaries of clustered subregions. The newly identified physical subregions of the Arctic Ocean will contribute to a more comprehensive understanding of the Arctic Ocean’s environmental response to accelerating climate change. Full article

Low-level jets (LLJs) are important features in the Arctic atmospheric boundary layer (ABL). In the present paper, a LLJ event during winter 2014/15 is investigated, which was observed at the Tiksi observatory (71.586° N, 128.918° E, 7 m asl) in the Laptev Sea region. Besides the routine synoptic observations, data from a meteorological tower and SODAR/RASS (sound detection and ranging/radio acoustic sounding system) were available. The latter yielded vertical profiles of wind and temperature in the ABL with a vertical resolution of 10 m and a temporal resolution of 20 min. In addition to the measurements, simulations were performed using the regional climate model CCLM with a 5 km resolution. CCLM was run with nesting in ERA5 data in a forecast mode, and the ABL measurements were used for comparison with a LLJ occurring from 31 December 2014 to 1 January 2015. The CCLM simulations agreed well with near-surface and SODAR observations and represented the LLJ development very well. The simulations showed that the LLJ at Tiksi was part of a downslope wind event and that LLJ structures were present over a large region. The flow was preconditioned by a barrier wind and channeling in the Lena Valley in the initial phase, but synoptic forcing from a low over the Laptev Sea dominated the mature and dissipation phases of the LLJ. High turbulence intensity occurred in the mature phase of the LLJ, which seemed to be associated with wave breaking. Downslope wind events are likely the reason for most LLJs at Tiksi. Full article

Per- and polyfluoroalkyl substances (PFAS) are increasingly detected in remote environments. This review aims to provide a comprehensive overview of the types and concentrations of PFAS found in the air, water, soil, sediments, ice, and precipitation across different remote environments globally. Most of the recent studies on PFAS remote occurrence have been conducted for the Arctic, the Antarctica, and the remote regions of China. Elevated perfluorooctane sulfonate (PFOS) in Meretta and Resolute Lakes reflects the impact of local sources like airports, while PFAS in lakes located in remote regions such as East Antarctica and the Canadian High Arctic suggest atmospheric deposition as a primary PFAS input. Long-chain PFAS (≥C7) accumulate in sediments, while short-chain PFAS remain in water, as shown in Hulun Lake. Oceanic PFAS are concentrated in surface waters, driven by atmospheric deposition, with PFOA and PFOS dominating across oceans due to current emissions and legacy contamination. Coastal areas display higher PFAS levels from local sources. Arctic sediment analysis highlights atmospheric deposition and ocean transport as significant PFAS contributors. PFAS in Antarctic coastal areas suggest local biological input, notably from penguins. The Tibetan Plateau and Arctic atmospheric data confirm long-range transport, with linear PFAS favoring gaseous states, while branched PFAS are more likely to associate with particulates. Climatic factors like the Indian monsoon and temperature fluctuations affect PFAS deposition. Short-chain PFAS are prevalent in snowpacks, serving as temporary reservoirs. Mountainous regions, such as the Tibetan Plateau, act as cold traps, accumulating PFAS from atmospheric precursors. Future studies should focus on identifying and quantifying primary sources of PFAS. Full article