Search Results (163)

To improve diagnostics and prediction of changes caused by increased impact of anthropogenic activity, it is necessary to increase the comparative analysis of measurements and modeling of ozone—one of the climatically important atmospheric gases due to the decisive influence of stratospheric ozone on the radiation balance of the Earth-atmosphere system and the role of tropospheric ozone, the third most significant anthropogenic factor contributing to the greenhouse effect. This task is particularly relevant for Russia, as its geographical location makes it more vulnerable to climate change than other countries, whereas its regional tendencies in ozone variability have not yet been studied in sufficient detail. An analysis of IKFS-2 tropospheric ozone content (TrOC) measurements for 2015–2022 revealed that in Siberian, Far Eastern, North Caucasian, and Southern federal districts of Russia TrOC maximum, caused by photochemical formation of ground-level ozone, is observed in July (up to 30–35 DU for monthly means in surface-400 hPa layer). In Northwestern federal district, TrOC maximum (up to 25–30 DU), determined by meridional transport, is observed in late spring. No statistically significant linear trends in TrOC are detected. The WRF-Chem model qualitatively describes the seasonal variations of TrOC as well as the anomalous increase in TrOC caused by forest fires. The variability of total ozone content (TOC) is analyzed by OMI (2005–2023) and IKFS-2 (2015–2022) measurements as well as by SOCOLv3 simulations. Ozone negative anomalies in spring (up to 15% for monthly means) are generally observed with positive Arctic oscillation index values and a westerly phase of Quasi-biennial oscillations. For the 2008–2022 period, a statistically significant increase in TOC (+1.6–1.7% per year) is obtained for European Russia and Western and Central Siberia in November. Full article

The main objective of this work is to characterize Sudden Stratospheric Warming (SSW) conditions and their impact on local weather forecasting and climate change, using SSW definition criteria. The SSWs strongly affect Arctic vortex structure and midlatitude weather conditions. This work evaluates the frequency, amplitude, and dynamical–thermal characteristics of SSWs under historical and Representative Concentration Pathway (RCP) 4.5 scenarios, focusing on stratospheric air temperature (T_s) and zonal wind speed (U_h) at the 10° N and 60° N latitudes. The fifth-generation ECMWF atmospheric reanalysis (ERA5) is employed as the reference dataset. Simulations of five Coupled Model Intercomparison Project Phase 5 (CMIP5) models, represented by M1 to M5, are analyzed. The primary group of models included 1) the Australian Community Climate and Earth-System Simulator, version 1.3 (ACCESS1-3, M1), 2) the Hadley Center Global Environmental Model, version 2—Carbon Cycle (HadGEM2-CC, M2), and 3) the Max Planck Institute Earth System Model—Medium Resolution (MPI-ESM-MR, M3). The analysis period covers SSW events related to the Quasi-Biennial Oscillation (QBO) in the Northern Hemisphere (NH) from 1980 to 2100. The key findings indicate that while M1, M2, and M3 simulate SSW occurrence correctly for the 21st century, they exhibit significant systematic deficiencies in capturing the structural dynamics of SSW events. Specifically, the M1, M2, and M3 models underestimate the polar stratospheric temperature amplitude (T_amp) by approximately 75–80% and zonal wind amplitude (U_amp) by more than 60% compared to the ERA5 analysis. Furthermore, ERA5 exhibits a strong negative correlation (R ≈ −0.8) between U_h and T_s that is not estimated accurately using the present models. The importance of the horizontal resolution of the models and wave–mean flow interactions in determining SSW intensity and occurrence is also found to be a critical metric. Results suggest that SSW definition criteria affect Arctic and midlatitude weather system prediction at a rate of 61–82%. It is concluded that the primary configurations of CMIP5 models for accurately capturing the dynamical structure and evolution of QBO–SSW interactions are needed, and that they affect future projections of SSW events. Full article

To effectively manage and conserve ecosystems, it is crucial to understand how vegetation changes over time and space and what drives these changes. The Normalized Difference Vegetation Index (NDVI) is a key measure of plant growth that is highly sensitive to climate variations. Despite its importance, there has been limited research on vegetation changes in the upper sections of the Irtysh River. In our study, we combined various datasets, including NDVI, temperature, precipitation, soil moisture, elevation, and land cover. We conducted several analyses, such as Theil–Sen median trend analysis, Mann–Kendall trend and mutation tests, partial correlation analysis, the geographical detector model, and wavelet analysis, to reveal the region’s pronounced warming and moistening trend in recent years, the response relationship between NDVI and the climate, and the primary drivers influencing NDVI variations. We also delved into the spatiotemporal evolution of NDVI and identified key factors driving these changes by analyzing atmospheric circulation patterns. Our main findings are as follows: (1) Between 1901 and 2022, the area’s temperature rose by 0.018 °C/a, with a noticeable increase in the rate of warming around 1990; precipitation increased by 0.292 mm/a. From 1950 to 2022, soil moisture exhibited a steady increase of 0.0002 m³ m⁻³/a. Spatial trend distributions indicated that increasing trends in temperature and precipitation were evident across the entire region, while trends in soil moisture showed significant spatial variation. (2) During 1982 to 2022, the vegetation greening trend was 0.002/10a, indicating a gradual improvement in vegetation growth in the study area. The spatial distribution of monthly average NDVI values revealed that the main growing season of vegetation spanned April to November, with peak NDVI values occurring in June–August. Combined with serial partial correlation and spatial partial correlation analysis, temperatures during April to May effectively promoted the germination and growth of vegetation, while soil moisture accumulation from June to August (or January to August) effectively met the water demand of vegetation during its growth process, with a significant promoting effect. Geographical detector results demonstrate that temperature exhibits the strongest explanatory power for NDVI variation, whereas land cover has the weakest. The synergistic promotional effect of multiple climatic factors is highly pronounced. (3) Wavelet analysis revealed that the periodic characteristics of NDVI and climate variables over a 2–15-year timescale may have been associated with the impacts of atmospheric circulation. Taking NDVI and climatic factors from June to August as an example, before 2000, temperature was the dominant influencing factor, followed by precipitation and soil moisture; after 2000, precipitation and soil moisture became the primary drivers. The North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) were the primary atmospheric circulation patterns influencing vegetation variability in the region. Their effects were reflected in the inverse relationship observed between NAO/AO indices and NDVI, with typical phases of high and low NDVI closely corresponding to shifts in NAO and AO activity. This study helps us to understand how plants have been changing in the upper parts of the Irtysh River. These insights are critical for guiding efforts to develop the area in a way that is sustainable and beneficial for the environment. Full article

Climate change can impact various facets of a region’s fire regime, such as the frequency and timing of fire ignitions. This study examines the temporal trends of monthly fire counts in the Northwest and Northeast Regions of Ontario, Canada, between 1960 and 2023. Fires ignited by human activities or lightning are analyzed separately. The significance of historical trends is investigated using the Cochrane–Orcutt method, which identifies decreasing trends in the number of human-caused fires for several months, including May through July. A complementary trend analysis of total area burned is also conducted. The forecasting of future months’ fire counts is explored using a Negative Binomial Autoregressive (NB-AR) model suitable for count time series data with overdispersion. In the NB-AR model, the use of climate teleconnections at a range of temporal lags as predictors is investigated, and their predictive skill is quantified through cross-validation estimates of Mean Absolute Error on a testing dataset. Considered teleconnections include the El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), North Atlantic Oscillation (NAO), and Atlantic Multidecadal Oscillation (AMO). The study finds the use of teleconnection predictors promising, with a notable benefit for forecasting human-caused fire counts but mixed results for forecasting lightning-caused fire counts. Full article

Under concurrent global warming and multi-scale climate anomalies, regional precipitation has become more uneven and less stable, and extreme events occur more frequently, amplifying water scarcity and ecological risk. Focusing on mainland China, we analyze nearly 70 years of monthly station precipitation records together with eight climate drivers—the Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), Multivariate ENSO Index (MEI), Arctic Oscillation (AO), surface air pressure (AP), wind speed (WS), relative humidity (RH), and surface solar radiation (SR)—and precipitation outputs from eight CMIP6 models. Using wavelet analysis and partial redundancy analysis, we systematically evaluate the qualitative relationships between climate drivers and precipitation and quantify the contribution of each driver. The results show that seasonal precipitation decreases stepwise from the southeast toward the northwest, and that stability is markedly lower in the northern arid and semi-arid regions than in the humid south, with widespread declines near the boundary between the second and third topographic steps of China. During the cold season, and in the northern arid and semi-arid zones and along the margins of the Tibetan Plateau, precipitation varies mainly with interdecadal swings of North Atlantic sea surface temperature and with the strength of polar and midlatitude circulation, and it is further amplified by variability in near-surface winds; the combined contribution reaches about 32% across the Northeast Plain, the Junggar Basin, and areas north of the Loess Plateau. During the warm season, and in the eastern and southern monsoon regions, precipitation is modulated primarily by tropical Pacific sea surface temperature and convection anomalies and by related changes in the position and strength of the subtropical high, moisture transport pathways, and relative humidity; the combined contribution is about 22% south of the Yangtze River and in adjacent areas. Our findings reveal the spatiotemporal variability of precipitation in China and its responses to multiple climate drivers and their relative contributions, providing a quantitative basis for water allocation and disaster risk management under climate change. Full article

We examine how the Arctic Oscillation (AO) shapes winter sea-surface-temperature (SST) variability in the East/Japan Sea, with a focus on sub-seasonal SST memory (how long anomalies persist) and air–sea coupling (where SST and atmospheric anomalies co-vary). Using daily OISST v2.1 and ERA5 reanalysis for 1993–2022, we first analyze winter persistence of SST and key atmospheric drivers and identify East Korea Bay and the Subpolar Front as hotspots of long-lived SST anomalies. A rank-reduced multivariate maximum covariance analysis then extracts the leading coupled mode between SST and a set of atmospheric fields under positive and negative AO phases; in both phases the coupled mode is front-anchored, but its amplitude and spatial focus differ. Finally, to quantify the mixed-layer memory, we construct Ornstein–Uhlenbeck-like time-integrated responses of the atmospheric principal components. The effective integration timescales, determined by maximizing zero-lag correlations with the SST mode, cluster at approximately 2–3 weeks for wind-stress curl and near-surface variables and 4–7 weeks for sea-level pressure and meridional wind, with longer timescales during negative AO. The time-integrated atmospheric responses exhibit SST-like persistence, confirming the mixed layer’s role as a stochastic integrator. These AO-conditioned memory windows define practical lead times over which integrated atmospheric indices can act as predictors of winter marine heatwaves and cold-surge-impacted SST anomalies. Full article

The semi-annual oscillation (SAO) dominates seasonal variability in the equatorial stratosphere and mesosphere. However, the seasonally dependent modulation of the SAO in the stratosphere (SSAO) and mesosphere (MSAO) by sudden stratospheric warmings (SSWs) in the Arctic has not been investigated in detail. In this study, we examine the seasonal evolution of the SAO during 16 major SSW events spanning 2004 to 2024 using the Japanese Atmospheric General Circulation Model for Upper Atmosphere Research Data Assimilation System Whole Neutral Atmosphere Re-analysis (JAWARA). Basic features of the SAO are well captured by JAWARA, as evidenced by the SSAO and MSAO appearing at around 50 km and 85 km, respectively. The different responses of the SAO to early and late winter SSWs are particularly strong during the Northern Hemisphere winter of 2023/24. Early winter SSWs tend to significantly intensify the westward SSAO, while late winter SSWs tend to weaken the eastward SSAO. Similarly, the eastward MSAO is amplified during early winter SSWs, whereas the westward MSAO is slightly weakened during late winter SSWs. The weak MSAO response is probably due to its smaller climatological magnitude. Modulation of the SAO by SSWs is related to meridional temperature changes during SSWs through the thermal wind balance. Our findings contribute to the understanding of coupling between the tropics and high latitudes, as well as interhemispheric coupling. Full article

Under global warming, the rising frequency and intensity of extreme climate events pose challenges to disaster prevention and sustainable development. Based on daily meteorological observations from 1970 to 2020 in Jilin Province, this study analyzes the spatiotemporal evolution and driving mechanisms of extreme temperature and precipitation events. Linear trend analysis and the Mann–Kendall test were employed to examine temporal trends and abrupt change years in extreme temperature and precipitation indices. Wavelet analysis was used to identify dominant periodicities and multi-scale variability. Empirical Orthogonal Function Analysis (EOF) revealed the spatial distribution characteristics of variability in extreme precipitation and temperature across Jilin Province, identifying high-incidence zones for extreme temperature and precipitation events. Additionally, Pearson correlation analysis was to investigate the correlation patterns between extreme climate indices in Jilin Province and geographical environmental factors alongside atmospheric circulation indicators. Results show that: (1) Warm-related temperature indices display significant upward trends, while cold-related indices generally decline, with abrupt changes mainly occurring in the 1980s–1990s and dominant periodicities of 3–5 years. Precipitation indices, though variable, show general increases with 3–4year cycles. (2) Spatially, most indices follow an east–high to west–low gradient. Temperature indices exhibit spatial coherence, while precipitation indices vary, especially between the northwest and central-southern regions. (3) The Arctic Oscillation (AO) exhibits a significant negative correlation with the extreme cold index, with correlation coefficients ranging from −0.31 to −0.46. It shows a positive correlation with the extreme warm index, with correlation coefficients between 0.16 and 0.18, confirming its regulatory role in cold air activity over Northeast China, particularly elevation and latitude, influence the spatial distribution of precipitation. These findings enhance understanding of extreme climate behaviors in Northeast China and inform regional risk management strategies. Full article

The understanding of coastal ecosystems regarding variability and resilience under climatic and anthropogenic forcing is reliant upon long-term ecological records. We examined the Helgoland Roads time series (1968–2017), which includes temperature, salinity, nutrients (nitrate, phosphate), and biological parameters (diatoms and Acartia spp.). We applied autocorrelation, multi-taper spectral analysis, and wavelet and cross-wavelet transforms to identify dominant temporal patterns and scale-dependent interactions. Sea surface temperature shows consistent long-term warming, and subdecadal (2–3-year) and decadal (7–8-year) oscillations reflect coherent patterns with the North Atlantic Oscillation and Arctic Oscillation. Salinity varied in anti-phase to Elbe River discharge at 6–7-year scales, reflecting control of seasonal, riverine freshwater, and salinity scenarios. Nutrients, as declining long-term trends (particularly phosphate), are associated with seasonal to multi-year variability linked to episodic discharge events. Biological parameters had strong annual periodicities reflective of bloom cycles but also variability above the annual limit. Diatoms responded to climatic, nutrient, and biological responses at the 3–5-year scale associated with this ecological context, particularly nitrate and phosphate; Acartia (spp.) respond to temperature, salinity, and resource availability (diatoms), reflecting climate/nutrient/trophic linkages. This study indicates that Helgoland Roads is represented as a multi-scale, non-stationary system, in which climate variability, riverine input, and ecological linkages are cascaded down to physical and chemical processes that structure biological communities. Spectral methods reveal scale-dependent synchrony and highlight the risks of trophic mismatch under climate change, emphasizing the importance of sustained high-frequency monitoring. Full article

The winter Arctic Oscillation (AO) modulates the East Asian climate and the East/Japan Sea (EJS) thermodynamics, yet the local, scale-dependent air–sea couplings remain unclear. Using 30 years of daily fields (1993–2022), we map at each grid point, the cross-persistence and scale-dependent cross-correlations between sea surface temperature anomalies (SSTA) and (i) atmospheric anomalies, (ii) turbulent heat-flux anomalies (sensible and latent), and (iii) oceanic anomalies. Detrended Fluctuation/Cross-Correlation Analyses (DFA/DCCA, 5–50 days) yield the Hurst exponent ($H, h_{XY}$) and the DCCA coefficient (${\rho }_{dcca}$). Significance is assessed with iterative-AAFT surrogates and Benjamini–Hochberg false discovery rate (FDR). Three robust features emerge: (1) during AO+, the East Korean Bay–Subpolar Front corridor shows large SSTA variance and high long-term memory ($H\ge$ 1.5); (2) turbulent heat-flux anomalies are anti-phased with SSTA and show little cross-persistence; (3) among oceanic fields, SSHA and meridional geostrophic velocity provide the most AO-robust positive coupling. Within a fractal frame, DFA slopes ($1<H<2$) quantify local self-similarity; interpreting winter anomalies as fBm implies a fractal-dimension proxy $D=3-H$, so AO+ hot spots exhibit $D\le 1.5$. These fractal maps, together with ${\rho }_{dcca}$, offer a compact way to pre-locate marine-heatwave-prone regions. The grid-point, FDR-controlled DFA/DCCA approach is transferable to other marginal seas. Full article

China’s Yangtze and Yellow River Basins exhibit divergent drought patterns, yet the underlying mechanisms driving these differences remain underexplored. This study compares their drought characteristics from 1961 to 2022 using the Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, and Palmer Drought Severity Index, and identifies their drivers through attribution models and interpretable machine learning. Our results reveal two distinct paradigms: the Yangtze Basin is characterized by high-frequency, over 14% in all seasons, short-duration droughts, reflecting a rapid hydrological response, while the Yellow River Basin experiences low-frequency, long-duration events indicative of strong soil moisture memory. Quantitative attribution demonstrates that atmospheric evaporative demand (VPD) plays a significantly greater role in the Yellow River Basin, contributing over 20% to soil drought, far exceeding its 14.4% contribution in the Yangtze Basin. Furthermore, their large-scale drivers differ fundamentally: the Yangtze Basin responds primarily to the Atlantic Multidecadal Oscillation (AMO) and Arctic Oscillation (AO), whereas the Yellow River Basin is mainly influenced by solar activity and the El Niño-Southern Oscillation (ENSO). These findings reveal that Yangtze drought is primarily driven by precipitation deficits, while Yellow River drought is a composite phenomenon amplified by evaporative demand. This distinction underscores the need for basin-specific water management strategies. Full article

Using station observations, the JRA-55 reanalysis dataset, and the CESM2 model, this study investigates the impacts of the stratospheric polar vortex (SPV) on winter near-surface wind speed (NSWS) over China across interannual and interdecadal timescales. On the interannual timescale, a strong SPV leads to a downward-extension signal that generates negative geopotential height anomalies over the Arctic, skewed toward the Atlantic sector. The associated surface response resembles the positive phase of the Arctic Oscillation (AO), resulting in reduced NSWS over northern China. In contrast, a weak SPV produces opposite effects. On the decadal timescale, a weakened SPV induces positive height anomalies over the Arctic that shift toward Northeast Eurasia. The surface response over the polar region stimulates a wave train, which drives a positive height anomaly over the North Pacific. The pressure gradient between East Asia and the North Pacific suppresses NSWS over eastern China. The response of China’s NSWS to interannual SPV variability is more pronounced than its response to interdecadal changes. CESM2 model simulations confirm these contrasting responses and the associated mechanisms. Full article

Variability in the Laptev Sea–East Siberian Sea circulation system modulates freshwater circulation in the Arctic Ocean, yet details of these wind-driven mechanisms remain poorly understood. Based on in situ observations from the 2018 Sino-Russian joint Arctic expedition, this study investigates the modulatory influence of wind on circulation structures and freshwater transport in the study area and examines the long-term variation characteristics of this circulation and its inherent connection with the Arctic wind. In situ measurements confirm two freshwater transport pathways: a coastal-current route and a geostrophic slope-current route. As the Beaufort High moves toward the Canadian Basin, it shifts wind patterns from anticyclonic to cyclonic, which regulates the transport of shelf water by influencing the prevailing wind direction. Furthermore, our analysis identifies two main modes of long-term changes in summer surface circulation: the first mode characterizes the coastal-current architecture, while the second mode delineates slope-current configurations. Crucially, large-scale modes of the Arctic wind play an important role in regulating circulation. Its first mode corresponds to the summer anticyclonic circulation pattern of the Arctic Ocean Oscillation, which drives the eastward strengthening of the coastal current, while the third mode presents a mechanism similar to the Arctic Dipole, which promotes the development of the slope current by enhancing the convergence of the polar current and wind. This has led to the long-term strengthening of the slope current. Full article

Wave climate variability, including seasonal cycles, long-term trends, and interannual anomalies of wave parameters, was investigated across five latitudinal sectors using ERA5 reanalysis data from 1980 to 2023. Pronounced seasonal cycles were observed in both Northern and Southern Hemisphere sectors, although the variability was more marked in the Northern Hemisphere. In contrast, the tropical region exhibited comparatively stable conditions throughout the year. Long-term trends revealed increases in both significant wave height and peak period across most sectors. The tropical region exhibited a trimodal regime driven by wind waves at low latitudes and remotely generated swells from both hemispheres. Teleconnections associated with the North Atlantic Oscillation (NAO) explained interannual variability in wind-wave direction in the tropics with an r² of 0.74 and wind-wave height variability in the Northern Hemisphere with an r² of 0.81. Additional indices, such as the Arctic Oscillation (AO), the Tropical North Atlantic (TNA) index, and the Northern Annular Mode (NAM), explained 30 to 60 percent of the directional variability. These results underscore the need to account for climate-driven variability in wave modeling frameworks to improve forecast accuracy and representation of directional trends. 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