Slow‑moving synoptic structures have been hypothesized to be a part of severe heatwave events over extended continental regions in summer, highlighting the potential impacts of the growing climate emergency associated with rising background temperatures. In this context, a slow‑moving synoptic structure from 16 to 19 July 2022 is investigated using a potential vorticity (PV) framework and Q‑vector analysis. The analyses reveal the development and maintenance of a temporally evolving three‑dimensional, self‑sustained structure associated with the extreme heatwave that affected Western and Northern Europe, centred over the United Kingdom (UK). PV anomalies at upper levels and the surface were semi-stationary and in lock-phase during this heatwave period, due to an environment in which a local circulation cell develops related to the sea surface temperature (SST) anomalies and the exit of the upper-level jet stream. Q-vector analysis emphasises that the location of the exit is also responsible for the evolution and movement of the heatwave over the UK. Both Q-vector and PV theories provide theoretical explanations for the mechanisms and characteristics of the dynamic heatwave drivers.

Statistical downscaling translates coarse-resolution climate model output into locally relevant information for climate services and impact assessment. Recent advances in artificial intelligence (AI) enable high-resolution, probabilistic, and computationally efficient approaches. This paper provides a perspective on the evolution from classical to AI-driven and hybrid downscaling approaches, assesses key challenges related to interpretability, uncertainty, data availability, and computational requirements, and outlines physically constrained and generative frameworks that support decision-making across sectors.

Environmental management and urban planning face complex intersectional challenges that are often underserved by current engineering solutions, particularly when relying on coarse-resolution climate models. This study explores the integration of social and cultural dimensions with AI-driven statistical downscaling of convection-permitting model outputs and Global Forecast System (GFS) data to better understand climate-driven hazards and their impacts on diverse communities. In regions with complex terrain, such as Oman, where intense rainfall events have led to significant loss of life, high-resolution models like Weather Research and Forecasting (WRF) offer more realistic simulations of extreme weather. We demonstrate how Convolutional Neural Networks (CNNs) trained on WRF and GFS outputs can produce statistically downscaled climate variables with strong spatial and temporal fidelity. The study includes a case from April 2024, comparing CNN outputs with observational data and showcasing the model’s ability to replicate WRF-like patterns. We use conditional probability and immersive visualization to trace how localised climate extremes translate into socio-economic risks. Immersive workshops using the resulting virtual reality environments were conducted to pilot a tool to engage stakeholders from varied socioeconomic backgrounds, facilitating discussions and reinforcing values for climate adaptation and energy planning. These workshops emphasize using immersive tools to explore ethical principles and intersectional impacts, including health, labour, gender roles, and cultural identity. By applying conditional probability to downscaled outputs, we propose a co-creation framework that supports participatory scenario development and socially responsive engineering design. By expressing downscaled outputs as conditional probabilities of impacts, this approach makes the AI-generated climate information more interpretable for non-experts, supports the use of tools like Life Cycle Assessment and Human-Centred Design, and bridges the gap between technical modelling and community needs. Ultimately, integrating AI-based forecasting with social impact frameworks enables more equitable and actionable climate resilience strategies.

El Niño Southern Oscillation (ENSO) is a climate phenomenon that affects the atmospheric circulation of the Northern Hemisphere and causes short-term variability in temperature and precipitation patterns. ENSO impacts over the Euro-Mediterranean (EM) region are commonly defined by using Niño3.4 and Niño3 indices. However, some recent studies indicate that the ENSO event represented by both Niño1+2 and Niño3.4 indices (shared ENSO) is more effective over EM region climate.In this study, we examine the response of the EM climate to ENSO events detected by Niño1+2 and Niño3.4 regions. NCEP/NCAR Reanalysis surface air temperature, precipitation, 500 hPa geopotential height, 850 hPa wind, and 300 hPa zonal wind datasets and SST-based ENSO indices from ERSSTv4 were used in the analysis for boreal winters between 1950 and 2019. For composite analysis, we separated ENSO events as El Niño and La Niña according to those observed in Niño1+2, Niño3.4, and both regions. We also tried to understand if there is any relation between ENSO and teleconnection patterns such as NAO, East Atlantic (EA), Trough Displacement Index for the Mediterranean Trough (TDI_MedT), and East Atlantic/Western Russia (EAWR) by using the cross-correlation analysis. Additionally, investigate the winter (December, January, February, DJF) ENSO’s possible lagged impacts on the teleconnection patterns in the subsequent seasons, spring (March-April-May, MAM), summer (June-July-August, JJA), and autumn (September-October-November, SON).The major finding of this study is that the shared ENSO event is more effective over the EM climate than the ENSO events detected only by Niño1+2 or Niño3.4 indices. Further, it is also important for the predictability of the EM climate. In the shared El Niño event, the Middle East and much of North Africa tend to become colder than climatology while Europe becomes warmer. The anticyclonic wind anomaly over western Europe causes drier air in southern Europe and wetter air in northern Europe. The shared El Niño event also modulates the westerly flows at the upper troposphere. The westerly flow accelerates over high latitudes while decelerates over European mid-latitudes, causing northern Europe to be wetter and the Mediterranean Basin to be drier. The cross-correlation analysis including all SST-based ENSO indices and teleconnection indices that the EA index has a significant correlation with the Niño1+2 index across all seasons.

Convection-permitting model outputs offer significant opportunities for training statistical downscaling approaches. The Coordinated Regional Climate Downscaling Experiment (CORDEX) on the urban environment and regional climate change ensemble simulations provide valuable insights into the uncertainties of numerical atmospheric models. Traditional weather generators, based on the Maximum Likelihood for the Generalised Linear Model approach, have been instrumental in modelling precipitation occurrence and amount. This study advances the statistical downscaling method by integrating Generative AI approaches, using deep learning to create stochastic precipitation ensembles.Compared to deterministic simulations, this new probabilistic approach allows for an exploration of the nonstationary statistical properties influenced by regional climate conditions through more feasible nonlinear representation for the weather generator parameters by deep learning. Emphasis is placed on the importance of probabilistic and agnostic methods in exploring, interpreting, and explaining uncertainties.Findings related to temperature variations for daily precipitation extremes attribute the roles of sensible and latent heat, which are further interpreted through regional processes. The integration of generative AI highlights the stochastic uncertainties in weather generators, emphasising the need for consistency between deterministic convection-permitting model outputs and observational data. By examining scaling relationships, the interpretability and explainability of model outputs, particularly concerning energy balance processes, are demonstrated.Through interpretable and explainable statistical downscaling, the approach to modelling precipitation extremes based on maximum likelihood theory fosters international collaboration in the Climate Collaboratorium* project (IIRCC; ‘Exploring climate solutions with interactive theatre’)This includes contributions from Canada, Germany, the UK, and the US, aimed at providing accessible science that can inform climate decisions in partnership with social science/arts and humanities researchers, tailored to place-based user needs. Advocacy for responsible AI in atmospheric and water sciences facilitates interdisciplinary climate adaptation and mitigation with Taiwanese and Brazilian communities. This approach promotes transparency and fairness through explainable and interpretable climate scenarios. By incorporating immersive experiences and smart decision-making processes, the pathway for human oversight remains central to fair climate action to achieve Sustainable Development Goal 13.*https://www.ukri.org/publications/international-science-partnerships-fund-iircc-initiative-funded-projects/international-joint-initiative-for-research-in-climate-change-adaptation-and-mitigation-project-overview/

Rapidly changing climate in polar regions not only impacts their local environments but also influences weather patterns in tropical and mid‐latitude regions. A key indicator of these changes is the accelerated decline of sea ice in polar areas. In this study, we investigated the atmospheric impacts of Antarctic sea ice reduction in response to intensified ocean surface winds. We employed the Polar‐WRF model, driven by ERA5 initial and boundary conditions between 2005 and 2011. The sea ice concentration (SIC) and sea surface temperature (SST) conditions were derived from two realistic regional Southern Ocean MITgcm simulations, consisting of a control and a wind sensitivity experiment. In the latter, the zonal wind stress over the Southern Ocean is increased by a factor of 1.5, leading to a significant decrease in SIC and an increase in SST. Our Polar‐WRF simulations indicate that the winter and spring seasons are marked by significant meteorological changes, including a notable increase in surface air temperature (over 2.4°C) and sea level pressure (over 2 hPa). These atmospheric changes are particularly large in the Bellingshausen Sea, adjacent to the Antarctic Peninsula and the Western Pacific Ocean. The intensified advection of warm‐moist air may further contribute to sea ice decline, with potential implications for increased melting of ice shelves in the Weddell and Ross Seas. The simulations illustrate that variations in wind stress could provide insights into the atmospheric‐sea ice dynamics driving recent record lows in Antarctic sea ice, underscoring the importance of such modeling for understanding and predicting changes.

Increasing spatial resolution to kilometre scales allows the deactivation of deep convection parameterisation schemes. As a result of various global initiatives for the next generation of climate studies, continental convection-permitting model (CPM) simulations are now accessible. Nonstationary local extremes, like heatwaves and intense precipitation, are probabilistically linked to regional circulation through scaling relationships. However, these relationships have not been extensively explored in the new simulations available in the early 2020s. Hourly time series data were extracted from the UK Climate Science for Service Partnership (CSSP) and the US South America Affinity Group (SAAG) CPM simulations to compare extreme characteristics of precipitation and temperature for 39 stations in a region of São Paulo, Brazil. Compared to reanalysis and satellite data, which exhibit lower variance in hourly time series, these two sets of CPM simulations have precipitation that is more similar to station observations than the ERA5 data and the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) data. The cross-correlation structures of the time series are investigated to quantify temporal dependence and reveal patterns between temperature and precipitation at an hourly timescale. Within a higher-dimensional probability space for joint risk, the cross-correlation structures between temperature and precipitation at different lags demonstrate the "memory" of these variables, indicating the influence of past values on future behaviour across multiple time points. Their forecasting power for these two variable based on each other is also explored to offer insights into the physical processes within the evolving simulated dynamic system. Overall, the results underscore the added value of convection-permitting models in providing more realistic simulations of local dynamics of extremes. The identified cross-correlation structures from the CPMs are valuable for exploring opportunities to design AI engines based on weather generator algorithms that use stochastic differential equations. Using CPM simulations, these weather generators can be employed to develop AI approaches for rapid decision support tools aimed at stakeholders facing extreme weather events related to compound risks of temperature and precipitation.

Atmospheric rivers (ARs) significantly impact hydrometeorological conditions by transporting large amounts of heat and water vapor, often resulting in extreme weather events and geohazards such as landslides. While the role of ARs in producing extreme rainfall and related landslides is well established, their influence on landslides through temperature-driven snowmelt remains poorly understood. Here, we examine this mechanism using 330 recorded landslides from February to April 2022 across the North Anatolian Mountains (T & uuml;rkiye). Our results demonstrate that ARs significantly contributed to snowmelt (up to 250 mm per event), stimulated by abrupt temperature increases (up to +6 degrees C) and rain-on-snow conditions, with rainfall and snowfall reaching up to 100 mm and 40 mm, respectively; all differences were statistically significant (p < 0.01) when comparing AR and non-AR days. These processes shifted landslide activity to higher elevations and steeper slopes over time, with median values rising from 330 m to 549 m and 16 degrees to 21 degrees, respectively. The results highlight the compound effect of ARs on landslide initiation and suggest that warming-driven snowmelt can substantially contribute to slope destabilization. This study provides a framework for understanding AR-related landslide hazards in other midlatitude mountain regions, including the Pacific Rim, the Andes, High Mountain Asia, and the Alps. As climate change is projected to amplify the frequency, intensity, and spatial extent of ARs, the risk of AR-induced geohazards is therefore likely to intensify further in such mountainous regions.

Landslides triggered by snowmelt, as one of the main hydrometeorological triggering factors, and their interaction with Atmospheric Rivers (ARs—long and narrow horizontal water vapor transport characterized by high water vapor content and strong low-level winds) and topographic conditions are not adequately elucidated. During the February–April 2022 period, extreme snowfalls in the Northern Anatolian Mountains, followed by a rapid snowmelt event, triggered more than 300 landslides. Accordingly, based on local and national news sources as well as public institution reports, an inventory was created by mapping 330 landslide events that occurred as a result of rapid snowmelt during this period. This landslide inventory compiled for the Northern Anatolia Region, one of the most susceptible regions in Europe, as well as Türkiye in terms of landslide events, provides a unique opportunity to understand the process dynamics underlying snowmelt-induced landslides. Revealing the combined and/or individual roles of meteorological weather events such as sudden temperature rises, heat waves, rain-on-snow events, and/or the foehn effect, associated with ARs or synoptic-scale weather events, in triggering these landslides is essential for better understanding possible such events in the near-future and to taking effective measures to mitigate socio-economic losses.The spatio-temporal distribution of snowmelt, air temperature, and snow-water equivalent (SWE) variables at daily and monthly scales for the February to April 2022 period according to long-term climatology (1993–2022) as well as landslide events triggered by ARs were analyzed. Additionally, the impacts of altitude and slope steepness on the spatio-temporal distribution of landslide events were revealed. Over the study area during February–April 2022, both monthly SWE and snowmelt values had positive anomalies, while air temperature values showed positive anomalies only for February and April. The analysis of landslide events triggered by ARs based on a 5-day window for AR passages showed that ARs as a triggering factor were responsible for 62% of total landslide events. On the other hand, as time progressed during the period February–April 2022, an increase in the altitude and slope steepness values at which landslide events occurred gradually increased. In addition to a gradual escalation of landslide occurrences to higher altitudes with time, we observed that landslides are limited to around 800 m, which further suggested that this may be caused either by limited soil thickness cover above a certain altitude or by the air temperature below the thawing degree.

Understanding the hydrometeorological impacts of atmospheric rivers (ARs) on mountain snowpack is crucial for water resources management in the snow-fed river basins such as the Euphrates-Tigris (ET). In this study, we investigate the contribution of wintertime (December-January–February) ARs to precipitation and snowpack in the headwater regions of the ET Basin for the period of 1979–2019 using a state-of-the-art AR catalog and ERA5 reanalysis data. The results show that AR days in the headwaters region could be warmer by up to 3 °C and wetter by over 5 mm day−1 compared to non-AR days. The contribution of ARs to the total winter precipitation varies from year to year, with a maximum contribution of over 80

AbstractAt the current juncture with climate change, centennial projections of species distributions in biodiversity hotspots, using dynamic vegetation models may provide vital insight into conservation efforts. This study aims to answer: (1) if climate change progresses under a business‐as‐usual scenario of anthropogenic emissions for this century, how may the forest ranges be affected? (2) will there be potential regional extinctions of the taxa simulated? (3) may any site emerge as a potential refugium? Study Area: Anatolian Peninsula and its surroundings, longitudes 24–50° E, latitudes 33–46° N. Time Period: 1961‐2100. Major Taxa Studied: 25 woody species and a C3 grass‐type. Method: Keeping a spatial window large enough to track potential changes in the vegetation range and composition especially in the mountain ranges within the study area, we parameterized a process‐based regional‐to‐global dynamic vegetation model (LPJ‐GUESS v 4.1), forced it with ERA5‐Land reanalysis for the historical period, and five different bias‐corrected centennial global circulation model (GCM) datasets under SSP5‐8.5, and simulated the dynamic responses of key forest species. Bivariate spatio‐temporal maps from the simulation results were constructed for final analysis. Results: A significant increase in woody taxa biomass for the majority of our study area, towards the end of the century was simulated, where temperate taxa with high tolerance for drought and a wider range of temperatures took dominance. The mountain ranges in our study area stood out as critical potential refugia for cold favoring species. There were no regional extinctions of taxa, however, important changes in areal dominance and potential future forest composition were simulated. Main Conclusions: Our simulation results suggest a high potential for future forest cover in our study region by the end of the century under a high emissions scenario, sans human presence, with important changes in vegetation composition, including encroachment of grasslands ecosystems by woody taxa.

The objective of this research is to propose an integrated approach to create a Geographic Information System (GIS) based spatial decision support system for sustainable hazelnut agriculture in the process of adaptation to climate change within the scope of a newly started interdisciplinary project. Hazelnut is a temperate climate fruit species. In Turkiye, the Black Sea coastal zone, which has favorable climatic conditions, is naturally a hazelnut growing area and hazelnut is one of the climax plant species. Hazelnut is one of the most important agricultural export products of Turkiye and according to 2023 data, Turkiye is ranked as the first place in the world hazelnut exports with 298.557 tons of hazelnut kernel. Since hazelnut is an economically valuable product and its cultivation is directly dependent on topographical and climatic conditions in large areas, monitoring and planning of hazelnut cultivation under changing climatic conditions is very important for the country's economy. Turkiye is one of the most affected Mediterranean countries due to climate change. The results of temperature, precipitation, and humidity simulations in regional climate models created especially for the Black Sea provide important findings in terms of the necessity for spatial planning based on the ecological requirements of hazelnut. Sustainable and rational use of land, determination of the suitability and quality of land, and integrated evaluation of information such as climate, topography, and soil properties are of great importance and are considered natural resources/heritage for future generations. For this purpose, the province of Sakarya, which is ranked as 3rd in hazelnut production in Turkiye with 82.581 tons, was selected as the study area in this research. In the process of creating the spatial decision support system in the study, many interrelated variables, such as climate, topography, geological conditions, and soil properties, will be evaluated using geostatistical methods and weights obtained from the literature and expert opinion surveys. With the data will be used in the spatial decision support system to be designed with the Analytic Hierarchy Process method approach will be used for the integrated analysis of various geospatial data related to the hazelnut cultivation to determine the most suitable hazelnut cultivation areas and the most appropriate hazelnut cultivars within the scope of adaptation to climate change. We will design a spatial decision support system in the process of climate change adaptation by determining the characteristics of the criteria based on varieties that support rural development in order to ensure sustainable production of hazelnut, which has strategic importance for Turkiye and strengthens competition in the global market.

Global or regional impacts of El Ni & ntilde;o Southern Oscillation (ENSO) have predominantly been investigated through the Ni & ntilde;o3.4 index, representing the sea surface temperature (SST) variations in the central Tropical Pacific. In this study, we comparatively evaluated the usefulness of Ni & ntilde;o1+2, a relatively less utilised index that represents SST variability in the Eastern Tropical Pacific. In our analyses, we focused on ENSO impacts on Euro-Mediterranean (EM) climate variability during boreal winter, using data from the NCEP/NCAR Reanalysis. The correlation analysis involving Ni & ntilde;o1+2 depicts more distinct temperature and precipitation patterns over the EM region. Amongst the SST-based Ni & ntilde;o indices, it has the highest correlation with the East Atlantic index (0.47, statistically significant at > 99% confidence level), a prominent regional teleconnection associated primarily with the strength of the East Atlantic ridge, which produces dipole-type climate patterns between East Atlantic/Western Europe and Central/Eastern Mediterranean. Moreover, its lagged correlations with the following spring (0.39), summer (0.31), and autumn (0.36) are all statistically significant at >= 99% confidence levels. The composite analysis shows that different Ni & ntilde;o regions have distinct effects on atmospheric circulation and climate in the EM region. The Ni & ntilde;o1+2 index is particularly helpful in identifying the years when warm SST anomalies of El Ni & ntilde;o extend to the Eastern Equatorial Pacific, which results in a reversal of temperatures across the EM region. Thus, this study suggests that Ni & ntilde;o1+2 is a useful index for studying climate variability and predictability in the EM region, especially when used in conjunction with other Ni & ntilde;o indices, as it captures some ENSO features that they may not encompass.

To improve sub-seasonal forecasts, different global initiatives generate continental convection-permitting simulations for resolution less than 10 kilometres for multiple decades. These simulations, however, are based on land use maps with only single urban type. In this study, we explore how the density and height information of the urban canopy based on Local Climate Zones (LCZs) affect the dynamics among temperatures, precipitation and land use types for the 2022 summer heatwave in the Southwest UK. Four numerical experiments at a 3 km grid are run by switching off the parameterization of deep-convection in the Weather Research and Forecasting (WRF) models. These experiments are based on (i) the no urban scenario, (ii) the default MODIS land use scheme, (iii) the building environment parameterization (BEP), and (iv) the building energy model (BEM).Results show that the cold advection over the UK led to downward motion according to a Q-vector analysis. The regional downward motion caused the formation of a heat dome. It is against the hypothesis that the 2022 summer heatwave was due to the hot circulation from Spain and equatorial Africa. Even though four land use schemes have similar simulated cold advection across the UK, our findings show that land use types affected water recycling due to local convection differently. These differences were related to the strength of rainstorms at the dissipating heatwave stage. Our results suggest that urban areas were more likely to have more persistent heatwaves since the intensity of rainstorms was affected by the lower local water recycling. This advanced understanding of the UK heatwave mechanism based on regional advection conditions and local convection processes will guide us on how to improve our sub-seasonal forecast in the urban area.

This study investigates the predictability of the dense advection fog over Istanbul on February 19, 2014, which significantly halted international as well as local transportation. Sensitivity simulations were conducted using the Weather Research and Forecasting (WRF) model forced by the ERA-Interim reanalysis data. A hierarchical approach was adopted. The first group of sensitivity simulations involving different microphysics schemes (WSM6, Morrison, Thompson-aerosol, NNSL, NNSL-CCN, and Milbrandt) indicated that the simulation with Milbrandt reproduced slightly better results for the fog event. Further sensitivity tests involving different planetary boundary layer (PBL) schemes (ACM2, BouLac, MYJ, MYNN2.5, MYNN, and YSU) were conducted. The YSU PBL scheme provided better diurnal air and dew point temperature variations compared to the observations at Ataturk and Sabiha Gokcen airports. We further investigated the performances of RRTMG, RRTMG-fast and Dudhia shortwave radiation schemes, and RRTMG and RRTM longwave radiation schemes. Our analyses revealed that simulation of the fog was very sensitive to radiation scheme. Although all PBL schemes were able to generate fog, a configuration with the YSU PBL scheme with Dudhia shortwave and RRTM longwave schemes produced comparatively low RMSE for temperature depression, 0.31 °C (0.23 °C), during the fog hours at Sabiha Gokcen (Ataturk) Airport. The model simulated the onset time of the afternoon fog well; however it reproduced the onset and dissipation times of the morning fog earlier than the observations. It is also found that the use of high-resolution initial and boundary condition data did not provide a significant improvement in the advection fog simulation.

El Nino Southern Oscillation (ENSO) is a phenomenon in the equatorial Pacific that could have profound effects on climate around the world. Although ENSO impacts are fairly well-defined for south and north America, Australia and south-eastern Asia, they are not very clear for Euro-Mediterranean region. Some studies indicate that the negative phase of ENSO in Nino3 and Nino3.4 indices have similar effects in the negative phase of North Atlantic Oscillation (NAO). ENSO impacts and teleconnection patterns are mostly studied using the Nino3.4 index. However, some recent studies indicate that the Nino1+2 index has higher correlation with climate variability over the Euro-Mediterranean region. In this study, we investigate impacts of ENSO over the Euro-Mediterranean climate variability and atmospheric dynamics using the Nino1+2 and Nino3.4 indices. Additionally, we also tried to understand if there is any relation between ENSO and the Mediterranean and East Asian troughs. NCEP/NCAR Reanalysis surface air temperature, precipitation and 500 hPa geopotential height datasets and SST-based ENSO indices from ERSSTv4 were used in the analysis for boreal winter (December-January-February) for a period of 1950 - 2019. We utilized the Pearson correlation analysis to reveal the relation between these indices and climate parameters and the composite analysis to define the pattern differences between the cold and warm phases of the indices. Our preliminary findings show that there is a distinct correlation pattern between Nino indices and surface air temperature over the region of interest. Nino1+2 index has a more distinct dipole pattern with a significant positive correlation pole over central Europe and negative pole over north-eastern Africa. However, Nino3.4 indicates a rather zonal correlation dipole pattern whose poles are over northwest Africa (strongly positive) and northeast Africa (negative). It is also found that the Mediterranean trough location is sensitive to the phase of ENSO for both indices. Namely, the Mediterranean trough tends to be in the west of its climatological location for La Nina phases of Nino1+2 and Nino3.4, which affects the distribution of surface temperature and precipitation over the Euro-Mediterranean and Middle East and Northern Africa (MENA) regions. We concluded that the La Nina phase of Nino1+2 seems to play a more distinctive role in the dipole pattern. The surface air temperature is colder over the entire Europe while it is opposite in the Middle East region including Turkey. This dipole pattern is also detected for the La Nina phase of Nino3.4, but it is mostly observed over southwestern Europe and northern Africa. Comparison between the La Nina and El Nino phases of the Nino1+2 index indicates that for the La Nina phase precipitation is larger over the Aegean Sea and Italy and smaller in northern Europe.

Atmospheric rivers (ARs) are important components of the global water cycle as they are responsible for over 90% of the poleward moisture transport at middle to high latitudes. ARs travelling thousands of kilometers over arid North Africa could interact with the highlands of the Mesopotamia and thus affect the hydrometeorology and water resources of the Euphrates-Tigris Basin. Here, we use a state-of-the-art AR tracking database, and reanalysis and observational datasets to investigate the climatology (1979-2017) and influences of these ARs in snowmelt season (March-April). The Red Sea and northeast Africa are found to be the major source regions of these ARs, which are typically associated with the eastern Mediterranean trough positioned over the Balkan Peninsula and a blocking anticyclone over the Near East-Caspian region, triggering southwesterly air flow towards the highlands of the Euphrates-Tigris Basin. AR days exhibit enhanced precipitation over the crescent-shaped orography of the Euphrates-Tigris Basin. Mean AR days indicate wetter (up to +2 mm day-1) and warmer (up to +1.5oC) conditions than all-day climatology. On AR days, while snowpack tends to decrease (up to 30%) in the Zagros Mountains, it can show decreases or increases in the Taurus Mountains depending largely on elevation. A further analysis with the aid of observations and reanalysis for the three extreme AR events indicates that ARs coinciding with large scale sensible heat transport can have notable impacts on the surface hydrometeorological conditions such as snowmelt, rain-on-snow precipitation and increasing daily discharges of the Euphrates and Tigris rivers. These results suggest that ARs can have notable impacts on the hydrometeorology and water resources of the basin, particularly of lowland Mesopotamia, a region that is famous with great floods in the ancient narratives.

The Southeastern Anatolia Region (SEAR), the third-lowest mean annual precipitation region in Turkey, has semi-arid climate and plateau characteristics. The proximity of the region to North Africa and the Middle East dust source areas enables long-range transport of desert dust particles toward the SEAR by strong winds. Among the other dust source regions, the Arabian Peninsula has a crucial role in terms of affecting the SEAR with a high-annual frequency and high dust concentration values. We investigated the atmospheric patterns of three extreme Arabian dust episodes that affect the SEAR in this study. Dust episodes were determined using present weather (SYNOP) codes of ten stations in the SEAR during the 2014–2019 period. The source regions were found using HYSPLIT backward trajectory analysis. In this study, we benefited from synoptic maps, in situ PM10 observations, numerical simulations of the WRF-Chem model, and MODIS satellite images to analyze the extreme dust episodes. The results showed that the surface low pressure over the Persian Gulf and strong southerly winds at the 700-hPa level enabled the transport of dust particles from the surface to the mid-atmospheric levels. If the center of the upper-level ridge extended from Saudi Arabia to southern Turkey, the atmospheric blocking mechanism prevented the dispersion of dense dust particles from the SEAR to its surrounding, which caused the observation of high dust concentrations in the SEAR. In general, the WRF-Chem model outputs are in good agreement with ground-based PM10 concentrations and MODIS true-color images in terms of temporal and spatial distributions of dust concentrations.

Bu çalışmada amacımız, Güney Okyanusu üzerindeki rüzgar dinamiklerini ve bunun okyanus devinim sirkülasyonu üzerindeki etkisini incelemektir. Bu amaçla, atmosfer ve bileşik okyanus-deniz buzu yüksek çözünürlüklü bölgesel modelleri ayrı ayrı koşturulmuştur. 2007 ve 2013 yılları arasında eşzamanlı olarak üç benzetim gerçekleştirilmiştir. İlk benzetim, gözlemlenen deniz yüzeyi sıcaklığı ve deniz buzu konsantrasyonu tarafından zorlanan sadece atmosfer bölgesel modelidir. Model, ortalama deniz seviyesi basıncı, 2 metre hava sıcaklığı, yukarı atmosfer jetleri ve Stratosferik Polar Vortex gibi önemli atmosferik özelliklerin mevsimselliğini başarıyla yakalamıştır. Model, Antarktika'daki gözlem istasyonlarıyla uyumluluk göstermektedir. İkinci benzetim, reanaliz atmosferik veri seti ile zorlanan kontrol okyanus-deniz buzu bileşik bölgesel modeldir. Okyanus modeli, deniz yüzeyi sıcaklık gradyanını doğru şekilde yakalamayı başarmıştır. Drake Geçidi'ndeki taşınım değerleri gözlemler dahilinde yaklaşık 152 Sv'dir. Son olarak, Güney Okyanusu üzerindeki bölgesel rüzgar gerilmesinin 1,5 kat arttığı bir duyarlılık benzetimi de yapılmış ve daha güçlü Drake Geçidi taşınımı ve Deacon Hücresi sirkülasyonu gözlemlenmiştir. Bu çalışma ileride gerçekleştirilebilecek Güney Okyanusu tamamen bütünleşik atmosfer-okyanus modeli geliştirilmesi için kapasite ve kabiliyetlerin ortaya konmasını sağlamıştır.