Search Results (1,176)

The Laptev Sea, as a marginal sea and a key source of sea ice for the Arctic Ocean, has a profound influence on the dynamic processes of sea ice evolution. Under a 2 °C global warming scenario, the accelerated ablation of Arctic sea ice is projected to greatly impact Arctic warming. The ocean regulates global climate through its interactions with the atmosphere, where sea surface temperature (SST) serves as a crucial parameter in exchanging energy, momentum, and gases. SST is also a key driver of sea ice concentration (SIC). In this paper, we analyze the spatiotemporal variability of SST and SIC, along with their interrelationships in the Laptev Sea, using daily optimum interpolation SST datasets from NCEI and daily SIC datasets from the University of Bremen for the period 2004–2023. The results show that: (1) Seasonal variations are observed in the influence of SST on SIC. SIC exhibited a decreasing trend in both summer and fall with pronounced interannual variability as ice conditions shifted from heavy to light. (2) The highest monthly averages of SST and SIC were in July and September, respectively, while the lowest values occurred in August and November. (3) The most pronounced trends for SST and SIC appeared both in summer, with rates of +0.154 °C/year and −0.095%/year, respectively. Additionally, a pronounced inverse relationship was observed between SST and SIC across the majority of the Laptev Sea with correlation coefficients ranging from −1 to 0.83. Full article

Within-field soil physical and chemical heterogeneity may affect spatio-temporal crop performance. Managing this heterogeneity can contribute to improving resource use and crop productivity. A simulation experiment based on comprehensive soil and crop data collected at the patchCROP landscape laboratory in Tempelberg, Brandenburg, Germany, an area characterized by heterogeneous soil conditions, was carried out to quantify the impact of within-field soil heterogeneities and their interactions with interannual weather variability on crop yield variability in summer and winter crops. Our hypothesis was that crop–soil water holding capacity interactions vary depending on the crop, with some crops being more sensitive to water stress conditions. Daily climate data from 1990 to 2019 were collected from a nearby station, and crop management model inputs were based on the patchCROP management data. A previously validated agroecosystem model was used to simulate crop growth and yield for each soil auger profile over the 30-year period. A total of 49 soil auger profiles were classified based on their plant available soil water capacity (PAWC), and the seasonal rainfall by crop was also classified from lowest to highest. The results revealed that the spatial variability in crop yield was higher than the temporal variability for most crops, except for sunflower. Spatial variability ranged from 17.3% for rapeseed to 45.8% for lupine, while temporal variability ranged from 10.4% for soybean to 36.8% for sunflower. Maize and sunflower showed a significant interaction between soil PAWC and seasonal rainfall, unlike legume crops lupine and soybean. As for winter crops, the interaction was also significant, except for wheat. Grain yield variations tended to be higher in years with low seasonal rainfall, and crop responses under high seasonal rainfall were more consistent across soil water categories. The simulated results can contribute to cropping system design for allocating crops and resources according to soil conditions and predicted seasonal weather conditions. Full article

Monitoring the snow cover variability and trends is crucial due to its significant contribution to river formation and sustenance. Using gap-filled MODIS data over the 2001–2024 period, the spatial distribution and temporal evolution of three snow cover metrics were studied: number of days, onset and end of the snow cover season across fourteen regions covering the Karakoram, Western Himalayas and Kunlun Mountains. The obtained signals exhibit considerable complexity, making it difficult to find a unique factor explaining their variability, even if elevation emerged as the most important one. The mean values of snow-covered days span from about 14 days in desert regions to about 184 days in the Karakoram region. Given the high interannual variability, the metrics show no significant trend across the study area, even if significant trends were identified in specific regions. The obtained results correlate well with the ERA5 and ERA5-Land values: the Taklamakan Desert and the Kunlun Mountains experienced a significant decrease in the snow cover extent possibly associated with an increase in temperature and a decline in precipitation. Similarly, the Karakoram and Western Himalayas region show a positive snow cover trend possibly associated with a stable temperature and a positive precipitation trend. Full article

Phytoplankton comprise the base of the food web in estuaries and their biomass and rates of growth (productivity) exert a bottom-up control in pelagic ecosystems. Reliable means to quantify biomass and productivity are crucial for managing estuarine ecosystems. In many estuaries, direct productivity measurements are rare and instead are estimated with biomass-based models. A seminal example of this is a light utilization model (LUM) used to predict productivity in the San Francisco Estuary and Delta (SFED) from long timeseries data using an efficiency factor, ψ. Applications of the LUM in the SFED, Chesapeake Bay, and the Dutch Scheldt Estuary highlight significant interannual and regional variability, indicating the model must be recalibrated often. The objectives of this study are to revisit the LUM approach in the SFED and assess a chlorophyll-a to carbon model (CCM) that produces a tuning parameter, Ω. To assess the estimates of primary productivity resulting from the models, productivity was directly measured with a ¹³C-tracer at nine locations during 22 surveys using field-derived phytoplankton incubations between March and November of 2023. For this study, ψ was determined to be 0.42 ± 0.02 (r² = 0.89, p < 0.001, CI₉₅ = 319). Modeling productivity using an alternative CCM approach (Ω = 3.47 × 10⁴ ± 1.7 × 10³, r² = 0.84, p < 0.001, CI₉₅ = 375) compared well to the LUM approach, expanding the toolbox for estuarine researchers to cross-examine productivity models. One practical application of this study is that it confirms an observed decline in ψ, suggesting a decline in light utilization by phytoplankton in the SFED. This highlights the importance of occasionally recalibrating productivity models in estuaries and leveraging multiple modeling approaches to validate estimations before application in ecological management decision making. Full article

Ecosystem water use efficiency (WUE) is a key indicator of the coupling between carbon and water cycles. With the increasing frequency of extreme climate events, WUE may also show trends of extremization. Understanding the dominant drivers behind extreme WUE variations is crucial for assessing the impact of climate variability on WUE. We investigate the main drivers and regional sensitivity of extreme WUE variations across seven geographical regions in China. The results reveal that extreme WUE variations are collectively influenced by gross primary productivity (GPP) and evapotranspiration (ET) (43.72%). GPP controls extreme WUE variations in 36.00% of the areas, while ET controls 20.17%. Furthermore, as the climate shifts from arid to humid regions, the area where GPP dominates extreme WUE variations increases, while the area dominated by ET decreases, suggesting a relationship with precipitation. Ridge regression analysis shows that vapor pressure deficit (VPD) is the primary driver of interannual WUE variation in China, with an average relative contribution of 38.64% and an absolute contribution of 0.025 gC·m⁻²·mm⁻¹·a⁻¹. We studied the changes in WUE and its driving mechanisms during extreme disaster events, providing a perspective focused on extreme conditions. In the future, these results may help regulate the carbon–water cycle in different regions, such as by guiding vegetation planting and land use planning based on the spatial characteristics of the dominant factors influencing extreme WUE variations to improve vegetation WUE. Full article

Mixed legume/grass grasslands are the most significant type of artificial grassland in rain-fed semi-arid regions. Understanding the contributions of legumes and grasses to grassland productivity, as well as the nitrogen-sharing mechanisms between them, is crucial to maintaining the sustainability, stability, and high yield of mixed grasslands. In this study, four commonly used cultivated species were selected: smooth bromegrass (Bromus inermis Leyss.), orchardgrass (Dactylis glomerata L.), sainfoin (Onobrychis viciifolia Scop.), and red clover (Trifolium pratense L.). Combinations of two and three species of legumes and grasses were established, with monoculture serving as the control. The results revealed that in all the monocultures and mixed grasslands comprising two or three species, the average dry matter yield (DMY) of mowed grasslands in 2017 was significantly higher than in 2018, while the average DMY of grazed summer regrowth in 2018 surpassed that of 2016 and 2017. Over the period from 2016 to 2018, smooth bromegrass and sainfoin gradually dominated the mixed grasslands, while orchardgrass and red clover exhibited a declining abundance. Over time, the ratio and amount of nitrogen (N) fixation in legumes significantly increased in both the monoculture and mixed grasslands. Similarly, the amount of nitrogen (N) received by grasses also increased significantly in mixed grasslands. However, the proportion of nitrogen fixed by legumes remained below 10% in 2016, 20% in 2017, and 30% in 2018. In contrast, nitrogen transfer from legumes to smooth bromegrass was less than 10%, while in orchardgrass, it was even lower, at less than 2%. The interannual variability in dry matter yield (DMY) and nitrogen contribution in the mixed grasslands of rain-fed semi-arid areas is primarily influenced by forage adaptability and average annual precipitation. Increasing the proportion of grazed forage relative to hay in annual forage consumption should be considered, as more extensive grazing can reduce damage from field rodents and provide higher forage quality at lower costs and energy consumption. To maintain grassland productivity, targeted grazing should be carefully planned and implemented. Full article

Many Caribbean low-latitude small island states lack wind maps tailored to capture their wind features at high resolutions. However, high-resolution mesoscale modeling is computationally expensive. This study proposes a statistical–dynamical downscaling (SDD) method that integrates an atmospheric-circulation-type (CT) approach with a high-resolution numerical weather prediction (NWP) model to map the wind resources of a case study, Trinidad and Tobago. The SDD method uses a novel wind class generation technique derived directly from reanalysis wind field patterns. For the Caribbean, 82 wind classes were defined from an atmospheric circulation catalog of seven types derived from 850 hPa daily wind fields from the NCEP-DOE reanalysis over 32 years. Each wind class was downscaled using the Weather Research and Forecasting (WRF) model and weighted by frequency to produce 1 km × 1 km climatological wind maps. The 10 m wind maps, validated using measured wind data at Piarco and Crown Point, exhibit a small positive average bias (+0.5 m/s in wind speed and +11 W m⁻² in wind power density (WPD)) and capture the shape of the wind speed distributions and a significant proportion of the interannual variability. The 80 m wind map indicates from good to moderate wind resources, suitable for determining priority areas for a detailed wind measurement program in Trinidad and Tobago. The proposed SDD methodology is applicable to other regions worldwide beyond low-latitude tropical islands. Full article

The Antarctic sea ice concentration (SIC) plays a crucial role in global climate dynamics by influencing atmospheric and oceanic circulation. This study examines SIC variability and its relationship with major climate modes, including the El Niño-Southern Oscillation (ENSO), Pacific-South American (PSA) pattern, Southern Annular Mode (SAM), and Antarctic Dipole (ADP). Using NSIDC satellite-derived sea ice data and ERA5 reanalysis from 1980 to 2022, we analyzed SIC anomalies in the Weddell, Ross, and Bellingshausen and Amundsen (B&A) Seas, assessing their response to climatic forcings across different timescales. Our findings reveal strong linkages between SIC variability and large-scale atmospheric circulation. ENSO-related teleconnections drive a dipolar SIC response, with warming in the Pacific sector and cooling in the Atlantic during El Niño, and the opposite pattern during La Niña. PSA and ADP further modulate this response by altering Rossby wave propagation and heat fluxes, leading to significant SIC fluctuations. The ADP emerges as a dominant driver of interannual SIC anomalies, showing an out-of-phase relationship between the Atlantic and Pacific sectors of the Southern Ocean. Regional SIC trends exhibit contrasting patterns: the Ross Sea shows a significant positive SIC trend, while the B&A and Weddell Seas experience persistent negative anomalies due to enhanced meridional heat transport and stronger westerly winds. SAM strongly influences SIC, particularly in the Atlantic sector, with delayed responses of up to six months, likely due to ice-albedo feedbacks and ocean memory effects. These results enhance our understanding of Antarctic sea ice variability and its sensitivity to large-scale climate oscillations. Given the observed trends and ongoing climate change, further research is needed to assess how these processes will evolve under future warming scenarios. This study highlights the importance of continuous satellite observations and high-resolution climate modeling for improving projections of Antarctic sea ice behavior and its implications for the global climate system. Full article

With the rising frequency and severity of wildfires that cause significant threats to ecosystems, public health and livelihoods, it is essential to have tools for evaluating and monitoring their impacts and the effectiveness of policy initiatives. This paper presents the development and implementation of a new calculation pipeline integrated with a web-based platform designed to provide georeferenced data on the burn severity of wildfires in mainland Portugal. The platform integrates a modular architecture that comprises a module in R and Google Earth Engine to compute standardized satellite-derived datasets on observed/historical severity for burned areas, integrated with a web portal module to facilitate the access, search, visualization, and downloading of the generated data. The platform provides open-access, multisource data from satellite missions, including MODIS, Landsat-5, -7, and -8, and Sentinel-2. It offers multitemporal burn severity products, covering up to 12 months post-fire, and incorporates three severity indicators, the delta NBR, relative difference NBR, and relativized burn ratio, derived from Normalized Burn Ratio (NBR) quarterly median composites. The platform’s modular and scalable framework also allows the integration of more spectral indices, burn severity indicators, and other wildfire perimeter databases. These design features also enable the platform to adapt to other contexts or regions beyond its current scope and regularly update burn severity products. Results from exploratory data analyses revealed the ability of satellite-based severity products to diagnose trends, assess interannual variability, and enable regional comparisons of burn severity, providing a basis for further research. In the face of climate change and societal challenges, the platform aims to support decision-making processes by providing authorities with standardized and updated information while promoting public awareness of wildfire challenges and, ultimately, contributing to the sustainability of rural landscapes. Full article

Poyang Lake, the largest freshwater lake of China, serves as a crucial wintering site for migratory birds in the East Asian–Australasian Flyway, where habitat quality is essential for maintaining diverse bird populations. Recently, the frequent alternation of extreme wet years, e.g., 2020, and dry years, e.g., 2022, have inflicted considerable perturbation on the local wetland ecology, severely impacting avian habitats. This study employed the spatiotemporal fusion method (ESTARFM) to obtain continuous imagery of Poyang Lake National Nature Reserve during the wintering seasons from 2020 to 2022. Habitat areas were identified based on wetland classification and water depth constraints. The results indicate that both extreme wet and dry conditions have exacerbated the fragmentation of migratory bird habitats. The shallow water habitats showed minor short-term fluctuations in response to water levels but were more significantly affected by long-term hydrological trends. These habitats exhibited considerable interannual variability across different hydrological years, affecting both their proportion within the overall habitat and their distribution within the study area. This study demonstrates the ability of ESTARFM to reveal the dynamic changes in migratory bird habitats and their responses to extreme hydrological conditions, highlighting the critical role of water depth in habitat analysis. The outcomes of this study improve the understanding of the impact of extreme water levels on migratory bird habitats, which may help expand knowledge about the protection of other floodplain wetlands around the world. Full article

This study investigates the potential of uninitialized global climate projections for providing 12-month (inter-annual) wind forecasts in Europe in light of the increasing demand for long-term climate predictions. This is important in a context where models based on the past climate may not fully account for the implications for climate variability of current warming trends, and where initialized 12-month forecasts are still not widely available (i.e., seasonal forecasts) and/or consolidated (i.e., decadal predictions). To this aim, we use two types of simulations: uninitialized climate projections from CMIP6 (Coupled Model Intercomparison Project Phase 6) and initialized 6-month seasonal forecasts (ECMWF’s SEAS5), using the latter as a benchmark. All the predictions are bias-corrected with five distinct approaches (quantile delta mapping, empirical quantile mapping, quantile delta mapping, scaling bias-adjustment and a proprietary quantile mapping) and verified against weather observations from the ECA&D E-OBS project (684 weather stations across Europe). It is observed that the quantile-mapping techniques outperform the other bias-correction algorithm in adjusting the cumulative distribution function (CDF) to the reference weather stations and, also, in reducing the mean bias error closer to zero. However, a simple bias -correction by scaling improves the time-series predictive accuracy (root mean square error, anomaly correlation coefficient and mean absolute scaled error) of CMIP6 simulations over quantile-mapping bias corrections. Thus, the results suggest that CMIP6 projections may provide a valuable preliminary framework for comprehending climate wind variations over the ensuing 12-month period. Finally, while baseline methods like climatology could still outperform the presented methods in terms of time-series accuracy (i.e., root mean square error), our approach highlights a key advantage: climatology is static, whereas CMIP6 offers a dynamic, evolving view of climatology. The combination of dynamism and bias correction makes CMIP6 projections a valuable starting point for understanding wind climate variations over the next 12 months. Furthermore, using workload schedulers within high-performance computing frameworks is essential for effectively handling these complex and ever-evolving datasets, highlighting the critical role of advanced computational methods in fully realizing the potential of CMIP6 for climate analysis. Full article

Flood modelling in snow-fed river basins is critical for understanding the impacts of climate change on hydrological extremes. The Zhabay River in northern Kazakhstan exemplifies a basin highly vulnerable to seasonal floods, which pose significant risks to infrastructure, livelihoods, and water resource management. Traditional flood forecasting in Central Asia still relies on statistical models developed during the Soviet era, which are limited in their ability to incorporate non-stationary climate and anthropogenic influences. This study addresses this gap by applying the Soil and Water Integrated Model (SWIM) to project climate-driven changes in the hydrological regime of the Zhabay River. The study employs a process-based, high-resolution hydrological model to simulate flood dynamics under future climate conditions. Historical hydrometeorological data were used to calibrate and validate the model at the Atbasar gauge station. Future flood scenarios were simulated using bias-corrected outputs from an ensemble of General Circulation Models (GCMs) under Representative Concentration Pathways (RCPs) 4.5 and 8.5 for the periods 2011–2040, 2041–2070, and 2071–2099. This approach enables the assessment of seasonal and interannual variability in flood magnitudes, peak discharges, and their potential recurrence intervals. Findings indicate a substantial increase in peak spring floods, with projected discharge nearly doubling by mid-century under both climate scenarios. The study reveals a 1.8-fold increase in peak discharge between 2010 and 2040, and a twofold increase from 2041 to 2070. Under the RCP 4.5 scenario, extreme flood events exceeding a 100-year return period (2000 m³/s) are expected to become more frequent, whereas the RCP 8.5 scenario suggests a stabilization of extreme event occurrences beyond 2071. These findings underscore the growing flood risk in the region and highlight the necessity for adaptive water resource management strategies. This research contributes to the advancement of climate-resilient flood forecasting in Central Asian river basins. The integration of process-based hydrological modelling with climate projections provides a more robust framework for flood risk assessment and early warning system development. The outcomes of this study offer crucial insights for policymakers, hydrologists, and disaster management agencies in mitigating the adverse effects of climate-induced hydrological extremes in Kazakhstan. Full article

Agricultural drought is a condition that threatens natural ecosystems, water security, and food security. The timely identification of an agricultural drought event is essential to mitigating its effects. However, achieving a reliable and accurate assessment is challenging due to the interannual variability of precipitation in a region. Therefore, the objective of this study was to identify the months with drought during the agricultural cycle of the maize crop (Zea mays L.) in the Atlacomulco Rural Development District (ARDD) as a study area using the SPI and SPEI indices and their impact on each phenological stage. The results show that when analyzing the historical period (1985–2017), the ARDD is a region prone to agricultural droughts with a duration of one month. The stages of grain filling and ripening were the most vulnerable, since SPI and SPEI-1 quantify that 25% and 31% of the total months with drought occur during those stages, respectively. Towards the 2041–2080 horizon, the MCG ACCESS-ESM1-5 with the SSP2-4.5 scenario identified an occurrence of dry periods with 17% and 20% by SPI and SPEI, respectively, while for SSP5-8.5, 17% and 22% of the total number of periods corresponded to dry months with SPI and SPEI, respectively. Greater recurrence will be observed in the future, specifically after the year 2061, meaning an increase in the frequency of agricultural drought events in the region, causing difficult and erratic productive conditions for each agricultural cycle and threatening sustainable development. Therefore, it is necessary to take action to mitigate the effects of climate change in this sector. Full article

This study reconstructed the annual lake surface area (LSA) and absolute lake water storage (LWS) changes of Lake Sarez, the world’s largest high-altitude landslide-dammed lake, from 1992 to 2023 using multi-source remote sensing data. All available Landsat images were used to extract the LSA using an improved multi-index threshold method, which incorporates a slope mask and threshold adjustment to enhance the boundary delineation accuracy (Kappa coefficient = 0.94). By combining the LSA with high-resolution DEM and the GLOBathy bathymetry dataset, the absolute LWS was reconstructed, fluctuating between 12.3 × 10⁹ and 12.8 × 10⁹ m³. A water balance analysis revealed that inflow runoff (IRO) was the primary driver of LWS changes, contributing 54.57%. The cross-wavelet transform and wavelet coherence analyses showed that the precipitation (PRE) and snow water equivalent (SWE) were key climatic factors that directly influenced the variability of IRO, impacting the interannual water availability in the lake, with PRE having a more sustained impact. Temperature indirectly regulated IRO by affecting SWE and potential evapotranspiration. Furthermore, IRO exhibited different resonance periods and time lags with various atmospheric circulation factors, with the Pacific Decadal Oscillation and North Atlantic Oscillation having the most significant influence on its interannual variations. These findings provide crucial insights into the hydrological behavior of Lake Sarez under climate change and offer a novel approach for studying water storage dynamics in high-altitude landslide-dammed lakes, thereby supporting regional water resource management and ecological conservation. Full article

In July 2023, 19 continuous days of very hot days in Hong Kong brought inconvenience to citizens and disasters to society. This long-lasting heat wave event is closely linked to the atmospheric variability on the quasi-biweekly to intraseasonal timescales. While extreme weather has aroused the attention of scientists and society, limited studies focus on quasi-biweekly to intraseasonal extreme (QBIE) weather. Thus, to address this issue, this study aims at examining the climatology and long-term variability of these QBIE events in Hong Kong. This study serves as one of the very few fundamental works that construct a century-long record of QBIE temperature events, based on in situ observation in Hong Kong, and further examines the climatology, diversity, and variability of these QBIE temperature events. A total of 382 QBIE heat waves and 510 QBIE cold surges are identified from 1885 to 2022, exhibiting various characteristics in their occurring time and seasonality. Based on ARIMA model and time series analyses, we find that while apparent interannual variability exists in QBIE heat wave and cold surge activity, short-term climate prediction of QBIE temperature events based on past patterns or common climate indices is largely unfeasible. This research provides a valuable historical reference for understanding QBIE weather in the Guangdong–Hong Kong–Macau Greater Bay Area and highlights the need for further studies on the predictability of QBIE weather in the future. Full article