Annual Anomalies Research Articles

Decadal trends and predictive insights into aquatic ecosystem health in the Namhan River, South Korea: Longitudinal analysis of hydro-environmental factors and stream health indices (2008–2022)

The main focus of this study is to explore the complex interactions between aquatic ecosystem health (AEH) and time-varying hydro-environmental factors. We measure AEH in the Namhan River of South Korea using stream health index (SHI) scores for the trophic diatom index (TDI), benthic macroinvertebrate index (BMI), and fish assessment index (FAI) for 2008 to 2022. Key hydro-environmental variables associated with AEH, including their time-lagged responses, are identified. Water quality parameters such as chemical oxygen demand (COD) are significantly and negatively correlated with TDI (r=−0.68), BMI (r=−0.87), and FAI (r=−0.70), highlighting the critical role of water quality in the sensitivities of diatoms, macroinvertebrates, and fish health. The apparent response lag of aquatic ecosystems to hydrological parameters, such as a one-year-lagged correlation of BMI with mean flow (r=0.74) and annual flow anomalies (r=0.73), suggests their potential use as predictors in AEH models. A longitudinal analysis of SHIs over a decade showed consistent changes in AEH from upstream to downstream, with notable trends in the deterioration and improvement of SHIs in different areas. The upstream TDI and BMI deteriorated, while the upstream FAI improved. A multiple-linear-regression–based predictive model demonstrated efficacy in predicting AEH under various hydro-environmental conditions, achieving R2 values up to 0.85 for BMI and also has the potential to simulate the impacts of anticipated climatic and hydrological changes.

Near‐surface wind variability in Prydz Bay and Amery Ice Shelf region, East Antarctica: A four‐decade SOM analysis

AbstractNear‐surface wind fields in the Prydz Bay and Amery Ice Shelf region of East Antarctica play a crucial role in the formation and variability of Antarctic Bottom Water, a cold, dense water mass that sinks and spreads across the deep ocean basins influencing ocean circulation and modulating earth's climate system. This study investigates the primary modes of variability of these wind fields using the self‐organizing map (SOM) method and data from the latest version of the European Centre for Medium‐Range Weather Forecasts (ECMWF) ReAnalyses (ERA5), spanning four decades from 1979 to 2020. While the wind field climatology, characterized by small seasonal variation, is dominated by katabatic and large‐scale forcing, the spatial patterns of the primary variability modes are mainly influenced by synoptic system activities. The overall trend in annual wind speed anomalies is positive across the study region, with the exception of the southwestern part and central Prydz Bay. However, significant trends are observed in only two out of nine SOM nodes (nodes 4 and 9), which collectively explain less than 30% of the averaged trends over the region. The interannual variability in the seasonal occurrences of certain nodes is linked to several well‐known climate modes, including the El Niño–Southern Oscillation, the Southern Annular Mode and Zonal Wavenumber 3. Our results provide a reference for forecasting the occurrence frequency of specific patterns, which could help mitigate the impact of extreme wind events through improved forecasting.

Modeling cheatgrass distribution, abundance, and response to climate change as a function of soil microclimate.

Exotic annual grass invasions in water-limited systems cause degradation of native plant and animal communities and increased fire risk. The life history of invasive annual grasses allows for high sensitivity to interannual variability in weather. Current distribution and abundance models derived from remote sensing, however, provide only a coarse understanding of how species respond to weather, making it difficult to anticipate how climate change will affect vulnerability to invasion. Here, we derived germination covariates (rate sums) from mechanistic germination and soil microclimate models to quantify the favorability of soil microclimate for cheatgrass (Bromus tectorum L.) establishment and growth across 30 years at 2662 sites across the sagebrush steppe system in the western United States. Our approach, using four bioclimatic covariates alone, predicted cheatgrass distribution with accuracy comparable to previous models fit using many years of remotely-sensed imagery. Accuracy metrics from our out-of-sample testing dataset indicate that our model predicted distribution well (72% overall accuracy) but explained patterns of abundance poorly (R2 = 0.22). Climatic suitability for cheatgrass presence depended on both spatial (mean) and temporal (annual anomaly) variation of fall and spring rate sums. Sites that on average have warm and wet fall soils and warm and wet spring soils (high rate sums during these periods) were predicted to have a high abundance of cheatgrass. Interannual variation in fall soil conditions had a greater impact on cheatgrass presence and abundance than spring conditions. Our model predicts that climate change has already affected cheatgrass distribution with suitable microclimatic conditions expanding 10%-17% from 1989 to 2019 across all aspects at low- to mid-elevation sites, while high- elevation sites (>2100 m) remain unfavorable for cheatgrass due to cold spring and fall soils.

Global scale assessment of urban precipitation anomalies

Urbanization has accelerated dramatically across the world over the past decades. Urban influence on surface temperatures is now being considered as a correction term in climatological datasets. Although prior research has investigated urban influences on precipitation for specific cities or selected thunderstorm cases, a comprehensive examination of urban precipitation anomalies on a global scale remains limited. This research is a global analysis of urban precipitation anomalies for over one thousand cities worldwide. We find that more than 60% of the global cities and their downwind regions are receiving more precipitation than the surrounding rural areas. Moreover, the magnitude of these urban wet islands has nearly doubled in the past 20 y. Urban precipitation anomalies exhibit variations across different continents and climates, with cities in Africa, for example, exhibiting the largest urban annual and extreme precipitation anomalies. Cities are more prone to substantial urban precipitation anomalies under warm and humid climates compared to cold and dry climates. Cities with larger populations, pronounced urban heat island effects, and higher aerosol loads also show noticeable precipitation enhancements. This research maps global urban rainfall hotspots, establishing a foundation for the consideration of urban rainfall corrections in climatology datasets. This advancement holds promise for projecting extreme precipitation and fostering the development of more resilient cities in the future.

Annual and seasonal mean daily discharge in natural and regulated rivers in Northern Finland: variability, trends, and links to climate teleconnections

ABSTRACT This study aimed to investigate long-term (1969–2023) variability and trends in annual mean daily discharge (AMDD) and seasonal mean daily discharge (SMDD) in 10 rivers throughout Northern Finland in connection to climate teleconnections. The lowest AMDDs were mostly recorded during the first 12 years (1969–1980) of our study period, while the highest ones were during the last 12 years (2012–2023). Significant trends in AMDD were found only in three natural rivers of Tornionjoki, Simojoki, and Kuivajoki during 1969–2023. Such variations and trends in AMDD were most significantly associated with the Scandinavia (SCA) pattern, which is also an influential climate teleconnection for annual precipitation anomalies across Northern Finland. The highest (lowest) SMDDs were experienced in spring (winter). Only increasing trends in SMDD were statistically significant (p < 0.05). Such substantial increases in SMDDs were detected in winter, spring, and summer (autumn) in association with the Arctic Oscillation or AO and the North Atlantic Oscillation or NAO (East Atlantic/West Russia or EA/WR). Generally speaking, recent milder and wetter climatic conditions in association with strong positive AO and NAO (negative EA/WR) phases could increase SMDD for winter, spring, and summer (autumn) seasons. Hence, both AMDD and SMDD in rivers throughout Northern Finland were primarily influenced by precipitation.

Uneven changes in air and crown temperatures associated with snowpack changes affect the phenology of overwintering cereals

Time series analysis of overwintering cereals in snowy areas has revealed several phenological patterns associated with climate changes in winter. Herein, to investigate the recent effect of climatic variations on overwintering cereals, we investigated the phenology over multiple decades at three snowy region sites with an air temperature (Tair) increase trend of 0.48–1.09 °C/decade. Our findings were as follows: heading trends differed within the same cultivar at different sites; phenology was promoted with increasing temperatures in cooler regions and decreasing snow duration in regions with heavy snow; crown temperature (Tcrown) was a more direct determinant than Tair in phenology estimation model in regions with heavy snow. A thermal gap of more than a few degrees Celsius between Tair and Tcrown, owing to the insulation effect of snowpack, affected the phenology of overwintering cereals. A shorter snow cover period promoted phenology in locations with temperatures >0 °C. Subsequently, we found that when the thermal gap was >0 °C of the growing temperature range, Tcrown directly helped determine the phenology of overwintering cereals, and irrespective of the warming trend, the periodic inflow of cold air into the northern mid-latitudes of the Northern Hemisphere and associated snow cover changes dominated Tcrown, resulting in annual phenological anomalies with a range of fluctuations of approximately 1 month. The trend of increasing Tair during spring in northern Japan is consistent with the global trend, with a pronounced trend of advancing phenology reaching >4 days/decade in a typical cooler location experiencing snowmelt in March.

State of polar climate in 2023

The year 2023 has become the warmest year on global record. As the Antarctic and Arctic are sensitive regions to global warming, the climate changes in 2023 in these regions have attracted widespread attention. In this study, using observations, reanalysis and remote sensing data, we reported detailed polar climate changes in 2023, including warming, sea ice, atmospheric composition and extreme events. Antarctic exhibited large east‒west regional differences and the coexistence of extreme warm and cold events. In Coats Land, Queen Maud Land and the Antarctic Peninsula, three and seven stations recorded the second and third highest autumn air temperatures in history, respectively. The Amundsen–Scott station experienced extreme warming event in July, with the temperature increasing by 40 °C in one day. Abnormal cooling was evident in the Ross Sea and neighboring regions which were predominantly winter (June–August) cold anomalies, with Marylin Station reaching the lowest winter temperature in history. The Arctic experienced the warmest summer after 1979, with an overall distribution of ‘warm land‒cold sea’ on annual average. Compared with the 1991–2020 average, the annual air temperature anomalies reached more than 2 °C in northern Canada and the Barents Sea–Kara Sea coast. Abnormal high summer temperature caused most severe wildfires in Canada on record and second largest daily cumulative melt area over the Greenland ice Sheet daily post-1979. Polar sea ice continued to decrease rapidly, with minimum sea ice extent in Antarctic and Arctic ranking the first and sixth lowest post-1979. For melt season, Arctic Ocean sea ice began to melt later in 2023 than the 2011–2023 average, and freeze onset was delayed due to high temperatures in summer and autumn. Additionally, the status of polar atmospheric greenhouse gases remains bleak, and major greenhouse gas concentrations continue to increase. The Antarctic ozone hole in 2023 formed approximately 10 d earlier and lasted longer than the 1979–2023 average, with a maximum daily area of 2.6 × 107 km2 on 21 September. This summary of polar climate changes in 2023 will help people better understand global climate change and draw attention to polar regions.

Modeling non-stationarity in extreme rainfall data and implications for climate adaptation: A case study from southern highlands region of Tanzania

The Southern Highlands region of Tanzania has witnessed an increased frequency of severe flash floods. This study examines rainfall data of four stations (Iringa, Mbeya, Rukwa, and Ruvuma) spanning 30 years (1991–2020) to investigate drivers of extreme rainfall and non-stationarity behavior. The Generalized Extreme Value (GEV) model, commonly used in hydrological studies, assumes constant distribution parameters, which may not be true due to climate variability, potentially leading to bias in extreme quantile estimation. Recent studies have introduced a technique for constructing non-stationary Intensity-Duration-Frequency (IDF) rainfall curves. The method incorporates trends in the parameters of the GEV distribution, only using time as a covariate. However, uncertainty exists about whether time is the most suitable covariate, highlighting the need to explore all potential covariates for modeling non-stationarity. The aim of this study is to assess the influence of other time-varying covariates on extreme daily rainfall events, considering seasonality and climate change in the rainfall data. Specifically, five processes (i.e., local temperature changes (LTC), urbanization, annual Global Temperature Anomaly (GTA), the Indian Ocean Dipole (IOD), and the El Niño-Southern Oscillation (ENSO) cycle) were studied as drivers of extreme rainfall events. Sixty two non-stationary GEV models are developed based on these covariates and their combinations, alongside two non-stationary GEV models using the time covariate to capture the seasonality of the unimodal rainfall in the region, and one stationary GEV model (S0). With the use of corrected Akaike Information Criterion (AICc), the best model for each duration (i.e., 1-, 3-, and 5-days) of rainfall series is chosen. Results indicate that local processes (i.e., LTC and urbanization) are the optimal covariates for 1 day-duration rainfall, while global processes (i.e, IOD, ENSO cycle, and GTA) are identified as the most suitable covariates for 3, and 5 day-duration rainfall across all stations. The identified best non-stationary model (with their best covariates) are then used to develop non-stationary rainfall IDF curves for all stations. According to the analysis of non-stationary extreme values, the return periods of extreme rainfall events concluded a notable decrease in comparison to the stationary approach. The study also revealed strong correlations between global climate indices (ENSO, IOD, GTA) and long-duration extreme rainfall in Tanzania’s Southern Highlands. Local factors like Urbanization and temperature changes also show significant associations with 1-day duration events. These findings emphasize the need for integrated climate forecasting to inform effective adaptation strategies. Finally, the study addresses associated uncertainties in our predictions of forthcoming extreme rainfall events through rigorous analysis. The study demonstrated that return levels for extreme rainfall events exhibit a rising trend with increasing return period, indicating heightened intensity over longer time spans, whereas, a relative uncertainty analysis illustrate escalating uncertainty with increasing return periods, emphasizing challenges in long-term prediction.

Predicting malaria hyper endemic zones in West Africa using a regional scale dynamical malaria model

Due to the continuing large number of malaria-related deaths in tropical Africa, the need to develop a robust Malaria Early Warning System (MEWS) for effective action is growing to guide cost-effective implementation of interventions. This study employs a two-stage hierarchical evaluation technique to evaluate the ability of the VECTRI malaria model to simulate malaria dynamics at seasonal time scale (1 - 7 months) over Nigeria and West Africa. Two sets of malaria simulations are considered. The first set is based on VECTRI simulations driven by observed rainfall and temperature datasets (hereafter referred to as control run). The second is based on malaria simulations driven by the European Centre for Medium-Range Weather Forecasting (ECMWF) System5 ensemble seasonal forecasting system (hereafter referred to as Forecast run). Different metrics are employed to assess the skill of the VECTRI malaria model. Results based on the control run indicate that the model can reproduce hyper-endemic zones and the evolution of malaria cases, particularly the observed increase in cases with decreasing population density. Despite having significant biases and low correlation, the model successfully predicts annual anomalies in malaria cases across Nigeria, particularly in the savannah region that experience large malaria burden. Annual correlations between the VECTRI Forecast run and the VECTRI Control run are relatively low at all lead times (LT) and for each start date (SD) across West Africa, although correlation generally increases from the Gulf of Guinea to the Sahel. Despite low correlations, the Rank Probability Skill Score (RPSS) reveals that the model has a statistically significant skill in predicting malaria occurrences across all categories of malaria cases, regardless of start date or lead time. While the Guinea Forest has the strongest RPSS, the increase or decrease in skill from the first to seventh lead time varies significantly across the region. In addition, the VECTRI malaria model has a good ability to discriminate variability in malaria cases across all regions, with an average Area Under the Relative Operating Characteristics (ROC) Curve (AUC) of approximately 0.62. Our findings suggest that the VECTRI malaria model could be used as a reliable Malaria Early Warning System (MEWS), particularly for identifying malaria hyper-endemic zones in West Africa at seasonal time scale.

Precipitation patterns and their variability in the southern region of Brazil

Climate change is a long-term transformation of climate patterns, which can be natural or anthropogenic. Signs of climate change can manifest themselves in different ways: through the occurrence of extreme events, rising ocean levels, melting glaciers, among others. Negative impacts are produced by climate change and are one of humanity's biggest concerns, since its effects reach the entire planet with different consequences. Considering the need for actions aimed at contributing to the process of mitigating climate change, the importance of knowing changes in regional climate patterns is emphasized. This study aims to analyze changes in precipitation patterns over time in the southern region of Brazil, including variability, trends, anomalies, droughts and excess rainfall. Daily precipitation data from 1961 to 2020 were used, collected at 15 meteorological stations in the region. Various statistical analyzes were carried out, such as averages, maximum and minimum values, seasonal and annual anomalies, frequency distribution, Mann-Kendall trend tests and the Normalized Precipitation Index (SPI). The results indicate that climate change trends are positive in terms of increasing rainfall volumes, altering rainfall dispersion patterns (volume). In other words, rainfall is more irregular in terms of distribution throughout the seasons. The big highlight was Florianópolis -SC, which during the summer had the highest volume of rain among the meteorological stations studied.

Evaluating Noah‐MP Simulated Runoff and Snowpack in Heavily Burned Pacific‐Northwest Snow‐Dominated Catchments

AbstractTerrestrial hydrology is altered by fires, particularly in snow‐dominated catchments. However, fire impacts on catchment hydrology are often neglected from land surface model (LSM) simulations. Western U.S. wildfire activity has been increasing in recent decades and is projected to continue increasing over at least the next three decades, and thus it is important to evaluate if neglecting fire impacts in operational land surface models (LSMs) is a significant error source that has a noticeable signal among other sources of uncertainty. We evaluate a widely used state‐of‐the‐art LSM (Noah‐MP) in runoff and snowpack simulations at two representative fire‐affected snow‐dominated catchments in the Pacific Northwest: Andrew's Creek in Washington and Johnson Creek in Idaho. These two catchments are selected across all western U.S. fire‐affected catchments because they are snow‐dominated and experienced more than 50% burning in a single fire event with minimal burning outside of this event, which allows analyses of distinct pre‐ and post‐fire periods. There are statistically significant shifts in model skills from pre‐to post‐fire years in simulating runoff and snowpack. At both study catchments, simulations miss enhancements in early‐spring runoff and annual runoff efficiency during post‐fire years, resulting in persistent underestimates of annual runoff anomalies throughout the 12‐year post‐fire analysis periods. Enhanced post‐fire snow accumulation and melt contributes to observed but unmodeled increases of spring runoff and annual runoff efficiency at these catchments. Informing simulations with satellite observed land cover classifications, leaf area index, and green fraction do not consistently improve the model ability to simulate hydrologic responses to fire disturbances.

Multiscale Analysis of Water Area, Level and Flow and Their Relationships for a Large Lake Connected to Rivers: A Case Study of Dongting Lake, China

Exploring the different characteristics of water status at different time scales is essential for the understanding of the complex hydrological processes in lakes connected to rivers. Taking Dongting Lake, a large-scale lake connected to the Yangtze River, as an example, this study employed wavelet analysis and multiple models to explore the temporal characteristics of water area, level, and flow and the water level–area and flow–area correlations from 2000 to 2021 on annual, seasonal, and monthly scales. The results show that the interannual variations in annual, seasonal, and monthly water area anomalies demonstrated rather consistent overall trends, but there were differences in the variation ranges at different temporal scales. The dominant amplitudes for annual and seasonal area anomalies were close, while for monthly anomalies, they were sometimes much larger. The dominant amplitudes of the water level and flow anomalies generally followed the order annual < seasonal < monthly. The water level–area and flow–area correlations are strongest at Chenglingji station and on the seasonal scale. This study provides a multiscale perspective on hydrological conditions and will support the management of water resources and the prevention of drought and flood disasters.

Quantifying the air pollution impacts on solar photovoltaic capacity factors and potential benefits of pollution control for the solar sector in China

Solar photovoltaic (PV) plays a crucial role in China's energy transition. However, air pollution diminishes solar radiation resources, thereby reducing PV power generation efficiency. This study aims to quantify the impacts of air pollution on PV capacity factors in China while emphasizing the geographically specific potential benefits of improved air quality for the future PV sector. Using a PV power generation evaluation model, this study estimated solar PV capacity factors from 1961 to 2016 under both all-sky and clear-sky scenarios. Also, the spatiotemporal patterns of air pollution-induced capacity factor changes were analyzed. The results indicate that the all-sky and clear-sky PV capacity factors showed consistent annual anomalies, suggesting that air pollution due to aerosol emissions drove PV capacity factor temporal evolutions and trends in China. From 1961 to 1990, the national average clear-sky PV capacity factors for fixed modules with optimal tilt angle showed a significant decreasing trend of −0.0053 per decade, whereas from 1990 to 2016, an upward trend of 0.0030 per decade was observed. Compared with fixed modules, tracking systems suffered from greater productivity losses due to air pollution. In comparison to 1961–1965 averages, the provincial PV capacity factors for 2012–2016 decreased by 0.48–13.54%, 1.15–11.40%, 1.06–14.83%, and 1.31–16.51% for fixed modules with optimal tilt angle, horizontal fixed, one-axis horizontal tracking, and two-axis tracking modules, respectively, with decreases of 7–17% in the central and southeast. Furthermore, according to the provincial PV installation targets projected in China's 14th Five-Year Plan, national solar PV power generation was expected to increase by 81.333 TWh to 1069.997 TWh in 2025 with average PV capacity factors for 1961–1965, compared to 2012–2016 averages. In a scenario of keeping grid parity, additional power generation would offer an economic benefit of approximately 30.102 billion CNY.

Basin‐scale responses of groundwater‐resource quality to drought and recovery, San Joaquin Valley, California

AbstractGroundwater‐resource quality is assumed to be less responsive to drought compared to that of surface water due to relatively long transit times of recharge to drinking‐supply wells. Here, we evidence dynamic perturbations in aquifer pressure dynamics during drought and subsequent recovery periods cause dramatic shifts in groundwater quality on a basin scale. We used a novel application of time‐series clustering on annual nitrate anomalies at >450 public‐supply wells (PSWs) across California's San Joaquin Valley during 2000–22 to group sub‐populations of wells with similar water‐quality responses to drought. Additionally, we statistically evaluated the direction and magnitude of multi‐constituent water‐quality changes across the San Joaquin Valley using a broader dataset of >3000 PSWs with data during two select hydrologic stress periods representing an extreme drought (2012–16) and subsequent recovery (2016–19). Results of time‐series clustering and stress‐period change analyses corroborate a predominant regional response to pumping stress characterized by increased concentrations of anthropogenic constituents (nitrate, total dissolved solids) and decreased concentrations of geogenic constituents (arsenic, fluoride), which largely reversed during recovery. Cluster analysis also identified a secondary, less commonly occurring group of PSWs where nitrate decreased during drought, but explanatory factor analysis was not able to discern hydrogeologic drivers for these two divergent response patterns. Long‐term tracer data support the hypothesis that the predominant regional signal of nitrate increase during drought is caused by enhanced capture of modern‐aged groundwater by PSWs during periods of pumping stress, which can drive rapid changes in water quality on seasonal and multiannual timescales. Pumping‐induced migration of modern, oxic groundwater to depth during drought may affect geochemical conditions in deeper portions of regional aquifers controlling the mobility of geogenic contaminants over the long term.

Historical Marine Cold Spells in the South China Sea: Characteristics and Trends

Marine cold spells (MCSs) are extreme ocean temperature events impacting marine organisms, yet their characteristics and trends in the South China Sea (SCS) historical period remain unclear. This study systematically analyzes sea surface temperature (SST) and MCSs in the SCS using satellite observation data (OISSTv2.1) from 1982 to 2022. The climatological mean SST ranges from 22 °C near the Taiwan Strait to 29 °C near the Nansha Islands, showing notable variations. Annual SST anomalies demonstrate a heterogeneous spatial trend of approximately 0.21 ± 0.16 °C/decade (p < 0.01) across the SCS, indicating an increase in SST over time. MCS analysis uncovers spatial non-uniformity in frequency, with higher values near the Beibu Gulf and Hainan Island, and longer durations in the northeastern coastal areas. Statistical analysis indicates normal distributions for frequency and duration trends but skewness for intensity and cumulative intensity, reflecting extreme values. Winter months exhibit larger MCS occurrence areas and higher mean intensities, illustrating seasonal variability. Anticipated changes will significantly impact the ecological structure and functioning of the SCS.

Environmental conditions on the Pacific halibut fishing grounds obtained from a decade of coastwide oceanographic monitoring, and the potential application of these data in stock analyses

Context Establishing baseline environmental characteristics of demersal fish habitat is essential to understanding future distribution changes and to identifying shorter-term anomalies that may affect fish density during monitoring efforts. Aims Our aim was to synthesise environmental data to provide near-bottom oceanographic baseline information on the Pacific halibut fishing grounds, to establish geographic groupings that may be used as co-variates in fish-density modelling and to identify temporal trends in the data. Methods Water-column profiler data were collected from 2009 to 2018 along the North American continental shelf, during summer fishery surveys focused on Pacific halibut. Key results In addition to establishing baseline information on the fishing grounds, this analysis illustrated that environmental variables can be grouped geographically into four regions that correspond to the four biological regions established by the International Pacific Halibut Commission. A spatio-temporal modelling approach is presented as an example of how to describe the relationship between environmental data and Pacific halibut distribution. Conclusions This study has highlighted the efficacy of environmental data in analysing fish distribution and density changes. Implications Oceanographic monitoring provides the ability to detect annual anomalies such as seasonal hypoxic zones that may affect fish density and to establish baseline information for future research.

ФОРМИРОВАНИЕ ПОЛОЖИТЕЛЬНЫХ АНОМАЛИЙ ТЕМПЕРАТУРЫ ПОВЕРХНОСТИ ЧЕРНОГО МОРЯ

Based on the satellite data at the points of a high-resolution regular grid for the period from 2008 to 2021, positive anomalies of the sea surface temperature (SST) in the northern Black Sea and off the southwestern coast of Crimea have been identified. It has been shown that there are vast areas with an exceeding of SST over the mean for the measurement period up to 4-5 and 5-6°C and a monthly total area (for example, in May 2018) about 200000 and 100000 km2, respectively, in the northern Black Sea. The analysis of available satellite data has led to the conclusion that warm SST anomalies occur mainly in May and October. The maximum (over the entire analyzed period) warm SST anomaly (exceeding 5°C) was reached in May 2012, when its area was 200000 km2. The spatial structure and some statistical characteristics of the identified SST anomalies have been described and analyzed. It has been shown that the typical frequency of vast SST anomalies is at least one case per several years, while they are generated by abnormal heat fluxes on the sea surface with the inflow of anomalously warm river water in shallow areas of the northwestern shelf and spread to the open sea areas due to the horizontal advection of predominantly wind-driven origin. The average annual SST anomalies off the northwestern coast of Crimea and in the northern Black Sea correlate at a level of 0.97.

Ледовитость российских арктических морей трассы Северного морского пути в современный климатический период

The paper considers the ice cover in the Arctic seas of the Northern Sea Route (NSR): Kara, Laptev, East Siberian and Chukchi since 1946. A comparison of the long-term course of integral average annual air temperature anomalies and seasonal average ice cover anomalies allows identifying a year 2005, starting from which the sea ice cover sharply decreases in a good agreement with the increase and accumulation of positive air temperature anomalies. The researchers reveal that the average level of ice cover has decreased by 1.7 times since 2005, however, the values of the range of fluctuations and standard deviation have remained almost unchanged, i.e. ice cover fluctuates in the same range, but at a lower average level. The main shipping routes are cleared about three decades earlier. The repeatability of complete cleansing of the seas has increased to 60—90%.

Comparative evaluation of the dynamics of terrestrial water storage and drought incidences using multiple data sources: Tana sub-basin, Ethiopia

Abstract Evaluating water storage changes and addressing drought challenges in areas like the Tana sub-basin in Ethiopia is difficult due to limited data availability. The aim of this study was to evaluate the dynamics of terrestrial water anomaly and drought incidences by employing multiple data source. The Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) datasets were used to assess the long-term water storage dynamics and drought incidences using the weighted water storage deficit index (WWSDI). WWSDI was used to identify drought periods, which ranged from severe to extreme drought. Despite the overall increase in average annual total water storage anomaly (TWSA) by 0.43 cm/year and a net gain of 50.68 cm equivalent water height from 2003 to 2022, there were instances of terrestrial water storage deficits, particularly in 2005, 2006, and 2009, during historical drought periods. The TWSA exhibited a strong correlation with Lake Tana water storage and precipitation anomalies after adjusting lag times. WWSDI displayed a high correlation with WSDI but a weak correlation with SPI and SPEI. Therefore, utilization of GRACE and GLDAS data is promising for evaluating terrestrial water storage and monitoring drought in data-deficient regions like the Tana sub-basin in Ethiopia.

Long-term trend of near-surface air temperature lapse rate over the Chinese mainland during 1961-2018

The near-surface air temperature lapse rate (SATLR) is a result of surface energy balance, and the long-term trend of SATLR is linked to elevation-dependent warming (EDW). The long-term trend of SATLR in China’s mainland was examined in the present study. It was found that the regional average values of annual, autumn, and winter SATLR anomalies in Tmean, Tmax, and Tmin decreased significantly during the period of 1961-2018. In terms of spatial distribution, most of the annual SATLR trends in Tmean across China’s mainland are negative. In spring, positive SATLR trends in Tmean are widespread in the northern mountainous areas of China, while in winter, strong negative trends are found in some areas of the central and eastern parts of the Tibetan Plateau and the Yunnan-Guizhou Plateau. Most of the negative annual SATLR trends in Tmax occur in the central part of China, but in the southwestern part of China for Tmin. Widespread negative SATLR trends in Tmin are usually found in winter, especially in the Hengduan Mountains of Southwestern China and the eastern part of the Tibetan Plateau. The significant positive SATLR trends in Tmax are observed in spring, mostly distributed in northern mountainous areas. In the context of global and regional warming, the long-term trend of SATLR has the potential to detect differences in altitude responses to climate change on a small spatial scale, and to explore the local effect of EDW.