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Recently, an ongoing rise in temperature for both land and ocean areas is recorded resulting from the Earth's warming climate. As a result, droughts we observe are getting more frequent, longer and more severe, exerting sustained impacts on humans, ecosystems leading to famine, poverty, mass migration, or agricultural and economic losses. The changes in climate are successfully monitored by analyzing Total Water Storage (TWS). For years, TWS has been successfully determined using geodetic techniques, such as gravity field variations observed by the Gravity Recovery and Climate Experiment (GRACE) missions or station position changes monitored by the Global Positioning System (GPS). As well, geodetic-derived data can be applied successfully to study of hydrometeorological events. To quantify droughts characteristics at different temporal and spatial scales, we recalculate the vertical displacements to Drought Severity Indices (DSI). We find that DSI based on GPS and GRACE are positively correlated at over 80% of stations around the world, highlighting both Americas and Europe as the most correlated areas. To validate results, we compare DSI based on GPS/GRACE with the Global Land Water Storage (GLWS) hydrological model, the traditional climate indices, and temperature anomalies. We show that GPS-DSIs are strongly temporally consistent with both the Standardized Precipitation Index (SPI) and the Soil Moisture Index (SMI) climate indices at 85% of stations, indicating weakly correlated areas at mid-latitudes. We further show a high potential of geodetic data to assess drought characteristics within climate zones as well as global studies. We note that moderate conditions dominate for all climate zones, for which dry moderate conditions are observed for 40% of the months analyzed. As a result, we note warning conditions at least 52% of global stations with extreme drying DSI trends above a value of 2–3 per year. We note that the global water changes are dominated by 9 month droughts at over 72% of stations, indicating the average drought duration around 12, 14, and 15 months for GPS-, GRACE-, and GLWS-DSI, respectively. The obtained results from geodetic measurements more reliably characterize the type and phase of drought, as well as how these droughts cascade into freshwater, enabling appropriate mitigation strategies. [ABSTRACT FROM AUTHOR]

Abstract Recently, an ongoing rise in temperature for both land and ocean areas is recorded resulting from the Earth’s warming climate. As a result, droughts we observe are getting more frequent, longer and more severe, exerting sustained impacts on humans, ecosystems leading to famine, poverty, mass migration, or agricultural and economic losses. The changes in climate are successfully monitored by analyzing Total Water Storage (TWS). For years, TWS has been successfully determined using geodetic techniques, such as gravity field variations observed by the Gravity Recovery and Climate Experiment (GRACE) missions or station position changes monitored by the Global Positioning System (GPS). As well, geodetic-derived data can be applied successfully to study of hydrometeorological events. To quantify droughts characteristics at different temporal and spatial scales, we recalculate the vertical displacements to Drought Severity Indices (DSI). We find that DSI based on GPS and GRACE are positively correlated at over 80% of stations around the world, highlighting both Americas and Europe as the most correlated areas. To validate results, we compare DSI based on GPS/GRACE with the Global Land Water Storage (GLWS) hydrological model, the traditional climate indices, and temperature anomalies. We show that GPS-DSIs are strongly temporally consistent with both the Standardized Precipitation Index (SPI) and the Soil Moisture Index (SMI) climate indices at 85% of stations, indicating weakly correlated areas at mid-latitudes. We further show a high potential of geodetic data to assess drought characteristics within climate zones as well as global studies. We note that moderate conditions dominate for all climate zones, for which dry moderate conditions are observed for 40% of the months analyzed. As a result, we note warning conditions at least 52% of global stations with extreme drying DSI trends above a value of 2–3 per year. We note that the global water changes are dominated by 9 month droughts at over 72% of stations, indicating the average drought duration around 12, 14, and 15 months for GPS-, GRACE-, and GLWS-DSI, respectively. The obtained results from geodetic measurements more reliably characterize the type and phase of drought, as well as how these droughts cascade into freshwater, enabling appropriate mitigation strategies.

When a hydrological drought occurs due to a decrease in water storage, there is no choice but to supply limited water. Because this has a devastating impact on the community, it is necessary to identify causes and make predictions for emergency planning. The state of change in dam inflow can be used to confirm hydrological drought conditions using the Standardized Runoff Index (SRI), and meteorological drought and climate variability are used to identify causal relationships. Multiple Linear Regression (MLR) and Generalized Additive Model (GAM) models are developed to predict accumulated hydrological drought for 6, 12, and 24 months in the Boryeong Dam basin, and the Nash-Sutcliffe model efficiency coefficient (NSE) exceeded 0.4, satisfying the suitability criteria. The estimation ability is highest when predicting a 12-month annual drought, and reliability can be further increased by reflecting some climate fluctuations in a non-linear form. The droughts of 6 month and 24 month cumulative scales are significantly influenced by the Western Hemisphere Warm Pool (WHWP) extending from the eastern North Pacific to the North Atlantic and by the Nino 3.4 region in the tropical Pacific. Furthermore, it is anticipated that the drought conditions of the inflow volume to the Boryeong Dam will worsen with increasing sea surface temperatures in both regions. [ABSTRACT FROM AUTHOR]

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To implement the geographical and future climate adaptation of building moisture design for building projects, practitioners need efficient tools, such as precalculated climate indices to assess climate loads. Among them, the Frost Decay Exposure Index (FDEI) describes the risk of freezing damage for clay bricks in facades. Previously, the FDEI has been calculated for 12 locations in Norway using 1961–1990 measurements. The purpose of this study is both updating the FDEI values with new climate data and future scenarios and assessing how such indices may be suitable as a climate adaptation tool in building moisture safety design. The validity of FDEI as an expression of frost decay potential is outside the scope of this study. Historical data from the last normal period as well as future estimated climate data based on 10 different climate models forced by two emission scenarios (representative concentration pathways 4.5 and 8.5) have been analyzed. The results indicate an overall decline in FDEI values on average, due to increased winter temperatures leading to fewer freezing events. Further, the variability between climate models and scenarios necessitates explicit uncertainty evaluations, as single climate model calculations may result in misleading conclusions due to high variability between models. [ABSTRACT FROM AUTHOR]

Drought prediction is crucial for early risk assessment, preventing negative impacts and the timely implementation of mitigation measures for sustainable water management. This study investigated the relationship between climate variations in three seas and the prediction of December meteorological droughts in South Korea, using the Standardized Precipitation Evapotranspiration Index (SPEI). Climate indices with multiple time lags were integrated into multiple linear regression (MLR) and Random Forest (RF) models and evaluated using Pearson's correlation coefficients (PCCs) and the Root Mean Square Error (RMSE). The results indicated that the MLR model outperformed RF model in the western inland region with a PCC of 0.52 for predicting SPEI-2. On the other hand, the RF model effectively predicted drought states of 'moderate drought' or worse (SPEI < −1) nationwide, achieving an average hit rate of 47.17% and Heidke skill score (HSS) of 0.56, particularly excelling in coastal areas. Nino 3.4 turned out to be the most influential factor for short-period extreme droughts (SPEI-2) with a three-month lag, contributed by the Pacific, Atlantic, and Indian Oceans. For periods of four months or longer, climate variations had a lower predictive value. However, integrating autocorrelation functions to account for the previous month's drought status improved the accuracy. A HYBRID model, which blends linear and nonlinear approaches, further enhanced reliability, making the proposed model more applicable for drought forecasting in neighboring countries and valuable for South Korea's drought monitoring system to support sustainable water management. [ABSTRACT FROM AUTHOR]

Glacial lake outburst floods (GLOF) hazards in alpine areas are increasing. The effects of climate change on GLOF hazards are unclear. This study examined 37 glacial lakes and climate data from 15 meteorological stations and explored the correlation between climate variations at different temporal scales. The results indicate that 19 GLOFs hazards occurred in El Niño (warm) years, 8 GLOFs hazards occurred in La Niña (cold) years, 3 GLOFs hazards occurred in cold/warm or warm/cold transition years, and 7 GLOFs hazards occurred in normal years. The higher the fluctuations, the higher the probability of GLOF hazards. Climatic conditions can be divided into three categories: extreme temperature and precipitation, as represented by the Guangxie Co GLOF; extreme precipitation, as represented by the Poge Co GLOF; and extreme temperature, as represented by the Tsho Ga GLOF. [ABSTRACT FROM AUTHOR]

The objective of this study is to examine the impact of climate and technology on forest efficiency (FE) in China's provinces from 2002 to 2020. First, the study used SBM-data envelopment analysis (SBM-DEA) to estimate Chinese provinces' FE using multidimensional forest inputs and outputs. The climate influence is assessed using temperature, precipitation, sunlight hours, and carbon dioxide levels in the second phase. A climate index was created using principal component analysis (PCA) for a complete estimation. In addition to prior research, we analyze the technology impact through two technological indicators: (i) research and development, and (ii) investment in forests. Furthermore, we explore the non-linear influence of economic development on both FE and climate quality. The regression study by CupFM and CupBC found that temperature and precipitation increase FE, whereas sunlight hours and carbon emissions decrease it. The positive association observed between Climate Index1, and the negative relationship noted for Climate Index2, suggests that forests positively influence climate conditions, signifying that an improvement in FE leads to an improvement in climate quality. Technology boosts forest productivity and climatic quality. The environmental Kuznets curve shows an inverted U-shape relationship between economic development and FE. Similarly, climate and economic development have an inverted U-shaped EKC relationship. Urbanization reduces FE due to human growth and activity. Our findings are important for forest management, climate change, and sustainable development policymakers and scholars. [ABSTRACT FROM AUTHOR]

Guided by entering the deep sea and achieving deep marine development in marine construction, the factors hindering marine construction cannot be ignored. Strong ocean winds have a devastating impact on tasks such as ship navigation, carrier aircraft take-off and landing, naval operations and military exercises, and affect the planning of sea routes and the development of the long-distance sea. This paper uses ERA5 wind field data and key climate indices to conduct a systematic analysis of catastrophic winds in the global ocean using methods such as climate statistical analysis, the Theil–Sen trend method, Pearson correlation and contribution rate calculation. It points out the spatiotemporal distribution, variation trend, climate index correlation and contribution rate characteristics of strong winds occurrence (SWO) and hopes that the results of this study can serve as a guide for maritime route planning and provide technical assistance and decision-making support for marine development and other needs. The results show the following: The high global SWO occurs in the Southern Ocean, the North Atlantic, the North Pacific, near Taiwan, China, the Arabian Sea and other locations, with the strongest SWO in summer. The growth trend of SWO in the Southern Ocean is strongest, with decreasing regions near the Arabian Sea and the Bay of Bengal, and the growth trend is reflected in all four seasons. The climate indices with the strongest correlation and highest contribution to the global SWO are AAO (Antarctic Oscillation) and EP–NP (East Pacific–North Pacific pattern) with a correlation between −0.5 and 0.5 and a contribution rate of up to −50%~50%. [ABSTRACT FROM AUTHOR]

Abstract The Loess Plateau of China, located to the west of the Liupan Mountains and north of the Qinling Mountains stretching across the Yellow River, is the main loess deposit area in Northwest China. The loess in the northwest of China has deposits with large thickness and extensive distribution. Scanning electron microscopy (SEM) and energy spectrum analysis of loess have been performed to study the relationship between the loess microstructures and the forming climates Era. The relevant indexes were evaluated including the sand‐dropping speed (Vn), certain sedimentary depths (hn sand particle volume (Vd) and element ratios of Ca/Fe, K/Al, Si/Al and Ca/Mg. The microstructure indexes of loess accumulation and evolution reflect paleoclimate conditions and time scales to a certain extent. The important discovery is the microscopic sand‐dropping speed (Vn) and the sedimentary depth hn) calculation method of different sedimentary ages. These indices are compared with the record of major aeolian‐forming climates from the Guliya ice core, and provide a reliable benchmark for studying climate change It also can be used as important indicators of monsoonal change and environmental evolution reconstruction. The index of sand sedimentation speed (Vn) got from loess microstructure could reflect sand‐dropping speed and loess deposition course. According the article can serve as new indicators of climatic changes of different forming loess layers. It can also be concluded that the climatic indexes obtained from loess microstructure can reflect climate conditions of loess forming. The loess forming climatic parameters are synchronous correspond to Tengger Desert and Guliya ice core for studying climate change, then microscopic parameters can also be used for preliminary analysis of loess climate formation and has be found corresponding evidence, and the loess climatic parameters correspond to the other two indexes. The analysis of loess microstructure indexes is very useful in researching climate change. Loess microstructure indexes can find new indicators and information about the monsoon climate evolution and paleoclimate changes.

This study presents a comprehensive assessment of the spatio-temporal variability of Sea Surface Temperature (SST) in the Black Sea at monthly to interannual scales, with a focus on understanding its connection to major large-scale atmospheric forcing during the period 2000-2022. Monthly variations of SST in the Black Sea reveal distinct seasonal patterns. The study evaluates the potential impacts of large-scale atmospheric patterns on interannual SST variations using climate indices such as the North Atlantic Oscillation (NAO), East Atlantic/West Russia (EA-WR) and El Niño-Southern Oscillation (ENSO) during the winter months. The results indicated that these large-scale atmospheric oscillations played a substantial role in influencing SST anomalies, with the NAO and EA-WR indices particularly affecting the Black Sea's SST anomalies. The NAO index exhibited negative values during warm winters and positive values during cold winters, with extreme cold and warm winters corresponding to specific years, as observed in 2003, 2006, 2012, 2017 (cold) and 2018, 2020, 2021 (warm). Notably, the relationship between NAO and SST anomalies was not as dominant during 2000-2022. This difference might be explained by the combined influence of NAO and ENSO, which is beyond the scope of this study. The EA-WR pattern was identified as another significant large-scale atmospheric dynamic affecting the Black Sea's SST. Although it explains the cold SST anomalies in certain years, it cannot account for extreme warm SST years. While the influence of ENSO remains somewhat inconclusive for the extreme warm period, the SST pattern between 2016-2022 aligns closely with El Niño events, particularly in 2018 and 2021 when positive SOI index values coincide with warm SST years in the Black Sea.

To implement the geographical and future climate adaptation of building moisture design for building projects, practitioners need efficient tools, such as precalculated climate indices to assess climate loads. Among them, the Frost Decay Exposure Index (FDEI) describes the risk of freezing damage for clay bricks in facades. Previously, the FDEI has been calculated for 12 locations in Norway using 1961–1990 measurements. The purpose of this study is both updating the FDEI values with new climate data and future scenarios and assessing how such indices may be suitable as a climate adaptation tool in building moisture safety design. The validity of FDEI as an expression of frost decay potential is outside the scope of this study. Historical data from the last normal period as well as future estimated climate data based on 10 different climate models forced by two emission scenarios (representative concentration pathways 4.5 and 8.5) have been analyzed. The results indicate an overall decline in FDEI values on average, due to increased winter temperatures leading to fewer freezing events. Further, the variability between climate models and scenarios necessitates explicit uncertainty evaluations, as single climate model calculations may result in misleading conclusions due to high variability between models.

Climate indices are used to reduce the complex climate system and its changes to simple measures. The data basis – whether observational data or climate model data – to which the climate indices are applied, is usually subject to uncertainties. For threshold-based climate indices, the data uncertainty influences the threshold value, and, hence, the uncertainty can influence the values for the climate index. What the actual impacts of these uncertainties are on threshold-based climate indices is examined in this paper. The focus is not only on the climate model uncertainty, but also on the observational data uncertainty. The general sensitivity of each of the chosen climate indices to arbitrary changes in the threshold is studied. This shows a higher sensitivity of indices assessing extremes (ice days, heavy precipitation days) to changes in the threshold than indices that integrate a quantity over a given time interval (coldsum, consecutive days). For assessing an ensemble of climate model data with respect to their ability to reproduce the index values for current climate, the reference data uncertainty is applied to the chosen threshold-based climate indices by changing their threshold value by its corresponding uncertainty. It is shown that the climate model uncertainty can be within the range of the reference data uncertainty. When using threshold-based climate indices to assess changes in future climate periods, uncertainties should always be taken into account and ideally corrected in an appropriate way. This is especially important for indices that assess extremes.

Among artificial intelligence (AI) models, the recurrent neural network (RNN)-based temporal AI, long short-term memory (LSTM) model has been successfully applied to hydroclimatological time series due to its long-lead-time predictions. However, few logical reasons and explanations for its performance by investigating and discovering its deep structure have been made. Therefore, research on the outlook for LSTM models was conducted in the current study by investigating its hidden states and was focused on the dependence structures and statistical behaviors. Here, the three most critical datasets of hydroclimatological variables were applied as the representative climate index data for the Pacific Decadal Oscillation (PDO) and two critical rivers, the Colorado River and Nile River. The results indicate that each hidden unit is responsible for different frequency variations in the input data and is sensitive to special occasions of input data. This separation of the roles of the hidden units leads to variations in the dependence structure along with the numbers of hidden units and the unique characteristics of statistical behaviors. Specifically, the dependence decreases along with the increase in the number of hidden units until the complex structure of the original input data is appropriately separated into the independent hidden units. Overall, the current study reveals that there is a relationship between attaining maturity of the deep learning LSTM model and the dependence structure of the hidden units, especially for hydroclimatological variables, and concludes that the dependence structure of the hidden units can provide valuable information to further extract the explanations of the deep learning model and to select an appropriate model structure, including the number of hidden units. This finding can help to simulate and predict climate and hydrologic conditions whose long-term behaviors are critical for water resource management. [ABSTRACT FROM AUTHOR]

The Loess Plateau of China, located to the west of the Liupan Mountains and north of the Qinling Mountains stretching across the Yellow River, is the main loess deposit area in Northwest China. The loess in the northwest of China has deposits with large thickness and extensive distribution. Scanning electron microscopy (SEM) and energy spectrum analysis of loess have been performed to study the relationship between the loess microstructures and the forming climates Era. The relevant indexes were evaluated including the sand‐dropping speed (Vn), certain sedimentary depths (hn sand particle volume (Vd) and element ratios of Ca/Fe, K/Al, Si/Al and Ca/Mg. The microstructure indexes of loess accumulation and evolution reflect paleoclimate conditions and time scales to a certain extent. The important discovery is the microscopic sand‐dropping speed (Vn) and the sedimentary depth hn) calculation method of different sedimentary ages. These indices are compared with the record of major aeolian‐forming climates from the Guliya ice core, and provide a reliable benchmark for studying climate change It also can be used as important indicators of monsoonal change and environmental evolution reconstruction. The index of sand sedimentation speed (Vn) got from loess microstructure could reflect sand‐dropping speed and loess deposition course. According the article can serve as new indicators of climatic changes of different forming loess layers. It can also be concluded that the climatic indexes obtained from loess microstructure can reflect climate conditions of loess forming. The loess forming climatic parameters are synchronous correspond to Tengger Desert and Guliya ice core for studying climate change, then microscopic parameters can also be used for preliminary analysis of loess climate formation and has be found corresponding evidence, and the loess climatic parameters correspond to the other two indexes. The analysis of loess microstructure indexes is very useful in researching climate change. Loess microstructure indexes can find new indicators and information about the monsoon climate evolution and paleoclimate changes. [ABSTRACT FROM AUTHOR]

Guided by entering the deep sea and achieving deep marine development in marine construction, the factors hindering marine construction cannot be ignored. Strong ocean winds have a devastating impact on tasks such as ship navigation, carrier aircraft take-off and landing, naval operations and military exercises, and affect the planning of sea routes and the development of the long-distance sea. This paper uses ERA5 wind field data and key climate indices to conduct a systematic analysis of catastrophic winds in the global ocean using methods such as climate statistical analysis, the Theil–Sen trend method, Pearson correlation and contribution rate calculation. It points out the spatiotemporal distribution, variation trend, climate index correlation and contribution rate characteristics of strong winds occurrence (SWO) and hopes that the results of this study can serve as a guide for maritime route planning and provide technical assistance and decision-making support for marine development and other needs. The results show the following: The high global SWO occurs in the Southern Ocean, the North Atlantic, the North Pacific, near Taiwan, China, the Arabian Sea and other locations, with the strongest SWO in summer. The growth trend of SWO in the Southern Ocean is strongest, with decreasing regions near the Arabian Sea and the Bay of Bengal, and the growth trend is reflected in all four seasons. The climate indices with the strongest correlation and highest contribution to the global SWO are AAO (Antarctic Oscillation) and EP–NP (East Pacific–North Pacific pattern) with a correlation between −0.5 and 0.5 and a contribution rate of up to −50%~50%.

This study aimed to systematically and globally evaluate the monthly precipitation forecasts of Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System ver. 2 (JMA/MRI-CPS2), a dynamical seasonal climate forecast (Dyn-SCF) system operated by the Japan Meteorological Agency, by comparing its forecasts with those of a statistical SCF (St-SCF) system using climate indices. We developed a new global St-SCF system using 17 climate indices and compared the monthly precipitation of this system with those of JMA/MRI-CPS2. Consequently, the skill of JMA/MRI-CPS2 was determined to be globally higher than that of the St-SCF for zero-month lead forecasts. Contrarily, for forecasts made with a lead time of 1 month or longer, the deterministic skill of JMA/MRI-CPS2 was comparable to that of the St-SCF, and the probabilistic skill of JMA/MRI-CPS2 remained slightly higher. In addition to evaluating the skill of JMA/MRI-CPS2, we identified several regions and seasons, for which JMA/MRI-CPS2 exhibited a low forecast skill, compared with the St-SCF. This indicated that JMA/MRI-CPS2 cannot sufficiently reproduce certain dynamics. In conclusion, comparing Dyn-SCFs with St-SCFs can elucidate the potential regions and seasons to improve the forecast skill of Dyn-SCFs. [ABSTRACT FROM AUTHOR]

Soil moisture is an important variable affecting land surface and climate interactions. This study used cross-wavelet and wavelet coherence methods to analyze the relationship between soil moisture and climatic factors in the study area based on the soil moisture data sequence and corresponding meteorological data observed on the surface of the desert steppe in Inner Mongolia. The results showed that soil moisture had a relatively high- or low-value period for months or even years. Soil moisture was significantly different between different slope positions and soil layers. The fluctuation and mean of soil moisture decreased gradually with the deepening of soil depth. The relationship between soil moisture and meteorological factors varied with time scales. The influence of precipitation on soil moisture was significant at time scales of 1–6 months and 10–15 months, while air temperature and soil temperature showed stable and continuous periodic influence on soil moisture at the time scale of 10–15 months. Climate indexes for the Pacific region, Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation index (NAO) were the main climatic factors controlling soil moisture in the Inner Mongolia desert steppe and strongly correlated with soil moisture primarily on time scales of 4–7 months and 10–15 months. Pacific Decadal Oscillation (PDO) and Indian Ocean basin-wide warming (IOBW) showed a strong lag effect on soil moisture. [ABSTRACT FROM AUTHOR]

Previously published results of the study [Khen et al., 2019b] are continued. Long-term changes of the sea surface temperature (SST) in the Far-Eastern Seas and North-West Pacific (NWP) are described for 1950–2019 and their relationship with large-scale climate processes described by climatic indices (AO, Nino 3.4, PDO, ALPI, NPI, PNA, SHI, and WP) is analyzed. SST has increased in all seasons, with higher rate in winter and autumn and less significant trend in summer. A prominent shift to warmer regime occurred in the Bering Sea in 1977 that coincided with a sharp change in dynamics of PDO, ALPI, NPI, and PNA indices. Such shifts were observed in the Okhotsk Sea in 1981 and in the Japan Sea in 1990, one year after the shifts in the time-series of AO, PDO, and PNA indices. Smaller shifts to warming happened in NWP in 2008 and 2018. Pacific Decadal Oscillation is the main contributor to temperature variability in the Bering Sea in all seasons, though the contribution of ALPI and PNA variation is considerable in winter and spring. Arctic Oscillation is the most important for the Okhotsk Sea. Variations of AO, SHI and WP are significant for the SST variability in the Japan Sea. Any single climatic index does not determine the SST variability in NWP, in all seasons. The set of climatic indices can be divided into two categories: western and eastern ones, according to their contribution to SST variability in certain regions. The western group includes AO, SHI, and WP, which contribute mostly to the variations in the western regions, westward from the longitude of Kamchatka. The most important indices in the eastern group are PDO, PNA and ALPI.

Simple Summary: A life cycle assessment (based on ISO 14040 and 14044) considering the climate change (CC) impact category on beekeeping was performed. To this aim, for two consecutive years, data from beekeeping farms were collected, including data on annual honey production, other hive products, geographical locations of the apiaries, the processing infrastructure, technologies used, and the fuel and energy consumption. The overall LCA result was estimated at 1.44 kg CO2e/kg honey, with transport and supplement feeding as main contributors to greenhouse gas (GHG) emissions. Migratory beekeeping systems were found to be more impactful than nonmigratory ones. Results of a climate index indicated that the scarcity of rainfall seems to negatively affect the honey yield, as well as increase the provision of supplemental feeding and the amount of GHG emissions. Despite the study limitations, the results obtained provide interesting insight to improve the sustainability of beekeeping practices in light of the EU Green Deal and Farm to Fork strategies. The objective of this study was to quantify the climate change (CC) impact of the honey supply chain in different beekeeping systems and farms, over two consecutive years. The CC impact category is quantified as kg CO2 equivalent and it evaluates the GHG emissions, mainly CO2, N2O, and CH4. The results ranged from 0.44 to 3.18 (p = 0.039) kg CO2e/kg honey with higher values in 2021 than 2020. The main contributors to climate change of the honey supply chain are represented by transport and supplemental feeding inputs. The beekeeping system (migratory or stationary) influenced CC: the contribution to CC for stationary farms was estimated at 0.58 kg CO2e/kg honey and 2.48 for migratory ones (p < 0.001). Given the close connection between honey yield and LCA results due to the unit of measurement of impact, i.e., kg of honey produced, an index was developed (wildflower honey climate index) as a simple benchmark tool for prediction of honey yield in the survey context. Using the data from the present study, we found that the index is positively related to honey yield (r = 0.504; p < 0.05) but negatively related to supplemental feeding (r = −0.918; p < 0.01) and overall carbon footprint (r = −0.657; p < 0.05). Further studies are needed to better explain the effects of weather on honey production, as well as environmental impact. [ABSTRACT FROM AUTHOR]

Whiting events are massive calcite precipitation events turning hardwater lake waters to a milky turquoise color. Herein, we use a multispectral remote sensing approach to describe the spatial and temporal occurrences of whitings in Lake Geneva from 2013 to 2021. Landsat-8, Sentinel-2, and Sentinel-3 sensors are combined to derive the AreaBGR index and identify whitings using appropriate filters. 95% of the detected whitings are located in the northeastern part of the lake and occur in a highly reproducible environmental setting. An extended time series of whitings in the last 60 years is reconstructed from a random forest algorithm and analyzed through a Bayesian decomposition for annual and seasonal trends. The annual number of whiting days between 1958 and 2021 does not follow any particular monotonic trend. The inter-annual changes of whiting occurrences significantly correlate to the Western Mediterranean Oscillation Index. Spring whitings have increased since 2000 and significantly follow the Atlantic Multidecadal Oscillation index. Future climate change in the Mediterranean Sea and the Atlantic Ocean could induce more variable and earlier whiting events in Lake Geneva. [ABSTRACT FROM AUTHOR]

A small change in the mean climate may lead to a dramatic change in the frequency and intensity of extreme climate events. In this study, the relationship between mean temperature (MT) and extreme precipitation and the influence from large-scale circulation were investigated in Hunan Province. The correlation between MT and the frequency of extreme precipitation events in different seasons (spring, summer, and autumn) and time periods (1960–2009) was used to obtain pairs of spatial patterns by the singular value decomposition method. The temporal expansion series displayed a consistent trend of temperature and extreme precipitation, and a mutation was observed to occur approximately during the 1980s–1990s. Temperature exhibited a warming trend after the mutation, but the frequency of extreme precipitation events exhibited obvious spatio-temporal variations. The causes of seasonal differences in the frequency of extreme precipitation events were determined by comparing interdecadal changes in three atmospheric circulation factors (850 hPa winds, the entire layer of vapor transportation fluxes and vapor flux divergence) before and after the mutation was revealed. HIGHLIGHTS The relationship between temperature and extreme rainfall events was investigated using the SVD method.; The temperature exhibited a sudden change during the 1980s–1990s.; The trend of temperature change was widely varied.; The correlation between temperature and extreme rainfall exhibited a significant spatial variation.; The wind anomaly field revealed the opposite trends before and after the abrupt change.;

Numerous studies have demonstrated the tourism industry to be especially sensitive to weather and climate variability. Snow-related tourism, being largely dependent on climatic resources, is particularly affected by climate change. Our study provides a new index to reflect the climatic suitability of a given destination for snow-related tourism activities, focusing on resorts with usually limited snowfall. The proposed Skiing Utility Index (SUI) is based purely on the weather preferences of skiers, extracted by questionnaires distributed at the Parnassos ski center (Greece). The index incorporates four different weather variables considered to be the most influential for this type of tourism. The ideal temperature for skiing was found to be close to 0 °C, the ideal wind speed between 0–3.3 m/s, the ideal cloud cover between 0–25% and the snowfall duration between 1–2 h, with the latter found to be the most important variable for skiing. For each climatic variable, a mean utility score profile was developed from all respondents. Following, a utility function was fitted via linear regression to the above-mentioned utility score. All four utility functions were aggregated into one total SUI score. When combined with climate projections, the SUI can support the assessment of climate change risks for snow-related tourism destinations. [ABSTRACT FROM AUTHOR]

Tourism activities are highly dependent on climatological conditions. The climatological suitability of tourism destinations is investigated by using a Tourism Climate Index (TCI) that is frequently used by researchers. The TCI varies between 0 and 100 and is created by using temperature, relative humidity, sunshine duration, wind and precipitation data. For TCI, 100 is for ideal and 0 is for extremely unfavorable conditions for tourism. In this study, the meteorological data covering the period of 1981–2020 for 98 stations is used to calculate the TCI of each station for all seasons and months. The Mann-Kendall trend test is used for TCI behavior of the entire country and Sen Innovative Trend Analysis method is used for four famous tourism destinations. For summer, coastal regions have smaller TCI values than inland regions due to the high amount of relative humidity. Most stations have TCI values in the "Very Good" category or better. In spring and autumn, the TCI values fall into the "Acceptable" category or better. The winter is the season with smallest TCI values. For summer, 54 of 98 stations have a decreasing trend at different levels of significance and four of them have an increasing trend. In autumn, 30 stations have an increasing trend and two stations have a decreasing trend at standard levels of significance. Similarly, for spring, 20 stations have an increasing trend and one has a decreasing trend. During winter, 14 stations have an increasing trend while one has decreasing trend. The Sen Innovative Trend test shows an increasing trend on average for four famous tourism destinations during May–September months. [ABSTRACT FROM AUTHOR]

The cold events and Precipitation conditions having special attention in the last years due to their impact on human health, ecosystems, and other aspects such as agriculture, hydrology. The ECMWF ERA-Interim 12-hourly (03 and 15 UTC) total precipitations and Tmin in a 1º x 1º grid covering Iraq, from 29° N to 38° N and from 39° W to 48° E, with a total of 10 by 10 cells, was used. At each grid point, extremes were defined as those events in which total precipitations were above 99th percentile for the 25 years period 1994-2018. For more investigation, the Hybrid Single-Particle Lagrangian Integrated The trajectory (HYSPLIT) model was used to study the dynamical mechanism that led to producing the cold events in Iraq. The number of extreme precipitations patterns shows an increasing behavior in the number of extreme events especially in the last decade, farther more there is a significant increase in the number of extreme precipitations in the last three years ago. No correlations were found with NAO, EA index, in contrast, there is a significant negative correlation with winter Arctic oscillations index. The aim of this work is studying the precipitation and cold extreme events in Iraq and their relations of most hemispheric pattern which influence in the Middle East region such as North Atlantic Oscillation (NAO), East Atlantic index (EA), Artic oscillation index (AO) and Mediterranean index (MOi). We speculate that the results of this study can provide a better understanding of extreme cold and precipitations anomalies in Iraq from a large-scale view.The cold events and Precipitations conditions having special attention in the last years due to their impact on human health, ecosystems and other aspects such as agriculture, hydrology. The ECMWF ERA-Interim 12-hourly (03 and 15 UTC) total precipitations and Tmin in a 1º x 1º grid covering the Iraq, from 29° N to 38° N and from 39° W to 48° E, with a total of 10 by 10 cells, was used. At each grid point, extremes were defined as those events in which total precipitations were above its 99th percentile for the 25 years’ period 1994-2018. For more investigation, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to study the dynamical mechanism that led to produce the cold events over Iraq. The number of extreme precipitations pattern shows an increasing behavior in the number of extreme events especially in last decade, farther more there is a significant increase in the number of extreme precipitations in last three years ago. No correlations were found with NAO, EA index, in contrast, there is a significant negative correlation with winter Arctic oscillations index. The aim of this work is studying the precipitation and cold extreme events in Iraq and their relations of most hemispheric pattern which influence in the Middle East region such as North Atlantic Oscillation (NAO), East Atlantic index (EA), Artic oscillation index (AO) and Mediterranean index (MOi). We speculate that the results of this study can provide a better understanding of extreme cold and precipitations anomalies in Iraq a large-scale view.

Understanding patterns of change in the distribution of species among their critical habitats is important for analyzing population dynamics and adaptive responses to environmental shifts. We investigated spatio-temporal changes in small yellow croaker (Larimichthys polyactis) using eight alternative models fitted to data from bottom trawl surveys conducted in the Bohai Sea each spring (spawning period) and summer during 1982–2018. These models included different combinations of local sea temperature, fishing pressure, and individual climate index (i.e., North Pacific index, NPI, and West Pacific index, WPI) as explanatory variables. Selection of the most parsimonious model for each season was based on Akaike’s Information Criterion (AIC). The model with NPI as its only explanatory variable was used as a base case for pre-analysis. In spring, a spatio-temporal model with sea temperature as a quadratic effect, plus the spatially varying effects of a climate index and fishing pressure was selected, as the AIC value of this model was reduced by 41.491 compared to the base case model without these effects. In the summer after spawning, the spatio-temporal model with WPI as a climate index covariate lagged by 1-year best explained the spatio-temporal distribution patterns of the stock. The results suggested that small yellow croaker populations significantly decreased in biomass in the Bohai Sea over the study period. A statistically significant northeastward shift in the center of gravity (COG) and a contraction in the distribution range occurred in summer throughout the study period (p

In this study, to determine the spatiotemporal variability of satellite-observed chlorophyll-a (Chl-a) concentrations in the East Sea (Japan Sea, EJS), monthly composite images were generated via noise processing using Level-2 MODIS Chl-a data from 2003 to 2020. Harmonic analysis was performed on time-series Chl-a data to present the spatial distribution of seasonal and intraseasonal variability with 1–4 cycles per year. In the EJS, seasonal cycles contributed less than approximately 30% to the total variance in Chl-a variability, indicating the existence of dominant interannual variability. Analysis of the temporal trend in Chl-a concentrations showed that they increased (< 0.06 mg m–3 yr–1) in most of the EJS over almost two decades (2003–2020). In recent years, in the areas showing positive trends in Chl-a, it tended to increase with time, especially in the northern part of the EJS. As a result of examining the trend associated with the physical environment that affects the long-term trend in Chl-a concentrations, sea surface temperature (SST) trends were mostly increased. The wind speeds showed a characteristic strengthening trend in the northeastern part of the EJS and the North Korean coast. Long-term changes in wind direction indicated strengthening of the northerly wind components on the Russian coast and the westerly components on the eastern coast of the Korean Peninsula. These wind changes were closely related to the Arctic Oscillation (AO) index variability in relation to the recent warming of the Arctic Ocean. When the AO index was greater than 1, the wind speed tended to be weakened and the SSTs showed a tendency to increase. This led to general increasing responses in Chl-a concentrations during positive AO. The summer SST anomaly revealed an inverse relationship between higher positive values during the La Niña period and lower ones during the El Niño period. When the amplitude of MEI (Multi-variate ENSO Index) was high (| MEI| > 1), the SST anomaly indicated an inverse correlation with the Chl-a concentration anomaly in the EJS. This study demonstrated the regional effects of climate change on Chl-a variability in the EJS in response to tropical–subtropical and arctic–subarctic interactions between ocean and atmospheric variations.

The study is continuing, which first results were published in 2019 [Khen et al., 2019]. The main patterns of long-term variability are considered for selected climate indices in the North Pacific and links between them are identified on the common methodological basis. The following indices are analyzed: AO (Arctic Oscillation), PDO (Pacific Decadal Oscillation), Nino 3.4 (index of El-Nino — South Oscillation), ALPI (Aleutian Low Pressure index), NPI (North Pacific index), PNA (Pacific/North American index), SHI (Siberian High index), and WP (West Pacific index). Their time-series are provided on websites of the world climate centers, with exception of the Siberian High index that was calculated from the reanalysis data on the sea level pressure provided by the USA National Center for Environmental Prediction (NCEP) — National Center for Atmospheric Research (NCAR) for 1950–2018. Data were analysed using standard statistical methods. Regime shifts are detected using Rodionov’s method of sequential regime shift detection including the regime shift index (RSI) and tools of automatic detection of the regime shifts with improved performance at the ends of time series. Variations of all indices since the middle 20th century correspond to warming that is not monotonous but combines phases of quick transition from one climatic regime to another — climate shifts and periods of relatively stable state between them. The most important climate shifts happened in 1977 and 1989 and they were noted for majority of the considered indices. Values of the indices heightened in the former shift and slightly lowered in the latter one, except of NPI that had opposite changes. PDO, WP and NPI had another positive shift in the recent years (2015–2017) that allows to assume transition to a new climate regime which will be warmer than the previous one in the last two decades. Long-term periodicity coincided with the 19-year cycle of lunar declination is revealed for PDO, ALPI, NPI and PNA; its spectral power amplifies considerably after removing of high-frequency variability by running 5-year averaging of the time series. Nino 3.4 showed a prominent 11-year cycle, possibly associated with the solar activity. SHI, AO and WP changed with periods about two decades: the main frequency is 26 years for SHI, 20 years for AO, and 17 years for WP, but the peaks of spectral power for the two latter indices is low, i.e. non-periodic oscillations dominate for them. Secondary peaks of spectral power are much lower than the main ones, they correspond to cycles of 7–8 years for AO and PDO, 11 years for WP, and 15 years for SHI. The indices of the North Pacific quartette (PDO, ALPI, NPI and PNA) are closely related between each other with high correlation coefficients (0.67–0.96). The Nino 3.4 index is also linked with them, but with lower correlation (0.45–0.56). SHI has statistically significant relationship with AO only, and WP correlates with Nino 3.4. Contribution of the large-scale climate processes to environmental variability in the Far-Eastern Seas of Russia and the Northwestern Pacific will be considered in the next issue.

Climatic indices reflecting the environmental conditions and patterns of their variability in the entire northern Pacific and in its local regions are overviewed. Their physical nature and mechanisms of the processes, their geography and methods of calculation are presented, with citing of the first descriptions. Among a variety of global and regional climatic indices concern ing the North Pacific, the following ones are described: the indices of Arctic Oscillation (AO), El Niño — Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Aleutian Low Pressure Index (ALPI), Siberian High Index (SHI), North Pacific Index (NPI), Pacific/North American (PNA) Index, and West Pacific Index (WP). AO is a large-scale index of atmospheric circulation reflecting the processes both in the troposphere and stratosphere, where «pumping» of air masses between the high and moderate latitudes occurs continuously. ENSO is also a large-scale index that reflects large-scale interactions in the fields of temperature, atmospheric pressure, wind, and cloudiness over the whole Pacific. Other indices are rather regional, since their influence does not extend far beyond the limits of the domains of their definition. Nevertheless, their role in environmental fluctuations in certain areas could be significant and their influence could be traced throughout the Northern Hemisphere.

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This article deals with the practices of French corporate environmental disclosure with a focus on climate‐related risks. In particular, it aims to analyse the compliance of CAC 40 firms with the recommendations of the Task Force on Climate‐related Financial Disclosures (2017), an international initiative made up by Financial Stability Board to enhance financial transparency. On the basis of a content analysis of firms' reference documents spanning 2015–2018, we constructed the Climate Compliance Index (CCI) to evaluate whether firms disclose information on climate risks and opportunities about governance, strategy, risk management and metrics. Our results highlight a gradual increase of the CCI despite disparities across sectors and management areas. The content analysis allows us to develop a set of indicators frequently reported by domain and to identify and define climate risks and opportunities and their financial impacts per sector, which is a first step to improve the disclosure of non‐financial information. [ABSTRACT FROM AUTHOR]

Climate change is likely to change the relationships between commonly used climate indices and underlying patterns of climate variability, but this complexity is rarely considered in studies using climate indices. Here, we show that the physical and ecological conditions mapping onto the Pacific Decadal Oscillation (PDO) index and North Pacific Gyre Oscillation (NPGO) index have changed over multidecadal timescales. These changes apparently began around a 1988/1989 North Pacific climate shift that was marked by abrupt northeast Pacific warming, declining temporal variance in the Aleutian Low (a leading atmospheric driver of the PDO), and increasing correlation between the PDO and NPGO patterns. Sea level pressure and surface temperature patterns associated with each climate index changed after 1988/1989, indicating that identical index values reflect different states of basinscale climate over time. The PDO and NPGO also show time dependent skill as indices of regional northeast Pacific ecosystem variability. Since the late 1980s, both indices have become less relevant to physical–ecological variability in regional ecosystems from the Bering Sea to the southern California Current. Users of these climate indices should be aware of nonstationary relationships with underlying climate variability within the historical record, and the potential for further nonstationarity with ongoing climate change. [ABSTRACT FROM AUTHOR]

Modern views on dynamics of some mass fish species, as walleye pollock Theragra chalcogramma , pacific salmon Oncorhynchus spp., and japanese sardine Sardinops melanosticta are critically analyzed. Recent tendency to formal and simplified approaches in explaining the status of populations and the factors limiting their abundance is noted, both for biologists and fishery oceanographers. It is reasoned by shortage of expeditionary researches combined with passion for formal indices and ignoring the role of population and endogenous factors in forming of new generations.

Forests are among the most valuable resources of any country not only as wood, but as a key component of bio-ecological system. Excluding anthropogenic factors, forests are mostly vulnerable by wildfire, droughts, pest invasions, hazardous and extreme weather events, etc. In fact, all these non-anthropogenic impacts could be significantly intensified by projected climate change in the 21st century. That is why future conditions for sustainable forest growth should be evaluated accounting for projected climate change preferably under different scenarios. It is well known that global climate change reveals different regional aspects. Therefore, special scenarios have been elaborated processing data of regional climate models (RCMs) from the FP-6 project ENSEMBLES with spatial resolution of 25 km. Verified over the territory of Ukraine ensembles of 10 RCMs for air temperature and 4 RCMs for precipitation calculated for IPCC scenario A1B, which is characterized by balanced consumption of fossil and renewable energy sources and considered by climate change science as one of most likely future development of the world, were applied. Taking into account that the expected dryness of regional climate could generate major challenges for vulnerability of Ukrainian forests, a modification of A1B scenario that is characterized by increasing temperature and decreasing precipitation (A1B+T-P) was proposed. In overall, the impacts of climate change on Ukrainian forests are diverse dependently upon geographical location, geomorphology and large land forms (mountains, plains), forest types and regimes of forest management. The biggest vulnerability was recognized in forests growing in steppe and southern forest steppe, where there is a high probability of impoverishment, degradation and death of forests over large areas. At the same time, there is also a threat of critical increase of vulnerability in other regions, particularly under more tough scenarios of climate change. The study was supported via the EU-funded ClimaEast project CEEF2015-036-UA “Building capacity for the assessment of vulnerability of Ukraine’s flatland forests to climate change”.

In order to study the rules of precipitation change in Nanchang City under thebackground of global warming, based on the precipitation data of Nanchang Station from 1960 to2018, this paper analyzes the change trend, periodic characteristics and abrupt change ofprecipitation by the Mann-Kendall test and wavelet analysis, and discusses the correlation betweenannual precipitation and some global climate indexes. The results show that: The annualprecipitation and the precipitation in the flood season are on the fluctuant rise;It is unsurethat whether there is an abrupt change on annual precipitation and precipitation in the floodseason; The annual precipitation and the precipitation in the flood season have an oscillatingperiod of 28 a, and there is a significant linear relationship between annual precipitation andAntarctic oscillation index.

Background: Although there are many reports of temperature being associated with the onset of acute coronary syndrome (ACS), few studies have examined differences in ACS due to climatic differences between Japan and Thailand. The aim of this joint Japan-Thailand study was to compare patients with myocardial infarction in Japanese and Thai hospitals in different climates. Methods and Results: We estimated the climate data in 2021 for the Wakayama Prefecture and Chonburi Province, two medium-sized cities in Japan and Thailand, respectively, and ACS patients who were treated at the Wakayama Medical University (WMU) and Burapha University Hospital (BUH), the two main hospitals in these provinces (ACS patient numbers: WMU, n=177; BUH, n=93), respectively. In the Chonburi Province, although the average temperature was above 25℃, the number of ACS cases in BUH varied up to threefold between months (minimum: July, 4 cases; maximum: October, 14 cases). In Japan and Thailand, there was a mild to moderate negative correlation between temperature-atmospheric pressure at the onset of ACS, but different patterns for temperature-humidity (temperature-atmospheric pressure, temperature-humidity, and atmospheric pressure-humidity: correlation index; r=-0.561, 0.196, and -0.296 in WMU vs. r=-0.356, -0.606, and -0.502 in BUH). Conclusions: The present study suggests that other climatic conditions and factors, not just temperature, might be involved in the mechanism of ACS., Competing Interests: All authors report that they have no relationships relevant to the content of this paper., (Copyright © 2024, THE JAPANESE CIRCULATION SOCIETY.)

Early and late extremes in the timing of snowmelt have recently been observed in the Pacific Arctic. Subseasonal‐to‐seasonal forecasts of this timing are important for industry, environmental management, and Arctic communities. In northern Alaska, the timing is influenced by the advection of marine air from the north Pacific by the Aleutian Low, modulated by high pressure centered in the Beaufort Sea. A new climate index that integrates their interaction could advance melt predictions. We define this index based on 850‐hPa geopotential height at four fixed locations and refer to it as the Aleutian Low‐Beaufort Sea Anticyclone (ALBSA). During positive ALBSA in May, advection of +0.5‐1.5 K/day is observed through the Bering Strait. ALBSA is correlated with both snowmelt in northern Alaska and the onset of sea ice melt over the adjacent seas. ALBSA therefore may be suitable for monitoring the relevant circulation patterns and for developing predictive tools. Plain Language Summary: Early and late extremes in the timing of snowmelt have recently been observed in northern Alaska. Forecasts of this timing with lead times of weeks to months are important for area stakeholders including industry, environmental managers, and Arctic communities. We find that transport of air masses from the north Pacific influences the timing of melt in the region. We introduce a new index called the Aleutian Low‐Beaufort Sea Anticyclone (ALBSA). When ALBSA is positive, warm Pacific air is observed over the Bering Strait and in the far western Arctic seas. ALBSA is correlated with the timing of spring melt and therefore may be suitable for both monitoring and prediction. Key Points: Record early and late anomalies in the timing of snowmelt in northern Alaska have been observed from 2015‐2018Timing of snowmelt is linked to atmospheric transport of air masses from the north PacificA climate index is introduced that is sensitive to the implicated advection and may be useful as a monitoring or prediction tool [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal regime of summer precipitation at local scales plays a key role in regional prevention and mitigation of floods disasters and water resources management. Previous works focused on spatiotemporal characteristics of a region as a whole but left the influence of associated physical factors on sub-regions unexplored. Based on the precipitation data of 77 meteorological stations in the Poyang Lake basin (PYLB) from 1959 to 2013, we have investigated regional characteristics of summer precipitation in the PYLB by integrating the rotated empirical orthogonal function (REOF) analysis with hierarchical clustering algorithm (HCA). Then the long-term variability of summer precipitation in sub-regions of the PYLB and possible links with large-scale circulations was investigated using multiple trend analyses, wavelet analysis and correlation analysis. The results indicate that summer precipitation variations in the PYLB were of very striking regional characteristics. The PYLB was divided into three independent sub-regions based on two leading REOF modes and silhouette coefficient (SC). These sub-regions were located in northern PYLB (sub-region I), central PYLB (sub-region II), and southern PYLB (sub-region III). The summer precipitation in different sub-regions exhibited distinct variation trends and periodicities, which was associated with different factors. All sub-regions show no trends over the whole period 1959–2013, rather they show trends in different periods. Trends per decade in annual summer precipitation in sub-region I and sub-region II were consistent for all periods with different start and end years. The oscillations periods with 2–3 years were found in summer precipitation of all the three sub-regions. Summer precipitation in sub-region I was significantly positively correlated with the previous Indian Ocean Dipole (IOD) event, but negatively correlated with East Asian Summer Monsoon (EASM). While summer precipitation in sub-region II and sub-region III showed weak teleconnections with climate indices. All of the results of this study are conducive to further understand both the regional climate variations in the PYLB and response to circulation patterns variations.

Precipitation is the major atmospheric source of surface water, thus, in order to build appropriate adaptation strategies for various economic sections related to water resources it is essential to provide projections for precipitation tendencies as exact as possible. Extreme precipitation events are especially important from this point of view since they may result in different environmental, economical, and/or even human health damages. Excessive precipitation for instance may induce floods, flash-floods, landslides, traffic accidents. On the other hand, lack of precipitation is not favorable either: long dry periods affect agricultural production quite negatively, and hence, food safety can be threatened. Several precipitation-related indices (i.e., describing drought or intensity, exceeding different percentile-based or absolute threshold values) are analyzed for the Carpathian region for 1961–2100. For this purpose 11 completed regional climate model simulations are used from the ENSEMBLES database. Before the thorough analysis, a percentile-based bias correction method was applied to the raw data, for which the homogenized daily gridded CarpatClim database (1961–2010) served as a reference. Absolute and relative seasonal mean changes of climate indices are calculated for two future time periods (2021–2050 and 2071–2100) and for three subregions within the entire Carpathian region, namely, for Slovakia, Hungary and Romania. According to our results, longer dry periods are estimated for the summer season, mainly in the southern parts of the domain, while precipitation intensity is likely to increase. Heavy precipitation days and high percentile values are projected to increase, especially, in winter and autumn.

Based on a three-month-scale standardized precipitation index (SPI-3) computed from the available rainfall data of 13 stations of Niger, meteorological drought trends, periodicities and the relationships with 10 oceanic-atmospheric variables were analysed using the Mann-Kendall test, continuous wavelet transform and cross-wavelet analysis, respectively. The results revealed a significant (p < 5%) increase in drought at five of the 13 stations. A common dominant drought periodicity of 2 years was found at all of the stations, whereas significant periodicities varied from 2 to 32 years at six stations. Among the considered climate indices, South Atlantic sea-surface temperature, Southern Oscillation Index, sea-level pressure, geopotential height and relative humidity from the Atlantic basin oscillated in anti-phase relative to the SPI-3 at an inter-annual to decadal time scale from 1960 to 1990. In this period, relative humidity from the Mediterranean basin and zonal wind oscillated in phase with the drought index. [ABSTRACT FROM AUTHOR]

When a Canada‐wide daily climate time series, covering the period 1950–2010, became available, an opportunity arose to analyze the time series for trends of a variety of temperature indices. The 6,833 climate grid cells covering Alberta, each with an area of 10 km by 10 km, allowed the detailed mapping of 30 temperature indices across the province. From each time series, an annual series was computed, which then enabled trend analyses using the non‐parametric Mann‐Kendall and Sen Slope tests. New maps could be created at an unprecedented spatial resolution, and an associated website was developed to access all trends and changes between 1950 and 2010 for all grid cells at albertaclimaterecords.com. The confidence levels of some temperature trends exceed 99%, while others are below 80%. In Alberta's south, annual average temperatures have increased by 1°C to 2°C since the 1950s, but in Alberta's north the increase is 2°C to 4°C. The growing season has lengthened by between one and five weeks since the 1950s, while the number of frost days has declined. The most significant trends observed were increases in mean annual and winter temperatures, and declines in the number of days below −20°C and heating degree days. [ABSTRACT FROM AUTHOR]