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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]

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]

As the largest watershed in Java Island, the Bengawan Solo watershed has experienced recurrent hydrometeorological hazards, leading to infrastructure damage, casualties, and environmental degradation. Research on extreme rainfall causing the hazards in the Bengawan Solo watershed is still limited. This study examines extreme rainfall events by analyzing daily rainfall data (1991-2020) from three observation stations namely Musuk, Tinap, and Lowayu, which represent the upstream, middle, and downstream of the Bengawan Solo watershed. The Extreme Value Theory (EVT) using the Block Maxima approach with a Generalized Extreme Value (GEV) method was used to determine the rainfall return period of 5, 10, 20, 30, and 50- year. We applied the Mann-Kendall test to assess the annual trends of extreme rainfall indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). The results found that the highest estimated annual maximum of daily rainfall was in Musuk station (226.7 mm), followed by Tinap station (159.3 mm) and Lowayu station (149.4 mm). While no significant trend was observed for Musuk, other stations showed a significant trend for the decrease of the daily rainfall intensity, the increase of the number of annual rainy days, the decrease of the annually maximum amount of five consecutive precipitation days, and the increase of the annually number of consecutive wet days. There is also an increase in the maximum amount of annual rainfall for one day (Rx1day) at Lowayu station, which indicates a higher risk of disaster due to high rainfall. Additionally, an increasing trend in the total annual rainfall (PRCPTOT) at Musuk, Tinap, and Lowayu stations suggests a greater potential for water storage to meet water needs in these areas. [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]

Under specific agroclimatic conditions, growth is an adaptability parameter resulting in reduced performance with decreasing animal comfort of a sheep breed. In this study, a total of 1011 lambs’ live weights (LW) and average daily weight gains (ADG) in 16 different farms between 2018 and 2020 (birth to 18 months) were determined in Akkaraman herds raised in Çankırı Province of Turkey. Comprehensive Climate Index (CCI) was examined to determine the effect of climate. Thermal comfort limits in CCI values were classified as CCI-1.cold (≤ 5 ℃), CCI-2.comfort (5–25 ℃), and CCI-3.warm (≥ 25 ℃). The climatic values that all lambs examined were exposed to according to index value groups in periods from birth to 18 months of age were prepared daily. The lambs were exposed to comfort environment in the majority of the days in the MP0–3 (77.7%) and MP12–18 (50.1%) periods. The lambs mostly in the MP3–6 period (72.2%) were above the warm stress threshold, but in the MP6–12 period, 54.7% were above the cold stress threshold. Overall mean LW of lambs at birth, at 3, 6, 12, and 18 months were 4.29 kg, 35.1 kg, 39.1 kg, 45.4 kg, and 52.6 kg, while the ADG values between birth–3 months, 3–6 months, 6–12 months, and 12–18 months were 341.8 g, 54.5 g, 42.9 g, and 56.8 g respectively. Periodically, CCI values were realized as 10–15, 25–30, and 18–22 in the growth periods of 0–3, 3–6, and 12–18 months, respectively, and ADG values of the lambs were found to be similar within their own periods. However, when the CCI values of the lambs increase to 5 and above in the 06–12-month period, there is a decrease in their live weight. As a result, climatic index values did not have a negative effect on the growth and development values of lambs in cold conditions in general, but warm conditions affected the animals negatively. [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]

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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]

Modelling rainfall extremes and dry periods over the Southeast Asia (SEA) region is challenging due to the characteristics of the region, which consists of the Maritime Continent and a mountainous region; it also experiences monsoonal conditions, as it is located between the Asian summer monsoon and the Australian summer monsoon. Representing rainfall extremes is important for flood and drought assessments in the region. This paper evaluates extreme rainfall climatic indices from regional climate models from CORDEX Southeast Asia and compares them with the results of high‐resolution global climate models with a comparable spatial resolution from the HighResMIP experiment. Observations indicate a high intensity of rainfall over areas affected by tropical cyclones and long consecutive dry day periods over some areas in Indochina and the southern end of Indonesia. In the model simulations, we find that both coupled and sea surface temperature‐forced HighResMIP model experiments are more similar to the observations than CORDEX model results. However, the models produce a poorer simulation of precipitation intensity‐related indices due to model biases in the rainfall intensity. This bias is higher in CORDEX than in HighResMIP and is evident in both the low‐ and high‐resolution HighResMIP model versions. The comparable performances of HighResSST (atmosphere‐only runs) and Hist‐1950 (coupled ocean–atmosphere runs) demonstrate the accuracy of the ocean model. Comparable performances were also found for the two different resolutions of HighResMIP, suggesting that there is no improvement in the performance of the high‐resolution HighResMIP model compared to the low‐resolution HighResMIP model. [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]

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

Ethiopia witnessed tremendous precipitation variability and extremes linked with large-scale climate anomalies. This study investigated the long-term spatiotemporal trends of precipitation extremes, significant change points, and its teleconnection to climate anomalies. We used a daily CHIRPS gridded precipitation dataset of the past four decades covering from 1981 to 2019. Eight extreme precipitation indices are defined here based on Expert Team on Climate Change Detection and Indices guidelines. We used the Mann–Kendall test, Sen's slope estimator, and Pettitt's test to investigate trends of the precipitation change in terms of the magnitude and change point of time series. Wavelet coherence and correlation coefficient are used to identify the relationship between precipitation extremes and climate indices. Our results show a significantly decreasing trend for the Kiremt season (June to Sept) and Belg season (Feb to May) over southeast Ethiopia. The majority of grid points experience a change in time series during 1990 to 2012. Most precipitation extreme indices show an increasing trend over the south and southwest region, except consecutive wet days (CWD), which shows a decreasing trend at similar locations. The multiscale analysis presents strong coherence between precipitation anomaly and Nino 3.4 and IOD over the south and southeast region. Similarly, spatial correlation shows that IOD and Nino 3.4 are positively correlated to R10mm, R25mm, PRCTOT, Rx5day, R95ptot, and R99ptot over south, southwest, and southeast parts of the country. A negative correlation is observed with CDD for similar locations along with NAO climate index for most precipitation extremes. [ABSTRACT FROM AUTHOR]

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.

Setipinna taty is one of the pivotal fishes in the Yellow Sea large marine ecosystem, and detecting and analyzing changes in the patterns of its distribution under multiple stresses is important in understanding its population dynamics and potential shifts in its ecological role. In this study, eight alternative spatio-temporal models were developed for S. taty using the Vector Autoregressive Spatio-Temporal (VAST) framework to analyse winter trawl survey data in the Yellow Sea from 2001–2021. These models included different combinations of local sea temperature, climate index and fishing pressure as explanatory variables. The model selected, based on Akaike's Information Criterion (AIC) and spatio-temporal variation explanation rate, indicated that the spatio-temporal distribution patterns of the population were driven by a combination of local temperature, climatic pressure, and fishing intensity. Among these factors, sea temperature is purportedly the most important driver. Results from the chosen model showed that the effective area occupied by the population decreased significantly during the study period (p <0.05). Prior to 2010, the climate and marine environment were relatively stable, and distribution changes were consistent with the basin model (BM, supported by density-dependent habitat selection theory), that individuals move to preferred habitats as biomass declines under fishing pressure (i.e. S. taty individuals were concentrated within the core distribution area as biomass declines). The climate and its driving oceanographic conditions (temperature) changed significantly after 2010 and, as a result, population distribution changes became complex (non-significant relationship between abundance and distribution area) and were no longer supported by BM, corresponding to a significant northward shift of the center of gravity (p <0.05) and a northward expansion of the northern population boundary. Our study highlights the synergistic drive of multiple stresses on spatio-temporal distribution patterns of the S. taty population. Future research should focus on developing a mechanistic understanding of the synergies between climate and anthropogenic stresses and how they affect population dynamics (distribution patterns), which is key to the successful management of the S. taty fishery and other fisheries in this ecosystem. • We developed a spatio-temporal model for the Yellow Sea Setipinna taty population. • Distribution patterns were driven by a combination of multiple stressors. • Among these factors, sea temperature is purportedly the most important driver. • The effective area occupied by the population significantly decreased. [ABSTRACT FROM AUTHOR]

The changes in the occurrence and intensity of precipitation have been widely observed around the world. However, exploring the causes of changes in precipitation remains challenging. To better explore the causes of precipitation changes, the evaporation sources of such precipitation were identified. The region from which evaporative sources contribute to precipitation in a sink region is called the precipitationshed. Based on the ERA5 reanalysis data, we identified the seasonal precipitationshed in the Pearl River Delta (PRD), one of the most densely urbanized regions in the world, using the Water Accounting Model‐2layer. The precipitationshed in the PRD has larger variation in summer and autumn, and differences in the responses of different types of precipitation to the change in the precipitationshed were identified. In autumn, results show that light precipitation increases when the evaporation contribution from the south is enhanced while heavy precipitation increases when the evaporation contribution from the northeast is enhanced in the PRD. The precipitationshed is influenced by natural climate variability. The identified differences in the response of different types of precipitation to the change in the precipitationshed further help us to better understand the influence of natural climate variability on precipitation. The influences of El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and North Atlantic Oscillation on different types of seasonal precipitation were investigated. For example, results show that more heavy precipitation is expected in the PRD in summer when the positive phase of IOD and the negative phase of ENSO occur simultaneously. Key Points: Evaporation sources for seasonal precipitation in the Pearl River Delta, China were identifiedDifferences in the responses of different types of precipitation to the change in the evaporation source were investigatedInfluences of climate indices on the variation of evaporation source in each season were quantified [ABSTRACT FROM AUTHOR]

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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]

Some teleconnection patterns characterized through various hydro‐climate indices (HCIs) have been shown to influence regional rainfall regimes over a range of time scales in Brazil. However, our current knowledge of how various HCI–rainfall relationships vary across regions in Brazil is still incomplete. In this study, simple correlation analysis is performed using sliding window sizes and lag times on the order of months to years to substantially reduce the effects of high‐frequency variability inherent in HCI and precipitation time series and reveal any lower‐frequency relationships that may exist. This analysis is applied to monthly rainfall and HCI data during the time‐span of 1961–2015. The strength and significance of each correlation were tested. HCIs considered in this analysis include those characterizing the El Niño–Southern Oscillation (ENSO), those characterizing the Madden–Julian Oscillation (MJO), as well as the Tropical Southern Atlantic index (TSA), the Caribbean index (CAR), and several others. A cluster analysis based on the k‐medoids algorithm was also applied to explore the relationship between local factors and the distribution of HCIs exhibiting maximum correlation at each site. A 60‐month sliding window and a 12‐month lag time were found to optimize the results of the correlation. The MJO was found to have a strong and significant link to annual rainfall throughout much of eastern Brazil, while other HCIs showing strong influence in other regions include ENSO, the CAR, and the TSA. The cluster analysis revealed a more spatially homogeneous response over the Amazon River basin and the southern regions than other regions of Brazil with complex topography, where the orographic effect attenuates the influence of the main atmospheric mechanisms at a local scale. In general, the rainfall–HCI relationship could be exploited for some operational applications (e.g., a regional drought early warning system), where the strongest correlations were observed. [ABSTRACT FROM AUTHOR]

This study sought to analyze the effect of the climate of tourists' region of origin on their length of stay in a specific inland destination as climate of origin has been ignored in previous analyses. The present study collected data from 674 valid surveys of visitors in the selected destination and applied a zero-truncated negative binomial regression model and a Poisson-inverse Gaussian regression model. The results for this destination suggest that climate of origin affects tourists' length of stay. This finding was obtained via the Poisson-inverse Gaussian regression model because of its greater tolerance to long tail distributions. Similarities and differences were found regarding results for other destinations found in the literature. The present findings further include the non-significant effect of reasons for traveling and tourists' satisfaction and the significant influence of tourists' mode of transport, income, and age on length of stay. Cheaper lodging categories also have an important impact on visitors who prefer extended stays. [ABSTRACT FROM AUTHOR]

Quantifying spatiotemporal influence of climate index on extreme precipitation will help to better understand the variability of extreme precipitation. The extreme precipitation is usually influenced by different climate indices, and mutual offset is unavoidable to occur, thus the rotated empirical orthogonal function was used to identify the different influences of climate indices on extreme precipitation in space and time. The variation of extreme precipitation in data‐scarce region is also concerned, hence, an improved spatiotemporal regional frequency analysis model was further developed, therein the identified spatiotemporal influences of climate indices on extreme precipitation were quantified using Bayesian hierarchical method. In this study, the in situ seasonal maximum one‐day precipitation amount (Rx1day) was used to represent seasonal precipitation extremes from 1957 to 2010 in the Poyang Lake basin, and spatiotemporal influences of El Niño‐Southern Oscillation (ENSO), North Atlantic oscillation (NAO) and Indian Ocean Dipole (IOD) on seasonal Rx1day were quantified. Results indicated that the seasonal Rx1day was influenced by different climate indices in the Poyang Lake basin, ENSO tends to affect spring and autumn Rx1day, IOD tends to affect summer Rx1day, and NAO tends to affect spring and winter Rx1day. The response of extreme precipitation on climate index is varied in different regions, and this was well distinguished and verified, such as negative ENSO (in the same year) events tends to cause spring Rx1day slight decrease in the southern part of the basin while increase about 15% in the northern part with center around the Poyang lake. [ABSTRACT FROM AUTHOR]

Environmental change is occurring at unprecedented rates in many marine ecosystems. Yet, environmental effects on fish populations are commonly assumed to be constant across time. In this study, I tested whether relationships between ocean conditions and productivity of North American sockeye salmon (Oncorhynchus nerka) stocks have changed over the past six decades. Specifically, I evaluated the evidence for non‐stationary relationships between three widely used ocean indices and productivity of 45 sockeye salmon stocks using hierarchical Bayesian models. The ocean indices investigated were the Pacific Decadal Oscillation (PDO), North Pacific Gyre Oscillation (NPGO), and sea surface temperature (SST). I found partial support for time‐varying salmon–ocean relationships. Non‐stationary relationships were strongest for the NPGO and weaker for the SST and PDO indices. Productivity–NPGO correlations tended to shift gradually over time with opposite trends for stocks in British Columbia (B.C.) and western Alaska; for B.C. stocks, the NPGO correlations shifted from significantly negative prior to 1980 to significantly positive after 1990, whereas for western Alaska stocks, the correlations shifted from positive to negative. Productivity–SST correlations showed declining trends for B.C. and Gulf of Alaska stocks, that is, correlations became more negative (B.C.) or less positive (Gulf of Alaska) over time. For the PDO, correlations weakened during the 1980s for western Alaska and B.C. stocks. Overall, these results provide evidence for time‐varying relationships between salmon productivity and environmental conditions over six decades, highlighting the need to recognize that historical responses of salmon populations to environmental change may not be indicative of future responses. [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]

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”.

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.)

This study presents the first global‐scale comprehensive climatology of marine fog and is based on ICOADS ship present weather observations for the period 1950–2007. In general, the median marine fog occurrence away from the polar oceans is low (0.2%). Substantially greater marine fog occurrences are limited to four regions, not including the polar region. Fog occurrence maxima along the western side of the sub‐polar ocean gyre occur during the warm season and over the shelf, which includes one centred over the Northwest Pacific Kuril Islands (60%) and one over the Northwest Atlantic Grand Banks (45%), while a third lies over the Argentinean shelf break. Fog maxima over seven marginal seas occur over the Okhotsk Sea, Sea of Japan, Yellow Sea, South China Sea, Nova Scotia, North Sea, and Baltic Sea. Five wind‐driven coastal upwelling zone maxima comprise the California‐Oregon, Namibia‐South Africa, Peru, Morocco and Arabian regions. Maximum upwelling fog occurrence during the warm season was found to be inversely proportional to the minimum sea surface temperature (SST). Most fog maximum occurrence locations lie over SST minima in shallow water during the warm season and are capped by a stable lower atmosphere. Positive correlations (up to 0.79) were found between 5‐year moving averages of fog in the Kuril Islands and the North Pacific Oscillation. Five‐year moving averages of fog in the Grand Banks were positively and significantly correlated (up to 0.56), with the North Atlantic Oscillation represented by the sea level pressure difference between Gibraltar and Reykjavik. In contrast, 5‐year moving averages of fog in the Grand Banks and the Atlantic Multidecadal Oscillation index were negatively and significantly correlated (up to −0.75). [ABSTRACT FROM AUTHOR]

The Middle East can experience extended wintertime spells of exceptionally hot weather, which can result in prolonged droughts and have major impacts on the already scarce water resources of the region. Recent observational studies point at increasing trends in mean and extreme temperatures in the Middle East, while climate projections seem to indicate that, in a warming weather scenario, the frequency, intensity and duration of warm spells will increase. The nonstationary warm spell frequency analysis approach proposed herein allows considering both climate variability through global climatic oscillations and climate change signals. In this study, statistical distributions with parameters conditional on covariates representing time, to account for temporal trend, and climate indices are used to predict the frequency, duration and intensity of wintertime warm spells in the Middle East. Such models could find a large applicability in various fields of climate research, and in particular in the seasonal prediction of warm spell severity. Based on previous studies linking atmospheric circulation patterns in the Atlantic to extreme temperatures in the Middle East, we use as covariates two classic modes of 'fast' and 'slow' climatic variability in the Atlantic Ocean (i.e., the Northern Atlantic Oscillation and the Atlantic Multidecadal Oscillation respectively). Results indicate that the use of covariates improves the goodness-of-fit of models for all warm spell characteristics. [ABSTRACT FROM AUTHOR]

• U.S. Maize and winter wheat yield variability are analyzed using PCA at the climate divisional level. • The first principal component of the yield variability is strongly associated with AMO. • Climate oscillations explained a larger percent of yield variability for maize than for winter wheat. • Responses of maize and winter wheat yields to climate oscillations are spatially dependent. • Both yield anomalies and categories at the division level can be well predicted using climate indices. Studies have shown linkages of climate oscillations with climate extreme events, such as floods and droughts, which may induce risks in summer and winter crop productions. The goal of this study is to explore spatial and temporal variability of a summer crop (maize) and a winter crop (wheat) yields and its linkages with influential climate oscillations in the United States. The county level yield data over 1960–2016 for maize and winter wheat were aggregated into each of the 260 climate divisions in the rainfed regions of the United States, with the linear yield trend being removed. The rotated Principal Component Analysis (PCA) reveals that the first five principal components explain 79% (maize) and 72% (winter wheat) of the spatial and temporal variability of crop anomalies. The first principal component of crop yield variability is strongly associated with the Atlantic Multidecadal Oscillation (AMO). The results of multiple linear regressions for predicting yield anomalies using climate indices show that, climate indices during the reproductive period of maize explained final yield better than the vegetation period (30% versus 26%), while climate indices for winter wheat during the dormant and reproductive growth periods are similar and not significant (25% versus 28%). Categorical yield forecasts using random forecast techniques show that the low (below 30th percentile) and high (above 70th percentile) yields are well predicted by climate indices. Spatially, AMO is identified as the most important predictor for maize in 46% climate divisions and for wheat in 33% climate divisions. The results from this study may contribute to understanding the risks of large-scale climate oscillations to local-scale crop production and improving crop yield predictions. [ABSTRACT FROM AUTHOR]

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]