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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.

The tourism industry is a significant contribution to the economy of many countries, including Pakistan. However, its activities often have a negative impact on the environment, particularly related to climate change. Notably, Pakistan ranks fifth among countries most affected by climate change, which requires a targeted analysis of the tourism sector to determine its potential impacts. Despite the critical nature of this issue, there is currently a lack of research that examines how climate change specifically impacts Pakistan's tourism industry. This study aims to address this gap by using the Tourism Climate Index (TCI) to assess the impact of climate change on the suitability of different tourism locations across the country. Our results show that popular tourist destinations such as Gilgit-Baltistan experience their peak season in the warmer third quarter (summer), which corresponds to the highest observed TCI values. This suggests that warmer temperatures could boost tourism activity and spending in these regions. Furthermore, our analysis shows that air temperature plays a crucial role in determining tourist comfort levels and significantly influences tourists' TCI values and sensory experiences. The results of this study show that the TCI methodology can be an effective tool for further research into the geographical impacts of climate change on tourism. By identifying necessary changes due to climatic changes, future studies could provide valuable insights into how the tourism industry can adapt to and reduce its environmental footprint.

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.

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%.

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.

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.;

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 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.

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.

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.

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.

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.

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.

The effects of the climate characters and climate index calculated by Thornthwaite-Mather method on diameter increment of Austrian pine trees in the seedling seed orchard on Jelova Gora was studied in the period from 1996 to 2002. The samples for dendrometric analysis were taken from 498 trees in 40 half-sib lines originating from the best natural sites of this species. A strong correlation was determined between the range of precipitation regime and Thornthwaite-Mather climate index, and the development of current diameter increment. The study results indicate that the diameter increment of Austrian pine increases considerably with the increase of humidity, regardless of the relatively modest demands regarding the site productive characteristics.

We analyze the relationship between four climate variables (maximum and minimum extreme temperatures, rainfall and snowfall) measured in winter (December to March) at 17 stations from 1950 to 2000 in the three hydroclimate regions of southern Quebec, and six seasonal climate indices using canonical correlation analysis (CCA) and the copula method. This analysis yielded these major results: (1) extreme temperatures are not correlated with the amount of winter rain or snow in southern Quebec; (2) winter seasonal climate indices show better correlations with climate variables than do fall climate indices; (3) winter extreme temperatures are best correlated (positive correlation) with the Atlantic Multidecadal Oscillation (AMO) in the eastern region, but show a negative correlation with the Arctic Oscillation (AO) in the southwestern region; (4) the total amount of winter snow is best correlated (negative correlation) with the Pacific Decadal Oscillation (PDO) in the three hydroclimate regions; (5) the total amount of winter rain is best (negatively) correlated with PDO in the eastern region, but shows a positive correlation with AO in the southeast region. Finally, the copula method revealed very little change in the dependence between climate indices and climate variables in the three hydroclimate regions.

Much research has been devoted to quantifying optimal or unacceptable climate conditions both generally and for specific tourism segments or activities over the last 10 years. This knowledge is not incorporated in the Tourism Climate Index (TCI), which has also been subject to other substantial critiques. To more accurately assess the climatic suitability of destinations for leisure tourism, the Holiday Climate Index (HCI) was developed. A major advancement of the HCI is that its variable rating scales and the component weighting system are based on this aforementioned literature of tourists’ stated climatic preferences. This paper will discuss the design of the HCI and how the limitations of the TCI were overcome. It then presents an inter-comparison of the results from HCI:Urban and TCI for geographically diverse urban destinations across Europe. The results illustrate how the HCI:Urban rates the climate of many cities higher than the TCI, particularly in shoulder seasons and the winter months, which is more consistent with observed visitation patterns. The results empirically demonstrate that use of the TCI should be discontinued.