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Modern climate change and variability of the natural environment have a significant impact on the quality of life and health of the population, as well as on the development of the recreational and touristic industry in resort cities. Based on the long-term observations (1993–2023), the changes in the following climatic characteristics have been analyzed: surface air temperature, total precipitation, and current microbioclimatic parameters of the resort city of Kislovodsk. It has been found for the past 30 years that the average annual temperature in Kislovodsk according to the scale of balneological significance can be identified as favorable, and the average annual minimum and maximum temperatures can be characterized as the most favorable. The statistical parameters of the analyzed characteristics and their extremes are presented. The microbioclimatic potential of the central part of the city is characterized by an increased level of natural air ionization and high purity of the surface atmosphere in the areas that do not experience high anthropogenic load. [ABSTRACT FROM AUTHOR]

The World Meteorological Organization (WMO) recommends that the most recent 30-year period, i.e., 1991–2020, be used to compute the climate normals of geophysical variables. A unique aspect of this recent 30-year period is that the satellite-based observations of many different essential climate variables are available during this period, thus opening up new possibilities to provide a robust, global basis for the 30-year reference period in order to allow climate-monitoring and climate change studies. Here, using the satellite-based climate data record of cloud and radiation properties, CLARA-A3, for the month of January between 1981 and 2020, we illustrate the difference between the climate normal, as defined by guidelines from WMO on calculations of 30 yr climate normals, and climatology. It is shown that this difference is strongly dependent on the climate variable in question. We discuss the impacts of the nature and availability of satellite observations, variable definition, retrieval algorithm and programmatic configuration. It is shown that the satellite-based climate data records show enormous promise in providing a climate normal for the recent 30-year period (1991–2020) globally. We finally argue that the holistic perspectives from the global satellite community should be increasingly considered while formulating the future WMO guidelines on computing climate normals. [ABSTRACT FROM AUTHOR]

Climate change and trade are closely related. Climate may alter the comparative advantages across countries, which may in turn trigger changes in trade patterns. Trade itself may constitute an adaptation strategy, moving excesses of agri-food supply to regions with shortages, and this in turn may explain changes in land-use. We investigate these linkages, showing that the changes in climate affect counties’ trade value and contribute to reshaping trade patterns. First, we quantify the long-term impacts of climate on the value of agri-food exports, implicitly considering the ability of countries to adapt, and show that higher marginal temperatures and rainfall levels tend to be beneficial for countries’ exports. Following a gravity model approach, we then link the evolving trade patterns to climate change adaptation strategies. We find that the larger the difference in temperatures and rainfall levels between trading partners, the higher the value of bilateral exports. Furthermore, while developed and developing exporters are both sensitive to climate change and to cross-countries heterogeneity in climate, we found their responses to changes in climate to be quite diverse.

Alps are an important geographical area of the European continent and, in this area, temperature increase is most evident. However, the 1991–2020 climate normal in the Alps has still not been thoroughly investigated. Aiming to fill this gap with a focus on high-elevation environments, minimum and maximum daily air temperature acquired by 23 automatic weather station were used. The results show that the mean annual values of minimum and maximum temperature for the 1991–2020 climate normal in the Alps are −2.4°C and 4.4°C, respectively, with a warming rate of 0.5°C/10 years. The mean annual temperature comparison between 1961–1990 and 1971–2000, 1961–1990 and 1981–2010, 1961–1990 and 1991–2020 climate normal show an increase of 0.3°C, 0.5°C and 0.9°C, respectively. The results also confirm that seasonal and annual temperatures are rising through the whole Alpine arc, mainly in summer and autumn. This work highlights that annual minimum and maximum temperature do not seem to be affected by a positive elevation-dependent warming. Instead, a positive elevation-dependent warming in the maximum values of the annual minimum temperature was found. If anthropogenic emissions maintain the trend of the last decades, the expected mean annual temperature of the 2001–2030 climate normal is −0.2°C, with an increase of 0.5°C if compared to the 1991–2020 climate normal and with an increase of 1.5°C if compared to the 1961–1990 climate normal. This study highlights the warming rate that is now present in the European Alps, provides indications on the warming rate that will occur in the coming years and highlights the importance of carrying out investigations that consider not only the last 30-year climate normal, but also the most recent 30-year climate normal by comparing them with each other. [ABSTRACT FROM AUTHOR]

In the advent of the new climate normal period (i.e., 1991–2020), questions are raised on what are the recent changes in the observed Philippine climatology. Here we present evidence that the Philippine climate has become warmer (i.e., increased annual surface temperatures) and wetter (i.e., increased annual rainfall) since the mid-1990s while an abrupt increase in tropical cyclone (TC) activity in the Philippines is detected in the mid-2000s. Such regime changes are mainly attributed with the shift of the Atlantic Multidecadal Oscillation (AMO) to its positive phase since the mid-1990s. A positive AMO enhances the Pacific Walker Circulation where the more intense convection center typically shifts towards the western Pacific – this translates to more rainfall, narrowing diurnal temperature range, warmer sea surface temperatures, and more intense TC activity in the Philippines. However, the recent positive AMO phase is reported as externally and possibly driven by anthropogenic warming rather than it is naturally oscillatory, which likely implies that the detected abrupt regime shifts in the Philippine climate, particularly in increased surface temperatures, are also externally driven. Our findings provide new insights on the long-term trends and variability of the Philippine climate in support of its disaster risk reduction preparedness and seasonal forecasting. [ABSTRACT FROM AUTHOR]

The World Meteorological Organization (WMO) recommends that the most recent 30-year period, i.e., 1991–2020, be used to compute the climate normals of geophysical variables. A unique aspect of this recent 30-year period is that the satellite-based observations of many different essential climate variables are available during this period, thus opening up new possibilities to provide a robust, global basis for the 30-year reference period in order to allow climate-monitoring and climate change studies. Here, using the satellite-based climate data record of cloud and radiation properties, CLARA-A3, for the month of January between 1981 and 2020, we illustrate the difference between the climate normal, as defined by guidelines from WMO on calculations of 30 yr climate normals, and climatology. It is shown that this difference is strongly dependent on the climate variable in question. We discuss the impacts of the nature and availability of satellite observations, variable definition, retrieval algorithm and programmatic configuration. It is shown that the satellite-based climate data records show enormous promise in providing a climate normal for the recent 30-year period (1991–2020) globally. We finally argue that the holistic perspectives from the global satellite community should be increasingly considered while formulating the future WMO guidelines on computing climate normals.

The study presents the results of analysis of spatiotemporal distribution of updated total ozone climate normals for the period of 1991–2020. It analyzes the changes since the last total ozone climate normals estimate conducted for the period of 1981–2010. The daily data retrieved using TOMS and OMI satellite instruments over the territory of Ukraine allowed calculation of multiyear average values, climate normals for each day of the year, amplitudes, phases and determination coefficients for total ozone seasonal variations. Use of the NCEP/NCAR reanalysis data allowed establishment of the relation between total ozone and meridional wind changes in the lower stratosphere. The research shows the existence of the regions with higher/lower total ozone content that are not in line with the characteristics of latitudinal distribution. They are formed due to more frequent recurrence of air advection with ozone content that is not typical for the Ukrainian territory, mostly from January to March. The study describes a typical temporal distribution of the total ozone climate normals varying from 285 to 375 Dobson units (D.u.) and analyzes the statistic patterns of deviations distribution and recurrence of extremely high/low total ozone content. The paper emphasizes a small variation of total ozone over the territory of Ukraine. The authors also researched the features of spatial distribution of seasonal variation amplitudes varying within the range of 42–46 D.u. and the specifics of maximum values periods observed from 19 to 30 of March, depending on a region. It was established that, since the previous update of the climate normals, the total ozone decreased for all months with its maximum decrease of about 8 D.u. in winter. However, no increase of recurrence of ultraviolet radiation high levels was observed. Total ozone changes are mainly associated with shifts of meridional wind values to negative ones. This process indicates the increase of recurrence of air advection from the north. The paper also emphasizes the complexness of total ozone changes during the period of research and the lack of consistency of such changes with the circulation factor of summer months.

This data article describes a new dataset on measures of climate distance for trade analyses. The dataset contains, (i) time-varying measures of long-run climate conditions for twenty economies accounting for two-third of global agri-food exports and representatives of different prevailing climates, and (ii) the difference in long-run climate conditions between country-pairs, here defined as Climate Distance . Measures of long-run climate conditions are computed, for each year in the sample (1996 to 2015), as 30-years rolling averages of weather conditions (i.e., climate normals or climatologies) in each country. Our climate measures are based on elaboration of data collected from the World Bank's Climate Change Knowledge Portal (CCKP), containing historical information on weather conditions. We also provide data on bilateral trade flows, tariffs, and policies, as provided by publicly available datasets. Our measures of climate change allow a more precise evaluation of structural changes in trade routes associated with climate comparative advantages., (© 2024 The Author(s).)

Thirty-year periods are treated in climatology as spans with relatively representative and stable climatic patterns, which can be used for calculating climate normals. Annual and seasonal series of circulation types were used to compare two 30-year sub-periods, 1961–1990 and 1991–2020, the second one being strongly influenced by recent global warming. This analysis was conducted according to the objective classification of circulation types and the climatic characteristics of sunshine duration, temperature, humidity, precipitation, and wind speed as calculated for the territory of the Czech Republic during the 1961–2020 period. For both sub-periods, their statistical characteristics were calculated, and the statistical significance of differences between them was evaluated. There was a statistically significant increase in the annual frequencies of anticyclonic circulation types and a significant decrease in cyclonic circulation types during 1991–2020 compared with 1961–1990. Generally, in both 30-year periods, significant differences in means, variability, characteristics of distribution, density functions, and linear trends appear for all climatic variables analysed except precipitation. This indicates that the recent 30-year “normal” period of 1991–2020, known to be influenced more by recent climate change, is by its climatic characteristics unrepresentative of the stable climatic patterns of previous 30-year periods.

Climate atlases summarize large sets of quantitative and qualitative data and are results of complex analytical cartographic work. These special geographical publications summarize long term meteorological observations, provide maps and figures which characterise different climate elements. Visual information is supplemented with explanatory texts. A lot of information on short and long term changes of climate elements were provided in published Lithuanian atlases (Atlas of Lithuanian SDR, 1981; Climate Atlas of Lithuania, 2013), as well as in prepared but unpublished Lithuanian Atlas (1989) and in upcoming new national atlas publications (National Atlas of Lithuania. 1st part, 2014). Climate atlases has to be constantly updated to be relevant and to describe current climate conditions. Comprehensive indicators of Lithuanian climate are provided in different cartographic publications. Different time periods, various data sets and diverse cartographic data analysis tools and visualisation methods were used in these different publications.

The climate normal, that is, the latest three full-decade average, of Arctic sea ice parameters is useful for baselining the sea ice state. A baseline ice state on both regional and local scales is important for monitoring how the current regional and local states depart from their normal to understand the vulnerability of marine and sea ice-based ecosystems to the changing climate conditions. Combined with up-to-date observations and reliable projections, normals are essential to business strategic planning, climate adaptation and risk mitigation. In this paper, monthly and annual climate normals of sea ice parameters (concentration, area, and extent) of the whole Arctic Ocean and 15 regional divisions are derived for the period of 1981−2010 using monthly satellite sea ice concentration estimates from a climate data record (CDR) produced by NOAA and the National Snow and Ice Data Center (NSIDC). Basic descriptions and characteristics of the normals are provided. Empirical Orthogonal Function (EOF) analysis has been utilized to describe spatial modes of sea ice concentration variability and how the corresponding principal components change over time. To provide users with basic information on data product accuracy and uncertainty, the climate normal values of Arctic sea ice extents (SIE) are compared with that of other products, including a product from NSIDC and two products from the Copernicus Climate Change Service (C3S). The SIE differences between different products are in the range of 2.3−4.5% of the CDR SIE mean. Additionally, data uncertainty estimates are represented by using the range (the difference between the maximum and minimum), standard deviation, 10th and 90th percentiles, and the first, second, and third quartile distribution of all monthly values, a distinct feature of these sea ice normal products.

Coastal upwelling has a significant local impact on marine coastal environment and on marine biology, namely fisheries. This study aims to evaluate climate and environmental changes in upwelling trends between 1950 and 2010. Annual, seasonal and monthly upwelling trends were studied in three different oceanographic areas of the Portuguese coast (northwestern—NW, southwestern—SW, and south—S). Two sea surface temperature datasets, remote sensing (RS: 1985−2009) and International Comprehensive Ocean—Atmosphere Data Set (ICOADS: 1950−2010), were used to estimate an upwelling index (UPWI) based on the difference between offshore and coastal sea surface temperature. Time series analyses reveal similar yearly and monthly trends between datasets A decrease of the UPWI was observed, extending longer than 20 years in the NW (1956−1979) and SW (1956−1994), and 30 years in the S (1956−1994). Analyses of sudden shifts reveal long term weakening and intensification periods of up to 30 years. This means that in the past 60 years a normal climate UPWI occurred along the Portuguese coast. An intensification of UPWI was recorded in recent decades regardless of the areas (RS: 1985−2009). Such an intensification rate (linear increase in UPWI) is only significant in S in recent decades (increase rate: ICOADS = 0.02 °C decade-1; RS = 0.11 °C decade-1) while in NW and SW the increase rate is meaningless. In NW more stable UPWI conditions were recorded, however average UPWI values increased in autumn and winter in NW in recently decades (RS: 1985−2009). An intensification rate of UPWI was recorded during summer (July, August and September) in SW and S in latter decades (RS: 1985−2009). The average UPWI values increased in recent decades in autumn in S. Marked phenological changes were observed in S in summer (before downwelling conditions prevail whilst recently when UPWI regimes prevail) with UPWI seasonal regime in S in recent decades becoming similar to those found in SW and NW. Results of this work can contribute to a better understanding of how upwelling dynamics affect/are correlated with biological data.

The traditional anomaly (TA) reference frame and its corresponding anomaly for a given data span changes with the extension of data length. In this study, the modulated annual cycle (MAC), instead of the widely used climatological mean annual cycle, is used as an alternative reference frame for computing climate anomalies to study the multi-timescale variability of surface air temperature (SAT) in China based on homogenized daily data from 1952 to 2004. The Ensemble Empirical Mode Decomposition (EEMD) method is used to separate daily SAT into a high frequency component, a MAC component, an interannual component, and a decadal-to-trend component. The results show that the EEMD method can reflect historical events reasonably well, indicating its adaptive and temporally local characteristics. It is shown that MAC is a temporally local reference frame and will not be altered over a particular time span by an extension of data length, thereby making it easier for physical interpretation. In the MAC reference frame, the low frequency component is found more suitable for studying the interannual to longer timescale variability (ILV) than a 13-month window running mean, which does not exclude the annual cycle. It is also better than other traditional versions (annual or summer or winter mean) of ILV, which contains a portion of the annual cycle. The analysis reveals that the variability of the annual cycle could be as large as the magnitude of interannual variability. The possible physical causes of different timescale variability of SAT in China are further discussed. [ABSTRACT FROM AUTHOR]

Thirty-year periods are treated in climatology as spans with relatively representative and stable climatic patterns, which can be used for calculating climate normals. Annual and seasonal series of circulation types were used to compare two 30-year sub-periods, 1961–1990 and 1991–2020, the second one being strongly influenced by recent global warming. This analysis was conducted according to the objective classification of circulation types and the climatic characteristics of sunshine duration, temperature, humidity, precipitation, and wind speed as calculated for the territory of the Czech Republic during the 1961–2020 period. For both sub-periods, their statistical characteristics were calculated, and the statistical significance of differences between them was evaluated. There was a statistically significant increase in the annual frequencies of anticyclonic circulation types and a significant decrease in cyclonic circulation types during 1991–2020 compared with 1961–1990. Generally, in both 30-year periods, significant differences in means, variability, characteristics of distribution, density functions, and linear trends appear for all climatic variables analysed except precipitation. This indicates that the recent 30-year "normal" period of 1991–2020, known to be influenced more by recent climate change, is by its climatic characteristics unrepresentative of the stable climatic patterns of previous 30-year periods. [ABSTRACT FROM AUTHOR]

Coastal upwelling has a significant local impact on marine coastal environment and on marine biology, namely fisheries. This study aims to evaluate climate and environmental changes in upwelling trends between 1950 and 2010. Annual, seasonal and monthly upwelling trends were studied in three different oceanographic areas of the Portuguese coast (northwestern—NW, southwestern—SW, and south—S). Two sea surface temperature datasets, remote sensing (RS: 1985–2009) and International Comprehensive Ocean—Atmosphere Data Set (ICOADS: 1950–2010), were used to estimate an upwelling index (UPWI) based on the difference between offshore and coastal sea surface temperature. Time series analyses reveal similar yearly and monthly trends between datasets A decrease of the UPWI was observed, extending longer than 20 years in the NW (1956–1979) and SW (1956–1994), and 30 years in the S (1956–1994). Analyses of sudden shifts reveal long term weakening and intensification periods of up to 30 years. This means that in the past 60 years a normal climate UPWI occurred along the Portuguese coast. An intensification of UPWI was recorded in recent decades regardless of the areas (RS: 1985–2009). Such an intensification rate (linear increase in UPWI) is only significant in S in recent decades (increase rate: ICOADS = 0.02 °C decade-1; RS = 0.11 °C decade-1) while in NW and SW the increase rate is meaningless. In NW more stable UPWI conditions were recorded, however average UPWI values increased in autumn and winter in NW in recently decades (RS: 1985–2009). An intensification rate of UPWI was recorded during summer (July, August and September) in SW and S in latter decades (RS: 1985–2009). The average UPWI values increased in recent decades in autumn in S. Marked phenological changes were observed in S in summer (before downwelling conditions prevail whilst recently when UPWI regimes prevail) with UPWI seasonal regime in S in recent decades becoming similar to those found in SW and NW. Results of this work can contribute to a better understanding of how upwelling dynamics affect/are correlated with biological data. [ABSTRACT FROM AUTHOR]