Tropical Nights Research Articles

Observational study of gadolinium-enhancing lesions in MRI in patients with multiple sclerosis from the Spanish Mediterranean coast: seasonal variability and relationship with climatic factors

IntroductionEnvironmental factors appear to play an important role in the development and course of Multiple Sclerosis (MS). Seasonal variability in disease activity has been described and it is postulated that it may vary according to geographical area. The aim of this study is to analyse the monthly distribution of activity observed on Magnetic Resonance Imaging (MRI) and to look for a possible relationship with climate in patients with relapsing remitting MS. Material and methodsRetrospective observational study, carried out in the population of one hospital on the Spanish Mediterranean coast. A total of 238 MRI scans of 51 patients were evaluated. Climatological data were obtained from the Spanish State Meteorological Agency from 2012 to 2016. Activity was defined as contrast-enhancing lesions. ResultsThe distribution of gadolinium-enhancing lesions was found to be non-uniform across months (p=0.008). Visual inspection suggests higher activity in July and August. Regarding weather, tropical nights (defined as days with a minimum temperature above 20°C) were associated with increased risk of MRI activity (OR=1.06, p=0.001). ConclusionThese findings suggest a non-uniform monthly distribution of gadolinium-enhancing lesions and an association between warmer nights and increased MRI activity, pointing to a potential impact of environmental factors on multiple sclerosis activity in neuroimaging.

The Increase in Urban Heat Due to Global Warming Can be Significantly Affected by the Structure of the Land Use and Land Cover

ABSTRACTUrban populations are increasingly exposed to excessive heat. Heat distribution in the urban environment can be affected by several factors, including the spatial arrangement of land use/land cover (LULC) that is specific to a given city. This study applies a climate model with urban canopy parameterisation to downscale future climate projections and simulate the spatio‐temporal pattern of heat in the urban environment to better understand the effect of LULC structure on its distribution. Heat conditions are characterised by climate indices that are well representative in two mid‐sized Central European cities of Brno and Ostrava (Czech Republic). Our results show that the annual number of hot days (HOT), summer days (SUD), tropical nights (TRN) and warm nights (WAN) will increase significantly (p < 0.01) in the 21st century in both cities. The model also simulates a more intensive increase and a higher spatio‐temporal variability in all indices in Brno compared to Ostrava. In Brno, the annual number of HOT and TRN is projected to be more than 500% of the 1981–2010 reference period's value by the end of the 21st century under the RCP 8.5 scenario. To determine the causes of the differences in heat distribution, we applied LULC configuration metrics and correlation analysis using various geographical factors. The higher risk of urban heat in Brno compared to Ostrava can be attributed to a more homogenised and less fragmented LULC structure and to the more substantial role of altitude in the complex terrain of Brno. Other factors, such as the presence of impervious surfaces and vegetation, have a similar effect on the variability of the studied indices in both cities. Urban planners should consider the role of the LULC structure and the changes that can be made in a city when designing adaptation measures to mitigate the effects of urban heat.

A Study of the Unexplored Aspect of Energy Poverty: Overheating Risk Analysis

Energy poverty, defined as the inability of households to afford adequate heating or cooling, is worsened by anthropogenic climate change, leading to increased energy demand and indoor comfort challenges. This study addresses a gap in summer fuel poverty research by focusing on overheating concerns and energy subsidies. This study defines the indoor temperature and overheating risk of low-income households compared with general dwellings based on field measurements and walkthrough surveys. The difference in indoor temperature and overheating risk according to the provision of energy subsidy is analyzed. We evaluate the impact of energy subsidies on indoor temperature and the adequacy of current subsidy programs. Findings show that households without energy vouchers experience indoor temperatures around 29°C, while voucher recipients see a 2°C decrease. During Tropical Night periods, households with energy vouchers maintain similar indoor temperatures to the overall period, while those without vouchers tend to experience a 2°C increase. Although energy vouchers significantly mitigate overheating risks by approximately 95% during the summer, they are not as effective in fully eliminating summer overheating risks compared to previous winter results. This highlights the persisting issue of traditional policies providing integrated electricity subsidies. Hence, addressing indoor overheating during hot summers requires improvements in alternative subsidy distributions or cooling strategies warrants change to the energy voucher payment method or the cooling method.

Quantifying urban climate response to large-scale forcing modified by local boundary layer effects

Over the past two decades, the joint manifestation of global warming and rapid urbanization has significantly increased the occurrence of heatwaves and the formation of urban heat islands in temperate cities. Consequently, this synergy has amplified the frequency and duration of periods with tropical nights (TNs) in these urban areas. While the occurrences of such extreme events demonstrate irregular and nonlinear annual patterns, they consistently manifest a discernible rising decadal trend in local or regional climatic data. In urban regions situated amidst hilly or mountainous landscapes, changing wind directions—often associated with uphill or downhill thermal flows—profoundly impact the spread and dispersion of heat-related pollution, creating unique natural ventilation patterns. Using the Lausanne/Pully urban area in Switzerland as examples of hilly and lakeshore temperate cities, this study explores the influence of wind patterns and natural urban ventilation on the nonlinearity of recorded climatic data within an urban environment. This study integrates a mesoscale numerical weather prediction model (COSMO-1), a microscale Computational Fluid Dynamics (CFD) model, field observations, variational mode decomposition technique, and statistical analysis to investigate how wind speed and direction critically influence the nonlinearity of recorded long-term trends of extreme events, specifically focusing on the frequency and duration of TNs in lakeshore and hilly cities. The results strongly indicate a direct correlation between the frequency of TNs and the occurrence of specific moderate wind patterns. These wind patterns are exclusively captured by the microscale CFD model, unlike the mesoscale model, which neglects both urban morphology and complex hilly terrains. The impact of temporal and spatial variability of the wind field on long-term observations at fixed measurement stations suggests that caution should be exercised when relying on limited spatial measurement points to monitor and quantify long-term urban climate trends, particularly in cities located in complex terrains.

A comparative analysis of temperature trends at Modena Geophysical Observatory and Mount Cimone Observatory, Italy

AbstractGlobal warming has become a critical environmental, social, and economic threat, with increasing frequency and intensity of extreme weather events. This study aims to analyse temperature trends and climate indices in the Po Valley, a significant economic and agricultural region in Italy, by examining data from two historical stations: the urban Modena Observatory and the rural Mount Cimone Observatory. The analysis extends previous studies to 2018, assessing the magnitude of climate changes since the 1950s and isolating the Urban Heat Island (UHI) effect in Modena. Significant warming trends were confirmed at both sites, with in maximum (TX) and minimum (TN) temperatures trends nearly doubling from 1981 to 2018 compared to 1951–2018. For example, TX trends reached 0.84°C·decade−1 in Modena and 0.62°C·decade−1 at Mount Cimone, while TN trends were 0.77 and 0.80°C·decade−1, respectively. Extreme climate indices showed a substantial increase in warm days and nights (TX90p and TN90p, respectively). Particularly we found TX90p of 27.5 days·decade−1 in Modena and 15 days·decade−1 at Mount Cimone while TN90p of 29.5 days·decade−1 in Modena, 22 days·decade−1 at Mount Cimone. The UHI effect significantly impacts Modena's temperature trends. Urbanization contributes up to 65% of the rise in warm nights. Specifically, frost days decreased by 1.88 days·decade−1 (37% of Urban Contribute, UC), tropical nights increased by 5.16 days·decade−1 (57% UC), warm nights increased by 12.7 days·decade−1 (65% UC), and cool nights decreased by 3.19 days·decade−1 (39% UC). Overall, the study underscores the importance of considering both global and local factors in regional climate trend analysis.

Assessing Climate Change Projections through High-Resolution Modelling: A Comparative Study of Three European Cities

Climate change is expected to influence urban living conditions, challenging cities to adopt mitigation and adaptation measures. This paper assesses climate change projections for different urban areas in Europe –Eindhoven (The Netherlands), Genova (Italy) and Tampere (Finland)—and discusses how nature-based solutions (NBS) can help climate change adaptation in these cities. The Weather Research and Forecasting Model was used to simulate the climate of the recent past and the medium-term future, considering the RCP4.5 scenario, using nesting capabilities and high spatial resolution (1 km2). Climate indices focusing on temperature-related metrics are calculated for each city: Daily Temperature Range, Summer Days, Tropical Nights, Icing Days, and Frost Days. Despite the uncertainties of this modelling study, it was possible to identify some potential trends for the future. The strongest temperature increase was found during winter, whereas warming is less distinct in summer, except for Tampere, which could experience warmer summers and colder winters. The warming in Genova is predicted mainly outside of the main urban areas. Results indicate that on average the temperature in Eindhoven will increase more than in Genova, while in Tampere a small reduction in annual average temperature was estimated. NBS could help mitigate the increase in Summer Days and Tropical Nights projected for Genova and Eindhoven in the warmer months, and the increase in the number of Frost Days and Icing Days in Eindhoven (in winter) and Tampere (in autumn). To avoid undesirable impacts of NBS, proper planning concerning the location and type of NBS, vegetation characteristics and seasonality, is needed.

Accuracy of daily extreme air temperatures under natural variations in thermometer screen ventilation

AbstractAccurate air temperatures underpin environmental research. Most professional meteorological air temperature measurements still expose thermometers within traditional, naturally ventilated screens. Their representation of true air temperature depends on screen airflow, and therefore local winds. Accuracies of daily maximum (Tmax) and minimum (Tmin) air temperatures are assessed by comparison between a naturally ventilated large conventional screen and a co‐located aspirated reference screen. In over 1200 days' data, the naturally ventilated Tmin and Tmax both showed small (median < 0.06°C) cold bias, but, in 1% of cases, warm Tmax bias and cold Tmin bias >|1°C|. The Tmin cold bias is associated with calm clear nights, and the Tmax warm bias events with calm winter days at low sun angles, allowing solar heating of the screen. The prevalence of poor natural ventilation, potentially affecting Tmin and Tmax, is estimated across European sites. Poor ventilation occurred at Tmin for 12% of values, and at Tmax for 4%. Climatological averaging will reduce these effects, but, without corroborating wind data, statistical changes in Tmin or Tmax, including identifying “Tropical Nights” (Tmin > 20°C) or occurrences of winter extremes, may have limited value. Wider adoption of aspirated thermometer screens, with an initial overlap period, will largely eliminate these effects.

Projected changes in high temperatures in coastal tourism destinations: A case study of the Turquoise Coast

The effects of climate change may manifest themselves in different ways, but when it comes to coastal tourism, extreme temperatures, which are the unavoidable consequences of climate change, come to the fore. The study examined the changes that extreme temperatures can make on major coastal tourism destinations by using such indices as hot summer days, tropical nights, and days with dangerous apparent temperature (> 40.6 °C). Under the RCP4.5 and RCP8.5 scenarios, increases in these indices are expected for Antalya, Aydın, Balıkesir, İzmir, and Muğla on the Turquoise Coast in the near (2021-2050) and distant (2071-2100) future. The projections based on both scenarios indicate that there might be a rise in the number of hot summer days, tropical nights, and days exceeding the critical human thermal comfort conditions on the Turquoise Coast. Expected increases may reach the highest values under the pessimistic scenario at the end of the century. The increase in these indices caused by rising temperatures may adversely affect the tourism sector due to changes in tourism demands, destinations, and seasonal shifts.

Assessing the impacts of temperature extremes on agriculture yield and projecting future extremes using machine learning and deep learning approaches with CMIP6 data

Climate change, particularly extreme weather events, has significantly affected various sectors, including agriculture, human health, water resources, sea levels, and ecosystems. It is anticipated that the intensity, duration, and frequency of these extremes will escalate in the future. This study aims to discover the association between temperature extremes and agricultural yield and to project these extremes using machine learning (ML) and deep learning (DL) models with CMIP6 (Coupled Model Intercomparison Project Phase 6) data under two SSPs (Shared Socioeconomic Pathways). A bi-wavelet coherence technique is employed to investigate the association, providing detailed information in both the frequency and time domains for the period of 1980–2014. Various ML and DL models are trained and tested for the periods of 1985–2004 and 2005–2014, respectively, with gradient boosting machine chosen for projecting temperature extremes based on its superior performance. Mann-Kendall test is used for trend analysis in the projected temperature extremes. The results indicate strong negative and positive associations between TN10p (Cold nights) and TN90p (Warm nights), respectively, with wheat production. Additionally, there is a long-term negative association of CSDI (Cold Spell Duration Indicator) and strong positive association of WSDI (Warm Spell Duration Indicator) with rice yield. Projected results show an increase and decrease under SSP2-4.5 and SSP5-8.5, respectively, in DTR (Diurnal Temperature Range) at most stations. TN10p will increase in the future at most stations, with exceptions such as Muree station where it decreases during 2025–2049 and then increases under both SSPs. Projections show that TXn (annual or monthly minimum value of daily maximum temp) will increase in the future, with Muree station exhibiting the lowest value close to zero, while the average maximum value is around 20 °C at Khanpur station. Trend analysis reveals significantly increasing trend in TR20 (Tropical nights) and decreasing trend in CSDI in future durations under both SSPs. These findings hold implications for policymakers and stakeholders in various departments, including agriculture, health, and water resources management.

Long-Term Survival after Coronary Artery Surgical Revascularization-Does Ambient Temperature Matter?

Background and Objectives: The progression of global warming results in an increased exposure to extreme heat, leading to exaggeration of preexisting diseases and premature deaths. The aim of the study was to present possible risk factors for all-cause long-term mortality in patients who underwent surgical revascularization, including an assessment of the influence of ambient temperature exposure. Materials and Methods: Retrospective analysis included 153 (123 (80%) males and 30 (20%) females) patients who underwent off-pump revascularization and were followed for a median time of 2533 (1035-3250) days. The demographical, clinical data and ambient temperature exposure were taken into analysis for prediction of all-cause mortality. Individual exposure was calculated based on the place of habitation. Results: In the multivariate logistic regression model with backward stepwise elimination method, risk factors such as dyslipidaemia (p = 0.001), kidney disease (p = 0.005), age (p = 0.006), and body mass index (p = 0.007) were found to be significant for late mortality prediction. In addition to traditional factors, environmental characteristics, including tropical nights (p = 0.043), were revealed to be significant. Conclusions: High night-time ambient temperatures known as tropical nights may be regarded as additional long-term mortality risk factor after surgical revascularization.

High-resolution surface temperature changes for Portugal under CMIP6 future climate scenarios

Future changes in the mean, maximum and minimum temperature in continental Portugal were investigated using high-resolution future climate projections based on the latest IPCC AR6 CMIP6 climate scenarios. The results show that the mean, maximum and minimum temperatures are projected to substantially increase in all continental Portugal, particularly at the south-central inland regions. For the near-term future (2046–2065 period), SSP3-7.0 is the future climate scenario that projects higher increases, of around 1 °C, 1.5 °C and 2 °C for the daily mean, maximum and minimum temperatures, respectively. For the long-term future (2081–2100 period), the projected warming is higher, particularly under the SSP5-8.5 future climate scenario with projected warmings of 3 °C, 3.5 °C and 2.5 °C for the daily mean, maximum and minimum temperatures, respectively. Occurrences of hot days (maximum temperature above 30 °C), very hot days (maximum temperature above 40 °C) and tropical nights (minimum temperature above 20 °C) are all projected to increase up to 35–40, 12–15 and 50 more days per year, respectively, mainly in the interior areas of Portugal. Oppositely, the occurrence of frost days is projected to decrease in practically all mountainous areas in Portugal. These results show a clear tendency of a significant increase in the surface temperatures and frequency of occurrence of extreme temperature episodes in continental Portugal, which can have severe impacts on the population, environment, economy and vital human activities such as agriculture.

Climate change impact on Mediterranean viticultural regions and site-specific climate risk-reduction strategies

The global increase in extreme weather and climate events may dramatically impact agriculture, food safety, and socioeconomic dynamics. The Mediterranean basin is already exposed to extreme climatic events, severely challenging viticulture, a pivotal Mediterranean agro–industry. This study aims to understand better how climate is expected to evolve in six viticulturally important Mediterranean regions in Portugal, Italy, Turkey and Morocco, using a 4–member ensemble of climatic model projections under Representative Concentration Pathways (RCP) 4.5 and 8.5 for 2041–2070, and using the 1981–2010 period as a baseline. By comparing the main specific challenges these locations will face, we comparatively define the best strategies to reduce the impacts of climate change at the national and regional levels. Projections show increases in overall temperatures, up to + 3.6°C than the historical baseline, whilst precipitation projections indicate decreases that could reach 36% of the overall annual precipitation. Biological effective degree days, consecutive dry days, growing season length, tropical nights, or very heavy precipitation days, also show challenging prospects for viticulture in these countries. A screening of the adaptative strategies already undertaken in the studied countries suggests that growers are taking reactive rather than preventive strategies. Moreover, the discussion of the most suitable strategies in this study is region–specific, i.e., prioritised by the specific needs of each location. The conclusions drawn herein may support local growers, improving their decision–making based on the most adequate adaptive strategies to their conditions, thus optimising their sustainable production under changing climates.

Feeling hot is being hot? Comparing the mapping and the surveying paradigm for urban heat vulnerability in Vienna

With rising global temperatures, cities increasingly need to identify populations or areas that are vulnerable to urban heat waves; however, vulnerability assessments may run into ecological fallacy if data from different scales are misconstrued as equivalent. We assess the heat vulnerability of 1983 residents in Vienna by measuring heat impacts, exposure, sensitivity and adaptive capacity with mirrored indicators in the mapping paradigm (i.e. census tract data referring to the geographic regions where these residents live) and the surveying paradigm (i.e. survey data referring to the residents' individual households). Results obtained in both paradigms diverge substantially: meteorological indicators of hot days and tropical nights are virtually unrelated to self-reported heat strain. Meteorological indicators are explained by mapping indicators (R2 of 15–40 %), but mostly not by surveying indicators. Vice versa, experienced heat stress and subjective heat burden are mostly unassociated with mapping indicators but are partially explained by surveying indicators (R2 of 2–4 %). The results suggest that the two paradigms do not capture the same components of vulnerability; this challenges whether studies conducted in the respective paradigms can complement and cross-validate each other. Policy interventions should first define which heat vulnerability outcome they target and then apply the paradigm that best captures the specific drivers of this outcome.

Exploring the spatial-temporal evolution characteristics of extreme heat events and driving factors in seven geographic regions of China (1980–2021)

Extreme heat events in China are occurring more frequently due to global warming. However, there is a lack of comprehensive studies analyzing the movement direction and factor interaction of extreme heat events in different regions of China. Based on daily temperature data from 2252 meteorological stations in China from 1980 to 2021, three extreme heat indicators were calculated: Hot days (SU35), Tropical nights (TR20), and Extreme maximum temperature (TXx). The study updated and compared the characteristics of spatiotemporal evolution and driving factors of extreme heat events in China and its seven geographical regions.The results show that: (1) SU35 and TR20 increase faster in summer, while TXx increases faster in winter. Nighttime high-temperature events have increased at a faster rate. (2) Extreme heat events in North, Northeast, and Northwest China have gradually moved southward and eastward, while in Central, East, and South China, such events have moved northward. (3) In northeast and northwest China, the Atlantic Multi-decadal Oscillation (AMO) is a strong driving force for extreme heat events, while urbanization is the most potent driving force in the remaining regions. The interaction between urbanization and atmospheric circulation, including AMO, Pacific Decadal Oscillation (PDO), and East Atlantic/West Russia (EA/WR), is non-linear or double factor enhancement and exhibits significant spatial heterogeneity. These results will be significant for urban adaptation to climate change and sustainable development.

Analysis of tropical nights in Spain (1970–2023): Minimum temperatures as an indicator of climate change

AbstractIn the current context of climate change, the increase in minimum temperatures recorded in recent decades has received special scientific attention due to its importance for the proper night's sleep and health of the population, among other considerations. One of the main indicators usually considered refers to the frequency of tropical nights (≥20°C), which have begun to become widespread in regions hitherto excluded from this type of event. This paper analyses tropical nights in Spain between 1970 and 2023, addressing their mean annual occurrence, their intensity, their monthly distribution and the average number of consecutive tropical nights recorded, as well as their relationship with relative humidity. The results, based on the analysis of 75 homogenized series located in 44 different provinces, allow us to differentiate seven large areas based on the minimum temperature in which there has been an increase in the frequency and intensity of tropical nights, which progressively extend the season in which they can appear. Similarly, a generalized increase in the maximum number of consecutive nights thermally above 20°C was identified, in addition to the presence of high relative humidity in coastal areas during these episodes.

Probabilistic medium-range forecasts of extreme heat events over East Asia based on a global ensemble forecasting system

This study suggests a methodology for probabilistic forecasts of the extreme heat events in East Asia based on an operational global ensemble prediction used by the Korea Meteorological Administration (KMA). It focuses on the medium range of up to 11 days, providing probabilities of heatwave and tropical night occurrence each day. Forecast validation in the summer from 2016 to 2021 shows that the deterministic heatwave forecast provides 5 days of optimal forecast range, while the probabilistic forecast can extend the practically predictable range up to 10 days in the Korean Peninsula and 7 days in Japan, respectively. Comparing prediction skills for heatwave and tropical night, the skills for tropical night tend to be inferior, presumably due to complex mechanisms of the tropical night and large uncertainty in the numerical model, such as microphysics and radiation. In addition, the coarse resolution of the operational system does not seem to resolve temperature variability at night. As a case study, this study also examines the forecast of the onset and offset of the 2018 South Korean heatwave event. The temporal evolution of the heatwave matches well with the changes in the upper-level atmospheric circulation pattern, which can be used for useful forecast guidance. This probabilistic forecast based on the global ensemble forecasting system is expected to provide reliable prediction information for heatwaves in advance, reducing exposure to extreme events.

Extreme phenomena in the Lake Baikal basin: tropical nights and hot days as indicators of climate warming (Eastern Siberia, Russia)

Extreme phenomena in the Lake Baikal basin: tropical nights and hot days as indicators of climate warming (Eastern Siberia, Russia)

Climate of the Pyrenees: Extremes indices and long-term trends

We utilized an extensive, multisource, cross-border dataset of daily meteorological observations from over 1500 stations in the Pyrenees, spanning from the mid-20th century to 2020, to examine the spatial and temporal climate patterns. Our focus was on 17 indices related to extreme precipitation and temperature events across the mountain range. The original data underwent rigorous quality control and homogenization processes, employing a comprehensive workflow that included spatial modeling based on environmental predictors. This process yielded two main outcomes: 1) a high-resolution gridded dataset (1 km2) of daily precipitation, maximum and minimum temperature from 1981 to 2020, allowing for a detailed analysis of spatial variations; and 2) an evaluation of long-term annual and seasonal trends from 1959 to 2020, using selection of high-quality data series that were homogenized to preserve their temporal structure and coherence. The findings revealed a clear elevation-related pattern in temperature indices (with the exception of tropical nights, which were predominantly observed on the Mediterranean side) and a distinct north-south latitudinal disparity in precipitation, turning longitudinal when focusing on extreme precipitation events. Overall, there was a notable and significant warming trend of 0.2 to 0.4 °C per decade, and a non-significant change of precipitation, with the exception of the southern and Mediterranean regions, where there was a notable decrease, approximately −3 % per decade, observed on an annual basis.

Decreasing the energy demand in public buildings using nature-based solutions: case studies from Novi Sad (Republic of Serbia) and Osijek (Republic of Croatia)

BackgroundNature-based solutions (NBS) in urban areas offer an opportunity to improve environmental conditions and to reduce CO2 emissions towards establishing climate-neutral cities in the next few decades. Furthermore, the implementation of NBSs—vertical or horizontal green infrastructures on public facilities—could in particular improve both climate, including outdoor thermal conditions on a micro-scale (especially during the summer season) and the energy demand of buildings as well as save heating energy during the winter period.ResultsOn both selected buildings, extensive green roofs were implemented as an NBS intervention. The analysed data were obtained using the monitoring systems (from 2019 to 2022) installed on two public buildings in Novi Sad (Republic of Serbia) and Osijek (Republic of Croatia), with a focus on climate/bioclimate characteristics and thermal transmission capacities. Four automatic weather stations (AWS) were used for microclimate monitoring, along with the heat flow meter (HFM) method, to measure the alterations in the thermal transmittance (U value) of a flat concrete roof before and after energy refurbishment and the installation of a green roof. The outcomes of this study show that the air temperatures (Ta) and globe temperatures (Tg) near the green roof are lower by 0–3 °C for Ta and by 0–16.5 °C for Tg than the values captured by the AWSs at other locations. An even more interesting fact is that the green roof has a constant cooling potential during tropical nights, and based upon this research, the cooling value is around 2 °C for Tg (the Ta value is not distinct). The thermal transmittance results show that more savings can be achieved by applying a green roof with an 8 cm thick substrate: U values decreased by 50–69%, as measured by two different heat flux sensors.ConclusionsNature-based solutions, such as the implementation of an extensive green roof, have positive effects on diverse aspects of urban environments and building energy savings, which are particularly evident in extreme seasons, both summer and winter. Applying the proposed monitoring and assessment system could help local communities in their efforts to reduce carbon-based emissions. This paper provides a good example of the implementation of NBSs on a local- and a micro-scale.

Forecasting some climate parameters of Türkiye using the SSP3-7.0 scenario for the years 2040–2059

This study employs the Coupled Model Inter-comparison Projects (CMIPs) and the Sixth phase of CMIPs (CMIP6) to unravel the multifaceted impacts of global climate change on climate of Türkiye. The CMIP6 data, fundamental to the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports, forms the basis for projecting future climate scenarios, specifically under the medium-high reference scenario SSP3-7. Utilizing a suite of global climate models, including the innovative Multi-Model Ensemble (MME) approach, this study combines predictions to enhance the precision climate projections of Türkiye. Historical data spanning from 1951 to 2020 were subjected to rigorous statistical analysis, including descriptive statistics and regression analysis. The findings reveal an unequivocal upward trajectory in Türkiye’s annual mean temperature, with an accelerated pace in recent decades. Despite a lack of a significant long-term trend in annual precipitation from 1951 to 2020, the rate of change in precipitation is accelerating, indicating potential future challenges. Projections for 2040-2059 under the SSP3-7.0 scenario indicate a non-uniform increase in mean temperature across Türkiye, with the southern and western regions facing the most significant impact. This warming trend poses imminent threats to agriculture, altering crop yields and increasing the risk of heat stress for livestock. Additionally, the projected decrease in precipitation, alongside a surge in hot days and tropical nights, underscores the urgency for adaptive measures. As Türkiye navigates the complex terrain of climate change, this study provides valuable insights, emphasizing the significance of robust climate modeling for informed decision-making. The results underscore the imminent challenges Türkiye faces and emphasize the critical importance of proactive climate action on both national and global fronts.