Abstract The investigation of wave climate is of primary concern for the successful implementation of offshore aquaculture systems as waves can cause significant loads on them. Up until now, site selection and design (or selection) of offshore cage system structures on extended sea areas do not seem to follow any specific guidelines. This paper presents a novel methodology for the identification of favorable sites for offshore aquaculture development in an extended sea area based on two important technical factors: (i) the detailed characterization of the wave climate, and (ii) the water depth. Long-term statistics of the significant wave height, peak wave period, and wave steepness are estimated on an annual and monthly temporal scale, along with variability measures. Extreme value analysis is applied to estimate the design values and associated return periods of the significant wave height; structures should be designed based on this data, to avoid partial or total failure. The Eastern Mediterranean Sea is selected as a case study, and long-term time series of wave spectral parameters from the ERA5 dataset are utilized. Based on the obtained results, the most favorable areas for offshore aquaculture installations have been identified.

Abstract Urbanization is closely associated with land use land cover (LULC) changes that correspond to land surface temperature (LST) variation and urban heat island (UHI) intensity. Major districts of Bangladesh have a large population base and commonly lack the resources to manage fast urbanization effects, so any rise in urban temperature influences the population both directly and indirectly. However, little is known about the impact of rapid urbanization on UHI intensity variations during the winter dry period in the major districts of Bangladesh. To this end, we aim to quantify spatiotemporal associations of UHI intensity during the winter period between 2000 and 2019 using remote-sensing and geo-spatial tools. Landsat-8 and Landsat-5 imageries of these major districts during the dry winter period from 2000 to 2020 were used for this purpose, with overall precision varying from 81% to 93%. The results of LULC classification and LST estimation showed the existence of multiple UHIs in all major districts, which showed upward trends, except for the Rajshahi and Rangpur districts. A substantial increase in urban expansion was observed in Barisal > 32%, Mymensingh > 18%, Dhaka > 17%, Chattogram > 14%, and Rangpur > 13%, while a significant decrease in built-up areas was noticed in Sylhet < −1.45% and Rajshahi < −3.72%. We found that large districts have greater UHIs than small districts. High UHI intensities were observed in Mymensingh > 10 ◦C, Chattogram > 9 ◦C, and Barisal > 8 ◦C compared to other districts due to dense population and unplanned urbanization. We identified higher LST (hotspots) zones in all districts to be increased with the urban expansion and bare land. The suburbanized strategy should prioritize the restraint of the high intensity of UHIs. A heterogeneous increase in UHI intensity over all seven districts was found, which might have potential implications for regional climate change.Our study findings will enable policymakers to reduce UHI and the climate change effect in the concerned districts.

Abstract Research on the relationship between microclimate and heritage buildings or historic buildings has increased dramatically in the last few decades. Research has focused on indoor climate or indoor microclimate or the environment or micro-environment, and the field of these studies regards several variables, physical—air temperature, air speed, relative humidity—or chemical, dust, CO2 , pollution, etc., all of which can have an effect or damage buildings or artifacts inside buildings. Moreover, all these variables should be monitored in a monitoring campaign following the standard EN 15757; in spite of this, scientific literature contains mistakes with regard to the words and objects of study. In this short contribution, the author proposes a common nomenclature in the research field of climate and microclimate in heritage buildings and heritage artifacts. A new nomenclature should be useful for the community of heritage scientists working on preventive measures to distinguish between climate and environment, or the object of study, e.g., the room (wall, wood structure, fresco,etc.) where the artifacts are or the air around them (painting, canvas, statue, piece of furniture,documents, books, etc.).

Abstract We address the following question: Are turning points of daily air temperature function a piece of relevant climatological information worth recording and analyzing? Diurnal Extrema Timing (DET) are daily occurrence times of air temperature minimum and maximum. Although unrecognized and unrecorded as a meteorological variable, the exact timing of daily temperature extrema plays a crucial role in the characterization of air temperature variability. In this study, we introduce the DET concept and assess the plausibility of this potential parameter in detecting temperature extrema timing changes. Conceptualization of the DET parameter has, for a primary goal, the supplementation of vital spatial information to the daily measurements of air temperature extrema. The elementary analysis of annual trends of daily DET examines the significance of this parameter in describing changes in the time domain of air temperature variability. The introduction of the new Climate Parameter Sensitivity Index (CPSI) for evaluating the usceptibility of climate parameters to climate change directs attention to the importance of the systematic acquisition of the timing of daily extrema in climate observations. The results of this study reveal the timing of daily air temperature maximum as the most vulnerable to climate change among temperature and timing extrema indices.

Abstract Long-term changes in precipitation and temperature indirectly impact aquifers through groundwater recharge (GWR). Although estimates of future GWR are needed for water resource management, they are uncertain in cold and humid climates due to the wide range in possible future climatic conditions. This work aims to (1) simulate the impacts of climate change on regional GWR for a cold and humid climate and (2) identify precipitation and temperature changes leading to significant long-term changes in GWR. Spatially distributed GWR is simulated in a case study for the southern Province of Quebec (Canada, 36,000 km2 ) using a water budget model. Climate scenarios from global climate models indicate warming temperatures and wetter conditions (RCP4.5and RCP8.5; 951–2100). The results show that annual precipitation increases of >+150 mm/yr or winter precipitation increases of >+25 mm will lead to significantly higher GWR. GWR is expected to decrease if the precipitation changes are lower than these thresholds. Significant GWR hanges are produced only when the temperature change exceeds +2 ◦C. Temperature changes of >+4.5 ◦C limit the GWR increase to +30 mm/yr. This work provides useful insights into the regional assessment of future GWR in cold and humid climates, thus helping in planning decisions as climate change unfolds. The results are expected to be comparable to those in other regions with similar climates in post-glacial geological environments and future climate change conditions.

Abstract Climate change is a current subject that is attracting more and more attention, whether from academics or the public. This public attention is mainly due to the frequently published news in the media, reporting consequences caused by extreme weather events. On the other hand, scientists are looking into the origins of the phenomenon, seeking answers that will somehow help to mitigate the effects of climate change. This article presents a review of some of the different possible approaches taken on climate change, to demonstrate the need to build a multidisciplinary perspective of the problem. It is understood that only the integration of different perspectives, presented by different areas of knowledge, such as natural sciences, social and economic sciences and human sciences,will make it possible to build modeling and predictive scenarios, which realistically may represent the development of the earth system under the influence of climate change. In this way, with the support of all areas of knowledge, the creation of forecast models where all possible changes to the different variables of the earth system may be simulated will allow for the mitigation measures presented to be analyzed in advance and, thus, prioritized. This review shows that a multi and inter disciplinary approach, based on the knowledge acquired from different knowledge and science fields,presents itself as the way to solve this global and complex problem caused by climate change.

Abstract If we are going to be able to fight climate change in an effective way there is a need for a profound sustainability transformation of society. The question is how everyday pro-environmental behavior such as climate-friendly food choices should be looked upon in this context: as something that hides the need for structural change, or as a starting point for a profound transformation? The aim is to discuss how emotions related to conflicts encountered when trying to make everyday climate-friendly food choices in a society that is not always sustainable can be used to promote transformational learning. Interviews were performed with 15 adolescents. Emotions felt in relation to conflicts and how the youth cope were explored. The results show that the youth mainly felt individualized emotions of guilt, helplessness, and irritation and that they coped primarily by distancing themselves from emotions felt, but also sometimes in a problem-focused way and through positive reappraisal. Results are discussed in relation to theories about critical emotional awareness and prefigurative politics. It is argued that by taking account of emotional aspects related to everyday conflicts in a critical manner, issues such as justice could be brought to the surface and transformative learning could be enhanced.

Abstract Despite the concern about climate change and the associated negative impacts, fossil fuels continue to prevail in the global energy consumption. This paper aimed to propose the first model that relates CO2 emissions of Sao Paulo, the main urban center emitter in Brazil, with gross national product and energy consumption. Thus, we investigated the accuracy of three different methods: multivariate linear regression, elastic-net regression, and multilayer perceptron artificial neural networks. Comparing the results, we clearly demonstrated the superiority of artificial neural networks when compared with the other models. They presented better results of mean absolute percentage error (MAPE = 0.76%) and the highest possible coefficient of determination (R2 = 1.00). This investigation provides an innovative integrated climate-economic approach for the accurate prediction of carbon emissions. Therefore, it can be considered as a potential valuable decision support tool for policymakers to design and implement effective environmental policies.

Abstract The Athabasca Oil Sands Area (AOSA) in Alberta, Canada, is considered to have a high density of weather stations. Therefore, our objective was to determine an optimal network for the wind data measurement that could sufficiently represent the wind variability in the area. We used available historical data records of the weather stations in the three networks in AOSA, i.e., oil sands monitoring (OSM) water quantity program (WQP) and Wood Buffalo Environmental Association (WBEA) edge sites (ES) and meteorological towers (MT) of the air program. Both graphical and quantitative methods were implemented to find the correlations and similarities in the measurements between weather stations in each network. The graphical method (wind rose diagram) was found as a functional tool to understand the patterns of wind directions, but it was not appropriate to quantify and compare between wind speed data of weather stations. Therefore, we applied the quantitative method of the Pearson correlation coefficient (r) and absolute average error (AAE) in finding a relationship between the wind data of station pairs and the percentage of similarity (PS) method in quantifying the closeness/similarity. In the correlation analyses, we found weak to strong correlations in the wind data of OSM WQP (r = 0.04–0.69) and WBEA ES (r = 0.32–0.77), and a strong correlation (r = 0.33–0.86) in most of the station pairs of the WBEA MT network. In the case of AAE, we did not find any acceptable value within the standard operating procedure (SOP) threshold when logically combining the values of the u and v components together. In the similarity analysis, minor similarities were identified between the stations in the three networks. Hence, we presumed that all weather stations would be required to measure wind data in the AOSA.

Abstract Fruit detection is crucial for yield estimation and fruit picking system performance. Many state-of-the-art methods for fruit detection use convolutional neural networks (CNNs). This paper presents the results for peach detection by applying a faster R-CNN framework in images captured from an outdoor orchard. Although this method has been used in other studies to detect fruits, there is no research on peaches. Since the fruit colors, sizes, shapes, tree branches, fruit bunches, and distributions in trees are particular, the development of a fruit detection procedure is specific. The results show great potential in using this method to detect this type of fruit. A detection accuracy of 0.90 using the metric average precision (AP) was achieved for fruit detection. Precision agriculture applications, such as deep neural networks (DNNs), as proposed in this paper, can help to mitigate climate change, due to horticultural activities by accurate product prediction, leading to improved resource management (e.g., irrigation water, nutrients, herbicides, pesticides), and helping to reduce food loss and waste via improved agricultural activity scheduling.

Abstract Precipitation is one of the most variable climatic parameters, as it is determined by many physical processes. The spatiotemporal characteristics of precipitation have been significantly affected by climate change during the past decades. Analysis of precipitation trends is challenging, especially in regions such as Greece, which is characterized by complex topography and includes several ungauged areas. With this study, we aim to shed new light on the climatic characteristics and inter annual trends of precipitation over Greece. For this purpose, we used ERA5 monthly precipitation data from 1950 to 2020 to estimate annual Theil–Sen trends and Mann–Kendall significance over Greece and surrounding areas. Additionally, in order to analyze and model the nonlinear relationships of monthly precipitation time series, we used generalized additive models (GAMs). The results indicated significant declining inter-annual trends of areal precipitation over the study area. Declining trends were more pronounced in winter over western and eastern Greece, but trends in spring, summer and autumn were mostly not significant. GAMs showcased that the trends were generally characterized by nonlinearity and precipitation over the study area presented high inter-decadal variability. Combining the results, we concluded that precipitation did not linearly change during the past 7 decades, but it first increased from the 1950s to the late 1960s, consequently decreased until the early 1990s and, afterwards, presented an increase until 2020 with a smaller rate than the 1950–1960s.

Abstract Climate change has challenged biodiversity conservation practitioners and planners. In this paper, we provide scalable guidance on integrating climate change into conservation planning and adaptive management that results in the most appropriate conservation strategies. This integrated “Climate-Smart Conservation Practice” focuses on analyzing the potential impact of climate change on species, ecosystems, and ecosystem services, combined with “conventional” (non-climate) threats, and incorporating this knowledge into projects. The guidance is based on the already widely-used “Open Standards for the Practice of Conservation”, an application of systems thinking and adaptive management, which has been successfully applied to thousands of conservation projects. Our framework emphasizes a methodical analysis of climate change impacts for projects to support more productive goals and strategy development. We provide two case studies showing the applicability and flexibility of this framework. An initial key element is developing “situation models” that document both current and future threats affecting biodiversity while showing the interactions between climate and conventional threats. Guidance is also provided on how to design integrated, climate-smart goals and strategies, and detailed theories of change for selected strategies. The information and suggestions presented are intended to break down the steps to make the process more approachable, provide guidance to teams using climate change information within a systematic conservation planning process, and demonstrate how climate scientists can provide appropriate information to conservation planners.

Abstract In recent years, outbreaks of kiwifruit bacterial canker (Pseudomonas syringae pv. actinidiae, Psa) have caused huge economic losses to two major global kiwifruit producers, Italy and New Zealand. To evaluate the potential global risk areas of Psa, three modelling methods (MaxEnt,CLIMEX and a Multi-Model Framework, including Support Vector Machine or SVM) were used.Current global occurrence data for Psa were collected from different sources. The long-term climate data were sourced from WorldClim and CliMond websites. The model results were combined into a consensus model to identify the hotspots. The consensus model highlighted the areas where two or three models agreed on climate suitability for Psa. All three models agreed with respect to the climate suitability of areas where Psa is currently present and identified novel areas where Psa has not established yet. The SVM model predicted large areas in Central Asia, Australia, and Europe as more highly suitable compared to MaxEnt and CLIMEX. Annual mean temperature and annual precipitation contributed most to the MaxEnt prediction. Both MaxEnt and CLIMEX showed the probability of Psa establishment increased above 5 ◦C and decreased above 20 ◦C. The annual precipitation response curve showed that excessive rain (>1200 mm/y) constrains Psa establishment.Our modelling results will provide useful information for Psa management by highlighting the climatically susceptible areas where Psa has not established, such as the USA, Iran, Denmark, Belgium and especially South Africa, where kiwifruit has been planted commercially in recent years.

Abstract The Russian city of Murmansk has about 300,000 inhabitants and is located inside the Arctic Circle in NE Scandinavia (Russia). It has one of the largest such concentrations of people in the Arctic. The city is a scientific, industrial, cultural, and transportation centre (an ice-free port in the so-called Northern Sea Route, connecting Europe with Asia). Currently, air pollution in the city is associated with outdated city heating technology, coal dust from the port and vehicular traffic, and so-called “small emissions”. The authors propose practical solutions based on known examples of Scandinavian cities with similar climatic conditions such as: the modernisation of heat energy acquisition; diversification of energy acquisition including renewable sources; thermal insulation of buildings; arrangement of urban greenery with dust-catching plants, and proposals for changing the habits within the population by promoting the use of public transport.

Abstract This work aims to assess potential changes in the mean and extreme precipitation and temperature across the Sacramento–San Joaquin Delta (Delta) in California in the 21st century. The study employs operative climate model projections from the Coupled Model Inter-comparison Project Phase 5 (CMIP5). Specifically, 64 individual downscaled daily projections (1/16 degree, approximately 6 by 6 km) on precipitation and temperature from 32 Global Circulation Models (GCMs) under two emission scenarios (RCP 4.5 and RCP 8.5) from 2020–2099 are utilized for the analysis. The results indicate increasing warming (in mean, minimum, and maximum temperature) further into the future under both emission scenarios. Warming also exhibits a strong seasonality, with winters expecting lower and summers expecting higher increases in temperature. In contrast, for mean annual total precipitation, there is no consistent wetter or drier signal. On average, the changes in annual total precipitation are minimal. However, dry season precipitation is projected to decline. The study also shows that the number of wet days is projected to decrease while the number of very wet (daily precipitation over 10 mm) and extremely wet (daily precipitation over 20 mm) days is projected to increase. Moreover, the study illustrates that only about half of the changes in total annual precipitation are projected to come from changes in the wettest 10% of wet days. In contrast, a majority of changes in variance of the annual precipitation comes from changes in variance of the wettest 10% of the wet days. This suggests that fluctuations in large storms are projected to dictate the variability of precipitation in the Delta. Additionally, a general upward trend in dry conditions measured by the Standardized Precipitation-Evapotranspiration Index is expected during the projection period. The trending signal is stronger at multi-year temporal scales (one to four years) and under the higher emission scenario. These change patterns are generally similar across three sub-regions of the Delta (i.e., North, South, and West), even though some changes in the South Delta are the most pronounced. This study further discusses challenges posed by these changes to the Delta’s water supply and ecosystems, along with the Delta’s resiliency and potential ways to address these challenges.

Abstract The food self-sufficiency policy has always featured as an unquestionable policy objective for Egypt. This is understandable when one considers both the high population growth and the social and political vulnerability associated with a dependence on food imports and world market food prices such as wheat. Intensive agriculture has led to a growing subsidy burden for the Egyptian government. In addition, the agricultural fields in Egypt are commonly distributed with relatively small sizes parcels that usually reduce the reliability of the agricultural sector, particularly in the delta region, to meet the national food policy. On top of that, climate change, through changing weather patterns and increased temperatures, is affecting agricultural yields and thus farmers’ livelihoods.A water profitability analysis was conducted for three governorates in the Nile Delta in Egypt to establish a baseline and assess the net return per unit of water of the main crops in each of these governorates; this can act as a reference of the water profitability of different crops before they are affected by climate change and other internal and external factors. The analysis was based on extensive in-person surveys in each governorate in addition to workshop discussions with farmers. The study has highlighted the impact of a lack of extension services, which limits farmers’ ability to increase their land and water productivity. Farmers with more access to subsidized production inputs managed to achieve higher levels of water profitability even on smaller lands. Finally, we drew from our findings key policy actions to improve water profitability and land productivity for farmers in the Nile Delta to achieve higher levels of food security. This will help build resilient food production systems that are reliable in the face of climate change and other rivers. In addition, an integrated nexus strategy and plan for the inter- and intra-country is recommended to address thechallenges related to food and climate security.