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The green transformation of the built environment is aimed at improving sustainability and can be supported by digitalization, which has become a significant tool to support the supply, integration, and management of information throughout the construction life cycle. In addition, climate change highly affects human comfort in the built environment and different strategies should be evaluated for adapting cities. This paper developed a digital workflow by integrating existing tools (i.e., Grasshopper, Ladybug, Honeybee, and Dragonfly) to evaluate how different green infrastructure strategies affected the thermal comfort by reducing the UTCI. The workflow was applied to a typical historical urban context (Catania, South of Italy), consisting of a square surrounded by three-floor buildings. Three basic scenarios were created that depended on the pavement material used in the built environment: a black stone pavement (reference material from Mount Etna), a permeable pavement, and grass. These three scenarios were combined with different green infrastructure strategies: tree pattern on the square, green walls and green roofs on the surrounding buildings, and the integrations of all these above-mentioned strategies. The results demonstrated that the integration of different green strategies (a grass square instead of pavement, with trees, and green walls and green roofs) increased the thermal comfort by reducing the UTCI by more than 8 °C compared to the existing urban context (black stone pavement and building envelope). However, this temperature reduction was highly affected by the location of the human body into the urban context and by the evaporation rates from vegetation. The workflow developed will be useful for designers to evaluate the effectiveness of different green strategies during the early-design stage in mitigating and adapting cities to climate change. [ABSTRACT FROM AUTHOR]

Australia experiences a variety of climate extremes that result in loss of life and economic and environmental damage. This paper provides a first evaluation of the performance of state-of-the-art Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models (GCMs) in simulating climate extremes over Australia. Here, we evaluate how well 37 individual CMIP6 GCMs simulate the spatiotemporal patterns of 12 climate extremes over Australia by comparing the GCMs against gridded observations (Australian Gridded Climate Dataset). This evaluation is crucial for informing, interpreting, and constructing multimodel ensemble future projections of climate extremes over Australia, climate-resilience planning, and GCM selection while conducting exercises like dynamical downscaling via GCMs. We find that temperature extremes (maximum-maximum temperature -TXx, number of summer days -SU, and number of days when maximum temperature is greater than 35 °C -Txge35) are reasonably well-simulated in comparison to precipitation extremes. However, GCMs tend to overestimate (underestimate) minimum (maximum) temperature extremes. GCMs also typically struggle to capture both extremely dry (consecutive dry days -CDD) and wet (99th percentile of precipitation -R99p) precipitation extremes, thus highlighting the underlying uncertainty of GCMs in capturing regional drought and flood conditions. Typically for both precipitation and temperature extremes, UKESM1-0-LL, FGOALS-g3, and GCMs from Met office Hadley Centre (HadGEM3-GC31-MM and HadGEM3-GC31-LL) and NOAA (GFDL-ESM4 and GFDL-CM4) consistently tend to show good performance. Our results also show that GCMs from the same modelling group and GCMs sharing key modelling components tend to have similar biases and thus are not highly independent. [ABSTRACT FROM AUTHOR]

Liaoning Province, a crucial agricultural region in Northeast China, has endured frequent drought disasters in recent years, significantly affecting both agricultural production and the ecological environment. Conducting drought research is of paramount importance for formulating scientific drought monitoring and prevention strategies, ensuring agricultural production and ecological safety. This study developed a Comprehensive Joint Drought Index (CJDI) using the empirical Copula function to systematically analyze drought events in Liaoning Province from 1981 to 2020. Through the application of MK trend tests, Morlet wavelet analysis, and run theory, the spatiotemporal variation patterns and recurrence characteristics of drought in Liaoning Province were thoroughly investigated. The results show that, compared to the three classic drought indices, Standardized Precipitation Index (SPI), Evaporative Demand Drought Index (EDDI), and Standardized Precipitation Evapotranspiration Index (SPEI), CJDI has the highest accuracy in monitoring actual drought events. From 1981 to 2020, drought intensity in all regions of Liaoning Province (east, west, south, and north) exhibited an upward trend, with the western region experiencing the most significant increase, as evidenced by an MK test Z-value of −4.53. Drought events in Liaoning Province show clear seasonality, with the most significant periodic fluctuations in spring (main cycles of 5–20 years, longer cycles of 40–57 years), while the frequency and variability of drought events in autumn and winter are lower. Mild droughts frequently occur in Liaoning Province, with joint and co-occurrence recurrence periods ranging from 1.0 to 1.8 years. Moderate droughts have shorter joint recurrence periods in the eastern region (1.2–1.4 years) and longer in the western and southern regions (1.4–2.2 years), with the longest co-occurrence recurrence period in the southern region (3.0–4.0 years). Severe and extreme droughts are less frequent in Liaoning Province. This study provides a scientific foundation for drought monitoring and prevention in Liaoning Province and serves as a valuable reference for developing agricultural production strategies to adapt to climate change. [ABSTRACT FROM AUTHOR]

Wind resources play a pivotal role in building sustainable energy systems, crucial for mitigating and adapting to climate change. With the increasing frequency of extreme events under global warming, effective prediction of extreme wind resource potential can improve the safety of wind farms and other infrastructure, while optimizing resource allocation and emergency response plans. In this study, we evaluate the seasonal prediction skill for summer extreme wind events over China using a 20-year hindcast dataset generated by a dynamical seamless prediction system designed by Shanghai Investigation, Design and Research Institute Co., Ltd. (Shanghai, China) (SIDRI-ESS V1.0). Firstly, the hindcast effectively simulates the spatial distribution of summer extreme wind speed thresholds, even though it tends to overestimate the thresholds in most regions. Secondly, high prediction skills, measured by temporal correlation coefficient (TCC) and normalized root mean square error (nRMSE), are observed in northeast China, central east China, southeast China, and the Tibetan Plateau (TCC is about 0.5 and the nRMSE is below 0.9 in these regions). The highest skills emerge in southeast China with a maximum TCC greater than 0.7, and effective prediction skill can extend up to a 5-month lead time. Ensemble prediction significantly enhances predictive skill and reduces uncertainty, with 24 ensemble members being sufficient to saturate TCC and 12–16 members for nRMSE in most key regions and lead times. Furthermore, we show that the prediction skill for extreme wind counts is strongly related to the prediction skill for summer mean wind speeds, particularly in southeast China. Overall, SIDRI-ESS V1.0 shows promising performance in predicting extreme winds and has great potential to provide services to the wind industry. It can effectively help to optimize wind farm operating strategies and improve power generation efficiency. However, further improvements are needed, particularly in areas where prediction skills for extreme winds are influenced by smaller-scale weather phenomena and areas with complex underlying surfaces and climate characteristics. [ABSTRACT FROM AUTHOR]

Mediterranean water-stressed areas face significant challenges from higher temperatures and increasingly severe droughts. We assess the effect of climate change on rainfed barley production in the aridity-prone province of Almería, Spain, using the FAO AquaCrop model. We focus on rainfed barley growth by the mid-century (2041–2070) and end-century (2071–2100) time periods, using three Shared Socio-economic Pathway (SSP)-based scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. Using the paired t-test, Spearman and Pearson correlation coefficient, Root Mean Squared Error, and relative Root Mean Squared Error, we verified AquaCrop's ability to capture local multi-year trends (9 or more years) using standard barley crop parameters, without local recalibration. Starting with a reference Initial Soil Water Content (ISWC), different soil water contents within barley rooting depth were modelled to account for decreases in soil water availability. We then evaluated the efficiency of different climate adaptation strategies: irrigation, mulching, and changing sowing dates. We show average yield changes of +14% to −44.8% (mid-century) and +12% to −55.1% (end-century), with ISWC being the main factor determining yields. Irrigation increases yields by 21.1%, utilizing just 3% of Almería's superficial water resources. Mulches improve irrigated yield performances by 6.9% while reducing irrigation needs by 40%. Changing sowing dates does not consistently improve yields. We demonstrate that regardless of the scenario used, climate adaptation of field barley production in Almería should prioritize limiting soil water loss by combining irrigation with mulching. This would enable farmers in Almería's northern communities to maintain their livelihoods, reducing the province's reliance on horticulture while continuing to contribute to food security goals. [ABSTRACT FROM AUTHOR]

Agricultural production across the African continent is subjected to various effects of climate variability. One of the main staple foods in Sub-Saharan Africa is maize. However, limited scientific research has recently focused on understanding the possible effects of hydro-climatic variability on maize production. The aim of the present work was to contribute to policy and climate adaptation, thus reducing the vulnerability of maize production to climate change over Equatorial Africa. This study firstly examined long-term trends of precipitation (PRE), soil moisture (SM), actual evapotranspiration (E), and potential evapotranspiration (Ep), as well as surface air temperatures, including the minimum (TMIN) and maximum (TMAX). Secondly, the relationship between maize production and these climate variables was quantified for 18 Equatorial African countries (EQCs) over 1980−2021. To assess the linear trends, Mann–Kendall and Sen's slope tests were used to quantify the magnitude of the hydro-climatic variable trends at the 5% significance level, and Pearson's correlation coefficient was used to evaluate the relation of these climate parameters with the maize production. The annual mean PRE declined at 0.03 mm day−110a−1. Other climate variables increased at different rates: SM at 0.02 mmday−110a−1, E at 0.03 mm day−110a−1, Ep at 0.02 mm day−1 10a−1, TMIN and TMAX at 0.01 °C day−110a−1. A regional analysis revealed heterogeneous significant wet–dry and warm–cool trends over the EQCs. While, spatially, dry and warm climates were observed in the central to eastern areas, wet and warm conditions dominated the western regions. Generally, the correlations of maize production with the E, Ep, TMAX, and TMIN were strong (r > 0.7) and positive, while moderate (r > 0.45) correlations of maize production with PRE and SM were obvious. These country-wide analyses highlight the significance of climate change policies and offer a scientific basis for designing tailored adaptation strategies in rainfed agricultural regions. [ABSTRACT FROM AUTHOR]

This review seeks to enhance the understanding of the critical concepts of vulnerability, adaptation, and resilience within the context of global environmental challenges, with a particular focus on climate change. Climate change is characterized by rising global temperatures and an increase in extreme weather events, making the comprehension and addressing of these concepts crucial for effective adaptation strategies. Despite widespread recognition of the interconnectedness of vulnerability, adaptation, and resilience, there remains a gap in a comprehensive understanding of how these concepts interrelate. Through synthesizing existing literature, this review provides a detailed examination of their definitions and the interrelationships among vulnerability, adaptation, resilience, and climate-related disasters. Additionally, it explores the impact of climate change on future disaster risk reduction efforts by analyzing the nexus between climate change adaptation and disaster risk reduction. Key findings highlight the necessity of incorporating social, institutional, economic, and environmental factors into adaptation planning and call for innovative approaches to boost adaptive capacity and resilience. This review not only furthers the discourse in research, policy, and practice in this vital area but also offers strategic insights for developing more resilient and adaptive societies amidst the challenges posed by climate change. [ABSTRACT FROM AUTHOR]

This paper explores how climate services are framed in the literature and possible implications for climate services' policies and projects. By critically exploring the frames around climate services, the wider objective is to encourage more reflexive and responsible research in the field, particularly given the huge challenge that climate change represents. By using a framing analysis based on an extensive literature review, five dominant frames were identified. Climate services are mainly framed (1) as a technological innovation, (2) as a market, (3) as an interface between users and producers, (4) as a risk management tool, and (5) from an ethical angle. The predominant frames influence how we think about climate services, shared assumptions, and the way in which policies and projects are designed. To prevent negative effects of climate services on the ground, such as inequalities, the main recommendations include establishing interdisciplinary and transdisciplinary dialogues between different communities of practice and players, increasing empirical and social science research to improve our understanding of this new field, and finally, re-thinking climate services in terms of adaptation rather than as the mere production of new information products. [ABSTRACT FROM AUTHOR]

Rainfall onset and cessation date greatly influence cropping calendar decisions in rain-fed agricultural systems. This paper examined trends of onsets, cessation, and the length of growing season over Northern Ghana using CHIRPS-v2, gauge, and farmers' perceptions data between 1981 and 2019. Results from CHIRPS-v2 revealed that the three seasonal rainfall indices have substantial latitudinal variability. Significant late and early onsets were observed at the West and East of 1.5° W longitude, respectively. Significant late cessations and longer growing periods occurred across Northern Ghana. The ability of farmers' perceptions and CHIRPS-v2 to capture rainfall onsets are time and location-dependent. A total of 71% of farmers rely on traditional knowledge to forecast rainfall onsets. Adaptation measures applied were not always consistent with the rainfall seasonality. More investment in modern climate information services is required to complement the existing local knowledge of forecasting rainfall seasonality. [ABSTRACT FROM AUTHOR]

Climate change (CC) is a worldwide environmental issue affecting all economic sectors, especially agriculture. Pakistan is one of the countries most affected by CC due to the country's vulnerability to catastrophic events and limited ability to adapt. Assessing existing activities for adaptation to CC at the farm level is critical to understanding their success and recommending additional government measures. This study analyzes possible farming practice modifications that Pakistani farmers may adopt to reduce the loss of agricultural output due to the rising prevalence of dangerous weather events by CC. Data for the current research were gathered from 432 wheat farmers in rural Pakistan. This article investigates many factors that impact farmers' decisions to CC adaptation in crop production utilizing binary logit (BL) and multivariate probit (MVP) models. Gender, education level, farming experience, farm size, level of damage, access to finance, and training participation are characteristics that substantially affect farmers' likelihood of adapting to CC. Farm size and participation in CC training were the most critical factors influencing farmers' CC adaptation decisions. Policy recommendations were presented to increase the farmers' resilience in the study areas to CC. These comprise expanding CC training courses, developing regulations to encourage agricultural integration, and integrating CC and adaptation to CC principles into the operations of regional organizations. Finally, based on the findings, policymakers will be better equipped to address the challenges posed by CC and create a more resilient agricultural sector. This, in turn, will contribute to improving food security, ensuring sustainable agricultural growth. [ABSTRACT FROM AUTHOR]

Climate extreme events have been observed more frequently since the 1970s throughout Ethiopia, which adversely affects the socio-economic development of the country, as its economy depends on agriculture, which, in turn, relies heavily on annual and seasonal rainfall. Climate extremes studies conducted in Ethiopia are mainly limited to a specific location or watershed, making it difficult to have insights at the national level. The present study thus aims to examine the observed climate extreme events in Ethiopia at both station and agro-ecological zone (AEZ) levels. Daily rainfall and temperature data for 47 and 37 stations, respectively (1986 up to 2020), were obtained from the National Meteorology Agency (NMA). The Modified Mann–Kendall (MMK) trend test and the Theil–Sen slope estimator were employed to estimate the trends in rainfall and temperature extremes. This study examines trends of 13 temperature and 10 rainfall extreme indices using RClimDex in R software. The results revealed that most of the extreme rainfall indices showed a positive trend in the majority of the climate stations. For example, an increase in consecutive dry days (CDD), very heavy rainfall days (R20), number of heavy rainfall days (R10) and consecutive wet days (CWD) were exhibited in most climate stations. In relation to AEZs, the greater number of extreme rainfall indices illustrated an upward trend in cool and sub-humid, cool and humid, and cool and moist AEZs, a declining trend in hot arid AEZ, and equal proportions of increasing and decreasing trends in warm semi-arid AEZs. Concerning extreme temperature indices, the result indicated an increasing trend of warm temperature extreme indices and a downward trend of cold temperature extreme indices in most of the climate stations, indicating the overall warming and dryness trends in the country. With reference to AEZs, an overall warming was exhibited in all AEZs, except in the hot arid AEZ. The observed trends in the rainfall and temperature extremes will have tremendous direct and indirect impacts on agriculture, water resources, health, and other sectors in the country. Therefore, the findings suggest the need for identifying and developing climate change adaptation strategies to minimize the ill effects of these extreme climate events on the social, economic, and developmental sectors. [ABSTRACT FROM AUTHOR]

The Integrated Agrometeorological Prediction System (IAPS) was a two-year project for the development of the long term forecast (LRF) for agricultural producers. Using LRF in decision-making, to reduce the risks and seize the opportunities, represents short-term adaptation to climate change. High-resolution ensemble forecasts (51 forecasts) were made for a period of 7 months and were initiated on the first day of each month. For the initial testing of the capacity of LRF to provide useful information for producers, 2017 was chosen as the test year as it had a very hot summer and severe drought, which caused significant impacts on agricultural production. LRF was very useful in predicting the variables which bear the memory of the longer period, such are growing degree days for the prediction of dates of the phenophases' occurrences and the soil moisture of deeper soil layers as an indicator for the drought. Other project activities included field observations, communication with producers, web portal development, etc. Our results showed that the selected priority forecasting products were also identified by the producers as being the highest weather-related risks, the operational forecast implementation with the products designed for the use in agricultural production is proven to be urgent and necessary for decision-making, and required investments are affordable. The total cost of the full upgrade of agrometeorological climate services to meet current needs (including monitoring, seamless forecasting system development and the development of tools for information dissemination) was found to be about three orders of magnitude lower than the assessed losses in agricultural production in the two extreme years over the past decade. [ABSTRACT FROM AUTHOR]

The green transformation of the built environment is aimed at improving sustainability and can be supported by digitalization, which has become a significant tool to support the supply, integration, and management of information throughout the construction life cycle. In addition, climate change highly affects human comfort in the built environment and different strategies should be evaluated for adapting cities. This paper developed a digital workflow by integrating existing tools (i.e., Grasshopper, Ladybug, Honeybee, and Dragonfly) to evaluate how different green infrastructure strategies affected the thermal comfort by reducing the UTCI. The workflow was applied to a typical historical urban context (Catania, South of Italy), consisting of a square surrounded by three-floor buildings. Three basic scenarios were created that depended on the pavement material used in the built environment: a black stone pavement (reference material from Mount Etna), a permeable pavement, and grass. These three scenarios were combined with different green infrastructure strategies: tree pattern on the square, green walls and green roofs on the surrounding buildings, and the integrations of all these above-mentioned strategies. The results demonstrated that the integration of different green strategies (a grass square instead of pavement, with trees, and green walls and green roofs) increased the thermal comfort by reducing the UTCI by more than 8 °C compared to the existing urban context (black stone pavement and building envelope). However, this temperature reduction was highly affected by the location of the human body into the urban context and by the evaporation rates from vegetation. The workflow developed will be useful for designers to evaluate the effectiveness of different green strategies during the early-design stage in mitigating and adapting cities to climate change.

Australia experiences a variety of climate extremes that result in loss of life and economic and environmental damage. This paper provides a first evaluation of the performance of state-of-the-art Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models (GCMs) in simulating climate extremes over Australia. Here, we evaluate how well 37 individual CMIP6 GCMs simulate the spatiotemporal patterns of 12 climate extremes over Australia by comparing the GCMs against gridded observations (Australian Gridded Climate Dataset). This evaluation is crucial for informing, interpreting, and constructing multimodel ensemble future projections of climate extremes over Australia, climate-resilience planning, and GCM selection while conducting exercises like dynamical downscaling via GCMs. We find that temperature extremes (maximum-maximum temperature -TXx, number of summer days -SU, and number of days when maximum temperature is greater than 35 °C -Txge35) are reasonably well-simulated in comparison to precipitation extremes. However, GCMs tend to overestimate (underestimate) minimum (maximum) temperature extremes. GCMs also typically struggle to capture both extremely dry (consecutive dry days -CDD) and wet (99th percentile of precipitation -R99p) precipitation extremes, thus highlighting the underlying uncertainty of GCMs in capturing regional drought and flood conditions. Typically for both precipitation and temperature extremes, UKESM1-0-LL, FGOALS-g3, and GCMs from Met office Hadley Centre (HadGEM3-GC31-MM and HadGEM3-GC31-LL) and NOAA (GFDL-ESM4 and GFDL-CM4) consistently tend to show good performance. Our results also show that GCMs from the same modelling group and GCMs sharing key modelling components tend to have similar biases and thus are not highly independent.

Since maize, peanut, and cotton are economically valuable crops in the southeast United States, their yield amount changes in a future climate are attention-grabbing statistics demanded by associated stakeholders and policymakers. The Crop System Modeling—Decision Support System for Agrotechnology Transfer (CSM-DSSAT) models of maize, peanut, and cotton are, respectively, driven by the North American Regional Climate Change Assessment Program (NARCCAP) Phase II regional climate models to estimate current (1971–2000) and future (2041–2070) crop yield amounts. In particular, the future weather/climate data are based on the Special Report on Emission Scenarios (SRES) A2 emissions scenario. The NARCCAP realizations show on average that there will be large temperature increases (~2.7 °C) and minor rainfall decreases (~−0.10 mm/day) with pattern shifts in the southeast United States. With these future climate projections, the overall future crop yield amounts appear to be reduced under rainfed conditions. A better estimate of future crop yield amounts might be achievable by utilizing the so-called weighted ensemble method. It is proposed that the reduced crop yield amounts in the future could be mitigated by altering the currently adopted local planting dates without any irrigation support. [ABSTRACT FROM AUTHOR]

Rainfall onset and cessation date greatly influence cropping calendar decisions in rain-fed agricultural systems. This paper examined trends of onsets, cessation, and the length of growing season over Northern Ghana using CHIRPS-v2, gauge, and farmers’ perceptions data between 1981 and 2019. Results from CHIRPS-v2 revealed that the three seasonal rainfall indices have substantial latitudinal variability. Significant late and early onsets were observed at the West and East of 1.5° W longitude, respectively. Significant late cessations and longer growing periods occurred across Northern Ghana. The ability of farmers’ perceptions and CHIRPS-v2 to capture rainfall onsets are time and location-dependent. A total of 71% of farmers rely on traditional knowledge to forecast rainfall onsets. Adaptation measures applied were not always consistent with the rainfall seasonality. More investment in modern climate information services is required to complement the existing local knowledge of forecasting rainfall seasonality.

This paper explores how climate services are framed in the literature and possible implications for climate services’ policies and projects. By critically exploring the frames around climate services, the wider objective is to encourage more reflexive and responsible research in the field, particularly given the huge challenge that climate change represents. By using a framing analysis based on an extensive literature review, five dominant frames were identified. Climate services are mainly framed (1) as a technological innovation, (2) as a market, (3) as an interface between users and producers, (4) as a risk management tool, and (5) from an ethical angle. The predominant frames influence how we think about climate services, shared assumptions, and the way in which policies and projects are designed. To prevent negative effects of climate services on the ground, such as inequalities, the main recommendations include establishing interdisciplinary and transdisciplinary dialogues between different communities of practice and players, increasing empirical and social science research to improve our understanding of this new field, and finally, re-thinking climate services in terms of adaptation rather than as the mere production of new information products.

Weather variations affect natural ecosystems, while in regions where climate change is anticipated to intensify extreme events such as droughts, the vitality of vulnerable species may be reduced. The sensitivity of key-species to the climatic conditions may illustrate their adjustability in specific areas and assist decision making towards proper mitigation and adaptation measures. Pinus nigra, commonly known as black pine, is an endemic species, forming many protected habitats in the Mediterranean. In this study, black pine tree-ring data from Greece are used to assess the response of tree growth to specific temperature-related (mean, max. and min. temperature and diurnal temperature range) and water-related (precipitation, evapotranspiration, relative humidity and vapor pressure deficit) meteorological parameters. Additionally, the effect of drought episodes is estimated using indices, including the well-established standardised precipitation index (SPI) and reconnaissance drought index (RDI), as well as two recently proposed modifications, namely, the agricultural SPI (aSPI) and the effective RDI (eRDI). The outcomes reveal several seasonal patterns, emphasising the sensitivity of black pine principally to water-related meteorological parameters, with winter and early spring conditions having a primary role on annual tree growth. Black pine seems to be tolerant to drought in the study region, in terms of its resilience; however, there are indications that multiyear droughts may have prolonged effects on tree growth, which may last approximately three years after drought ends. Additionally, it is derived that both aSPI and eRDI illustrate more efficiently tree growth response to drought, indicating that these modifications provide increased accuracy regarding drought characterisation in the forest environment. [ABSTRACT FROM AUTHOR]