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This study investigated monthly variations of surface urban heat island intensity (SUHII) and the applicability of the local climate zones (LCZ) scheme for land surface temperature (LST) differentiation within three spatial contexts, including urban, rural and their combination, in Shenyang, China, a city with a monsoon-influenced humid continental climate. The monthly SUHII and LST of Shenyang were obtained through 12 LST images, with one in each month (within the period between 2018 and 2020), retrieved from the Thermal InfraRed Sensor (TIRS) 10 in Landsat 8 based on a split window algorithm. Non-parametric analysis of Kruskal-Wallis H test and a multiple pairwise comparison were adopted to investigate the monthly LST differentiations with LCZs. Overall, the SUHII and the applicability of the LCZ scheme exhibited spatiotemporal variations. July and August were the two months when Shenyang underwent strong heat island effects. Shenyang underwent a longer period of cool than heat island effects, occurring from November to May. June and October were the transition months of cool–heat and heat–cool island phenomena, respectively. The SUHII analysis was dependent on the definition of urban and rural boundaries, where a smaller rural buffering zone resulted in a weaker SUHI or surface urban cool island (SUCI) phenomenon and a larger urban area corresponded to a weaker SUHI or SUCI phenomenon as well. The LST of LCZs did not follow a fixed order, where in July and August, the LCZ-10 (Heavy industry) had the highest mean LST, followed by LCZ-2 (Compact midrise) and then LCZ-7 (Lightweight low-rise). In comparison, LCZ-7, LCZ-8 (Large low-rise) and LCZ-9 (Sparsely built) had the highest LST from October to May. The LST of LCZs varied with urban and rural contexts, where LCZ-7, LCZ-8 and LCZ -10 were the three built LCZs that had the highest LST within urban context, while LCZ-2, LCZ-3 (Compact low-rise), LCZ-8, LCZ-9 and LCZ-10 were the five built LCZs that had the highest LST within rural context. The suitability of the LCZ scheme for temperature differentiation varied with the month, where from July to October, the LCZ scheme had the strongest capability and in May, it had the weakest capability. Urban context also made a difference to the suitability, where compared with the whole study area (the combination of urban and rural areas), the suitability of built LCZs in either urban or rural contexts weakened. Moreover, the built LCZs had a higher level of suitability in an urban context compared with a rural context, while the land-cover LCZs within rural had a higher level of suitability.

This study focuses on the northward shift of homogeneous agro-climate zones in Europeanalyzed for the observed past and projected climate conditions for the next decades. Statisticalcluster analysis is used to derive eight main agro-climatic zones driven by two agro-meteorologicalindicators, namely, active temperature sum and thermal growing season length. The northward shift ofhomogeneous agro-climate zones and the corresponding change of crop growth suitability are analyzedtogether with the change of exposure of crops to temperature-related climate extremes during the growingseason. Gradual warming over Europe has contributed to a lengthening of the growing season and anincreased active temperature accumulation, accompanied by more frequent occurrence of warm extremeclimate events. Using a set of five high-resolutionregional climate scenarios, we calculate that a major partof Europe will be affected by further northward climate zone migration. In the next decades, the migrationof agro-climatic zones in Eastern Europe may reach twice the velocity observed during the period1975–2016. Several regions of the Mediterranean may lose suitability to grow specific crops in favor ofnorthern European regions. This indicator-based assessment suggests that the potential advantages of thelengthening of the thermal growing season in northern and eastern Europe are often outbalanced by therisk of late frost and increased risk of early spring and summer heat waves.

Aim Investigate the relative abilities of different bioclimatic models and data sets to project species ranges in novel environments utilizing the natural experiment in biogeography provided by Australian Acacia species. Location Australia, South Africa. Methods We built bioclimatic models for Acacia cyclops and Acacia pycnantha using two discriminatory correlative models (MaxEnt and Boosted Regression Trees) and a mechanistic niche model (CLIMEX). We fitted models using two training data sets: native-range data only ('restricted') and all available global data excluding South Africa ('full'). We compared the ability of these techniques to project suitable climate for independent records of the species in South Africa. In addition, we assessed the global potential distributions of the species to projected climate change. Results All model projections assessed against their training data, the South African data and globally were statistically significant. In South Africa and globally, the additional information contained in the full data set generally improved model sensitivity, but at the expense of increased modelled prevalence, particularly in extrapolation areas for the correlative models. All models projected some climatically suitable areas in South Africa not currently occupied by the species. At the global scale, widespread and biologically unrealistic projections by the correlative models were explained by open-ended response curves, a problem which was not always addressed by broader background climate space or by the extra information in the full data set. In contrast, the global projections for CLIMEX were more conservative. Projections into 2070 indicated a polewards shift in climate suitability and a decrease in model interpolation area. Main conclusions Our results highlight the importance of carefully interpreting model projections in novel climates, particularly for correlative models. Much work is required to ensure bioclimatic models performed in a robust and ecologically plausible manner in novel climates. We explore reasons for variations between models and suggest methods and techniques for future improvements.

Identifying the main factors influencing the land surface temperature (LST) of each local climate zone (LCZ) built type is of great significance for controlling LST. This study investigated the main factors influencing the LST of each LCZ built type in two Asian megacities: Tokyo and Shanghai. Each area in both megacities was classified according to the LCZ scheme. The diurnal LST, pervious surface fraction (PSF), surface albedo (SA), average building height (⟨BH⟩), and gross building coverage ratio (λp) of each LCZ were also calculated. Finally, the influence of the properties of each LCZ built type on LST was investigated. The results demonstrated that the main factors influencing LST of different LCZ built types differed in Tokyo and Shanghai. ⟨BH⟩ was the main factor influencing LST for compact mid-rise and open high-rise types in Tokyo, and the compact mid-rise type in Shanghai; PSF was the main factor influencing LST for other LCZ built types. Moreover, both ⟨BH⟩ and PSF negatively correlated with LST. Based on the above results and characteristics of each LCZ built type, specific LST mitigation strategies for each LCZ built type were proposed for Tokyo and Shanghai. The approach in this study can contribute to perspectives for urban planners and policymakers to develop highly feasible and reasonable LST mitigation strategies.

Using the local climate zone (LCZ) framework and multiple Earth observation input features, an LCZ classification was developed and established for Riyadh City in 2017. Four land-cover-type and four urban-type LCZs were identified in the city with an overall accuracy of 87%. The bare soil/sand (LCZ-F) class was found to be the largest LCZ class, which was within the nature of arid climate cities. Other land-cover LCZs had a lower coverage percentage (each class with <7%). The compact low-rise (LCZ-3) class was the largest urban type, as urban development in arid climate cities tends to extend horizontally. The daytime surface thermal characteristics of the developed LCZs were analyzed at seasonal timescales using land surface temperature (LST) estimated from multiple Landsat 8 satellite images (June 2017–May 2018). The highest daytime mean LST was found over large low-rise (LCZ-8) class areas throughout the year. This class was the only urban-type LCZ class that demonstrated a positive LST departure from the overall mean LST across seasons. Other urban-type LCZ classes showed lower LSTs and negative deviations from the overall mean LSTs. The overall thermal results suggested the presence of the surface urban heat island sink phenomenon as urban areas experienced lower LSTs than their surroundings. Thermal results demonstrated that the magnitudes of LST differences among LCZs were considerably dependent on the way the region of interest/analysis was defined. This was related to the types of LCZ classes presented in the study area and the spatial distribution and abundance of these LCZ classes. The developed LCZ classification and thermal results have several potential applications in different areas including planning and urban design strategies and urban health-related studies.

Understanding changes in urban internal structure and land surface temperature (LST) is essential. The local climate zone (LCZ) scheme has been extensively applied to characterize urban spatial structure, which has potential for urban climate research. We combined optical imagery and synthetic aperture radar (SAR) data (Landsat-5 and PALSAR for 2008; Sentinel-2 and PALSAR-2 for 2020) to map the LCZs in Shanghai, China. The results showed that the areas of open high-rise and open mid-rise buildings significantly increased from 2008 to 2020. Then, we investigated the spatiotemporal variations in LST based on the LCZ data from 2008 to 2020 using the grid method. The mean daytime LST (obtained from Landsat-5 and Landsat-8) was higher in 2020 than in 2008 for each LCZ type in spring. The mean daytime LSTs of compact mid-rise, compact low-rise, large low-rise and heavy industry zones were higher than those of other LCZ types in spring and summer. The mean nighttime LST (obtained from ASTER) in the downtown area was higher than that in the suburbs in summer. Furthermore, the mean nighttime LST of the built types was also generally higher than that of the natural types in summer. A comparison of the mean daytime LSTs in 2008 and 2020 revealed that the expansion trend of the higher LST areas in spring and summer is consistent with the expansion areas of the mid-rise and high-rise built types.

Local climate zone (LCZ) maps have been used widely to study urban structures and urban heat islands. Because remote sensing data enable automated LCZ mapping on a large scale, there is a need to evaluate how well remote sensing resources can produce fine LCZ maps to assess urban thermal environments. In this study, we combined Sentinel-2 multispectral imagery and dual-polarized (HH + HV) PALSAR-2 data to generate LCZ maps of Nanchang, China using a random forest classifier and a grid-cell-based method. We then used the classifier to evaluate the importance scores of different input features (Sentinel-2 bands, PALSAR-2 channels, and textural features) for the classification model and their contribution to each LCZ class. Finally, we investigated the relationship between LCZs and land surface temperatures (LSTs) derived from summer nighttime ASTER thermal imagery by spatial statistical analysis. The highest classification accuracy was 89.96% when all features were used, which highlighted the potential of Sentinel-2 and dual-polarized PALSAR-2 data. The most important input feature was the short-wave infrared-2 band of Sentinel-2. The spectral reflectance was more important than polarimetric and textural features in LCZ classification. PALSAR-2 data were beneficial for several land cover LCZ types when Sentinel-2 and PALSAR-2 were combined. Summer nighttime LSTs in most LCZs differed significantly from each other. Results also demonstrated that grid-cell processing provided more homogeneous LCZ maps than the usual resampling methods. This study provided a promising reference to further improve LCZ classification and quantitative analysis of local climate.

Sea breezes are important in a coastal urban climate; however, the impact of urban development on the effects of sea breezes, which decrease air temperature and increase humidity, has not been understood quantitatively. To quantitatively evaluate this impact in Sendai, Japan over the past twenty years, this study analyzed the heat balance mechanisms in urban spaces based on the simulation results of the Weather Research and Forecasting (WRF) model coupled with Local Climate Zone (LCZ) maps. Compared to the observation data on air temperature, specific humidity, and wind in August 2002, results of the numerical simulation, using the 2002 LCZ map and the meteorological conditions of August 2002, confirmed that the WRF model could reproduce meteorological factors well. Thereafter, two numerical simulations using the LCZ maps from 2002 and 2022 were conducted based on the same meteorological condition, from 25 July to 1 September 2008, to extract the impact of urban development on the effects of sea breeze. Consequently, when land use changed from urban built-up land to natural land cover, both the effects of sea breeze—decreasing air temperature and increasing humidity—decreased. Additionally, increases in LCZ 3 (compact low rise), mainly from LCZ 6 (open low rise) and LCZ 9 (sparsely built), decreased the effects of sea breeze (decreasing air temperature and increasing humidity) by 5% and 10%, respectively, in areas around Sendai Station. This was because the consumption of the sea breeze’s potential to decrease air temperature and increase humidity increased and the wind speed of sea breezes decreased in the windward areas of Sendai Station. These results provide new insights into the impact of urban development on the effects of sea breeze and quantitatively reveal changes in the effects of sea breeze.

Targeting existing residential buildings for retrofit improvement presents significant prospects for global reduction of energy-usage and carbon footprints. Energy consumption of the existing single-family building in the hot-humid climate zone needs to be targeted for improvement due to their potential energy-savings and sizeable market share. This paper proposes and evaluates energy savings and cost-effectiveness of a whole building retrofit package for single-family residential buildings built between 1950 and 1970. The study outlined a survey conducted using the clustering data mining technique on Florida Single-Family Residential (SFR) homes to determine the essence of the building envelope, patterns of construction, and Heating, Ventilation, and Air-Conditioning (HVAC) systems. The evaluation of the energy efficiency measures (EEMs) effectiveness is performed utilizing Autodesk Revit and a Six-Step modeled framework. This framework consists of baseline model development, validation of the model with an actual case study building, identification of potential EMMs, evaluation of EEMs individually and incorporating the selected EMMs into retrofit package and maximizing the energy-saving and cost-effectiveness of the proposed retrofit package. The study develops proposed retrofit improvement package detailing replacement & improvement EEMs, implementation cost, annual energy savings (kWh), cost-saving ($), and payback period (years) for each individual EEM as well as the combined/total energy improvement package. The paper further explored the usage of solar photovoltaics (PV) energy generation options to offset the remaining energy-use after the implementation of the proposed retrofit package.