Climate

In view of global climate change, studies on long-term changes in Near-Surface Air Temperature (NSAT) in the Brazil region are highly relevant. Climate warming requires the government to develop various adaptation strategies to these changes in order to maintain marine and terrestrial ecosystems in a stable state. Pearson correlation analysis, with significance assessment of the obtained results, was used to analyze NSAT data from 39 weather stations, along with the ERA5 reanalysis dataset and 33 CMIP6 models, for the different SSP scenarios of greenhouse gas emissions in the Brazil region (10° N–40° S; 75–25° W). The increase in NSAT of the Brazil region for 1964–2023 was 0.4 ± 0.2 °C based on meteorological stations and 0.3 ± 0.1 °C based on the ERA5 reanalysis. NSAT by ERA5 data changed little from 1940 to 1970, after which a relatively rapid increase began at a rate of +0.18 °C/10 years. The increase in NSAT for the period 1940–2023 was 0.66 ± 0.17 °C based on the CMIP6 model ensemble. The CMIP6 models show the increase in the average NSAT across the region of 0.75–1.08 °C from 1994–2023 to 2024–2053. The average NSAT in the region under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios shows an increase of 1.05, 1.89, 2.75, and 3.53 °C by the end of the 21st century, respectively. Moreover, NSAT over land in the study region is increasing faster than over the ocean. According to the CMIP6 ensemble, NSAT over land in the Brazil region increased by an average of 0.6–1.0 °C from 1940–1969 to 1994–2023, while over the ocean near the Brazilian coastline it increased by approximately 0.5 °C. From 1994–2023 to 2070–2099, the projected warming over land is expected to be 1.0–1.4, 1.6–2.6, 2.4–3.8, and 3.2–5.0 °C for the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, respectively. Over the ocean, however, the estimated warming is substantially smaller—from 0.8 to 2.8 °C, depending on the SSP scenario, which may be explained by the stabilizing role of the ocean.

Small urban parks are the dominant form of green spaces in most Japanese cities and hold great potential for heat stress mitigation. However, most research has focused on large urban parks, leaving a knowledge gap in how small parks can be designed to mitigate heat. Given that small parks are primarily used for rest, we focused on resting areas and assessed their thermal conditions in three typical small parks in Kyoto, Japan. We then examined how the spatial arrangements of park elements influenced thermal conditions. Results revealed that nearly half of the resting areas were uncomfortable, underscoring the urgent need for spatial design improvements. Linear mixed-effects models showed that while shade elements, such as tree canopies and roofs, most effectively enhanced thermal perception, their effectiveness was distance- and orientation-dependent. We also found a critical mismatch between green ground and shade elements that adversely affected thermal conditions. Our findings highlight that strategic spatial design, particularly the thoughtful placement of shade elements and resting areas, is the key to providing thermal comfort in small urban parks. This study provides evidence that small parks can act as urban heat spots if poorly designed, but with appropriate design they can become cool refuges.

The increasing occurrence of extreme rainfall events often leads to flash floods, infrastructure damage, loss of human life, and significant economic impacts. There is a pressing need for data-driven assessments and the application of robust analytical approaches to better understand these changes. Analyzing ground-level daily rainfall data from 1985 to 2023 from 26 monitoring stations, this study first employs the Mann–Kendall test using robust statistics including minimum, median, various quartiles, and maximum rainfall values for detecting long-term trends across Saudi Arabia. Next, the k-means clustering technique is applied to characterize the annual rainfall cycles across different regions of the country. Finally, the Peaks Over Threshold (POT) approach within Extreme Value Theory (EVT) is employed to identify site-specific thresholds for extreme rainfall using the Generalized Pareto Distribution (GPD). This automated, data-driven method offers a more objective alternative to the commonly used ad hoc percentile-based threshold selection, thereby enhancing the rigour and reproducibility of extreme rainfall analysis. Local specific thresholds were computed ranging from about 16 to 47 mm from Arar and Jazan, respectively. These thresholds were then used to calculate the frequency and intensity of extreme rainfall events. The fitted GPD parameters were further used to estimate return levels (RLs) for different return periods (2-, 5-, 10-, 20-, 50-, and 100-year) into the future. The results underscore considerable spatial variability in extreme rainfall behaviour across Saudi Arabia, with a higher likelihood of intense and infrequent precipitation events in the coming decades.