Machine learning reveals complex effects of climatic means and weather extremes on wheat yields during different plant developmental stages

Rising weather volatility poses a growing challenge to crop yields in many global breadbaskets. However, empirical evidence regarding the effects of extreme weather conditions on crop yields remains incomplete. We examine the contribution of climate and weather to winter wheat yields in Ukraine, a leading crop exporter with some of the highest yield variabilities observed globally. We used machine learning to link daily climatic data with annual winter wheat yields from 1985 to 2018. We differentiated the impacts of long-term climatic conditions (e.g., temperature) and weather extremes (e.g., heat waves) on yields during the distinct developmental stages of winter wheat. Our results suggest that climatic and weather variables alone explained 54% of the wheat yield variability at the country level. Heat waves, tropical night waves, frost, and drought conditions, particularly during the reproductive and grain filling phase, constitute key factors that compromised wheat yields in Ukraine. Assessing the impacts of weather extremes on crop yields is urgent to inform strategies that help cushion farmers against growing production risks because these extremes will likely become more frequent and intense with climate change.

Quantitative evaluation of the mitigation effect of low-impact development pavement materials on urban heat island and tropical night phenomena

Rapid urbanization has led to altered thermal circulations in major cities that are responsible for the increasing occurrence of urban heat islands (UHIs) and events such as tropical nights and heat waves. To effectively mitigate such events, low-impact development (LID) and green infrastructure strategies have been developed. In Korea, LID techniques focus mainly on road pavement materials; however, issues regarding the reliability of measurements due to differences in the measurement equipment and studied specimens persist. This study presents the design of a green infrastructure surface temperature measurement (GSTM) instrument and a reliable methodology developed to evaluate the performance of pavement materials under controlled climate conditions. The developed GSTM instrument and methodology were tested by monitoring the surface temperature of materials based on LID practices and dense-graded asphalt and evaluating their ability to mitigate UHI and tropical night phenomena. The experiments were conducted under controlled climate conditions, using summer climate conditions of Seoul's typical meteorological year data. The UHI and tropical night phenomena mitigation performance of the pavement materials was evaluated by analyzing the correlation between the pavement materials' albedo and surface temperature using porous block specimens of different colors and LID-based pavement materials. The greening block recorded the most significant reduction in surface temperature, showing a difference of 22.6 °C, 185 min to the dense-graded asphalt. The white and yellow porous blocks showed surface temperature differences of 10.2 °C and 8.2 °C respectively compared to the dense-graded asphalt. The results revealed that pavement materials with higher albedo, more evaporation, and lower heat capacity have superior performance in mitigating UHI and tropical night events.

Human and natural drivers of the recent contrasting trends between daytime and nighttime hot extremes

The frequency and duration of extreme heat events, including heat waves (HWs, daytime hot extremes) and tropical night (TNs), are increasing significantly as the climate warms, adversely affecting human health, agriculture, and energy consumption. Although many detection and attribution studies have examined extreme heat events, the underlying mechanisms associated with the recent increase in HWs and TNs remain unclear. In this study, we analyze the controlling factors behind the distinct increases in HW and TN events over the Northern Hemisphere during boreal summer (June to August). We found that the occurrence of HW events has been increasing gradually since 1980, mostly due to anthropogenic forcing. However, the occurrence of TN events increased abruptly during the late 1990s and has changed little since then. We demonstrate that this sudden increase in TN events is closely associated with low frequency variability in sea surface temperature, including the Pacific Decadal Oscillation, indicating its natural origin. We further found that CMIP5 climate models fail to capture the observed non-linear increases in TN events, implying potentially large uncertainty in future projections of nighttime heat events and its impacts on human society and ecosystem.

Data for the geoecology of solution karst dolines, with particular attention to climatic, soil and biogenic environment

In this study the changes in the nighttime heat load in Carpathian Basin cities during the 21st century were examined. To quantify the heat load, the tropical night climate index was used. The MUKLIMO_3 local scale climate model was used to describe the urban processes and the land use classes were defined by the local climate zones. The expected change was examined over three periods: the 1981–2010 was taken as reference period using the Carpatclim database and the 2021–2050 and 2071–2100 future periods using EURO-CORDEX regional model simulation data for two scenarios (RCP4.5 and RCP8.5). To combine the detailed spatial resolution and the long time series, a downscaling method was applied. Our results show that spectacular changes could be in the number of tropical nights during the 21st century and the increasing effect of the urban landform is obvious. In the near future, a slight increase can be expected in the number of tropical nights, which magnitude varies from city to city and there is no major difference between the scenarios. However, at the end of the century the results of the two scenarios differ: the values can be 15-25 nights in case of RCP4.5 and 30-50 nights in case of RCP8.5. The results show that dwellers could be exposed to high heat load in the future, as the combined effect of climate change and urban climate, thus developing various mitigation and adaptation strategies is crucial.

Projected changes in heat load in Carpathian Basin cities during the 21st century

In this study the changes in the nighttime heat load in Carpathian Basin cities during the 21st century were examined. To quantify the heat load, the tropical night climate index was used. The MUKLIMO_3 local scale climate model was used to describe the urban processes and the land use classes were defined by the local climate zones. The expected change was examined over three periods: the 1981–2010 was taken as reference period using the Carpatclim database and the 2021–2050 and 2071–2100 future periods using EURO-CORDEX regional model simulation data for two scenarios (RCP4.5 and RCP8.5). To combine the detailed spatial resolution and the long time series, a downscaling method was applied. Our results show that spectacular changes could be in the number of tropical nights during the 21st century and the increasing effect of the urban landform is obvious. In the near future, a slight increase can be expected in the number of tropical nights, which magnitude varies from city to city and there is no major difference between the scenarios. However, at the end of the century the results of the two scenarios differ: the values can be 15-25 nights in case of RCP4.5 and 30-50 nights in case of RCP8.5. The results show that dwellers could be exposed to high heat load in the future, as the combined effect of climate change and urban climate, thus developing various mitigation and adaptation strategies is crucial.

A Building Block Urban Meteorological Observation Experiment (BBMEX) over Seoul City, Korea

<p>For the purpose of understanding the detailed distribution of surface and air temperatures in a high-rise building block, a 3-dimensional Building-Block Meteorological observation EXperiment (BBMEX) campaign has been carried out over typical commercial area (Gwanghwamun) in Seoul Metropolitan Area, Korea during the heat-wave and tropical night periods (5-6 August) in 2019. Several types of fixed and mobile instruments were deployed in the experiment domain: A thermal infrared imager (TIR) monitored the surface temperature with 320×240 pixels including building wall, road, sidewalks at every 10 min; 6 automatic weather stations obtained air temperature and relative humidity, and wind speed and direction at every 1 min; a mobile weather vehicle (MOVE4) monitored road surface temperatures and 4-components of radiation at 1 s on roadway; a mobile cart for meteorological observation (MCMO) monitored surface, 0.5m, 1.5m, and 2.5m air temperatures at 1 s on the sidewalk and square. The TIR exhibited that east-face of a building was strongly heated during the morning time, while horizontal surface was strongly heated near noon. Air temperatures at 2 m high in 2×2 km<sup>2</sup> exhibited 1.5 ℃ temperature range at 06 LST, while 4.0 ℃ temperature range at 15 LST on 6 August 2019, depending on the location of site in building blocks. Air temperatures in Gwanghwamun Square were 1.5-1.7 ℃ and 0.1-2.2 ℃ higher than those observed at the Seoul synoptic station (1 km apart) in night and day, respectively. Surface and 0.5, 1,5, and 2.5m temperatures was 49.1 ℃, 38.7 ℃, 38.1 ℃, and 37.9 ℃, respectively, at 1500 LST on 6 August 2019, when the hottest air temperature in the year 2019 (36.9 ℃) was recorded at the Seoul station. Surface and air temperatures were found to be affected by many factors in a building-block such as shades, trees, building height and density, aspect ratio of building canyon, sky-view, ground-fountain, waterway, etc.</p>

Hotspots of Extreme Heat under Global Warming

<p>We evaluate how hotspots of different types of the most extreme summer heat change under global warming increase of up to 4°C, to determine the level of global warming that allows us to avert the risk of these hotspots considering the irreducible range of possibilities defined by well-sampled internal variability. We use large samples of low-probability extremes simulated by the 100-member Max Planck Institute Grand Ensemble (MPI-GE) for five metrics of extreme heat: maximum reachable temperatures, return periods of extreme temperatures, maximum temperature variability, sustained tropical nights, and wet bulb temperatures. At 2°C of warming, MPI-GE projects maximum summer temperatures below 50°C over most of the world. Beyond 2°C, this threshold is overshot in all continents, with projected temperatures above 60°C in hotspots such as the Arabic Peninsula. Extreme 1-in-100-years pre-industrial temperatures occur every 10-25 years already at 1.5°C of warming. At 4°C, these 1-in-100-years extremes are projected to occur every one to two years over most of the world. The range of maximum temperature variability increases by 10-50% at 2°C of warming, and by 50-100% at 4°C. Beyond 2°C, heat stress is aggravated substantially over non-adapted areas by sustained tropical night and hot and humid conditions that occur rarely in a pre-industrial climate. At 4°C of warming, tropical night hotspots spread polewards globally, and prevail for at least 95% of the summer months; whilst extreme monthly mean wet bulb temperatures beyond 26°C spread over large tropical as well as mid-latitude regions.</p>