Combining socio-economic and climate projections to assess heat risk

The assessments of future climate risks are common; however, usually, they focus on climate projections without considering social changes. We project heat risks for Finland to evaluate (1) what kind of differences there are in heat vulnerability projections with different scenarios and scales, and (2) how the use of socio-economic scenarios influences heat risk assessments. We project a vulnerability index with seven indicators downscaled to the postal code area scale for 2050. Three different scenario sets for vulnerability are tested: one with five global Shared Socioeconomic Pathways (SSPs) scenarios; the second with three European SSPs (EUSSPs) with data at the sub-national scale (NUTS2); and the last with the EUSSPs but aggregated data at the national scale. We construct projections of heat risk utilizing climatic heat hazard data for three different Representative Concentration Pathways (RCPs) and vulnerability and exposure data for five global SSPs up to 2100. In the vulnerability projections, each scenario in each dataset shows a decrease in vulnerability compared to current values, and the differences between the three scenario sets are small. There are evident differences both in the spatial patterns and in the temporal trends when comparing the risk projections with constant vulnerability to the projections with dynamic vulnerability. Heat hazard increases notably in RCP4.5 and RCP8.5, but a decrease of vulnerability especially in SSP1 and SSP5 alleviates risks. We show that projections of vulnerability have a considerable impact on future heat-related risk and emphasize that future risk assessments should include the combination of long-term climatic and socio-economic projections.

An urban climate service to manage heat risks in UK and Chinese cities

<p>Recent extreme heat events in the UK are likely to become more frequent over the 21st Century and exacerbated in cities due to the urban heat island effect. Due to high population densities and a concentration of assets, urban areas are more vulnerable to climatic extremes with impacts that traverse health, infrastructure, built environment and economic activity. Risks to health, well-being and productivity from high temperatures is one of six priority areas from in UK Climate Change Risk Assessment (2017) where more action is needed to manage risks, prompting local authorities to understand heat risks within their city. <br>City based climate services are needed for day-to-day operations in cities, emergency response and to inform urban design and development.  Recent advances in high resolution modelling enable better representation of urban processes and provide greater understanding of extreme events.  By exploiting such advances in underpinning science, the Met Office is generating urban climate services for city stakeholders to plan for and manage heat stress in their city.<br>The Met Office has been engaging with local authorities and city stakeholders in the UK and China to co-produce a prototype, two tier, urban heat climate service to enhance the resilience of urban environments to extreme heat events.  The prototype is based on a strong requirement from several cities to develop an evidence base of the heat hazard and understand current and future hot spots vulnerable to extremes of heat within the city.  Tier 1 uses observations and high-resolution climate data to provide city specific information of the heat hazard in a graphical factsheet format.  This includes information on future changes in temperature, extreme heat indicators, frequency and duration of heatwave events, and spatial distribution of heat across the city.  Tier 2 involves working closely with city stakeholders to combine the hazard information with data on health, built environment and socio-economics, to provide tailored information on heat exposure and vulnerability.  This will allow users to identify highly vulnerable parts of the city network and neighbourhoods for priority action.  This two-tier service can provide an evidence base to inform urban policy, design and adaptation strategies, and prepare authorities and city stakeholders for future demand on city services. </p>

Analysis of the Universal Thermal Climate Index during heat waves in Serbia

The objective of this paper is to assess the bioclimatic conditions in Serbia during summer in order to identify biothermal heat hazard. Special emphasis is placed on the bioclimatic index UTCI (Universal Thermal Climate Index), whose purpose is to evaluate the degree of thermal stress that the human body is exposed to. For this research, mean daily and hourly (07:00 and 14:00 CET) meteorological data from three weather stations (Zlatibor, Novi Sad and Niš) have been collected for the period from 1998 to 2017. In order to identify patterns of biothermal heat stress conditions, the thresholds of the daily UTCI (UTCI ≥38 ∘C, referred to as very strong heat stress, VSHS) at 14:00 CET are compared with the thresholds of daily maximum air temperature (tmax⁡≥35 ∘C, referred to as hot days, HDs), which are further termed as heat wave events (HWEs). The findings show that the UTCI heat stress category “very strong heat stress“ at 14:00 CET indicates heat waves. The most extreme heat wave events occurred in 2007, 2012, 2015 and 2017. Moreover, three HWEs at Niš that occurred in July 2007 lasted 3, 10 and 4 d. HWEs and very strong heat stress events (VSHSEs) recorded in July 2007 (lasted 10 d each), 2012 (lasted 9 and 12 d) and 2015 (lasted 7 and 10 d) were of the longest duration and are considered to be the indicators of biothermal heat hazard. The daily UTCI14 h heat stress becomes more extreme in terms of severity and heat wave duration up to very strong heat stress.

EER test and analysis of surface ice cooling system for coal mine

With the depth increasing and the improvement of mining mechanization of the coal mine, heat hazard is becoming more and more serious, the ice cooling system is gradually promoted and applied in China for recent years, but the cooling capacity and energy efficiency ratio (EER) test and analysis for such system is seldom reported. The cooling capacity and EER of the refrigerating equipment of an ice cooling system of a coal mine are tested and analyzed in Shandong Province in this paper. The test result indicates that the refrigerating capacity and EER are lower and both the pre-cooling units and the ice making system have a large variable range. It points out that it should not evaluate the ice cooling system by the test data only in this mine but expect more test data and analysis of the ice cooling system in other coal mines to get a more scientific conclusion. And the test, analysis and comparison for different kinds of cooling system are also expected to explore suitable cooling system while a full consideration of the geological conditions and other factors of the mine are taken into consideration.

Assessing Urban Risk to Extreme Heat in China

Many cities are experiencing persistent risk in China due to frequent extreme weather events. Some extreme weather events, such as extreme heat hazard, have seriously threatened human health and socio-economic development in cities. There is an urgent need to measure the degree of extreme heat risk and identify cites with the highest levels of extreme heat risk. In this study, we presented a risk assessment framework of extreme heat and considered risk as a combination of hazard, exposure, and vulnerability. Based on these three dimensions, we selected relevant variables from historical meteorological data (1960–2016) and socioeconomic statistics in 2016, establishing an indicator system of extreme heat risk evaluation. Finally, we developed an extreme heat risk index to quantify the levels of extreme heat risk of 296 prefecture-level cities in China and revealed the spatial pattern of extreme heat risk in China in 2016 and their dominant factors. The results show that (1) cities with high levels of extreme heat hazard are mainly located in the south of China, especially in the southeast of China; (2) the spatial distribution of the extreme heat risk index shows obvious agglomeration characteristics; (3) the spatial distribution of the extreme heat risk is still mostly controlled by natural geographical conditions such as climate and topography; (4) among the four types of hazard-dominated, exposure-dominated, vulnerability-dominated, and low risk cities, the number of vulnerability-dominated cities is the largest. The results of this study can provide support for the risk management of extreme heat disasters and the formation of targeted countermeasures in China.

A mobile sensor-based Approach for Analyzing and Mitigating Urban Heat Hazards

<p>High-quality temperature data at a finer spatial-temporal scale is critical for analyzing the risk of heat hazards in urban environments. The variability of urban landscapes makes cities a challenging landscape for quantifying heat exposure. Most of the existing heat hazard studies have inherent limitations on two fronts: the spatial-temporal granularities are too coarse and the ability to track the actual ambient air temperature instead of land surface temperature. Overcoming these limitations requires radically different research approaches, both the paradigms for collecting the temperature data and developing models for high-resolution heat mapping. We present a comprehensive approach for studying urban heat hazards by harnessing a high-quality hyperlocal temperature dataset from a network of mobile sensors and using it to refine the satellite-based temperature products. We mounted vehicle-borne mobile sensors on thirty city buses to collect high-frequency (5 sec) temperature data from June 2018 to Nov 2019. The vehicle-borne data clearly show significant temperature differences across the city, with the largest differences of up to 10℃ and morning-afternoon diurnal changes at a magnitude around 20℃. Then we developed a machine learning approach to derive a hyperlocal ambient air temperature (AAT) product by combining the mobile-sensor temperature data, satellite LST data, and other influential biophysical parameters to map the variability of heat hazard over areas not covered by the buses. The machine learning model output highlighted the high spatio-temporal granularity in AAT within an urban heat island. The seasonal AAT maps derived from the model show a well-defined hyperlocal variability of heat hazards which are not evident from other research approaches. The findings from this study will be beneficial for understanding the heat exposure vulnerabilities for individual communities. It may also create a pathway for policymakers to devise targeted hazard mitigation efforts such as increasing green space and developing better heat-safety policies for workers.</p>