A High Spatiotemporal Resolution Global Gridded Dataset of Historical Human Discomfort Indices.

Meteorological human discomfort indices or bioclimatic indices are important metrics to gauge potential risks to human health under varying environmental thermal exposures. Derived using sub-daily meteorological variables from a quality-controlled reanalysis data product (Global Land Data Assimilation System—GLDAS), a new high-resolution global dataset referred to as “HDI_0p25_1970_2018” is presented in this study. The dataset includes the following daily indices at 0.25° × 0.25° gridded resolution: (i) Apparent Temperature indoors (ATind); (ii) two variants of Apparent Temperature outdoors in shade (ATot); (iii) Heat Index (HI); (iv) Humidex (HDEX); (v) Wet Bulb Temperature (WBT); (vi) two variants of Wet Bulb Globe Temperature (WBGT); (vii) Thom Discomfort Index (DI); and (viii) Windchill Temperature (WCT). Spanning 49 years over the period 1970–2018, HDI_0p25_1970_2018 fills gaps in existing climate indices datasets by being the only high-resolution historical global-gridded daily time-series of multiple human discomfort indices based on different meteorological parameters, thus offering applications in wide-ranging climate zones and thermal-comfort environments

The role of residential air circulation and cooling demand for electrification planning: Implications of climate change in sub-Saharan Africa.

Nearly 1 billion people live without electricity at home. Energy poverty limits their ability to take autonomous actions to improve air circulation and the cooling of their homes. It is therefore important that electricity-access planners explicitly evaluate the current and future air circulation and cooling needs of energy-poor households, in addition to other basic energy needs. To address this issue, we combine climate, socio-economic, demographic and satellite data with scenario analysis to model spatially explicit estimates of potential cooling demand from households that currently lack access to electricity. We link these demand factors into a bottom-up electrification model for sub-Saharan Africa, the region with the world's highest concentration of energy poverty. Accounting for cooling needs on top of baseline household demand implies that the average electrification investment requirements grow robustly (a scenario mean of 65.5% more than when considering baseline household demand only), mostly due to the larger generation capacity needed. Future climate change could increase the investment requirements by an additional scenario mean of 4%. Moreover, the share of decentralised systems as the lowest-cost electrification option falls by a scenario mean 4.5 percentage points of all new connections. The crucial determinants for efficient investment pathways are the adoption and use of cooling appliances, the extent of climate change, and the baseline electricity demand. Our results call for a more explicit consideration of climate-adaptative energy needs by infrastructure planners in developing countries

Intraseasonal Precipitation Variability over West Africa under 1.5 °C and 2.0 °C Global Warming Scenarios: Results from CORDEX RCMs.

This study assessed the performance of 24 simulations, from five regional climate models (RCMs) participating in the Coordinated Regional Climate Downscaling Experiment (CORDEX), in representing spatiotemporal characteristics of precipitation over West Africa, compared to observations. The top five performing RCM simulations were used to assess future precipitation changes over West Africa, under 1.5 °C and 2.0 °C global warming levels (GWLs), following the representative concentration pathway (RCP) 8.5. The performance evaluation and future change assessment were done using a set of seven ‘descriptors’ of West African precipitation namely the simple precipitation intensity index (SDII), the consecutive wet days (CWD), the number of wet days index (R1MM), the number of wet days with moderate and heavy intensity precipitation (R10MM and R30MM, respectively), and annual and June to September daily mean precipitation (ANN and JJAS, respectively). The performance assessment and future change outlook were done for the CORDEX–Africa subdomains of north West Africa (WA-N), south West Africa (WA-S), and a combination of the two subdomains. While the performance of RCM runs was descriptor- and subregion- specific, five model runs emerged as top performers in representing precipitation characteristics over both WA-N and WA-S. The five model runs are CCLM4 forced by ICHEC-EC-EARTH (r12i1p1), RCA4 forced by CCCma-CanESM2 (r1i1p1), RACMO22T forced by MOHC-HadGEM2-ES (r1i1p1), and the ensemble means of simulations made by CCLM4 and RACMO22T. All precipitation descriptors recorded a reduction under the two warming levels, except the SDII which recorded an increase. Unlike the WA-N that showed less frequency and more intense precipitation, the WA-S showed increased frequency and intensity. Given the potential impact that these projected changes may have on West Africa’s socioeconomic activities, adjustments in investment may be required to take advantage of (and enhance system resilience against damage that may result from) the potential changes in precipitation

Global vulnerability of crop yields to climate change.

Using a newly-available panel dataset of gridded annual crop yields in conjunction with a dynamic econometric model that distinguishes between farmers' short-run and long-run responses to weather shocks and accounts for adaptation, we investigate the risk to global crop yields from climate warming. Over broad spatial domains we observe only slight moderation of short-run impacts by farmers' long-run adjustments. In the absence of additional margins of adaptation beyond those pursued historically, projections constructed using an ensemble of 21 climate model simulations suggest that the climate change could reduce global crop yields by 3–12% by mid-century and 11–25% by century's end, under a vigorous warming scenario

SHARE-ENV: A Data Set to Advance Our Knowledge of the Environment–Wellbeing Relationship

Climate change interacts with other environmental stressors and vulnerability factors. Some places and, owing to socioeconomic conditions, some people, are far more at risk. The data behind current assessments of the environment–wellbeing nexus is coarse and regionally aggregated, when considering multiple regions/groups; or, when granular, comes from ad hoc samples with few variables. To assess the impacts of climate change, we require data that are granular and comprehensive, both in the variables and population studied. We build a publicly accessible data set, the SHARE-ENV data set, which fulfills these criteria. We expand on EU representative, individual-level, longitudinal data (the SHARE survey), with environmental exposure information about temperature, radiation, precipitation, pollution, and flood events. We illustrate through four simplified multilevel linear regressions, cross-sectional and longitudinal, how full-fledged studies can use SHARE-ENV to contribute to the literature. Such studies would help assess climate impacts and estimate the effectiveness and fairness of several climate adaptation policies. Other surveys can be expanded with environmental information to unlock different research avenues

Increased energy use for adaptation significantly impacts mitigation pathways.

Climate adaptation actions can be energy-intensive, but how adaptation feeds back into the energy system and the environment is absent in nearly all up-to-date energy scenarios. Here we quantify the impacts of adaptation actions entailing direct changes in final energy use on energy investments and costs, greenhouse gas emissions, and air pollution. We find that energy needs for adaptation increase considerably over time and with warming. The resulting addition in capacity for power generation leads to higher greenhouse gas emissions, local air pollutants, and energy system costs. In the short to medium term, much of the added capacity for power generation is fossil-fuel based. We show that mitigation pathways accounting for the adaptation-energy feedback would require a higher global carbon price, between 5% and 30% higher. Because of the benefits in terms of reduced adaptation needs, energy system costs in ambitious mitigation scenarios would be lower than previous estimates, and they would turn negative in well-below-2-degree scenarios, pointing at net gains in terms of power system costs

Reconstructing individual-level exposures in cohort analyses of environmental risks: an example with the UK Biobank.

Recent developments in linkage procedures and exposure modelling offer great prospects for cohort analyses on the health risks of environmental factors. However, assigning individual-level exposures to large population-based cohorts poses methodological and practical problems. In this contribution, we illustrate a linkage framework to reconstruct environmental exposures for individual-level epidemiological analyses, discussing methodological and practical issues such as residential mobility and privacy concerns. The framework outlined here requires the availability of individual residential histories with related time periods, as well as high-resolution spatio-temporal maps of environmental exposures. The linkage process is carried out in three steps: (1) spatial alignment of the exposure maps and residential locations to extract address-specific exposure series; (2) reconstruction of individual-level exposure histories accounting for residential changes during the follow-up; (3) flexible definition of exposure summaries consistent with alternative research questions and epidemiological designs. The procedure is exemplified by the linkage and processing of daily averages of air pollution for the UK Biobank cohort using gridded spatio-temporal maps across Great Britain. This results in the extraction of exposure summaries suitable for epidemiological analyses of both short and long-term risk associations and, in general, for the investigation of temporal dependencies. The linkage framework presented here is generally applicable to multiple environmental stressors and can be extended beyond the reconstruction of residential exposures. IMPACT: This contribution describes a linkage framework to assign individual-level environmental exposures to population-based cohorts using high-resolution spatio-temporal exposure. The framework can be used to address current limitations of exposure assessment for the analysis of health risks associated with environmental stressors. The linkage of detailed exposure information at the individual level offers the opportunity to define flexible exposure summaries tailored to specific study designs and research questions. The application of the framework is exemplified by the linkage of fine particulate matter (PM2.5) exposures to the UK Biobank cohort

Air conditioning and electricity expenditure: The role of climate in temperate countries

This paper investigates how households adopt and use air conditioning to adapt to climate change and increasingly high temperatures, which pose a threat to the health of vulnerable populations. The analysis examines conditions in eight temperate, industrialized countries (Australia, Canada, France, Japan, the Netherlands, Spain, Sweden, and Switzerland). The identification strategy exploits cross-country and cross-household variations by matching geocoded households with climate data. Our findings suggest that households respond to excess heat by purchasing and using air conditioners, leading to increased electricity consumption. Households on average spend 35%–42% more on electricity when they adopt air conditioning. Through an illustrative analysis, we show that climate change and the growing demand for air conditioning are likely to exacerbate energy poverty. The number of energy poor who spend a high share of income on electricity increases, and households in the lowest income quantile are the most negatively affected

Optimal heat stress metric for modelling heat‐related mortality varies from country to country

Combined heat and humidity is frequently described as the main driver of human heat-related mortality, more so than dry-bulb temperature alone. While based on physiological thinking, this assumption has not been robustly supported by epidemiological evidence. By performing the first systematic comparison of eight heat stress metrics (i.e., temperature combined with humidity and other climate variables) with warm-season mortality, in 604 locations over 39 countries, we find that the optimal metric for modelling mortality varies from country to country. Temperature metrics with no or little humidity modification associates best with mortality in ~40% of the studied countries. Apparent temperature (combined temperature, humidity and wind speed) dominates in another 40% of countries. There is no obvious climate grouping in these results. We recommend, where possible, that researchers use the optimal metric for each country. However, dry-bulb temperature performs similarly to humidity-based heat stress metrics in estimating heat-related mortality in present-day climate