Projecting Health Impacts of Future Temperature: A Comparison of Quantile-Mapping Bias-Correction Methods

Abstract

Health impact assessments of future environmental exposures are routinely conducted to quantify population burdens associated with the changing climate. It is well-recognized that simulations from climate models need to be bias-corrected against observations to estimate future exposures. Quantile mapping (QM) is a technique that has gained popularity in climate science because of its focus on bias-correcting the entire exposure distribution. Even though improved bias-correction at the extreme tails of exposure may be particularly important for estimating health burdens, the application of QM in health impact projection has been limited. In this paper we describe and apply five QM methods to estimate excess emergency department (ED) visits due to projected changes in warm-season minimum temperature in Atlanta, USA. We utilized temperature projections from an ensemble of regional climate models in the North American-Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). Across QM methods, we estimated consistent increase in ED visits across climate model ensemble under RCP 8.5 during the period 2050 to 2099. We found that QM methods can significantly reduce between-model variation in health impact projections (50–70% decreases in between-model standard deviation). Particularly, the quantile delta mapping approach had the largest reduction and is recommended also because of its ability to preserve model-projected absolute temporal changes in quantiles.

Climate projections of human thermal comfort for indoor workplaces

“…To make EURO-CORDEX data comparable to ERA5 data, EURO-CORDEX data were bias corrected relative to ERA5-Land data before their use as input data for the ANNs. ERA5-Land data of 1970–1999 at a temporal resolution of 3 h were used as input data for workplace-specific ANN models to calculate the indoor thermal comfort at every workplace for the entire 30-year historical period 1970–1999 and to adjust the EURO-CORDEX data to the ERA5-Land data according to the quantile delta mapping (QDM) method using the historical modeled EURO-CORDEX data from 1970 to 1999 for all used variables of each climate projection (Cannon et al 2015 ; Fauzi et al 2020 ; Qian and Chang 2021 ). QDM is an improved version of quantile mapping (Qian and Chang 2021 ; Tong et al 2021 ), a commonly used method in climate downscaling to bias-correct climate projections against observations (Thrasher et al 2012 ; Fiddes et al 2022 ).…”

Impacts of Climate Change on Temperature and Precipitation in Nepal: Projections and Bias Correction

“…The estimation of a flexible quantile mapping function g() such that F-1(τ)=g[F-1(τ)] is done using NLLS based on the ten closest data points indicated in the quantilequantile plot for each quantile level τ = 0, 0.01, 0.02,….., 0.99, 1.0. The future projection at time 't' is then derived through the expression: (Qian and Chang, 2021).…”

Identifying Cost-Effective Low-Impact Development (LID) under Climate Change: A Multi-Objective Optimization Approach

“…Therefore, the projected precipitation is commonly downscaled and bias corrected using statistical relationships with the observed precipitation [69]. Many statistical techniques are available to downscale and bias correct the projected precipitation, with the delta change (DC), quantile mapping (QM), and equidistance quantile mapping (EQM) methods being the most commonly used [70].…”