Implementation and comparison of a suite of heat stress metrics within the Community Land Model version 4.5

J R Buzan,M Huber,K Oleson

doi:10.5194/gmd-8-151-2015

Abstract

Abstract. We implement and analyze 13 different metrics (4 moist thermodynamic quantities and 9 heat stress metrics) in the Community Land Model (CLM4.5), the land surface component of the Community Earth System Model (CESM). We call these routines the HumanIndexMod. We limit the algorithms of the HumanIndexMod to meteorological inputs of temperature, moisture, and pressure for their calculation. All metrics assume no direct sunlight exposure. The goal of this project is to implement a common framework for calculating operationally used heat stress metrics, in climate models, offline output, and locally sourced weather data sets, with the intent that the HumanIndexMod may be used with the broadest of applications. The thermodynamic quantities use the latest, most accurate and efficient algorithms available, which in turn are used as inputs to the heat stress metrics. There are three advantages of adding these metrics to CLM4.5: (1) improved moist thermodynamic quantities; (2) quantifying heat stress in every available environment within CLM4.5; and (3) these metrics may be used with human, animal, and industrial applications. We demonstrate the capabilities of the HumanIndexMod in a default configuration simulation using CLM4.5. We output 4× daily temporal resolution globally. We show that the advantage of implementing these routines into CLM4.5 is capturing the nonlinearity of the covariation of temperature and moisture conditions. For example, we show that there are systematic biases of up to 1.5 °C between monthly and ±0.5 °C between 4× daily offline calculations and the online instantaneous calculation, respectively. Additionally, we show that the differences between an inaccurate wet bulb calculation and the improved wet bulb calculation are ±1.5 °C. These differences are important due to human responses to heat stress being nonlinear. Furthermore, we show heat stress has unique regional characteristics. Some metrics have a strong dependency on regionally extreme moisture, while others have a strong dependency on regionally extreme temperature.

Highlights

Heat-related conditions are the number one cause of death from natural disaster in the United States – more than tornadoes, flooding, and hurricanes combined (NOAAWatch, 2014)
We show that the advantage of implementing these routines into CLM4.5 is capturing the nonlinearity of the covariation of temperature and moisture conditions
The primary focus of this paper is on atmospheric-variablebased heat stress metrics that we introduce into the HumanIndexMod

Summary

Introduction

Heat-related conditions are the number one cause of death from natural disaster in the United States – more than tornadoes, flooding, and hurricanes combined (NOAAWatch, 2014). Long-term exposure (heat waves or seasonally high heat), even without working, may drastically increase morbidity and mortality (Kjellstrom et al, 2009a). There is high uncertainty in the number of deaths, the 2003 European heat wave killed 40 000 people during a couple weeks in August (García-Herrera et al, 2010), and tens of thousands more altogether for the entire summer (Robine et al, 2008). Each index that we chose uses a combination of atmospheric variables: temperature (T ), humidity (Q), and pressure (P ). We chose these metrics because they are in operational use globally by industry, governments, and weather services. These metrics may be applied to the broadest range of uses: climate and weather forecasting models, archive data sets, and local weather stations

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Conclusion