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Call for Papers: Anticipating the Tokyo Olympic Games

A multi-scalar climatological analysis in preparation for extreme heat at the Tokyo 2020 Olympic and Paralympic Games

ORCID Icon, , ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 191-214
Received 11 Dec 2019
Accepted 06 Feb 2020
Published online: 19 Mar 2020
 

ABSTRACT

Extreme heat can be harmful to human health and negatively affect athletic performance. The Tokyo Olympic and Paralympic Games are predicted to be the most oppressively hot Olympics on record. An interdisciplinary multi-scale perspective is provided concerning extreme heat in Tokyo—from planetary atmospheric dynamics, including El Niño Southern Oscillation (ENSO), to fine-scale urban temperatures—as relevant for heat preparedness efforts by sport, time of day, and venue. We utilize stochastic methods to link daytime average wet bulb globe temperature (WBGT) levels in Tokyo in August (from meteorological reanalysis data) with large-scale atmospheric dynamics and regional flows from 1981 to 2016. Further, we employ a mesonet of Tokyo weather stations (2009–2018) to interpolate the spatiotemporal variability in near-surface air temperatures at outdoor venues. Using principal component analysis, two planetary (ENSO) regions in the Pacific Ocean explain 70% of the variance in Tokyo’s August daytime WBGT across 35 years, varying by 3.95°C WGBT from the coolest to warmest quartile. The 10-year average daytime and maximum intra-urban air temperatures vary minimally across Tokyo (<1.2°C and 1.7°C, respectively), and less between venues (0.6–0.7°C), with numerous events planned for the hottest daytime period (1200–1500 hr). For instance, 45% and 38% of the Olympic and Paralympic road cycling events (long duration and intense) occur midday. Climatologically, Tokyo will present oppressive weather conditions, and March–May 2020 is the critical observation period to predict potential anomalous late-summer WBGT in Tokyo. Proactive climate assessment of expected conditions can be leveraged for heat preparedness across the Game’s period.

Acknowledgments

The authors would like to thank Dr. Andrew Perrin for assistance with stochastic modeling; Dr. Walter Kolczynski for support in extracting meta-data files; Dr. Glenn McGregor for very detailed and helpful insight on the analysis and writing; Dr. Jan Null for providing feedback on analysis ideas for ENSO strength; and Dr. Kevin Trenberth, a renowned ENSO expert at the National Center for Atmospheric Research (NCAR), for his time to read and comment on early findings.

Disclosure statement

In accordance with Taylor & Francis policy and my ethical obligation as researchers, the following are potential COIs:

DJC, AJG, YH:

● Have a potential COI as members of the IOC Adverse Weather Impact expert working Group for the Olympic Games Tokyo 2020; not receiving honorarium.

WMT:

● Consultant interest that may arise from the research reported in the enclosed paper. Those interests are fully to Taylor & Francis with an approved plan for managing any potential conflicts arising from this reporting, such as publicly disclosing errors or corrections for the benefit of evolving the science and protecting it from error.

Abbreviations

AMeDAS

Automated Meteorological Data Acquisition System

AQMS

Air Quality Monitoring System

AWS

Automatic Weather Station

BOH

Bonin/Ogasawara High

CP

Cerebral palsy

DJF

December, January, February

EHI

Exertional heat illness

ENSO

El Niño Southern Oscillation

HRI

Heat related illness

JJA

June, July, and August

JMA

Japan Meteorological Agency

MAM

March, April, and May

MERRA-2

Modern-Era Retrospective Analysis for Research & Applications

MS

Multiple sclerosis

NCEI

National Centers for Environmental Information

NDVI

Normalized difference vegetation index

NTL

Brightness of nighttime lights

PCA

Principal component analysis

PJI

Pacific Japan Index

PJO

Pacific Japan Oscillation

RFRK

Random forests-based regression kriging

RH

Relative humidity (%)

SCI

Spinal cord injury

SON

September, October, November

SST

Sea-surface temperature (°C)

Ta

Air temperature (°C)

Tˉa

Mean daytime air temperature (°C)

Tmax

Daytime high air temperature (°C)

Tˉmax

Mean daytime high air temperature (°C)

Tsfc

surface temperature (°C)

USGS

U.S. Geological Survey

VSE

Very Strong El Niño (or Super El Niño)

WBGT

Wet bulb globe temperature (°C)

WC

Walker-Circulation

WJ

West-Asia Jet

WJI

West-Asia Jet Index

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

The authors of this paper received no funding for this work.

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