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

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.

Keywords: ENSO; Tokyo; athletes; extreme heat; wet bulb globe temperature.

Figure 1.

Overview of scales and atmospheric predictors affecting Tokyo’s weather. (a) Large-scale (planetary) displaying Niño Regions. We used NINO3 and NINO3.4 in this study [Source: National Oceanic and Atmospheric Administration (NOAA, 2019), National Center for Environmental Intelligence (NCEI)]. (b) Synoptic scale – Country of Japan. (c) Regional scale showing MERRA-2 grid boxes over the Tokyo region at the meso-scale. BH, Bonin High. WJ (upper-level flow) and PJO (lower-level flow) also shown. See Figure 2 for intra-urban microscale and locations of venues within the city region. Conceptually, our large-scale analysis uses A, B, and C; the local microclimate was assessed separately in the current paper based on the weather station network to create Figures 4 and 5.

Figure 2.

General timeline used within the current study to relate prior months of the year to the WBGT levels experienced in Tokyo, based on El Nino Southern Oscillation (ENSO) seasons. SON: September, October, November (Season 1); DJF: December, January, February (Season 2); MAM: March, April, May (Season 3); JJA: June, July, August (Season 4).

Figure 3.

Tokyo map of outdoor venue locations. See Table 2 for venue names and sports at each location.

Figure 4.

Intraurban temperature variability of “daytime” (0500–1900 hr) air temperatures at a 500 m scale, showing (a) average across the day, and (b) average of the maximum daytime air temperatures, for August 2008–2018 in the Tokyo region using random forests-based regression kriging (RFRK). Circles indicate venue numbers and star is Olympic stadium (see Table 2 & Figure 3 for reference). To clearly show the spatial variations between Figures 4 and 5, the scales of mean temperature and the scales of max temperature are matched.

Figure 5.

Intraurban temperature variability of midday (1200–1500 hr) air temperatures, showing (a) average across the 3-hr period, and (b) average of the maximum within the 3-hr period, for August 2008–2018 in the Tokyo region using random forest-based regression kriging (RFRK). Circles indicate venue numbers and star is Olympic stadium (see Table 2 & Figure 3 for reference).

Figure 6.

(a) Mean air temperature and (b) and relative humidity diurnally for August across 10 years (2009–2018) from AQMS stations (20) and AMEDAS stations (4).