Numerical Simulations of Summer Mesoscale Heat-Stress around the Seto Inland Sea, Japan

Abstract

We simulated mesoscale distributions of summertime human heat stress around the Seto Inland Sea in western Japan, using a mesoscale numerical model: WRF-ARW at 2 km horizontal resolution. In this study, the Heat Index, which indicates the real human-felt temperature, was adopted as a heat-hazard index that can represent mesoscale spatial distributions of human heat stress. Our simulations specified regions of undesirably high daytime Heat Index around the Seto Inland Sea for the year 2007, an extremely hot summer. Although the daytime high surface air temperature in the Osaka Plain was the most prominent of the calculation domain (at 1400 JST, August air temperature means of 33-35 °C), high Heat Index regions were broadly found in other land areas around the Seto Inland Sea in addition to the Osaka Plain: the Tokushima, Okayama, Sanuki, and Nakatsu Plains (at 1400 JST, August Heat Index means of 37-39 °C). Daytime differences between air temperature and Heat Index were large in the four previously mentioned plains (excluding the Osaka Plain), showing a difference of 5-7 °C in our simulation. The large differences were caused by the difference of relative humidity in the region. Hence, residents in these plains should carefully avoid high heat stress, because they may be feel hotter than the actual air temperature.To discuss mechanisms increasing the mesoscale Heat Index, sensitivity experiments for mountains and sea surface temperature were conducted. They revealed that those factors worked selectively in the above regions. The existence of mountains induced thermal effects due to a valley-like terrain, and higher sea surface temperature produced warm and moist air transports from the sea. Daytime local circulations, which were topographically and thermally driven, were important for both factors. These mountain and high sea surface temperature influences produced increases of Heat Index of 1-2 °C in the aforementioned four regions.

Subtropical Marine Low Stratiform Cloud Deck Spatial Errors in the E3SMv1 Atmosphere Model

“…Many previous studies may have unintentionally confounded spatial errors for amplitude errors if they focused on target regions defined based on observations (e.g., the boxes defined by Klein & Hartmann, 1993, as used in Caldwell et al, 2013, and Koshiro & Shiotani, 2014 or defined by a condition (such as subsidence) from reanalyses (e.g., Dolinar et al, 2015). This is because cloud decks may be misplaced at least partially outside of such fixed regions.…”