Tropical Cyclone Simulation in A High-Resolution Atmosphere-Ocean Coupled General Circulation Model

A global high-resolution coupled general circulation model (CGCM) consisting of a T319 atmosphere general circulation model and an eddy-permitted ocean general circulation model is examined in terms of the reproducibility of the northern hemisphere tropical cyclone (TC) activity as well as the large-scale environmental conditions. The CGCM successfully simulates the realistic TC structure, TC-induced ocean response, and TC genesis frequency. The global TC genesis frequency simulated by the high-resolution CGCM is much closer to the observed, compared that simulated by the medium-resolution (T106) CGCM. In addition, the high-resolution CGCM partially reproduces the bimodal seasonal cycle of the North Indian Ocean cyclogenesis, while the medium-resolution CGCM fails to simulate it. The high-resolution CGCM also reasonably reproduces the environmental conditions favorable for the TC genesis: warm sea surface temperature, low-level cyclonic circulation, weak vertical wind shear, and high relative humidity in the mid-troposphere. The eastward extension of monsoon-trough is well simulated by the high-resolution CGCM as observed, compared to the medium-resolution CGCM. There are, however, still some discrepancies between the modeled and observed TC activity. We discuss about the following two discrepancies from the view point of the simulated large-scale environmental conditions: the high-resolution CGCM fails to reproduce the bimodal seasonal cycle of the Arabian cyclogenesis during the pre-monsoon period, and the western North Pacific TC genesis locations are confined in the southwestern part of the western North Pacific. It is found that less Arabian cyclogenesis during the pre-monsoon period is due to the weak low-level cyclonic circulation in the Arabian Sea during this period, although the weak vertical wind shear is well simulated as observed. For the western North Pacific, less TC genesis in the southeastern part of the western North Pacific is found to be due to the failure to simulate the eastward extension of the monsoon-trough up to the international dateline. Compared to a medium-resolution CGCM, one of the advantages of the high-resolution CGCM is the reproduction of the intense TC. Surface wind speed exceeding 20~40 ms-1 is successfully simulated by the high-resolution CGCM, while the TC wind speed simulated by the medium-resolution CGCM is less than 20~30 ms-1. The frequency distribution of TC surface wind speed simulated by the high-resolution CGCM is closer to the observed compared to the medium-resolution CGCM.Edited by K. Oouchi and H. Fudeyas

Extreme fire weather is the major driver of severe bushfires in southeast Australia

In Australia, the proportion of forest area that burns in a typical fire season is less than for other vegetation types. However, the 2019–2020 austral spring-summer was an exception, with over four times the previous maximum area burnt in southeast Australian temperate forests. Temperate forest fires have extensive socio-economic, human health, greenhouse gas emissions, and biodiversity impacts due to high fire intensities. A robust model that identifies driving factors of forest fires and relates impact thresholds to fire activity at regional scales would help land managers and fire-fighting agencies prepare for potentially hazardous fire in Australia. Here, we developed a machine-learning diagnostic model to quantify nonlinear relationships between monthly burnt area and biophysical factors in southeast Australian forests for 2001–2020 on a 0.25° grid based on several biophysical parameters, notably fire weather and vegetation productivity. Our model explained over 80% of the variation in the burnt area. We identified that burnt area dynamics in southeast Australian forest were primarily controlled by extreme fire weather, which mainly linked to fluctuations in the Southern Annular Mode (SAM) and Indian Ocean Dipole (IOD), with a relatively smaller contribution from the central Pacific El Nino Southern Oscillation (ENSO). Our fire diagnostic model and the non-linear relationships between burnt area and environmental covariates can provide useful guidance to decision-makers who manage preparations for an upcoming fire season, and model developers working on improved early warning systems for forest fires