Future changes in tropical cyclone (TC) activity and structure are investigated using the outputs of a 14-km mesh climate simulation. A set of 30-yr simulations was performed under present-day and warmer climate conditions using a nonhydrostatic icosahedral atmospheric model with explicitly calculated convection. The model projected that the global frequency of TCs is reduced by 22.7%, the ratio of intense TCs is increased by 6.6%, and the precipitation rate within 100 km of the TC center increased by 11.8% under warmer climate conditions. These tendencies are consistent with previous studies using a hydrostatic global model with cumulus parameterization. The responses of vertical and horizontal structures to global warming are investigated for TCs with the same intensity categories. For TCs whose minimum sea level pressure (SLP) reaches less than 980 hPa, the model predicted that tangential wind increases in the outside region of the eyewall. Increases in the tangential wind are related to the elevation of the tropopause caused by global warming. The tropopause rise induces an upward extension of the eyewall, resulting in an increase in latent heating in the upper layers of the inclined eyewall. Thus, SLP is reduced underneath the warmed eyewall regions through hydrostatic adjustment. The altered distribution of SLP enhances tangential winds in the outward region of the eyewall cloud. Hence, this study shows that the horizontal scale of TCs defined by a radius of 12 m s−1 surface wind is projected to increase compared with the same intensity categories for SLP less than 980 hPa.

Abstract. The Nonhydrostatic ICosahedral Atmospheric Model (NICAM), a global model with an icosahedral grid system, has been under development for nearly two decades. This paper describes NICAM16-S, the latest stable version of NICAM (NICAM.16), modified for the Coupled Model Intercomparison Project Phase 6, High Resolution Model Intercomparison Project (HighResMIP). Major updates of NICAM.12, a previous version used for climate simulations, included updates of the cloud microphysics scheme and land surface model, introduction of natural and anthropogenic aerosols and a subgrid-scale orographic gravity wave drag scheme, and improvement of the coupling between the cloud microphysics and the radiation schemes. External forcings were updated to follow the protocol of the HighResMIP. A series of short-term sensitivity experiments were performed to determine and understand the impacts of these various model updates on the simulated mean states. The NICAM16-S simulations demonstrated improvements in the ice water content, high cloud amount, surface air temperature over the Arctic region, location and strength of zonal mean subtropical jet, and shortwave radiation over Africa and South Asia. Some long-standing biases, such as the double intertropical convergence zone and smaller low cloud amount, still exist or are even worse in some cases, suggesting further necessity for understanding their mechanisms, upgrading schemes and parameter settings, and enhancing horizontal and vertical resolutions.

We propose a deep learning approach for identifying tropical cyclones (TCs) and their precursors. Twenty year simulated outgoing longwave radiation (OLR) calculated using a cloud-resolving global atmospheric simulation is used for training two-dimensional deep convolutional neural networks (CNNs). The CNNs are trained with 50,000 TCs and their precursors and 500,000 non-TC data for binary classification. Ensemble CNN classifiers are applied to 10 year independent global OLR data for detecting precursors and TCs. The performance of the CNNs is investigated for various basins, seasons, and lead times. The CNN model successfully detects TCs and their precursors in the western North Pacific in the period from July to November with a probability of detection (POD) of 79.9-89.1% and a false alarm ratio (FAR) of 32.8-53.4%. Detection results include 91.2%, 77.8%, and 74.8% of precursors 2, 5, and 7 days before their formation, respectively, in the western North Pacific. Furthermore, although the detection performance is correlated with the amount of training data and TC lifetimes, it is possible to achieve high detectability with a POD exceeding 70% and a FAR below 50% during TC season for several ocean basins, such as the North Atlantic, with a limited sample size and short lifetime.

Thirty-one successive daily experiments for extended-range (30 day) forecasts are conducted using a global nonhydrostatic atmospheric model without convective parameterization. The model successfully reproduces tropical cyclogenesis (TCG) in six out of eight cases in the western North Pacific in August 2004, up to 2 weeks prior to cyclone formation. Detailed analyses reveal that Typhoon Songda's genesis is related to the eastward extension of the monsoon trough associated with the intraseasonal variability (ISV). The successful simulation of the migration and extension of the monsoon trough leads to a 2 week forecast for Songda's genesis. These findings highlight the need for a model capable of predicting the modulation of large-scale fields by ISV for TCG forecasts and that a global nonhydrostatic cloud-system-resolving model is a promising tool for TCG forecasts.

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