The Canadian Seasonal to Interannual Prediction System (CanSIPS) became operational at Environment Canada's Canadian Meteorological Centre (CMC) in December 2011, replacing CMC's previous two-tier system. CanSIPS is a two-model forecasting system that combines ensemble forecasts from the Canadian Centre for Climate Modeling and Analysis (CCCma) Coupled Climate Model, versions 3 and 4 (CanCM3 and CanCM4, respectively). Mean climate as well as climate trends and variability in these models are evaluated in freely running historical simulations. Initial conditions for CanSIPS forecasts are obtained from an ensemble of coupled assimilation runs. These runs assimilate gridded atmospheric analyses by means of a procedure that resembles the incremental analysis update technique, but introduces only a fraction of the analysis increment in order that differences between ensemble members reflect the magnitude of observational uncertainties. The land surface is initialized through its response to the assimilative meteorology, whereas sea ice concentration and sea surface temperature are relaxed toward gridded observational values. The subsurface ocean is initialized through surface forcing provided by the assimilation run, together with an offline variational assimilation of gridded observational temperatures followed by an adjustment of the salinity field to preserve static stability. The performance of CanSIPS historical forecasts initialized every month over the period 1981–2010 is documented in a companion paper. The CanCM4 model and the initialization procedures developed for CanSIPS have been employed as well for decadal forecasts, including those contributing to phase 5 of the Coupled Model Intercomparison Project.

Rapid intensification (RI) is an essential process in the development of strong tropical cyclones and a major challenge in prediction. RI in offshore regions is more threatening to coastal populations and economies. Although much effort has been devoted to studying basin-wide temporal-spatial fluctuations, variations of global RI events in offshore regions remain uncertain. Here, we show that compared with open oceans, where the annual RI counts do not show significant changes, offshore areas within 400 km of the coastline have experienced a significant increase in RI events, with the count tripling from 1980 to 2020. Furthermore, thermodynamic environments present more favorable conditions for this trend, and climate models show that global ocean warming has enhanced such changes. This work yields an important finding that an increasing threat of RI in coastal regions has occurred in the preceding decades, which may continue under a future warming climate.

In situ observation of a buoys/moorings array and a model simulation were used to study the modulation of upper ocean thermal structure by Typhoon Kalmaegi in September 2014. The inertial period signals were significant after forcing of Kalmaegi, but they did not account for the net heat change. Removing the inertial period signals showed that the net thermal response biased to the right of Kalmaegi's track. Vertical mixing caused surface cooling with an inverted‐cone structure and subsurface warming with a double‐wing structure. Net upwelling converted the left wing of the subsurface warming to cooling, while net downwelling warmed the upper ocean in front and on both sides of the net upwelling zone. Horizontal advection was not as important as vertical mixing and vertical advection in modulating the thermal structure but contributed to the net outward advection of thermal anomaly in the mixed layer during the forced stage and also in the net along‐track recovery of subsurface anomaly during the relaxation stage. In general, horizontal and vertical advection modulated thermal anomalies in the upper ocean across a broader horizontal range and into the deeper ocean compared with the effect of vertical mixing. Our results indicate the need to consider both mixing and advection (rather than only mixing) when studying the effects of tropical cyclones on local ocean heat uptake and global ocean heat transport.

In the present research, Indian Ocean Dipole (IOD) prediction was explored using statistical methods based on deep learning techniques. First, convolutional neural network (CNN) models were trained using sea‐surface temperature anomaly (SSTA) maps of the Indian Ocean from 1854 to 1989, and the properly trained CNN models were then validated with the period from 1991 to 2019. The results indicate that the deep learning approach is capable of forecasting the IOD at lead times up to 7 months. The forecast skills of CNN are superior to those of the dynamic models in the North American Multi‐Model Ensemble (NMME). The CNN outperforms the NMME with lower sensitivity to predictability barriers and fewer systematic errors. Moreover, the gradient heat map analysis demonstrates that the triggering precursors selected by CNN models for IOD events are novel and physically sensible. These results suggest the CNN to be a new and effective tool for both IOD prediction and comprehension.