Wind‐wave hindcast data have many applications including climatology assessments for renewable energy projects, maritime engineering design, event‐based impact assessments, generating boundary conditions for further downscaling, amongst others. Here, we present a global wave hindcast with nested high‐resolution grids for the Exclusive Economic Zones of Australia and south west Pacific Island Countries, that is extended in time monthly. The model employs strategic methods to incorporate the effects of subgrid sized features such as small islands and islets. Various bulk wave parameters are available hourly from January 1979 to present, along with the full wave spectra at a set of 3,683 predetermined points distributed globally.

The 2020 marine heatwave (MHW) in the Great Barrier Reef (GBR) and Coral Sea led to mass coral bleaching. Sea surface temperature anomalies reached +1.7 °C for the whole of the GBR and Coral Sea and exceeded +2 °C across broad regions (referenced to 1990–2012). The MHW reached Category 2 (Strong) and warm anomalies peaked between mid-February and mid-March 2020. The MHW’s peak intensity aligned with regions of reduced cloud cover and weak wind speeds. We used a MHW framework to assess the ability of an operational coupled ocean-atmosphere prediction system (Australian Community Climate and Earth System Simulator Seasonal version 1) to capture the MHW’s severity, duration, and spatial extent. For initial week predictions, the predicted MHW severity generally agreed with the magnitude and spatial extent of the observed severity for that week. The model ensemble mean did not capture the MHW’s development phase at lead times beyond the first week. The model underestimated the MHW’s spatial extent, which reached up to 95% of the study area with at least Moderate severity and up to 43% with at least Strong severity. However, most forecast ensemble members correctly predicted the period of Strong severity in the first week of the model forecast. The model correctly predicted MHW conditions to persist from mid-February to mid-March but did not capture the end of the MHW. The inability to predict the end of the event and other periods of less skilful prediction were related to subseasonal variability owing to weather systems, including the passage of tropical cyclones not simulated in the model. On subseasonal time scale, evaluating daily to weekly forecasts of ocean temperature extremes is an important step toward implementing methods for developing operational forecast extremes products for use in early warning systems.

Coastal high water level events are increasing in frequency and severity as global sea‐levels rise, and are exposing coastlines to risks of flooding. Yet, operational seasonal forecasts of sea‐level anomalies are not made for most coastal regions. Advancements in forecasting climate variability using coupled ocean‐atmosphere global models provide the opportunity to predict the likelihood of future high water events several months in advance. However, the skill of these models to forecast seasonal sea‐level anomalies has not been fully assessed, especially in a multi‐model framework. Here, we construct a 10‐model ensemble of retrospective forecasts with future lead times of up to 11 months. We compare predicted sea levels from bias‐corrected forecasts with 20 years of observations from satellite‐based altimetry and shore‐based tide gauges. Forecast skill, as measured by anomaly correlation, tends to be highest in the tropical and subtropical open oceans, whereas the skill is lower in the higher latitudes and along some continental coasts. For most locations, multi‐model averaging produces forecast skill that is comparable to or better than the best performing individual model. We find that the most skillful predictions typically come from forecast systems with more accurate initializations of sea level, which is generally achieved by assimilating altimetry data. Having relatively higher horizontal resolution in the ocean is also beneficial, as such models seem to better capture dynamical processes necessary for successful forecasts. The multi‐model assessment suggests that skillful seasonal sea‐level forecasts are possible in many, though not all, parts of the global ocean.

The Tasman Sea has been identified as a climate hotspot and has experienced several marine heatwaves (MHWs) in recent years. These events have impacted coastal regions of New Zealand (NZ), which has had a follow-on effect on local marine and aquaculture industries. Advance warning of extreme marine heat events would enable these industries to mitigate potential losses. Here we present an assessment of the forecast skill of the Australian Bureau of Meteorology’s seasonal prediction system, Australian Community Climate and Earth-System Simulator-Seasonal v1.0 (ACCESS-S1), for three key aquaculture regions around NZ: Hauraki Gulf, Western Cook Strait and Foveaux Strait. We investigate the skill of monthly sea surface temperature anomaly (SSTA) forecasts, and forecasts for SSTA exceeding the 90th percentile, which is an accepted MHW threshold. We find that the model has skill for predicting extreme heat events in all three regions at 0–2 month lead times. We then demonstrate that ACCESS-S1 was able to capture observed monthly SSTA exceeding the 90th percentile around coastal NZ during the 2019 Tasman Sea MHW at a lead time of 1 month. Finally, we discuss the relationship between SSTA in the Tasman Sea and SSTA in coastal regions of NZ, and thus the Tasman Sea as a source of model SSTA skill in the three key coastal regions. Results from this study show that skilful forecasts of ocean heat extremes in regional areas have the potential to enable marine operators in the aquaclture industry to mitigate losses due to MHWs, especially in a warming climate.