No abstract

Global and regional ocean and sea ice reanalysis products (ORAs) are increasingly used in polar research, but their quality remains to be systematically assessed. To address this, the Polar ORA Intercomparison Project (Polar ORA-IP) has been established following on from the ORA-IP project. Several aspects of ten selected ORAs in the Arctic and Antarctic were addressed by concentrating on comparing their mean states in terms of snow, sea ice, ocean transports and hydrography. Most polar diagnostics were carried out for the first time in such an extensive set of ORAs. For the multi-ORA mean state, we found that deviations from observations were typically smaller than individual ORA anomalies, often attributed to offsetting biases of individual ORAs. The ORA ensemble mean therefore appears to be a useful product and while knowing its main deficiencies and recognising its restrictions, it can be used to gain useful information on the physical state of the polar marine environment.

In light of the growing recognition of the role of surface temperature variations in the Indian Ocean for driving global climate variability, the predictive skill of the sea surface temperature (SST) anomalies associated with the Indian Ocean dipole (IOD) is assessed using ensemble seasonal forecasts from a selection of contemporary coupled climate models that are routinely used to make seasonal climate predictions. The authors assess predictions from successive versions of the Australian Bureau of Meteorology Predictive Ocean-Atmosphere Model for Australia (POAMA 15b and 24), successive versions of the NCEP Climate Forecast System (CFSv1 and CFSv2), the ECMWF seasonal forecast System 3 (ECSys3), and the Frontier Research Centre for Global Change system (SINTEX-F) using seasonal hindcasts initialized each month from January 1982 to December 2006.The lead time for skillful prediction of SST in the western Indian Ocean is found to be about 5-6 months while in the eastern Indian Ocean it is only 3-4 months when all start months are considered. For the IOD events, which have maximum amplitude in the September-November (SON) season, skillful prediction is also limited to a lead time of about one season, although skillful prediction of large IOD events can be longer than this, perhaps up to about two seasons. However, the tendency for the models to overpredict the occurrence of large events limits the confidence of the predictions of these large events. Some common model errors, including a poor representation of the relationship between El Niñ o and the IOD, are identified indicating that the upper limit of predictive skill of the IOD has not been achieved. APPENDIXComputational Formulas for COR, RMSE, s Mem , SPD, BIAS, HR, and FAR a. CORForecast anomalies X9 iv (im, iy, ie, lt) from model version iv and ensemble member (i.e., that verify at month DECEMBER 2012 S H I E T A L .

ACCESS-S1 will be the next version of the Australian Bureau of Meteorology's seasonal prediction system, due to become operational in early 2018. The multiweek and seasonal performance of ACCESS-S1 has been evaluated based on a 23-year hindcast set and compared to the current operational system, POAMA. The system has considerable enhancements compared to POAMA, including higher vertical and horizontal resolution of the component models and state-ofthe-art physics parameterisation schemes. ACCESS-S1 is based on the UK Met Office GloSea5-GC2 seasonal prediction system, but has enhancements to the ensemble generation strategy to make it appropriate for multi-week forecasting, and a larger ensemble size.ACCESS-S1 has markedly reduced biases in the mean state of the climate, both globally and over Australia, compared to POAMA. ACCESS-S1 also better predicts the early stages of the development of the El Niño Southern Oscillation (through the predictability barrier) and the Indian Ocean Dipole, as well as multi-week variations of the Southern Annular Mode and the Madden-Julian Oscillation — all important drivers of Australian climate variability. There is an overall improvement in the skill of the forecasts of rainfall, maximum temperature (Tmax) and minimum temperature (Tmin) over Australia on multi-week timescales compared to POAMA. On seasonal timescales the differences between the two systems are generally less marked. ACCESS-S1 has improved seasonal forecasts over Australia for the austral spring season compared to POAMA, with particularly good forecast reliability for rainfall and Tmax. However, forecasts of seasonal mean Tmax are noticeably less skilful over eastern Australia for forecasts of late autumn and winter compared to POAMA.The study has identified scope for improvement of ACCESS-S in the future, particularly 1) reducing rainfall errors in the Indian Ocean and Maritime Continent regions, and 2) initialising the land surface with realistic soil moisture rather than climatology. The latter impacts negatively on the skill of the temperature forecasts over eastern Australia and is being addressed in the next version of the system, ACCESS-S2.

ACCESS-S2 is a major upgrade to the Australian Bureau of Meteorology's multi-week to seasonal prediction system. It was made operational in October 2021, replacing ACCESS-S1. The focus of the upgrade is the addition of a new weakly coupled data assimilation system to provide initial conditions for atmosphere, ocean, land and ice fields. The model is based on the UK Met Office GloSea5-GC2 seasonal prediction system and is unchanged from ACCESS-S1, aside from minor corrections and enhancements. The performance of the assimilation system and the skill of the seasonal and multi-week forecasts have been assessed and compared to ACCESS-S1. There are improvements in the ACCESS-S2 initial conditions compared to ACCESS-S1, particularly for soil moisture and aspects of the ocean, notably the ocean currents. More realistic soil moisture initialisation has led to increased skill for forecasts over Australia, especially those of maximum temperature. The ACCESS-S2 system is shown to have increased skill of El Nino-Southern Oscillation forecasts over ACCESS-S1 during the challenging autumn forecast period. Analysis suggests that ACCESS-S2 will deliver improved operational forecast accuracy in comparison to ACCESS-S1. Assessments of the operational forecasts are underway. ACCESS-S2 represents another step forward in the development of seasonal forecast systems at the Bureau of Meteorology. However, key rainfall and sea surface temperature biases in ACCESS-S1 remain in ACCESS-S2, indicating where future efforts should be focused.