The Australian Community Climate and Earth System Simulator coupled model (ACCESS-CM) has been developed at the Centre for Australian Weather and Climate Research (CAWCR), a partnership between CSIRO 1 and the Bureau of Meteorology. It is built by coupling the UK Met Office atmospheric unified model (UM), and other sub-models as required, to the ACCESS ocean model, which consists of the NOAA/GFDL 2 ocean model MOM4p1 and the LANL 3 sea-ice model CICE4.1, under the CERFACS 4 OASIS3.2-5 coupling framework. The primary goal of the ACCESS-CM development is to provide the Australian climate community with a new generation fully coupled climate model for climate research, and to participate in phase five of the Coupled Model Inter-comparison Project (CMIP5). This paper describes the ACCESS-CM framework and components, and presents the control climates from two versions of the ACCESS-CM, ACCESS1.0 and AC-CESS1.3, together with some fields from the 20 th century historical experiments, as part of model evaluation. While sharing the same ocean sea-ice model (except different setups for a few parameters), ACCESS1.0 and ACCESS1.3 differ from each other in their atmospheric and land surface components: the former is configured with the UK Met Office HadGEM2 (r1.1) atmospheric physics and the Met Office Surface Exchange Scheme land surface model version 2, and the latter with atmospheric physics similar to the UK Met Office Global Atmosphere 1.0 including modifications performed at CAWCR and the CSIRO Community Atmosphere Biosphere Land Exchange land surface model version 1.8. The global average annual mean surface air temperature across the 500-year preindustrial control integrations show a warming drift of 0.35 °C in ACCESS1.0 and 0.04 °C in AC-CESS1.3. The overall skills of ACCESS-CM in simulating a set of key climatic fields both globally and over Australia significantly surpass those from the preceding CSIRO Mk3.5 model delivered to the previous coupled model inter-comparison. However, ACCESS-CM, like other CMIP5 models, has deficiencies in various aspects, and these are also discussed.

No abstract

Abstract. Analysis of the variability of the last 18 yr (1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) of a 32 yr run of a new near-global, eddy-resolving ocean general circulation model coupled with biogeochemistry is presented. Comparisons between modelled and observed mean sea level (MSL), mixed layer depth (MLD), sea level anomaly (SLA), sea surface temperature (SST), and chlorophyll a indicate that the model variability is realistic. We find some systematic errors in the modelled MLD, with the model generally deeper than observations, which results in errors in the chlorophyll a, owing to the strong biophysical coupling. We evaluate several other metrics in the model, including the zonally averaged seasonal cycle of SST, meridional overturning, volume transports through key straits and passages, zonally averaged temperature and salinity, and El Niño-related SST indices. We find that the modelled seasonal cycle in SST is 0.5-1.5 • C weaker than observed; volume transports of the Antarctic Circumpolar Current, the East Australian Current, and Indonesian Throughflow are in good agreement with observational estimates; and the correlation between the modelled and observed NINO SST indices exceeds 0.91. Most aspects of the model circulation are realistic. We conclude that the model output is suitable for broader analysis to better understand upper ocean dynamics and ocean variability at mid-and low latitudes. The new model is intended to underpin a future version of Australia's operational short-range ocean forecasting system.

No abstract

No abstract

Abstract. We introduce ACCESS-OM2, a new version of the ocean–sea ice model of the Australian Community Climate and Earth System Simulator. ACCESS-OM2 is driven by a prescribed atmosphere (JRA55-do) but has been designed to form the ocean–sea ice component of the fully coupled (atmosphere–land–ocean–sea ice) ACCESS-CM2 model. Importantly, the model is available at three different horizontal resolutions: a coarse resolution (nominally 1∘ horizontal grid spacing), an eddy-permitting resolution (nominally 0.25∘), and an eddy-rich resolution (0.1∘ with 75 vertical levels); the eddy-rich model is designed to be incorporated into the Bluelink operational ocean prediction and reanalysis system. The different resolutions have been developed simultaneously, both to allow for testing at lower resolutions and to permit comparison across resolutions. In this paper, the model is introduced and the individual components are documented. The model performance is evaluated across the three different resolutions, highlighting the relative advantages and disadvantages of running ocean–sea ice models at higher resolution. We find that higher resolution is an advantage in resolving flow through small straits, the structure of western boundary currents, and the abyssal overturning cell but that there is scope for improvements in sub-grid-scale parameterizations at the highest resolution.

The generation and evolution of eddies in the ocean are largely due to instabilities that are unpredictable, even on short time-scales. As a result, eddyresolving ocean reanalyses typically use data assimilation to regularly adjust the model state. In this study, we present results from a second-generation eddy-resolving ocean reanalysis that is shown to match both assimilated and with-held observations more closely than its predecessor; but involves much smaller adjustments to the model state at each assimilation. We compare version 2 and 3 of the Bluelink ReANalysis (BRAN) in the Australian region.Overall, the misfits between the model fields in BRAN3 and observations are 5-28% smaller than the misfits for BRAN2. Specifically, we show that for BRAN3 (BRAN2) the sea-level, upper ocean temperature, upper-ocean salinity, and near-surface velocity match observations to within 7.7 cm (9.7 cm), 0.68 tively. We also show that the increments applied to BRAN3 -the artificial adjustments applied at each assimilation step -are typically 20-50% smaller than the equivalent adjustments in BRAN2. This leads us to conclude that the performance of BRAN3 is more dynamically consistent than BRAN2, rendering it more suitable for a range of applications, including analysis of ocean variability, extreme events, and process studies.

No abstract