Identifier code: ACCESS-S2
First operational forecast run: 20 October 2021
Is the model coupled to an ocean model? Yes: Atmosphere, land, ocean and sea-ice.
Coupling frequency: Hourly
The coupled model is described in Hudson et al (2017) and Wedd et al (2022).
| Model | Global Atmosphere 6.0 (GA6): The Unified Model version 8.6 (UM; Williams et al. 2015; Walters et al. 2017). Global Land 6.0 (GL6): Joint UK Land Environment Simulator (JULES; Best et al. 2011; Walters et al. 2017) |
|---|---|
| Horizontal resolution and grid | N216 (~60km in the mid-latitudes) |
| Atmosphere vertical resolution | 85 levels |
| Top of atmosphere | 85 km |
| Soil levels | Four soil levels |
| Time step |
Detailed documentation:
| Ocean model | NEMO v3.4 (Madec et al. 2023; Megann et al. 2014) |
|---|---|
| Horizontal resolution | ORCA 0.25 |
| Vertical resolution | L75. Level thicknesses range from 1 m near the surface to ~200 m near the bottom (6000-m depth) |
| Time step | |
| Sea ice model | CICE v3.1 (Hunke and Lipscomb 2010; Rae et al. 2015) |
| Sea ice model resolution | ORCA 0.25 |
| Sea ice model levels | |
| Wave model | N/A |
| Wave model resolution | N/A |
Detailed documentation: NEMO documentation, CICE documentation
| Greenhouse gases | |
|---|---|
| Ozone | |
| Tropospheric aerosols | |
| Volcanic aerosols | |
| Solar forcing |
Detailed documentation:
| Hindcast | Forecast | |
|---|---|---|
| Atmosphere initialization | ERA-Interim (Dee et al. 2011) | ACCESS-G3, the Bureau’s 4D-Var analysis (Bureau of Meteorology 2019) |
| Atmosphere IC perturbations | See Hudson et al 2017 | See Hudson et al 2017 |
Land Initialization | Climatological fields with weakly coupled data assimilation | Climatological fields with weakly coupled data assimilation |
| Land IC perturbations | None | None |
| Soil moisture initialization | Climatological fields with weakly coupled data assimilation | Climatological fields with weakly coupled data assimilation |
| Snow initialization | ||
| Unperturbed control forecast? | None | None |
Data assimilation method for control analysis:
Horizontal and vertical resolution of perturbations:
Perturbations in +/- pairs:
Detailed documentation:
| Hindcast | Forecast | |
|---|---|---|
| Ocean initialization | EN4 | Bureau realtime ocean data assimilation (Wedd et al 2022) |
| Ocean IC perturbations | No | None |
| Unperturbed control forecast? | No | None |
Detailed documentation:
| Model dynamics perturbations | None |
|---|---|
| Model physics perturbations | None |
If there is a control forecast, is it perturbed? | No control |
Detailed documentation:
| Forecast frequency | Daily |
|---|---|
| Forecast ensemble size | 11 per day out to 6 months 22 per day out to 6 weeks |
| Hindcast years | September 1981- December 2018 |
| Hindcast ensemble size | 27-member time-lagged ensemble: 3 per start date out to 9 months back 9 days, 6 per start date out to 6 weeks back 3 days |
| On-the-fly or static hindcast set? | Static |
| Calibration (bias correction) period | September 1981- December 2018 |
ACCESS-S system:
Hudson, D., Alves, O., Hendon, H.H., Lim, E., Liu, G., Luo J.-J., MacLachlan, C., Marshall, A.G., Shi, L., Wang, G., Wedd, R., Young, G., Zhao, M., Zhou X., 2017: ACCESS-S1: The new Bureau of Meteorology multi-week to seasonal prediction system. Journal of Southern Hemisphere Earth Systems Science, 67:3 132-159 doi: 10.22499/3.6703.001.
Wedd R., Alves O., de Burgh-Day C., Down C., Griffiths M., Hendon H.H., Hudson D., Li S., Lim E., Marshall A.G., Shi L., Smith P., Smith G., Spillman C.M., Wang G., Wheeler M.C., Yan H., Yin Y., Young G., Zhao M., Yi X. and Zhou X., 2022: ACCESS-S2: The upgraded Bureau of Meteorology multi-week to seasonal prediction system. Journal of Southern Hemisphere Earth System Science, 72 (3), 218-242.
Post-processing:
Griffiths M, Smith P, Yan H, Spillman C, Young G, Hudson D, 2023 ACCESS-S2: Updates and improvements to postprocessing pipeline Bureau Research Report, No. 082, Bureau of Meteorology Australia.
Other selected papers:
King AD, Hudson D, Lim, E-P, Marshall AG, Hendon HH, Lane TP, Alves O. 2020: Sub-seasonal to seasonal prediction of rainfall extremes in Australia. Quarterly Journal of the Royal Meteorological Society, https://doi.org/10.1002/qj.3789.
Lim E, Hudson DA, Wheeler M et al, 2021: Why Australia was Not Wet during Spring 2020 despite La Niña. Scientific Reports. https://www.nature.com/articles/s41598-021-97690-w
Lim E, Hendon HH and co-authors, 2021: The 2019 Southern Hemisphere polar stratospheric warming and its impacts. Bulletin of the American Meteorological Society, https://doi.org/10.1175/BAMS-D-20-0112.1
Marshall AG, Gregory PA, de Burgh-Day CO, and Griffiths M, 2021: Subseasonal drivers of extreme fire weather in Australia and its prediction in ACCESS-S1 during spring and summer. Climate Dynamics. https://doi.org/10.1007/s00382-021-05920-8
Marshall AG, Wang G, Hendon HH and others (2023) Madden–Julian Oscillation teleconnections to Australian springtime temperature extremes and their prediction in ACCESS-S1. Climate Dynamics 61, 431–447. https://doi.org/10.1007/s00382-022-06586-6
Smith GA and Spillman CM (2024) Global ocean surface and subsurface temperature forecast skill over subseasonal to seasonal timescales. Journal of Southern Hemisphere Earth Systems Science, https://doi.org/10.1071/ES23020.
Spillman CM and Smith GA, 2021: A New Operational Seasonal Thermal Stress Prediction Tool for Coral Reefs Around Australia. Frontiers in Marine Science, https://doi.org/10.3389/fmars.2021.687833.