1. Forecast system version
Identifier code: ACCESS-S2
First operational forecast run: 20 October 2021
2. Configuration of the forecast model
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).
2.1 Atmosphere and land surface
| 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 | 15 minutes |
Detailed documentation:
2.2 Ocean and cryosphere
| 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 | 22.5 minutes |
| 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 | Five categories and open water (Hunke et al 2010; Rae et al 2015) |
| Wave model | N/A |
| Wave model resolution | N/A |
Detailed documentation: NEMO documentation
3. Boundary conditions - climate forcings
| Greenhouse gases | Set to observed values up to the year 2005 and after this the emissions follow the Intergovernmental Panel on Climate Change (IPCC) RCP4.5 scenario. |
|---|---|
| Ozone | The SPARC (Cionni et al 2011) observational climatology is used for ozone, which includes a seasonal cycle. |
| Tropospheric aerosols | Climatologies with a seasonal variation (MacLachlan et al. 2015) |
| Volcanic aerosols | N/A |
| Solar forcing | Inter-annual variation |
Detailed documentation:
4. Initialization and initial condition (IC) perturbations
4.1 Atmosphere and land
| Hindcast | Forecast | |
|---|---|---|
| Atmosphere initialization | ERA-Interim (Dee et al. 2011) | 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 | Land surface (soil moisture and soil temperature) evolves in response to the atmosphere forcing (i.e., indirect initialisation of the land surface through nudging) | Land surface (soil moisture and soil temperature) evolves in response to the atmosphere forcing (i.e., indirect initialisation of the land surface through nudging) |
| Land IC perturbations | None | None |
| Soil moisture initialization | Land surface (soil moisture and soil temperature) evolves in response to the atmosphere forcing (i.e., indirect initialisation of the land surface through nudging) | Land surface (soil moisture and soil temperature) evolves in response to the atmosphere forcing (i.e., indirect initialisation of the land surface through nudging) |
| Snow initialization | None | None |
| Unperturbed control forecast? | None | None |
Data assimilation method for control analysis:
Horizontal and vertical resolution of perturbations:
Perturbations in +/- pairs:
Detailed documentation:
4.2 Ocean and cryosphere
| Hindcast | Forecast | |
|---|---|---|
| Ocean initialization | Weakly coupled ensemble optimal interpolation method (Wedd et al 2022), based on the EnKF-C software (Sakov 2014) | Weakly coupled ensemble optimal interpolation method (Wedd et al 2022), based on the EnKF-C software (Sakov 2014) |
| Ocean IC perturbations | None | None |
| Unperturbed control forecast? | None | None |
Detailed documentation:
Sakov P. (2014) EnKF-C user guide. arXiv: Computer Science 1410.1233, v1. doi:10.48550/arXiv.1410.1233
5. Model Uncertainties perturbations:
| Model dynamics perturbations | None |
|---|---|
| Model physics perturbations | Atmosphere stochastic physics scheme, SKEB2 (Bowler et al 2009) |
If there is a control forecast, is it perturbed? | No control |
Detailed documentation:
6. Forecast system and hindcasts
| Forecast frequency | Daily |
|---|---|
| Forecast ensemble size | 11 per day out to 6 months 22 per day out to 6 weeks |
| Hindcast years | 38 (January 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 (Also see next section) |
| On-the-fly or static hindcast set? | Static |
| Calibration (bias correction) period | January 1981- December 2018 |
7. Other relevant information
Hindcast configuration employs a time-lagged ensemble approach in which the number of ensemble members is dependent on the start date of the hindcast.
1) Three-member ensembles (out to 279 days) six times per month on the 1st, 6th, 11th, 16th, 21st and 26th to support climatologies and calibration of the real-time system. Real-time forecasts utilise the closest prior climatology date for bias correction or calibration.
2) A 27-member time-lagged ensemble once per month, valid on the 1st of the month, to support calculation of seasonal skill. This comprises three 279-day ensemble members on 9 successive days (the 1st of the month and the 8 prior days of the previous month).
3) A 27-member time-lagged ensemble twice per month, valid on the 1st of the month and the 16th of the month, to support calculation of multi-week skill. This comprises nine 42-day ensemble members from 3 successive days: (1)on the 1st of the month plus the 2 days prior and (2) on the 16th of the month plus the 15th and 14th.
Interpolation details
The outputs from ACCESS-S2 in vertical pressure levels haven't been postprocessed to fill the holes below the terrain. In those grid points where the pressure at the surface is lower than a given pressure level the variables have been set to a missing value indicator and therefore they have not been extrapolated to produce a globally complete field.
8. Where to find more information
ACCESS-S system:
Hudson D, Alves O, Hendon HH, Lim E, Liu G, Luo JJ, MacLachlan C, Marshall AG, 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 HH, Hudson D, Li S, Lim E, Marshall AG, Shi L, Smith P, Smith G, Spillman CM, Wang G, Wheeler MC, Yan H, Yin Y, Young G, Zhao M, Yi X, 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:
de Burgh-Day C, Griffiths M, Yan H, Young G, Hudson D, Alves O (2020) An adaptable framework for development and real time production of experimental sub-seasonal to seasonal forecast products, Bureau Research Report, No. 42. Bureau of Meteorology Australia.
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.
