Evaluation of ACCESS-S1 seasonal forecasts of growing season precipitation for Western Australia’s wheatbelt region
Rebecca Firth
A * , Jatin Kala A , Debra Hudson B and Fiona Evans C
+ Author Affiliations
- Author Affiliations
A Environmental and Conservation Sciences and Harry Butler Institute, Centre for Terrestrial Ecosystem Science and Sustainability, Murdoch University, Murdoch, WA 6150, Australia.
B Australian Bureau of Meteorology, Melbourne, Vic., Australia.
C Centre for Crop and Food Innovation, Murdoch University, Murdoch, WA 6150, Australia.
Journal of Southern Hemisphere Earth Systems Science 73(2) 131-147 https://doi.org/10.1071/ES22031
Submitted: 24 September 2022 Accepted: 16 May 2023 Published: 7 June 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of the Bureau of Meteorology. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
Abstract
Seasonal forecasts are increasingly important tools in agricultural crop management. Regions with Mediterranean-type climates typically adopt rain-fed agriculture with minimal irrigation, hence accurate seasonal forecasts of rainfall during the growing season are potentially useful in decision making. In this paper we examined the bias and skill of a seasonal forecast system (ACCESS-S1) in simulating growing season precipitation (GSP) for south-west Western Australian (SWWA), a region with a Mediterranean-type climate and significant cereal crop production. Focusing on July–September (3-month) and May–October (6-month) forecasts, with 0- and 1-month lead times, we showed that overall ACCESS-S1 had a dry bias for SWWA rainfall and a tendency to simulate close to average rainfall during both wetter and drier than average rainfall years. ACCESS-S1 showed particularly poor skill at these timeframes for very wet and very dry years. The limitations in ACCESS-S1 for SWWA GSP were associated with inaccuracies in the timing of heavy rainfall events. In addition, limitations of the ACCESS-S1 model in accurately capturing SST and wind anomaly patterns over the tropical Indian Ocean during extreme rainfall years also contributed to errors in SWWA GSP forecasts. Model improvements in these regions have the potential to improve seasonal rainfall forecasts for SWWA.
Keywords: ACCESS-S1, agriculture, Bureau of Meteorology, model evaluation, rainfall, seasonal climate forecasting, south-west Western Australia, wheatbelt.
References
Alves O, Wang G, Zhong A, Smith N, Tseitkin F, Warren G, Schiller A, Godfrey S, Meyers G (2003) POAMA: Bureau of Meteorology operational coupled model seasonal forecast system. In ‘Science for Drought: Proceedings of National Drought Forum’, 15–16 April 2003, Brisbane, Qld, Australia. (Eds R Stone, I Partridge) pp. 22–32. (Queensland Department of Primary Industries) Available at https://www.ecmwf.int/sites/default/files/elibrary/2003/7694-poama-bureau-meteorology-coupled-model-seasonal-forecast-system.pdfBest MJ, Pryor M, Clark DB, Rooney GG, Essery RLH, Ménard CB, Edwards JM, Hendry MA, Porson A, Gedney N, Mercado LM, Sitch S, Blyth E, Boucher O, Cox PM, Grimmond CSB, Harding RJ (2011) The Joint UK Land Environment Simulator (JULES), model description – Part 1: energy and water fluxes. Geoscientific Model Development 4, 677–699.
| The Joint UK Land Environment Simulator (JULES), model description – Part 1: energy and water fluxes.Crossref | GoogleScholarGoogle Scholar |
Blockley EW, Martin MJ, McLaren AJ, Ryan AG, Waters J, Lea DJ, Mirouze I, Peterson KA, Sellar A, Storkey D (2014) Recent development of the Met Office operational ocean forecasting system: an overview and assessment of the new Global FOAM forecasts. Geoscientific Model Development 7, 2613–2638.
| Recent development of the Met Office operational ocean forecasting system: an overview and assessment of the new Global FOAM forecasts.Crossref | GoogleScholarGoogle Scholar |
Camp J, Wheeler MC, Hendon HH, Gregory PA, Marshall AG, Tory KJ, Watkins AB, MacLachlan C, Kuleshov Y (2018) Skilful multiweek tropical cyclone prediction in ACCESS-S1 and the role of the MJO. Quarterly Journal of the Royal Meteorological Society 144, 1337–1351.
| Skilful multiweek tropical cyclone prediction in ACCESS-S1 and the role of the MJO.Crossref | GoogleScholarGoogle Scholar |
Charles AN, Duell RE, Wang X, Watkins AB (2015) Seasonal forecasting for Australia using a dynamical model: improvements in forecast skill over the operational statistical model. Australian Meteorological and Oceanographic Journal 65, 356–375.
| Seasonal forecasting for Australia using a dynamical model: improvements in forecast skill over the operational statistical model.Crossref | GoogleScholarGoogle Scholar |
Cowan T, Stone R, Wheeler MC, Griffiths M (2020) Improving the seasonal prediction of northern Australian rainfall onset to help with grazing management decisions. Climate Services 19, 100182
| Improving the seasonal prediction of northern Australian rainfall onset to help with grazing management decisions.Crossref | GoogleScholarGoogle Scholar |
Cowan T, Wheeler MC, Sharmila S, Narsey S, de Burgh-Day C (2022) Forecasting northern Australian summer rainfall bursts using a seasonal prediction system. Weather and Forecasting 37, 23–44.
| Forecasting northern Australian summer rainfall bursts using a seasonal prediction system.Crossref | GoogleScholarGoogle Scholar |
Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Hólm EV, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette J-J, Park B-K, Peubey C, de Rosnay P, Tavolato C, Thépaut J-N, Vitart F (2011) The ERA‐Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society 137, 553–597.
| The ERA‐Interim reanalysis: Configuration and performance of the data assimilation system.Crossref | GoogleScholarGoogle Scholar |
Department of Primary Industries and Regional Development (2018) Western Australian grains industry. Available at https://www.agric.wa.gov.au/grains-research-development/western-australian-grains-industry
England MH, Ummenhofer CC, Santoso A (2006) Interannual rainfall extremes over southwest Western Australia linked to Indian Ocean climate variability. Journal of Climate 19, 1948–1969.
| Interannual rainfall extremes over southwest Western Australia linked to Indian Ocean climate variability.Crossref | GoogleScholarGoogle Scholar |
Evans FH, Guthrie MM, Foster I (2020) Accuracy of six years of operational statistical seasonal forecasts of rainfall in Western Australia (2013 to 2018). Atmospheric Research 233, 104697
| Accuracy of six years of operational statistical seasonal forecasts of rainfall in Western Australia (2013 to 2018).Crossref | GoogleScholarGoogle Scholar |
Gentilli J (1972) ‘Australian climate patterns.’ (Thomas Nelson: Melbourne, Vic., Australia)
Geographic Information Services (2016) Potentially arable areas in the Western Australian wheatbelt. (Department of Primary Industries and Regional Development, Western Australia: Perth, WA, Australia) Available at https://library.dpird.wa.gov.au/gis_maps/20/
Giunta F, Motzo R, Deidda M (1993) Effect of drought on yield and yield components of durum wheat and triticale in a Mediterranean environment. Field Crops Research 33, 399–409.
| Effect of drought on yield and yield components of durum wheat and triticale in a Mediterranean environment.Crossref | GoogleScholarGoogle Scholar |
Gregory PA, Camp J, Bigelow K, Brown A (2019) Sub‐seasonal predictability of the 2017–2018 Southern Hemisphere tropical cyclone season. Atmospheric Science Letters 20, e886
| Sub‐seasonal predictability of the 2017–2018 Southern Hemisphere tropical cyclone season.Crossref | GoogleScholarGoogle Scholar |
Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz‐Sabater J, Nicolas J, Peubey C, Radu R, Schepers D, Simmons A, Soci C, Abdalla S, Abellan X, Balsamo G, Bechtold P, Biavati G, Bidlot J, Bonavita M, Chiara G, Dahlgren P, Dee D, Diamantakis M, Dragani R, Flemming J, Forbes R, Fuentes M, Geer A, Haimberger L, Healy S, Hogan RJ, Hólm E, Janisková M, Keeley S, Laloyaux P, Lopez P, Lupu C, Radnoti G, Rosnay P, Rozum I, Vamborg F, Villaume S, Thépaut JN (2020) The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society 146, 1999–2049.
| The ERA5 global reanalysis.Crossref | GoogleScholarGoogle Scholar |
Hope P, Keay K, Pook M, Catto J, Simmonds I, Mills G, McIntosh P, Risbey J, Berry G (2014) A comparison of automated methods of front recognition for climate studies: a case study in southwest Western Australia. Monthly Weather Review 142, 343–363.
| A comparison of automated methods of front recognition for climate studies: a case study in southwest Western Australia.Crossref | GoogleScholarGoogle Scholar |
Hudson D, Alves O, Hendon HH, Lim E-P, Liu G, Luo J-J, MacLachlan C, Marshall AG, Shi L, Wang G, Wedd R, Young G, Zhao M, Zhou X (2017a) ACCESS-S1: the new Bureau of Meteorology multi-week to seasonal prediction system. Journal of Southern Hemisphere Earth Systems Science 67, 132–159.
| ACCESS-S1: the new Bureau of Meteorology multi-week to seasonal prediction system.Crossref | GoogleScholarGoogle Scholar |
Hudson D, Shi L, Alves O, Hendon H, Young G (2017b) Performance of ACCESS-S1 for key horticultural regions. Bureau Research Report Number BRR020. (Bureau of Meteorology: Melbourne, Vic., Australia) Available at http://www.bom.gov.au/research/publications/researchreports/BRR-020.pdf [Verified 18 May 2023]
Hunke E, Lipscomb W (2008) ‘The Los Alamos sea ice model documentation and software user’s manual, Version 4.0.’ (Los Alamos National Laboratory: Los Alamos, NM, USA)
Jones DA, Wang W, Fawcett R (2009) High-quality spatial climate data-sets for Australia. Australian Meteorological and Oceanographic Journal 58, 233–248.
| High-quality spatial climate data-sets for Australia.Crossref | GoogleScholarGoogle Scholar |
Kala J, Andrys J, Lyons TJ, Foster IJ, Evans BJ (2015) Sensitivity of WRF to driving data and physics options on a seasonal time-scale for the southwest of Western Australia. Climate Dynamics 44, 633–659.
| Sensitivity of WRF to driving data and physics options on a seasonal time-scale for the southwest of Western Australia.Crossref | GoogleScholarGoogle Scholar |
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 146, 2228–2249.
| Sub-seasonal to seasonal prediction of rainfall extremes in Australia.Crossref | GoogleScholarGoogle Scholar |
Lim E, Hendon H, Hudson D, Zhao M, Shi L, Alves O, Young G (2016) Evaluation of the ACCESS-S1 hindcasts for prediction of Victorian seasonal rainfall. Bureau Research Report 19. (Bureau of Meteorology Australia) Available at http://www.bom.gov.au/research/publications/researchreports/BRR-019.pdf
MacLachlan C, Arribas A, Peterson KA, Maidens A, Fereday D, Scaife AA, Gordon M, Vellinga M, Williams A, Comer RE, Camp J, Xavier P, Madec G (2015) Global seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system. Quarterly Journal of the Royal Meteorological Society 141, 1072–1084.
| Global seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system.Crossref | GoogleScholarGoogle Scholar |
Madec G, The NEMO team (2016) NEMO ocean engine: Note du pole de modélisation de l’Institut Pierre-Simon Laplace nombre 27. (IPSL: Guyancourt, France) Available at https://www.nemo-ocean.eu/doc/
Marshall AG, Hendon HH (2018) Multi-week prediction of the Madden–Julian Oscillation with ACCESS-S1. Climate Dynamics 52, 2513–2528.
| Multi-week prediction of the Madden–Julian Oscillation with ACCESS-S1.Crossref | GoogleScholarGoogle Scholar |
Marshall AG, Hendon HH, Hudson D (2021a) Influence of the Madden–Julian Oscillation on multiweek prediction of Australian rainfall extremes using the ACCESS-S1 prediction system. Journal of Southern Hemisphere Earth Systems Science 71, 159–180.
| Influence of the Madden–Julian Oscillation on multiweek prediction of Australian rainfall extremes using the ACCESS-S1 prediction system.Crossref | GoogleScholarGoogle Scholar |
Marshall AG, Gregory PA, de Burgh-Day CO, Griffiths M (2021b) Subseasonal drivers of extreme fire weather in Australia and its prediction in ACCESS-S1 during spring and summer. Climate Dynamics 58, 523–553.
| Subseasonal drivers of extreme fire weather in Australia and its prediction in ACCESS-S1 during spring and summer.Crossref | GoogleScholarGoogle Scholar |
Megann A, Storkey D, Aksenov Y, Alderson S, Calvert D, Graham T, Hyder P, Siddorn J, Sinha B (2014) GO 5.0: the joint NERC–Met Office NEMO global ocean model for use in coupled and forced applications. Geoscientific Model Development 7, 1069–1092.
| GO 5.0: the joint NERC–Met Office NEMO global ocean model for use in coupled and forced applications.Crossref | GoogleScholarGoogle Scholar |
Meza FJ, Hansen JW, Osgood D (2008) Economic value of seasonal climate forecasts for agriculture: review of ex-ante assessments and recommendations for future research. Journal of Applied Meteorology and Climatology 47, 1269–1286.
| Economic value of seasonal climate forecasts for agriculture: review of ex-ante assessments and recommendations for future research.Crossref | GoogleScholarGoogle Scholar |
Pook MJ, Risbey JS, McIntosh PC (2012) The synoptic climatology of cool-season rainfall in the central wheatbelt of Western Australia. Monthly Weather Review 140, 28–43.
| The synoptic climatology of cool-season rainfall in the central wheatbelt of Western Australia.Crossref | GoogleScholarGoogle Scholar |
Saji NH, Goswami BN, Vinayachandran PN, Yamagata T (1999) A dipole mode in the tropical Indian Ocean. Nature 401, 360–363.
| A dipole mode in the tropical Indian Ocean.Crossref | GoogleScholarGoogle Scholar |
Shao Y, Wang QJ, Schepen A, Ryu D (2022) Introducing long‐term trends into sub‐seasonal temperature forecasts through trend‐aware post‐processing. International Journal of Climatology 42, 4972–4988.
| Introducing long‐term trends into sub‐seasonal temperature forecasts through trend‐aware post‐processing.Crossref | GoogleScholarGoogle Scholar |
Smith IN, McIntosh P, Ansell TJ, Reason CJC, McInnes K (2000) Southwest Western Australian winter rainfall and its association with Indian Ocean climate variability. International Journal of Climatology 20, 1913–1930.
| Southwest Western Australian winter rainfall and its association with Indian Ocean climate variability.Crossref | GoogleScholarGoogle Scholar |
Stephens DJ, Lyons TJ (1998) Rainfall–yield relationships across the Australian wheatbelt. Australian Journal of Agricultural Research 49, 211–224.
| Rainfall–yield relationships across the Australian wheatbelt.Crossref | GoogleScholarGoogle Scholar |
Stone RC, Hammer GL, Marcussen T (1996) Prediction of global rainfall probabilities using phases of the Southern Oscillation Index. Nature 384, 252–255.
| Prediction of global rainfall probabilities using phases of the Southern Oscillation Index.Crossref | GoogleScholarGoogle Scholar |
Ummenhofer CC, Sen Gupta A, Pook MJ, England MH (2008) Anomalous rainfall over southwest Western Australia forced by Indian Ocean sea surface temperatures. Journal of Climate 21, 5113–5134.
| Anomalous rainfall over southwest Western Australia forced by Indian Ocean sea surface temperatures.Crossref | GoogleScholarGoogle Scholar |
Vitart F, Ardilouze C, Bonet A, Brookshaw A, Chen M, Codorean C, Déqué M, Ferranti L, Fucile E, Fuentes M, Hendon H, Hodgson J, Kang HS, Kumar A, Lin H, Liu G, Liu X, Malguzzi P, Mallas I, Manoussakis M, Mastrangelo D, MacLachlan C, McLean P, Minami A, Mladek R, Nakazawa T, Najm S, Nie Y, Rixen M, Robertson AW, Ruti P, Sun C, Takaya Y, Tolstykh M, Venuti F, Waliser D, Woolnough S, Wu T, Won DJ, Xiao H, Zaripov R, Zhang L (2017) The Subseasonal to Seasonal (S2S) Prediction Project Database. Bulletin of the American Meteorological Society 98, 163–173.
| The Subseasonal to Seasonal (S2S) Prediction Project Database.Crossref | GoogleScholarGoogle Scholar |
Walters D, Boutle I, Brooks M, Melvin T, Stratton R, Vosper S, Wells H, Williams K, Wood N, Allen T, Bushell A, Copsey D, Earnshaw P, Edwards J, Gross M, Hardiman S, Harris C, Heming J, Klingaman N, Levine R, Manners J, Martin G, Milton S, Mittermaier M, Morcrette C, Riddick T, Roberts M, Sanchez C, Selwood P, Stirling A, Smith C, Suri D, Tennant W, Vidale PL, Wilkinson J, Willett M, Woolnough S, Xavier P (2017) The Met Office unified model global atmosphere 6.0/6.1 and JULES global land 6.0/6.1 configurations. Geoscientific Model Development 10, 1487–1520.
| The Met Office unified model global atmosphere 6.0/6.1 and JULES global land 6.0/6.1 configurations.Crossref | GoogleScholarGoogle Scholar |
Wedd R, Alves O, de Burgh-Day C, Down C, Griffiths M, Hendon HH, Hudson D, Li S, Lim E-P, Marshall AG, Shi L, Smith P, Smith G, Spillman CM, Wang G, Wheeler MC, Yan H, Yin Y, Young G, Zhao M, Xiao Y, Zhou X (2022) ACCESS-S2: the upgraded Bureau of Meteorology multi-week to seasonal prediction system. Journal of Southern Hemisphere Earth Systems Science 72, 218–242.
| ACCESS-S2: the upgraded Bureau of Meteorology multi-week to seasonal prediction system.Crossref | GoogleScholarGoogle Scholar |
Williams KD, Harris CM, Bodas-Salcedo A, Camp J, Comer RE, Copsey D, Fereday D, Graham T, Hill R, Hinton T, Hyder P, Ineson S, Masato G, Milton SF, Roberts MJ, Rowell DP, Sanchez C, Shelly A, Sinha B, Walters DN, West A, Woollings T, Xavier PK (2015) The Met Office Global Coupled Model 2.0 (GC2) configuration. Geoscientific Model Development 8, 1509–1524.
| The Met Office Global Coupled Model 2.0 (GC2) configuration.Crossref | GoogleScholarGoogle Scholar |
Wittwer G, Adams PD, Horridge M, Madden JR (2002) Drought, regions and the Australian economy between 2001-02 and 2004-05. Australian Bulletin of Labour 28, 231–246.
Wright PB (1974) Seasonal rainfall in southwestern Australia and the general circulation. Monthly Weather Review 102, 219–232.
| Seasonal rainfall in southwestern Australia and the general circulation.Crossref | GoogleScholarGoogle Scholar |
Zhao T, Wang QJ, Schepen A (2019a) A Bayesian modelling approach to forecasting short-term reference crop evapotranspiration from GCM outputs. Agricultural and Forest Meteorology 269-270, 88–101.
| A Bayesian modelling approach to forecasting short-term reference crop evapotranspiration from GCM outputs.Crossref | GoogleScholarGoogle Scholar |
Zhao T, Wang QJ, Schepen A, Griffiths M (2019b) Ensemble forecasting of monthly and seasonal reference crop evapotranspiration based on global climate model outputs. Agricultural and Forest Meteorology 264, 114–124.
| Ensemble forecasting of monthly and seasonal reference crop evapotranspiration based on global climate model outputs.Crossref | GoogleScholarGoogle Scholar |
Zhao P, Wang QJ, Wu W, Yang Q (2021) Which precipitation forecasts to use? Deterministic versus coarser‐resolution ensemble NWP models. Quarterly Journal of the Royal Meteorological Society 147, 900–913.
| Which precipitation forecasts to use? Deterministic versus coarser‐resolution ensemble NWP models.Crossref | GoogleScholarGoogle Scholar |
