Australian rainfall anomalies and Indo-Pacific driver indices: links and skill in 2-year-long forecasts
I. G. Watterson
A * , T. J. O’Kane B , V. Kitsios A and M. A. Chamberlain B
+ Author Affiliations
- Author Affiliations
A Climate Science Centre, CSIRO, Aspendale, Vic. 3195, Australia.
B Climate Science Centre, CSIRO, Hobart, Tas. 7004, Australia.
* Correspondence to: ian.watterson@csiro.au
Journal of Southern Hemisphere Earth Systems Science 71(3) 303-319 https://doi.org/10.1071/ES21008
Submitted: 16 April 2021 Accepted: 18 November 2021 Published: 9 December 2021
© 2021 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of BoM. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
Abstract
Two-year-long simulations of the atmosphere and ocean by the Commonwealth Scientific and Industrial Research Organisation's (CSIRO) Climate Analysis Forecast Ensemble (CAFE) modelling system are analysed, with a focus on Indo-Pacific sea surface temperature (SST) climate drivers and their teleconnection to Australian rainfall. The simulations are 11-member ensemble forecasts (strictly, hindcasts) initiated each month from 2002 to 2015, supplemented by a 100-year-long control simulation. Using correlations r between seasonal and annual means, it is shown that the links between the interannual variations of All-Australia precipitation (AApr) and the standard driver indices, together with the Pacific-Indian Dipole (PID), are mostly similar to those derived from observational data. The vertically integrated meridional flux of moisture towards northern Australia is linked to both the SSTs and AApr. Correlations between ensemble averages and observations are used as a measure of forecast skill, calculated for each start month and for lead time after start. Positive correlations hold over the first year for much of the low-latitude Pacific and for the drivers. The forecasts become more skillful than persistence, with r for PID averaging 0.3 higher over lead times of 7–13 months. The forecast of seasonal AApr has moderate to good correlations (r 0.4–0.8) for seasons centred on September–February. This is largely consistent with skill in both the flux and in the SST drivers. Correlations are also good for 1-year and 2-year means. This apparent skill is currently being explored using a new larger suite of CAFE forecasts.
Keywords: atmospheric moisture flux, Australian rainfall, ENSO, ERA5, Indo-Pacific climate drivers, Pacific‐Indian Dipole, seasonal and annual forecasts, teleconnection.
References
Barnston AG, Tippett MK, Ranganathan M, L’Heureux ML (2019) Deterministic skill of ENSO predictions from the North American multimodel ensemble. Climate Dynamics 53, 7215–7234.| Deterministic skill of ENSO predictions from the North American multimodel ensemble.Crossref | GoogleScholarGoogle Scholar | 31929685PubMed |
Bi D, Dix M, Marsland S, O’Farrell S, Sullivan A, Bodman R, Law R, Harman I, Srbinovsky J, Rashid HA, Dobrohotoff P, Mackallah C, Yan H, Hirst A, Savita A, Boeira Dias F, Woodhouse M, Fiedler R, Heerdegen A (2020) Configuration and spin-up of ACCESS-CM2, the new generation Australian community climate and Earth system simulator coupled model. Journal of Southern Hemisphere Earth Systems Science 70, 225–251.
| Configuration and spin-up of ACCESS-CM2, the new generation Australian community climate and Earth system simulator coupled model.Crossref | GoogleScholarGoogle Scholar |
Cai W, Wu L, Lengaigne M, Li T, McGregor S, Kug J-S, Yu J-Y, Stuecker MF, Santoso A, Li X, Ham Y-G, Chikamoto Y, Ng B, McPhaden MJ, Du Y, Dommenget D, Jia F, Kajtar JB, Keenlyside N, Lin X, Luo J-J, Martín-Rey M, Ruprich-Robert Y, Wang G, Xie S-P, Yang Y, Kang SM, Choi J-Y, Gan B, Kim G-I, Kim C-E, Kim S, Kim J-H, Chang P (2019) Pantropical climate interactions. Science 363, eaav4236
| Pantropical climate interactions.Crossref | GoogleScholarGoogle Scholar | 30819937PubMed |
Christiansen B (2019) Analysis of ensemble mean forecasts: the blessings of high dimensionality. Monthly Weather Review 147, 1699–1712.
| Analysis of ensemble mean forecasts: the blessings of high dimensionality.Crossref | GoogleScholarGoogle Scholar |
Copernicus Climate Change Service (C3S) (2017) ERA5: fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus climate change service climate data store (CDS). Available at https://cds.climate.copernicus.eu/cdsapp#!/home
Delworth TL, Broccoli AJ, Rosati A, Stouffer RJ, Balaji V, Beesley JA, Cooke WF, Dixon KW, Dunne J, Dunne KA, Durachta JW, Findell KL, Ginoux P, Gnanadesikan A, Gordon CT, Griffies SM, Gudgel R, Harrison MJ, Held IM, Hemler RS, Horowitz LW, Klein SA, Knutson TR, Kushner PJ, Langenhorst AR, Lee H-C, Lin S-J, Lu J, Malyshev SL, Milly PCD, Ramaswamy V, Russell J, Schwarzkopf MD, Shevliakova E, Sirutis JR, Spelman MJ, Stern WF, Winton M, Wittenberg AT, Wyman B, Zeng F, Zhang R (2006) GFDL’s CM2 global coupled climate models. Part I: formulation and simulation characteristics. Journal of Climate 19, 643–674.
| GFDL’s CM2 global coupled climate models. Part I: formulation and simulation characteristics.Crossref | GoogleScholarGoogle Scholar |
Dey R, Lewis SC, Arblaster JM, Abram NJ (2019) A review of past and projected changes in Australia’s rainfall. Wiley Interdisciplinary Reviews: Climate Change 10, e577
| A review of past and projected changes in Australia’s rainfall.Crossref | GoogleScholarGoogle Scholar |
Evans A, Jones D, Smalley R, Lellyett S (2020) An enhanced gridded rainfall analysis scheme for Australia. Bureau Research Report 41. Australian Bureau of Meteorology, p. 45.
Frederiksen CS, Grainger S, Zheng X (2018) Potential predictability of Australian seasonal rainfall. Journal of Southern Hemisphere Earth Systems Science 68, 65–100.
| Potential predictability of Australian seasonal rainfall.Crossref | GoogleScholarGoogle Scholar |
Gu G, Adler RF (2019) Precipitation, temperature, moisture transport variations associated with two distinct ENSO flavors during 1979–2014. Climate Dynamics 52, 7249–7265.
| Precipitation, temperature, moisture transport variations associated with two distinct ENSO flavors during 1979–2014.Crossref | GoogleScholarGoogle Scholar |
Hauser S, Grams CM, Reeder MJ, McGregor S, Fink AH, Quinting JF (2020) A weather system perspective on winter–spring rainfall variability in southeastern Australia during El Niño. Quarterly Journal of the Royal Meteorological Society 146, 2614–2633.
| A weather system perspective on winter–spring rainfall variability in southeastern Australia during El Niño.Crossref | GoogleScholarGoogle Scholar |
Hersbach H, Bell B, Berrisford P, et al. (2020) The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society 146, 1999–2049.
| The ERA5 global reanalysis.Crossref | GoogleScholarGoogle Scholar |
Holgate CM, Evans JP, van Dijk AIJM, Pitman AJ, Di Virgilio G (2020) Australian precipitation recycling and evaporative source regions. Journal of Climate 33, 8721–8735.
| Australian precipitation recycling and evaporative source regions.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 (2017) 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 |
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 |
Klingaman NP, Woolnough SJ, Syktus J (2013) On the drivers of inter-annual and decadal rainfall variability in Queensland, Australia. International Journal of Climatology 33, 2413–2430.
| On the drivers of inter-annual and decadal rainfall variability in Queensland, Australia.Crossref | GoogleScholarGoogle Scholar |
Lai AW-C, Herzog M, Graf H-F (2018) ENSO Forecasts near the spring predictability barrier and possible reasons for the recently reduced predictability. Journal of Climate 31, 815–838.
| ENSO Forecasts near the spring predictability barrier and possible reasons for the recently reduced predictability.Crossref | GoogleScholarGoogle Scholar |
Mahlstein I, Bhend J, Spirig C, Martius O (2019) Developing an automated medium-range flood awareness system for switzerland based on probabilistic forecasts of integrated water vapor fluxes. Weather and Forecasting 34, 1759–1776.
| Developing an automated medium-range flood awareness system for switzerland based on probabilistic forecasts of integrated water vapor fluxes.Crossref | GoogleScholarGoogle Scholar |
Marshall AJ (2019) Variation in growing season water balance in central Victoria, Australia, in relation to large-scale climate drivers. Journal of Southern Hemisphere Earth Systems Science 69, 131–145.
| Variation in growing season water balance in central Victoria, Australia, in relation to large-scale climate drivers.Crossref | GoogleScholarGoogle Scholar |
Marshall AG, Hendon HH (2019) 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 |
O’Kane TJ, Sandery PA, Monselesan DP, Sakov P, Chamberlain MA, Matear RJ, Collier MA, Squire DT, Stevens L (2019) Coupled data assimilation and ensemble initialization with application to multiyear ENSO prediction. Journal of Climate 32, 997–1024.
| Coupled data assimilation and ensemble initialization with application to multiyear ENSO prediction.Crossref | GoogleScholarGoogle Scholar |
O’Kane TJ, Squire DT, Sandery PA, Kitsios V, Matear RJ, Moore TS, Risbey JS, Watterson IG (2020) Enhanced ENSO prediction via augmentation of multi-model ensembles with initial thermocline perturbations. Journal of Climate 33, 2281–2293.
| Enhanced ENSO prediction via augmentation of multi-model ensembles with initial thermocline perturbations.Crossref | GoogleScholarGoogle Scholar |
O’Kane TJ, Sandery PA, Kitsios V, Sakov P, Chamberlain MA, Collier MA, Fiedler R, Moore TS, Chapman CC, Sloyan BM, Matear RJ (2021) CAFE60v1: a 60-year large ensemble climate reanalysis. Part I: system design, model configuration and data assimilation. Journal of Climate 34, 5153–5169.
| CAFE60v1: a 60-year large ensemble climate reanalysis. Part I: system design, model configuration and data assimilation.Crossref | GoogleScholarGoogle Scholar |
Power S, Haylock M, Colman R, Wang X (2006) The predictability of interdecadal changes in ENSO activity and ENSO teleconnections. Journal of Climate 19, 4755–4771.
| The predictability of interdecadal changes in ENSO activity and ENSO teleconnections.Crossref | GoogleScholarGoogle Scholar |
Reynolds RW, Rayner NA, Smith TM, Stokes DC, Wang W (2002) An improved in situ and satellite SST analysis for climate. Journal of Climate 15, 1609–1625.
| An improved in situ and satellite SST analysis for climate.Crossref | GoogleScholarGoogle Scholar |
Smith I (2004) An assessment of recent trends in Australian rainfall. Australian Meteorological Magazine 53, 163–173.
Taminiau C, Haarsma RJ (2007) Projected changes in precipitation and the occurrence of severe rainfall deficits in central Australia caused by global warming. Australian Meteorological Magazine 56, 167–175.
Van Rensch P, Arblaster J, Gallant AJE, Cai W, Nicholls N, Durack PJ (2019) Mechanisms causing east Australian spring rainfall differences between three strong El Niño events. Climate Dynamics 53, 3641–3659.
| Mechanisms causing east Australian spring rainfall differences between three strong El Niño events.Crossref | GoogleScholarGoogle Scholar |
Watterson IG (2020) Australian rainfall anomalies in 2018-2019 linked to indo-pacific driver indices using ERA5 reanalyses. Journal of Geophysical Research 125, e2020JD033041
| Australian rainfall anomalies in 2018-2019 linked to indo-pacific driver indices using ERA5 reanalyses.Crossref | GoogleScholarGoogle Scholar |
Watterson IG, Keane RJ, Dix M, Ziehn T, Andrews T, Tang Y (2021) Analysis of CMIP6 atmospheric moisture fluxes and the implications for projections of future change in mean and heavy rainfall. International Journal of Climatology 41, E1417–E1434.
| Analysis of CMIP6 atmospheric moisture fluxes and the implications for projections of future change in mean and heavy rainfall.Crossref | GoogleScholarGoogle Scholar |
Wittenberg AT, Rosati A, Delworth TL, Vecchi GA, Zheng F (2014) ENSO modulation: is it decadally predictable. Journal of Climate 27, 2667–2681.
| ENSO modulation: is it decadally predictable.Crossref | GoogleScholarGoogle Scholar |
Ye C, Zhang H, Moise A, Mo R (2020) Atmospheric rivers in the Australia-Asian region: a BoM-CMA collaborative study. Journal of Southern Hemisphere Earth Systems Science 70, 3–16.
| Atmospheric rivers in the Australia-Asian region: a BoM-CMA collaborative study.Crossref | GoogleScholarGoogle Scholar |
Zhang W, Villarini G, Vecchi GA (2019) Impacts of the Pacific meridional mode on rainfall over the maritime continent and Australia: potential for seasonal predictions. Climate Dynamics 53, 7185–7199.
| Impacts of the Pacific meridional mode on rainfall over the maritime continent and Australia: potential for seasonal predictions.Crossref | GoogleScholarGoogle Scholar |
Zhao S, Li J, Li Y, Jin F-F, Zheng J (2019) Interhemispheric influence of Indo-Pacific convection oscillation on Southern Hemisphere rainfall through southward propagation of Rossby waves. Climate Dynamics 52, 3203–3221.
| Interhemispheric influence of Indo-Pacific convection oscillation on Southern Hemisphere rainfall through southward propagation of Rossby waves.Crossref | GoogleScholarGoogle Scholar |
