1. Introduction
Allan and D’Arrigo (1999) were the first to publish the concept of multiyear “protracted” ENSO episodes in the scientific literature, and this term has been used in subsequent papers such as Allan (2000, 2006), Allan et al. (2003), Gergis and Fowler (2009), Arora and Kumar (2019), and Arora (2022). In the last 10 years, terms such as “double or triple dip” have been coined for two particular temporal lengths of the generic phenomenon of protracted ENSO episodes, of either El Niño or La Niña phase (Hu et al. 2014; DiNezio et al. 2017; Gao et al. 2023; Li et al. 2022). It is also important to note that durational differences occur between ENSO events or episodes and the asymmetry of their El Niño and La Niña phases, as well as their underlying physics, and these have been well documented (e.g., Allan et al. 1996; Allan 2000, 2006; Okumura and Deser 2010; Okumura et al. 2011; Hu et al. 2014). Isolation of the ENSO signal from the noise prior to analyses is advocated strongly in papers such as Compo and Sardeshmukh (2010). The above elements of ENSO nature and characteristics are all discussed in Allan et al. (1996) and Allan (2000, 2006) and more recently in Allan et al. (2019).
In the Allan and D’Arrigo (1999) paper, we examined historical and paleoclimate evidence for protracted ENSO episodes and showed the nature of the global rainfall response during each year in the course of the 1990–95 protracted El Niño episode, plus the evolution, similarities, and differences of the Southern Oscillation index (SOI), Niño-3 and Niño-4 surface temperature (SST) region anomalies during four protracted El Niño episodes and six protracted La Niña episodes since 1878. In Allan (2000), a spectral analysis was used to determine the frequency ranges of the specific quasi-biennial, interannual, and quasi-decadal signals of ENSO, and then a joint multi-taper method–singular value decomposition (MTM-SVD) examination of the historical SST and atmospheric pressure signatures in each of those frequency ranges was used to show that the quasi-decadal signal “carried” the dominance of Niño-4 SSTs and was where the protracted ENSO signal occurred. This chapter also examined the global precipitation correlations at each of the above frequency ranges, illustrating the similarities and differences between the seasonal rainfall responses on quasi-biennial, interannual, and quasi-decadal time scales. Meinke et al. (2005) again examined the global precipitation correlations at each of the above ENSO frequency ranges, with a longer dataset on a seasonal basis. This paper also verified the statistical robustness of not just the protracted ENSO signal, but its higher-frequency quasi-biennial and interannual signals indicative of more “classical” El Niños and La Niñas. For Australia, the September–November (SON) season, where the strongest interannual ENSO rainfall correlations were found, showed that the quasi-decadal signal of protracted ENSO episodes had even stronger rainfall correlations. Interestingly, in Allan et al. (1990), an examination of ENSO correlations in the Australian region showed that seasonal sea level correlations with Australian rainfall were even stronger than those with the SOI or Niño-3, Niño-3.4, and Niño-4 SST region anomalies.
In Allan et al. (2019), we built upon the Allan and D’Arrigo (1999) paper that addressed the physical nature and teleconnections and some of the impacts of protracted ENSO episodes (of both El Niño and La Niña phases) using historical and paleo-environmental data. This was in order to put the focus of that paper, the 2014–16 protracted El Niño, into a longer spatiotemporal context with other episodes. We do likewise in this paper but focus on the 2018–20 protracted El Niño episode and its wider impacts on Australia within a historical perspective.
Allan et al. (2019) showed that protracted ENSO episodes were just another “flavor” of ENSO (Capotondi et al. 2015; Timmermann et al. 2018), and that their signature pattern of periods of over 2 yr of persistent western equatorial Pacific Niño-4 region (5°N–5°S, 160°E–150°W) SST anomalies was analogous to the Modoki El Niño and La Niña phases (Ashok et al. 2007; Weng et al. 2007; Ashok and Yamagata 2009) and central Pacific (CP) ENSOs (Ashok and Yamagata 2009; Capotondi et al. 2015; Timmermann et al. 2018). It was also noted that the ocean–atmosphere interactions underlying classical events, which occur on interannual time scales, also underlie protracted episodes, but that the latter involve the interplay of quasi-biennial, interannual, and quasi-decadal ENSO signals (Tourre et al. 2001).
It is also important to view protracted ENSO episodes within the wider framework of pantropical ocean–atmosphere interactions underlying ENSO (Cai et al. 2019; Wang 2019; Feng et al. 2021b). Since the papers of Saji et al. (1999) and Webster et al. (1999), which promoted the Indian Ocean dipole (IOD) as a new entity potentially independent of ENSO [see schematics of Risbey et al. (2009) as opposed to Allan (1988)], there has been contention in the climate community about the IOD and its relationship to ENSO (Allan et al. 2001; Dommenget and Latif 2002, 2003; Krishnamurthy and Kirtman 2003; Dommenget et al. 2006; Dommenget 2007; Hannachi and Dommenget 2009; Dommenget and Jansen 2009; Dommenget 2011; Frauen and Dommenget 2012; Zhao and Nigam 2015; Stuecker et al. 2017; Feng et al. 2021b). For instance, the IOD is not independent of ENSO in the recent analyses of Stuecker et al. (2017) and Feng et al. (2021b). In fact, basic long-term (1870–2021) simultaneous seasonal correlations between SST anomalies in the Niño-4 region and globally (Fig. 1) show a significant protracted ENSO and an IOD pattern occurring together during the peak July–September (JAS)–October–December (OND) season of the latter. The spatial structure of the Pacific SST response to protracted ENSO episodes mirrors that seen in the quasi-decadal phases of Tourre et al. (2001) and White and Tourre (2007). This is also shown in the discussion of delayed action oscillator (DAO) mechanisms underlying both ENSO and quasi-decadal (protracted ENSO) modes operating in the Indo-Pacific domain in Fig. 9 of White and Tourre (2007). Furthermore, Compo and Sardeshmukh (2010) have shown that rigorously isolating the ENSO signal in the climate system results in SST anomaly patterns across the Indo-Pacific domain in which the IOD occurs as part of ENSO. Importantly, Cai et al. (2019) and Feng et al. (2021b) both show this as well, and their findings are especially significant in that they refocus climate dynamical thinking on an integrated ENSO system involving the IOD and tropical interbasin interactions.
Seasonal (JFM, AMJ, JAS, and OND) correlations of anomalies of Niño-4 region SSTs with global HadISST 1 SSTs from 1870 to 2021 (significance p < 1%), with protracted ENSO and IOD concurrence most evident in JAS–OND. These correlation patterns have field significance pfield < 0.1%. SST anomalies are from the 1991–2020 mean. Source: Climate Explorer (https://climexp.knmi.nl/).
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-22-0096.1
Following Allan et al. (2019), we define protracted episodes in the historical context as when both the SOI and the Niño-4 SST anomalies (shown in Fig. 3, described in more detail below) are of either sign for 2 yr or more, with any sign change in that period being for a maximum of only two consecutive months. Although they both impact similar global regions, especially across the Indo-Pacific domain (Allan 2000; Meinke et al. 2005), classical ENSO episodes usually last for between 12 and 18 months, while the historical to paleo analyses of Allan and D’Arrigo (1999) and Allan et al. (2019) indicate that protracted ENSO episodes have lasted from 2 to 7 yr in the historical instrumental record and up to 9 yr in the paleo data. Although measured by the same indices, protracted ENSO episodes are usually weaker in magnitude than major classical ENSO events, but it is the longer duration of the former that can magnify their impacts on societal, agricultural, and marine regimes dramatically.
This paper focuses initially on defining the physical nature and teleconnections associated with the 2018–20 protracted El Niño episode and interlinked IODs that, together with climate change, underlie the climate pattern that caused the recent major Australian drought (https://public.wmo.int/en/media/news/australia-suffers-devastating-fires-after-hottest-driest-year-record) and wildfires (Boer et al. 2020). Importantly, when examined since 1990 in the sections below, the longer duration of protracted ENSO episodes of either phase shows that they temporally extend, and can dramatically magnify, the ecophysiological impacts associated with classical ENSO events, posing an additional set of problems for all sectors. The focus then expands to place the wider Australian agricultural plus terrestrial and marine environmental impacts during this episode in their longer-term context. Note that as with ENSO events, protracted ENSO episodes of either phase show both similarities and differences in their impacts over their duration. All of this indicates that the potential to forecast protracted ENSO episodes, and thus enhance management decisions, must be established (see DiNezio et al. 2017).
2. Data
The SOI is defined as the normalized mean sea level pressure (MSLP) difference between Papeete in Tahiti and Darwin in Australia (Allan et al. 1996), and the series used in this study is from the NOAA/National Weather Service Climate Prediction Center (CPC) (https://www.cpc.ncep.noaa.gov/data/indices/soi). The equatorial SOI is calculated as the standardized anomaly of the difference between the area-averaged monthly sea level pressure in the eastern equatorial Pacific (80°–130°W, 5°N–5°S) and over Indonesia (90°–140°E, 5°N–5°S) (https://www.cpc.ncep.noaa.gov/data/indices/reqsoi.for).
Global, Niño-3, and Niño-4 SST anomalies (detrended and from the 1991–2020 mean) (used in Fig. 1 and in Fig. 5, described in more detail below) were generated from the HadISST 1 dataset (1870–2021) and are available as monthly series values on Climate Explorer (https://climexp.knmi.nl/). Tropical equatorial Pacific Niño-1 + 2, Niño-3, Niño-3.4, and Niño-4 regions (shown in Fig. 3, described in more detail below) are from the online NCAR/UCAR Climate Data Guide (https://climatedataguide.ucar.edu/). The Niño-4 SST anomalies are also used in framing the definition of protracted episodes.
Anomalies of upper-level (200 hPa) standardized seasonal velocity potential fields and precipitation fields (http://iridl.ldeo.columbia.edu/maproom/ENSO/Tropical_Atm_Circulation/PRCP_Std_Vpot.html) in Fig. 4 (see below) are from the IRI Map Room. Velocity potential is a measure of the irrotational or divergent component of the wind flow at a designated level in the atmosphere, with positive (negative) values denoting regions of convergence (divergence). The dipole mode index (DMI) (used in Fig. 5, described in more detail below) was taken from the Climate Explorer (https://climexp.knmi.nl/).
Australian coastal and marine station sea level anomalies (in Fig. 6, described below) are reproduced from the Australian Bureau of Meteorology (BoM) Australian Baseline Sea Level Monitoring Project (ABSLMP) February 2022 report (National Operations Centre Tidal Unit 2022; http://www.bom.gov.au/ntc/IDO60201/IDO60201.202202.pdf). They are generated from the observations made at the BoM ABSLMP’s coastal tide gauges [see their locations in Fig. S2 in the supplemental material of Allan et al. (2019)].
As noted in Allan et al. (2019), we were careful in our use of indices like the Niño-1 + 2, Niño-3, Niño-3.4, and Niño-4 region SSTs that have been interpolated, include satellite remote sensing data, use multiple data sources, and display warming biases due to the effect of climate change (Newman et al. 2018; Turkington et al. 2019). Comparisons (not shown) using observations-only versions of them revealed almost imperceptible differences.
Detailed assessments of impacts in Australian agricultural and environmental sectors (in Figs. 8–10, described in more detail below) are taken from the Australian Bureau of Agricultural and Resource Economics (ABARES; Wittwer and Waschik 2021) and Meat and Livestock Australia (2022), while evidence for modulations and extreme impacts on Australian coastal marine systems and species stocks is drawn from a wide range of sources and presented in section 5 (Figs. 12–14, described in more detail below).
3. The 2018–20 protracted El Niño episode
During the major Australian drought and intense wildfires in the 2018–20 period (Boer et al. 2020), seasonal forecasts and outlooks issued by national meteorological services reported the cause of these conditions as being the result of a strong, positive phase of the IOD, with ENSO being in a neutral phase (http://www.bom.gov.au/climate/updates/articles/a037.shtml and https://www.climate.gov/news-features/blogs/enso/april-2020-enso-update-alternative-communication), together with the influence of climate change. These ENSO assessments were based primarily on the nature of Niño-3 and/or Niño-3.4 SST anomalies in the Pacific, which did not exceed their criteria for an El Niño event. This is still seen in some contemporary thinking, even when Niño-4 SSTs are considered (Feng et al. 2021b). However, since the 2018–20 Australian drought and wildfires, several papers have appeared in the literature that attribute this climatic extreme to a combination of the influences of climate change and both the IOD and warm CP SSTs/Modoki El Niño influences (Doi et al. 2020; Wang and Cai 2020; Abram et al. 2021; Zhang et al. 2021).
An examination of monthly Niño-4 SST anomalies shows that they remained positive from the period of March–April 2018 to April–May 2020 (Fig. 2), with the SOI being almost consistently negative (allowing for only any two consecutive months to have gone positive), from June 2018 to 2020, thus passing the Allan et al. (2019) criteria for a protracted El Niño episode (see https://iridl.ldeo.columbia.edu/maproom/ENSO/Time_Series/SOI.html). In addition, the three-month assessments of both statistical and numerical model forecasts in the Fast Break newsletters (https://agriculture.vic.gov.au/support-and-resources/newsletters/the-break) of Agriculture Victoria in Australia during this period found that the forecasts of the statistical SOI phase system (Stone et al. 1996; Cobon and Toombs 2013) often captured as much of the observed South Australian, Victorian, southern New South Wales, and Tasmanian rainfall and temperature patterns as the numerical model forecasts and IOD measures. Hence, the nature of the SOI signal is displayed in Fig. 3, where the monthly Niño-4 SST anomalies and SOI (plus the equatorial SOI) defining the protracted El Niño episode from 2018 to 2020 are shown.
Time–longitude section (Hovmöller plot) of anomalous SST (°C) averaged between 5°N and 5°S during the 2018–20 protracted El Niño episode (highlighted in the red-outlined rectangle that defines the Niño-4 SST region). The contour interval is 0.5°C. Dashed contours indicate negative anomalies. Anomalies are departures from the 1991–2020 base period means. Source: Climate Prediction Center 2020 (https://www.cpc.ncep.noaa.gov/products/CDB/Tropics/.shtml).
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-22-0096.1
Monthly Niño-4 SST anomalies (°C; blue shading), SOI (red bars), and equatorial SOI (green bars) from 2018 to 2020. Sources: Niño-4 SST is from https://www.cpc.ncep.noaa.gov/data/indices/sstoi.indices, SOI is from https://www.cpc.ncep.noaa.gov/data/indices/soi, and equatorial SOI is from https://www.cpc.ncep.noaa.gov/data/indices/reqsoi.for.
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-22-0096.1
To examine this situation further, global seasonal upper-level (200 hPa) standardized velocity potential and precipitation anomalies are shown for the 2018–20 period in Fig. 4. The panels for 2018, 2019, and 2020 all show enhanced precipitation, as a result of deep convection, over the western Pacific region. The latter is seen in the 200-hPa seasonal standardized 2020 velocity potential anomaly fields, with an area of upper-level divergent winds emanating from the Niño-4 region, and an outflow directed toward eastern Australia indicative of a regional teleconnection (area covered by the red circles) that promoted large-scale subsidence and suppressed rainfall (brown shaded regions) across eastern Australia during the 2018–20 period. Importantly, the seasonal panels also show the Indian Ocean conditions indicative of positive IODs in 2018 and 2019 (Wang and Cai 2020; Zhang et al. 2021), with reduced northwest cloud band [see general schematic in Fig. 9 in Reid et al. (2019)] formation and suppressed rainfall across southern Australia (Zhang et al. 2021). The IOD is strongest in the 2019 JAS-OND seasons, with a zonal east–west overturning pattern supporting upper-level subsidence (enhanced convection) and reduced (increased) rainfall over the central-eastern Indian Ocean and Maritime Continent (western tropical Indian Ocean). This is linked dynamically to the strong negative SSTs off the south of Sumatra–Java (https://iridl.ldeo.columbia.edu/maproom/Global/Ocean_Temp/Seasonal.html?T=Sep-Nov%202019), positive outgoing longwave radiation (OLR) anomalies (W m−2) (extending from the south of Sumatra–Java across northwest Australia), and suppressed northwest cloud band activity, especially from September to December 2019 (https://iridl.ldeo.columbia.edu/maproom/Global/Precipitation/Monthly_OLR_anom.html?T=Sep%202019 to https://iridl.ldeo.columbia.edu/maproom/Global/Precipitation/Monthly_OLR_anom.html?T=Dec%202019). It is reinforced further in the full tropospheric divergent circulation anomalies in Figures T29-T30 of the monthly Climate Diagnostics Bulletin (https://www.cpc.ncep.noaa.gov/products/CDB/CDB_Archive_pdf/pdf_CDB_archive.shtml). Thus, these are all interlinked dynamical signatures of the Indo-Pacific protracted El Niño episode.
Global seasonal (JFM, AMJ, JAS, OND) 200-hPa standardized velocity potential (VP) and precipitation (P) anomalies during 2018, 2019, and 2020 (only JFM and AMJ). The P anomaly shading values in millimeters are shown below the panels. Areas shaded in brown or green respectively represent negative or positive P anomalies from the 1991–2020 mean. Blue contours represent the seasonal standardized VP anomalies; solid lines are positive anomalies from the 1991–2020 mean, and dashed lines are negative anomalies. The standardized VP anomalies are contoured at an interval of 0.5 std dev, and the scale is shown below the panels. The blue vectors indicate the gradient of the seasonal VP anomalies, representing the divergent part of the wind. The magnitude of the gradient is indicated by the vector length. The region of a major teleconnection associated with large-scale subsidence over eastern Australia is shown by the red circles. Source: http://iridl.ldeo.columbia.edu/maproom/ENSO/Tropical_Atm_Circulation/PRCP_Std_Vpot.html.
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-22-0096.1
As reported in Feng et al. (2021b), Niño-4 SST anomalies are also an influence on southern Chinese rainfall, and this appears to result from a teleconnection into the Northern Hemisphere from the Niño-4 region toward China. In this analysis, this teleconnection is observed most prominently in OND 2018 (Fig. 4) through January–March (JFM) and April–June (AMJ) 2019 (Fig. 4), and again in JFM 2020 (Fig. 4).
4. Protracted El Niño and La Niña episodes since 1990
Further perspectives on protracted ENSO–IOD relationships and the response of coastal sea level in the Australian region are addressed in Figs. 5 and 6. As the best set of high-resolution coastal sea level anomalies around the Australian coast is only available from a group of some 16 monitoring sites operating since 1990 [in Fig. 6 from the National Operations Centre Tidal Unit (2022)], the focus in Fig. 5 is on the four protracted El Niño and two protracted La Niña episodes that occurred during that time.
Plots of monthly Niño-3 and Niño-4 region SST anomalies (°C) and the DMI for the 2018–20 and 2014–16 protracted El Niño episodes and the 2010–12 protracted La Niña episode and for the 2001–05 protracted El Niño episode, the 1998–2001 protracted La Niña episode, and the 1990–95 protracted El Niño episode.
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-22-0096.1
Monthly sea level anomalies (m; red is positive, and blue is negative) from 1990 to June 2022 counterclockwise around the Australian coast. The transparent yellow-shaded regions show 1990–95, 2002–05, 2014–16, and 2018–20 protracted El Niño episodes. The transparent green-shaded regions show 1998–2001, 2010–12, and ongoing 2020 protracted La Niña episodes. Sea level anomalies are the residuals after tides; annual and semiannual seasonal cycles and linear slope have been removed by way of harmonic tidal analysis of the complete record. Source: National Operations Centre Tidal Unit 2022 (http://www.bom.gov.au/ntc/IDO60201/IDO60201.202202.pdf).
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-22-0096.1
The first three plots of Fig. 5 show plots of monthly Niño-3 and Niño-4 region SST anomalies and the DMI for the 2018–20 and 2014–16 protracted El Niño episodes and the 2010–12 protracted La Niña episode. In the second three plots of Fig. 5, the sequence is completed with plots of monthly Niño-3 and Niño-4 region SST anomalies and the DMI for the 2001–05 protracted El Niño episode, the 1998–2001 protracted La Niña episode, and the 1990–95 protracted El Niño episode. In all of the protracted episodes shown in Fig. 5, there are, to varying degrees, signatures of positive and negative IOD events (indicated by the DMI) occurring in conjunction with the Niño-3 and Niño-4 region SST anomalies. Interestingly, in Fig. 5, the 2014–16 protracted El Niño episode evolves into a distinct full-blown major El Niño event during 2015/16. El Niño events also evolve at various stages during the protracted El Niño episodes in 1991/92 (strong), 1994/95 (moderate), and 2002/03 (moderate) in Fig. 5, while La Niña events occur during protracted La Niña episodes in 1998/99 and 1999/2000 (both strong) in Fig. 5 and in 2010/11 (strong) and 2011/12 (moderate) in Fig. 5 (https://ggweather.com/enso/oni.htm).
Global to regional sea level responses to ENSO are well established in the literature (e.g., Becker et al. 2012; Zhang and Church 2012; Zinke et al. 2015; Muis et al. 2018; Lowe et al. 2021). In Fig. 6, both classical and protracted El Niños (La Niñas) since 1990 are associated with a lowering (rising) of sea level around the Australian coast [National Operations Centre Tidal Unit 2022]. Along the west coast of Australia, this is primarily as a consequence of modulations of the Indonesian Throughflow (Feng et al. 2021b) that contribute to the strength of the Leeuwin Current. This sea level response was discussed for the 2014–16 protracted El Niño episode in Allan et al. (2019), where it was also shown that the integrating effect of these ENSO-influenced sea level anomalies (from the combination of MSLP, SST, wind influences, etc.) means that they correlate more strongly with Australian rainfall anomalies than do the SOI or regional and Niño region SSTs alone (see also Allan et al. 1990). Supplemental material in Allan et al. (2019) (Fig. S4) also showed significant simultaneous correlations between Darwin, Fremantle, and Townsville sea level anomalies with Indo-Pacific SSTs, plus oceanic heat content and sea surface salinity in the Australian region.
The ENSO-influenced sea level signal extends counterclockwise around the Australian coast from the northern tropics, southward down the west coast, through the southern midlatitudes from west to east, and northward up the east coast. During classical ENSO events of either phase, the sea level signal tends to propagate a farther distance around the coast. During protracted ENSO episodes of either phase, the sea level signal tends to propagate a smaller distance around the coast. Protracted ENSO sea level anomalies since 1990 usually extend into southern latitudes but rarely up the Australian east coast, the only exception in this time period being the 2014–16 protracted El Niño episode (which evolved into a major classical El Niño event by 2015/16). Thus, these sea level responses are physical signatures of protracted ENSO activity and influences on the Australian marine environment. Their wider manifestations and impacts are examined in the following sections, with the criteria for a protracted La Niña episode from around July 2020 having just been exceeded at the end of July 2022, and it is continuing at the time of writing (this episode will be discussed in a future paper).
5. Australian agricultural, terrestrial, and marine environmental impacts
Periods of drought are an intrinsic part of the Australian climate, and efforts to forecast and mitigate their impacts on society, infrastructure, agriculture, and both the terrestrial and marine environments are ongoing (Smith et al. 2007; Howden et al. 2014). Protracted ENSO episodes provide an added dimension in that their impacts last longer than those experienced during classical ENSO events, and their presence over several years can markedly overstress and expose society, infrastructure, the natural environment, and agricultural systems (https://public.wmo.int/en/media/news/australia-suffers-devastating-fires-after-hottest-driest-year-record; https://www.metoffice.gov.uk/research/news/2020/causes-of-extreme-fire-weather-in-australia; Harris and Lucas 2019; Boer et al. 2020; Filkov et al. 2020; Feng et al. 2021a; Abram et al. 2021). Thus, it is not surprising that there is growing evidence that such climatic extremes also impact the wider Australian agricultural and environmental sectors (e.g., enhanced frost, reduced pasture growth and yields) (Crimp et al. 2016) and marine systems and species (e.g., suppressed sea levels, marine heatwaves, coral bleaching, changes in fish stocks) (Frölicher and Laufkötter 2018; Kamenos and Hennige 2018; Heidemann and Ribbe 2019; Feng et al. 2021a; Holbrook et al. 2020; Oliver et al. 2021). As shown by Feng et al. (2018, 2021b), the Indonesian Throughflow plays an important role as a conduit for the passage of fresh, warm Pacific waters into the Indian Ocean, with an important component spreading southward down the Western Australian coastal margins, as the Leeuwin Current, affecting marine systems and generating marine heatwaves. Thus, protracted ENSO episodes provide an added dimension, in that their impacts last longer than those experienced during classical ENSO events, and their presence over several years can markedly overstress and expose society, infrastructure, the natural environment, and agricultural systems. These terrestrial and marine impacts are examined in the following section.
6. Terrestrial: Links to protracted ENSO episodes
Drought and agricultural production
Much of New South Wales and southern Queensland, in particular, suffered from particularly extreme drought from 2017 to 2019/20 (Fig. 7). A detailed assessment by ABARES (Wittwer and Waschik 2021) noted that the protracted drought of this period resulted in a drop of national real GDP to 0.7% or more below the baselines of 2018/19 and 2019/20. In the above report, New South Wales’s real GDP fell relative to forecasts by 1.1% or AUD $6.9 billion in 2018/19 and by 1.6% or AUD $10.2 billion in 2019/20. These impacts reflected a severe decrease in farm output and also contributed to the conditions that produced the bushfires (which had a further impact on agriculture and the economy). The ABARES report notes that the protracted drought (and bushfire destruction) depleted farm capital through depressed investment and also diminished herd numbers. The net present value of the national welfare loss amounted to AUD $63 billion, split between AUD $53 billion in losses from the drought and AUD $10 billion from bushfires (Wittwer and Waschik 2021).
Aggregated soil water recharge status (percentage) for the main sorghum-growing region of Australia, as it was on 1 Dec 2019. A short 7‐month winter fallow that captures the fall in antecedent moisture levels was simulated from 1 Apr 2019 to the end of November 2019. Major sorghum production areas (shires) in NSW and QLD are shown. Source: Seasonal Crop Outlook (Queensland Alliance for Agriculture and Food Innovation 2019).
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-22-0096.1
A more detailed analysis of responses in various Australian states indicates a reduction in the area sown to the core cash crops of (austral summer) sorghum (Figs. 8a,c) and (austral winter) wheat (Figs. 9a,c) in the main impacted regions of northeast New South Wales (NSW) and Queensland (QLD), with substantial decreases being most dramatic in 2019/20 for summer sorghum (Fig. 8a) and in 2018–20 for winter wheat (Fig. 9a) during the 2018–20 protracted El Niño episode. Reduction in yield (per 1000 ha sown) also shows substantial impacts from the 2018–20 protracted El Niño episode (Figs. 8b and 9b,d). The likely major decrease in aggregated soil moisture during the initial stages of this episode (especially in sorghum growing areas, Fig. 7) exacerbated the major impacts of substantially reduced in-crop growing rainfall during 2019.
1989–2020 (a) land area used for summer sorghum production in NSW, (b) sorghum yield in kilotons per 1000 ha in NSW, (c) land area used for summer sorghum production in QLD, and (d) sorghum yield in kilotons per 1000 ha in QLD. Transparent yellow-shaded regions show 1990–95, 2001–05, 2014–16, and 2018–20 protracted El Niño episodes, and transparent green-shaded regions show 1998–2001, 2010–12, and ongoing 2020 protracted La Niña episodes (after ABARES 2020).
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-22-0096.1
1989–2020 (a) land area used for winter wheat production in QLD, (b) winter wheat yield in kilotons per 1000 ha in QLD, (c) land area used for winter wheat production in NSW, and (d) winter wheat yield in kilotons per 1000 ha in NSW. Transparent yellow-shaded regions show 1990–95, 2001–05, 2014–16, and 2018–20 protracted El Niño episodes, and transparent green-shaded regions show 1998–2001, 2010–12, and ongoing 2020 protracted La Niña episodes.
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-22-0096.1
Several of the other protracted El Niño (and La Niña) episodes since 1990 are associated with decreases (increases) in NSW and QLD austral summer sorghum and austral winter wheat area sown and/or yields, respectively (Figs. 8 and 9). This is most evident in parts of the 1998–2001 protracted La Niña and 2001–05 protracted El Niño episodes and is supported by the Australian rainfall patterns in the above NSW and QLD regions (see http://www.bom.gov.au/climate/maps/rainfall/?variable=rainfall&map=totals&period=daily®ion=nat&year=2022&month=08&day=29). However, the 2018–20 period shows the most substantial agricultural impacts in NSW and QLD since 1990–95 (also a major protracted El Niño episode), which is not surprising as it is in the region (eastern Australia) most affected by the teleconnection from the Niño-4 SST region in Fig. 4.
Figure 10 completes the picture, showing winter wheat yields in the Australian states of Victoria (Vic), South Australia (SA), and Western Australia (WA) during the period since 1990 (sorghum is not produced in any quantity in these states). As in QLD and NSW (Fig. 9), reduction in winter wheat yield (per 1000 h sown) is seen in SA during all, but in Vic and WA only during parts, of the 2018–20 protracted El Niño episode. In general, the SA, Vic, and WA winter wheat yields have similar responses to the 1998–2001 and 2010–12 protracted La Niña and 2001–05 protracted El Niño episodes (Figs. 10a–c) as in QLD or NSW (Figs. 9a,b). Interestingly, the reduction in yield (per 1000 h sown) for winter wheat in NSW, SA, and Vic around the 2006/07 period (Figs. 9d and 10a,b) occurred during a classical El Niño event. Overall, these results show the major influence of both the Niño-4 SST region and the IOD-modulated northwest cloud band components of ENSO on Australian agricultural production.
1989–2020 (a) winter wheat yield in kilotons per 1000 ha in Vic, (b) winter wheat yield in kilotons per 1000 ha in SA, and (c) winter wheat yield in kilotonnes per 1000 ha in WA. Transparent yellow-shaded regions show 1990–95, 2001–05, 2014–16, and 2018–20 protracted El Niño episodes, and transparent green-shaded regions show 1998–2001, 2010–12, and ongoing 2020 protracted La Niña episodes
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-22-0096.1
Cattle numbers across Australia dropped by over 7% during the protracted 2018–20 drought. Industry estimates suggested it may take many years for herd numbers to recover (Fig. 11) (Meat and Livestock Australia 2022).
Cattle number decline associated with 2018–20 protracted Australian drought (Meat and Livestock Australia 2022). The transparent yellow-shaded region shows the 2018–20 protracted El Niño episode.
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-22-0096.1
7. Marine
a. Marine heatwaves and cold spells: Links to protracted ENSO episodes
The term marine heatwave (MHW) was first proposed by Pearce et al. (2011) after the extreme 2011 Western Australian marine heatwave event. The nature, characteristics, and causes of MHWs have been examined in the Australian context in papers such as Pearce and Phillips (1988), Pearce and Feng (2013), Hobday and Pecl (2014), Zinke et al. (2014, 2015), Caputi et al. (2016, 2019), Le Nohaïc et al. (2017), Schlegel et al. (2017), Frölicher and Laufkötter (2018), Sprogis et al. (2018), Chandrapavan et al. (2019), Heidemann and Ribbe (2019), Akhir et al. (2020), Feng et al. (2021a), Holbrook et al. (2020), Molony et al. (2021), and Oliver et al. (2021) (see the MHW forecasting project at https://research.csiro.au/mri-research-portfolio/home/climate-impacts-adaptation/marine-heatwaves/forecasting-marine-heat). Together with the opposite phenomena, marine cold spells (MCSs), defined in papers such as Schlegel et al. (2017), Wang et al. (2020), and Feng et al. (2021a), these terms reflect major warm- and cold-water events affecting western, southern, and eastern Australian coastal environments. Impacts of MHWs are most dramatically visible when they initiate coral bleaching events [see https://www.aims.gov.au/docs/research/climate-change/coral-bleaching/bleaching-events.html and Kamenos and Hennige (2018)]. Other major impacts on marine ecosystems, fish, and shellfish stocks are far less visible but are as dramatic and threatening to environmental sustainability and stability.
Around the world, physical links have been shown to exist between MHWs, MCSs, and ENSO events (Fig. 12) (Holbrook et al. 2020). In the Australian region, such relationships have been established and discussed in the literature in papers such as Le Nohaïc et al. (2017), Oliver et al. (2017), Heidemann and Ribbe (2019), Holbrook et al. (2019), Sen Gupta et al. (2020), and Kajtar et al. (2021). The important ecophysiological impacts of MHWs on global coral reef environments are discussed in Fordyce et al. (2019), with their wider economic impacts detailed in Hobday et al. (2021) and Smith et al. (2021).
Major marine heatwave (MHW) events since 1995. The MHW intensity scale, from moderate to extreme, represents conditions corresponding to the peak date of the event, with categories identified successively as multiples of the 90th percentile. Source: Holbrook et al. (2020).
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-22-0096.1
Details of the modulations and ecophysiological impacts of Australian MHWs and MCSs by ENSO events and protracted ENSO episodes since 1990 are summarized below. A detailed set of ecophysiological case studies in various regions around the Australian coast [Western Australia, Torres Strait, Great Barrier Reef (GBR), Tasman Sea, and South Australian Basin] is given in Kajtar et al. (2021):
b. 1990–95 protracted El Niño episode
A negative relationship between the scallop catch in Shark Bay and the strength of the Leeuwin Current and water temperature was established after the El Niño events of 1982 and 1986 (Joll and Caputi 1995) when high abundance of 0+ recruits were detected. Therefore, it was not surprising that the protracted El Niño episode in the period 1990–95 resulted in four years of record scallop catches in Shark Bay during the period 1991–94 (Fig. 13) (Caputi et al. 1996).
Scallop catches in Shark Bay from 1982 to 2019. Source: Kangas et al. (2021).
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-22-0096.1
A similar negative relationship has been assessed for the scallop abundance in the Abrolhos Islands and the environmental variables, ENSO, Leeuwin Current, and water temperature, with above-average catches achieved in 1982/83 and the period 1993–96 during El Niño and protracted El Niño conditions, with a weak Leeuwin Current and cool water SSTs (Fig. 13) (Caputi et al. 2016).
However, a number of years of low western rock lobster puerulus settlement were also seen during this period (Caputi et al. 1996).
c. 1998–2001 protracted La Niña episode
This was a period of strong Leeuwin Current and warm SST, with the second strongest MHW occurring off Western Australia during this period (Le Nohaïc et al. 2017; Kajtar et al. 2021). These conditions generally had a negative effect on scallop stocks in Shark Bay and the Abrolhos Islands and a positive effect on the western rock lobster puerulus settlement (Caputi et al. 2021).
Prior to the 1998–2001 protracted La Niña episode, the austral summer of the major 1997/98 El Niño event caused the GBR to experience bleaching due to high SSTs across 74% of the inshore reefs. In the Palm Island area, some 70% of corals died (https://www.aims.gov.au/docs/research/climate-change/coral-bleaching/bleaching-events.html).
There were also mass coral bleaching episodes in Papua New Guinea and the southwest Pacific Islands (Fiji and Solomon Islands) in 1999/2000 (Zinke et al. 2015).
d. 2001–05 protracted El Niño episode
In the austral summer of 2001/02, the GBR experienced an MHW coral bleaching event marginally more severe than that in 1997/98. According to the Australian Institute of Marine Science (https://www.aims.gov.au/docs/research/climate-change/coral-bleaching/bleaching-events.html), in this period, “aerial surveys revealed bleaching in 54% of the 641 reefs observed. Nearly 41% of offshore and 72% of inshore reefs had moderate or high levels of bleaching.”
Heidemann and Ribbe (2019) report on a 2005/06 MHW event during the period of the 2001–05 protracted El Niño episode along the southeastern coastal region of Queensland that resulted in bleaching of GBR corals. Importantly, McGowan and Theobald (2017) state that “synoptic-scale weather patterns and local atmosphere-ocean feedbacks related to El Niño–Southern Oscillation (ENSO) and not large-scale SST warming due to El Niño alone and/or global warming are often the cause of coral bleaching on the GBR.” The latter was established by examining the GBR coral bleachings in 1983, 1987, 1992, 1993, 1998, 2010, and 2016. We suspect that the same can be said for protracted El Niño episodes.
e. 2010–12 protracted La Niña episode
This event resulted in the extreme 2011 Western Australian MHW that affected 2000 km of the Western Australian coast, centered at the midwest Australian coast during the 2010/11 austral summer (Fig. 14 a) (Pearce and Feng 2013; Wernberg et al. 2013; Caputi et al. 2014). Water temperature anomalies of 2°–4°C persisted for more than 10 weeks (Wernberg et al. 2013). The MHW was influenced by a strong Leeuwin Current during an extreme protracted La Niña episode and an anomalously high heat flux (Pearce and Feng 2013; Feng et al. 2013).
Large-scale patterns of SST anomalies within the eastern Indian Ocean along WA, monthly averaged for the periods when SST anomalies were near maximum: (a) February 2011 during La Niña and (b) April 2016 during El Niño. SST anomalies are based on NOAA ¼° daily optimally interpolated SST, version 2 (OISSTv2), relative to a 1971–2000 climatological mean for the respective month using data available online (https://www.ncdc.noaa.gov/oisst/data-access). Source: Le Nohaïc et al. (2017).
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-22-0096.1
The lower west coast of Australia is a hotspot of SST increases in the Indian Ocean, with a 1°C increase over the past 40 yr (Pearce and Feng 2007), and has also been classified as one of 24 global warming hotspots (Hobday and Pecl 2014). The austral summers of 2011/12 and 2012/13 also experienced above-average SSTs after the 2010/11 summer MHW event (Caputi et al. 2014), which have exacerbated the effect of the MHW on the marine ecosystem. The Shark Bay (Fig. 13) and Abrolhos Island scallop fisheries were shut for 3 and 5 yr, respectively. The biodiversity patterns of temperate seaweeds, sessile invertebrates, and demersal fish were significantly different after the 2010/11 MHW, which led to a reduction in abundance of habitat-forming seaweeds, a subsequent shift in community structure, and a southward distribution shift in tropical finfish communities (Wernberg et al. 2013). Seagrasses in Shark Bay (Fraser et al. 2014) and Exmouth Gulf were also significantly affected by the MHW. Hobday et al. (2021) state that “The indirect economic loss from this event was estimated to be U.S.$3.1 billion (A$4.14 billion) per year, based on the ecosystem service value of the seagrass’ capacity to store carbon.” Coral bleaching followed by high mortality were recorded in areas of the Ningaloo and Abrolhos Islands (Depczynski et al. 2013; Le Nohaïc et al. 2017), and there was a widespread southward expansion of tropical fish and the collapse of crustacean and shellfish fisheries (Pearce et al. 2011; Pearce and Feng 2013; Caputi et al. 2016, 2019; Feng et al. 2013, 2021a).
Extremely high rainfall in Queensland during each of the summers of 2008/09 and 2010/11 resulted in large amounts of freshwater flooding and bleaching of the nearshore reefs of the GBR (https://www.aims.gov.au/docs/research/climate-change/coral-bleaching/bleaching-events.html).
f. 2014–16 and 2018–20 protracted El Niño episodes
A major MHW occurred in the Tasman Sea region in 2015/16, which led to oyster disease outbreaks, mollusc mortalities, and salmon aquaculture impacts (https://www.nature.com/articles/s43017-020-0068-4; Oliver et al. 2017). It was the longest in duration in the observed record (318 days) according to Kajtar et al. (2021). Smith et al. (2021) note that there were major monetary losses due to Pacific oyster mortality [$19 million industry (U.S. dollars)], reduced salmon production [$545 million industry (U.S. dollars)], and less wild-caught abalone [$62 million industry (U.S. dollars)].
Although record SSTs were recorded in the Australian region in 2016/17, GBR coral bleaching was quite variable, being worst between Cape York and Port Douglas (https://www.aims.gov.au/docs/research/climate-change/coral-bleaching/bleaching-events.html). Smith et al. (2021) note that economic “loss related to bleaching unknown; gains related to ‘last chance tourism’ also unknown.”
Bleaching of corals and giant clams in the Torres Strait domain (132°–148°E, 18°–9°S) were reported during the first half of 2016 (Kajtar et al. 2021)
These protracted El Niño episodes also resulted in MCSs along the western coast of Australia in the 2016–19 period (Fig. 14b), which were linked to a weakened Leeuwin Current and warm Niño-4 SSTs during both protracted episodes (Le Nohaïc et al. 2017; Feng et al. 2021b). These MCSs helped in the recovery of the scallop stocks in Shark Bay (Fig. 13) and the Abrolhos Islands, plus Shark Bay blue swimmer crabs and Roe’s abalone in the Perth metropolitan area (Feng et al. 2021b).
An MHW occurred again in the Tasman Sea region in 2018/19. It was modulated by a combination of influences of the East Australian Current and atmospheric heat input (https://www.nature.com/articles/s43017-020-0068-4).
The MHW in 2019/20 resulted in GBR and Coral Sea mass coral bleaching. This occurred under the most widespread MHW event since 1981 and covered more than 50% of the GBR for 36 days (Benthuysen et al. 2021).
8. Conclusions
The extreme 2019/20 Australian drought and wildfires focused attention on the enhanced impacts that can occur when natural climatic variability and climate change influences “collide” (https://www.metoffice.gov.uk/research/news/2020/causes-of-extreme-fire-weather-in-australia) (Harris and Lucas 2019; Boer et al. 2020; Filkov et al. 2020; Abram et al. 2021). In this paper, we have shown that from March–April 2018 to April–May 2020, a protracted El Niño episode, defined by positive SSTs in the Niño-4 SST region, together with positive IODs in 2018 and 2019, led to not only catastrophic drought and bushfire conditions over Australia, but significant terrestrial agricultural production and marine ecophysiological impacts. Dynamically, this was expressed by the conjunction of two components of El Niño phases, suppressed northwest cloud bands and southern Australia rainfall, and a persistent teleconnection generated by the Niño-4 region SSTs initiating strong subsidence and drought over eastern Australia. This finding was also put into context by reference to, and examination of, other protracted ENSO episodes since 1990.
We have also illustrated the extent to which this particular climatic extreme impacted not just the wider Australian agricultural and environmental sectors (e.g., depressed yields and production plus wildfires and reduced pasture growth) (ABARES 2020; Wittwer and Waschik 2021; Meat and Livestock Australia 2022), but also extended into surrounding marine systems and species (e.g., suppressed sea levels, MHWs and MCSs, coral bleaching, changes in aquaculture stocks) (Frölicher and Laufkötter 2018; Heidemann and Ribbe 2019; Holbrook et al. 2020; Feng et al. 2021a; Oliver et al. 2021). These observations highlight the fact that synoptic-scale weather patterns and local atmosphere–ocean feedbacks related to protracted ENSO episodes, and not just large-scale SST warming, are often important factors in initiating ecophysiological impacts.
Where possible, we have emphasized the substantial economic costs of these ecophysiological impacts in both terrestrial and marine spheres. Noting that there have also been some “winners” associated with these events, such as the effect of the 2016–19 MCS on some invertebrate fisheries, helped to recover some of the losses from the 2011–13 MHW. Consequently, we would reiterate the urgent need to investigate the potential to forecast protracted ENSO episodes. This is especially needed considering that another round of impacts has been initiated by the protracted La Niña episode that has occurred from around July 2020 and is still ongoing. When taken together, these episodes of protracted agricultural impacts and ecophysiological extremes, plus their knock-on effects, permeate, overstress, aggravate, and expose society, infrastructure, and livelihoods to longer temporal-scale pressures than those experienced during shorter classical ENSO events.
Acknowledgments.
Rob Allan is supported by funding from the U.K. Newton Fund, which is managed by the U.K. Department for Business, Energy and Industrial Strategy (BEIS), under its CSSP China and WCSSP South Africa projects. Allan also acknowledges the University of Southern Queensland, Toowoomba, Australia, and the Centre for Maritime Historical Studies, University of Exeter, Exeter, United Kingdom, where he is an adjunct and honorary professor, respectively. Roger Stone acknowledges support from the University of Southern Queensland in the establishment and maintenance of his honorary position as emeritus professor in Climate Science. Joëlle Gergis and Zak Baillie acknowledge funding from the following Australian National University Futures Scheme Project: “using historical weather extremes to improve future climate change risk assessment.” Hanna Heidemann is supported by the Northern Australia Climate Program funded by Meat and Livestock Australia, the Queensland Government’s Drought and Climate Adaptation Program, and the University of Southern Queensland, as well as the DeRISK International Climate Initiative. Nick Caputi acknowledges the support of scientists from the Department of Primary Industries and Regional Development and CSIRO in Western Australia, who have contributed to the assessment of MHWs and MCSs and their effect on fisheries. Rosanne D’Arrigo acknowledges support from the National Science Foundation (NSF), including NSF PIRE (Grant 1743738): Climate Research Education in the Americas Using Tree-Ring and Cave Sediment; NSF-GEO-NERC (2102759): Understanding Trans-Hemispheric Modes of Climate Variability: A Novel Tree-Ring Data Transect Spanning from the Himalaya to Southern Ocean; the NSF Paleoclimatic Perspectives on Climatic Change (Grant 1903634): Hydroclimatic Response of El Niño-Southern Oscillation to Natural and Anthropogenic Radiative Forcing; and NSF PIRE: SUNYA 18-28-79761: Cimate Research Education in the Americas. Christa Pudmenzky acknowledges support from the University of Southern Queensland.
Data availability statement.
The SOI (https://www.cpc.ncep.noaa.gov/data/indices/soi) and the equatorial SOI (https://www.cpc.ncep.noaa.gov/data/indices/reqsoi.for) are available from the NOAA/National Weather Service Climate Prediction Center (CPC). Global, Niño-3, and Niño-4 SST anomalies were generated from the HadISST 1 dataset (1870–2021) and are available as monthly series values on Climate Explorer (https://climexp.knmi.nl/). Tropical equatorial Pacific Niño-1 + 2, Niño-3, Niño-3.4, and Niño-4 regions are from the online NCAR–UCAR Climate Data Guide (https://climatedataguide.ucar.edu/). Anomalies of upper-level (200 hPa) standardized seasonal velocity potential fields and precipitation fields are from the IRI Map Room (http://iridl.ldeo.columbia.edu/maproom/ENSO/Tropical_Atm_Circulation/PRCP_Std_Vpot.html). The dipole mode index (DMI) was taken from the Climate Explorer (https://climexp.knmi.nl/). Australian coastal and marine station sea level anomalies are reproduced from the February 2022 report of the Australian Bureau of Meteorology (BoM) Australian Baseline Sea Level Monitoring Project (ABSLMP) (National Operations Centre Tidal Unit 2022; http://www.bom.gov.au/ntc/IDO60201/IDO60201.202202.pdf). They are generated from the observations made at the BoM ABSLMP’s coastal tide gauges [see their locations in Fig. S2 in the supplemental material of Allan et al. (2019)].
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