1. Introduction
Atmospheric rivers (ARs) are narrow regions of enhanced water vapor transport in the lower troposphere (Zhu et al. 1998). Globally, ARs are associated with natural hazards such as extreme rainfall and floods (Kingston et al. 2016; Lavers et al. 2011; Ralph et al. 2006), extreme winds (Waliser and Guan 2017) and landslides (Wills et al. 2016). A recent review from Payne et al. (2020) discussing the response of ARs to climate change highlighted that ARs in the Australasian region are particularly understudied, although there has been some work showing ARs play a considerable role in New Zealand hydrology (Kingston et al. 2016; Little et al. 2019; Prince et al. 2021; Reid et al. 2021). Global studies of atmospheric rivers have shown that ARs do occur over Australia (Guan and Waliser 2017; Knippertz et al. 2013). A more detailed Australian-focused study is warranted given the observed effects of ARs on natural hazards.
In Australia, ARs are usually discussed in the context of the northwest cloudband (NWCB): a large-scale cloudband that extends across the continent from the northwest of Australia to the south or southeast and can be associated with an AR (Tapp and Barrell 1984). As a result, the few studies of ARs in Australia that have been published have only focused on a specific type of AR—those that resemble the NWCB (e.g., Black et al. 2021; Chen et al. 2020). However, recent global work has suggested that ARs are predominantly extratropical (Lora et al. 2020; Zhang et al. 2019), whereas the NWCB is thought of as a tropical–extratropical interaction (Wright 1997). The official definition by the American Meteorological Society describes ARs as typically occurring ahead of a cold front of an extratropical cyclone (Ralph et al. 2018). However, this definition was based on observation studies of ARs in the extratropics (Ralph et al. 2005, 2018). In comparison, cool season NWCBs are typically associated with convergence of warm moist tropical air and cool, dry midlatitude air due to a refracted Rossby wave synoptic pattern in the subtropics (Reid et al. 2019). This question of whether ARs correspond to the same meteorological pattern was recently discussed in a perspective paper by Gimeno et al. (2021). This led us to one of the main questions addressed in this paper: Is there a difference between tropical and extratropical atmospheric rivers in the Australian region? To investigate this question, we categorized ARs based on where they formed. We then examined the differences and similarities in frequency, rainfall, and vertical structure of ARs that form at low-latitudes compared to midlatitudes.
We selected three source regions for Australian ARs (shown in Fig. 2): northwest Australia and parts of the Indian Ocean (NW; 25°–5°S, 80°–130°E), eastern Australia and parts of the Pacific Ocean (PAC; 35°–10°S, 142°–179°E), and over the Southern Ocean (SO; 55°–35°S, 61°–150°E). NW ARs were chosen to represent the canonical NWCB. NWCBs form in the east Indian Ocean and are associated with rainfall in the northwest, central and southern Australian regions (Reid et al. 2019; Telcik and Pattiaratchi 2014). Recent work on Australian rainfall sources suggested that a considerable portion of moisture that precipitated over the continent originated in waters east of Australia (Holgate et al. 2020). Additionally, Hauser et al. (2020) showed that wet winter–spring months over eastern Australia are often driven by enhanced warm conveyor belt activity from the east. Dacre et al. (2019) found that warm conveyor belts and ARs are strongly linked. Additionally, Guan and Waliser (2019) found the waters off the east coast of Australia to be a main AR genesis location for the Southern Hemisphere. For these reasons, we selected a region off the east coast of Australia as our second origin location (PAC). Finally, given ARs are typically associated with cold fronts and extratropical cyclones, it was pertinent to include an extratropical region. Southern Australian rainfall typically is associated with the passage of fronts and extratropical cyclones (Pook et al. 2013); however, the role of ARs in this region has not yet been analyzed.
ARs have been identified as major sources of mean and extreme rainfall particularly in the midlatitudes (Lavers et al. 2011; Ralph et al. 2006; Viale et al. 2018). While there has not been a study on the AR contribution to rainfall in Australia at a regional scale, there are global studies that include this region (e.g., Arabzadeh et al. 2020; Guan and Waliser 2015). The Paltan et al. (2017) global analysis of ARs and hydrological extremes in land surface models suggested that ARs contributed up to about 10%–15% of annual soil moisture in Australia with the maximum contributions occurring in the northwest, southeast and southwest of the continent. However, given the limitations of modeling global rainfall, there was a need for an in situ observations based rainfall analysis to confirm the role of ARs in Australian hydrology. Arabzadeh et al. (2020) estimate that ARs contribute between 30% and 80% of Australia’s rainfall with the greatest contribution over the southern states. However, the AR identification method used tends to overestimate AR frequency in the interior of continents (e.g., Fig. 6b from Rutz et al. 2019).
The vertical structure of extratropical ARs has been well documented due to multiple field campaigns involving dropsonde observations off the west coast of North America (e.g., CALJET and PACJET; Ralph et al. 2004, 2005). Dropsonde observations typically showed a low-level jet at about 900 hPa reaching wind speeds of between 14 and 32 m s−1 for different ARs (Ralph et al. 2005). Mixing ratio values in the jet were between 7 and 10 g kg−1. A more recent observational study by Rauber et al. (2020) of an AR over the Southern Ocean near Australia found a similar vertical structure to the earlier Northern Hemisphere studies. They found the maximum specific humidity in the AR was around 11–12 g kg−1, the low-level jet was at about 850 hPa and wind speeds in the jet were about 15 m s−1. We explore the difference in vertical structure of tropical, subtropical and extratropical ARs in this study.
The link between ARs and some climate modes has been well established in the Northern Hemisphere (Payne and Magnusdottir 2014). In the North Pacific, there is a strong poleward shift in AR frequency during La Niña and equatorward shift during El Niño, which is likely due to the modulation of the storm tracks with El Niño–Southern Oscillation (ENSO; Mundhenk et al. 2016). However, they also noted considerable regional and seasonal variations in AR response to ENSO. For example, along the West Coast of the United States, El Niño was associated with increased AR frequencies in boreal winter but decreased frequencies in boreal summer. In Australia, there are well established links between rainfall and ENSO/Indian ocean dipole (IOD; Risbey et al. 2009; Ummenhofer et al. 2009), so a relationship between these climate modes and ARs is likely to exist. The southern annular mode (SAM) is known to influence the frequency of landfalling fronts over southern Australia (Meneghini et al. 2007), and therefore is likely to impact Southern Ocean ARs. The ENSO–AR, IOD–AR, and SAM–AR relationships have not been explored previously over Australia. The modulation of ARs by various climate drivers in the Australian region will therefore be assessed in this study.
Subseasonal variability in ARs has also been linked with climate modes for other parts of the world (e.g., Guan et al. 2012). Previous studies have found increases in AR frequency in the central and eastern Pacific during the Madden–Julian oscillation (MJO) phases 7–8 (Wheeler and Hendon 2004), while it was found that AR frequency decreases over large areas of the North Pacific during phases 3–4 (Mundhenk et al. 2016; Zhou et al. 2021). Additionally, Mundhenk et al. (2018) and Baggett et al. (2017) found that skillful subseasonal forecasts of anomalous AR frequency along the U.S. West Coast were possible five weeks in advance using the MJO–AR relationship. Sellars et al. (2017) showed that the quasi-biennial oscillation (QBO) can affect AR frequency over southeast Australia, and the QBO and MJO have been found to be strongly linked (Martin et al. 2021). Given that the MJO can impact ARs in the western United States, we hypothesized that the MJO–AR relationship will be considerably stronger over Australia since MJO activity is physically closer to Australia and there are studies linking the MJO to Australian rainfall variability (e.g., Wheeler et al. 2009). Therefore, the potential for skillful subseasonal forecasts of ARs may be even greater over Australia that the western United States.
The aim of this paper is to provide a comprehensive analysis of ARs over Australia and fill this key regional gap in the AR literature. We have tested to what extent ARs are a key driver of rainfall over Australia and whether Australia is affected by both the canonical extratropical ARs ahead of a cold front and ARs originating from the tropics (e.g., Knippertz et al. 2013). We have analyzed the rainfall associated with ARs and modulation of AR frequency by climate drivers and whether these results depend on where the AR originates. The difference in vertical structure of ARs was examined to test the hypothesis that the structure and processes governing tropical, subtropical and extratropical ARs may vary. This could have implications for climate change projections of ARs due to the thermodynamic versus dynamic drivers of ARs responding differently to warming. Finally, we aim to understand whether ARs in Australia are strongly modulated by ENSO, IOD, SAM, and the MJO, as this may allow for skillful subseasonal forecasts of ARs and related rainfall in this region.
2. Methods
a. Datasets
We used gridded daily rainfall data from the Australian Water Availability Project (AWAP; Jones et al. 2009) at a 0.25° × 0.25° resolution. Although higher resolutions of AWAP are available, this coarser resolution was used due to low station density over large regions of Australia. Sea Surface Temperature (SST) data are from the Hadley Centre Global Sea Ice and Sea Surface Temperature dataset (HADISST v1.1; Rayner et al. 2003; National Center for Atmospheric Research Staff 2021). The Niño-3.4 index was calculated from HadISST SST data using the method described by Trenberth and National Center for Atmospheric Research Staff (2020). The dipole mode index (DMI) was also calculated using HadISST SST data and is the difference in the mean temperature anomaly between the west Indian (10°N–10°S, 50°–70°E) and east Indian (0°–10°S, 90°–110°E) Oceans. The phase and amplitude of the Madden–Julian oscillation (MJO) was obtained from the Australian Bureau of Meteorology (Bureau of Meteorology 2021) and is defined by the Real-time Multivariate MJO index (RMM index) described in Wheeler and Hendon (2004). The SAM index was obtained from the National Weather Service Climate Prediction Centre (Mo 2000).
b. Atmospheric river identification
We used an objective automated algorithm (Reid et al. 2020) to identify ARs between 1980 and 2019 using hourly integrated water vapor transport (IVT) from the European Centre for Medium-Range Weather Forecasts Reanalysis at the native resolution (0.25° × 0.25°) (ERA5; Hersbach et al. 2019). The AR dataset was produced as part of the Atmospheric River Tracking Method Intercomparison Project Tier 2 Reanalyses (Shields et al. 2018). The specific algorithm is the “Reid500” algorithm (Reid et al. 2020), which is so named because it uses an absolute IVT threshold of 500 kg m−1 s−1 to extract AR-like objects. The same threshold is used for all latitudes. For an object to be defined as an AR, its length must exceed 2000 km and the length to width ratio of the object must exceed two. While the 500 kg m−1 s−1 threshold is more restrictive than most other absolute methods (Rutz et al. 2019), higher thresholds are less sensitive to specific dataset parameters such as resolution and regridding methods making the results more robust (Reid et al. 2020).
c. Atmospheric river tracking
For this study, we added a tracking tool to the Reid500 identification algorithm. This allowed us to identify the origin of ARs and track them during their full life cycle. Rather than tracking the entire AR object using overlapping shapes as in other AR literature (e.g., Guan and Waliser 2019; Zhou et al. 2018), we tracked the center of mass (centroid) of the AR similar to front tracking techniques (e.g., Simmonds et al. 2012). We located the AR centroid at time t and searched within a 500-km radius for another centroid at time t + 1 h using hourly AR data described in section 2b. In the rare case of two centroids being located within the search radius, the algorithm selects the closest centroid to track. The size of the search radius was determined via analyses of case studies. We repeated this until the AR was defined as terminated, i.e., a centroid was not identified nearby for 5 h. The 5-h threshold for termination was to account for “flickering” where ARs may temporarily drop below the IVT threshold for a few time steps especially as they cross land. Without this requirement, the tracking algorithm may separate AR events which, dynamically, should be the same event. In addition, the centroid of an AR is the center of mass of the ellipse that is fitted to the region of enhanced IVT (described in Reid et al. 2020) and therefore the change in centroid location between time steps is not solely dependent on the translational movement of the AR but also changes to the AR intensity and geometry. This is another reason for why we allow 5 h before declaring the AR terminated. For our analyses, we only included AR events that lasted longer than 3 h. We recorded a mean lifetime propagation speed of 53.6 km h−1, which is comparable to other methods. For example, Zhou et al. (2018) calculate a mean speed of ∼60 km h−1 while Guan and Waliser (2019) found a mean propagation speed of ∼40 km h−1.
d. Atmospheric river–related rainfall and vertical structure
After identifying and tracking each AR event, we produced 0.25° × 0.25° masks of ARs over Australia for each day (using the sum of the hourly masks) between 1980 and 2019 to match the gridded rainfall data. The masks represent the footprint of IVT > 500 kg m−1 s−1 for all identified AR objects. AR and non-AR rainfall was separated using these masks. We tested the assumption that AR associated rainfall could reasonably occur within IVT values less than 500 kg m−1 s−1 by dilating the masks by up to 0.5°. However, we found this did not alter our results noticeably leading us to conclude that most of the rainfall likely occurs within the high intensity IVT regions. Therefore, our AR masks were able to capture most of the AR associated rainfall. This is consistent with Fig. 15 from Ralph et al. (2004).
The annual maximum n-day rainfall, Rxn, was used to analyze the probability of AR associated multiday rainfall events. The moving sum of daily rainfall was calculated and attributed to an AR event if an AR was present on 1, 2, and 3 of the days for 1-, 3-, and 5-day rainfall totals, respectively. We analyzed the vertical structure of ARs by examining the specific humidity (q), wind speed (|U|) and magnitude of water vapor flux [product of q and (|U|) at each of the 20 pressure levels in ERA5 between 1000 and 300 hPa] at the location of the centroid for each time step in every AR event.
3. Frequency analyses
There is strong interannual variability in AR frequency in the Australasian region (Fig. 1a). ARs are most frequent in austral summer and autumn (DJF and MAM; Fig. 1b), and AR events are least frequent in austral spring SON. There is a positive trend of 1.18 AR events per year in the Australasian region which is significant at p < 0.01 using Monte Carlo simulations. We have separated the ARs that affect Australia into tropical, subtropical and extratropical flavors based on the origin regions of ARs. Figures 2a–c shows the three origin regions for each of the three flavors as well as the frequency footprint of ARs that are first detected in those regions. From 1980 to 2019, approximately 750 AR events were first detected in the NW region, 1500 in the PAC and 7800 in the SO. Figures 2d–f shows the seasonal occurrence of ARs from each region.
Annual AR count per (a) year and (b) season from 1980 to 2019 for the Australasian region (domain indicated in Fig. 2).
Citation: Journal of Climate 35, 9; 10.1175/JCLI-D-21-0606.1
Percentage of days (1980–2019) with AR at each grid box for ARs that originate in the (a) northwest, (b) Pacific Ocean, and (c) Southern Ocean. Boxes indicate region of origin. Note the nonlinear color bar. Total number of AR events for each season (1980–2019) for ARs originating in the (d) northwest, (e) Pacific Ocean, and (f) Southern Ocean.
Citation: Journal of Climate 35, 9; 10.1175/JCLI-D-21-0606.1
For the NW and PAC regions, AR occurrence peaks in austral summer (DJF). Prince et al. (2021) found a similar seasonal cycle of ARs over New Zealand (more frequent in the warmer months) despite using a different AR detection method. Similarly, Guan and Waliser (2019) showed anomalous AR genesis to the northwest of Australia during November–March. Studies of East Asian ARs also found a peak in frequency during the warm season (Kamae et al. 2017; Pan and Lu 2020) as did Knippertz et al. (2013) in their climatology of Tropical Moisture Exports over Australia. This warm season peak is important because most preliminary studies of ARs were focused on the western coast of North America and Europe where ARs are primarily thought of as a cool season phenomenon (e.g., Ralph et al. 2005). Therefore, analyses of the structure of Northern Hemisphere, wintertime, extratropical ARs may not be reflective of ARs globally. The seasonal cycle of Southern Ocean ARs is less pronounced than tropical NW and subtropical PAC events. We observe that extratropical AR frequency also peaks in the warmer seasons likely due to enhanced moisture availability (in DJF) and strong meridional temperature gradients (in MAM).
The results of Fig. 2 suggest that ARs originating in the tropics and subtropics sometimes shift poleward and interact with the midlatitudes. For example, northwest cloudbands can converge with cold fronts and bring enhanced rainfall to Southern Australia (Reid et al. 2019). Conversely, ARs originating in the extratropics tend to follow a more zonal path (likely driven by the prevailing westerlies) with little evidence of substantial equatorward movement.
4. ARs and Australian rainfall
The AR dataset, outlined in section 2, includes a binary mask of all identified ARs at daily time steps on a 0.25° × 0.25° grid. This allows us to separate the precipitation into AR and non-AR associated precipitation. We note that our estimates are conservative given we use a restrictive AR identification algorithm with a relatively high IVT threshold. However, this gives us confidence that the rainfall we attribute to ARs is associated with these systems or mesoscale convective activity embedded within the ARs. Figure 3 shows the percentage of total rainfall over the entire period (1980–2019) associated with all ARs for each season. Regions of low station density have been masked out (i.e., over parts of central and Western Australia). During DJF, up to about 35% of rainfall in the east is associated with ARs. There is a very weak signal in the northwest which is unexpected because DJF is when ARs originating from the NW are most frequent. We suspect this result is because the most extreme rainfall in the northwest during the warmer months comes from Tropical Cyclones and about one-third of rainfall comes from isolated thunderstorms (Clark et al. 2018; Lavender and Abbs 2012). Even though ARs do contribute to rainfall in the northwest (e.g., Fig. 4), the relative contribution is small compared to other weather systems during DJF. Moreover, this means that the horizontal transport of moisture alone is not sufficient to cause substantial rainfall. Instability from terrain or a front, or for the moisture to be transported sufficiently poleward so that through adiabatic ascent the air can become saturated are potential necessary processes required for the enhanced horizontal moisture transport to translate into enhanced precipitation.
Annual mean seasonal rainfall percentage (relative to total seasonal rainfall) associated with ARs for (a) DJF, (b) MAM, (d) JJA, and (e) SON. Total percentage of rainfall (all seasons) (c) associated with ARs and (f) not associated with ARs. Gray regions correspond to low station density.
Citation: Journal of Climate 35, 9; 10.1175/JCLI-D-21-0606.1
Mean daily rainfall attributable to ARs originating in the (a) northwest, (b) Pacific Ocean, and (c) Southern Ocean during AR rain days. Gray regions correspond to low station density.
Citation: Journal of Climate 35, 9; 10.1175/JCLI-D-21-0606.1
In the Murray–Darling basin (MDB; Fig. 5) region, 30%–50% of precipitation is associated with ARs during Austral autumn and winter. This result emphasizes the importance of understanding ARs in this region. The MDB is a key agriculture region in Australia, and its economic and societal value depends strongly on rainfall (Adamson et al. 2009). In spring (SON), there are generally fewer ARs; however, they contribute approximately 20% of southeast Australia’s rainfall. Spring rainfall in the southeast is mostly associated with the passage of Southern Ocean ARs ahead of fronts (Fig. 4). The maximum in annual AR rainfall over southeast Australia (Fig. 3c) and magnitude of over 30% was also observed by Guan and Waliser (2015; Fig. 9i) in their global analysis which used a different method for identifying ARs.
Number of the top 100 rainfall days (ranked by amount) at each grid box that occurred during an AR. Gray regions correspond to low station density. Dashed outline indicates the boundary of the Murray–Darling basin.
Citation: Journal of Climate 35, 9; 10.1175/JCLI-D-21-0606.1
Figure 4 illustrates the mean daily rainfall attributable to ARs from the three origin locations. ARs from the northwest generally bring rain to the northern half of Australia. In fact, Fig. 4 suggests that NW ARs do not cause significant rain in the southeast. This appears to contradict the notion in NWCB literature that NWCBs cause rain over southern Australia (e.g., Reid et al. 2019; Telcik and Pattiaratchi 2014; Wright 1988). However, this is likely due to the restrictive AR identification algorithm used in this study. Moisture crossing the dry continent from the northwest would likely have diverged such that the IVT threshold was no longer met (i.e., Fig. 2a where AR frequency decreases with increasing latitude toward the southeast). ARs from the PAC generally lead to rainfall over the northeast of Australia. We observe some orographic enhancement of precipitation along the east coast in Fig. 4 as easterly ARs interact with the Great Dividing Range. Finally, the most frequent ARs, those originating in the Southern Ocean, lead to rainfall over most of southern Australia. It is likely that many of these are linked to fronts. ARs contribute up to 250 mm yr−1 in Western Tasmania where orography and the position of the winter storm tracks lead to high frontal rainfall amounts (not shown).
A major motivation for researching ARs is to understand their relationship with extreme rainfall and flooding events. First, we ranked the top 100 rainfall days (by daily total) at each grid point and assessed whether an AR was present over that grid point for each of those 100 days. Figure 5 shows the number of the top 100 rainfall days that occurred coincident with an AR. For most of the country, 5–15 of the heaviest 100 rainfall days occurred during ARs. However, for most of New South Wales and the Murray–Darling basin region, almost half of the top 100 extreme rainfall days coincided with an AR. Waliser and Guan (2017) also found a similar frequency of AR contribution to extreme rainfall over southeast Australia (36%–48%) despite using a different AR identification method and rainfall dataset. This suggests that these results are likely robust and not sensitive to the method used.
Probability ratio of multiday rainfall extremes associated with ARs to non-AR events. Rxn is the period with the greatest n-day rainfall amount for each year (1980–2019) for all wet days (rainfall > 1 mm). Gray regions correspond to regions of low station density or low AR occurrence. Stippling indicates regions where we have high confidence in the direction of the probability ratio [Eq. (1)] either exceeding or being below one due to bootstrapping (at least 90% of iterations agree on being above or below one).
Citation: Journal of Climate 35, 9; 10.1175/JCLI-D-21-0606.1
5. Vertical structure of tropical, subtropical, and extratropical originating ARs
The results from sections 3 and 4 indicate that there are differences in the frequency and precipitation of tropical and subtropical, and extratropical originating ARs. Therefore, we wanted to understand why this was the case by examining the vertical structure of ARs first detected in three different domains. Figure 7 shows the range in vertical profiles of water vapor flux for each AR domain. We observed that the strength of the SO ARs is less variable than for the NW and PAC ARs. Overall, the strongest ARs in the Australian region originate in the tropical–subtropical Pacific.
The range of vertical profiles of water vapor flux (kg kg−1 m s−1) for ARs originating in the northwest (blue), Pacific Ocean (orange), and Southern Ocean (yellow). Solid lines indicate the 5th percentile of the water vapor flux at each pressure level, dashed lines are the 50th percentile, and dotted lines are the 95th percentile.
Citation: Journal of Climate 35, 9; 10.1175/JCLI-D-21-0606.1
A key question for this study was whether intensity changes in ARs were driven more by changes in moisture or changes in wind. We attempted to answer this by assessing the mean vertical profile of specific humidity and wind speeds for the weakest 5%, middle 5%, and strongest 5% of AR events at each of the three origin locations (Fig. 8). Given the intensity of water vapor flux should be somewhat evenly spaced between the weak, middle, and strong ARs as in Fig. 7, the spacing between the weak, middle, and strong profiles of q and u should indicate whether the wind or moisture was more important for driving AR intensity.
Mean vertical profiles of specific humidity (kg kg−1) and wind speed (m s−1) for the strongest, middle, and weakest 5% of AR events originating in the (a)–(c) northwest, (d)–(f) Pacific Ocean, and (g)–(i) Southern Ocean. The plots in (c), (f), and (i) show the normalized (by the values in the strongest 5% profiles) difference between the vertical profiles of q and u for strongest and middle 5% of ARs.
Citation: Journal of Climate 35, 9; 10.1175/JCLI-D-21-0606.1
Figure 8a shows the mean life cycle vertical profile of specific humidity for NW ARs. The moisture profiles of the weak (blue), middle (red), and strongest (green) ARs are very similar. Conversely, the increase in windspeeds between the weak, middle and strong ARs is much greater (Fig. 8b) This indicates that wind speed dictates the difference between a middle and strong AR in the NW. We show this quantitatively in Fig. 8c. Figures 8c, 8f, and 8i represent the normalized difference between the strongest and middle vertical profiles of both specific humidity (yellow line) and wind speed (purple line). The difference was normalized by the values in the vertical profiles of the strongest ARs so that we could compare the relative contribution of the thermodynamics and wind in strengthening ARs. We observe similar behavior in the Pacific where the specific humidity profiles between middle and strong ARs are similar, while the wind speeds are much stronger for more intense ARs.
The Southern Ocean ARs show a different relationship. The difference between the wind speeds in middle and strong ARs are smaller than the difference between the specific humidity. This suggests that in the tropics and subtropics, where atmospheric moisture is abundant, dynamic forces are the main driver of intense ARs, whereas in the extratropics, where wind speeds are generally faster, moisture content dictates the strength of ARs. In their study of “wet” versus “windy” ARs along the U.S. West Coast, Gonzales et al. (2020) hypothesized that moisture dominant ARs are associated with anticyclonic wave breaking, while wind dominant ARs are associated with cyclonic wave breaking. While Gonzales et al. and this study explore similar concepts of moisture versus wind dominance in ARs, it is difficult to compare our results given the strong regional dependence (e.g., role of Aleutian low and latitude differences).
The magnitude of the specific humidity and wind speeds of the extratropical (Southern Ocean) ARs are within the same range as the dropsonde observations reported by Ralph et al. (2005). However, the tropical and subtropical ARs generally have greater specific humidity values and smaller wind speeds than the extratropical ARs. Given our large sample size, we are confident these results are likely to be physical. This result may have implications for understanding the impact of climate change on ARs. The moisture driven extratropical ARs may be more susceptible to the thermodynamic effects of climate change whereas the wind driven tropical ARs could be more susceptible to the more uncertain dynamical changes.
6. Relationships with climate modes
Probability ratio [Eq. (2)] of AR occurrence during (a) MJO phases and (b) ENSO, IOD, and SAM phases. Blue bars show results for northwest ARs, orange bars show results for Pacific Ocean ARs, and yellow bars show results for Southern Ocean results. Error bars show the 80% confidence interval (10th–90th percentiles) based on bootstrapping.
Citation: Journal of Climate 35, 9; 10.1175/JCLI-D-21-0606.1
The MJO is a key driver of climate variability over the Australian region, but on subseasonal time scales. Similarly, to Mundhenk et al. (2016), we found AR activity increased in a region (NW or PAC) when the peak of the convection associated with the MJO was over or near that region. For example, AR activity in the NW peaked during MJO active phases 3–5, and between phases 5–7 in the PAC. Whereas AR activity decreased in the suppressed phases of the MJO i.e., phases 7–1 for the NW and 1–3 for the PAC (Fig. 9a). ARs originating in the Southern Ocean did not appear to have a strong relationship with the MJO, although previous work did find statistically significant frequency variations in ARs, associated with the MJO, at mid- and high latitudes in the Northern Hemisphere (Mundhenk et al. 2018). Specifically, they found AR frequency differences of up to 8% depending on location and AR phase. We note that we tested whether there was a lagged relationship between SO ARs and the MJO too but did not find a clear relationship. Although our method is different from Mundhenk et al. our results in Fig. 9a suggest that tropical and subtropical AR frequency modulation by the MJO in the Australian region is almost an order of magnitude stronger than the modulation of extratropical ARs in the North Pacific and Southern Ocean. This is an important finding because, as discussed in section 1, the MJO–AR relationship can be used to make skillful subseasonal predictions of ARs.
In addition, we found that El Niño events tend to be associated with suppressed AR formation and La Niña with enhanced AR formation particularly in the PAC region. This is not surprising given La Niña is associated with increased rainfall over Eastern Australian and warmer SSTs in the western Pacific, while El Niño is associated with reduced rainfall over Eastern Australia. Moreover, the positive phase of the IOD is associated with suppressed AR formation in the NW and PAC likely due to colder SSTs to the northwest of Australia. A negative IOD is associated with increased AR formation in the NW and PAC. The relationship between ARs and SAM is more complicated with the SAM mostly affecting the PAC ARs in summer (DJF) and NW ARs in winter (JJA).
7. Summary and conclusions
In this study, we analyzed atmospheric river climatology, impacts and structure over Australia, thereby filling in a key regional gap in AR literature. We found that ARs are more frequent in the warmer months, similar to other studies of nearby locations. However, we found that ARs contribute up to about 50% of mean cool season rainfall in the Murray–Darling basin and over large regions of southern Australia. Elsewhere, ARs typically contribute 0%–20% of mean rainfall. About 15%–50% of the most extreme rainfall days over the Murray–Darling basin region are associated with ARs. In the west, multiday rainfall extremes are up to 5 times more likely during AR events.
Australia is affected by both extratropical and tropical ARs. Extratropical ARs are much more frequent and cause rainfall over most of southern Australia. Tropical ARs typically produce higher daily rainfall totals and also impact large regions. The IOD and the MJO only have a measurable influence on tropical and subtropical originating ARs. ENSO and the SAM modulate ARs from all three origin locations with ENSO having the greatest impact on ARs originating east of Australia. However, the modulation of extratropical AR frequency by the SAM and ENSO is weak. The relationship between the MJO and tropical and subtropical ARs over Australia is strong and indicates the possibility for skillful subseasonal forecasts.
To the authors’ knowledge, we have conducted the first direct comparison of the vertical structure of tropical, subtropical and extratropical ARs. We found that in the moisture abundant tropics and subtropics, wind speeds dictate the intensity of ARs, whereas in the extratropics moisture is the main driver of whether an AR will be moderate or intense. This implies that global climate change projections of ARs need to simulate both the dynamic and thermodynamic response to warming to realistically forecast changes to AR intensity.
Atmospheric rivers are major source of mean and extreme rainfall over Australia especially in the agriculturally important Murray–Darling region. The vertical structure and resulting rainfall of ARs varies depending on where they first are identified. The most intense ARs typically originate in the PAC region, while the weakest but most common occur in the Southern Ocean region. Studying ARs is vital for understanding horizontal moisture transport over Australia and this paper provides a necessary foundation for further work in this area especially on topics such as subseasonal forecasting of rainfall and understanding hydrological climate extremes.
Acknowledgments.
The work of K. J. Reid was funded by an Australian Government Research Training Program (RTP) Scholarship and the Australian Research Council (ARC; DE180100638), the work of A. D. King was funded by the ARC (DE180100638), and the work of T.P. Lane was funded by the ARC Centre of Excellence for Climate Extremes (CE170100023). This research was undertaken with the assistance of resources from the National Computational Infrastructure (NCI Australia) supported by the Australian government. We wish to thank Prof. Julie Arblaster for useful discussions.
Data availability statement.
The link for Tier 1 AR algorithm (uses MERRA2 instead of ERA5) is https://www.earthsystemgrid.org/dataset/ucar.cgd.ccsm4.artmip.tier1.html. The algorithm is the same, but the Tier 2 data (using ERA5) will be available following the publication of the ARTMIP Tier 2 overview studies. The link to ERA5 data is https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form. Precipitation data are only available upon request from the Australian Bureau of Meteorology.
REFERENCES
Adamson, D., T. Mallawaarachchi, and J. Quiggin, 2009: Declining inflows and more frequent droughts in the Murray–Darling basin: Climate change, impacts and adaptation. Aust. J. Agric. Resour. Econ., 53, 345–366, https://doi.org/10.1111/j.1467-8489.2009.00451.x.
Arabzadeh, A., M. R. Ehsani, B. Guan, S. Heflin, and A. Behrangi, 2020: Global intercomparison of atmospheric rivers precipitation in remote sensing and reanalysis products. J. Geophys. Res. Atmos., 125, e2020JD033021, https://doi.org/10.1029/2020JD033021.
Baggett, C. F., E. A. Barnes, E. D. Maloney, and B. D. Mundhenk, 2017: Advancing atmospheric river forecasts into subseasonal-to-seasonal time scales. Geophys. Res. Lett., 44, 7528–7536, https://doi.org/10.1002/2017GL074434.
Black, A. S., and Coauthors, 2021: Australian Northwest cloudbands and their relationship to atmospheric rivers and precipitation. Mon. Wea. Rev., 149, 1125–1139, https://doi.org/10.1175/MWR-D-20-0308.1.
Bureau of Meteorology, 2021: Madden–Julian Oscillation (MJO) monitoring. Bureau of Meteorology, accessed 26 April 2021, http://www.bom.gov.au/climate/mjo/.
Chen, J., H. Zhang, C. Ye, H. Chen, and R. Mo, 2020: Case studies of atmospheric rivers over China and Australia: New insight into their rainfall generation. J. South. Hemisphere Earth Syst. Sci., 70, 17–35, https://doi.org/10.1071/ES19026.
Clark, S., M. J. Reeder, and C. Jakob, 2018: Rainfall regimes over northwestern Australia. Quart. J. Roy. Meteor. Soc., 144, 458–467, https://doi.org/10.1002/qj.3217.
Dacre, H. F., O. Martínez-Alvarado, and C. O. Mbengue, 2019: Linking atmospheric rivers and warm conveyor belt airflows. J. Hydrometeor., 20, 1183–1196, https://doi.org/10.1175/JHM-D-18-0175.1.
Gimeno, L., I. Algarra, J. Eiras-Barca, A. M. Ramos, and R. Nieto, 2021: Atmospheric river, a term encompassing different meteorological patterns. Wiley Interdiscip. Rev.: Water, 8, e1558, https://doi.org/10.1002/wat2.1558.
Gonzales, K. R., D. L. Swain, E. A. Barnes, and N. S. Diffenbaugh, 2020: Moisture‐ versus wind‐dominated flavors of atmospheric rivers. Geophys. Res. Lett., 47, e2020GL090042, https://doi.org/10.1029/2020GL090042.
Guan, B., and D. E. Waliser, 2015: Detection of atmospheric rivers: Evaluation and application of an algorithm for global studies. J. Geophys. Res. Atmos., 120, 12 514–12 535, https://doi.org/10.1002/2015JD024257.
Guan, B., and D. E. Waliser, 2017: Atmospheric rivers in 20 year weather and climate simulations: A multimodel, global evaluation. J. Geophys. Res. Atmos., 122, 5556–5581, https://doi.org/10.1002/2016JD026174.
Guan, B., and D. E. Waliser, 2019: Tracking atmospheric rivers globally: Spatial distributions and temporal evolution of life cycle characteristics. J. Geophys. Res. Atmos., 124, 12 523–12 552, https://doi.org/10.1029/2019JD031205.
Guan, B., D. E. Waliser, N. P. Molotch, E. J. Fetzer, and P. J. Neiman, 2012: Does the Madden–Julian oscillation influence wintertime atmospheric rivers and snowpack in the Sierra Nevada? Mon. Wea. Rev., 140, 325–342, https://doi.org/10.1175/MWR-D-11-00087.1.
Hauser, S., C. M. Grams, M. J. Reeder, S. McGregor, A. H. Fink, and J. F. Quinting, 2020: A weather system perspective on winter-spring rainfall variability in southeastern Australia during El Niño. Quart. J. Roy. Meteor. Soc., 146, 2614–2633, https://doi.org/10.1002/qj.3808.
Hersbach, H., and Coauthors, 2019: Global reanalysis: Goodbye ERA-Interim, hello ERA5. ECMWF Newsletter, No. 159, ECMWF, Reading, United Kingdom, 17–24, https://doi.org/10.21957/vf291hehd7.
Holgate, C. M., J. P. Evans, A. I. J. M. van Dijk, A. J. Pitman, and G. Di Virgilio, 2020: Australian precipitation recycling and evaporative source regions. J. Climate, 33, 8721–8735, https://doi.org/10.1175/JCLI-D-19-0926.1.
Jones, D. A., W. Wang, and R. Fawcett, 2009: High-quality spatial climate data-sets for Australia. Aust. Meteor. Oceanogr. J., 58, 233–248, https://doi.org/10.22499/2.5804.003.
Kamae, Y., W. Mei, S.-P. Xie, M. Naoi, and H. Ueda, 2017: Atmospheric rivers over the northwestern Pacific: Climatology and interannual variability. J. Climate, 30, 5605–5619, https://doi.org/10.1175/JCLI-D-16-0875.1.
Kingston, D. G., D. A. Lavers, and D. M. Hannah, 2016: Floods in the Southern Alps of New Zealand: The importance of atmospheric rivers. Hydrol. Processes, 30, 5063–5070, https://doi.org/10.1002/hyp.10982.
Knippertz, P., H. Wernli, and G. Gläser, 2013: A global climatology of tropical moisture exports. J. Climate, 26, 3031–3045, https://doi.org/10.1175/JCLI-D-12-00401.1.
Lavender, S. L., and D. J. Abbs, 2012: Trends in Australian rainfall: Contribution of tropical cyclones and closed lows. Climate Dyn., 40, 317–326, https://doi.org/10.1007/s00382-012-1566-y.
Lavers, D. A., R. P. Allan, E. F. Wood, G. Villarini, D. J. Brayshaw, and A. J. Wade, 2011: Winter floods in Britain are connected to atmospheric rivers. Geophys. Res. Lett., 38, L23803, https://doi.org/10.1029/2011GL049783.
Little, K., D. G. Kingston, N. J. Cullen, and P. B. Gibson, 2019: The role of atmoshperic rivers for extreme ablation and snowfall events in the Southern Alps of New Zealand. Geophys. Res. Lett., 46, 2761–2771, https://doi.org/10.1029/2018GL081669.
Lora, J. M., C. A. Shields, and J. J. Rutz, 2020: Consensus and disagreement in atmospheric river detection: ARTMIP global catalogues. Geophys. Res. Lett., 47, e2020GL089302, https://doi.org/10.1029/2020GL089302.
Martin, Z., S. W., Son, A., Butler, H., Hendon, H., Kim, A., Sobel, S. Yoden, and C. Zhang, 2021: The influence of the quasi-biennial oscillation on the Madden–Julian Oscillation. Nat. Rev. Earth Environ., 2, 477–489, https://doi.org/10.1038/s43017-021-00173-9.
Meneghini, B., I. Simmonds, and I. N. Smith, 2007: Association between Australian rainfall and the southern annular mode. Int. J. Climatol., 27, 109–121, https://doi.org/10.1002/joc.1370.
Mo, K. C., 2000: Relationships between low-frequency variability in the Southern Hemisphere and sea surface temperature anomalies. J. Climate, 13, 3599–3610, https://doi.org/10.1175/1520-0442(2000)013<3599:RBLFVI>2.0.CO;2.
Mundhenk, B. D., E. A. Barnes, and E. D. Maloney, 2016: All-season climatology and variability of atmospheric river frequencies over the North Pacific. J. Climate, 29, 4885–4903, https://doi.org/10.1175/JCLI-D-15-0655.1.
Mundhenk, B. D., E. A. Barnes, E. D. Maloney, and C. F. Baggett, 2018: Skillful empirical subseasonal prediction of landfalling atmospheric river activity using the Madden–Julian oscillation and quasi-biennial oscillation. npj Climate Atmos. Sci., 1, 20177, https://doi.org/10.1038/s41612-017-0008-2.
National Center for Atmospheric Research Staff, 2021: The Climate Data Guide: SST data: HadiSST v1.1. NCAR, accessed 8 February 2021, https://climatedataguide.ucar.edu/climate-data/sst-data-hasisst-v11.
Paltan, H., D. Waliser, W. H. Lim, B. Guan, D. Yamazaki, R. Pant, and S. Dadson, 2017: Global floods and water availability driven by atmospheric rivers. Geophys. Res. Lett., 44, 10 387–10 395, https://doi.org/10.1002/2017GL074882.
Pan, M., and M. Lu, 2020: East Asia atmospheric river catalog: Annual cycle, transition mechanism, and precipitation. Geophys. Res. Lett., 47, e2020GL089477, https://doi.org/10.1029/2020GL089477.
Payne, A. E., and G. Magnusdottir, 2014: Dynamics of landfalling atmospheric rivers over the North Pacific in 30 years of MERRA reanalysis. J. Climate, 27, 7133–7150, https://doi.org/10.1175/JCLI-D-14-00034.1.
Payne, A. E., and Coauthors, 2020: Responses and impacts of atmospheric rivers to climate change. Nat. Rev. Earth Environ., 1, 143–157, https://doi.org/10.1038/s43017-020-0030-5.
Pook, M. J., J. S. Risbey, and P. C. McIntosh, 2013: A comparative synoptic climatology of cool-season rainfall in major grain-growing regions of southern Australia. Theor. Appl. Climatol., 117, 521–533, https://doi.org/10.1007/s00704-013-1021-y.
Prince, H. D., N. J. Cullen, P. B. Gibson, J. Conway, and D. G. Kingston, 2021: A climatology of atmospheric rivers in New Zealand. J. Climate, 34, 4383–4402, https://doi.org/10.1175/JCLI-D-20-0664.1.
Ralph, F. M., P. J. Neiman, and G. A. Wick, 2004: Satellite and CALJET aircraft observations of atmospheric rivers over the eastern North Pacific Ocean during the winter of 1997/98. Mon. Wea. Rev., 132, 1721–1745, https://doi.org/10.1175/1520-0493(2004)132<1721:SACAOO>2.0.CO;2.
Ralph, F. M., P. J. Neiman, and R. Rotunno, 2005: Dropsonde observations in low-level jets over the northeastern Pacific Ocean from CALJET-1998 and PACJET-2001: Mean vertical-profile and atmospheric-river characteristics. Mon. Wea. Rev., 133, 889–910, https://doi.org/10.1175/MWR2896.1.
Ralph, F. M., P. J. Neiman, G. A. Wick, S. I. Gutman, M. D. Dettinger, D. R. Cayan, and A. B. White, 2006: Flooding on California’s Russian river: Role of atmospheric rivers. Geophys. Res. Lett., 33, L13801, https://doi.org/10.1029/2006GL026689.
Ralph, F. M., M. D. Dettinger, M. M. Cairns, T. J. Galarneau, and J. Eylander, 2018: Defining “atmospheric river”: How the Glossary of Meteorology helped resolve a debate. Bull. Amer. Meteor. Soc., 99, 837–839, https://doi.org/10.1175/BAMS-D-17-0157.1.
Rauber, R. M., H. Hu, F. Dominguez, S. W. Nesbitt, G. M. McFarquhar, T. J. Zaremba, and J. A. Finlon, 2020: Structure of an atmospheric river over Australia and the Southern Ocean. Part I: Tropical and midlatitude water vapor fluxes. J. Geophys. Res. Atmos., 125, e2020JD032513, https://doi.org/10.1029/2020JD032513.
Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.
Reid, K. J., I. Simmonds, C. L. Vincent, and A. D. King, 2019: The Australian Northwest cloudband: Climatology, mechanisms, and association with precipitation. J. Climate, 32, 6665–6684, https://doi.org/10.1175/JCLI-D-19-0031.1.
Reid, K. J., A. D. King, T. P. Lane, and E. Short, 2020: The sensitivity of atmospheric river identification to integrated water vapor transport threshold, resolution, and regridding method. J. Geophys. Res. Atmos., 125, e2020JD032897, https://doi.org/10.1029/2020JD032897.
Reid, K. J., S. M. Rosier, L. J. Harrington, A. D. King, and T. P. Lane, 2021: Extreme rainfall in New Zealand and its association with atmospheric rivers. Environ. Res. Lett., 16, 044012, https://doi.org/10.1088/1748-9326/abeae0.
Risbey, J. S., M. J. Pook, P. C. McIntosh, M. C. Wheeler, and H. H. Hendon, 2009: On the remote drivers of rainfall variability in Australia. Mon. Wea. Rev., 137, 3233–3253, https://doi.org/10.1175/2009MWR2861.1.
Rutz, J. J., and Coauthors, 2019: The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying uncertainties in atmospheric river climatology. J. Geophys. Res. Atmos., 124, 13 777–13 802, https://doi.org/10.1029/2019JD030936.
Sellars, S. L., B. Kawzenuk, P. Nguyen, F. M. Ralph, and S. Sorooshian, 2017: Genesis, pathways, and terminations of intense global water vapor transport in association with large-scale climate patterns. Geophys. Res. Lett., 44, 12 465–12 475, https://doi.org/10.1002/2017GL075495.
Shields, C. A., and Coauthors, 2018: Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Project goals and experimental design. Geosci. Model Dev., 11, 2455–2474, https://doi.org/10.5194/gmd-11-2455-2018.
Simmonds, I., K. Keay, and J. A. T. Bye, 2012: Identification and climatology of Southern Hemisphere mobile fronts in a modern reanalysis. J. Climate, 25, 1945–1962, https://doi.org/10.1175/JCLI-D-11-00100.1.
Tapp, R. G., and S. L. Barrell, 1984: The north-west Australian cloud band: Climatology, characteristics and factors associated with development. J. Climatol., 4, 411–424, https://doi.org/10.1002/joc.3370040406.
Telcik, N., and C. Pattiaratchi, 2014: Influence of northwest cloudbands on southwest Australian rainfall. J. Climatol., 2014, 671394, https://doi.org/10.1155/2014/671394.
Trenberth, K., and National Center for Atmospheric Research Staff, 2021: The Climate Data Guide: Nino SST Indices (Nino 1+2,2,3.4,4; ONI and TNI) (last modified 21 Jan 2020). NCAR, accessed 8 February 2021, https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4-oni-and-tni.
Ummenhofer, C. C., M. H. England, P. C. McIntosh, G. A. Meyers, M. J. Pook, J. S. Risbey, A. S. Gupta, and A. S. Taschetto, 2009: What causes southeast Australia’s worst droughts? Geophys. Res. Lett., 36, L04706, https://doi.org/10.1029/2008GL036801.
Viale, M., R. Valenzuela, R. D. Garreaud, and F. M. Ralph, 2018: Impacts of atmospheric rivers on precipitation in southern South America. J. Hydrometeor., 19, 1671–1687, https://doi.org/10.1175/JHM-D-18-0006.1.
Waliser, D., and B. Guan, 2017: Extreme winds and precipitation during landfall of atmospheric rivers. Nat. Geosci., 10, 179–183, https://doi.org/10.1038/ngeo2894.
Wheeler, M. C., and H. H. Hendon, 2004: An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Mon. Wea. Rev., 132, 1917–1932, https://doi.org/10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2.
Wheeler, M. C., H. H. Hendon, S. Cleland, H. Meinke, and A. Donald, 2009: Impacts of the Madden–Julian Oscillation on Australian rainfall and circulation. J. Climate, 22, 1482–1498, https://doi.org/10.1175/2008JCLI2595.1.
Wills, C., F. Perez, and D. Branum, 2016: New method for estimating landslide losses from major winter storms in California and application to the ARkStorm scenario. Nat. Hazards Rev., 17 (4), https://doi.org/10.1061/(ASCE)NH.1527-6996.0000142.
Wright, W. J., 1988: The low latitude influence on winter rainfall in Victoria, south-eastern Australia-I. Climatological aspects. J. Climatol., 8, 437–462, https://doi.org/10.1002/joc.3370080502.
Wright, W. J., 1997: Tropical–extratropical cloudbands and Australian rainfall: I. Climatology. Int. J. Climatol., 17, 807–829, https://doi.org/10.1002/(SICI)1097-0088(19970630)17:8<807::AID-JOC162>3.0.CO;2-J.
Yin, Y., X. Pan, X. Yang, X. Wang, G. Wang, and S. Sun, 2019: Spatiotemporal changes and frequency analysis of multiday extreme precipitation in the Huai River basin during 1960 to 2014. Adv. Meteor., 2019, 6324878, https://doi.org/10.1155/2019/6324878.
Zhang, Z., F. M. Ralph, and M. Zheng, 2019: The relationship between extratropical cyclone strength and atmospheric river intensity and position. Geophys. Res. Lett., 46, 1814–1823, https://doi.org/10.1029/2018GL079071.
Zhou, Y., H. Kim, and B. Guan, 2018: Life cycle of atmospheric rivers: Identification and climatological characteristics. J. Geophys. Res. Atmos., 123, 12 715–12 725, https://doi.org/10.1029/2018JD029180.
Zhou, Y., H. Kim, and D. E. Waliser, 2021: Atmospheric river lifecycle responses to the Madden–Julian Oscillation. Geophys. Res. Lett., 48, e2020GL090983, https://doi.org/10.1029/2020GL090983.
Zhu, Y., R. E. Newell, Y. Zhu, and R. E. Newell, 1998: A proposed algorithm for moisture fluxes from atmospheric rivers. Mon. Wea. Rev., 126, 725–735, https://doi.org/10.1175/1520-0493(1998)126<0725:APAFMF>2.0.CO;2.