Atmospheric Rivers: An Overlooked Threat to the Marginal Snowpack of the Australian Alps

Hamish McGowan aAtmospheric Observations Research Group, University of Queensland, Brisbane, Queensland, Australia

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Kara Borthwick aAtmospheric Observations Research Group, University of Queensland, Brisbane, Queensland, Australia

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Andrew Schwartz aAtmospheric Observations Research Group, University of Queensland, Brisbane, Queensland, Australia

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John Nik Callow bSchool of Agriculture and Environment, University of Western Australia, Perth, Western Australia, Australia

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Shane Bilish cSnowy Hydro Ltd., Cooma, New South Wales, Australia

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Stuart Browning dRisk Frontiers, St Leonards, New South Wales, Australia

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Abstract

Atmospheric rivers (ARs) are tropospheric corridors that provide ~90% of poleward water vapor transport. They are predicted to increase in frequency and intensity if global warming continues unabated. Here we present a case study of the first direct observations of the impact of AR rain-on-snow (RoS) events on the marginal snowpack of the Australian Alps. Reanalysis data show ARs embedded within strong northwesterly airflow extended over 4000 km from the eastern Indian Ocean to southeast Australia, where orographic processes enhanced RoS. We quantify for the first-time radiation and turbulent energy flux exchanges using eddy covariance and the contribution of rain heat flux to the snowpack during the AR RoS events. The hydrological response of an above snow line catchment that includes Australia’s highest peak during the events was rapid, with discharge increasing by nearly two orders of magnitude above historical mean winter discharge. This reflects the isothermal properties of the marginal Australian snowpack, where small increases in energy from RoS can trigger rapid snowmelt leading to flash flooding. Discharge decreased quickly following the passage of the ARs and onset of cold air advection. Based on climate projections of ≈+2.5°C warming in the Australian Alps by midcentury combined with an already historically, close-to-ripe snowpack, we postulate that AR induced RoS events will accelerate the loss of snow cover.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Hamish McGowan, h.mcgowan@uq.edu.au

Abstract

Atmospheric rivers (ARs) are tropospheric corridors that provide ~90% of poleward water vapor transport. They are predicted to increase in frequency and intensity if global warming continues unabated. Here we present a case study of the first direct observations of the impact of AR rain-on-snow (RoS) events on the marginal snowpack of the Australian Alps. Reanalysis data show ARs embedded within strong northwesterly airflow extended over 4000 km from the eastern Indian Ocean to southeast Australia, where orographic processes enhanced RoS. We quantify for the first-time radiation and turbulent energy flux exchanges using eddy covariance and the contribution of rain heat flux to the snowpack during the AR RoS events. The hydrological response of an above snow line catchment that includes Australia’s highest peak during the events was rapid, with discharge increasing by nearly two orders of magnitude above historical mean winter discharge. This reflects the isothermal properties of the marginal Australian snowpack, where small increases in energy from RoS can trigger rapid snowmelt leading to flash flooding. Discharge decreased quickly following the passage of the ARs and onset of cold air advection. Based on climate projections of ≈+2.5°C warming in the Australian Alps by midcentury combined with an already historically, close-to-ripe snowpack, we postulate that AR induced RoS events will accelerate the loss of snow cover.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Hamish McGowan, h.mcgowan@uq.edu.au

1. Introduction

Located on the poleward margins of subtropical ridges in both hemispheres are the largest expanses of marginal winter montane snowpacks. These snowpacks can be classified as maritime and ephemeral using Sturm et al.’s (1995) classification, being warm with melt features including ice layers and percolation columns. Basal melting is common and changes in ambient air temperatures warm/cold may result in significant change in snow cover extent, snow depth and persistence. Global warming is resulting in an expansion of these marginal snowpacks both upslope and into higher latitudes, and they are increasingly affected by rain-on-snow (RoS) events contributing to decline in snowpack and snow water storage (e.g., Mote et al. 2005; Kapnick and Delworth 2013; Klos et al. 2014; Musselman et al. 2018).

Sun et al. (2019) conducted a multimodel study of the impact of global warming on the Sierra Nevada (United States) snowpack finding warming will likely cause a pronounced decrease of snow cover below 2500 m, even though many models predicted an overall increase in precipitation. The increasing occurrence of precipitation phase change from snow to rain as temperature increases will disproportionately affect marginal snowpack regions and, their expanding periphery causing change in hydrological and ecological systems. Musselman et al. (2018) using the representative concentration pathway 8.5 (RCP8.5) scenario modeled end-of-century RoS runoff. They found >200% increase for runoff from marginal warm snowpack regions including the southern Sierra Nevada and Colorado River headwaters. These extreme runoff events will increase flood risk placing communities and hundreds of billions of dollars of infrastructure at risk of loss (Corringham et al. 2019). A declining snow cover combined with an increased number of RoS events may also alter water resource availability, resulting in a contraction in the duration of alpine snowmelt supplying water to downstream areas (Musselman et al. 2018).

Atmospheric rivers (AR) are tropospheric corridors that provide ~90% of poleward water vapor transport, primarily sourced from the tropical and subtropical oceans (Zhu and Newell 1998; Guan and Waliser 2015; Guan et al. 2016; Nash et al. 2018; Ralph et al. 2018). They are in general several thousand kilometers in length, and a few hundred kilometers in width (Newell et al. 1992). Four to five ARs are present at any one time in the Southern and Northern Hemispheres (Zhu and Newell 1998) with the majority of tropospheric moisture contained within the lowest 3000 m of these systems. ARs are predicted to increase in frequency over this century with a greater potential to carry moisture as the atmosphere warms (Gao et al. 2015; Ramos et al. 2016).

Landfall of ARs often triggers high-intensity precipitation in response to topographic enhancement leading to extreme precipitation events. Lavers et al. (2020) attributed ARs as responsible for >90% of flood damages in coastal areas of the western United States, while Kamae et al. (2017) found ARs responsible for 40% of the annual precipitation in Japan and the western North Pacific (WNP). Some ARs cause RoS events as they convey warm tropical moisture into the midlatitudes. Chen et al. (2019) modeled the impact of ARs and non-ARs precipitation events on runoff from snow and no snow covered catchments. They found that for preexisting snowpack environments, runoff/precipitation ratio almost doubled for AR RoS events, thereby contributing significantly to floods. If global warming continues, the associated warming of ARs will shift their winter snow-to-rain ratio leading to more frequent RoS events and greater magnitude RoS floods (Gonzales et al. 2019; Whan et al. 2020). These floods will likely increase in size in response to the greater water holding capacity of the atmosphere (≈+7% K−1) via the Clausius–Clapeyron relation (Payne et al. 2020). Modeling by Whan et al. (2020) indicates a near 100% increase in AR winter precipitation maxima in Norway with a doubling of ARs in Britain under end-of-century RCP8.5 (Lavers et al. 2013).

Regional studies of ARs are rare in the Southern Hemisphere, despite global climatology patterns favoring a significant potential role in Australasia, Chile, South Africa, and southern Brazil. Paltan et al. (2017) found that ARs contribute up to 80% of high and low flows in the 1.06 million km2 Murray–Darling basin, Australia, and to flood and drought sequences in the Parana River catchment in southern Brazil. Waliser and Guan (2017) showed that ARs affect New Zealand on around 40 days annually and are associated with more than half of recorded extreme coastal wind events. The climatology of Southern Hemisphere AR events highlights a strong association with the circumpolar westerlies of the mid- to high latitudes, with another region of more frequent AR occurrences in the central and eastern Pacific Ocean. In the Australian region, the historical climatology shows that ARs are most frequent over southern Australia with minima over the north (Waliser and Guan 2017). However, AR climatology does not identify Australian northwest cloud bands (NWCB), which are baroclinic convergences of AR scale that transport tropical moisture from the eastern Indian Ocean to southeast Australia.

Reid et al. (2019) found during winter, NWCB increase the probability of extreme rainfall by 12 times in northwest Australia and occur more frequently during negative phases of the Indian Ocean dipole (IOD). The negative IOD phase favors warmer than average sea surface temperatures (SSTs) off northwestern Australia, creating the potential for an enhanced oceanic moisture supply through evaporation to NWCB. These conveyors of tropical moisture are recognized as a significant source of precipitation over the seasonally snow-covered Australian Alps (Ummenhofer et al. 2009a; Theobald et al. 2015; Schwartz et al. 2020).

Here we present a case study analysis of the impact of two winter NWCB ARs on the marginal snowpack of the Australian Alps in southeast Australia. We show that AR induced RoS events triggered a pronounced increase in alpine catchment discharge and present a first-of-its-kind direct measurement of surface–atmosphere energy exchanges during AR RoS events.

2. Geographic context

Located in southeastern Australia (SEA), the Australian Alps form the highest part of the Great Dividing Range and includes Australia’s highest peak, Mount Kosciuszko, at 2228 m. Theobald et al. (2015) found annual precipitation from daily totals > 10 mm (the amount required to trigger a streamflow response from 1958 to 2021) in high-elevation regions of the Snowy Mountains varied from 760 to 2800 mm yr−1. Around 70% of this precipitation was associated with tropical connected synoptic weather patterns that occur most often in autumn (MAM) and spring (SON) (Theobald et al. 2015). Winter precipitation in the Australian Alps from cold fronts and closed lows is declining, resulting in reductions in peak snow depth (Nicholls 2005; Hennessy et al. 2008). Monthly mean temperature for the longest running high-altitude (1957 m) site in the Australian Alps–Thredbo AWS (36.49°S, 148.29°E), ranges from +7°C in January to −5.1°C in July (1967–2020) with a midwinter (July) warming trend in mean monthly minimum temperature of +0.3°C decade−1.

The snowpack of the Australian Alps is classified predominantly as “maritime” at the higher elevations using the Sturm et al. (1995) classification system (Sanecki et al. 2006), becoming more ephemeral at elevations below 1600 m and on aspects prone to ablation. Snow depth days > 0.5 m at Deep Creek snow course (36.035°S, 148.374°E; 1620 m MSL) are the lowest now for the past 2000 years (McGowan et al. 2018). Di Luca et al. (2018) predict snow cover extent will decrease by 15%–60% between 2030 and 2070, respectively. These dramatic declines in snowpack are in response to increases in near surface temperature and reductions in the snowfall/rainfall ratio. Winter precipitation at present is dominated by synoptic circulation systems with cyclonic vorticity advection (cold fronts and cutoff lows) (Theobald et al. 2015). The highest winter precipitation totals are associated with warm air advection from northwest of Australia during NWCB events (Theobald et al. 2015), that may result in RoS events causing widespread snowmelt (Bilish et al. 2019).

3. Hydrometeorological data

Synoptic systems with tropical connections that deliver precipitation to the Australian Alps include NWCB that account for 26% of all days with over 10 mm of precipitation (Theobald et al. 2015). Using the AR criteria of Zhu and Newell (1998) we identify the spatial properties of two winter AR NWCB events that resulted in RoS. This approach computes integrated vapor transport (IVT) through the layer 1000 to 100 hPa (Shields et al. 2018) from gridded reanalysis data. IVT is used as it incorporates both wind and moisture allowing the intensity of an AR to be clearly identified (Shields et al. 2018), while AR precipitation and hydrological impacts generally scale with IVT (Albano et al. 2020). Here we use ERA5 reanalysis data with a spatial resolution of approximately 31 × 31 km2 and 137 levels (Hersbach et al. 2020). Precipitation data are from an ETI Instruments NOAH II precipitation gauge with a 6-m diameter (half-size) double fence intercomparison reference (DFIR) shield (Rasmussen et al. 2012) located at Perisher (1761 m MSL) (Fig. 1). Streamflow data are from the Snowy River above Guthega Pondage (catchment area 76.8 km2) recorded at 36°23′26.001″S, 148°21′38.9988″E (1630 m MSL; Fig. 1).

Fig. 1.
Fig. 1.

(a) Location map showing Snowy Mountains region, (b) the eddy covariance system, and (c) Perisher Valley, upper Snowy River catchment above Guthega Pondage and the eddy covariance site.

Citation: Journal of Hydrometeorology 22, 10; 10.1175/JHM-D-20-0293.1

Meteorological data were collected by a Campbell Scientific eddy covariance (EC) system located at 36.417°S, 148.422°E at 1828 m MSL on an open and level grassland area surrounded by a mixture of living and dead Eucalyptus pauciflora (Snow Gum) trees, fens, and alpine bogs (Schwartz et al. 2020) (Fig. 1b). The EC system made measurements at 10 Hz at a height of 3 m above the ground surface using a Campbell Scientific CSAT3 sonic anemometer and EC150 open path gas analyzer with 30-min block averages logged by a Campbell Scientific CR3000 micrologger. Radiation transfers were monitored by a Kipp and Zonen CNR4 radiometer (3.0 m) with 30-min averages logged. All EC data underwent double coordinate rotation (Stiperski and Rotach 2016); corrections for sensor response delays, volume averaging and, the separation distance of the sonic anemometer and gas analyzer. Corrections were made to account for vertical velocities in response to changing air mass density through fluxes of heat and water vapor following the method of Webb et al. (1980). The sign convention is that positive fluxes are directed toward the snow surface. Further details of instrumentation, gap filling, and quality control procedures are detailed in Schwartz et al. (2020).

Precipitation heat flux (Qr) was calculated following Bilish et al. (2018), where the method of Stull (2011) is used to approximate wet bulb temperature (Tw). The fraction of precipitation falling as rain (1 − Psnow) was calculated following Michelson (2004). Snow depth was monitored using a Sommer USH-8 50-kHz ultrasonic snow depth sensor at the EC site with a measurement resolution of 0.1% at a range of 0–8 m.

4. Wintertime atmospheric river events

Here we present an analysis of two winter AR RoS events that triggered rapid snowmelt in the Australian Alps. The objective is to investigate the links between synoptic-scale atmospheric circulation and local hydrometeorology in a seasonally snow covered catchment. There are no dedicated and permanent above snow line alpine flux tower sites within Australia and this study therefore presents a unique opportunity to understand both the synoptic meteorology of winter AR events and, their impact on snowpack energy exchanges and hydrometeorology. As a result, we present the first study to quantify the effects of AR RoS events on the surface energy balance of a winter snowpack using EC and above snow line catchment discharge. These unique observations are the first step in developing understanding of the impact of AR RoS events on alpine hydrometeorology in settings where winter snow cover is marginal or predicted to become marginal in the future due to global warming.

a. Case study 1: 21–22 July 2016

The mean sea level pressure (MSLP) analysis for 1000 EST (0000 UTC) 22 July (Fig. 2a) shows prefrontal northwesterly flow directed over southeast Australia including the Australian Alps ahead of a series of cold fronts in a disturbed westerly airflow. A surface trough extended north into central Australia ahead of a ridge of high pressure with a NWCB stretching from the eastern Indian Ocean northwest of Australia over 4200 km to the Australian Alps (Fig. 2b). The progression of the synoptic cloud field associated with this event from 20 to 23 July 2016 is presented in the online supplemental material.

Fig. 2.
Fig. 2.

(a) 22 Jul 2016: mean sea level pressure at 1000 Australian eastern standard time (AEST) (UTC + 10 h), (b) Himawari-8 visible satellite image 1200 AEST, (c) integrated water vapor transport, sea surface temperature anomaly (reference period 1981–2010), and wind speed at 1000 AEST, and (d) aerological sounding referenced to the local ground surface elevation of 1828 m above sea level at 1000 AEST.

Citation: Journal of Hydrometeorology 22, 10; 10.1175/JHM-D-20-0293.1

The core of the AR exceeded 4000 km in length with maximum IVT values > 800 kg m−1 s−1 located proximal to the Australian Alps on 22 July 2016 (Fig. 2c). These were associated with the delta region of a northwesterly jet at 700 hPa (Fig. 2c) which transported the moisture to southeast Australia from the anomalously warm seas off the northwest coast (Fig. 2c). Orographic forcing of the moisture rich warm northwesterly flow by the approximately 2000-m-high Australian Alps undoubtedly enhanced precipitation over the mountains.

Aerological profiles developed from ERA5 reanalysis data for the closest grid point to the EC and precipitation gauge (25 km north of the EC station) highlight the warm northwesterly airflow over the region on the 22 July (Fig. 2d). This prevailed from the surface to a level around 600 hPa above which the airflow became westerly and increased in speed to >50 kt (1 kt ≈ 0.51 m s−1) above 450 hPa. The profile data clearly show two layers of moisture with the first from the surface to 700 hPa and the second from around 500 to 350 hPa. Orographic lifting of the lower level moist airflow likely enhanced precipitation totals over the Australian Alps with deeper convection tapping into moisture at higher levels further contributing to the recorded high rainfall totals.

Figure 3 presents local hydrometeorology data for the EC at an elevation of 1828 m MSL, and nearby precipitation and river gauging stations, from 0000 Australian eastern standard time (AEST) 21 July to 2400 AEST 23 July 2016. Air temperature on the 21 July was above 3°C increasing to 5°C at midday before onset of light rain, which continued through to 2200 AEST (Fig. 3a). Warm air temperatures and rain through this period along with a positive radiation flux in response primarily to a positive shortwave flux that peaked at 1200 AEST (Fig. 3b), triggered an initial increase in streamflow from around 8 m3 s−1 at midday to 22 m3 s−1 at 2300 AEST 21 July (Fig. 3d). Air temperatures then remained above freezing increasing steadily into the morning of the 22 July with the onset of rain again around 0600 AEST, which continued to 1800 AEST with the passage of the AR over the Australian Alps and Perisher Valley. Heat flux into the snowpack from the warm rain peaked at 157 W m−2 around 1500 EST, while sensible heat flux remained positive throughout this period. Latent heat flux became increasingly negative from 0930 AEST 22 July (Fig. 3c), while net radiative flux (Q*) reached a daily maximum of only 76 W m−2 at midday (Fig. 3b).

Fig. 3.
Fig. 3.

(a) Event 1 hydrometeorological observations 21–23 Jul 2016. (b) Precipitation and air temperature, radiation transfers (K in, solar radiation; K out, reflected solar radiation; L in, longwave in; L out, longwave out) recorded at the Pipers Creek EC, (c) latent and sensible heat flux recorded at the Pipers Creek EC and precipitation heat flux calculated using meteorological data from the Pipers Creek EC and Perisher Valley precipitation gauge, and (d) discharge from Upper Snowy River above Guthega Pondage.

Citation: Journal of Hydrometeorology 22, 10; 10.1175/JHM-D-20-0293.1

Total daily energy fluxes during the event are presented in Table 1. There was a net gain of energy by the snow surface on the 21 July with K*, Qh, Qe, and Qr contributing energy to the snowpack at 1.92, 1.74, 0.64, and 0.19 MJ m−2 day−1 respectively. On the 22 July there was a net gain of energy at the surface also (Table 1) with Qr and Qh the dominant energy sources contributing 2.87 and 2.75 MJ m−2 day−1 to the snowpack respectively. Only Qe was negative at −0.92 MJ m−2 day−1 directed away from the snow surface. On the 23 July, clearing weather, lower air temperatures and occasional light snow flurries were associated with a net loss of energy from the snow surface with Qe at −2.62 MJ m−2 day−1, K* of 1.03 MJ m−2 day−1, and L* of −0.83 MJ m−2 day−1. Sensible heat flux was variable becoming predominately negative after midday (Fig. 3c), which was most likely associated with loss of heat to the colder air associated with snowfall (Fig. 3a).

Table 1.

Net daily energy fluxes (positive values are directed toward the surface).

Table 1.

Streamflow in the Snowy River displayed an almost immediate response (<30-min data logging interval) to the onset of warm RoS with discharge increasing from 23 m3 s−1 0700 EST to a maximum of 314 m3 s−1 at 1600 AEST 22 July. This represented a 13-fold increase in discharge and was almost two orders of magnitude above the mean winter (JJA) discharge (1965–2020) of 3.5 m3 s−1 (Fig. 3d). The estimated return period of this maximum streamflow using discharge data for the Snowy River above Guthega Pondage (1965–2020) is 1:100 years. Rapid loss of snowpack occurred across the entire alpine area with the warm rain and above freezing air temperatures, leading to flash flooding at Perisher Valley including at local ski resorts.

Onset of colder conditions from late afternoon on the 22 July as the surface level cold front passed over the Australian Alps resulted in a transition from warm rain to snow showers (Fig. 3a), and a return to clearer conditions on the 23 July as evidenced in radiation flux data (Fig. 3b). Streamflow decreased rapidly with the onset of colder temperatures (Fig. 3d) highlighting the flashy nature of alpine catchment hydrology in response to RoS events, and the associated sudden changes in the surface energy balance caused by the passage of the AR.

b. Case study 2: 31 August–1 September 2016

The AR RoS event of 31 August–1 September 2016 was characterized by a trough extending from northwest to southeast Australia (Fig. 4a) with a ridge of high pressure along the east coast directing a northwesterly airflow toward the Australian Alps below 650 hPa. This was associated with a NWCB (Fig. 4b) originating from the tropical eastern Indian Ocean displaying a north-northwest to south-southeast arc with the highest IVT values located north of the Australian Alps (Fig. 4c). Maximum IVTs peaked at around 600 kg m−1 s−1 in conjunction with convergence of flow at 700 hPa over southwest Queensland around 1000 km northwest of the Australian Alps (Fig. 4c). The progression of the synoptic cloud field associated with this event from 30 August to 2 September 2016 is presented in the online supplemental material.

Fig. 4.
Fig. 4.

(a) 31 Aug 2016: mean sea level pressure at 0400 AEST, (b) Himawari-8 visible satellite image at 1200 AEST, (c) integrated water vapor transport, sea surface temperature anomaly (reference period 1981–2010), and wind speed at 0400 AEST, and (d) aerological sounding referenced to the local ground surface elevation of 1828 m above sea level at 1000 AEST.

Citation: Journal of Hydrometeorology 22, 10; 10.1175/JHM-D-20-0293.1

The aerological profile for this event shows moisture transport to the Australian Alps above approximately 550 hPa in a strong westerly airflow with winds at lower levels from the northwest (Figs. 4c,d). Two regions of high IVT are evident in Fig. 4c with one located to the northwest of the Australian Alps and the other to the southeast (Fig. 4d). This indicates possible blocking by the mountains with drier conditions in the immediate lee of the barrier as shown by the small cloud free region in Fig. 4b.

Daytime air temperatures peaked at midday on 30 August 2016 at 7.5°C, with the onset of rain at 2330 AEST (Fig. 5a). Intermittent rain then continued over the following 18 h contributing at times up to 44 W m−2 of heat flux (Qr) to the snowpack. Short periods of negative Qh on the 30 and 31 August around midday may have been associated with advection of cooler air onto the EC footprint between showers (Fig. 5c). Rain showers were recorded again on the morning of the 1 September before clearing as indicated by increased receipt of solar radiation and higher air temperatures (Figs. 5 a,b) with Qh reaching a minimum of approximately −140 W m−2 soon after midday (Fig. 5c) following the daily temperature minimum around 0700 AEST and showers (Fig. 5a).

Fig. 5.
Fig. 5.

Event 2 hydrometeorological observations 30 Aug–2 Sep 2016. (a) Precipitation and air temperature, (b) radiation transfers (K in, solar radiation; K out, reflected solar radiation; L in, longwave in; L out, longwave out) recorded at the Pipers Creek EC, (c) latent and sensible heat flux recorded at the Pipers Creek EC and precipitation heat flux calculated using meteorological data from the Pipers Creek EC and Perisher Valley precipitation gauge, and (d) discharge from Upper Snowy River above Guthega Pondage.

Citation: Journal of Hydrometeorology 22, 10; 10.1175/JHM-D-20-0293.1

Total daily energy fluxes for the event are presented in Table 2 with substantially higher daily Q* values the result of increased incoming solar radiation and lower albedo following the AR RoS on the 31 August. Onset of rain late on 30 August and into 31 August (Fig. 5a) resulted in Qr contributing 0.01 and 0.86 MJ m−2 day−1 of energy to the snow surface respectively. Combined with 5.26 MJ m−2 day−1 of K* and 1.54 MJ m−2 day−1 of Qh this resulted in the snowpack at the EC site ablating completely on 31 August. On 1 September L*, Qh and Qe were negative at −2.25, −1.13, and −1.47 MJ m−2 day−1, respectively (Table 2), reflecting clearing skies following showers and associated evaporation.

Table 2.

Net daily energy fluxes (positive values are directed toward the surface).

Table 2.

Streamflow in the Snowy River increased rapidly from around 5 m3 s−1 at 0300 AEST to 55 m3 s−1 8 h later at 1100 AEST—a tenfold increase in discharge (Fig. 5d). Discharge then remained around this level until late afternoon with a slight decrease between 1200 and 1500 AEST possibly caused by localized variability in precipitation in the headwaters of the Snowy River not monitored by the precipitation gauge (Fig. 1c). Discharge then began to decrease slowly following cessation of RoS. Warm daytime air temperatures on 1 September caused the rapid decrease in river flow to moderate, with extensive snow cover still prevalent at higher elevations in the Upper Snowy River catchment following the AR RoS.

5. Discussion

ARs have attracted growing interest over the past two decades because of their potential to break droughts and/or trigger natural disasters through riverine flooding in midlatitude alpine catchments (Rhoades et al. 2020; Lavers et al. 2020). This occurs as narrow midlevel atmospheric baroclinic systems convey oceanic sourced moisture to impact midlatitude topographic barriers (Neiman et al. 2011; Little et al. 2019). Orographic forcing then causes ascent of the AR leading to high-intensity precipitation events. As climate warms in response to anthropogenic carbon emissions to the atmosphere, these events are predicted to become more extreme and frequent due to the increased water holding capacity of the atmosphere (Rhoades et al. 2020; Payne et al. 2020). Rhoades et al. (2020) modeled the impact of global warming under RCP8.5 on end-century (2070–2100) ARs impacting the West Coast of the United States. They showed AR precipitation totals increased by +270% with a shift toward category 4 and 5 events (Rhoades et al. 2020). Of particular concern, are AR RoS events where the combined effect of rainfall and snowmelt trigger unexpected and at times catastrophic flooding (Lackmann and Gyakum 1999; Kingston et al. 2016). In the western United States, AR floods currently result in an average annual damages bill of approximately $1.1 billion (Corringham et al. 2019).

In global AR climatology, AR events are shown to occur, yet are understudied, in the Indian and Southern Oceans (Waliser and Guan 2017; Paltan et al. 2017). Over Australia ARs occur approximately eight times per year and vary in strength with IVTs ranging from 400 to over 2000 kg m−1 s−1 with such extreme high values often due to the co-occurrence with tropical cyclones. In 2016 nine ARs impacted Australia with four events affecting the Snowy Mountains including the two case study events presented here. This was the highest number of ARs to affect this region between 1998 and 2019 (Borthwick and McGowan 2021, manuscript submitted to Int. J. Climatol.). Here we have shown for the first time that ARs are responsible for winter RoS events in the Australian Alps, where rain and snowmelt can combine to trigger substantial increases in alpine catchment discharges, at times leading to severe flooding as occurred during case study 1. This hydrological response is amplified by the isothermal nature of the Australian snowpack, which means small additions of heat to the snowpack can trigger rapid snowmelt. Our observations quantify for the first-time energy flux transfers over a snowpack during AR RoS events and show that K*, Qh, and Qr were the largest energy fluxes to the snowpack during the two winter case studies (Tables 1 and 2). Event 1 had maximum rainfall intensities of approximately 20 mm h−1, which ablated most of the snowpack below 1900 m MSL, resulting in flash flooding and closure of ski fields (Fig. 6). During the early spring event (31 August–1 September 2016), net shortwave was the largest energy flux followed by Qh and Qr (Table 2), and while causing snowmelt, reduced rainfall totals meant that increases in river discharge were less pronounced but still flashy in nature.

Fig. 6.
Fig. 6.

Flooding in Perisher Village, Perisher Valley, Snowy Mountains at 1452 AEST 22 Jul 2016 (Steph Raphael).

Citation: Journal of Hydrometeorology 22, 10; 10.1175/JHM-D-20-0293.1

Marks et al. (1998) used an energy balance snowmelt model to simulate energy fluxes during a RoS event in February 1996 in the Central Cascade Mountains of Oregon, United States. They found that 60%–90% of the energy for snowmelt came from Qh and Qe even though the event resulted in 349–410 mm of RoS. Air temperatures during RoS were mostly around 5° to 8°C, therefore being comparable to conditions during the two case studies presented here. Mazurkiewicz et al. (2008) used the physically based snow energy balance model (SNOBAL) to model energy flux contributions to snowmelt during RoS events in the western Oregon Cascade Mountain Range of the Pacific Northwest of the United States over the period 1996–2003. They found net radiation was the main energy source during RoS snowmelt events with turbulent fluxes contributing the most (42%) at the Vanilla Leaf (1273 m) (VANMET) site, while modeled Qr only ranged from 10% to 15%. Differences in the relative contributions of energy fluxes to snowmelt during RoS computed by such modeling studies compared to direct measurement as presented here highlight the limitations of assumptions made in modeling snowmelt during RoS events.

The AR RoS events presented here were both associated with NWCB (Figs. 2b and 3b) although the links between these two phenomena have not previously been investigated. NWCB have been shown to be responsible for the majority of winter rainfall in southeast Australia, with increased occurrence during negative IOD events (Evans et al. 2009; Ummenhofer et al. 2009a,b; Reid et al. 2019). In winter, a meridional SST gradient between the Indonesian Archipelago and the east Indian Ocean creates an increase in the gradient temperature aloft and enhanced wind shear (Reid et al. 2019). This strong transequatorial flow feeds warm moist air into the region northwest of Australia, driving convection (McGowan et al. 2019; Reid et al. 2019). When coupled with the cool equatorward flow from a quasi-permanent anticyclone to the southwest of Australia (associated with the descending limb of the Hadley cell) a strong baroclinic zone is formed along the northwest–southeast convergence axis. A northwesterly airflow is developed along the leading edge of this convergence zone, transporting a stream of warm moist air toward southeast Australia as seen in both case studies, enhanced by anomalous warm SSTs in the eastern Indian Ocean (−IOD).

NWCBs are flanked by regions of drier subsiding air—a common component of ARs. Reid et al. (2019) also identified a large-scale wave pattern propagating across the Indian Ocean during winter NWCB days. This is consistent with the idea that NWCBs are associated with refracted Rossby waves and that in the Southern Hemisphere they may be influenced by longwave circulations (Zhu and Newell 1998). As the moisture-laden northwesterly airstream on the northern side of the baroclinic convergence encounters the Australian Alps, orographic lifting enhances precipitation, which falls as rain. During event 1, AR moisture was clearly visible as a NWCB and mostly confined in the lowest 2000 m of the troposphere with another layer of moisture between 5500 and 7500 m MSL (Fig. 2d). During event 2, AR moisture seems to have been transported mostly to the Australian Alps at levels above 4000 m MSL (Fig. 3d) but still visible as a NWCB. This is approximately 1500 m above the highest peak in the study area and very different to the conceptual AR model of for example, Payne et al. (2020), where the majority of AR moisture is transported below 2500 m as in event 1. Nonetheless coupling between topography and the AR in event 2 would seem to have occurred resulting in the RoS.

Payne et al. (2020) highlight the urgent need to understand future interaction between ARs and topographic features in response to global warming, as this will lead to marked change in the characteristics of AR precipitation and the response of hydrological systems. For example, numerical modeling studies suggest under end-of-century RCP8.5 an approximate 60% increase in AR with a 20% increase in IVT strength will occur in the Southern Hemisphere midlatitudes (Espinoza et al. 2018). Coupled with the marginal nature of the Australian snowpack, reductions in snow depth (Nicholls 2005), wintertime precipitation (Chubb et al. 2011; Theobald and McGowan 2016), and predicted reductions in snow cover extent of 15% by 2030 and 60% by 2070 (Di Luca et al. 2018), increased AR frequency will accelerate further loss of snow cover. Collectively, these factors may lead to an entirely ephemeral snow cover in the Australian Alps by midcentury or sooner.

6. Conclusions

Here we have presented through a case study approach the first direct measurements of the impact of ARs on the hydrometeorology and surface–atmosphere energy exchanges in the Australian Alps. These measurements have helped to explicitly quantify the heat flux of the RoS during the AR and the response of an alpine catchment, which showed river discharge increasing by one to two orders of magnitude within approximately 6 h of AR RoS onset.

During the winter case study Qh and Qr were the largest energy fluxes to the snowpack, while during the spring case study AR RoS event K*, Qh, and Qr were the largest energy fluxes in descending order of magnitude. These observations are the first to present in detail the snow–atmosphere energy exchanges during an Australian RoS event and in doing so begin to shed light on the drivers of snowmelt during AR RoS events. Understanding these energy transfers and the corresponding response in snowmelt and runoff should be a research priority as continued global warming is forecast to increase both the frequency and intensity of ARs and RoS (Musselman et al. 2018; Gershunov et al. 2019; Rhoades et al. 2020; Payne et al. 2020). This is essential to increase accuracy of flood forecasts during AR RoS events to mitigate risk to life and property. Our case studies have also shown for the first time the link between NWCBs, AR, and RoS events in the Australian Alps. Future research will extend this study to investigate annual and interannual variability in NWCB, AR, and RoS events and the impact on alpine hydrology in the Australian Alps.

Acknowledgments

The authors acknowledge the support of their host institutions in undertaking this research. Funding for this research was provided by Snowy Hydro Limited. Andrew Schwartz was supported by an Australian Government Research Training Program (RTP) scholarship and the University of Queensland’s School of Earth and Environmental Sciences.

Data availability statement

Access to the data underpinning this research can be obtained by contacting the corresponding author.

REFERENCES

  • Albano, C. M., M. D. Dettinger, and A. A. Harpold, 2020: Patterns and drivers of atmospheric river precipitation and hydrologic impacts across the western United States. J. Hydrometeor., 21, 143159, https://doi.org/10.1175/JHM-D-19-0119.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bilish, S. P., H. A. McGowan, and J. N. Callow, 2018: Energy balance and snowmelt drivers of a marginal sub-alpine snowpack. Hydrol. Processes, 32, 38373851, https://doi.org/10.1002/hyp.13293.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bilish, S. P., J. N. Callow, G. S. McGrath, and H. A. McGowan, 2019: Spatial controls on the distribution and dynamics of a marginal snowpack in the Australian Alps. Hydrol. Processes, 33, 17391755, https://doi.org/10.1002/hyp.13435.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, X., L. R. Leung, M. Wigmosta, and M. Richmond, 2019: Impact of atmospheric rivers on surface hydrological processes in western U.S. watersheds. J. Geophys. Res. Atmos., 124, 88968916, https://doi.org/10.1029/2019JD030468.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chubb, T. H., S. T. Siems, and M. J. Manton, 2011: On the decline of wintertime precipitation in the Snowy Mountains of southeastern Australia. J. Hydrometeor., 12, 14831497, https://doi.org/10.1175/JHM-D-10-05021.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Corringham, T. W., F. M. Ralph, A. Gershunov, D. R. Cayan, and C. A. Talbot, 2019: Atmospheric Rivers drive flood damages in the western United States. Sci. Adv., 5, eaax4631, https://doi.org/10.1126/sciadv.aax4631.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Di Luca, A., J. P. Evans, and F. Ji, 2018: Australian snowpack in the NARCliM ensemble: Evaluation, bias correction and future projections. Climate Dyn., 51, 639666, https://doi.org/10.1007/s00382-017-3946-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Espinoza, V., D. E. Waliser, B. Guan, D. A. Lavers, and F. M. Ralph, 2018: Global analysis of climate change projection effects on atmospheric rivers. Geophys. Res. Lett., 45, 42994308, https://doi.org/10.1029/2017GL076968.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evans, A. D., J. M. Bennett, and C. M. Ewenz, 2009: South Australian rainfall variability and climate extremes. Climate Dyn., 33, 477493, https://doi.org/10.1007/s00382-008-0461-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, Y., J. Lu, L. R. Leung, Q. Yang, S. Hagos, and Y. Qian, 2015: Dynamical and thermodynamical modulations on future changes of landfalling atmospheric rivers over western North America. Geophys. Res. Lett., 42, 71797186, https://doi.org/10.1002/2015GL065435.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gershunov, A. T., and Coauthors, 2019: Precipitation regime change in western North America: The role of atmospheric rivers. Sci. Rep., 9, 9944, https://doi.org/10.1038/s41598-019-46169-w.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gonzales, K. R., D. L. Swain, K. M. Nardi, E. A. Barnes, and N. S. Diffenbaugh, 2019: Recent warming of landfalling atmospheric rivers along the west coast of the United States. J. Geophys. Res. Atmos., 124, 68106826, https://doi.org/10.1029/2018JD029860.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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 51412 535, https://doi.org/10.1002/2015JD024257.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guan, B., D. E. Waliser, F. M. Ralph, E. J. Fetzer, and P. J. Neiman, 2016: Hydrometeorological characteristics of rain-on-snow events associated with atmospheric rivers. Geophys. Res. Lett., 43, 29642973, https://doi.org/10.1002/2016GL067978.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hennessy, K., P. Whetton, K. Walsh, I. N. Smith, J. M. Bathols, M. Hutchinson, and J. Sharples, 2008: Climate change effects on snow conditions in mainland Australia and adaptation at ski resorts through snowmaking. Climate Res., 35, 255270, https://doi.org/10.3354/cr00706.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

  • Kamae, Y., W. Mei, and S. Xie, 2017: Climatological relationship between warm season atmospheric rivers and heavy rainfall over East Asia. J. Meteor. Soc. Japan, 95, 411431, https://doi.org/10.2151/jmsj.2017-027.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kapnick, S. B., and T. L. Delworth, 2013: Controls of global snow under a changed climate. J. Climate, 26, 55375562, https://doi.org/10.1175/JCLI-D-12-00528.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, 50635070, https://doi.org/10.1002/hyp.10982.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klos, P. Z., T. E. Link, and J. T. Abatzoglou, 2014: Extent of the rain-snow transition zone in the western U.S. under historic and projected climate. Geophys. Res. Lett., 41, 45604568, https://doi.org/10.1002/2014GL060500.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lackmann, G. M., and J. R. Gyakum, 1999: Heavy cold-season precipitation in the northwestern United States: Synoptic climatology and an analysis of the flood of 17–18 January 1986. Wea. Forecasting, 14, 687700, https://doi.org/10.1175/1520-0434(1999)014<0687:HCSPIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lavers, D. A., R. P. Allan, G. Villarini, B. Lloyd-Hughes, D. J. Brayshaw, and A. J. Wade, 2013: Future changes in atmospheric rivers and their implications for winter flooding in Britain. Environ. Res. Lett., 8, 034010, https://doi.org/10.1088/1748-9326/8/3/034010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lavers, D. A., and Coauthors, 2020: Improved forecasts of atmospheric rivers through systematic reconnaissance, better modelling, and insights on conversion of rain to flooding. Commun. Earth Environ., 1, 39, https://doi.org/10.1038/s43247-020-00042-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Little, K., D. G. Kingston, N. J. Cullen, and P. B. Gibson, 2019: The role of atmospheric rivers for extreme ablation and snowfall events in the Southern Alps of New Zealand. Geophys. Res. Lett., 46, 27612771, https://doi.org/10.1029/2018GL081669.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marks, D., J. Kimball, D. Tingey, and T. Link, 1998: The sensitivity of snowmelt processes to climate conditions and forest cover during rain-on-snow: A case study of the 1996 Pacific Northwest flood. Hydrol. Processes, 12, 15691587, https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1569::AID-HYP682>3.0.CO;2-L.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mazurkiewicz, A. B., D. G. Callery, and J. J. McDonnell, 2008: Assessing the controls of the snow energy balance and water available for runoff in a rain-on-snow environment. J. Hydrol., 354, 114, https://doi.org/10.1016/j.jhydrol.2007.12.027.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McGowan, H. A., J. S. Soderholm, J. N. Callow, G. S. McGrath, and M. L. Campbell, 2018: Global warming in the context of 2000 years of Australian alpine temperature and snow cover. Sci. Rep., 8, 4394, https://doi.org/10.1038/s41598-018-22766-z..

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McGowan, H. A., J. N. Callow, S. Bilish, A. Schwartz, and A. Theobald, 2019: Atmospheric rivers: An overlooked threat to the Australian snowpack in a warming world. 2019 Fall Meeting, San Francisco, CA, Amer. Geophys. Union, https://doi.org/10.1002/essoar.10501665.1.

    • Crossref
    • Export Citation
  • Michelson, D. B., 2004: Systematic correction of precipitation gauge observations using analyzed meteorological variables. J. Hydrol., 290, 161177, https://doi.org/10.1016/j.jhydrol.2003.10.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mote, P. W., A. F. Hamlet, M. P. Clark, and D. P. Lettenmaier, 2005: Declining mountain snowpack in western North America. Bull. Amer. Meteor. Soc., 86, 3950, https://doi.org/10.1175/BAMS-86-1-39.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Musselman, K. N., F. Lehner, K. Ikeda, M. P. Clark, A. F. Prein, C. Liu, M. Barlage, and R. Rasmussen, 2018: Projected increases and shifts in rain-on-snow flood risk over western North America. Nat. Climate Change, 8, 808812, https://doi.org/10.1038/s41558-018-0236-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nash, D., D. Waliser, B. Guan, H. Ye, and F. M. Ralph, 2018: The role of atmospheric rivers in extratropical and polar hydroclimate. J. Geophys. Res. Atmos., 123, 68046821, https://doi.org/10.1029/2017JD028130.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neiman, P. J., L. J. Schick, F. M. Ralph, M. Hughes, and G. A. Wick, 2011: Flooding in western Washington: The connection to atmospheric rivers. J. Hydrometeor., 12, 13371358, https://doi.org/10.1175/2011JHM1358.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Newell, R. E., N. E. Newell, Y. Zhu, and C. Scott, 1992: Tropospheric rivers? – A pilot study. Geophys. Res. Lett., 19, 24012404, https://doi.org/10.1029/92GL02916.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nicholls, N., 2005: Climate variability, climate change and the Australian snow season. Aust. Meteor. Mag., 54, 177185.

  • 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 38710 395, https://doi.org/10.1002/2017GL074882.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Payne, A. E., and Coauthors, 2020: Responses and impacts of atmospheric rivers to climate change. Nat. Rev. Earth Environ., 1, 143157, https://doi.org/10.1038/s43017-020-0030-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, 837839, https://doi.org/10.1175/BAMS-D-17-0157.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ramos, A. M., R. Tomé, R. M. Trigo, M. L. R. Liberato, and J. G. Pinto, 2016: Projected changes in atmospheric rivers affecting Europe in CMIP5 models. Geophys. Res. Lett., 43, 93159323, https://doi.org/10.1002/2016GL070634.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rasmussen, R., and Coauthors, 2012: How well are we measuring snow? The NOAA/FAA/NCAR winter precipitation test bed. Bull. Amer. Meteor. Soc., 93, 811829, https://doi.org/10.1175/BAMS-D-11-00052.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, 66656684, https://doi.org/10.1175/JCLI-D-19-0031.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rhoades, A. M., and Coauthors, 2020: The shifting scales of western U.S. landfalling atmospheric rivers under climate change. Geophys. Res. Lett., 47, e2020GL089096, https://doi.org/10.1029/2020GL089096.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sanecki, G. M., K. Green, H. Wood, and D. Lindenmayer, 2006: The characteristics and classification of Australian snow cover: An ecological perspective. Arct. Antarct. Alp. Res., 38, 429435, https://doi.org/10.1657/1523-0430(2006)38[429:TCACOA]2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, A., H. McGowan, A. Theobald, and N. Callow, 2020: Quantifying the impact of synoptic weather types, patterns, and trends on energy fluxes of a marginal snowpack. Cryosphere, 14, 27552774, https://doi.org/10.5194/tc-14-2755-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shields, C. A., and Coauthors, 2018: Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Project goals and experimental design. Geosci. Model Dev., 11, 24552474, https://doi.org/10.5194/gmd-11-2455-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stiperski, I., and M. W. Rotach, 2016: On the measurement of turbulence over complex mountainous terrain. Bound.-Layer Meteor., 159, 97121, https://doi.org/10.1007/s10546-015-0103-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stull, R. B., 2011: Wet-bulb temperature from relative humidity and air temperature. J. Appl. Meteor. Climatol., 50, 22672269, https://doi.org/10.1175/JAMC-D-11-0143.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sturm, M., J. Holmgren, and G. E. Liston, 1995: A seasonal snow cover classification system for local to global applications. J. Climate, 8, 12611283, https://doi.org/10.1175/1520-0442(1995)008<1261:ASSCCS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, F., N. Berg, A. Hall, M. Schwartz, and D. Walton, 2019: Understanding end-of-century snowpack changes over California’s Sierra Nevada. Geophys. Res. Lett., 46, 933943, https://doi.org/10.1029/2018GL080362.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Theobald, A., and H. McGowan, 2016: Evidence of increased tropical moisture in south-east Australian alpine precipitation during ENSO. Geophys. Res. Lett., 43, 10 90110 908, https://doi.org/10.1002/2016GL070767.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Theobald, A., H. McGowan, J. Speirs, and N. Callow, 2015: A synoptic classification of inflow-generating precipitation in the Snowy Mountains, Australia. J. Appl. Meteor. Climatol., 54, 17131732, https://doi.org/10.1175/JAMC-D-14-0278.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, 2009a: What causes southeast Australia’s worst droughts? Geophys. Res. Lett., 36, L04706, https://doi.org/10.1029/2008GL036801.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ummenhofer, C. C., A. S. Gupta, A. S. Taschetto, and M. H. England, 2009b: Modulation of Australian precipitation by meridional gradients in East Indian Ocean sea surface temperature. J. Climate, 22, 55975610, https://doi.org/10.1175/2009JCLI3021.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Waliser, D. E., and B. Guan, 2017: Extreme winds and precipitation during landfall of atmospheric rivers. Nat. Geosci., 10, 179183, https://doi.org/10.1038/ngeo2894.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Webb, E. K., G. I. Pearman, and R. Leuning, 1980: Correction of flux measurements for density effects due to heat and water vapour transfer. Quart. J. Roy. Meteor. Soc., 106, 85100, https://doi.org/10.1002/qj.49710644707.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Whan, K., J. Sillmann, N. Schaller, and R. Haarsm, 2020: Future changes in atmospheric rivers and extreme precipitation in Norway. Climate Dyn., 54, 20712084, https://doi.org/10.1007/s00382-019-05099-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, Y., and R. E. Newell, 1998: A proposed algorithm for moisture fluxes from atmospheric rivers. Mon. Wea. Rev., 126, 725735, https://doi.org/10.1175/1520-0493(1998)126<0725:APAFMF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

Supplementary Materials

Save
  • Albano, C. M., M. D. Dettinger, and A. A. Harpold, 2020: Patterns and drivers of atmospheric river precipitation and hydrologic impacts across the western United States. J. Hydrometeor., 21, 143159, https://doi.org/10.1175/JHM-D-19-0119.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bilish, S. P., H. A. McGowan, and J. N. Callow, 2018: Energy balance and snowmelt drivers of a marginal sub-alpine snowpack. Hydrol. Processes, 32, 38373851, https://doi.org/10.1002/hyp.13293.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bilish, S. P., J. N. Callow, G. S. McGrath, and H. A. McGowan, 2019: Spatial controls on the distribution and dynamics of a marginal snowpack in the Australian Alps. Hydrol. Processes, 33, 17391755, https://doi.org/10.1002/hyp.13435.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, X., L. R. Leung, M. Wigmosta, and M. Richmond, 2019: Impact of atmospheric rivers on surface hydrological processes in western U.S. watersheds. J. Geophys. Res. Atmos., 124, 88968916, https://doi.org/10.1029/2019JD030468.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chubb, T. H., S. T. Siems, and M. J. Manton, 2011: On the decline of wintertime precipitation in the Snowy Mountains of southeastern Australia. J. Hydrometeor., 12, 14831497, https://doi.org/10.1175/JHM-D-10-05021.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Corringham, T. W., F. M. Ralph, A. Gershunov, D. R. Cayan, and C. A. Talbot, 2019: Atmospheric Rivers drive flood damages in the western United States. Sci. Adv., 5, eaax4631, https://doi.org/10.1126/sciadv.aax4631.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Di Luca, A., J. P. Evans, and F. Ji, 2018: Australian snowpack in the NARCliM ensemble: Evaluation, bias correction and future projections. Climate Dyn., 51, 639666, https://doi.org/10.1007/s00382-017-3946-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Espinoza, V., D. E. Waliser, B. Guan, D. A. Lavers, and F. M. Ralph, 2018: Global analysis of climate change projection effects on atmospheric rivers. Geophys. Res. Lett., 45, 42994308, https://doi.org/10.1029/2017GL076968.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evans, A. D., J. M. Bennett, and C. M. Ewenz, 2009: South Australian rainfall variability and climate extremes. Climate Dyn., 33, 477493, https://doi.org/10.1007/s00382-008-0461-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, Y., J. Lu, L. R. Leung, Q. Yang, S. Hagos, and Y. Qian, 2015: Dynamical and thermodynamical modulations on future changes of landfalling atmospheric rivers over western North America. Geophys. Res. Lett., 42, 71797186, https://doi.org/10.1002/2015GL065435.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gershunov, A. T., and Coauthors, 2019: Precipitation regime change in western North America: The role of atmospheric rivers. Sci. Rep., 9, 9944, https://doi.org/10.1038/s41598-019-46169-w.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gonzales, K. R., D. L. Swain, K. M. Nardi, E. A. Barnes, and N. S. Diffenbaugh, 2019: Recent warming of landfalling atmospheric rivers along the west coast of the United States. J. Geophys. Res. Atmos., 124, 68106826, https://doi.org/10.1029/2018JD029860.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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 51412 535, https://doi.org/10.1002/2015JD024257.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guan, B., D. E. Waliser, F. M. Ralph, E. J. Fetzer, and P. J. Neiman, 2016: Hydrometeorological characteristics of rain-on-snow events associated with atmospheric rivers. Geophys. Res. Lett., 43, 29642973, https://doi.org/10.1002/2016GL067978.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hennessy, K., P. Whetton, K. Walsh, I. N. Smith, J. M. Bathols, M. Hutchinson, and J. Sharples, 2008: Climate change effects on snow conditions in mainland Australia and adaptation at ski resorts through snowmaking. Climate Res., 35, 255270, https://doi.org/10.3354/cr00706.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

  • Kamae, Y., W. Mei, and S. Xie, 2017: Climatological relationship between warm season atmospheric rivers and heavy rainfall over East Asia. J. Meteor. Soc. Japan, 95, 411431, https://doi.org/10.2151/jmsj.2017-027.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kapnick, S. B., and T. L. Delworth, 2013: Controls of global snow under a changed climate. J. Climate, 26, 55375562, https://doi.org/10.1175/JCLI-D-12-00528.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, 50635070, https://doi.org/10.1002/hyp.10982.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klos, P. Z., T. E. Link, and J. T. Abatzoglou, 2014: Extent of the rain-snow transition zone in the western U.S. under historic and projected climate. Geophys. Res. Lett., 41, 45604568, https://doi.org/10.1002/2014GL060500.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lackmann, G. M., and J. R. Gyakum, 1999: Heavy cold-season precipitation in the northwestern United States: Synoptic climatology and an analysis of the flood of 17–18 January 1986. Wea. Forecasting, 14, 687700, https://doi.org/10.1175/1520-0434(1999)014<0687:HCSPIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lavers, D. A., R. P. Allan, G. Villarini, B. Lloyd-Hughes, D. J. Brayshaw, and A. J. Wade, 2013: Future changes in atmospheric rivers and their implications for winter flooding in Britain. Environ. Res. Lett., 8, 034010, https://doi.org/10.1088/1748-9326/8/3/034010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lavers, D. A., and Coauthors, 2020: Improved forecasts of atmospheric rivers through systematic reconnaissance, better modelling, and insights on conversion of rain to flooding. Commun. Earth Environ., 1, 39, https://doi.org/10.1038/s43247-020-00042-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Little, K., D. G. Kingston, N. J. Cullen, and P. B. Gibson, 2019: The role of atmospheric rivers for extreme ablation and snowfall events in the Southern Alps of New Zealand. Geophys. Res. Lett., 46, 27612771, https://doi.org/10.1029/2018GL081669.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marks, D., J. Kimball, D. Tingey, and T. Link, 1998: The sensitivity of snowmelt processes to climate conditions and forest cover during rain-on-snow: A case study of the 1996 Pacific Northwest flood. Hydrol. Processes, 12, 15691587, https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1569::AID-HYP682>3.0.CO;2-L.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mazurkiewicz, A. B., D. G. Callery, and J. J. McDonnell, 2008: Assessing the controls of the snow energy balance and water available for runoff in a rain-on-snow environment. J. Hydrol., 354, 114, https://doi.org/10.1016/j.jhydrol.2007.12.027.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McGowan, H. A., J. S. Soderholm, J. N. Callow, G. S. McGrath, and M. L. Campbell, 2018: Global warming in the context of 2000 years of Australian alpine temperature and snow cover. Sci. Rep., 8, 4394, https://doi.org/10.1038/s41598-018-22766-z..

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McGowan, H. A., J. N. Callow, S. Bilish, A. Schwartz, and A. Theobald, 2019: Atmospheric rivers: An overlooked threat to the Australian snowpack in a warming world. 2019 Fall Meeting, San Francisco, CA, Amer. Geophys. Union, https://doi.org/10.1002/essoar.10501665.1.

    • Crossref
    • Export Citation
  • Michelson, D. B., 2004: Systematic correction of precipitation gauge observations using analyzed meteorological variables. J. Hydrol., 290, 161177, https://doi.org/10.1016/j.jhydrol.2003.10.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mote, P. W., A. F. Hamlet, M. P. Clark, and D. P. Lettenmaier, 2005: Declining mountain snowpack in western North America. Bull. Amer. Meteor. Soc., 86, 3950, https://doi.org/10.1175/BAMS-86-1-39.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Musselman, K. N., F. Lehner, K. Ikeda, M. P. Clark, A. F. Prein, C. Liu, M. Barlage, and R. Rasmussen, 2018: Projected increases and shifts in rain-on-snow flood risk over western North America. Nat. Climate Change, 8, 808812, https://doi.org/10.1038/s41558-018-0236-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nash, D., D. Waliser, B. Guan, H. Ye, and F. M. Ralph, 2018: The role of atmospheric rivers in extratropical and polar hydroclimate. J. Geophys. Res. Atmos., 123, 68046821, https://doi.org/10.1029/2017JD028130.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neiman, P. J., L. J. Schick, F. M. Ralph, M. Hughes, and G. A. Wick, 2011: Flooding in western Washington: The connection to atmospheric rivers. J. Hydrometeor., 12, 13371358, https://doi.org/10.1175/2011JHM1358.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Newell, R. E., N. E. Newell, Y. Zhu, and C. Scott, 1992: Tropospheric rivers? – A pilot study. Geophys. Res. Lett., 19, 24012404, https://doi.org/10.1029/92GL02916.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nicholls, N., 2005: Climate variability, climate change and the Australian snow season. Aust. Meteor. Mag., 54, 177185.

  • 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 38710 395, https://doi.org/10.1002/2017GL074882.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Payne, A. E., and Coauthors, 2020: Responses and impacts of atmospheric rivers to climate change. Nat. Rev. Earth Environ., 1, 143157, https://doi.org/10.1038/s43017-020-0030-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, 837839, https://doi.org/10.1175/BAMS-D-17-0157.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ramos, A. M., R. Tomé, R. M. Trigo, M. L. R. Liberato, and J. G. Pinto, 2016: Projected changes in atmospheric rivers affecting Europe in CMIP5 models. Geophys. Res. Lett., 43, 93159323, https://doi.org/10.1002/2016GL070634.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rasmussen, R., and Coauthors, 2012: How well are we measuring snow? The NOAA/FAA/NCAR winter precipitation test bed. Bull. Amer. Meteor. Soc., 93, 811829, https://doi.org/10.1175/BAMS-D-11-00052.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, 66656684, https://doi.org/10.1175/JCLI-D-19-0031.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rhoades, A. M., and Coauthors, 2020: The shifting scales of western U.S. landfalling atmospheric rivers under climate change. Geophys. Res. Lett., 47, e2020GL089096, https://doi.org/10.1029/2020GL089096.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sanecki, G. M., K. Green, H. Wood, and D. Lindenmayer, 2006: The characteristics and classification of Australian snow cover: An ecological perspective. Arct. Antarct. Alp. Res., 38, 429435, https://doi.org/10.1657/1523-0430(2006)38[429:TCACOA]2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, A., H. McGowan, A. Theobald, and N. Callow, 2020: Quantifying the impact of synoptic weather types, patterns, and trends on energy fluxes of a marginal snowpack. Cryosphere, 14, 27552774, https://doi.org/10.5194/tc-14-2755-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shields, C. A., and Coauthors, 2018: Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Project goals and experimental design. Geosci. Model Dev., 11, 24552474, https://doi.org/10.5194/gmd-11-2455-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stiperski, I., and M. W. Rotach, 2016: On the measurement of turbulence over complex mountainous terrain. Bound.-Layer Meteor., 159, 97121, https://doi.org/10.1007/s10546-015-0103-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stull, R. B., 2011: Wet-bulb temperature from relative humidity and air temperature. J. Appl. Meteor. Climatol., 50, 22672269, https://doi.org/10.1175/JAMC-D-11-0143.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sturm, M., J. Holmgren, and G. E. Liston, 1995: A seasonal snow cover classification system for local to global applications. J. Climate, 8, 12611283, https://doi.org/10.1175/1520-0442(1995)008<1261:ASSCCS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, F., N. Berg, A. Hall, M. Schwartz, and D. Walton, 2019: Understanding end-of-century snowpack changes over California’s Sierra Nevada. Geophys. Res. Lett., 46, 933943, https://doi.org/10.1029/2018GL080362.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Theobald, A., and H. McGowan, 2016: Evidence of increased tropical moisture in south-east Australian alpine precipitation during ENSO. Geophys. Res. Lett., 43, 10 90110 908, https://doi.org/10.1002/2016GL070767.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Theobald, A., H. McGowan, J. Speirs, and N. Callow, 2015: A synoptic classification of inflow-generating precipitation in the Snowy Mountains, Australia. J. Appl. Meteor. Climatol., 54, 17131732, https://doi.org/10.1175/JAMC-D-14-0278.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, 2009a: What causes southeast Australia’s worst droughts? Geophys. Res. Lett., 36, L04706, https://doi.org/10.1029/2008GL036801.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ummenhofer, C. C., A. S. Gupta, A. S. Taschetto, and M. H. England, 2009b: Modulation of Australian precipitation by meridional gradients in East Indian Ocean sea surface temperature. J. Climate, 22, 55975610, https://doi.org/10.1175/2009JCLI3021.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Waliser, D. E., and B. Guan, 2017: Extreme winds and precipitation during landfall of atmospheric rivers. Nat. Geosci., 10, 179183, https://doi.org/10.1038/ngeo2894.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Webb, E. K., G. I. Pearman, and R. Leuning, 1980: Correction of flux measurements for density effects due to heat and water vapour transfer. Quart. J. Roy. Meteor. Soc., 106, 85100, https://doi.org/10.1002/qj.49710644707.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Whan, K., J. Sillmann, N. Schaller, and R. Haarsm, 2020: Future changes in atmospheric rivers and extreme precipitation in Norway. Climate Dyn., 54, 20712084, https://doi.org/10.1007/s00382-019-05099-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, Y., and R. E. Newell, 1998: A proposed algorithm for moisture fluxes from atmospheric rivers. Mon. Wea. Rev., 126, 725735, https://doi.org/10.1175/1520-0493(1998)126<0725:APAFMF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fig. 1.

    (a) Location map showing Snowy Mountains region, (b) the eddy covariance system, and (c) Perisher Valley, upper Snowy River catchment above Guthega Pondage and the eddy covariance site.

  • Fig. 2.

    (a) 22 Jul 2016: mean sea level pressure at 1000 Australian eastern standard time (AEST) (UTC + 10 h), (b) Himawari-8 visible satellite image 1200 AEST, (c) integrated water vapor transport, sea surface temperature anomaly (reference period 1981–2010), and wind speed at 1000 AEST, and (d) aerological sounding referenced to the local ground surface elevation of 1828 m above sea level at 1000 AEST.

  • Fig. 3.

    (a) Event 1 hydrometeorological observations 21–23 Jul 2016. (b) Precipitation and air temperature, radiation transfers (K in, solar radiation; K out, reflected solar radiation; L in, longwave in; L out, longwave out) recorded at the Pipers Creek EC, (c) latent and sensible heat flux recorded at the Pipers Creek EC and precipitation heat flux calculated using meteorological data from the Pipers Creek EC and Perisher Valley precipitation gauge, and (d) discharge from Upper Snowy River above Guthega Pondage.

  • Fig. 4.

    (a) 31 Aug 2016: mean sea level pressure at 0400