1. Introduction

Across many parts of the globe, large-scale cloudbands stretch from the tropics to the extratropics, and transport latent heat and moisture. These bands have been observed over the South Pacific convergence zone (Vincent 1994), Brazil (Ferreira et al. 2001), northwest Africa (Knippertz 2003), southern Africa (Harrison 1984), and the Mediterranean (de Felice and Viltard 1976), and have also been associated with severe weather over North America (Kessler 1981). Australian northwest cloudbands (NWCBs) are synoptic-scale bands of cloud that extend from the tropical east Indian Ocean to southern Australia bringing precipitation across the continent. Australian NWCBs are unique in that they occur predominantly over land, whereas Fröhlich et al. (2013) showed that other cloudbands tend to occur to the east (west) of the main landmasses in the Southern (Northern) Hemisphere.

While there is no standard definition or climatology of NWCBs, there have been attempts at identifying them (both manually and automatically) and constructing a record of these events. Tapp and Barrell (1984, hereafter TB84) established criteria with respect to the structure and origin of NWCBs, and they manually applied their scheme to 4 years of 3-hourly infrared (IR) satellite images (September 1978–August 1982). They observed an average of 1.5–2.5 bands per month between March and November, and found a maximum in NWCB occurrence during austral winter [June–August (JJA)], which will be referred to as simply “winter” in this study (analogous terminology will be used for the other three seasons). Kuhnel (1990, hereafter K90) built on TB84’s work to assemble a cloudband climatology for all calendar months using 5 years (1979–83) of 3-hourly IR satellite data from the Geostationary Meteorological Satellite-1 (GMS-1). He found TB84’s location for the origin (approximately 80°–120°E) to be too restrictive and extended it to 145°E. Furthermore, K90 indicated that NWCB frequency peaked in summer [December–February (DJF)] or autumn [March–May (MAM)] months, and was at a minimum in spring [September–November (SON)]. Wright (1997, hereafter W97) produced a 15-yr record for the cool-season months of April–October of NWCBs using 3-hourly IR and visible GMS images. He used similar criteria to K90, while adding the criteria that cloudbands must persist as a discrete entity for at least 24 h and must have a coherent link with the tropics. W97 commented that TB84’s strict criteria excluded cloudband features that developed over land.

Recently, the meteorological community has tended to turn away from the subjective identification and characterization of synoptic “objects” and has increasingly relied on automated algorithms. In connection with Australian precipitation, Pezza et al. (2008) employed a cyclone identification scheme, while Hope et al. (2014) made use of several automated frontal detection algorithms. There are many advantages to using automated systems including efficiency and reproducibility (Murray and Simmonds 1991). Telcik and Pattiaratchi (2014, hereafter TP14) created an automatic NWCB identification scheme based on conditions used by the earlier manual schemes. They applied their scheme from March to November and extended their NWCB index back to 1950 using total cloud cover (TCC) data from the NCEP–NCAR reanalysis (Kalnay et al. 1996). TP14 found that the highest number of cloudband days occurred in austral autumn, whereas September had the fewest. They also found a peak in cool-season cloudband frequency in the late 1950s, a decrease to the late 1960s, and then a steady increase until the end of their study period (1999). One caveat in their investigation is the use of the NCEP–NCAR reanalysis to identify cloud cover. Their TCC is derived from the model moisture fields and therefore is influenced by uncertainty in the cloud cover analysis. Weare (1997) showed that the NCEP–NCAR reanalysis underestimated cloud cover off the northwest coast of Australia by about 20% relative to satellite observations. Fröhlich et al. (2013) performed a global analysis of “tropical plumes,” which can be thought of as cloudbands. They used brightness temperature anomaly measurements from 1983 to 2006 and identification criteria based on the geographical extent and geometry of such plumes. A main criterion they employed was the plume had to cross the 15°-latitude circle, a condition that could exclude genuine cloudbands that form in the higher latitudes of the tropics. Our study included all cloudbands with a tropical and extratropical component where the tropics are defined as 0°–23.5° latitude.

The association between NWCBs and mean and extreme precipitation is underexplored in the literature due to shortcomings in NWCB identification and climatology discussed above. The early NWCB records were too short to make robust conclusions about rainfall associated with NWCBs (e.g., TB84 and K90), while the studies with longer NWCB records used sparse station networks (e.g., W97) or focused on a single region (e.g., TP14). Moreover, analyses of drivers of Australian mean and extreme precipitation variability (e.g., Risbey et al. 2009; King et al. 2014) have largely focused on interannual time scales. As such, the associations between NWCBs and mean and extreme precipitation anomalies are not well understood.

From this overview, three main gaps in the work on NWCB identification schemes and climatology are apparent. First, the NWCB record does not extend much into the twenty-first century, so any connection with recent climate change and precipitation changes will not be captured by previous studies. Second, analyses of NWCB mechanisms are usually limited to winter NWCBs. Finally, investigations of the links between NWCBs and precipitation have relied on short cloudband records and sparse station networks such that NWCB–precipitation relationships are poorly constrained. This study fills these gaps by producing a climatology of Australian NWCB days from 1984 to 2014 using satellite observations. This climatology is used to analyze the physical drivers of NWCBs, and the association between NWCBs and precipitation.

2. Data and methods

b. Methods

We have built on the work of previous studies to develop a new NWCB identification scheme that is suitable for identifying cloudbands using cloud cover revealed in satellite data. Our proposed scheme uses the same longitudinal origin region as TP14 (80°–130°E), while the formation region was extended to 20°S to include cloudbands forming over land. Continental cloudbands can cause widespread precipitation (W97) and, hence, for studying precipitation in Australia due to NWCBs, it is appropriate to include this type of cloudband.

In constructing our automated scheme, we were guided by the results of a manual NWCB analysis we performed for a subperiod of the entire interval considered here. This covered 4 years (2006–09) and was based on 3-hourly satellite data [infrared (~11 μm), visual (~0.65 μm), and water vapor (~6.7 μm)] from NOAA’s GIBBS service (Global ISCCP B1 Browse System; Knapp 2008). The principles governing manual identification on a given day were that the cloudband must

• exist over part of continental Australia,

• be continuous,

• form over 0°–20°S, 80°–130°E,

• be northwest-to-southeast oriented, and

• have a length of at least 2000 km

The criteria for the automatic scheme were based on these same conditions. We translated the partially qualitative conditions of the manual scheme to purely quantitative and objective criteria that could be applied to gridded data. The domain for the automatic scheme was 10°–40°S, 110°–155°E. Given the automatic scheme does not track specific cloudbands in time, we used a more conservative domain for the automatic scheme to avoid identifying cloudband-like structures in the Indian Ocean that may not reach Australia. The criteria for the automatic scheme are the following:

1. There must be cloud in at least 50% of the meridians of the 1° × 1° gridded domain. This condition indirectly ensures the cloudband extends at least approximately 2000 km. However, in cases where a cloudband was more meridional than usual, this condition becomes more lenient by allowing a minimum of 30% of the meridians to contain cloud, so long as the magnitude of the northwest–southeast spatial gradient exceeds 0.25° longitude per 1° latitude.

2. Maximum cloud cover along each meridian must not lie solely on the coastline. This condition is important because cloudiness caused by other phenomena such as cyclones and fronts over the Great Australian Bight will reside near the southern boundary, and that cloud could be misattributed to an NWCB. Similarly, in the north, the monsoon trough may cause cloudband-like features.

3. The cloudband must be continuous across the continent, and the zonal distance between cloudy grid boxes of consecutive meridians must not exceed 4°. Moreover, parts of the cloudband must reside in both the tropics and extratropics.

4. The orientation of the cloudband must be northwest to southeast. This was determined by calculating the slope of the line of best fit through the points of maximum cloud cover at each longitude. The magnitude of the slope must exceed 0.1° longitude per 1° latitude.

5. The cloud fraction had to exceed 80% for a grid space to be considered cloudy in this study.

6. Similar to W97, a minimum duration condition was included; however, a period of 12 h rather than 24 h was used based on our observations of satellite imagery. This shorter duration is preferable as the start and end of cloudbands are challenging to identify and may not meet all the conditions as the cloudband starts to dissipate. Since some cloudbands may only be identified by the scheme once they have reached their peak intensity, it follows that a 24-h-duration condition may lead to an underestimation of cloudbands. If all conditions were met for a minimum of 12 consecutive hours, the day was deemed a cloudband day.

The criteria for the automatic scheme were refined by systematic comparison and validation against the outcomes of the manual scheme. Table 1 summarizes past NWCB identification schemes and our proposed scheme.

Table 1.

Summary of Australian NWCB identification scheme characteristics.

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For the 4-yr period of overlap of the manual and automatic identification schemes, we evaluated the automatic scheme using the probability of detection [Eq. (1)] and probability of false detection [Eq. (2)] calculated from a contingency table based on the daily identifications (Table 2).

Table 2.

Contingency table and probability of detection and probability of false detection scores for chosen automatic identification scheme using ISCCP-H and ERA-I data.

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Auto and Manual refer to the automatic and manual schemes, respectively, and “NWCB” and “No NWCB” refer to whether or not a cloudband was identified.

$\text{Probability}\text{of}\text{Detection}\left(\text{POD}\right)={\displaystyle \frac{\mathrm{Hits}}{\mathrm{Hits}+\mathrm{Misses}}},$(1)

$\text{Probability}\text{of}\text{False}\text{Detection}\left(\text{POFD}\right)={\displaystyle \frac{\mathrm{False}\text{Alarms}}{\mathrm{Correct}\text{Negatives}+\mathrm{False}\text{Alarms}}}.$(2)

The POD scores (Table 2) for the chosen dataset (ISCCP) are rather modest, and a few of the causes of this are discussed below. Some sources of misses and false positives are the days immediately before and after a cloudband. For example, the start and end times of a cloudband can be ambiguous. TB84 noted that these can be difficult to distinguish due to other cloud masses nearby and because the end of a cloudband event was gradual. Additionally, situations where there are multiple synoptic systems present over Australia can lead to ambiguity when identifying cloudbands both manually and automatically. However, ambiguous days that the automatic identification scheme may have trouble with are often challenging to identify using a manual method as well. Furthermore, weather systems do not always look like the ideal case so some uncertainty with identifying them is expected. We also note there is high variability in the POD score between years especially 2009 and 2006. This may be due to anomalously low SSTs northwest of Australia during 2006 leading to NWCBs with more homogeneous stratiform cloud.

We applied the scheme to TCC from ERA-I to assess the potential and/or validity of extending the NWCB record back in time using reanalysis data. Table 2 shows that the satellite data are superior to reanalysis data for detecting NWCBs this study. In reanalyses, TCC and precipitation parameters are indirectly constrained by temperature and humidity observations, so uncertainties can accumulate since such products in general have problems reproducing moisture budgets (Goswami et al. 2017). For example, Lorenz and Kunstmann (2012) found major shortcomings in the moisture budgets of ERA-I, the Modern-Era Retrospective Analysis for Research and Applications (MERRA; NASA) and the Climate Forecast System Reanalysis (CFSR; NCEP). Notably, they found a dry bias in summer precipitation over northwest Australia in ERA-I, and Dee et al. (2011) showed that ERA-I tends to underestimate daily precipitation over continental Australia. Free et al. (2016) showed that ERA-I underestimated mean annual TCC by about 10% in their study of the contiguous United States. For these reasons, and the results from the verification, it was concluded that cloud cover from ERA-I did not capture the NWCB structure adequately and therefore it was not appropriate to use reanalysis products to extend the cloudband record beyond the ISCCP observational period.

We compared the annual distribution of NWCB days as identified manually and using our automatic scheme (Fig. 1a). Both schemes produce frequency maxima during summer and autumn, and frequency minima in late winter to early spring. This result provides confidence that the automatic scheme is able to reproduce a physically realistic cloudband distribution with the caveat that the automatic scheme overestimates the number of summer cloudband days due to ambiguity in manually distinguishing between NWCBs and cloudband-like structures associated with the monsoon trough or tropical cyclones.

(a) Mean number of cloudband days per month, 2006–09, using the manual (gray) and automatic (black) identification schemes, and (b) box-and-whisker plot of number of NWCB days, 1984–2014, using the automatic scheme. Blue box is the interquartile range (IQR), red line is the median, black lines are the most extreme points not considered outliers, and red crosses are outliers defined where the difference from the median is greater than 1.5 times the IQR.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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(a) Mean number of cloudband days per month, 2006–09, using the manual (gray) and automatic (black) identification schemes, and (b) box-and-whisker plot of number of NWCB days, 1984–2014, using the automatic scheme. Blue box is the interquartile range (IQR), red line is the median, black lines are the most extreme points not considered outliers, and red crosses are outliers defined where the difference from the median is greater than 1.5 times the IQR.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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(a) Mean number of cloudband days per month, 2006–09, using the manual (gray) and automatic (black) identification schemes, and (b) box-and-whisker plot of number of NWCB days, 1984–2014, using the automatic scheme. Blue box is the interquartile range (IQR), red line is the median, black lines are the most extreme points not considered outliers, and red crosses are outliers defined where the difference from the median is greater than 1.5 times the IQR.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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To analyze the typical synoptic patterns during cloudband events, composite plots of geopotential height, SST, MSLP, TCWV, Eady growth rate (EGR), relative vorticity, and vertical velocity (all from ERA-I) during cloudband and non-cloudband days were constructed. These gave insights into which mechanisms were likely to influence the genesis and maintenance of NWCBs, despite the fact that ERA-I does not appear to properly capture the structure of the NWCBs. Although one must be cautious interpreting composites (Boschat et al. 2016), in our context they provide valuable insights. The EGR, a measure of baroclinic instability, was calculated with the following equation from Simmonds and Lim (2009):

$\begin{array}{c}\begin{array}{c}\mathrm{EGR}=0.3098\left|f\right|{\displaystyle \frac{\left|{\displaystyle \frac{\partial \mathbf{u}\left(z\right)}{\partial z}}\right|}{N}}\end{array}\end{array},$

where f is the Coriolis parameter, u(z) is the horizontal wind vector, $N=\sqrt{\left(g/\theta \right)\left(\partial \theta /\partial z\right)}$ is the Brunt–Väisälä frequency, g is the acceleration due to gravity, θ is the potential temperature, and z is height. To calculate the EGR at 500 and 850 hPa, the differences between potential temperature and horizontal winds at 550 and 450 hPa and at 875 and 825 hPa were used. The change in height δz between pressure levels was calculated from the change in geopotential (since Φ = gz).

3. NWCB climatology

We first show the annual cycle of NWCB days as identified by the automatic scheme. Figure 1b displays a frequency minimum in late winter/early spring. The numbers of cloudband days per month are generally lower than those of TP14, but higher than those of W97. The peak in May in W97 and TB84 does not appear in our results or in those of TP14. K90 found May 1979 had an anomalously high number of cloudband days. The peak in the May mean from the W97 and TB84 studies is likely due to the anomalous cloudband days in May 1979. The signal of that anomalous year would be stronger over their shorter study periods whereas, in our longer study periods, the mean would not be as sensitive to a specific year. Similar to the results obtained by K90 and Fröhlich et al. (2013), we find that summer had the most cloudband days.

The seasonal counts in Fig. 1b seem to be out of phase with those of TB84, but that study only included cloudbands that formed over the ocean and therefore would have excluded cloudbands that can form over the continent, which are very common in summer. W97 found that as the frequency of oceanic cloudbands decreased in late spring, the number of continental cloudbands began to increase. In addition, TB84 counted the frequency of cloudbands rather than the frequency of days with a cloudband, which the other NWCB studies (including this study) used. K90 found that cloudbands that formed east of 120°E were more common in summer and generally have a longer duration than those that formed west of 120°E. Since TB84’s definition of a cloudband excluded those that formed east of 130°E and duration was not factored into their climatology, it follows that TB84’s study was unlikely to identify as many summer cloudbands. Differences in cloudband definitions, the time period studied, and the scope of the various projects account for some variation in annual frequency distributions found between cloudband climatology studies. As Fig. 1b indicates by the size of the interquartile ranges (blue boxes), there is high interannual variability in the number of NWCB days, particularly in summer and autumn. For this reason, a climatology based on a 4- or 5-yr-long record is unlikely to reflect the true climatology of NWCBs. Hence, the 31-yr record used here greatly increases the robustness of NWCB climatologies.

We also present the variation in duration of NWCB events (Fig. 2). Most events lasted 1–2 days, and the maximum duration observed was 12 days. We found that NWCB events last longer in summer and autumn, followed by winter, and then spring. We hypothesize that this seasonal variability is likely associated with moisture availability over northwest Australia. During the wet season there is more moisture available over the Australian continent, allowing the cloudband to persist longer, whereas during winter and spring the continent is drier so the NWCB would decay faster. Figure 3 shows the time series of the total number of cloudband days per year identified by the new automatic identification scheme. Similar to TP14, we found a general increase in NWCB days during the period 1984–99. Moreover, we identified anomalous years such as 1998 and 1994 that had higher and lower counts of NWCB days respectively, consistent with TP14. We applied a least squares regression and found an annual increase of 0.87 cloudband days per year from 1984 to 2014, which was statistically significant at p < 0.01 using Monte Carlo simulations. Our results not only confirm the trend detected by TP14, but also suggest that the positive trend continued after 1999. The figure also shows the number of cloudband days per year for each season. We observe a significant positive trend in spring of 0.23 (p < 0.05) cloudband days per year, while the positive summer, autumn, and winter trends (0.11, 0.20, and 0.14, cloudband days per year, respectively) were not statistically significant. We acknowledge that there may be some uncertainty in the magnitude of the trend, especially during austral summer, due to uncertainty in the identification scheme discussed above. However, given that another study (TP14) also found a positive trend, from 1984 to 1999, using a different identification scheme, we are confident in the direction of the trend. We also note that there was no trend in the duration of NWCB events, indicating that the increase in NWCB days is likely due to an increase in the number of events rather than the duration of events. Moreover, we assessed the linear correlation between monthly NWCB frequency and phase of the Indian Ocean dipole, Madden–Julian oscillation, and El Niño–Southern Oscillation but found only weak associations.

Histogram of NWCB event duration for austral summer (blue), autumn (orange), winter (green), and spring (purple) NWCB events.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Histogram of NWCB event duration for austral summer (blue), autumn (orange), winter (green), and spring (purple) NWCB events.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Histogram of NWCB event duration for austral summer (blue), autumn (orange), winter (green), and spring (purple) NWCB events.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Time series of number of NWCB days (top) in total and (middle),(bottom) for each season, 1984–2014. Red line is the least squares regression line. Annual and austral spring trend lines are statistically significant at p < 0.05 using Monte Carlo simulations.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Time series of number of NWCB days (top) in total and (middle),(bottom) for each season, 1984–2014. Red line is the least squares regression line. Annual and austral spring trend lines are statistically significant at p < 0.05 using Monte Carlo simulations.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Time series of number of NWCB days (top) in total and (middle),(bottom) for each season, 1984–2014. Red line is the least squares regression line. Annual and austral spring trend lines are statistically significant at p < 0.05 using Monte Carlo simulations.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Figure 4 illustrates the mean location and intensity of cloud during NWCB days and suggests high seasonal variability in the cloudband location. Winter NWCBs tend to be narrow with high cloud amount and less spatial variability, whereas in summer we observe a broader cloudband region with smaller magnitudes of cloud amount.

Composite of cloud amount during austral (a) summer, (b) autumn, (c) winter, and (d) spring NWCB days. Cloud amount is the percentage of cloudy pixels in a grid space. The table shows the magnitude of the slope of mean maximum cloud amount in units of degree eastward per degree southward.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Composite of cloud amount during austral (a) summer, (b) autumn, (c) winter, and (d) spring NWCB days. Cloud amount is the percentage of cloudy pixels in a grid space. The table shows the magnitude of the slope of mean maximum cloud amount in units of degree eastward per degree southward.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Composite of cloud amount during austral (a) summer, (b) autumn, (c) winter, and (d) spring NWCB days. Cloud amount is the percentage of cloudy pixels in a grid space. The table shows the magnitude of the slope of mean maximum cloud amount in units of degree eastward per degree southward.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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4. Synoptic drivers of NWCBs

Where previous studies have largely focused on the cool season, we examine all calendar months. Case studies have shown summer monsoon depressions propagating from northwest Australia to southeast Australia, interacting with extratropical systems to form large-scale cloudbands and bringing heavy precipitation across inland Australia (Zhao and Mills 1991; Kong and Zhao 2010). Furthermore, heat lows that form over continental Australia in summer can trigger large cloudbands and inland precipitation (W97). Previous studies on NWCBs neglected summer cloudbands and some argued that summer cloudbands did not have a baroclinic zone as in cool-season cloudbands and therefore could not be considered NWCBs (TB84, W97, TP14). There has been considerably more research on the mechanisms of winter cloudbands. In their early study, TB84 found that NWCBs occurred from autumn to spring when warm, moist tropical air moving along the western side of an anticyclone over east and north Australia is lifted over the colder, drier air in a midlatitude trough. The intrusion of midlatitude air into the tropics creates a strong baroclinic zone along the leading edge of the trough, thus enhancing uplift. Similarly, K90 suggested that an upper-level trough west of Australia moves equatorward during autumn and spring, bringing more NWCBs. Simmonds and Rocha (1991) and Frederiksen and Frederiksen (1996) conducted early modeling studies on NWCBs and found a relationship between the SSTs to the north and west of the continent and NWCB-like structures. This paper will quantify some of the key synoptic conditions associated with summer and winter cloudbands to determine which factors are the most important. We hypothesize that the physical mechanisms will differ between seasons.

a. Winter NWCBs

Figure 5 shows the daily anomalies of geopotential at 750 and 250 hPa during winter (JJA) NWCB days. There is a trough over the NWCB region during winter at both levels, while an anticyclone over northeastern Australia, noted by TB84, is apparent at 250 hPa. Another anticyclone is located southwest of Western Australia (WA) at all levels (see also Fig. 6a). A Rossby wave pattern, most prominent at the upper level, is present across the Southern Hemisphere.

Composite of geopotential height anomalies (m) during winter (JJA) cloudband days at (a) 750 and (b) 250 hPa. Contour intervals are 60 m in (a) and 150 m in (b). Dashed line indicates statistical significance at p < 0.05 using the Student’s t test.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Composite of geopotential height anomalies (m) during winter (JJA) cloudband days at (a) 750 and (b) 250 hPa. Contour intervals are 60 m in (a) and 150 m in (b). Dashed line indicates statistical significance at p < 0.05 using the Student’s t test.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Composite of geopotential height anomalies (m) during winter (JJA) cloudband days at (a) 750 and (b) 250 hPa. Contour intervals are 60 m in (a) and 150 m in (b). Dashed line indicates statistical significance at p < 0.05 using the Student’s t test.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Composite of daily (a) MSLP, (b) vertical velocity at 750 hPa, (c) Eady growth rate at 850 hPa, (d) TCWV, and (e) SST anomalies during winter cloudband days. Dashed line indicates statistical significance at p < 0.05 using the Student’s t test.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Composite of daily (a) MSLP, (b) vertical velocity at 750 hPa, (c) Eady growth rate at 850 hPa, (d) TCWV, and (e) SST anomalies during winter cloudband days. Dashed line indicates statistical significance at p < 0.05 using the Student’s t test.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Composite of daily (a) MSLP, (b) vertical velocity at 750 hPa, (c) Eady growth rate at 850 hPa, (d) TCWV, and (e) SST anomalies during winter cloudband days. Dashed line indicates statistical significance at p < 0.05 using the Student’s t test.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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There is a wide band of anomalous ascending air situated northwest–southeast across Australia flanked by anomalous descent at 750 hPa during NWCB days (Fig. 6b). The band of rising air extends north almost to the equator, while in the south it begins to decay over Victoria (VIC). Moreover, a large-scale wave pattern can be seen propagating across the Indian Ocean. This is consistent with the idea that NWCBs may be associated with a refracted Rossby wave (Fröhlich et al. 2013).

Figure 6c shows the location of baroclinic zones during winter cloudband days. Positive EGR anomalies extend into the tropics and reach their maxima off the northwest coast of Australia. Warmer SSTs are usually associated with enhanced convection in the tropics due to increased latent heat flux, but the typical origin area of winter NWCBs lies between a region of positive and negative SST anomalies (Fig. 6e). Frederiksen and Balgovind (1994) used a global climate model to show that a strong SST gradient between the Indonesian Archipelago and the central Indian Ocean leads to increases in the frequency, extent, and intensity of NWCBs. Physically, a stronger SST gradient would increase the atmospheric thickness gradient, which in turn leads to an increase in baroclinic instability. This can be seen in Fig. 6c where the positive Eady growth rate anomaly over northwest Australia approximately coincides with the zero-anomaly SST contour. This might suggest that the SST gradient is more important than the SST magnitude for cloudband development. Figures 6c and 6e provide quantitative evidence for the hypothesis posed by W97 that an SST anomaly gradient in the eastern Indian Ocean is reflected in the atmosphere by a baroclinic gradient downstream, and that this baroclinic zone is necessary for cloudband formation.

Ummenhofer et al. (2009) studied May–September SST anomalies for dry and wet years for Australia. They found that during wet years there was a warm SST anomaly in the northeast portion of the south Indian Ocean and a cool anomaly in the southeast Indian Ocean similar to what we observe in Fig. 6e. In addition, they showed that the precipitation anomalies during wet and dry years have a northwest to southeast orientation with a similar signal to precipitation anomalies due to NWCBs. Furthermore, Nicholls (1989) found a positive correlation between SSTs off the northwest coast of Australia and the first principal component of Australian winter precipitation (an NWCB-like band stretching from northwest to southeast Australia), and a negative correlation between SSTs off the southwest coast of Australia and the same principal component. From this result, it follows that SST anomalies may drive winter cloudbands, and the frequency of winter NWCBs may affect whether a winter is wet or dry.

Unsurprisingly, Fig. 6d shows a large band of positive TCWV over the typical NWCB region during winter cloudband days indicating the air is more moist than usual during a cloudband day. The maximum TCWV anomaly occurs over the northwest coast of Australia and dissipates over the continent. Furthermore, regions of drier than usual air flank the cloudband. This flanking pattern was also observed in the precipitation deficits in southwest and northeast Australia during cloudband days.

b. Summer NWCBs

For the summer (DJF) composites, Figs. 7a and 7b show a low-level trough over the typical cloudband region and an upper-level positive geopotential height anomaly. This suggests that low-level convergence is associated with divergence aloft. A positive geopotential height anomaly persists southwest of the Australian continent. This feature was identified in previous studies on NWCBs including those that ignore summer cloudbands (Downey et al. 1981; TP14).

Composite of daily (a) geopotential height at 750 hPa, (b) geopotential height at 250 hPa, (c) relative vorticity at 1000 hPa, (d) vertical velocity at 750 hPa, (e) Eady growth rate at 850 hPa, and (f) TCWV anomalies during summer cloudband days. Dashed line indicates statistical significance at p < 0.05 using the Student’s t test.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Composite of daily (a) geopotential height at 750 hPa, (b) geopotential height at 250 hPa, (c) relative vorticity at 1000 hPa, (d) vertical velocity at 750 hPa, (e) Eady growth rate at 850 hPa, and (f) TCWV anomalies during summer cloudband days. Dashed line indicates statistical significance at p < 0.05 using the Student’s t test.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Composite of daily (a) geopotential height at 750 hPa, (b) geopotential height at 250 hPa, (c) relative vorticity at 1000 hPa, (d) vertical velocity at 750 hPa, (e) Eady growth rate at 850 hPa, and (f) TCWV anomalies during summer cloudband days. Dashed line indicates statistical significance at p < 0.05 using the Student’s t test.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Figure 7c displays an anomalous anticyclone over north and east Australia and a trough extending from the tropics to the midlatitudes during summer cloudbands. Moreover, the anticyclone over the ocean southwest of Australia would contribute to transporting cold air toward the continent. When this cold air interacts with the warmer tropical air, it forces the moist tropical air upward and enhances convection. Figure 7e shows an enhanced baroclinic zone over southern and eastern Australia during summer cloudband days. Once the cloudband penetrates into southeastern Australia, it reaches this zone, which enhances instability and promotes convection, thus helping to maintain the continental-scale cloudband.

Figure 7d shows vertical motion at 750 hPa during summer cloudband days. The anticyclone southwest of Australia is associated with sinking air at upper and lower levels. Air is rising over the typical cloudband location; however, cause and effect between the vertical motion of the air and the presence of a cloudband cannot be determined from Fig. 7d. Rising air can cause a cloudband or, conversely, cloudbands might trigger convection and cause air to rise. At the 750-hPa level there are two regions of enhanced convection. The first is over the ocean immediately northwest of Australia, which is expected given the typical origin of a NWCB. The second is over the state of South Australia (SA). This second region of enhanced convection coincides with the beginning of the baroclinic zone over SA as shown in Fig. 7e. This supports the hypothesis that the warm season cloudbands may begin as tropical systems but their shift toward the extratropics and subsequent interaction with the baroclinic zone over southern and eastern Australia reinvigorates the cloudband, allowing it to maintain its structure and extend across the continent. Additionally, Fig. 7d indicates compensating downward circulations flanking the main belt of upward motion occurring in a band across Australia. Air is rising in the southeast and sinking in the northeast, while the reverse is happening in the west where air is rising in the north and sinking in the south.

Figure 7c shows a negative relative vorticity anomaly off the coast of northwestern Australia. This indicates that there is typically surface level cyclonic flow near the origin of summer cloudbands. Furthermore, there is anomalous cyclonic circulation over the center of Australia. The relative vorticity anomaly peaks over the northern region of the rising vertical velocity anomaly over central Australia in Fig. 7d. The cyclonic anomaly advects warmer air toward the Great Australian Bight. As the low latitude air travels over the cooler midlatitude air, it rises, and some of the convection may occur downstream.

Summer MSLP anomalies also show a low pressure system in the NW, a weak low over central Australia, and high pressure flanking the cloudband region. Similarly to winter, there is a region of anomalous TCWV over the typical NWCB region during summer cloudband days (Fig. 7f). However, unlike winter, the anomalies in the surrounding regions such as southwest WA are weak. Moreover, the maximum positive TCWV anomaly during summer is only over land while the winter maximum crosses the coast.

5. Proposed conceptual seasonal models for NWCBs

a. Winter NWCB conceptual model

Guided by our results and the findings in the literature, we propose a schematic for the regional synoptic conditions associated with winter NWCB days (Fig. 8). The meridional SST gradient between the Indonesian Archipelago and the east Indian Ocean appears to be a key driver of winter cloudbands. This gradient at the surface leads to an increase in the gradient aloft and enhanced wind shear. These factors lead to an increase in baroclinic instability, which drives convection northwest of Australia. There is a quasi-permanent anticyclone to the southwest of Australia associated with the descending branch of the Hadley cell. This high pressure anomaly is enhanced during cloudband days, and advects cool midlatitude air over the warm continent maintaining the meridional temperature gradient and baroclinic instability over land.

Proposed conceptual model for winter (JJA) NWCBs.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Proposed conceptual model for winter (JJA) NWCBs.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Proposed conceptual model for winter (JJA) NWCBs.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Earlier modeling studies established a causal relationship between winter precipitation and Indian Ocean SSTs (Simmonds 1990; Simmonds and Rocha 1991). Simmonds and Rocha (1991) imposed positive SST anomalies to the northwest of Australia and negative anomalies to the west and southwest, creating a meridional sea surface temperature gradient in the east Indian Ocean using a GCM. This forcing led to significant winter precipitation increases over more than half the Australian continent. It was then shown that the SST gradients in the east Indian Ocean were important for Australian precipitation because of the anomalous circulation patterns they set up, such as low pressure anomalies, rather than because of the direct thermal impact (Simmonds et al. 1992).

b. Summer NWCB conceptual model

Our analyses indicate both similarities and differences in the typical synoptic patterns during summer and winter cloudband days. A high pressure anomaly to the southwest of Australia advecting cool midlatitude air to the tropics is common in both seasons, as is the moist trough extending from northwest to southeast Australia. A key difference appears to be in the initiation mechanisms. While winter cloudband days are associated with a long, narrow region of baroclinic instability, summer cloudbands seem to be driven by low pressure anomalies over northwest and central Australia.

Kong and Zhao (2010) proposed a conceptual model for heavy precipitation due to monsoon depressions interacting with midlatitude systems following their case study of an event from 1 to 4 January 2005, which created a northwest–southeast rainband and heavy precipitation across the continent. We also identified a NWCB during this period. Kong and Zhao’s (2010) conceptual model has similarities with patterns shown by the summer cloudband composites, notably including the two low pressure anomalies along the cloudband region.

Based on Kong and Zhao’s (2010) model and the results above for typical synoptic patterns during summer cloudbands days, we propose a conceptual model for summer NWCBs (Figs. 9a,b). A cyclonic disturbance off the northwest coast of Australia, such as a monsoon depression, tropical cyclone, or tropical low, leads to convection in the northwest. Using observations and coupled climate modeling, Shi et al. (2008) showed the positive trend in summer precipitation in northwest Australia was associated with anomalous low mean sea level pressure off the northwest coast. The cyclone in the northwest, along with an anticyclonic activity in the northeast, advects warm, moist air over the northwest Australian continent. An anticyclone southwest of Australia advects cold, midlatitude air inland. The moist tropical air rises over the cooler midlatitude air. In his 5-yr study of cloudbands, K90 found that blocking to the southwest of Australia was present in 70% of all (summer and winter) cloudband occurrences, and argued that blocking reduced zonal flow and increased meridional movement. Tropical–midlatitude interactions can bring heavy rain to NW Australia (Clark et al. 2018). Figure 7c shows a strong cyclonic anomaly over SA, which is accompanied by rising air (Fig. 7d) and the beginning of a zone of baroclinic instability (Fig. 7e). Hence, the conceptual model includes an extratropical cyclone over SA that triggers further convection and allows the cloudband to continue to propagate into the enhanced baroclinic zone. Without this extratropical influence the cloudband would likely decay over inland Australia. The baroclinic zone over southeast Australia enables further convection and development, and the cloudband can bring heavy precipitation as far south as the southeast Australian coast. Furthermore, an upper-level high, which can be seen in Fig. 7b, promotes divergence aloft, supporting the convection across the continent. This also leads to a circulation in the meridional–vertical directions as shown by Fig. 7d. We found a very small meridional gradient in SSTs northwest of Australia during summer NWCB days; however, there does not appear to be an atmosphere–ocean coupling in the form of enhanced baroclinic instability in the northwest as in winter, and the cyclonic anomaly in northwest Australia seems to be a stronger driver of NWCBs.

Proposed conceptual model for summer (DJF) NWCBs: (a) surface to 750 hPa and (b) upper level (250 hPa).

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Proposed conceptual model for summer (DJF) NWCBs: (a) surface to 750 hPa and (b) upper level (250 hPa).

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Proposed conceptual model for summer (DJF) NWCBs: (a) surface to 750 hPa and (b) upper level (250 hPa).

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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6. Precipitation associated with NWCBs

To assess the impact of NWCBs on Australian precipitation, seasonal composites of daily precipitation anomalies on cloudband days from 1984 to 2014 were constructed. They show positive anomalies of up to 3 mm in northwest Australia and mostly positive anomalies across central Australia (Fig. 10). There is a tendency for reduced daily precipitation in the east of Australia during such days. The precipitation anomaly pattern shifts with season. For example, in summer there is a distinct east–west divide between positive and negative anomalies, whereas in winter positive precipitation anomalies penetrate into southeast Australia. This may be due to an increased frequency of cold fronts in winter, which can interact with the cloudband. Moreover, Fig. 4 indicates the orientation and shape of cloudbands vary with season, and summer NWCBs tend to be more meridional than winter NWCBs, and hence the rainfall anomalies follow a similar pattern. Additionally, in all seasons except summer, southwest WA experiences reduced precipitation during cloudband days. As shown earlier, cloudbands are usually flanked by positive geopotential height anomalies aloft, which are associated with sinking air over southwest WA. Furthermore, in winter, when the negative precipitation anomaly in southwest WA is largest, the composite plots show there is a tendency for a negative vertical velocity anomaly (Fig. 6b) and a negative Eady growth rate anomaly (Fig. 6c) over the region in question. This indicates that NWCBs are associated with baroclinically stable air over southwest Australia in autumn, winter, and spring, which may explain the observed negative precipitation anomalies.

Composite of daily precipitation anomalies from the calendar day mean (mm) during NWCB days for (a) summer, (b) autumn, (c) winter, and (d) spring. Dashed contour indicates statistical significance at p < 0.05 using the Student’s t test.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Composite of daily precipitation anomalies from the calendar day mean (mm) during NWCB days for (a) summer, (b) autumn, (c) winter, and (d) spring. Dashed contour indicates statistical significance at p < 0.05 using the Student’s t test.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Composite of daily precipitation anomalies from the calendar day mean (mm) during NWCB days for (a) summer, (b) autumn, (c) winter, and (d) spring. Dashed contour indicates statistical significance at p < 0.05 using the Student’s t test.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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NWCBs have been linked to precipitation extremes in SA (Evans et al. 2009). W97 identified cases of cloudbands causing heavy precipitation between April and October over east and west Australia. He concluded that cloudbands are responsible for four or five significant rain events per season at Wagga Wagga, an inland station in eastern Australia. Here, we extend this work by using the high-quality AWAP gridded dataset covering the entire continent.

We have calculated the conditional probabilities between cloudband days and extreme precipitation events for each 0.25° × 0.25° grid box in the AWAP dataset. For this analysis, an extreme precipitation day was identified as follows. The 95th percentile of precipitation amount during rain days (with rain days defined as daily rainfall >0.2 mm) for each calendar day was calculated for each grid box over the period of the cloudband dataset, 1984–2014. For each calendar day, over the 31 years of data, only 1 or 2 days would exceed the 95th-percentile threshold. This means the data are noisy. Given this, we followed the method proposed by King et al. (2013) to obtain a better-constrained estimate of the 95th percentile on a calendar day by applying a 21-day binomially weighted moving average to smooth the time series, then the new weighted average for each calendar day was used as the extreme precipitation threshold.

Next, we calculated the probability of extreme precipitation given a cloudband day by separating the NWCB days from the non-NWCB days for each season, then counting the number of times the 95th percentile of rainfall was exceeded at each grid space and dividing by the number of NWCB days in each season. We used bootstrapping to determine the statistical significance of the conditional probability of extreme precipitation during a cloudband day.

Figure 11 shows the pattern of enhanced or reduced probability of extreme precipitation given a cloudband day relative to all days for each season. The color bar is the probability ratio or P(A|B)/P(A), where A is an extreme precipitation event and B is a cloudband day. In northwest Australia, extreme precipitation is 3 times more likely on a cloudband day than on other days, whereas in most of eastern Australia, and particularly Queensland, there is a reduced chance of extreme precipitation during cloudband days. This general pattern is not surprising when considering the typical synoptic conditions during cloudband days. There tends to be a trough from northwest to southeast Australia during cloudband days and anticyclones over the southern Indian Ocean and over northeast Australia. This implies that while convection is probable over northwest, central, and south Australia, subsidence due to the anticyclone would likely inhibit convection in the east. A similar result was found by O’Brien and Reeder (2017), who showed that, in connection with southeastern Australian heatwaves, upper-level anticyclones were often associated with precipitation in the northwest, and they argued that a refracted Rossby wave created a ridge–trough–ridge pattern over Australia.

Probability ratio (Pr) of extreme precipitation given a cloudband day relative to the probability of extreme precipitation on any day for austral (a) summer, (b) autumn, (c) winter, and (d) spring. Colored regions are statistically significant at p < 0.05 using bootstrapping; white regions indicate no significance.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Probability ratio (Pr) of extreme precipitation given a cloudband day relative to the probability of extreme precipitation on any day for austral (a) summer, (b) autumn, (c) winter, and (d) spring. Colored regions are statistically significant at p < 0.05 using bootstrapping; white regions indicate no significance.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Probability ratio (Pr) of extreme precipitation given a cloudband day relative to the probability of extreme precipitation on any day for austral (a) summer, (b) autumn, (c) winter, and (d) spring. Colored regions are statistically significant at p < 0.05 using bootstrapping; white regions indicate no significance.

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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While there is a general pattern of enhanced precipitation over west and central Australia and reduced precipitation in the east in all seasons, there are distinct differences in the magnitude of the probability ratio and the spatial pattern of enhanced or reduced precipitation. In summer, the magnitude of the probability ratio during a cloudband day is much lower in northwestern Australia than all other seasons. Summer is the wet season for northern Australia, and systems other than NWCBs, such as tropical cyclones and the monsoon depression, can cause extreme precipitation in the northwest of the country. Therefore, extreme precipitation on non-cloudband days is more common in summer, which explains why the probability ratio during summer cloudband days is less than in other seasons. Conversely, winter is the dry season for northern Australia and is outside both the tropical cyclone and monsoon seasons. Therefore, NWCBs become one of the main drivers of winter precipitation in northwestern Australia. There is a clear reduction in the probability of extreme precipitation along the eastern seaboard during winter cloudband days. Pepler et al. (2014) found a similar result when analyzing above-median wet winter years during the negative phase of the Indian Ocean dipole (with no La Niña). While most of western, central, and southern Australia were wetter than usual, the eastern seaboard had fewer years with above median precipitation.

Autumn and spring can be seen as a hybrid of the summer and winter patterns. Autumn extreme precipitation probability is enhanced over western, central, and southern Australia, and the region of reduced probability is confined to the northeast. The spring result appears to be noisy in the east and therefore conclusions about extreme spring precipitation in eastern Australia due to cloudbands should be made with caution. Last, we acknowledge that artifacts may be apparent over central Australia in Figs. 10–12 over 20°–30°S, 120°–130°E due to the sparser station network in that region.

Daily winter precipitation anomalies during (left) NWCB and front days, (center) NWCB only, and (right) front only days when there is a front over (top) VIC (140°–155°E), (middle) SA (125°–140°E), or (bottom) WA (110°–125°E).

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Daily winter precipitation anomalies during (left) NWCB and front days, (center) NWCB only, and (right) front only days when there is a front over (top) VIC (140°–155°E), (middle) SA (125°–140°E), or (bottom) WA (110°–125°E).

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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Daily winter precipitation anomalies during (left) NWCB and front days, (center) NWCB only, and (right) front only days when there is a front over (top) VIC (140°–155°E), (middle) SA (125°–140°E), or (bottom) WA (110°–125°E).

Citation: Journal of Climate 32, 20; 10.1175/JCLI-D-19-0031.1

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7. NWCB–front interactions

Following the investigations of Wright (1988) and others, we hypothesize that cloudbands and cold fronts can interact, and the nature of the interaction, including how precipitation is affected, will depend on where such interaction occurs. For example, a cold front over WA, which interacts with a cloudband, may promote precipitation to the west while a cold front over VIC may enhance precipitation in the southeast. To test this hypothesis, Australia was divided into three longitudinal segments, which approximately correspond to the three states: WA (110°–125°E), SA (125°–140°E), and VIC (140°–155°E). We used the frontal identification results of Rudeva and Simmonds (2015), based on ERA-I wind data and the identification algorithm of Simmonds et al. (2012). We identified front days and binned them based on the location of the front (WA, SA, or VIC), and whether there was a front and cloudband simultaneously, a front only, or a cloudband only over the region. A latitude range of 30°–45°S was used as the domain for selecting fronts, and there had to be greater than or equal to three frontal points in the domain during the 6-h time step. We show the results for winter when fronts occur more frequently over the Australian continent (Simmonds et al. 2012).

When fronts over VIC interact with cloudbands, positive precipitation anomalies appear to shift away from the southwest of Australia toward the southeast. The maximum precipitation anomalies in the northwest shift slightly farther north. Moreover, the magnitude of precipitation anomalies in the southeast is smaller during cloudband and front days compared to front-only days. Wright (1988) found that cloudbands interacting with fronts contributed to about 50% of northern VIC’s precipitation, but the natural barrier of the Great Dividing Range inhibited interacting fronts from having as large an influence in south and southeastern VIC. In contrast, with the benefit of a longer satellite record, high-quality gridded precipitation data and an automatic identification method for fronts, we show in Fig. 12 that precipitation anomalies can penetrate into southern VIC and southeastern VIC, yielding average or above-average precipitation when fronts over VIC interact with tropical systems.

When fronts over SA interact with cloudbands, precipitation in the southwest is enhanced while southeast Australian precipitation is reduced compared to cloudband-only days. A similar shift in precipitation pattern occurs when fronts over WA interact with cloudbands. Furthermore, the magnitude of northwest Australia precipitation anomalies seems to be reduced when there are fronts over SA and WA compared to on cloudband only days. This pattern is likely due to there being more front-only days than cloudband and front or cloudband-only days. Moreover, strong fronts that penetrate into the tropics over northwest Australia could be misidentified as a cloudband. As a result, the strength of the cloudband and front day signal would increase while the front-only signal would decrease.

The method of separating fronts into three regions did not exclude days when fronts may have also been present in one of the other regions. It should be noted that, for example, when a front was identified over WA there may also be fronts over SA and VIC. Therefore, the precipitation anomalies over VIC during WA front and cloudband days may be due to another front or cyclone rather than the interaction in the west causing anomalous precipitation in eastern Australia. Additionally, vertical velocity and TCWV anomalies are larger along the cloudband region during NWCB and front days. This leads us to suggest that the interaction between fronts and NWCBs manifests as enhanced uplift and atmospheric moisture content over the typical NWCB region, thus resulting in enhanced precipitation.

8. Summary and conclusions

In this study, an objective identification scheme for Australian NWCBs was developed and applied to 31 years of cloud amount data from geostationary satellites. We built on NWCB definitions used in earlier studies but added extra criteria that cloudbands must exist for at least 12 h, cloud amount must exceed 80% to be considered a cloudy grid space, and NWCBs may form in the higher latitudes of the tropics. We also quantified the slope and continuity parameter thresholds. We found that the number of NWCB days is maximum during summer and autumn, and minimum during late winter to early spring. A key finding from this study is that the number of NWCB days increased by an average of 0.87 days yr⁻¹ from 1984 to 2014. This finding may have implications for increased precipitation over northwest Australia and reduced precipitation over parts of eastern Australia (Taschetto and England 2009).

We also analyzed the typical synoptic conditions associated with NWCBs and found both similarities and differences in the physical drivers during winter and summer. In both seasons, NWCBs are associated with an alternating ridge–trough–ridge pattern oriented northwest–southeast across the Australian continent and an enhanced high pressure anomaly southwest of Australia. However, winter cloudbands seem to be driven by baroclinic instability associated with a meridional SST gradient in the east Indian Ocean, while summer cloudbands are associated with tropical low pressure anomalies causing convection in the northwest and extratropical instability allowing the cloudband to propagate to the midlatitudes.

We showed that NWCBs are linked with considerable precipitation anomalies across Australia, especially in winter. While NWCBs enhance rainfall across northwest, central, and parts of southern Australia, they can inhibit precipitation in east and southwest Australia. Similarly, NWCBs can enhance or reduce the probability of extreme rainfall across Australia, and extreme rainfall in northwest Australia in winter may be 12 times more likely during a NWCB day than any other day. The mean location of cloud during a NWCB day (Fig. 4) may help explain the observed relationship between NWCBs and extreme precipitation. When the cloud amount is concentrated over a smaller region, one would expect more intense precipitation. Last, we found that the interaction between NWCBs and cold fronts can enhance precipitation across southern Australia, which is likely due to increased instability and moisture availability during an interaction.

Future work planned on this topic includes analyzing how well cloudbands are simulated in global climate models to determine whether the increasing trend observed in this study may be related to anthropogenic climate change. This is especially relevant to understanding rainfall trends in the future climate.