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    Map of northwestern Victoria showing the network of high-quality rainfall stations used in the analysis. The larger of the two dotted boxes drawn on the map of Australia in the top of the diagram defines the region within which the analysis of synoptic systems was confined. The smaller box shows the region of southeastern Australia included in the lower part of the diagram.

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    Mean monthly rainfall (mm) for six of the eight stations in northwestern Victoria that have been selected for their “high quality” status (Lavery et al. 1997). Note that Sea Lake and Narraport have been omitted for the sake of clarity.

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    Monthly means (1970–2002) of MSLP (hPa) for the months of (a) April, (b) May, (c) June, (d) July, (e) August, (f) September, and (g) October (NNR data).

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    Mean geopotential thickness (m) of the 1000–500-hPa atmospheric layer (1970–2002) for (a) the austral summer (December–February) and (b) the austral winter (June–August) (NNR data).

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    Schematic representation of the broad frontal types encountered in the analysis: (a) a simple or common cold front, (b) a complex cold front, and (c) a wave on a cold front.

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    The mean percentages of growing-season rainfall attributed to particular synoptic systems for the period of 1970–2002 according to the classification scheme in section 4.

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    The monthly distribution and variability of cutoff rain and frontal rain averaged over the eight station network for the months of April–October: (a) the monthly mean percentage of rain (y axis) contributed by cutoff lows and frontal systems, and (b) the std dev (mm, y axis) for each month for cutoff rain and frontal rain.

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    Time series of growing-season rainfall, rain resulting from cutoff lows, and rain resulting from frontal systems for the eight-station network over the period of 1970–2002.

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    Percentage of rain attributed to cutoff lows and frontal systems for daily rainfall in intervals of 5 mm averaged over the eight-station network.

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    Number of days on which cutoff lows were identified in the analysis region (cutoff days) and the average rain attributed to cutoff lows per station per cutoff day for each growing season.

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    Mean, std dev, and median for the number of days on which cutoff lows were identified in the analysis region (cutoff days) in each month and for the total growing season (April–October).

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    Correlation coefficients of the month-by-month relationship between the BI and the number of cutoff days at (a) 140° and (b) 150°E for the growing-season months of April–October.

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    Hovmöller representation of the correlation coefficient between the monthly mean of the number of cutoff lows in the analysis region and the monthly mean BI at the meridian. Values of the correlation index greater than 0.35 are significant at the 0.05 level and are shown by the shaded area.

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The Synoptic Decomposition of Cool-Season Rainfall in the Southeastern Australian Cropping Region

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  • 1 CSIRO Marine and Atmospheric Research, Hobart, Tasmania, Australia
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Abstract

Daily rainfall during the April–October growing season in a major cropping region of southeastern Australia has been related to particular types of synoptic weather systems over a period of 33 yr. The analysis reveals that cutoff lows were responsible for at least 50% of all growing-season rainfall and accounted for 80% of daily rainfall events exceeding 25 mm per station. The proportion of rainfall contributed by cutoff lows varies throughout the growing season. It is highest in austral autumn and spring (55% and 57%, respectively) and falls to a minimum in July (42%). By way of contrast, the total contribution of all types of frontal systems to growing-season rainfall is about 32%, although the monthly value reaches a maximum of 41% in July when mean cutoff rainfall reaches a minimum. Rainfall associated with fronts is strongly concentrated in the lower range of daily falls (less than 10 mm per station). Frontal rainfall is found to be more consistent from year to year than is cutoff rainfall. The number of cutoff lows per season is highly variable, and there is a significant correlation between the number of cutoff days and atmospheric blocking in the region south of Australia in each month of the growing season. The mean amount of rainfall per cutoff day is also variable and has declined by approximately 0.8 mm over the analysis period. An understanding of the mechanisms controlling year-to-year variability of cutoff rainfall is therefore an important step in improving seasonal forecasts for agriculture in southeastern Australia.

Corresponding author address: Dr. Michael Pook, CSIRO Marine and Atmospheric Research, GPO Box 1538, Hobart, Tasmania 7001, Australia. Email: mike.pook@csiro.au

Abstract

Daily rainfall during the April–October growing season in a major cropping region of southeastern Australia has been related to particular types of synoptic weather systems over a period of 33 yr. The analysis reveals that cutoff lows were responsible for at least 50% of all growing-season rainfall and accounted for 80% of daily rainfall events exceeding 25 mm per station. The proportion of rainfall contributed by cutoff lows varies throughout the growing season. It is highest in austral autumn and spring (55% and 57%, respectively) and falls to a minimum in July (42%). By way of contrast, the total contribution of all types of frontal systems to growing-season rainfall is about 32%, although the monthly value reaches a maximum of 41% in July when mean cutoff rainfall reaches a minimum. Rainfall associated with fronts is strongly concentrated in the lower range of daily falls (less than 10 mm per station). Frontal rainfall is found to be more consistent from year to year than is cutoff rainfall. The number of cutoff lows per season is highly variable, and there is a significant correlation between the number of cutoff days and atmospheric blocking in the region south of Australia in each month of the growing season. The mean amount of rainfall per cutoff day is also variable and has declined by approximately 0.8 mm over the analysis period. An understanding of the mechanisms controlling year-to-year variability of cutoff rainfall is therefore an important step in improving seasonal forecasts for agriculture in southeastern Australia.

Corresponding author address: Dr. Michael Pook, CSIRO Marine and Atmospheric Research, GPO Box 1538, Hobart, Tasmania 7001, Australia. Email: mike.pook@csiro.au

1. Introduction

There is a particular need for accurate seasonal climate predictions in Australia. With its predominantly dry and highly variable climate, Australia poses substantial challenges for agricultural producers. This is especially the case for grain farmers, a sector for which input costs are high and the risk of crop failure is significant. Grain-growing areas are found in many parts of the country across a range of climatic regimes. In southern Australia (poleward of about 30°S) there is a major dependence on cool-season rainfall. In this paper we have concentrated our attention on climate processes in one grain-growing area of southeastern Australia, the northwest portion of the state of Victoria (see Fig. 1).

The growing season for grains in southeastern Australia is generally considered to extend from about April to the end of October, the period during which the region receives the majority of its annual rainfall, evaporation is lowest, and the number of rain days per month is highest. However, growing-season rainfall over the majority of the area is relatively low (typically less than 300 mm) and infrequent (less than 35% of days receive at least 0.2 mm of rain), and crop success depends critically on soil moisture at time of planting, evapotranspiration, and the amount and timing of rainfall during the season.

Seasonal rainfall represents the integrated contribution of a finite number of synoptic weather systems, and we propose that an improved understanding of the rain-producing mechanisms associated with these systems is an important part of developing an improved capability for seasonal climate prediction. Consequently, we have employed the methods of synoptic climatology to identify and categorize the systems responsible for day-to-day rainfall events and use the results of this analysis to deconstruct the key climate influences in this region. The purpose of this approach is to identify the mechanisms that control seasonal climate variability.

Most studies of Australian climate have investigated monthly, seasonal, or annual rainfall and have attempted to associate atmospheric and, in some cases, oceanic circulation characteristics over these time scales with rainfall variability and longer-term trends (e.g., Gentilli 1971; Pittock 1973; Streten 1981; Whetton 1988; Nicholls 1989; Allan and Haylock 1993; Suppiah 2004). Daily rainfall has been employed in investigations of trends in extreme rainfall events (Haylock and Nicholls 2000). Until recently, relatively few studies have been carried out in Australia to document the synoptic systems associated with daily rainfall over an extended period. A notable exception is the study of Wright (1989), extending from 1971 to 1982, which employed high-frequency rainfall from a network of six pluviographs distributed around the state of Victoria to classify rainfall events during the June–September period into one of five synoptic types. Northwestern Victoria was represented by a single pluviograph at the Mildura meteorological station (see Fig. 1), and it was assumed that the “synoptic processes affecting Mildura would be common to most of this area” (Wright 1989, p. 220).

Godfred-Spenning and Gibson (1995) performed an analysis of the synoptic weather systems that produced rainfall over the hydroelectric catchments of Tasmania, Australia’s southernmost state, during an approximately 30-yr period. The intensities of frontal systems and associated extratropical depressions embedded in the westerly airstreams were considered to determine the amount of precipitation deposited in individual weather situations and the rainfall rates at various stages of the event.

Qi et al. (1999) produced a climatology for southern Australia of an important class of synoptic system, the cutoff low, and presented a case history of one extreme rainfall event associated with an intense depression of this type. From 14 yr (1983–96) of Australian Bureau of Meteorology analyses, they identified synoptic-scale cyclones that had closed circulations at the surface and the 500-hPa level, and developed statistics of the relative frequency of occurrence of these systems in designated regions. Qi et al. (1999) also identified preferred tracks of cutoff lows and noted the high degree of interannual variability in the occurrence of these systems, but their study did not explore possible relationships with regional rainfall variability.

During the past decade, there has been a renewed interest in associating daily rainfall with patterns of atmospheric fields, particularly over the southwest of Australia where a major project, the Indian Ocean Climate Initiative, is being conducted into significant rainfall decline in that region (Ryan and Hope 2005). These studies have employed statistical techniques to develop downscaling models for application in climate change research using atmospheric general circulation models (e.g., Charles et al. 1999; Timbal 2004).

In this study, we identify the main synoptic systems responsible for daily rainfall during the growing season in northwestern Victoria within the period of 1970–2002 and investigate the seasonal and interannual variability of the dominant synoptic types. The paper is structured in the following manner. The next section provides a brief description of the significant features of the regional climate of northwestern Victoria, followed in section 3 by a discussion of the particular climatic constraints on cereal crop production in the region. The data sources and method used in our analysis are presented in section 4, and descriptions of each of the synoptic types in the analysis scheme are given in section 5. The results of the analysis are presented in section 6. The apparent association of cutoff lows with atmospheric blocking is explored in section 7, and the paper concludes with further discussion and conclusions in section 8.

2. Regional climate of northwestern Victoria

The study region includes the district known as the Mallee and the northern part of the Wimmera District, and forms part of the dry inland plains of Australia. Elevations there are generally below 200 m and for the most part are 100 m or less. This area mainly falls within the “warm (summer drought) grassland” classification of Australian climate according to the objective classification scheme developed by Stern et al. (2000), which is a modification of the steppe subdivision of the original Köppen “dry group” (see Trewartha 1954). Median annual rainfall varies from less than 250 mm in parts of the northern Mallee to nearly 500 mm in the southern Wimmera and 550 mm at Bendigo on the eastern edge (Bureau of Meteorology 1988). A notable feature of the mean monthly rainfall distribution is the abrupt change during autumn from the dry summer to an evenly distributed rainfall regime during the months from May to October. Figure 2 shows the monthly rainfall distribution for a selection of stations in the study region.

Attempts to explain this rainfall distribution by reference to the monthly mean sea level pressure (MSLP) field emphasize the role of the subtropical ridge (STR) of high pressure as it migrates equatorward and then poleward during the growing season. On annual time scales, Pittock (1973) developed a time series of the latitude of the STR from a selection of climate stations at about 150°E and obtained correlations between the annual mean latitude of this statistic and district mean annual rainfall around Australia. However, we demonstrate in this paper that the STR undergoes changes in position, intensity, and orientation from May to October while the mean monthly rainfall over the region of our study remains almost constant (Fig. 2). A clue to the explanation of this rainfall distribution lies in the fact that rain over southern Australia in winter is regularly observed to fall from middle-level clouds such as altostratus and nimbostratus when a well-developed anticyclone is present at the surface (Gentilli 1971), a phenomenon associated with upper-air troughs and cyclones (Foley 1956). Additionally, Qi et al. (1999) report a high incidence of cold-cored “cutoff lows” in the midtroposphere over southern Australia (south of 25°S) during the May–October period. Consequently, we argue that an explanation of the growing-season rainfall distribution must consider the mid- and upper troposphere in addition to the annual cycle of surface pressure.

Figure 3 shows the mean monthly position and intensity of the STR over Australia during the growing season. The STR is closest to the equator in the July–August period and the belt of westerly winds on its southern flank becomes established over southern Australia at this time. This period of well-developed westerly winds has been associated with an increased frequency of cold fronts crossing southern Australia and producing regular precipitation. Nevertheless, this part of the year does not exhibit any notable increase in mean monthly rainfall in the stations of northwestern Victoria shown in Fig. 1, except for Kaniva, in the southwest of the region, which experiences a slow climb to a maximum in August followed by a steady decline in spring.

In the mid- and upper troposphere there are significant changes in atmospheric circulation from summer to winter. One major change occurs with the intensification and equatorward movement of the subtropical jet stream to about 30°S when the STR is in its northernmost position (Radok 1971; Sturman and Tapper 1996; Hurrell et al. 1998). Additionally, the transition from summer to winter is delineated by the development of a “split” in the westerly flow over the Tasman Sea and New Zealand region in association with a general weakening of the westerlies and a maximum frequency of occurrence of atmospheric blocking (Trenberth and Mo 1985; Bals-Elsholz et al. 2001).

The changes in circulation in the upper air are partly explained by the major changes in the land–sea temperature contrast between southeastern Australia and surrounding oceans from summer to winter (Taljaard 1972). The cooling over the continental southeast results in a steady decrease in 1000–500-hPa thicknesses during autumn and the development of a winter thermal minimum in this region. The split in the westerlies forms in response to this cooling of the atmospheric column and the intensification and retrogression of the high-latitude thickness ridge (poleward of 45°S) from the central Pacific Ocean to about 160°E.

Figure 4 illustrates the contrast in mean 1000–500-hPa atmospheric thickness across the Australian region between summer and winter. In summer, there are two significant thermal ridges over the Australian continent, but in winter a trough develops over the southeast corner of the continent while a ridge persists south of about 45°S in these longitudes. This configuration of the thermal wind suggests that systems moving into the region are subject to reduced strength of the mean steering current and that there is a tendency for a horizontal shearing effect to occur. It helps to explain the secondary maximum of cyclonic activity over southeastern Australia and the Tasman Sea during winter (Leighton and Deslandes 1991; Keable et al. 2002). Clearly, the asymmetric cooling of the atmosphere over southern Australia has a significant influence on the mid- and upper-tropospheric circulation, because the climatological thermal trough over the southeast during winter is present when the STR at the surface is at its maximum intensity. Hence, there is a strong case for examining more closely the role of the upper air in synoptic-scale rain events in the study region.

3. Grain growing in northwestern Victoria

Northwestern Victoria is an important grain-growing region of Australia and produces the majority of grain grown in Victoria, the state that contributes about 11% of the total Australian wheat crop (ABARE 2005). Wheat is the dominant cereal grown but barley, oats, and canola are included in the crop mix of many growers, as well as legumes such as lentils, lupins, and chickpeas, which are in rotation. Crops are sown into predominantly solonized brown soils (mallee soils), which are moderately fertile despite low organic matter (McGarity 1987).

Major constraints on wheat growth in this region include the timing of autumn rains for sowing, duration of the winter minima in solar radiation and temperature, timing of the earliest “safe ear” emergence in relation to frost occurrence, and the rapid increase in temperature and evapotranspiration in late spring (Nix 1987). Water supply is the most critical factor affecting wheat yields in this as in other parts of Australia (French and Schultz 1984; Stephens and Lyons 1998a), and water deficiencies are expressed in reduced grain number per ear of grain and reduced grain size (Donald and Puckridge 1987).

Cereal crops are normally sown in May and June, but varying seasonal conditions and farming methods may dictate that sowing begins as early as April or as late as August (Stephens and Lyons 1998b). Some farmers wait for the “autumn break” rains before sowing, but an increasing number employ dry sowing and minimum tillage techniques to preserve soil structure and limit erosion and, hence, plant their crops according to a schedule rather than in response to rain events. Because crops rely almost exclusively on natural watering there are considerable risks in committing to a sowing program early in the season, but the potential gains in yield of sowing when soil temperatures are favorable for germination—survival and plant vigor—are very attractive to farmers (Stephens and Lyons 1998a).

Improved prediction of seasonal and growing-season rain offers the prospect of improving efficiency by influencing the total area sown, controlling production costs such as those associated with seed, fertilizer, and fuel, and minimizing the risk of crop failure. Predictions based on the ENSO state are unreliable in the austral autumn because of the so-called “predictability barrier” (McIntosh et al. 2005), and there is a need for a fresh approach to the problem of predicting conditions for sowing and crop growth at this early stage of the season.

Current operational seasonal climate prediction systems available to farmers in Australia include the Bureau of Meteorology system, based on SST around Australia (information online at http://www.bom.gov.au/climate/ahead/), and the Queensland Department of Primary Industries and Mines system, based on the phase of the Southern Oscillation index (Stone et al. 1996). Each of these prediction systems presents a 3-month seasonal climate outlook for rainfall, with maximum and minimum temperatures expressed only as a probability of exceeding the median.

In this paper we identify the synoptic rain-producing systems that supply the water requirements of the crops grown in the region. We have adopted this physical approach in order to better understand the mechanisms that influence climatic variability during the growing season.

4. Data and method

a. Data sources

Eight rainfall stations were selected to represent northwestern Victoria from a high-quality Australian historical dataset (Lavery et al. 1997). Daily rainfall totals from these stations were supplied by the Bureau of Meteorology for all years of record. The locations are shown in Fig. 1 and the station coordinates and elevations listed in Table 1. Where gaps occur in the record we have employed the patch point dataset supplied by the Queensland Department of Natural Resources and Mines (Jeffrey et al. 2001) to provide a consistent technique to insert estimates of missing data. The patched point dataset uses original Bureau of Meteorology measurements for a particular meteorological station, but interpolated data are inserted to fill any gaps in the observation record.

The synoptic analysis was conducted using the National Centers for Environmental Prediction–National Center for Atmospheric Research climate reanalysis dataset (Reanalysis 1), which will be referred to as the NNR dataset (Kalnay et al. 1996; Kistler et al. 2001). The NNR data consist of four analyses per day (at 6-hourly intervals from 0000 UTC) at a resolution of 2.5° latitude × 2.5° longitude for the standard atmospheric levels from the surface to the lower stratosphere. For the purposes of this study, fields were extracted and displayed for MSLP, the 500-hPa pressure surface, and the (computed) 1000–500-hPa atmospheric thickness. In addition, the 1000–500-hPa thickness anomaly relative to the long-term climatology for a particular month was calculated and displayed for each analysis.

The NNR dataset was used in parallel with daily weather maps at 0000 UTC published in the Australian Bureau of Meteorology’s “Monthly Weather Review” series (Simmonds and Richter 2000). These charts include frontal analysis, which has been performed manually by expert analysts employing interpretation of satellite imagery, in addition to the standard analysis of synoptic data (Guymer 1978) and evaluation of output from a suite of numerical weather prediction models. This gives us confidence that these charts provide the most reliable method of determining the occurrence of cold fronts, which are important rain-producing systems in southern Australia. The incorporation of the manual analyses was considered to be an essential component of the analysis project because the NNR numerical reanalyses lack this human input and do not have sufficient horizontal resolution to locate fronts reliably.

We chose 1970 as the starting point for the analysis for two main reasons. First, a systematized technique for incorporating the subjective interpretation of satellite cloud imagery into an objective analysis scheme became established practice in the World Meteorological Center, in Melbourne, around that time (Guymer 1978; Seaman and Hart 2003). Second, NNR analyses for the Southern Hemisphere have been found to have systematic biases prior to 1970, particularly at high latitudes (Hines et al. 2000).

b. Method

For each day on which rainfall was recorded at any of the eight rainfall stations, an assessment was made of the nature of the synoptic system responsible for the precipitation event. Having regard to typical velocities of synoptic systems, the analysis region was defined by a fixed box with limits of 30°S, 125°E; 30°S, 147.5°E; 45°S, 147.5°E; and 45°S, 125°E as shown in Fig. 1. Synoptic systems were classified according to the scheme that is discussed below. A rain-producing system was required to have been located within the box during the 24-h period to which the rainfall applied. In addition, a second analysis was conducted to determine all days during which a cutoff low could be identified in the analysis scheme, whether or not rain was reported.

5. Classification scheme

From our preliminary analysis trials and applying the experience in the analysis and forecasting practice of one of the authors (MP), the main categories of synoptic systems that we believe include the dominant mechanisms responsible for growing-season rainfall in northwestern Victoria were identified. Similar approaches have been taken by Wright (1989) for southeastern Australia and in the Northern Hemisphere by Rex (1950) and Berger et al. (2002). The three categories are cold-frontal systems of all types, cold-cored lows that have become cut off from the westerly airstream (cutoff lows), and a combined category designated “others,” which includes particular airstream types, waves in the easterlies, and open troughs aloft. A brief description of the characteristics of the synoptic categories follows.

a. Cold-frontal systems

The conventional cold front is located near the surface pressure trough and within the region of maximum gradient on the 1000–500-hPa thickness chart (Guymer 1978; Sturman and Tapper 1996) and is represented on a synoptic chart by a single line. Such simple systems are rarely encountered in practice and in many cases there are complicating features, and the front usually exists as a narrow zone of discontinuity of airmass properties and is identified on satellite imagery by its characteristic cloud band appearance. Nevertheless, on the scale of synoptic analysis, the cold front can be, and in operational analysis programs is, represented in this simplistic fashion.

Cold fronts that are preceded by a well-defined prefrontal trough are particularly common in summer when they are referred to as “cool changes” (Reeder and Smith 1998), but are less common in the growing season. However, early autumn and late spring are occasionally associated with this type of system, as well. Other subcategories of cold fronts include quasi-stationary fronts, which may have an east–west orientation at some stage in their life cycles, as well as cases of multiple fronts and those involving interaction with preexisting subtropical cloud bands in a manner similar to that described by Wright (1989, 1997) and classified as “interacting frontal types” in his classification system. These were initially classified as “complex fronts” in our preliminary analysis system.

A significant number of frontal systems approaching southeastern Australia undergo a process in which a wave develops on a section of the front and the northern and southern portions of the system move at different speeds. Zillman and Price (1972), Streten and Troup (1973), and Guymer (1978) have identified systems of this type from satellite imagery. In certain circumstances these systems may develop into individual closed low pressure systems (Troup and Streten 1972; Streten and Troup 1973). We initially classified these fronts as “frontal waves.” Figure 5 shows the typical MSLP pattern for the three common types of cold fronts encountered over southern Australia.

Although these frontal types appear distinct and may provide insight into the underlying rainfall mechanisms, there is an element of subjectivity involved in separating them into individual categories. Therefore, in the final analysis all the variations of cold fronts discussed above have been combined into one “frontal” category.

b. Cutoff lows

The term “cutoff low” is used somewhat loosely in the literature and some authors have departed from the original concept of a cold-cored closed low that develops in the mid- to upper troposphere and “extends downwards towards the surface” (McIntosh 1963, p. 69), preferring to include in their classification scheme any closed cyclonic circulation located equatorward of the main midlatitude westerly belt regardless of the upper-air temperature structure. In this analysis, we have selected criteria that strictly adhere to the traditional concept of an upper-air disturbance associated with a “cold pool” in the midtroposphere, while acknowledging that some circulations are more significant in their surface or low-level expressions at particular stages in their development. The criteria that we have adopted in this study are either of the following:

  1. A closed low is present at 500 hPa with an associated cold trough evident in the 1000–500-hPa thickness field. A negative thickness anomaly from the long-term mean (NNR analysis) of at least 20 geopotential meters exists within the designated box.
  2. A closed low is present in the surface MSLP field (≤1008 hPa) and an associated cold trough is located aloft with negative thickness anomaly from the long-term mean (NNR analysis) of at least 20 geopotential meters.

c. Other synoptic systems

This category contains the balance of the synoptic types not covered by the previous classes. Pre- and postfrontal airstreams are significant components and, importantly, this category includes warm-cored cyclones such as waves in the easterlies and the deep troughs in the easterlies over eastern Australia, which are commonly known as “easterly dips” (Sturman and Tapper 1996; Reeder and Smith 1998) or “tropical dips” (Langford 1965). It also contains cold troughs in the middle and upper atmosphere where a closed circulation is not analyzed at 500 hPa, but the rainfall is believed to have been produced or enhanced by convergence and/or instability associated with the trough.

It is important to note that the region under consideration is largely devoid of topographical features and most of the rainfall stations are located at a considerable distance from the nearest coast. Consequently, onshore airstreams are not significant contributors to rainfall at the eight stations that constitute the network.

6. Results and statistics

Analysis of the results of this study for the months of April–October over the period of 1970–2002 reveals that cutoff lows account for approximately 50% of the rainfall recorded by the selected stations. For the eight-station network in northwestern Victoria the figure is 51% while for the single station of Birchip (Woodlands) in the southern Mallee region the result is almost 54%, and this figure rises to 56% at Mildura in the extreme north of the region. The results of the analysis are displayed in Fig. 6.

Cold-frontal systems account for approximately one-third of growing-season precipitation in the region (Fig. 6). The remainder (13%–17%) is associated with pre- and postfrontal airstreams, waves in the easterlies (warm cored), and upper-level troughs where a closed low is not identified. Comparison of the two individual stations in Fig. 6, Mildura and Birchip, reveals that the northernmost location (Mildura) has a higher percentage of rain attributed to cutoff lows and rain associated with the “others” category but has a noticeably lower contribution from frontal rain than Birchip, which is about 200 km to the south.

Although cutoff lows are clearly associated with the highest proportion of growing-season rainfall there is variability in the monthly mean proportionality of the cutoff low to frontal rain. Figure 7a indicates that cutoff lows account for the largest proportion of rain in all months, but the difference between the two classes decreases in winter. When averaged across the eight-station network the combined frontal category contributes its highest percentage of rain (approximately 41%) in July. Nevertheless, Fig. 7a demonstrates that the cutoff rain component is still marginally higher (approximately 42%). In all months, Fig. 7b shows that cutoff rain is more highly variable than frontal rain, as indicated by a comparison of the standard deviations of each component.

There is a high degree of interannual variability in growing-season rainfall and its components (Fig. 8). Table 2 demonstrates that the component of rain resulting from cutoff lows has the highest coefficient of variation (0.46) when compared with frontal rain (0.36) and the mean rainfall for the growing season (0.32). The growing-season rainfall in each year is highly correlated with the cutoff rain component (r = 0.85, n = 33) and displays a moderate correlation with the frontal rain component (r = 0.61, n = 33). Values of the Pearson product moment correlation coefficient greater than 0.45 are significant at the 99% level for a one-tailed test. However, the correlation between cutoff-low rain and frontal rain in any growing season is very low (r = 0.17, n = 33), indicating that the two most important rain-producing systems act independently of each other.

Inspection of Fig. 8 reveals that cutoff rain varies at predominantly interannual time scales, while frontal rain during the growing season varies with a lower frequency. This qualitative inference is confirmed by spectral analysis, which indicates that there is significantly higher power for the cutoff rain component at periods of less than 3 yr. Although mean cutoff rain is considerably higher than mean frontal rain the contribution from the combined frontal rain category exceeds rain associated with cutoff lows in six of the individual years of the study. Three out of the four driest growing seasons in the analysis period (1972, 1982, and 2002) were associated with extremely low cutoff rain (between 1.4 and 1.8 standard deviations below the mean), while frontal rain in those years was more reliable. Both 1972 and 1982 are well-known El Niño events, and 2002 was a weak El Niño year.

Extending the analysis to specified rainfall ranges in a 24-h period reveals that the highest falls were closely associated with the presence of a cutoff low in the defined area. For the eight-station network mean daily rain per station in the range from 15 to 24.9 mm was associated with a cutoff low on more than 70% of the occasions. The figure rose to 80% for rainfall in the range of 25–34.9 mm and for all occasions that mean daily rainfall exceeded 35 mm (Fig. 9). However, Fig. 9 shows that for daily rainfall below 10 mm the percentage contribution from frontal systems increases and exceeds that resulting from cut offs for amounts of 5 mm or less. For the single station of Birchip (not shown) the results are similar: 61% of daily rainfall events in the range of 10–20 mm were associated with a cutoff low, 80% in the range from 20 to 29.9 mm (34 events), about 75% of the occasions that daily rainfall was measured in the 30–39.9-mm range (10 events), and all situations producing at least 40 mm in 24 h (eight events). It is evident from Fig. 9 that the proportion of daily rain associated with cutoff lows rises steadily as the daily rainfall amount increases, but our analysis also shows that the number of events resulting in daily falls of 20 mm or more per station is on average less than one per growing season for the whole network and approximately 1.6 per season at Birchip.

The number of cutoff lows per season is highly variable and not all lows identified in the analysis produce rain at the network stations. Figure 10 presents the number of days when cutoff lows were identified in the analysis region for each growing season (cutoff days), whether or not they produced rain. The decade of the 1980s stands out as a period with high numbers of cutoff days, culminating in the peak value of 73 days in 1989, a year in which atmospheric blocking was particularly persistent during the winter and early spring and resulted in low winter rainfall over the hydroelectric catchments in Tasmania in the far southeast of our analysis region (Pook and Gibson 1999). Although there has been an apparent decline in the number of cutoff days in the latter part of the record, there is no significant trend in the number of cutoff days per growing season over the complete analysis period. In support of this result, Fuenzalida et al. (2005) found that the trend in the annual number of cutoff lows for the Australian sector is not significant over the period of their analysis (1969–99), but they report a rising trend for the American sector, which is significant at the 95% level.

The lack of a long-term trend in the number of cutoff days contrasts with the trend in the average amount of rain per station for each cutoff day (also shown in Fig. 10), which has been declining, particularly since 1996. The mean decrease in rain per cutoff day is 0.025 mm yr−1, which is significantly different from zero at the 75% confidence level (Draper and Smith 1998). Wright and Jones (2003) have commented on the sudden change to a drier rainfall regime in southeastern Australia in October 1996. Despite this recent trend, the lowest rainfalls per cutoff day have occurred in growing seasons during the three El Niño events mentioned previously (1972, 1982, and 2002).

Breaking down the seasonal occurrence of cutoff days into the monthly distribution reveals that the monthly mean number of cutoff days is at a minimum in April (5.5), rises to a maximum in May (7.4), remains approximately constant in winter (6.8–7.1), and decreases slowly in spring (see Fig. 11). The average number of cutoff days per growing season over the 33-yr analysis period is 45.9, which is approximately 21% of the growing-season days.

Qi et al. (1999) used slightly different criteria from ours to produce a climatology of cutoff lows across the southern Australian region between 25° and 40°S for the period of 1983–96. They found peak numbers for the entire region in May (11.9), June (12), and September (11.9), with the highest frequency of occurrence (44%) in the western sector (110°–120°E), just to the west of our analysis region. Despite differences in the latitudinal extent of the respective analyses, the much longer period of our study, and different analysis sets, a comparison of combined monthly means in their areas 2, 3, and 4 with our analysis region gives good agreement. The Qi et al. (1999) analysis implies a mean number of cutoff days across these sectors of 4.5 in April, rising to a peak value of about 6.7 in May, June, and September (cf. Fig. 10). Averaged over the April–October period in order to make a direct comparison with the growing season in our study, the mean number of cutoff days identified per season in the combined areas 2, 3, and 4 by Qi et al. (1999) is 43.2, which compares to our mean of 45.9 for the number of cutoff days per growing season.

7. Cutoff lows and blocking in the Australian region

Atmospheric blocking in the Australian region is regularly accompanied by the development of a cyclone or low pressure trough equatorward of the anticyclone, and many of these systems could be classified as cutoff lows (Taljaard 1972). Sturman and Tapper (1996) describe how a blocking anticyclone distorts the westerly airstream on its western flank and forces embedded fronts to fracture and frontolyze. In practice, a portion is often observed to shear and move to the equatorward side while the remainder moves toward Antarctica on the poleward side of the ridge, occasionally crossing the Antarctic coast (Pook and Gibson 1999; Massom et al. 2004).

The main characteristics of atmospheric blocking in the Southern Hemisphere have been presented by many authors (e.g., van Loon 1956; Coughlan 1983; Lejenäs 1984; Trenberth and Mo 1985; Sinclair 1996; Wiedenmann et al. 2002; Renwick 2005). Others have concentrated more specifically on the Pacific Ocean and the Australian and New Zealand region (e.g., Wright 1974; Hirst and Linacre 1981; Renwick 1998; Pook and Gibson 1999). In each case the major emphasis has been on the anticyclonic portions of the blocking phenomenon, and detailed studies of the cyclonic portions of blocking dipoles have not received similar attention. This situation has occurred despite the assertion by Wright (1974) that the cyclonic systems involved cannot be regarded as less important than others.

Studies of the cyclonic and anticyclonic components of blocking patterns have mainly been carried out separately. However, Hopkins and Holland (1997) related an increased incidence of heavy rainfall events associated with east coast lows on the Australian east coast to a poleward shift of the STR, and Qi et al. (1999) associated the development and intensification of a cutoff low over the Great Australian Bight in August 1997 to the presence of a blocking anticyclone over the Tasman Sea. In the case study of Qi et al. (1999), the low subsequently produced heavy rain over southern and eastern Australia. They concluded in their 14-yr climatology of cutoff lows that the development and maintenance of blocking to the southeast of Australia played an important part in the cyclogenesis and deepening of cutoff lows in general, but provided no evidence of such a symbiotic association. Other studies of cutoff lows have concentrated on the meteorological conditions contributing to the associated heavy rainfall events (e.g., Hill 1969; Mills and Wu 1995; Griffiths et al. 1998).

There are difficulties associated with the study of these low pressure systems because their dimensions are considerably smaller than their anticyclonic counterparts and they do not characteristically persist in a region for more than a day or two. Nevertheless, there is a clear secondary maximum of cyclonic activity over southeastern Australia and the Tasman Sea during winter (Leighton and Deslandes 1991; Keable et al. 2002) and a proportion of the cyclones contributing to this belt of activity could be considered to form the cyclonic portions of blocking dipoles.

To study the relationship between cutoff lows over southeastern Australia and blocking, we have employed a blocking index (BI), which was developed by the Bureau of Meteorology to quantify the intensity of blocking at a particular meridian (Pook and Gibson 1999). The BI has the following form:
i1558-8432-45-8-1156-e1
where, at a given meridian, Ux represents the zonal component of the mean 500-hPa wind at latitude x. In this case, we have calculated the BI from NNR reanalysis data.

Following the finding of Wright (1974) that the surface anticyclone in a blocking event in the Australian region is often a transitory feature, the BI is applied at the 500-hPa level. Correlations have been calculated between the occurrence of cutoff lows in our analysis region and the BI at selected meridians. Figure 12 gives the monthly cycle of correlation coefficients (r) between the mean BI at 140° and 150°E, respectively, and the number of cutoff days. At 140°E the maximum values of r (>0.7, n = 33) are found in July and October, but at 150°E the correlation between the number of cut offs and the BI remains almost constant (0.63 ≤ r ≤ 0.67) from July to October. Values of r ≥ 0.45 are significant at the 0.01 level.

While there is a significant correlation between the number of cutoff days and the BI in the region south of Australia in each month of the growing season, there are as yet unexplained month-to-month variations in the strength of this relationship. In Fig. 13 our Hovmöller representation of the growing-season correlations shows that correlation coefficients are highest in the longitude band from 125° to 155°E but undergo changes in magnitude throughout the growing season. Initially, the meridian of maximum correlation with the number of cutoff days migrates westward from about 145°E in April to 140°E in July. It then moves eastward in August and September before making an abrupt jump westward to approximately 130°E in October. Hirst and Linacre (1981) noted from their analysis of data taken from Wright (1974) that two maxima of blocking frequency are observed in the Australian region during the year and apparently track from west to east, always separated by at least 4 months. At 130°E, Hirst and Linacre (1981) found peaks of blocking activity in June and November, suggesting that the pattern shown in Fig. 13 may be associated with a seasonal shift in blocking frequency of this character.

8. Discussion and conclusions

Daily rainfall during the growing season for grains measured across a network of stations in northwestern Victoria has been related to particular types of synoptic weather systems over a 33-yr analysis period. The resulting climatology reveals that cutoff lows are responsible for at least 50% of all growing-season rainfall and about 80% of daily rainfall events, which result in at least 25 mm per station. Moreover, the mean proportion of rainfall contributed by cutoff lows varies throughout the growing season. It is highest in the autumn and spring months included in the growing season (52%–57%), and falls to a minimum in July (42%). By way of contrast, the total contribution of all types of frontal systems to growing-season rain is about 32% overall, but the percentage contribution by month reaches a maximum (about 41%) in July. Frontal rain is strongly concentrated in the lower range of daily falls (less than 10 mm per station).

Our results differ significantly from those of Wright (1989) who found that cutoff lows contributed 28% of the June–September rainfall at Mildura in the extreme north of our study region. This figure is just over one-half of the percentage of growing-season rainfall in our network that we attribute to cutoff lows. For Mildura itself, we found that 56% of growing-season rainfall was due to cutoff lows. We believe that there are several reasons for this discrepancy. In the first instance, our study extends over the growing season from April to October, while Wright (1989) examined June–September rainfall. Our Fig. 7 demonstrates that the proportion of rain associated with frontal systems rises in June and July while there is a corresponding decrease in the contribution from cutoff lows. Second, the period of our investigation is 33 yr as compared with the 12 employed in Wright’s study and should give a more reliable result. We have employed NNR data to ensure a consistent analysis product and our inclusion of a strict set of criteria for the identification of cutoff lows results in more consistency in classification of these systems. In support of this argument, Qi et al. (1999) report mean monthly numbers of cutoff lows over a similar region of southern Australia, which are in broad agreement with our results.

It is clear that there is always an element of subjectivity in this type of analysis and each analyst brings a different range of skills and experience to the task. Nevertheless, we believe that the selection of a network of recognized high-quality rainfall stations and the application of the techniques we have employed should ensure that the analysis is more objective and consistent and offers the possibility of automation, particularly for the cutoff-low component.

This study has demonstrated that the number of cutoff lows varies markedly from one growing season to another but does not exhibit a significant long-term trend. Not all cutoff lows identified in the analysis region produce rain over the station network. The mean rainfall per station per cutoff day is also highly variable but has gradually declined over the analysis period, and more significantly so in the recent past. The reduction in cutoff rain is closely associated with the lower-than-average growing-season rainfall for much of the past decade. Variability in the circulation characteristics leading to the genesis of cutoff lows and variability in the availability of moisture to fuel precipitation events are not well understood and further research is required into these areas if seasonal predictions are to be improved.

The contribution of frontal rain to growing-season rain, although smaller on average than the cutoff component, is more reliable and its variability operates on a lower frequency as demonstrated in Fig. 8. Frontal rain also increases in the winter months when mean cutoff rain reaches its minimum with frontal and cutoff rain making approximately equal contributions in July. This is a key finding of our study and has important implications for crop production because sowing of a successful crop has normally to be completed prior to the end of June. If soil moisture is insufficient at the start of winter then the more regular but lower daily rainfall amounts associated with frontal systems are unlikely to provide adequate support to a growing crop.

It is noteworthy that there is a significant correlation between the number of cutoff lows identified in each month in the growing season and the incidence of atmospheric blocking occurring to the south of Australia in that month. This relationship is most strongly expressed in July and October where more than 50% of the variance is explained. In an extension of this study the possible connection of atmospheric blocking and cutoff lows in the Australian region with broad-scale environmental forcing such as sea surface temperature will be examined. In addition, a major aspect of future research will involve the identification of possible teleconnections between the variability of the contributions of particular synoptic types to growing-season rainfall and key components of the atmospheric circulation of the Southern Hemisphere.

Acknowledgments

We thank Neil Adams of the Bureau of Meteorology and ACE CRC for calculating and supplying the blocking-index data and Lea Crosswell for her help with the figures. We are grateful for the constructive comments of three anonymous reviewers. This work was funded by a grant from the Managing Climate Variability Program of Land and Water, Australia.

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Fig. 1.
Fig. 1.

Map of northwestern Victoria showing the network of high-quality rainfall stations used in the analysis. The larger of the two dotted boxes drawn on the map of Australia in the top of the diagram defines the region within which the analysis of synoptic systems was confined. The smaller box shows the region of southeastern Australia included in the lower part of the diagram.

Citation: Journal of Applied Meteorology and Climatology 45, 8; 10.1175/JAM2394.1

Fig. 2.
Fig. 2.

Mean monthly rainfall (mm) for six of the eight stations in northwestern Victoria that have been selected for their “high quality” status (Lavery et al. 1997). Note that Sea Lake and Narraport have been omitted for the sake of clarity.

Citation: Journal of Applied Meteorology and Climatology 45, 8; 10.1175/JAM2394.1

Fig. 3.
Fig. 3.

Monthly means (1970–2002) of MSLP (hPa) for the months of (a) April, (b) May, (c) June, (d) July, (e) August, (f) September, and (g) October (NNR data).

Citation: Journal of Applied Meteorology and Climatology 45, 8; 10.1175/JAM2394.1

Fig. 4.
Fig. 4.

Mean geopotential thickness (m) of the 1000–500-hPa atmospheric layer (1970–2002) for (a) the austral summer (December–February) and (b) the austral winter (June–August) (NNR data).

Citation: Journal of Applied Meteorology and Climatology 45, 8; 10.1175/JAM2394.1

Fig. 5.
Fig. 5.

Schematic representation of the broad frontal types encountered in the analysis: (a) a simple or common cold front, (b) a complex cold front, and (c) a wave on a cold front.

Citation: Journal of Applied Meteorology and Climatology 45, 8; 10.1175/JAM2394.1

Fig. 6.
Fig. 6.

The mean percentages of growing-season rainfall attributed to particular synoptic systems for the period of 1970–2002 according to the classification scheme in section 4.

Citation: Journal of Applied Meteorology and Climatology 45, 8; 10.1175/JAM2394.1

Fig. 7.
Fig. 7.

The monthly distribution and variability of cutoff rain and frontal rain averaged over the eight station network for the months of April–October: (a) the monthly mean percentage of rain (y axis) contributed by cutoff lows and frontal systems, and (b) the std dev (mm, y axis) for each month for cutoff rain and frontal rain.

Citation: Journal of Applied Meteorology and Climatology 45, 8; 10.1175/JAM2394.1

Fig. 8.
Fig. 8.

Time series of growing-season rainfall, rain resulting from cutoff lows, and rain resulting from frontal systems for the eight-station network over the period of 1970–2002.

Citation: Journal of Applied Meteorology and Climatology 45, 8; 10.1175/JAM2394.1

Fig. 9.
Fig. 9.

Percentage of rain attributed to cutoff lows and frontal systems for daily rainfall in intervals of 5 mm averaged over the eight-station network.

Citation: Journal of Applied Meteorology and Climatology 45, 8; 10.1175/JAM2394.1

Fig. 10.
Fig. 10.

Number of days on which cutoff lows were identified in the analysis region (cutoff days) and the average rain attributed to cutoff lows per station per cutoff day for each growing season.

Citation: Journal of Applied Meteorology and Climatology 45, 8; 10.1175/JAM2394.1

Fig. 11.
Fig. 11.

Mean, std dev, and median for the number of days on which cutoff lows were identified in the analysis region (cutoff days) in each month and for the total growing season (April–October).

Citation: Journal of Applied Meteorology and Climatology 45, 8; 10.1175/JAM2394.1

Fig. 12.
Fig. 12.

Correlation coefficients of the month-by-month relationship between the BI and the number of cutoff days at (a) 140° and (b) 150°E for the growing-season months of April–October.

Citation: Journal of Applied Meteorology and Climatology 45, 8; 10.1175/JAM2394.1

Fig. 13.
Fig. 13.

Hovmöller representation of the correlation coefficient between the monthly mean of the number of cutoff lows in the analysis region and the monthly mean BI at the meridian. Values of the correlation index greater than 0.35 are significant at the 0.05 level and are shown by the shaded area.

Citation: Journal of Applied Meteorology and Climatology 45, 8; 10.1175/JAM2394.1

Table 1.

Coordinates, elevations, and year of first record for the Australian Bureau of Meteorology rainfall stations employed in the analysis. Note: Bendigo Airport replaced Bendigo Prison as the Bureau’s official station in 1991.

Table 1.
Table 2.

Long-term mean, std dev, and coefficient of variation of growing-season rainfall and its components for the eight-station network (1970–2002).

Table 2.
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