A reanalysis climatology of cool-season tornado environments over southern Australia

Transcription

1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 29: (2009) Published online 9 February 2009 in Wiley InterScience ( A reanalysis climatology of cool-season tornado environments over southern Australia R. Kounkou, a * G. Mills b and B. Timbal b a Ecole Nationale de la Météorologie, Météo-France, Toulouse, France b Centre for Australian Weather and Climate Research, Bureau of Meteorology, Melbourne, Australia ABSTRACT: It is of great interest to assess the likely changes in occurrence of extreme weather events such as those associated with severe convective storms in future (changed) climates. However, while events such as tornadoes cannot be resolved by climate models, or even the higher-resolution operational numerical weather prediction (NWP) models, it has been shown that some of the environments in which they occur can be predicted. In Australia, the so-called cool-season tornado (CST) environment is defined as the area where the forecast 700 hpa Surface Lifted Index and the surface to 1-km vertical wind shear exceed specified thresholds. CST threat area forecasts are issued with each operational mesoscale NWP forecast by the Australian Bureau of Meteorology. In this paper, the application of this diagnostic indicator of CST occurrence to typical climate model resolutions is tested using the NCEP/NCAR and ERA-40 reanalysis data sets. The techniques used to apply the diagnostic to these data sets are described, together with the calibration necessary to specify separate thresholds for each analysis set. Using these thresholds 40-year climatologies of CST risk over Australia are constructed for each reanalysis data set. It is shown that the two data sets indicate very similar spatial distributions of risk. Trends in frequency of occurrence are problematic, with significant discontinuities in frequency after the introduction of satellite sounding data in 1979, with an increased frequency, but little trend in numbers, of CST environments since that time. Relating inter-annual variations in the frequency of CST environments to large-scale circulation indices show little relation with regional sea surface temperature anomalies or with the Southern Oscillation Index, but showed significant negative correlation with the Southern Annular Mode Index. Copyright 2009 Royal Meteorological Society KEY WORDS climatology; reanalysis; cool season tornadoes Received 1 September 2007; Revised 3 December 2008; Accepted 11 December Introduction The adverse impact of climate on society is mostly felt through extreme events. One such class of events is the violent phenomena associated with severe convection: hail, flash flooding, damaging winds, and tornadoes. Tornadoes are the most intense of all atmospheric circulations on the local scale, and while they have a short lifetime, they cause extreme damage. A widely used measure of the intensity of an individual tornado is a scale based on the resulting damage caused to infrastructure developed by Fujita and Pearson (1973). More than 700 tornadoes that have occurred in Australia since European settlement in the late 1700s are documented in the Bureau of Meteorology s (the Bureau) Severe Storms Data Base (Deslandes, 2002), with the loss of more than 40 lives (Bureau of Meteorology, 2000). However, the true number of tornadoes is likely to be much higher due to the low population density over much of Australia, and also due to variable reporting practices for much of the period. Since the mid-1980s storm spotter networks have been established in Australia, and in * Correspondence to: R. Kounkou, Meteo-France, 2 Avenue Rapp, Paris Cedex 07, France. raphaelle.kounkou@meteo.fr the decade from 1987 to 1996 some 162 tornadoes were reported (Hanstrum et al., 2002). A feature of the Australian tornado climatology is that almost half the reported events occur in the cooler months of May September. Hanstrum et al. (2002), in a proximity sounding analysis of these cool-season tornadoes (CST), showed that in contrast to the high buoyancy environments of the tornadoes that occur during the warm season, these CST occur in lesser, although still positively buoyant, environments, and with high values of low-level shear. They found that thresholds of a variety of stability indices, such as Convective Available Potential Energy (CAPE), or Surface Lifted Index to 700 hpa (SLI7), together with low-level shear thresholds, could be used to discriminate and operationally forecast environments in which such tornadoes could occur. An alternative perspective is that CST does not occur unless these thresholds are exceeded. Synoptically (Hanstrum et al., 2002; Mills, 2004a), these CSTs are associated with low-topped supercells in cold-frontal cloudbands, cold-pool (comma-cloud) convection, convective bands or lines in strong cyclonic flow, or active convective development in a frontogenetic region, and often near the axis of a low-level equivalent potential temperature maximum. Their favoured region of occurrence is around 10 equatorward of an Copyright 2009 Royal Meteorological Society

2 2080 R. KOUNKOU ET AL. intense mid-latitude cyclone, close to the axis of the strongest surface to 850 hpa vertical wind shear (VWS) and immediately downstream of a very strong low-level wind maximum. The locations tend to be just downstream of an SLI7 minimum (instability maximum), with lowlevel convergence at the event location, downstream of a sharply diffluent upper trough, and on the cyclonic side of a strong upper-tropospheric jet streak. Hanstrum et al. (2002) showed that most CSTs are reported in two concentrated geographic areas the southwest of Western Australia (WA) and the coastal regions of central South Australia (SA) (Figure 1). While these two areas are the most densely populated areas of those states, and thus this distribution might be interpreted as being a consequence of this population density, Hanstrum et al. (2002) show using a radiosonde climatology of instability and shear that the CST environments are also more common in those areas. A numerical weather prediction (NWP) model climatology showed a similar distribution (Mills, 2004a). While the forecasting of individual tornadoes is essentially impossible at all but nowcasting timescales, the clear discrimination of a CST environment where both shear and instability thresholds are jointly exceeded, together with the fact that these environments are associated with strong synoptic systems that would be expected to be forecast with some skill by contemporary mesoscale NWP systems, led Hanstrum et al. (2002) to develop a forecast aid based on mesoscale NWP model forecast that identifies threat areas for CST up to 48 hours ahead. The system outlines those areas where VWS from surface to 850 hpa are forecast to be greater than 10 m s 1, and where SLI7 is less than 0.5 K, subject to the condition that the NWP model also forecasts ascent in the lowest 150 hpa, indicative of the necessary convergence criterion for convection. The operational branch of the Bureau, the National Meteorological and Oceanographic Centre (NMOC), has issued operational forecasts of CST Figure 1. Geographical distribution of the CST in the reported events database ( ) and regions used in this study: Western Australia (WA), South Australia (SA), south-east (SE) and east (E). threat areas using this diagnostic since 2000, based on the operational mesoscale NWP model, meso-limited Area Prediction System (LAPS) a grid spacing version of the LAPS (Puri et al., 1998). It is recognized that these parameters in no way predict an individual tornado, but the aim of this product is to alert forecasters to the areas where environments are such that a tornado may be sustained, and with a lead time of h. It is also recognized that a number of other parameters have been proposed that provide greater discrimination between tornado and non-tornado environments (e.g. Craven et al., 2002; Rasmussen, 2003), however some of these are not well predicted by NWP models, while others are at least partly implicit in the parameters chosen. For example, strong low-level VWS is associated with frontal boundaries, while axes of high low-level moisture or equivalent potential temperature contribute to more unstable values of SLI7. A verification of these operational forecasts for the four cool seasons (May September) from 1999 to 2002 inclusive has been presented in Mills (2004a,b), using a set of tornado reports from the Bureau s monthly Severe Weather Summaries. With the surge of community interest in the likely change in occurrence of extreme weather events under changing climate scenarios, and in understanding the year-to-year variability in extreme weather event occurrence as a function of large-scale circulation anomalies, it is appealing to apply this type of proven operational decision support diagnostic to reanalysis data sets and to the output of climate change models. However, the development and application of this CST diagnostic has been based on mesoscale NWP model forecast data, with typical grid spacings of km. The applicability of using this approach with coarse resolution (typically km grid spacing) reanalysis and general circulation model data sets remains to be demonstrated, although Brooks et al. (2003) have used a similar approach with the NCEP/NCAR reanalysis (NNR) (Kalnay et al., 1996; Kistler et al., 2001) data. As the first stage in such a study, we adapt the Australian operational techniques for forecasting CST environments to global reanalysis data sets as these have resolutions similar to those of contemporary climate change models. While parameters other than those used in the Hanstrum et al. (2002) diagnostic have been shown to provide additional discrimination of tornadic supercell environments (eg Craven et al., 2002) we have chosen to restrict this study to the parameter set used in operational practice in Australia as this has been extensively documented (Mills, 2004a,b). This provides a climate of diagnosed CST environments over some 40 years using reanalysis data sets, thus removing some of the uncertainty associated with general circulation model simulations of climate, and also provides a reference climatology against which future climate projections can be compared. The paper is structured as follows. The data sets used in this study are first introduced before detailing the diagnostic used and how it is adapted from highresolution NWP models to coarser resolution analyses.

3 REANALYSIS CLIMATOLOGY OF CST ENVIRONMENT OVER AUSTRALIA 2081 Then the climatology of CST risk arising from the application of this tool to two sets of global reanalyses is discussed. The main characteristics of the CST risk climatology are discussed. Its inter-annual variability is described and compared with a number of larger-scale circulation indices such as El Niño Southern Oscillation (ENSO) or Southern Annular Mode (SAM) indices. The future directions and challenges of applying these techniques to climate change models are discussed in the final section. 2. Presentation of the data sets Global reanalysis data sets provide a powerful tool to obtain a detailed description of the atmosphere (surface and upper level) over the long time period necessary for climate studies. Two sets of reanalyses are used in this study: ERA-40 from the European Center for Mediumrange Weather Forecasting (ECMWF) (Kallberg et al., 2005) and the NNR data. While NNRs are available from 1948 to the present, ERA-40 exists from the 1st of September 1957 to the 31st of August 2002 and therefore are only available for the entire austral cool seasons from 1958 to For the sake of consistency, the climatology of CST risk is developed using both reanalyses for the common period of inclusive. Observations used in NNR and ERA-40 come from surface, radiosonde, buoy, aircraft, and satellite observations. The ERA-40 and NNR atmospheric reanalyses are produced by three-dimensional variational data assimilation using six-hourly cycling. The assimilating atmospheric model of ERA-40 (NNR) reanalyses has a T159 (T62) spectral truncation in the horizontal, corresponding to 125 km (210 km) grid spacing, and 60 levels (17 levels) in the vertical from ground level up to 0.1 hpa (10 hpa). Both reanalyses provide standard upper air meteorological fields at 6 hours intervals, on a regular latitude longitude resolution grid, and at 17 pressure levels. In the case of NNR, surface fields are on a grid except for the mean sea-level pressure (MSLP) provided on a grid while for ERA-40 all surface fields are on the same grid. In order to have all NNR fields on the same grid, we interpolated the MSLP and upper levels fields onto the grid. Therefore, in this study, the two reanalyses are used with slightly different grid spacings: 2.5 for ERA-40 and for NNR, although the effective grid spacing of the upper level fields in NNR is 2.5. Observations of tornado events are not particularly suitable for climate or climate change studies as the number of events is small and the uncertainties in reporting and detection make interpretation of trends or variability problematic. Comparison of diagnostic threat areas from NWP models or reanalyses with known events does, however, provide the opportunity to validate and calibrate the diagnostic developed by Hanstrum et al. (2002) to the NWP model or reanalysis data set being used. Mills (2004b) lists 33 tornado events (Table I), extracted from the web sites maintained by the Severe Weather Section of the Western Australian Regional Office of the Bureau and the Severe Weather Summaries issued monthly by the Bureau, and which occurred between May and September in either 1999, 2000, 2001, or All cases in 2002 were included since they occurred early in the season and hence both ERA40 (which stops at the end of August) and NNR could be used. These events are used here to characterize the largescale environment resolved by the reanalyses, and to document the synoptic situation of each individual case. We divided the non-tropical part of Australia into four regions: a south-western region, which corresponds to the southern part of WA, a southern region, which corresponds to SA and two regions in south-eastern Australia [the western part of New South Wales, Tasmania and Victoria (SE) and the coastal zones of New South Wales (E)]. The vast majority of the CST documented occur in two preferred areas (Figure 1): the southwest of WA and the south-east coastal areas of SA (Hanstrum et al., Table I. List of the cases in the reported events database for Western Australia (WA), South Australia (SA), Victoria (Vic.) and New South Wales (NSW) and nearest corresponding analysis time of the reanalyses. case date hour State 1 17/06/ UTC WA 2 22/06/ UTC WA 3 13/07/ UTC WA 4 14/07/ UTC WA 5 18/07/ UTC SA 6 25/08/ UTC WA 7 02/09/ UTC WA 8 20/06/ UTC WA 9 21/06/ UTC VIC 10 22/06/ UTC VIC 11 22/06/ UTC VIC 12 30/06/ UTC WA 13 13/07/ UTC WA 14 14/07/ UTC WA 15 20/07/ UTC SA 16 20/07/ UTC SA 17 23/07/ UTC SA 18 24/07/ UTC SA 19 02/08/ UTC WA 20 08/08/ UTC VIC 21 10/08/ UTC SA 22 10/08/ UTC SA 23 09/09/ UTC VIC 24 25/07/ UTC NSW 25 07/09/ UTC SA 26 08/09/ UTC SA 27 18/05/ UTC SA 28 13/06/ UTC WA 29 15/06/ UTC SA 30 24/07/ UTC WA 31 03/08/ UTC VIC 32 03/08/ UTC VIC 33 12/08/ UTC VIC

4 2082 R. KOUNKOU ET AL. 2002; Mills, 2004b). As these areas correspond to the most densely populated areas of these states, it cannot be excluded that this distribution reflects population density rather than event density (reporting bias). However, comparisons with both radiosonde and LAPS model climatology (Hanstrum et al., 2002; Mills, 2004a) suggest that this distribution is likely to be a real climatological feature. 3. Diagnosing favourable CST environments Hanstrum et al. (2002) characterized the CST environment using instability thresholds based on CAPE and on SLI7, and VWS thresholds based on surface to 850 hpa shear. The SLI is the difference between the temperature at a level above the surface and the temperature of a surface parcel (with a known temperature and dewpoint) that is lifted adiabatically to saturation, and then saturated pseudo-adiabatically to the given level (Mills, 2004a). The VWS between surface (10 m) and 850 hpa level winds is defined as: VWS = (u 850hPa u surf ) 2 + (v 850hPa v surf ) 2 (1) The CST forecast product developed at the Bureau defined threat areas from mesoscale NWP model forecasts based on the coincidence of (1) values of 700 hpa SLI that exceeded a threshold value, (2) values of VWS and (3) low-level convergence. Threshold criteria for the first two of these parameters were (based to proximity soundings) set to: CAPE between 200 and 400 J kg 1, slightly negative 700 hpa SLI, and VWS greater than 10 m s 1. While Hanstrum et al. (2002) set thresholds for both CAPE and SLI, it was found that SLI provided better discrimination in the CST environments, where the instability is concentrated in the lower troposphere. The process by which these thresholds were determined for NWP models is described in detail in Hanstrum et al. (2002) and Mills (2004a,b). In addition, in operational practice two sets of thresholds are used to delineate weak and strong threat areas, which can be interpreted as lower and higher probabilities of CST occurrence (Mills, 2004a,b). In this study, due to the relatively coarse grid spacing of the reanalysis data sets compared with the latitude/longitude grid of the operational NWP model used to generate these forecast products, we have chosen to simplify the system by ignoring the convergence criterion, and also by using only a single set of thresholds rather than weak and strong threat areas. By doing so, the number of large-scale fields required to calculate these threat areas is reduced. The calculation of the 700 hpa SLI requires surface specific humidity (Q at 2 m), surface temperature (T at 2 m), temperature at 700 hpa, and Mean Sea Level Pressure (MSLP). Zonal and meridional (u and v) at the surface (10 m) and 850 hpa wind components are needed for the calculation of the VWS. Applying this diagnostic to any model requires a careful assessment of the model biases for the various individual components in order to adapt the thresholds used for the two parameters (Hanstrum et al., 2002). This issue was faced when adapting the diagnostic to the evolving operational NWP models: these biases change over time as the models are upgraded and improved. The threshold parameters listed for SLI and VWS were changed each time the operational model has changed (Mills, 2004b). Applying the diagnostic to reanalyses is not different. While the issue of temporal inconsistency in the model resolution or analysis methodology is eliminated, there are still potential inhomogeneities in the analyses due to changes in observing systems with time, and also potential biases in the analysis fields themselves. The issue of any potential temporal inconsistencies due to changes in observing systems will be addressed later in this report. In order to determine appropriate parameter thresholds for each reanalysis data set, we use the 33 events from 1999 to 2002 listed in Mills (2004b). To explain our methodology, the application of the diagnostic to the reanalyses is illustrated for one of the cases listed in Table I. The same exercise was repeated for all the cases in Table I. Case 6 occurred on the 25th of August, 1999, when a tornado crossed the far southwest of WA, at around 115 E longitude and 32 S latitude. The storm in this event was part of a convective cloud-band associated with a surface trough embedded in a broad westerly flow, as diagnosed from the ERA-40 synoptic analysis. A deep low is well to the south and the 300 hpa flow is strongly westerly, with two jet streaks (>100 kt) which join offshore of WA (Kounkou et al., 2007). In both reanalyses, a maximum VWS area (Figure 2) is situated just east of the tornado location, i.e. downstream of the maximum near-surface flow. However, the maximum VWS is higher in NNR (>13 m s 1 )thaninera-40(>11 m s 1 ). SLI values at the tornado location range from 1 to2 C inera-40 and 3 to2 C in NNR (Figure 2). The tornado location is within an SLI gradient, and upstream of the VWS maximum (Figure 2). Hanstrum et al. (2002) and Mills (2004b) both point to the overlap between zones (SLI and VWS) as being the threat area for CST. In order to determine appropriate threshold values for SLI and VWS, we determined the values of each of these parameters for each case in Table I, selecting the nearest gridpoint and nearest analysis time to the particular event to calculate these parameters. The operation was done separately for each reanalysis dataset. In effect we are calibrating each of the reanalyses against the Bureau s operational mesoscale NWP system for these known CST events. The spatial distribution of VWS and SLI for the 33 events is very similar in both reanalyses but the intensity differs; NNR is characterized by stronger VWS and stronger (more negative) SLI. This is a systematic difference between the values obtained with ERA-40 and

5 REANALYSIS CLIMATOLOGY OF CST ENVIRONMENT OVER AUSTRALIA 2083 Figure hpa Surface Lifted Index (in  C, left) and Vertical Wind Shear (in ms 1, right) over Australia on 25 August 1999, at 0000 UTC, with ERA-40 (top) and NNR (bottom). NNR, which leads us to define SLI and VWS thresholds separately for the two sets of reanalyses. For the 24 cases in WA and SA the thresholds obtained are: 700 hpa SLI less than 0 C for ERA-40 and than 2 C for NNR, and VWS greater than 8 m s 1 for ERA-40 and than 10 m s 1 for NNR. In addition, the risk area is identified if a VWS maximum (>10 m s 1 for ERA-40 and >13 m s 1 for NNR) is present downstream of the gridpoint at which the above two criteria were satisfied, and that the candidate gridpoint is in a zone of SLI gradient. These thresholds were deduced from all WA and SA cases reported in the events database. Hanstrum et al. (2002) and Mills (2004a,b) discuss the issue of setting these thresholds. If they are set too low, then a very large over-forecast results, while if they are set too high, the probability of detection (POD) decreases. It was partly for this reason that the weak and strong thresholds were used in the operational threat area products. All 33 cases were tested as both the ERA40 and NNR reanalyses exist until the end of August 2002, and hence include all the observed cases in In this study, a relatively low set of thresholds have been chosen, so that all 33 events were selected, as it was felt important that the parameters chosen encompass these known events. This implicitly includes allowing for variations from event to event, and makes some allowance for model bias, error, and perhaps resolution limitations, but leads to an over-estimate in frequency of occurrence. The implications of this overestimation are discussed in Section 4. To diagnose suitable environments for the other (eastern) regions, it was found that thresholds had to be set at very low values considerably lower than for WA and SA (Kounkou et al., 2007). This appears due to the fact that the CST in this region is frequently associated with weather systems which have a small lateral scale, and that are thus poorly resolved in the reanalyses [e.g. cases 24 and 33 in Mills, (2004a) and further analysed in Kounkou et al., (2007)] and is perhaps also influenced by the small number of CST events in the eastern states. As a consequence, the environment favourable to CST in these locations is less marked in the reanalyses. The fact that the thresholds had to be lower, while the same laws of physics apply, is problematic and suggests that the reanalyses may not be able to capture all forms of large-scale CST forcing. The consequence of the low thresholds was that far too many favourable environments were diagnosed over south-eastern Australia in comparison with the few events reported (Kounkou et al., 2007). As the diagnostic tool applied to coarse grid spacing data sets appears less meaningful for the smaller-scale environment characteristics of the CSTs that occurred in the eastern states, these regions are not considered in the remainder of this paper. We will only show results for WA and SA where CSTs are more prevalent, and their environments appear to be better captured by coarse grid reanalyses. However, even in the region where the diagnostic was found useful, it is important to keep in mind

6 2084 R. KOUNKOU ET AL. that this environment favourable to CST defines a risk of tornado occurrence and not the occurrence of a tornado itself. Following the adaptation of this technique to both sets of reanalyses, and the earlier adaptation to a suite of highresolution NWP models, it is interesting to summarize the impact of resolution on setting thresholds, as this is likely to be one source of differences among models, together with differences due to the model physics. We have already seen that although the results obtained with each of NNR and ERA-40 had similarities, different thresholds were used. This result is consistent with operational practice whenever a new NWP model is introduced (Mills, 2004b). Our sample is based on MESO LAPS, the Bureau s high-resolution regional model, LAPS, the Bureau s NWP regional model, NNR and ERA- 40 with a coarse grid. SLI thresholds appear to have erratic variation in relation to resolution, and there is no suggestion that the SLI threshold depends on model resolution (Figure 3). Arguably, for VWS the correlation between threshold value and model resolution is high (R 2 = 0.78), but this relationship is only based on few cases. The main conclusion, from this comparison, is that applying this diagnostic to different models requires a careful assessment of meaningful thresholds on a model by model basis. Only part of the differences will directly depend on the model s resolution while other model characteristics, including the physics, will also be important. Following the definition of the required thresholds for both sets of reanalyses, the application of the diagnostic tool to the entire period of the reanalyses gives a climatology of the favourable environments for CST occurrence, or a climatology of the CST risk over the past 50 years. described earlier to each gridpoint over Australia for each six-hourly reanalysis, the number of CST conducive environments (the number of gridpoints satisfying the joint threshold criteria) per year can be calculated. Figure 4 shows the geographical distribution of observed CST over Australia and observed environments favourable to CST over Australia (climatology of the number of days during May to September when 0 1 km shear greater than 15 m s 1 occurred in conjunction with negative 700 hpa SLI). These figures are based on observations from 1967 to 1997 (Hanstrum et al., 2002), and shows that the highest frequencies occur south of Adelaide and south of Perth, with maxima around 8 days (per year) in both of these regions; these numbers are on the same order as the numbers of observed events. There is also a close match between this spatial distribution and the distribution of observed events. The monthly frequency of potential tornado days peaks in July at Perth and Albany, and in July August at Adelaide and Mount Gambier. Their conclusion is that although this analysis does not show that a high-shear environment necessarily leads to tornadic thunderstorms, it does indicate that a forecast tool based on these buoyancy/shear criteria could predict the climatological spatial and temporal distribution of cool-season tornadoes in Australia reasonably well. As stated earlier, the diagnostic of CST risk is likely to overestimate the number of true occurrences of tornadoes. The real number of occurrences is hard to estimate as these phenomena are small scale and short lived. While the lower bound (based on actual sightings) is about ten cases a year (mostly in WA and SA), the real number is likely to be much larger across the two states which are sparsely populated in large areas. The total number of cases obtained from both set of reanalyses is shown 4. Climatology of the CST risk The environment is deemed to be favourable for CST when there is an overlap of the zones where SLI and VWS thresholds are exceeded. Applying the thresholds Figure 3. SLI (diamonds and dashed line) and shear (crosses and plain line) thresholds as functions of the model resolution for MESO LAPS (0.125 ), LAPS (0.375 ), NNR (1.875 ) and ERA-40 (2.5 ), with a linear best fit between the four points. Figure 4. Geographical distribution of days with environments favourable to CST over Australia in the period (from Hanstrum et al., 2002, figure 15).

7 REANALYSIS CLIMATOLOGY OF CST ENVIRONMENT OVER AUSTRALIA 2085 Figure 5. Monthly distribution of the number of favourable environments over WA and SA between 1958 and 2001 obtained with ERA-40 (left) and NNR (right). Table II. Cases of CST risk diagnosed in WA, SA, and over the entire non-tropical part of Australia using both reanalyses over the year Model WA SA Southern Australia Cases Percent Cases Percent Cases Percent ERA % % % NNR % % % The diagnosed cases are also expressed in percent of the total possible cases. in Table II. Results are based on four outputs per day between the first of May and the 30th of September over 44 years, for a total of six-hourly time steps, on a grid of 168 (348) points for ERA-40 (NNR) between 25 S and 45 S and between 110 E and 160 E with a resolution of 2.5 (1.875 ). A positive case is counted when all criteria which define a favourable environment are satisfied for one grid point. While the percentage of cases is low, in absolute terms this is around 300 cases (for NNR) per year across WA and SA, while the number of observations of tornadoes (Table I) was more like ten. This apparent overestimate is a consequence of our selecting thresholds that ensured a high POD for the events listed in Table I, and this decision has the inevitable consequence that a high false alarm ratio (FAR) will result. However, the focus of the study is the geographical and inter-annual distribution of CST environments, not of the tornadoes themselves, and this reduces the importance of these large apparent FAR statistics. The difference between the two sets of reanalyses is more surprising since individual thresholds were selected for each reanalysis. However, the numbers of diagnosed cases differ between the two sets by less than 20%. In percentage terms, the difference is larger; this is likely to be a consequence of the coarser resolution in ERA40 since most cases are diagnosed in very few grid boxes near the coast (roughly the same for both reanalyses) which represent a lesser spatial coverage in NNR due to the higher resolution and hence leading to a lower percentage. Across the cool months from May to September, favourable environments peak in the middle of the season (Figure 5) in July (ERA-40 and NNR) with high frequencies in June (NNR) and August (ERA-40). While the observed frequency peaks in June (Hanstrum et al., 2002), this difference is inconclusive since the observation record is based on a shorter period ( ) which may not well represent the longer period of the reanalyses ( ). Furthermore, it is well known that not all tornado events are reported as they can occur in uninhabited regions. Therefore, this mismatch does not necessarily indicate a problem with the diagnostic or with the reanalyses. Over the period (Figure 6, left), for both reanalyses, the inter-annual variability of the number of favourable environment is large: m = 407 (317) cases and σ = 95 (114) cases for ERA-40 (NNR). Both reanalyses show a marked positive trend: the slope of a linear regression fitted over the entire dataset gives an increase of 3.8 (3.2) cases per year for ERA-40 (NNR). It is reassuring to note that despite differences in the means, the number of events per year from the two reanalyses is highly correlated (R = 0.73, significant at 99% level). The consistency of the reanalyses and the marked positive slope might indicate that favourable environments over WA and SA have increased since However, a limitation on the temporal consistency and homogeneity of reanalyses is the introduction of new data that can affect the model. Satellite data are perhaps the most important dataset introduced during the period, due to their quantity, quality, and continuous improvement. A significant change in the amount of satellite data occurred in 1978 and the assimilation of these data began in both reanalyses in that year. Their impact was larger in the Southern Hemisphere where conventional data were more sparse (Renwick, 2005), particularly for upper-level fields. Indeed, linear trends of favourable environments over WA and SA (Figure 6, right) before and during the satellite era show a very different picture. Since the introduction of satellite data in 1978, more favourable environments are diagnosed in both reanalyses, suggesting that satellite data altered the representation of the lower troposphere with, in particular, a better representation of the more intense synoptic systems. The mean is 28% higher in ERA-40 and 29% in NNR (Table III)

8 2086 R. KOUNKOU ET AL. Figure 6. Evolution of the annual number of environments favourable to CST over Australia between 1958 and 2001 obtained with ERA-40 (solid line) and NNR (dotted line), with a linear best fit applied to the entire record length (left) and for two separate period before and after 1978 (right). Table III. Evolution of the CST risk in the 20th century over Australia: mean, standard deviation, and trend of the evolution of the number of environments favourable to CST for , , and Model Total period: Without satellite data: With satellite data: Mean Std dev Trend Mean Std dev Trend Mean Std dev Trend ERA NNR after Positive trends remain apparent prior to 1979 in both reanalyses: 2.3 (2.9) cases per year with ERA- 40 (NNR), but in the latter period the trends are small. This suggests that at least for the most reliable (latter) period, the risk of CST has only slightly increased in WA and SA. The trend over the whole reanalysis period is mostly explained by an early trend in the 1960s and 1970s for which a regular improvement of the observations network in the Southern Hemisphere is likely to be the cause and a discontinuity in following the introduction of satellite data. Interestingly, the two reanalyses are more highly correlated after 1978 (0.73) than before 1978 (0.62). This is somewhat intuitive since it is expected that both reanalyses are more reliable products in the satellite era. It is outside the scope of this study to evaluate and compare the reanalyses performance in order to assess their likely skill at producing the best estimate of the climatology of CST risk in the real climate system. Both CST risk climatologies obtained are regarded as equally likely and provide a range of plausible outcomes from which an error estimate of the real climatology can be drawn. 5. Relationship with large-scale modes of natural variability Beyond the mean climatology, it is of interest to investigate the inter-annual variability of the diagnosed CST risk and its relation to large-scale modes of natural variability, starting with the influence of near-by oceans. A simple thermodynamic argument would suggest that a warmer ocean is likely to provide more energy and low-level moisture and hence enhance the SLI and thus the CST risk. To assess the possible influence of neighbouring sea surface temperature (SST) anomalies on CST risk in the WA and the SA regions, we compared time series of SST over an East Indian Ocean domain (latitude S, longitude E, directly upstream of the main CST prone area) with the number of CST events per year over the WA area, and over a South-east Indian Ocean area (latitude S, longitude E) for the SA region (Figure 7). SST anomalies were calculated from the release 2 of the COADS dataset (Woodruff et al., 1998). A weak correlation 0.29 (0.20) exists between the East Indian Ocean SST and the CST risk over WA for ERA-40 (NNR). This positive correlation is reduced between the South Indian Ocean and the CST risk over SA (0.19) with ERA-40 and is negligible with NNR. Overall, there is very little evidence to suggest that neighbouring SSTs have a strong influence on CST risk. While the result might appear counter-intuitive from a thermodynamic view point, it is important to note that during the CST season (the cool months from May to September) SSTs are typically low (around 15 C) along the southern coast of Australia and thus it might not be all that surprising that SST anomalies do not play an important role. However, stronger conclusions would require additional evidence to ensure that it is not a consequence of the analysis technique used here. Since local SSTs do not seem to have a direct influence, it is interesting to investigate the possible teleconnections with remote tropical SST anomalies such as the influence of the tropical Pacific and the ENSO. This is a planetaryscale phenomenon which involves a major atmospheric pressure shift between the west and the south-east tropical Pacific regions and has been shown to influence many aspects of the Australian climate (McBride and Nicholls,

9 REANALYSIS CLIMATOLOGY OF CST ENVIRONMENT OVER AUSTRALIA 2087 Figure 7. Variation of the mean SST from May to September (dotted line) and the risk of CST (solid line) during the period , obtained with ERA-40 (left column) and NNR (right column), for WA (top row) and East Indian Ocean (latitude S and longitude E) and SA (bottom row) and South-East Indian Ocean (latitude S and longitude ). 1983). The Southern Oscillation Index (SOI) indicates the intensity and phase of ENSO and is calculated from the monthly anomalies of air pressure differences (Troup, 1965) between Papeete (Tahiti) and Darwin (Australia). Large negative values of the SOI, or El Niño episodes, are usually accompanied by sustained warming of the central and eastern tropical Pacific Ocean, a decrease in the strength of the Pacific trade winds, and a reduction in rainfall over eastern and northern Australia. Positive values of the SOI, or La Niña episodes, are associated with stronger Pacific trade winds and warmer SST to the north of Australia. Correlations between SOI and the numbers of CST environments across both WA and SA (not shown) are weak and barely significant. However, the impact of ENSO on the Australian climate is not uniform across the continent and is known to be stronger in the eastern part (Nicholls, 1989). To take into account these regional differences, correlations are calculated separately for WA and SA (Figure 8). Weak positive correlations exist: 0.15 (0.23) in WA with ERA- 40 (NNR), and slightly larger in SA (0.22 for ERA-40 and 0.25 with NNR). Only the latter correlation reaches a significance level greater than 90%. Overall, ENSO has only a very small positive correlation with the risk of CST in the southern part of non-tropical Australia; based on the length of the record this influence is barely significant. Finally, since CST affect the southern part of the Australian continent outside the Tropics, it is interesting to investigate the possible role of the SAM, the largest mode of natural variability at high latitudes in the Southern Hemisphere (Thompson and Wallace, 2000). SAM has been shown to influence the circulation around the south of Australia as well as rainfall and temperature (Hendon et al., 2007). Positive values of SAM indicate higher pressure over mid-latitudes of the Southern Hemisphere and lower pressure at 65 S. This leads to the spatial reduction of the circumpolar vortex and a strengthening of the associated westerlies at high latitudes (westerlies move towards high latitudes), and a weakening of the westerlies at the latitude of the Australian continent (Thompson and Solomon, 2002). Several indices to measure the intensity of SAM exist: as we were interested in a long-term perspective, we used the index proposed by Marshall (2003) which has been calculated as far back as Variations of SAM and CST risk over Australia between 1958 and 2001 (Figure 9) are negatively correlated: R = 0.59 ( 0.50) with ERA-40 (NNR), each significant at the 99% level. The SAM influence has similar magnitude in both WA and SA regions. The risk of CST is higher when SAM is negative, i.e. when the circumpolar vortex is larger and associated westerlies strengthen in the mid-latitudes near southern Australia. This correlation is higher during the last 20 years when, as was argued before, the diagnostic of CST risk should be more reliable due to the enhanced quality of the reanalyses since the satellite era. As the relation with SAM appears the only meaningful long-term relationship between local CST risk and largescale modes of variability, it is interesting to investigate further the possible role of SAM during the observed cases of CSTs. Using the Climatic Data Centre (CDC) calculation of a daily index for SAM based on NNR (Hendon et al., 2007) for the 33 days in when

10 2088 R. KOUNKOU ET AL. Figure 8. Inter-annual variability from 1958 to 2001 of the SOI (dotted lines) and the risk of CST (solid line) deduced from ERA-40 (left column) and NNR (right column), over WA (top row) and SA (bottom row). Figure 9. Variation of the annual SAM (dotted line) and the risk of CST over south-western Australia (solid line) during the period , obtained with ERA-40 (left), and NNR (right). Note: A different scale is used for CST cases (divided by 5). Table IV. Distribution of the 33 observed cases of CST according to a daily SAM index (I) and standard deviation (σ ) (from the Climatic Data Centre, CDC). Daily ily SAM index σ<i σ<i < 0 0 < I <σ I >σ Case (in %) CSTs are reported in the event database, it appears that the distribution is skewed towards more cases during strong negative SAM days (Table IV). Very few CSTs are reported when SAM is in a strong positive phase (i.e. SAM index greater than 1σ from the mean). Numbers are evenly distributed between the mean, ±1σ and when SAM is less than 1σ. Although of limited value from an NWP perspective, this indicates that SAM has a relationship with observed cases of CST, thus supporting the negative relationship between SAM index and the CST risk. 6. Conclusions and final discussion Extreme events are a great challenge for climate models as many occur at a scale much smaller than can be resolved by the coarse resolution commonly used to integrate these models for long climate timescales. The aim of this study was to test the possibility of adapting a diagnostic tool developed in Australia for high-resolution NWP models and operational forecasting products to climate-type model resolutions. The tool was applied to typical coarse climate model resolution using two sets of reanalyses: ERA-40 and the NNR. This diagnostic allows one to determine the CST potential by identifying

11 REANALYSIS CLIMATOLOGY OF CST ENVIRONMENT OVER AUSTRALIA 2089 the occurrence of the environment favourable to the formation of CST. Such an environment is defined once thresholds for two parameters are exceeded: Surface Lifted Index (SLI) to 700 hpa and VWS between the surface and 850 hpa. The first step was to define thresholds that are appropriate for these resolutions. It was found that while in general agreement, the thresholds differ from one reanalysis dataset to another and from those used in NWP applications. Differences are only partly attributable to the model resolution for VWS, with inherent model differences between reanalyses affecting the SLI thresholds. The CST risk climatology, which corresponds to the spatial overlap between areas where individual thresholds are exceeded, is the product of a joint distribution. No attempt was made to obtain the same number of CST risk environments from both datasets as the focus was on a consistent analysis of the individual parameters using different thresholds, and thus the differences for individual parameters generate different climatologies: a greater number of favourable environments in ERA-40 but larger inter-annual variability in NNR. Despite the differences in total number and variability, both climatologies are highly correlated and are treated as equally plausible representations of the real climate system. In the course of the adaptation of the tool to the reanalyses it was found that although results were meaningful for two regions of non-tropical Australia (WA and SA), choosing appropriate thresholds was more problematic in the rest of the southern half of the continent (Kounkou et al., 2007). The application of the tool there leads to dubious results and was later abandoned; it appears that the relatively coarse resolution of the reanalyses is unable to resolve the relatively small horizontal scale of some of the environments of the events observed in the south-east of the continent. This limitation must be recognized in interpreting the results or applying this technique (or similar ones) to relatively coarse reanalyses or climate models. In the future additional parameters, identified from emerging tornado research, may assist the discrimination of these south-eastern Australian tornado environments from coarser resolution numerical model output. Higher-resolution regional reanalysis data sets, or regional climate model output, may also ameliorate these limitations. The climatology of the CST risk was found to have high inter-annual variability, as noted on a shorter period (Mills, 2004a). The time-evolution of the risk of CST since 1958 is inconclusive. The frequency of occurrence of favourable environments over the second half of the 20th century shows a mark positive trend, but most if not all of this trend may be related to issues with data assimilation; most notably, this trend is discontinuous after the introduction of the assimilation of satellite data from 1978 to Once removed, the only marked positive trend is limited to the pre-satellite era and again is likely to be related to the slow improvement of the observation network during the 1960s and 1970s. From 1979 onwards, there is essentially no trend. Large-scale forcings likely to influence the occurrence of CST environments were investigated. It was found that regional SSTs anomalies had little influence on the CST risk. It was argued that this result, despite a postulated thermodynamic link between warmer SSTs, surface moisture and instability, might be explained by the cool SSTs observed during winter months around the southern coast of Australia. Among the planetary modes of variability, it was found that ENSO, despite its well-known influence on the Australian climate, did not appear to explain a significant part of the inter-annual variability of the CST risk. However, the SAM was found to be significantly anti-correlated with the CST risk. This correlation is moderate and explains about a third of the inter-annual variance, but appears meaningful and is highly significant. During a negative phase of SAM, westerly winds expand from more southerly latitude towards the southern part of Australia and are likely to be associated with the strong synoptic events conducive to CST formation in WA and SA. Further than simply noting the correlation with the risk of occurrence (i.e. the frequency of occurrence of conducive environments), it was found that, for the cases where CST occurrences were observed (33 cases between 1999 and 2002), there was indeed a tendency towards days with a strongly negative SAM index. The most important conclusion of this study is that it is possible to apply such a diagnostic to coarse resolution models and hence to infer the impact of climate change on the risk of CST formation. Using two sets of reanalyses, we were able to quantify the uncertainties associated with diagnosing such environment from coarse grid models. As expected, since the diagnostic relies on high-resolution analysis of extreme meteorological conditions, uncertainties are large, as shown by the differences between the two equally plausible reanalyses. We were able to establish a climatology to benchmark the future application of the technique to long climate model integrations of the 20th century as well as to future climate projections. The methodology appears suitable to infer the impact of climate change on this phenomenon. Assessing the climatology of extreme events such as CST is still in its infancy and requires further work. This type of research is important as the most felt impacts of climate change are through extreme events such as severe thunderstorms and other such hazards. Acknowledgements This manuscript benefits from earlier feedbacks from J.-F. Royer, M. Wheeler, and H. Richter. RK was supported by the Ecole Nationale de la Météorologie of Météo- France during her visit to CAWCR. BT was supported by the Australian Greenhouse Office (AGO) through the Australian Climate Change Science Program (ACCSP). We acknowledge our reviewers for their positive and constructive recommendations.