Bridging the gap between weather and seasonal forecasting: intraseasonal forecasting for Australia

Transcription

1 Quarterly Journal of the Royal Meteorological Society Q. J. R. Meteorol. Soc. 137: , April 2011 A Bridging the gap between weather and seasonal forecasting: intraseasonal forecasting for Australia Debra Hudson,* Oscar Alves, Harry H. Hendon and Andrew G. Marshall Centre for Australian Weather and Climate Research (CAWCR), Melbourne, Victoria, Australia *Correspondence to: D. Hudson, Bureau of Meteorology, CAWCR, GPO Box 1289, Melbourne, Victoria 3001, Australia. D.Hudson@bom.gov.au This study examines the potential use of the Predictive Ocean Atmosphere Model for Australia (POAMA), the Bureau of Meteorology s dynamical seasonal forecast system, as an intraseasonal prediction tool for Australia. This would fill the current prediction capability gap between weather forecasts and seasonal outlooks for Australia. The intraseasonal forecast skill of a 27-year hindcast dataset is investigated, focusing on precipitation and minimum and maximum temperatures over Australia in the second fortnight (average days of the forecast). Most of the skill for forecasting precipitation and maximum temperature in the second fortnight is focused over eastern Australia, during austral winter and spring for precipitation and during spring for maximum temperature. For this region and seasons the forecast of the second fortnight performs generally better than using climatology, persistence of observed, or persistence of the forecast for the first fortnight (average days 1 14). The model has generally poor skill in predicting minimum temperatures. The role of key drivers of Australian climate variability for providing predictability at intraseasonal time-scales is investigated. This is done for the austral winter and spring seasons, when POAMA s skill for predicting precipitationis highest. Forecast skill is found to be increased during extremes of the El Niño Southern Oscillation, the Indian Ocean Dipole and the Southern Annular Mode. The regions of impact of these modes of climate variability on forecast skill are similar to those regions identified in observed studies as being influenced by the respective drivers. In contrast, there is no significant relationship between intraseasonal forecast skill for precipitation and the amplitude of the Madden Julian Oscillation (MJO) in winter and spring, although the analysis does not distinguish between the phases of the MJO. The results indicate that the use of POAMA for intraseasonal forecasting is promising. Copyright c 2011 Royal Meteorological Society Key Words: monthly forecasts; prediction skill; coupled ocean-atmosphere model Received 31 May 2010; Revised 24 November 2010; Accepted 10 December 2010; Published online in Wiley Online Library 21 March 2011 Citation: Hudson D, Alves O, Hendon HH, Marshall AG Bridging the gap between weather and seasonal forecasting: intraseasonal forecasting for Australia. Q. J. R. Meteorol. Soc. 137: DOI: /qj Introduction The Bureau of Meteorology has been providing weather forecasts since 1908 and seasonal climate prediction since 1989 (Day, 2007). However, there is a notable gap in prediction capability beyond 1 week and shorter than a season. This is because it is notoriously difficult to provide skilful predictions for this intraseasonal or monthly timescale, particularly from the second week to the first month of the forecast. As noted by Vitart (2004), after about the first week the forecast system has typically lost most of the information from the atmospheric initial conditions, which Copyright c 2011 Royal Meteorological Society

2 674 D. Hudson et al. are the basis for weather forecasts. Also, in the first month the ocean state probably has not changed much since the start of the forecast; hence it is difficult to beat persistence as a forecast (Vitart, 2004). However, over the past few years, with the improvement of numerical prediction models, ensemble prediction techniques and initialization, skilful intraseasonal predictions based on general circulation models are now being delivered operationally (e.g. by the European Centre for Medium-Range Weather Forecasts, ECMWF; Vitart et al., 2008) and there is an increasing focus on dynamical intraseasonal prediction (e.g. Toth et al., 2007; Gottschalck et al., 2008). Forecast information on intraseasonal time-scales is potentially useful for a range of sectors of society, such as agriculture (e.g. Hammer et al., 2000; Meinke and Stone, 2005), energy (e.g. Roulston et al., 2003; Taylor and Buizza, 2003), water management (e.g. Sankarasubramanian et al., 2009) and the financial markets and insurance (e.g. Zeng, 2000; Jewson and Caballero, 2003). In Australia there has been increasing demand for intraseasonal forecasts from the agricultural community in particular (e.g. CliMag, 2009). Many farmers currently respond to climate variability through flexibility in their practices. Reliable intraseasonal forecasts may be valuable for decision making related to the scheduling of planting and harvesting, as well as within-season decisions, such as those related to fertilizer or pesticide application (Meinke and Stone, 2005; CliMag, 2009). Some farmers are already integrating existing intraseasonal forecasts into their decision-making framework. For example, some cotton growers in Queensland schedule the timing of their cotton harvests based on the expected passage of the next Madden Julian Oscillation (MJO) (Meinke and Stone, 2005). Forecasts on the intraseasonal time-scale would add to existing climate information available to farmers, assisting in the development of better risk management strategies. Currently at the Bureau of Meteorology dynamical seasonal prediction is based on the Predictive Ocean Atmosphere Model for Australia (POAMA), which is a coupled ocean atmosphere climate model and data assimilation system (Alves et al., 2003; Wang et al., 2008; Hendon et al., 2009; Lim et al., 2009; Spillman and Alves, 2009; Zhao and Hendon, 2009; Hudson et al., 2011). Although the real-time version of POAMA routinely produces an intraseasonal forecast from realistic atmospheric initial conditions, the skill of these intraseasonal forecasts has not, until recently, been assessed. This is because the atmosphere and land components of the hindcasts of the original POAMA-1 system were initialized from unrealistic atmospheric initial conditions; the atmospheric initial conditions were derived from Atmospheric Model Intercomparison Project (AMIP)-style atmosphere-only simulations. These initial atmosphere conditions, although appropriate for seasonal prediction where ocean initial conditions dominate, did not capture the true intraseasonal atmospheric/land surface state. However, the most recent version of POAMA (version 1.5) has a new Atmosphere and Land Initialization scheme (ALI; Hudson et al., 2011), which provides realistic atmospheric initial conditions for both the hindcasts and real-time forecasts. Thus this updated POAMA-1.5 system has the potential to bridge the gap between weather and seasonal forecasting, since forecasts in the 10- to 60-day range are influenced by initial conditions of the atmosphere and land, as well as the ocean. In this paper, intraseasonal forecast skill of the hindcasts from the POAMA-1.5 system is examined, focusing on fortnightly precipitation and maximum and minimum temperature anomalies over Australia. Section 2 describes the POAMA-1.5 forecast system, the hindcast dataset and the verification method. Section 3 documents the intraseasonal skill for precipitation, maximum and minimum temperature over Australia. Section 4 examines how some of the drivers of intraseasonal variability of rainfall overaustralia are related to POAMA s skill in predicting intraseasonal variations of Australian rainfall. Conclusions are presented in section Methods 2.1. POAMA-1.5 forecast system and hindcast experiments The atmospheric model component of POAMA-1.5 is the Bureau of Meteorology s atmospheric model (BAM version 3.0) (Colman et al., 2005; Wang et al., 2005; Zhong et al., 2006), which has a T47 horizontal resolution and 17 levels in the vertical. This horizontal resolution, together with the grid configuration, means that the southernmost state of Australia, the island of Tasmania, is not resolved as land in POAMA; therefore our analysis is restricted to mainland Australia. The land surface component is a simple bucket model for soil moisture (Manabe and Holloway, 1975) and has three soil levels for temperature. The ocean model is the Australian Community Ocean Model version 2 (ACOM2) (Schiller et al., 1997, 2002) and is based on the Geophysical Fluid Dynamics Laboratory Modular Ocean Model (MOM version 2). The ocean grid resolution is 2 in the zonal direction and in the meridional direction it is 0.5 at the Equator and gradually increases to 1.5 near the poles. The atmosphere and ocean models are coupled using the Ocean Atmosphere Sea Ice Soil (OASIS) coupling software (Valcke et al., 2000). POAMA-1.5 obtains ocean initial conditions from the POAMA ocean data assimilation system (PODAS; Smith et al., 1991) and atmospheric initial conditions from the ALI scheme (Hudson et al., 2011). ALI involves the creation of a new reanalysis dataset by continuously nudging the atmospheric model of POAMA toward a global atmospheric analysis. ALI nudges to the analyses from ERA-40 (Uppala et al., 2005) for the period 1980 to August 2002, and to the Bureau of Meteorology s operational global NWP analysis thereafter. The ALI scheme thus generates realistic atmospheric initial conditions that are more balanced for the POAMA atmospheric model, as well as producing land surface initial conditions that are in balance with the atmospheric forcing. The hindcast dataset is a 10-member ensemble starting on the first day of every month for The ensemble is generated through perturbing the atmospheric initial conditions by successively initializing each member with the atmospheric analysis 6 h earlier (i.e. the 10th member was initialized 2.25 days earlier than the first member). The ocean initial conditions are from the analyses provided by PODAS for the first of each month and are not perturbed. Forecast skill is assessed using anomalies from the hindcast climatology. These anomalies are created by producing a lead-time dependent ensemble mean climatology from the hindcasts. The ensemble mean forecast (or individual ensemble member) is compared against this climatology to create anomalies, and in so doing a first-order linear

3 Intraseasonal Forecasting for Australia 675 correction for model bias or drift is made (e.g. Stockdale, 1997) Verification methodology Hindcast performance of the POAMA-1.5 system for seasonal time-scales has been thoroughly assessed (e.g. Wang et al., 2008; Hendon et al., 2009; Lim et al., 2009; Spillman and Alves, 2009; Zhao and Hendon, 2009; Hudson et al., 2011). Some aspects of the forecast skill of the MJO have also been determined (Rashid et al., 2011; Marshall et al., 2011). In this paper, the skill of POAMA in predicting the first and second fortnight (average of days 1 14 and respectively) of the forecast for precipitation and temperature anomalies over Australia is assessed. The Bureau of Meteorology National Climate Centre gridded daily analyses (averaged into fortnights) of precipitation, maximum and minimum temperature are used for the verification. These gridded analyses are produced from quality-controlled station data by the application of a threepass Barnes successive-correction analysis (Mills et al., 1997). They are available on a 0.25 grid and are averaged onto the POAMA T47 atmospheric grid. Since POAMA uses realistic atmosphere initial conditions, derived from nudging towards a high-quality global analysis (i.e. using the ALI scheme), deterministic forecast skill in thefirstweekishigh.thatis,inthefirstweekpoama is essentially behaving as a global NWP system. After the first week, forecast spread is large and the forecast needs to be delivered and assessed in a probabilistic fashion. Deterministic verification is, however, shown in the form of correlation, which measures the linear correspondence between the ensemble mean forecast and observed, and root mean square error (RMSE), which provides information on errors in forecast amplitude. For probabilistic verification, focus is placed on probabilistic forecasts of exceeding tercile thresholds, and the relative operating characteristic (ROC) score, ROC curve, reliability diagram and Brier skill score metrics are used (e.g. Mason and Graham, 1999, 2002; Joliffe and Stephenson, 2003; Wilks, 2006; Mason and Stephenson, 2008). For calculation of the tercile thresholds, anomaly data from all the ensemble members are used. Calculation of the terciles from both the model and observations and the verification of the forecasts are subject to leaveone-out cross-validation. ROC scores, Brier scores and reliability diagrams are used for verifying the performance of dichotomous (yes/no) predictions, e.g. whether the forecast precipitation anomaly falls within the upper tercile or not. They are based on contingency tables of the number of observed occurrences and non-occurrences of the event occurring in predefined forecast probability bins. In this study, five probability bins are defined: 0 0.2, , , and This was done, rather than using the full set of 11 probability values that are available from the 10-member ensemble, in order to avoid sparseness of some of the probability categories. The ROC score (also referred to here as the ROC area) measures the ability of the forecasting system to discriminate between events and non-events, thereby providing information on forecast resolution. The ROC curve is produced by plotting the hit rate (fraction of observed events that were correctly forecast) against the false alarm rate (fraction of non-events that were incorrectly forecast as events) calculated for each probability bin, and by definition passes through the points (0.0, 0.0) and (1.0, 1.0). The no-skill line on an ROC curve is the diagonal, where hit rates equal false alarm rates. A forecast system with positive skill has a curve which lies above the diagonal and bends towards the top left corner (0.0, 1.0), such that hit rates exceed false alarm rates. The area under the ROC curve is often used to summarize the skill. It is normalized such that a perfect forecast system has an area of 1 (i.e. the curve passes through (0.0, 0.0), (0.0, 1.0) and (1.0, 1.0)) and a curve lying on the diagonal (no skill) has an area of 0.5. Statistical significance of the area under the ROC curve is determined using the Mann Whitney U- statistic (Mason and Graham, 2002; Wilks, 2006). The ROC area is rescaled into a Mann Whitney U-statistic and the statistical significance is evaluated in the context of a normal distribution (for large samples, the distribution of the U- statistic approximates the normal distribution) (Mason and Graham, 2002; Wilks, 2006). A reliability diagram shows the conditional relative frequency of occurrence of an event (observed relative frequency) as a function of the forecast probability, thereby providing information on forecast reliability. The diagonal line on a reliability diagram indicates perfect reliability, the horizontal line represents the observed climatological frequency and the vertical line the model climatology. If a set of forecasts is not reliable, then the corresponding curve will lie away from the diagonal. If the curve is shallower (steeper) than the diagonal, then the forecast system is overconfident (underconfident). A curve lying on or near the horizontal line indicates a forecast system that has no resolution. Deviations from the perfect reliability diagram can be due to sampling limitations rather than necessarily true deviations from reliability (Jolliffe and Stephenson, 2003). As such, a reliability diagram is usually accompanied by an indication of the sample size in each probability bin, such as a histogram. The histogram can therefore be used to indicate the confidence associated with the result for each probability bin, as well as the sharpness of the forecast system. If all the forecasts fell in the model climatology probability bin, then the system would have no sharpness (sharpness is the tendency to forecast extreme values). The Brier score measures the squared difference between predicted and observed probabilities (Brier, 1950). The Brier score can be decomposed into three components, two of which measure forecast resolution and reliability (Murphy, 1973). The third term is a function of the climatological frequency of the occurrence of the event. TherelativequalityofthescoreismeasuredwiththeBrier skill score (BSS), which is defined as the improvement of the probabilistic forecast relative to a reference forecast, usually the climatological frequency of the event. Positive BSS values (with a maximum value of 1) indicate forecasts that are better than climatology. In contrast, BSS values below zero mean that there is no skill and the forecast is worse than a climatological forecast. The BSS with reference to climatology is, however, negatively biased for hindcasts with small ensemble sizes, such as in the present study, due to sampling errors in the forecast probabilities (Müller et al., 2005). Essentially, imperfectly estimated probabilities from the sample of forecasts are unfairly compared to perfectly estimated climatological probabilities (Mason and Stephenson, 2008). This problem can be addressed by the addition of an uncertainty term to the BSS calculation which takes into account the number of ensemble members

4 676 D. Hudson et al. (Mason and Stephenson, 2008; Weigel et al., 2007). This new formulation of the BSS is referred to as the debiased BSS (BSS D ) and is what has been used in the current study. 3. Intraseasonal skill 3.1. Precipitation Figure 1(a) (d) shows the temporal correlation for precipitation in the first and second fortnights for all forecast start months over the hindcast period The degradation in skill in the second half of the month is very clear (Figure 1(c)), and most of the skill in the first fortnight (Figure 1(a)) comes from the first week of the forecast (not shown). Model forecast skill for both fortnights beats persistence of observed (Figure 1(a) (d)). The latter is calculated by persisting the average of the observed fortnight immediately prior to the forecast start date. Correlation skill in the second fortnight varies a great deal as a function of region and forecast start month. It is highest in winter and spring (June November, JJASON) over southern and eastern Australia (Figure 1(e)). The winter months (JJA) contribute more to the skill over southwestern Australia and the southern regions of eastern Australia, whereas the spring months (SON) contribute more to the skill over the northern regions of eastern Australia (not shown). The correlations over these regions and at these times of the year are clearly greater than those from forecasts of persistence of observed (Figure 1(f)). The average RMSE over Australia in the second fortnight in JJASON is 0.79 mm per day, compared to 1.05 mm per day for persistence forecasts. ROC curves and ROC areas provide information on forecast resolution by measuring the ability of a forecast to discriminate between the occurrence and non-occurrence of an event. An ROC area or score below 0.5 implies a skill lower than climatology. Figure 2 shows the ROC area (normalized area under the ROC curve) of the probability that the precipitation anomaly averaged over the first or second fortnight is in the lower or upper tercile, for the winter and spring forecast start months (i.e. same months shown in Figure 1(e) and (f); JJASON). The skill over the east and southeast is apparent in both fortnight and tercile categories. In the second fortnight, for both categories, much of the southeast has skill greater than the climatological value of 0.5, suggesting that the model has some ability in distinguishing between the occurrence of rainfall falling in the upper (lower) tercile and its non-occurrence. In Figure 1 persistence of observed is used as a baseline of comparison of the skill of the second fortnight forecasts. Perhaps a stricter baseline of comparison would be the skill from the persistence of the probabilities obtained from the first forecast fortnight, given the high skill that occurs in the first week of the forecast. This is following the approach of Vitart (2004). In other words, does the extended dynamical forecast provide anything useful over and above what we could get from persistence of the first 2 weeks of the forecast? The model does indeed perform better over certain regions, particularly the southeast, in the second fortnight compared to persisting forecast probabilities of the first fortnight forthistimeofyear(comparefigure2(c)and(e)and Figure 2(d) and (f)). ROC and reliability curves for southeastern Australia for the first and second fortnights are displayed in Figure 3. The curves are obtained by averaging the contingency tables obtained from each grid box (29 boxes in the region) falling within the masked area shown, for forecasts starting in JJASON months. The ROC curves exhibit a decline in skill from the first fortnight (ROC area, A, for the upper tercile = 0.68; lower tercile A = 0.71) to the second fortnight (upper tercile A = 0.66; lower tercile A = 0.63) for precipitation falling in the upper and lower tercile respectively, although skill (ROC area greater than 0.5) still prevails in the second fortnight (Figure 3(a) and (b)). As shown in Figure 2, the model provides more skill in the second fortnight than simply persisting the probabilities from the first fortnight (Figure 3(b), solid versus dashed lines). The reliability diagrams show that the forecast system is over-confident, with over-forecasting biases associated with large forecast probabilities (Figure 3(c) and (d)). The forecasts have some reliability; they correctly indicate increases and decreases in the probability of precipitation falling in the lower (upper) tercile, but the changes in probability are exaggerated. This is a common situation for seasonal forecasting (Mason and Stephenson, 2008). There may be some potential for improving this forecast reliability through statistical calibration, e.g. by inflating the ensemble spread (Doblas- Reyes et al., 2005; Johnson and Bowler, 2009). Currently, the only post-processing performed is the removal of model bias using the hindcast model climatology. The histogram accompanying the reliability diagram for the forecast in the second fortnight indicates that the forecast probabilities peak in frequency at the climatological probability (Figure 3(d)). This is in contrast to the results from the first fortnight, where the frequency of forecasts peaks in the lowest and highest probability bins for both the upper and lower tercile cases (Figure 3(c)). This indicates that as the forecast lead time progresses the forecasts become less sharp (i.e. there is a tendency towards forecasts of climatology). The BSS incorporates reliability and resolution attributes of forecast quality and measures the accuracy of the forecast relative to a reference forecast, taken here as climatology. The debiased BSSs for the reliability curves for the first fortnight, shown in Figure 3(c), indicate increases in skill over climatology of 13% and 2% for precipitation falling in the lower and upper terciles, respectively. For the second fortnight the corresponding values are 7% and 8% for the lower and upper terciles, respectively (for the reliability curves in Figure 3(d)). Figure 4 shows spatial plots of the debiased BSS for the second fortnight over Australia. Regions where the BSS is positive indicate that the model is more skilful than climatology. The skill in both cases (upper and lower tercile) is modest, with a 10 15% improvement in skill over climatology in parts of the southeast Temperature Correlation skill for maximum temperature is generally higher than that for precipitation, particularly for the first fortnight (Figure 5 compared to Figure 1). However, it is clear that the skill for persistence of observed is also higher for maximum temperature compared to precipitation; thus it is more difficult to beat the persistence forecasts (e.g. over northern Australia in the first fortnight for maximum temperature, Figure 5(a) and (b)). For the second fortnight, correlation skill is greatest during spring (SON) (Figure 5(e)). This skill is focused over eastern Australia and beats persistence of observed (Figure 5(f)). The average RMSE over Australia in the second fortnight

5 Intraseasonal Forecasting for Australia 677 Figure 1. Correlation of precipitation anomalies with observed for the first (top row) and second (middle and bottom rows) fortnights of the forecast from POAMA (left column) and from persistence of observed (i.e. persisting the observed fortnight prior to the start date; right column). Plots (a) (d) show the skill from all forecast start months (n = 324), and (e) and (f) are for winter and spring (JJASON) forecast start months (n = 162). Correlations significantly different from zero are shaded (t-test, n = 324 (162), r > 0.1 (0.2) is significant at p = 0.05) and the contour interval is 0.1. during spring (SON) is 1.89 C, compared to 2.35 Cfor persistence forecasts. The second fortnight ROC area and curves for maximum temperature falling in the upper tercile show some skill for the spring forecast start months (SON, Figures 6 and 7(a)). The ROC area is highest over eastern and southeastern Australia (ROC areas > 0.7), but a large proportion of the country exhibits ROC scores significantly (95% confidence) greater than 0.5, suggesting that the model performs better than climatology (Figure 6). In addition, for the second fortnight, the model is able to provide more skill than persisting the forecast probabilities from the first fortnight (Figures 6 and 7(a); in the latter, the ROC area is 0.70 for fortnight 2 and 0.59 for persistence of fortnight 1). The reliability diagram for southeastern Australia indicates an over-forecasting bias, particularly for large forecast probabilities (Figure 7(b)). The histogram showing the frequency of forecasts in different forecast probability bins suggests that the forecasts are still relatively sharp in the second fortnight (frequencies do not peak at the climatological frequency) (Figure 7(b)), although they are still much less sharp than those of the first fortnight, where the histogram exhibits a characteristic U shape (not shown). According to the debiased BSS the skill improvement over climatology over eastern Australia at this skilful time of year (SON) ranges from 10% to 25% (Figure 8). Minimum temperature is the least skilful of the three variables analysed here, particularly for the second fortnight

6 678 D. Hudson et al. Figure 2. ROC area of the probability that precipitation averaged over the first (a, b) and second fortnight (c, d) is in the lower (left column) or upper (right column) tercile for winter and spring (JJASON) forecast start months. (e) and (f) show the ROC areas obtained by persisting the probabilities from the first forecast fortnight. ROC areas significant at the 5% significance level are shaded (Mann Whitney U-test) and the contour interval is (Figure 9). This is also true for the probabilistic forecasts (not shown). An analysis of 3-month rolling seasons shows that the correlation at any time or grid point for the second fortnight is mostly less than 0.3, and there are no times or regions of cohesive appreciable skill (not shown). An investigation of the reasons why the model has less skill for minimum temperature compared to maximum temperature is beyond the scope of this paper. It is may be related to model error, but it is possible that it may also reflect reduced predictability for minimum temperature. In an examination of the relationship between the El Niño Southern Oscillation (ENSO) and Australian land surface temperature, Jones and Trewin (2000) found that statistically significant correlations between the Southern Oscillation Index (SOI) and seasonal mean minimum temperature were less widespread than for maximum temperature (although there were locally high correlations). Similarly, Hendon et al. (2007) found that the relationship of the Southern Annular Mode (SAM) with daily minimum temperatures over Australia was weaker than the relationship with daily maximum temperatures. 4. Sources of predictability The impact of key drivers of Australian rainfall variability on the skill of forecasting intraseasonal rainfall in POAMA is investigated. This investigation is motivated by the need to understand what is providing the predictability diagnosed

7 Intraseasonal Forecasting for Australia 679 Figure 3. ROC (a, b) and reliability (c, d) diagrams of the probability that precipitation averaged over the first (left column) or second (right column) fortnight is in the upper (black lines with star symbols) or lower (grey lines with square symbols) tercile. The dashed lines in (b) are the ROC curves obtained by persisting the probabilities from the first forecast fortnight. The diagonal line in the ROC diagrams (a, b) represents the no-skill line. In the reliability diagrams (c, d), the histogram represents the frequency of forecasts, i.e. the relative population of each forecast probability bin. The dashed horizontal line represents a no-resolution forecast (observed climatology) and the dashed vertical line represents a no-sharpness forecast (model climatology). The solid diagonal line represents perfect reliability. Both the ROC and reliability diagrams are obtained from winter and spring (JJASON) forecast start months for southeastern Australia (map inset). Figure 4. Debiased BSS (reference score is climatology) for the probability that precipitation averaged over the second fortnight is in the (a) lower or (b) upper tercile for winter and spring(jjason) forecast start months. The contour interval is Grid boxes with positive (negative) BSSs greater (less) than 0.05 ( 0.05) are dark (light) shaded. in section 3, as well as to provide insight into how model shortcomings may be limiting forecast skill. We focus on the effect of the ENSO, the Indian Ocean Dipole (IOD), the SAM and the MJO on Australian precipitation during winter and spring (JJASON), when POAMA s skill for predicting precipitation is highest (Figure 1(e)) ENSO and the IOD ENSO is the primary driver of predictable interannual variations of Australian rainfall, particularly over eastern regions during winter (JJA) and spring (SON) (e.g. Risbey et al., 2009). POAMA can skilfully predict tropical sea

8 680 D. Hudson et al. Figure 5. Correlation of maximum temperature anomalies with observed for the first (top row) and second (middle and bottom rows) fortnights of the forecast from POAMA (left column) and from persistence of observed (i.e. persisting the observed fortnight prior to the start date; right column). Plots (a) (d) show the skill from all forecast start months (n = 324), and (e) and (f) are for spring (SON) forecast start months (n = 81). Correlations significantly different from zero are shaded (t-test, n = 324 (81), r > 0.1 (0.2) is significant at p = 0.05) and the contour interval is 0.1. surface temperature (SST) anomalies associated with ENSO two to three seasons in advance (Wang et al., 2008) and, on a seasonal time-scale, can depict the teleconnection to Australian rainfall (Lim et al., 2009). Here, the impact of ENSO on intraseasonal rainfall prediction skill in POAMA is examined by stratifying forecasts into El Niño/La Niña cases and neutral cases. The observed monthly (3-month running mean imposed) ENSO index from the US National Weather Service Climate Prediction Center (CPC; monitoring/ ensostuff/ensoyears.shtml) is used to classify forecast start months as El Niño/La Niña or neutral events, using thresholds of ±0.5 C. The analysis is performed for all forecast start times in June November (JJASON). The stratification used in this paper does not distinguish between El Niño and La Niña cases because the sample size is too small. Hence we focus on the impact of ENSO being in an extreme versus being neutral. The correlation skill of predicting rainfall in the second fortnight is substantially higher over eastern-coastal, northeastern, northern and southwestern Australia in El Niño and La Niña years (Figure 10(a)) compared to neutral years (Figure 10(c)). These regions of higher skill are also where ENSO tends to have an impact on Australian rainfall on a seasonal time-scale, particularly in spring (e.g. Risbey et al., 2009) and where POAMA successfully simulates the teleconnection to rainfall (Lim et al., 2009). In these ENSO extreme cases, the model provides additional skill

9 Intraseasonal Forecasting for Australia 681 Figure 6. ROC area of the probability that maximum temperature averaged over the second fortnight is in the upper tercile for spring (SON) months from (a) POAMA and (b) that obtained for the second fortnight by persisting the probabilities from the first forecast fortnight. ROC areas significant at the 5% significance level are shaded (Mann Whitney U-test) and the contour interval is Figure 7. (a) ROC and (b) reliability diagrams of the probability that maximum temperature averaged over the second fortnight is in the upper tercile. The dashed line in (a) is the ROC curve obtained by persisting the probabilities from the first forecast fortnight. The diagonal line in the ROC diagram represents the no-skill line. In (b) the histogram represents the frequency of forecasts, i.e. the relative population of each forecast probability bin. The dashed horizontal line represents a no-resolution forecast (observed climatology) and the dashed vertical line represents a no-sharpness forecast (model climatology). The solid diagonal line represents perfect reliability. Both the ROC and reliability diagrams are obtained for spring (SON) forecast start months for the southeast of Australia (map inset). Figure 8. Debiased BSS (reference score is climatology) for the probability that maximum temperature averaged over the second fortnight is in the uppertercileforthespring(son)forecast start months. The contour interval is Grid boxes with positive (negative)bsss greater (less) than 0.05 ( 0.05) are dark (light) shaded. in the second fortnight compared to that obtained from persistence of observed (Figure 10(a) and (b)). Persistence is also a better forecast in extreme ENSO cases than in neutral cases over northeastern Australia (Figure 10(b) and (d)). It thus appears that some of the enhanced skill during ENSO extreme cases may stem from increased persistence, which POAMA faithfully replicates. Low-frequency coupled ocean atmosphere variability in the Indian Ocean, namely the IOD, has been shown to affect rainfall overaustralia (Ansellet al., 2000; Saji and Yamagata, 2003; Meyers et al., 2007; Risbey et al., 2009; Ummenhofer et al., 2009). The IOD is defined as the SST anomaly difference between the western (50 70 E; 10 S 10 N) and eastern ( E; 10 S 0 N) tropical Indian Ocean (Saji et al., 1999). POAMA can skilfully predict the peak phase of the occurrence of the IOD in austral spring (SON) with about 4 months lead time (Zhao and Hendon, 2009) and the teleconnection to rainfall across southern Australia is faithfully represented in POAMA (Lim et al., 2009). To investigate the impact of the IOD on the skill of predicting precipitation in the second fortnight in POAMA, the IOD at the initial forecast time is classified based on monthly data (JJASON months). The IOD is calculated from the Reynolds OI.v2 SSTs (Reynolds et al., 2002) for June 1982 to November 2006 and prior to this (June 1980 to November 1981) from the HadISST dataset (Rayner et al., 2003). The classification stratifies months into those when the IOD is

10 682 D. Hudson et al. Figure 9. Correlation of minimum temperature anomalies with observed for all forecast start months for the first (top row) and second (bottom row) fortnights of the forecast. (a) and (c) are the skill from POAMA and (b) and (d) are the skill obtained from persistence of observed (i.e. persisting the observed fortnight prior to the start date). Correlations significantly different from zero are shaded (t-test, n = 324, r > 0.1 issignificantatp = 0.05) and the contour interval is 0.1. strong or extreme (greater than the mean ±0.5 standard deviation; n = 91) and those when it is weak or neutral (within the mean ±0.5 standard deviation; n = 71). The mean and standard deviation are calculated from all JJASON months in the analysis period ( ). There is a marked increase in forecast skill in the second fortnight obtained when forecasts are initialized in months when the magnitude of the IOD is large (Figure 11(a)) compared to when it is small (Figure 11(c)) (note that there is no discrimination between positive and negative IOD events). In addition, there is increased skill in POAMA s second fortnight over southeastern and southwestern regions compared to the skill from persistence of observed (Figure 11(a) and (b)). Again, as for ENSOextreme forecasts, some of the increased skill for IODextreme forecasts may stem from increased persistence, particularly over southern Australia (Figure 11(b) and (d)). The pattern of the correlations in fortnight 2 for the strong IOD case (Figure 11(a)) is similar to that obtained for the El Niño/La Niña case (Figure 10(a)). This may be related to the fact that the IOD is not independent of ENSO (e.g. Saji et al., 2006; Meyers et al., 2007). In order to examine the impact of the IOD with the effect of ENSO removed, those cases that are associated with El Niño or La Niña events are removed. As before, the observed monthly (3-month running mean imposed) ENSO index from the US National Weather Service CPC ( monitoring/ ensostuff/ensoyears.shtml) is used, and warm and cold events are defined based on thresholds of ±0.5 C. In the updated IOD classification, IOD-extreme months are analysed only if the corresponding ENSO index falls within 0.5 and+0.5 C (i.e. a neutral ENSO event). The results show that most of the rainfall skill in POAMA that is attributable to the IOD is located over the southeast and southern regions of the country (Figure 12(a)). However, the skill from POAMA over central southern regions is not appreciably higher than that obtained from persistence of observed (Figure 12(a) and (b)). Removing the effect of ENSO removes significant correlations over the eastern and northern regions (everything north of 25 S) as well as over the southwest (Figure 11(a) compared to Figure 12(a)). Similar results were obtained by Risbey et al. (2009) and Lim et al. (2009) when looking at the observed correlation between Australian rainfall and the IOD. They found that when the IOD was considered without the effects of ENSO, then significant correlations in regions where ENSO dominates disappear (primarily the northeast), but the IOD influence on rainfall remains over a broad part of southern Australia. From the above analysis of skill during ENSO and IOD extremes, it is clear that some of the skill in the second fortnight can be attributed to seasonal variability appearing on the intraseasonal time-scale. In other words, the week to month persistence of ENSO and IOD SST anomalies creates a tendency for equally persistent precipitation anomalies that appear as skill on intraseasonal time-scales, but which actually arise from longer time-scale phenomena. This is

11 Intraseasonal Forecasting for Australia 683 Figure 10. Correlation skill of forecasting precipitation in the second fortnight in JJASON forecast start months for El Niño and La Niña cases (n = 76) (top row) from (a) POAMA and (b) persistence of observed, versus in neutral cases (n = 86) (bottom row) from (c) POAMA and (d) persistence of observed. Correlations significantly different from zero are shaded (t-test, n = 76 (86), r > 0.2issignificantatp = 0.05) and the contour interval is 0.1. evident to some extent from the skill seen in the persistence forecasts (Figures 10(b), 11(b) and 12(b)). However, it would be interesting to know how much of the intraseasonal skill to which ENSO and the IOD contribute is genuinely from intraseasonal variability or processes. To try and address this issue we have high-pass filtered the forecast and observed cases in order to isolate the intraseasonal anomalies. For each event identified (e.g. an IOD large event for a particular forecast start month and year) we remove the mean of the first 2 months of the forecast from the forecast for the second fortnight. The same is done for the corresponding observed data and the correlation skill is obtained using this filtered data. Figure 13 shows the skill in the second fortnight from this filtered data during extremes of ENSO (Figure 13(a)) and the IOD (with the effect of ENSO removed ) (Figure 13(b)) and can be compared to the skill obtained from the unfiltered data (Figures 10(a) and 12(a), respectively). Under the extremes of ENSO, much of the skill seen in the unfiltered data (Figure 10(a)) disappears in the filtered data (Figure 13(a)), particularly over northern and northeastern Australia. This suggests that much of that skill in the second fortnight has its origins in longer-time-scale, seasonal phenomena. This is confirmed by the skill of the persistence forecast (Figure 10(b)). Figure 13(a) suggests that some of the skill over southwestern Australia, in particular, may be due to intraseasonal processes which operate differently, and are more predictable under ENSO extreme cases compared to ENSO neutral cases (the skill is lower over this region for neutral events in the filtered data; not shown). Under extremes of IOD, the difference in skill between the filtered (Figure 13(b)) and unfiltered (Figure 12(a)) data is less dramatic. There is a slight reduction in the magnitude and spatial extent of the skill in the filtered data, but in general the skill over southern and southeastern Australia remains (Figure 13(b)). The skill of the persistence forecasts shows that there is less skill from the persistent anomalies under IOD extremes (Figure 12(b)) compared to ENSO extremes (Figure 10(b)), and that the main region of skill from persistent anomalies under IOD extremes is over northwestern Australia (Figure 12(b)). Correspondingly, we do see a reduction in skill over this region in the filtered data (Figure 13(b)) compared to the unfiltered data (Figure 12(a)), although the skill in both cases is poor. The results suggest that, in contrast to ENSO, a significant proportion of the skill under extremes of IOD can be attributed to the predictability of intraseasonal processes, rather than just skill from predicting seasonal variability which manifests at the intraseasonal time-scale. It is beyond the scope of this study to actually identify which intraseasonal processes are operating under the extremes of ENSO or IOD to bring about improved intraseasonal prediction skill, but will form part of future work SAM The SAM (also known as the Antarctic Oscillation or the High Latitude Mode) is an important mode of variability for high and middle latitudes and is characterized by shifts in the strength of the zonal flow between about

12 684 D. Hudson et al. Figure 11. Correlation skill of forecasting precipitation in the second fortnight in JJASON forecast start months when the magnitude of the IOD is large (i.e. greater than the mean ±0.5 standard deviations, n = 91) (top row) from (a) POAMA and (b) persistence of observed, versus when the magnitude of the IOD is small (i.e. within the mean ±0.5 standard deviations, n = 71) (bottom row) from (c) POAMA and (d) persistence of observed. Correlations significantly different from zero are shaded (t-test, n = 91(71), r > 0.2issignificantatp = 0.05) and the contour interval is S and S (e.g. Gong and Wang, 1999; Thompson and Wallace, 2000). SAM has been shown to be an important contributor to rainfall variability over Australia (Hendon et al., 2007; Risbey et al., 2009). Although the decorrelation time of the SAM is relatively short ( 15 days), its long time-scale relative to synoptic weather may be a source of multi-week predictability. To stratify our dataset based on extremes of the SAM, the observed daily SAM index for JJASON months is examined. The daily SAM index was obtained upon request from the US National Weather Service CPC ( daily ao index/aao/aao index.html). The index is constructed by projecting daily 700 hpa height anomalies onto the leading empirical orthogonal function (EOF) of monthly mean 700 hpa height poleward of 20 S (data available extended to May 2005; thus the stratification is from ). The classification here stratifies the hindcast dataset into forecast start months where the observed SAM index, averaged over the first 7 days of the month, is strong (greater than the mean ±0.5 standard deviation; n = 84) and when it is weak (within the mean ±0.5 standard deviation; n = 66). The mean and standard deviation of the observed SAM index, used in the threshold calculations, are calculated from all the days falling in JJASON months from 1980 to The classification does not distinguish between positive and negative SAM events. There has not been any previous research on the skill of POAMA in predicting the daily SAM index. In order to investigate the capability of POAMA for predicting the SAM, the daily SAM index is calculated for each POAMA hindcast in a similar fashion to that observed: daily anomalies are projected onto the observed EOF pattern of monthly mean 700 hpa height poleward of 20 S. Using the observed EOF for calculating the predicted SAM index allows for a direct comparison with the observed SAM index. The correlation is computed between the daily observed SAM index and the ensemble mean SAM index, stratified for large and small SAM events at the initial time (defined above), for lead times out to 30 days. Figure 14 shows that for large SAM events the correlation remains above 0.5 out to about 13 days. The correlation drops below 0.5 after about 6 days for forecasts initialized during neutral SAM. Because of the shorter timescale and prediction lead time for the SAM than for ENSO and the IOD, we assess the prediction skill during extremes of the SAM for the fortnight comprising weeks 2 and 3, rather than weeks 3 and 4. There is clearly more skill in forecasting precipitation in weeks 2 and 3 over eastern and southwestern Australia when the SAM is strong compared to when it is weak (Figure 15(a) and (c)). When the SAM is strong, most of the increased skill over a persistence forecast occurs over southwestern and central-eastern Australia (Figure 15(a) and (b)), which are regions where the SAM has an appreciable impact in winter and spring, respectively (e.g. Hendon et al., 2007; Risbey et al., 2009). In contrast to what might be expected from observed studies, our results do not show improved rainfall skill over the extreme southeast (Figure 15(a)). This

13 Intraseasonal Forecasting for Australia 685 Figure 12. As Figure 11, but for non-enso cases (see text for details). Correlations significantly different from zero are shaded (t-test, n = 48 for IOD large case and n = 38 for IOD small case, r > 0.3issignificantatp = 0.05) and the contour interval is 0.1. Figure 13. Correlation skill of forecasting precipitation in the second fortnight in JJASON forecast start months for (a) El Niño and La Niña cases (i.e. same cases as for Figure 10(a), n = 76) and (b) when the magnitude of the IOD is large (with the effect of ENSO removed ; i.e. same cases as for Figure 12(a), n = 48). The data are high-pass filtered: the mean of the first 2 months of the forecast is removed from the second fortnight for each case (see text for details). Correlations significantly different from zero are shaded and the contour interval is 0.1. may be related to the relatively low resolution of POAMA, such that the Victorian Alps are poorly resolved. In winter and spring the observed SAM is uncorrelated with ENSO and removal of ENSO variation does not affect the observed correlation of SAM with Australian rainfall (Hendon et al., 2007; Risbey et al., 2009). However, this is not necessarily the case in the forecast model. To test this, only non-enso cases are examined, as done in section 4.1. The resultant effect was to remove the high correlations over northeastern Australia (Figure 16(a) compared to Figure 15(a)), which makes the results more comparable with observed studies. The corresponding SAM neutral stratification with the effect of ENSO removed shows hardly any significant correlations for both POAMA and persistence of observed (not shown) MJO The MJO is possibly a significant source of predictability on intraseasonal time-scales (Waliser et al., 2006) and is

14 686 D. Hudson et al. not surprising that POAMA does not have any extra skill in forecasting rainfall over Australia during strong MJO periods. Even if POAMA forecasts the large-scale tropical structure of the MJO perfectly, at week 2 it does not simulate the correct relationship between the MJO and rainfall over most of Australia during winter and spring (Marshall et al., 2011). 5. Summary and conclusions Figure 14. Correlation skill of the SAM index for large SAM events (solid line) (see text for details) in JJASON months (n = 84) and small SAM events (dashed line) (n = 66) as a function of lead time (days). important for Australia, given its direct and remote impacts on tropical and extratropical rainfall (Hendon and Liebmann, 1990; Hall et al., 2001; Wheeler and Hendon, 2004; Risbey et al., 2009; Wheeler et al., 2009). The MJO has its greatest impact on Australian rainfall over northern regions in summer (DJF). During winter (JJA) and spring (SON) the MJO has an impact on extratropical Australian rainfall (particularly the southeast), associated with remotely forced vertical motion occurring within anomalous extratropical highs and lows (Wheeler et al., 2009). The POAMA model is able to simulate reasonably realistic MJO events (Zhang et al., 2006; Marshall et al., 2008) and can predict the largescale structure of the MJO in the Tropics out to about 21 days (measured by the bivariate correlation of an MJO index exceeding 0.5; Rashid et al., 2011). To investigate the impact of the MJO on the skill of predicting Australian rainfall in winter and spring, the hindcast dataset is stratified basedontheexistenceorabsenceofanmjo(independent of the phase of the MJO) using the daily observed real-time multivariate MJO (RMM) index of Wheeler and Hendon (2004). These data are available online at An MJO is defined as being strong if it has an RMM amplitude greater than 1 (based on the average amplitude of the first 7 days of the month) and weak or absent if it is less than 1. The impact on the skill of predicting Australian rainfall in the second fortnight of the forecast, as well as the fortnight obtained from averaging weeks 2 and 3, is examined. The correlation results obtained are inconclusive and many regions showing significant correlations (rainfall prediction skill) are higher in the weak MJO case rather than the strong MJO case (not shown). There is no clear significant relationship between precipitation skill in winter and spring and the amplitude of the MJO. Unfortunately, the hindcast dataset is too small to allow useful stratification of cases by, for example, the phase of the MJO and season. Such stratification may highlight phases of the MJO for which skill in predicting Australian rainfall exists. Marshall et al. (2011) examined the relationship between rainfall and the MJO in POAMA and found that in JJA and SON the model was unable to simulate the teleconnection between the MJO and extratropical (or tropical) Australian rainfall across the different MJO phases. Given their result, it is This paper investigates the potential of the POAMA seasonal forecast system to fill the current prediction capability gap between weather forecasts and seasonal outlooks in Australia. This initial examination of the intraseasonal skill of POAMA is promising. There are definite indications of useful skill for certain regions at certain times of the year. Most of the skill for forecasting precipitation and maximum temperature in the second fortnight (average days of the forecast) is focused over eastern and southeastern Australia, during austral winter and spring for precipitation and during spring for maximum temperature. Over these regions at these times, the forecast of the second fortnight from the model performs generally better than forecasts of persistence of observed, better than persistence of the forecast of the first fortnight (average days 1 14) and better than climatology. The model shows very little skill in forecasting minimum temperature in the second fortnight. In the second half of the paper, the source of the intraseasonal predictability of rainfall was explored. The analysis is performed on the austral winter and spring seasons, when POAMA has demonstrable rainfall skill. Results from this initial investigation indicate that ENSO, the IOD and the SAM are all important contributors to the intraseasonal rainfall forecast skill evident in winter and spring. There are other possible drivers of intraseasonal variability that may provide intraseasonal predictability that have not been considered in this study, for example the roles of antecedent soil moisture, blocking and stratosphere troposphere interactions. Even though ENSO is an interannual phenomenon, our results show that it has a clear impact on intraseasonal time-scales. There is significantly higher skill in predicting rainfall over eastern-coastal, northeastern, northern and southwestern Australia in the second fortnight during El Niño/La Niña cases compared to neutral cases. These regions of higher skill are also where ENSO tends to have an impact on Australian rainfall on a seasonal time-scale, particularly in spring (e.g. Risbey et al., 2009) and where POAMA successfully simulates the teleconnection to rainfall (Lim et al., 2009). In addition, during El Niño/La Niña cases the model beats the skill obtained from forecasts of persistence of observed. Some of the higher skill during ENSO extremes compared to neutral years, particularly over northern and north-eastern Australia, stems from enhanced persistence. The IOD has been shown to influence rainfall over the southern portion of Australia (Ansell et al., 2000; Saji and Yamagata 2003; Meyers et al., 2007; Risbey et al., 2009; Ummenhofer et al., 2009). Since the IOD is not entirely independent of ENSO (e.g. Saji et al., 2006; Meyers et al., 2007), the impact of the IOD with and without the effect of ENSO removed is assessed. Removing the effect of ENSO mainly removes the significant skill found over eastern and northern regions. Most of the rainfall skill in POAMA that is attributable to the IOD (for non-enso cases) is located over