The influence of local sea surface temperatures ... - Wiley Online Library

PUBLICATIONS Journal of Geophysical Research: Atmospheres RESEARCH ARTICLE 10.1002/2016JD025495 Key Points: • Warm coastal sea surface temperatures increase the likelihood of weak cyclones on the Australian east coast • Strong cyclones and those with strong upper level forcings are less affected by changed sea surface temperatures • Warmer sea surface temperatures increase the likelihood of cyclones with strong winds and heavy rain

Supporting Information: • Supporting Information S1 Correspondence to: A. S. Pepler, [email protected]

Citation: Pepler, A. S., L. V. Alexander, J. P. Evans, and S. C. Sherwood (2016), The influence of local sea surface temperatures on Australian east coast cyclones, J. Geophys. Res. Atmos., 121, 13,352–13,363, doi:10.1002/ 2016JD025495. Received 10 JUN 2016 Accepted 2 NOV 2016 Accepted article online 7 NOV 2016 Published online 25 NOV 2016

The influence of local sea surface temperatures on Australian east coast cyclones Acacia S. Pepler1, Lisa V. Alexander1, Jason P. Evans1, and Steven C. Sherwood1 1

Climate Change Research Centre and ARC Centre of Excellence for Climate System Science, University of New South Wales, Sydney, New South Wales, Australia

Abstract

Cyclones are a major cause of rainfall and extreme weather in the midlatitudes and have a preference for genesis and explosive development in areas where a warm western boundary current borders a continental landmass. While there is a growing body of work on how extratropical cyclones are influenced by the Gulf Stream and Kuroshio Current in the Northern Hemisphere, there is little understanding of similar regions in the Southern Hemisphere including the Australian east coast, where cyclones that develop close to the coast are the main cause of severe weather and coastal flooding. This paper quantifies the impact of east Australian sea surface temperatures (SSTs) on local cyclone activity and behavior, using three different sets of sea surface temperature boundary conditions during the period 2007–2008 in an ensemble of Weather Research and Forecasting Model physics parameterizations. Coastal sea surface temperatures are demonstrated to have a significant impact on the overall frequency of cyclones in this region, with warmer SSTs acting as a trigger for the intensification of weak or moderate cyclones, particularly those of a subtropical nature. However, sea surface temperatures play only a minor role in the most intense cyclones, which are dominated by atmospheric conditions.

1. Introduction Cyclones are one of the most important synoptic features of the midlatitudes, causing a large proportion of total rainfall and severe weather events [e.g., Pfahl and Wernli, 2012]. While cyclones can develop throughout the midlatitudes, both cyclogenesis and explosive development are particularly favored in maritime environments to the east of major continents [Sinclair, 1995; Lim and Simmonds, 2002; Hoskins and Hodges, 2002, 2005; Allen et al., 2010] such as near the Australian east coast. In this region, the cyclones locally known as East Coast Lows (ECLs) are responsible for more than 60% of extreme rainfall [Lavender and Abbs, 2013], at least 57% of major floods [Callaghan and Power, 2014], at least 70% of the most severe coastal sea level anomalies [McInnes and Hubbert, 2001], and the majority of days with wave heights greater than 5 m [Dowdy et al., 2014]. The areas of enhanced explosive cyclogenesis described in Allen et al. [2010] are associated with strong temperature contrasts between the coast and ocean, particularly in the winter near warm western boundary currents. Several studies have now attempted to quantify the effect of local sea surface temperatures (SSTs) on cyclone activity in the Gulf Stream [Brayshaw et al., 2011; Booth et al., 2012; Nelson and He, 2012] and the Kuroshio Current [Hayasaki et al., 2013; Hirata et al., 2015, 2016]. Higher SSTs and stronger SST gradients are associated with deeper minimum pressures [Booth et al., 2012], while changes in the location of highest SSTs can also change the preferred location of cyclone tracks [Hayasaki et al., 2013]. To date, there have been fewer attempts to assess the importance of SSTs in the East Australian Current (EAC) to local cyclone development. McInnes et al. [1992] performed numerical simulations for 24 h of four ECLs where SSTs were enhanced by 2–3°C and identified a 2–7 hPa change in the minimum central pressure with a corresponding increase in both the total precipitation and the areal extent of gale force winds. Using a variety of observations Hopkins and Holland [1997] found no link between cyclone frequency and observed SST anomalies, with some relationship suggested between cyclone intensification and SST gradients near the coast. Finally, Chambers et al. [2014, 2015] assessed the influence of SST resolution for four ECLs in a highresolution regional climate model. Increased SST resolution and improved representation of eddies had little effect on the overall intensity or development of the low, but resulted in shifts in the locations of thunderstorms and peak rainfall, of considerable interest to forecasters.

©2016. American Geophysical Union. All Rights Reserved.

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The EAC is a “hot spot” for projected global warming, with SSTs in parts of the Tasman Sea expected to warm by more than 2°C between the 1990s and 2060s [Oliver et al., 2013] and median projected changes by 2090

SSTS AND AUSTRALIAN EAST COAST CYCLONES

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Figure 1. The CORDEX (50 km) WRF model domain, with topography shown in color and the smaller (10 km) model region indicated by a black box. The dashed lines indicate the ECL identification region.

under RCP8.5 close to 4°C [Lenton et al., 2015]. While global climate models project a decline in extratropical cyclones in Australian latitudes over the coming century [Dowdy et al., 2014; Grieger et al., 2014], their resolutions generally inhibit accurate representation of the EAC, with large uncertainty in how changes in SST may affect ECLs in areas such as the Australian east coast. Recent regional climate model studies suggest a weaker decline in the frequency of ECLs directly adjacent to the coast [Pepler et al., 2016], which could be related to projected SST increases. For these reasons, it is important to more deeply understand the role local sea surface temperatures play on the formation, intensification, characteristics, and impacts of cyclones in this region. In this paper we will present a long, 2 year case study of the effect of the EAC on Australian ECLs. Using an ensemble of three regional climate models, we assess the impact of changing the magnitude of the EAC in the prescribed SST boundary conditions on the frequency, behavior, and impacts of all ECLs over a 2 year period, 2007 to 2008. This allows us to more systematically characterize the importance of coastal SSTs on the development of cyclones from a range of different atmospheric conditions. The paper is organized as follows. In section 2, we will describe the model setup as well as the methods used to identify, track, and classify cyclones. We then present the main results from the model simulations, identifying the impacts of SST changes on cyclone frequency, locations, characteristics, and impacts. Finally, we compare the relationships identified from model simulations to the relationship between observed SSTs and cyclone frequency.

2. Methods 2.1. Model Setup Model simulations were performed with version 3.6 of the Weather Research and Forecasting Model [Skamarock et al., 2008]. All model simulations use 30 vertical levels, with a model top at 50 hPa and atmospheric boundary conditions provided by the ERA-Interim reanalysis [Dee et al., 2011]. The model was run at a 50 km resolution across the CORDEX-Australasia [Giorgi et al., 2009] domain shown in Figure 1, with the 50 km resolution results providing boundary conditions for a 10 km resolution model run over a smaller domain covering southeast Australia and the adjacent ocean. Wind velocities and geopotential height in the low-resolution domain are also spectrally nudged toward the boundary conditions at model levels above 19, with a relaxation time of 1 h (0.0003 s 1) and for wave numbers above 18 in the x direction and 12 in the y direction, corresponding to approximately 600 km in both directions. The first ECL in the model simulations occurs on 4 January, so the first three days of the model simulations are not considered. Simulations were run for the 2 year period 2007–2008. This period includes 45 ECLs as identified using gridded sea level pressure data from the ERA-Interim reanalysis and is longer than most similar studies, which typically study a small number of ECLs. This is close to the long-term average ECL frequency of 22 per year [Speer et al., 2009] and incorporates a wide range of events with different intensities, impacts, and synoptic

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a

Table 1. The Physics Parameters Used for the Three WRF Regionally Downscaled Models RCM Name Cumulus physics PBL scheme Surface physics Longwave radiation physics Shortwave radiation physics

R1

R2

R3

Kain-Fritsch Mellor-Yamada-Janjic Eta Rapid Radiative Transfer Model Dudhia

Betts-Miller-Janjic Mellor-Yamada-Janjic Eta Rapid Radiative Transfer Model Dudhia

Kain-Fritsch Yonsei University MM5 NCAR Community Atmosphere Model NCAR Community Atmosphere Model

a

The WRF Double Moment 5-class (WDM5) microphysics scheme is used in all cases. For more information on the specifics of the model parameters, see Skamarock et al. [2008].

situations, including the very severe ECLs of June 2007 [Mills et al. 2010] and the ECLs used for WRF model evaluation in Evans et al. [2012] and Ji et al. [2013]. The large number of ECLs should identify any systematic impacts and their significance, as well as enabling the identification of types of systems that may be more or less impacted by SSTs. While most similar studies [e.g., Booth et al., 2012; Chambers et al., 2014] use a single set of physics parameterizations, the representation of cyclones and their associated impacts can vary substantially with the choice of model physics, and it is likely that the choice of physics and convection schemes will influence the sensitivity of ECL behavior to SSTs. Consequently, in this study we use an ensemble of three different sets of physics parameterizations (Table 1). These three parameter combinations were selected to maximize model skill and independence of errors, following a comprehensive analysis of 36 different combinations of physics parameterizations over eight significant ECLs [Evans et al., 2012; Ji et al., 2013] and have previously been used for climate projections in this region [Evans et al., 2014; Pepler et al., 2016]. All three sets of parameterizations have previously been shown to represent the observed frequency and characteristics of ECLs when driven by the NCEP1 reanalysis [Di Luca et al., 2016], with the control runs in this study producing between 42 and 55 ECLs, similar to the frequency in ERA-Interim. The models tend to overestimate rainfall on the eastern seaboard, particularly over areas of elevated topography and during the summer months, with average eastern seaboard rainfall in the R1 and R3 simulations 41 to 46% higher than in a 5 km resolution gridded rainfall product derived from station data [Jones et al., 2009]. R2 is closer to the observed rainfall, with average biases of +5% for the 50 km resolution and +18% for the 10 km resolution. All model simulations used prescribed time-varying SSTs, with each of the three physics parameterizations run using four different SST data sets for a total of 12 simulations. The control run uses daily sea surface temperature data from the Bluelink reanalysis [Oke et al., 2013], which is available at a 10 km resolution, providing better representation of eddy activity in the EAC. This is the data set used by Chambers et al. [2014, 2015], who found that simulations of ECLs using these SSTs had peak rainfall intensities closer to the observations, but caused little change in the overall ECL frequency or characteristics when compared to results using lowresolution SSTs. To test the sensitivity of model output to small changes in SSTs, model runs are also produced using the default SST data set provided with ERA-Interim, which is referred to as “low-resolution” SSTs in this paper. In addition, two other SST data sets were developed from the Bluelink reanalysis, “No EAC” and “DoubleEAC,” which aim to preserve some of the small-scale SST variability while removing (or doubling) the apparent strength of the EAC. We considered the EAC to be defined as the area within the region 20–50°S and 140–160°E where SSTs are abnormally warm relative to their latitude, giving an EAC pattern that varies with time. For each day, and every point in this region, we first calculated the SST anomaly relative to the average SST for the same day and latitude between 160 and 165°E. We then identify all points where the (centered) 29 day running average anomaly was positive, which is considered the EAC pattern for that day. The daily NoEAC SST field was created by subtracting this EAC pattern from all points where the daily SST anomaly is positive, while the DoubleEAC field was created by adding the EAC pattern to all points with positive anomalies. The average difference between the SSTs in the control and NoEAC cases is given in Figure 4, with widespread anomalies greater than 1°C and average anomalies greater than 2°C on parts of the northern coast. However, the strength and spatial extent of the pattern varies throughout the study period, with a stronger average intensity in 2007 (0.9°C) than 2008 (0.6°C).

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2.2. ECL Tracking There are a large number of automated approaches for cyclone detection and tracking. While the interannual variability and characteristics of cyclones are sensitive to the choice of method and parameters, the majority of methods have similar skill for detecting large, intense, or impactful events [Neu et al., 2013; Pepler et al., 2015a]. In this paper we use the University of Melbourne tracking scheme [Murray and Simmonds, 1991; Simmonds et al., 1999], which identifies cyclones from maxima in the Laplacian of mean sea level pressure before identifying a corresponding closed low. This is a widely used approach Figure 2. The average EAC SST anomaly field (K) in southeast Australia, with [e.g., Flocas et al., 2010; Pinto et al., the ECL region shown by a black box. 2005] and has been previously evaluated for ECLs in comparison with three other approaches in comparison to a subjective ECL database in Pepler et al. [2015a]. Of all the methods compared, this was found to have the most skill at identifying observed ECLs and was the least biased toward particular seasons or subcategories of ECLs. The algorithm is applied to the 6-hourly mean sea level pressure (MSLP) from both the ERA-Interim reanalysis [Dee et al., 2011] and the WRF model output, bilinearly regridded from the curvilinear model grid to a regular 0.5° grid. The intensity of a cyclone is given by the average of the Laplacian for a 200 km radius around the center. A cyclone is considered to be an ECL if it has a closed low with an intensity of at least 1 hPa.(deg lat) 2 for at least 6 h (two consecutive fixes) and is located within the ECL domain (Figure 2) in at least one instance, as per Pepler et al. [2015a]. A weaker intensity threshold of 0.5 hPa.(deg lat) 2 is also used in some cases to investigate weak lows. Following the method of Dowdy et al. [2013], we calculated the 2 day running average maximum 500 hPa geostrophic vorticity (GV) within the region bounded by 24–34°S and 145.5–160.5°E for each time step, with high GV indicated by values greater than 20 × 10 5 s 1. This is only weakly correlated with the maximum surface intensity for a given ECL, by between 0.35 and 0.4, and is a measure of the strength of the associated upper level conditions. As measures of ECL impacts we calculated both the spatial average and point maxima of the instantaneous 10 m wind speed and the 6-hourly accumulated rainfall centered on the time of the low within a radius of 250 km or 500 km around the low center. As the east coast of Australia is notable for a large frequency of subtropical cyclones [Yanase et al., 2014], we also assessed whether impacts were more significant for extratropical or subtropical systems. Following the method of Hart [2003] as applied by Yanase et al. [2014], a system was classified as extratropical where it had a strong lower level cold core, while a subtropical (hybrid) cyclone had a lower level warm core but an upper cold core. As the vast majority of cyclones reached the intensity threshold of 1 hPa.(deg lat) 2 inside the ECL domain, events were listed by the classification that occurred most frequently during the period they were within the ECL domain, with “Mixed” cyclones not falling clearly into either category. While ex-Tropical Cyclones can impact the region [Speer et al., 2009; Callaghan and Power, 2014], there were no ECLs that consistently featured a warm core aloft when within the ECL domain, so this category is discounted from analysis. 2.3. Observations “Observed” ECLs are taken from applying the ECL tracking scheme described above to the ERAInterim reanalysis [Dee et al., 2011] for the period 1980–2014. This data set has been previously

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Figure 3. Total number of ECLs in 2007 and 2008 across the different WRF versions and SST data sets. Note that the ECL frequency using lowresolution SSTs are also plotted with the “Control” frequency, as an indication of the spread due to small changes in sea surface temperatures.

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shown to well replicate a subjective ECL data set [Speer et al., 2009] over the common period 1980–2006. The Bluelink highresolution SST reanalysis used for the WRF model input in this study is only available for the 20 years 1993–2012 [Oke et al., 2013], which is too short for climate analysis. Instead, for observed SSTs we use three different monthly SST data sets: HadISST V1.1 [Rayner et al., 2003], NOAA ERSST V3 [Smith et al., 2008], and NOAA OISST V2 [Reynolds et al., 2002]. As OISST begins in 1982, the common period 1982–2014 is used for assessing climatological relationships.

3. SSTs and ECLs in a Regional Climate Model 3.1. ECL Frequency and Characteristics The total number of ECLs identified during the period varies between the three sets of WRF physics parameterizations, with slightly fewer ECLs identified with the R2 set of parameterizations. The frequency of cyclones is also sensitive to both the resolution of MSLP data and the choices made in applying the ECL tracking scheme, such as the minimum intensity threshold. For a given model setup and resolution, the number of ECLs identified is relatively insensitive to small changes in the spatial patterns of sea surface temperatures, with no change in total ECL frequency between the control simulations and those using lowresolution (ERA-Interim) SSTs (Figure 3). In comparison, there are strong and robust changes in ECL frequency across all WRF models with changes to the EAC. The number of ECLs decreases by an average of 21% when the EAC is removed and increases by 24% when the EAC is doubled. R1 and R3 have similar changes in ECL frequency with SST changes, while R2 gives a larger decrease for the NoEAC runs ( 29%) but a smaller increase for 2EAC (+17%). The magnitude of the changes in ECL frequency were relatively insensitive to the choice of resolution or the averaging radius used in calculating intensity thresholds, when intensity thresholds were chosen to give a consistent number of ECLs in the control simulation (not shown). The sign of the change was also robust to changes in intensity thresholds, although the magnitude of changes was smaller when using a weaker intensity threshold such as 0.5 hPa.(deg lat) 2. This threshold gives 59 ECLs per year in the control case, substantially higher than observed frequencies [e.g., Speer et al., 2009], which declines by 11% for a removed EAC or increases by 13% when the EAC is doubled. While the magnitude of the change was similar for both the case of removed and doubled EAC strength, the details of the impacts were different. When the EAC is removed, the decline in ECLs is predominantly due to a decrease in the frequency of weak or moderate ECLs. There is no change in the frequency of ECLs with maximum intensities exceeding 2 hPa.(deg lat) 2, which occur on average 6 times per year. This decrease is also independent of how cyclones are classified in the Hart [2003] phase space and is strongest directly over the region of decreased sea surface temperatures (Figure 4). The increase in ECL frequency when the strength of the EAC is doubled is more evenly distributed across space and across the intensity distribution, with a larger increase in the frequency of strong lows above 2 hPa.(deg lat) 2 (+46%) than weaker systems (+18%). The majority of the change is due to an increase in the frequency of subtropical cyclones, which occur at an average of 8.6 times per year in the control case but 14.1 times per year when the strength of the EAC is doubled.

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Figure 4. Percentage change in the number of hours where an ECL has its center located within a grid box, relative to the control case, averaged across three WRF versions at two resolutions: (left) NoEAC and (right) DoubleEAC. Crosses indicate cells where 5/6 members agree on the sign of the change, as an indication of robustness.

Changes in ECL frequency for a doubling of the EAC are also less robust over time, with an average increase in ECL frequency of 46% in 2007 but just 7% in 2008. This may be related to the magnitude of SST changes, with an average EAC SST anomaly of 0.9°C in 2007 and 0.6°C in 2008. However, there is little difference in the average decline in ECL frequency between the two years when the EAC is removed. In contrast to surface lows, there is no change in the distribution of GV (using a Kolmogorov-Smirnov test) or the frequency of high GV (greater than 20 × 10 5 s 1) between model runs using the different SST data sets. There is correspondingly little change (below 10%) in the frequency of ECLs associated with high GV, an indication of upper level atmospheric conditions favorable for ECL development [Dowdy et al., 2013, 2014]. There is a large decrease ( 29%) in the frequency of ECLs developing with weaker upper level forcings when the EAC is removed and a corresponding increase (+33%) when the strength of the EAC is doubled. 3.2. Matched Cyclones Our model runs employ spectral nudging, which pushes the middle and upper level atmospheric conditions in the model runs toward that in the driving data. While different WRF parameters influence the realization of these conditions into individual ECLs, on average 77% of ECLs in the ERA-Interim reanalysis also have a corresponding ECL present in any given WRF model within 6 h. This consistency offers some capacity to assess how changes in SSTs influence the development of individual ECLs. While 77% of ECLs are matched between the control case and a set of model runs using lower resolution SSTs, an indication of the sensitivity of ECLs to initial conditions, only 63% of ECLs present in the control case had a corresponding ECL when the EAC was removed. This is almost entirely due to weak or borderline ECLs from the control case that are missing from the NoEAC simulations; while close to 50% of events with maximum intensities below 1.5 hPa.(deg lat) 2 in the Control case are missing from the NoEAC simulations, almost all events with intensities above 2 hPa.(deg lat) 2 are present even when the EAC is removed (Figure 5). ECLs that are missing also tend to have much weaker forcing in the upper atmosphere, as indicated by the geostrophic vorticity at the 500 hPa level (Figure 6). We can combine these two intensity factors by defining a moderate ECL as having a maximum surface Laplacian greater than 1.5 hPa.(deg lat) 2 and/or a maximum 500 hPa geostrophic vorticity greater than 15 × 10 5 s 1. Where neither condition is satisfied, there is a 64% chance that an ECL will be absent in the NoEAC case; in comparison, only 6% of ECLs are missed when both conditions are satisfied. When only one condition is satisfied, there is a 40% chance of the ECL being missed. However, where an event is present in both data sets, there is no statistically significant change in its average intensity, central pressure, or upper level geostrophic vorticity. Of the events “missing” in the NoEAC case, 71% have a low-pressure system present in the ECL domain using a weaker intensity threshold of 0.5 hPa.(deg lat) 2, with an average intensity 0.4 hPa.(deg lat) 2 lower than in the control case. Interestingly, there is little consistency between the three different sets of WRF physics parameterizations as to which low-pressure systems fail to develop into fully fledged ECLs in the NoEAC case, PEPLER ET AL.


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making it difficult to identify additional synoptic factors that make an ECL more likely to be affected by changed SSTs. In comparison, 86% of ECLs in the control case were also present in the DoubleEAC runs. There were also a large number of additional ECLs, with only 69% of 2EAC ECLs matched by a corresponding event in the control runs, and just 52% of 2EAC ECLs having a corresponding event in the Figure 5. Average number of ECLs in the control run by intensity threshold, NoEAC model runs. Consistent with across three WRF versions and two resolutions. Shading indicates the proporearlier results, the new events in the tion of ECLs that were matched by a corresponding event in the NoEAC run. Double EAC runs, with no corresponding ECL in the control case, tend to have weaker upper level support (Figure 7), and 80% of “extra” events had a corresponding cyclone present in the region using a weaker intensity threshold. Unlike the results for NoEAC, in cases where an ECL was present in both the control and 2EAC case, there was a small strengthening of the low for several different intensity metrics. This includes the minimum central pressure ( 1.5 hPa), maximum Laplacian (+0.22 hPa (deg lat) 2), and maximum wind gust within 500 km of the low center (+1.5 m/s), all of which are statistically significant at the 5% level using a Student’s t test. This is also predominantly due to an increase in the strength of moderate ECL events, with an average increase of 0.31 hPa.(deg lat) 2 for events with maximum intensities of between 1 and 1.5 hPa.(deg lat) 2, but only 0.15 hPa.(deg lat) 2 for stronger events. 3.3. Changes in ECL-Associated Rainfall and Impacts The warm EAC is a source of atmospheric moisture; compared to the control simulations, the moisture flux over the region 24–40°S, 150–160°E is on average 252 mm lower per year in the NoEAC simulations, with a corresponding decrease in precipitation of 214.7 mm or 17%, using the high-resolution domain. There is a slightly larger increase in both the moisture flux (317 mm) and precipitation (25%) in the DoubleEAC case, reflecting a small nonlinearity in the impact of SSTs on moisture. The changes in precipitation are strongest directly over the EAC and on the far northeast of the east coast (Figure 8), with the total east coast rainfall declining by 12% when the EAC is removed and increasing just 8% when the EAC is enhanced. Previous studies have identified anywhere between 15 and 50% of east coast rainfall as being associated with closed low-pressure systems, and as much as 60% of extreme rainfall, with these proportions sensitive to the choice of rainfall data set, how cyclones are defined, and the time and space scales used for attributing rainfall to cyclones [Pfahl and Wernli, 2012; Lavender and Abbs, 2013; Pepler et al., 2014]. In the WRF model output, only 8–10% of rainfall on the east coast is associated with a low-pressure center within 500 km and 14% of rainfall in the full ECL region (24–40°S, 150–160°E), with the R2 set of parameterizations giving the largest contribution of ECLs to rainfall. This is generally below the contribution of ECLs to rainfall in observations and reanalyses, which may reflect flaws in model representation of cyclone-related precipitation, the well-known “drizzle problem” with too large a proportion of model rainfall associated with low rainfall intensities, or simply due to different choices of cyclone intensity thresholds and the method of attributing rainfall to cyclone centers. Consequently, the changes in total rainfall are dominated by the changes in non-ECL rainfall. There was no change in the average rainfall recorded within a 250 km or 500 km radius of an ECL associated with either of the changes to EAC SSTs, despite the changes to overall rainfall. However, increasing SSTs increased the likelihood of ECLs being associated with heavy rainfall around the low center, both for the average rainfall within 250 km of the low center as well as extreme grid point rainfall (Table 2). There were also increases in the frequency of ECLs associated with strong winds, likely related to the increase in average cyclone intensity. Changes were less robust for the NoEAC case, with some models showing no change in the frequency of ECLs associated with heavy rain.

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Figure 6. Average MSLP (hPa; black) and 500 hPa geostrophic vorticity (10 s ; grey) for the first instance of a cyclone 2 stronger than 1 hPa.(deg lat) in the 50 km resolution WRF ensemble for the control case where the ECL has (left) no corresponding ECL in the NoEAC case and (right) a corresponding NoEAC ECL.

4. Comparison With Observations To assess the influence of east coast SST anomalies on observed ECL frequency, we can compute the Pearson’s correlation coefficient between ECL frequency and the average SST anomaly in the east coast region, shown in Figure 2. Correlations are only statistically significant during autumn (Table 3), but are sensitive to the intensity threshold chosen for identifying ECLs, with autumn correlations weaker than 0.1 when a stronger intensity threshold of 1.5 hPa.(deg lat) 2 was used (giving 10 ECLs per year). Correlations are similar in magnitude when instead calculating the SST over the coastal area between 150–155°E or calculating the largest SST anomaly anywherein this region as anindicator of locallyhighSSTs. In comparison,an indexidentifyingmonths wherecoastal SSTs are anomalously warm, given by the average difference between SSTs in the region 24–41°S, 148–155°E and the same latitude at 160–165°E is negatively correlated with the frequency of observed ECLs, although the only statistically significant correlation is for ERSST during the summer (correlation coefficient of 0.37). The weak relationships between SSTs near the east coast and the frequency of ECLs in observations have been previously noted (A. Dowdy, personal communication, 2014), although some relationship may exist between certain subtypes of ECLs and broader regional SST patterns [Browning and Goodwin, 2016]. The spatial resolution of observational data sets is also too low to be able to explicitly identify warm eddies, which are known to affect the location of cyclone development [e.g., Vianna et al., 2010; Chambers et al., 2014]. The weakness of observed relationships between ECLs and SSTs likely reflects a large number of confounding factors, such as the importance of favorable atmospheric conditions for ECL development and the influence of

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Figure 7. Average MSLP (hPa; black) and 500 hPa geostrophic vorticity (10 s ; grey) for the first instance of a cyclone 2 stronger than 1 hPa.(deg lat) in the 50 km resolution WRF ensemble for the 2EAC case where the ECL has (left) no corresponding ECL in the control case and (right) a corresponding ECL in the control case.

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Figure 8. Percentage change in precipitation in 2007–2008 in the 50 km resolution domain with the EAC (left) removed or (right) doubled. The east coast region is indicated in red.

broader climate drivers such as the El Niño–Southern Oscillation on Australian SSTs. Rather than invalidating the results presented in section 3, this highlights the value of using climate models to answer questions that are difficult to assess using the observational data available.

5. Discussion Several studies have investigated the influences of local sea surface temperatures on the behavior and intensity of individual extratropical cyclones [McInnes et al., 1992; Booth et al., 2012; Chambers et al., 2014; Hirata et al., 2015]. However, few studies have systematically examined the influence of changed SSTs on the frequency or behavior of cyclones, despite changes in SST resolution having clear impacts on the spatial structure of the North Atlantic storm tracks [Woollings et al., 2010]. This paper presents a detailed assessment of the influence of the East Australia Current, a western boundary current off the east coast of Australia, on the frequency, characteristics, and impacts of local midlatitude cyclones during the 2 year period 2007–2008. The EAC is demonstrated to have a strong influence on the frequency of ECLs of weak to moderate intensity, particularly those without the high geostrophic vorticity in the upper atmosphere which favors ECL development. With the EAC removed, there is a 21% decrease in the total number of ECLs during the period 2007 to 2008, almost entirely driven by a decrease in low-intensity systems and those which develop directly over the EAC, which is robust across several sets of regional climate model parameterizations. There is little change in the development or frequency of intense ECLs, which have similar characteristics regardless of SST changes. This may partly explain the weak relationships between ECL activity and observed SST variability in this region. A doubling of the intensity of the EAC, by 1–2°C, resulted in a corresponding 24% increase in the frequency of ECLs, predominantly due to a 65% increase in the frequency of subtropical cyclones. As with the previous case, this was not due to a change in the frequency of ECL-favorable conditions in the upper atmosphere, but an Table 2. Changes in Frequency of ECLs Associated With Severe Weather Within 250 km of the Low Center at Least Once While the Low Center Was Within the a ECL Domain

Average (control) Change in NoEAC Change in 2EAC a

Mean Rain >6 mm/6 h

Mean Rain > 12 mm/6 h

Max Rain > 50 mm/6 h

Mean Wind >50 km/h

Max Wind > 80 km/h

26.2 17% +33%

9.5 18% +28%

19.0 2% +37%

6.0 +24% +84%

11.7 5% +57%

Data are presented in bold where all six WRF models and resolutions have a change of the same sign, with italics indicating agreement across 5/6.

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Journal of Geophysical Research: Atmospheres Table 3. Pearson Correlation Coefficient Between Seasonal ECL Frequency in the ERA-Interim Reanalysis and the Mean Tasman Sea SSTs for Three a Different Data Sets Between 1982 and 2014 SST Database

MAM

JJA

SON

DJF

HadISST ERSST OISST

0.41 0.32 0.39

0.13 0.08 0.00

0.06 0.03 0.05

0.20 0.20 0.26

a

Bold data indicate statistical significance for p < 0.05, with autocorrelation of data not considered.

10.1002/2016JD025495

increase in the likelihood of weaker ECLs developing despite less favorable atmospheric conditions. The increased SSTs were also associated with a small but significant increase in the mean intensity, central pressure, and wind speeds associated with ECLs, consistent with results from case studies of individual cyclones [McInnes et al., 1992; Booth et al., 2012].

Although different sets of WRF parameterizations influenced the overall frequency of ECLs and rainfall climatology in the regional climate model simulations, as well as the impact of SSTs on individual cyclones, the effect of the EAC on ECLs was relatively insensitive to the choice of physics and convection schemes. As noted in previous studies [e.g., Pepler et al., 2015b], the R2 set of parameterizations is the most different from the other methods in terms of both climatology and the impact of changes, demonstrating the importance of the choice of cumulus scheme to the simulation of large synoptic-scale systems. While changes in coastal SSTs result in large and significant changes to moisture flux and rainfall immediately above the affected region, the influence on rainfall on the Australian east coast is relatively minor, with only a 10% decline in rainfall on the east coast when the EAC is removed. There is also little change in the average rainfall for a given ECL, although increased local SSTs are also associated in an increased frequency of ECLs associated with heavy rainfall. However, there was no robust decrease in the frequency of ECLs associated with severe rainfall when the EAC was removed. Changes in the EAC have little impact on changes in upper level geostrophic vorticity, which is strongly associated with the development of severe ECLs [e.g., Dowdy et al., 2013]. Consequently, changes in ECL frequency are most evident for cyclones without strong upper level support, which may explain the weak influence of SST changes on strong or impactful cyclones as well as the weak correlations between interannual SST variability and ECL frequency. However, it is important to note that the use of spectral nudging in the upper layers contributes to the strong agreement in upper level GV distributions between the different model runs. Future work could employ unnudged simulations to better identify the effects of the EAC on the broader atmospheric circulation, including the frequency of upper atmosphere conditions favorable to ECLs.

Acknowledgments The authors would like to thank Alejandro Di Luca for his help in setting up and running the WRF regional climate model. Three anonymous reviewers significantly improved the clarity of this manuscript. This research was supported by the Australian Research Council Centre of Excellence for Climate System Science grant CE110001028 and Linkage project grant LP120200777. This research was undertaken with the assistance of the NCI National Facility systems and Raijin supercomputer at the Australian National University through the National Computational Merit Allocation Scheme supported by the Australian Government. All model output generated in this study will be archived as part of the UNSW Data Archive, with analyzed data available by request from Acacia Pepler ([email protected])

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Pepler et al. [2016] observed that while the global projections for a decrease in midlatitude cyclone frequency is broadly true for the ECL region, future projections are less certain for ECLs directly near the east coast as well as those during the warm season, when ECLs tend to be weaker and of a more subtropical nature. Results from the current study suggest that this may partly be explained by the very strong warming projected in the EAC region [e.g., Oliver et al., 2013]. As a stronger EAC is associated with an increase in ECL frequency directly near the coast, particularly those with subtropical characteristics and those associated with heavy rainfall, this projected coastal warming may act to counteract some of the declines caused by broader atmospheric changes, limiting their impact on local water resource availability. These results shown here indicate two main effects of coastal SSTs on ECL activity. First, the presence of the EAC increases the likelihood of weak or moderate ECLs developing off the east coast given weaker atmospheric forcing. Second, warmer SSTs result in a weak increase in the overall intensity of the surface low, and a larger increase in the likelihood of heavy rainfall for a given event. However, coastal SSTs play only a minor role in the development of the most severe ECLs, which are most strongly influenced by atmospheric conditions, which may explain the weak interannual correlations between observed SSTs and cyclone frequency.

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