type and air quality in Sydney, a subtropical coastal-basin environment ... Implementation of the two-phase batch self-organising map (SOM) ... To train this. SOM ...
Visualising the relationships between synoptic circulation type and air quality in Sydney, a subtropical coastal-basin environment Ningbo Jiang*, 1, Yvonne Scorgie, Melissa Hart, Matthew L. Riley, Jagoda Crawford, Paul J. Beggs, Grant C. Edwards, Lisa Chang, David Salter, Giovanni Di Virgilio
1. New South Wales Office of Environment and Heritage, Sydney, Australia 2. ARC Centre of Excellence for Climate System Science and Climate Change Research Centre, University of New South Wales, Sydney, Australia 3. Australian Nuclear Science and Technology Organisation, Sydney, Australia 4. Department of Environmental Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, Australia
Supporting information Appendix S1. Implementation of the two-phase batch self-organising map (SOM) classification procedure In the present study, the warm-month synoptic catalogue was derived from the geopotential height dataset via a two-phase batch SOM procedure (CP2) detailed in Jiang et al. (2012, 2015a). CP2 can be run in either clustering or data projection mode depending on the intended application. Here, CP2 was run in clustering mode in order to identify the major, distinctive synoptic features influencing east Australia. The first phase was to capture a rough estimation of the global patterns in the data, while the second phase was to fine-tune the mapping to achieve local optima and thus obtain the final groupings. A weather map (geopotential height field) was assigned to a SOM node (type) from which it has the smallest squared Euclidean distance. Since relatively large variability in geopotential height occurs at high latitudes in the study domain (Jiang et al., 2015a), CP2 was implemented on the standardised time series (i.e. taking the difference from the mean divided by the standard deviation of the raw geopotential height time series for each grid point) so as to suppress the effects of spatial heterogeneity (in variability) on the classification results. The final synoptic patterns were obtained by de-standardising the SOM nodes after the implementation of CP2. Several SOM map sizes (i.e., total number of nodes/types on the SOM grid) were considered for the geopotential height dataset. It was found that the 4x3 SOM mapping (i.e., 12 types) is sufficient to reproduce the typical warmmonth synoptic types identified in the literature for the same region (e.g., Jiang et al., 2012). To train this SOM, the neighbourhood function was set to Gaussian (default), with the neighbourhood width shrinking
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from 3 to 1 in the first phase within 50,000 iterations, but 2 to 0 for the second phase within 90,000 iterations. A synoptic classification/catalogue was then constructed by allocating each 1000hPa geopotential height map to one of the 12 types on the SOM obtained.
Appendix S2. Definition of high pollution event and exceedance day The Australian national standards for ambient air 1-hour average O3, 4-hour (rolling) average O3 and 24-hour average PM10 are 10pphm, 8pphm and 50µg/m3, respectively (NEPC, 2003). The NSW OEH sets the visibility standard of 2.1 10-4/m in 1-hour NEPH (nephelometer) reading from its network. Based on these standards, following Jiang and Riley (2015), a site was designated a poor air quality day if one of the above standards was reached or exceeded – for convenience, the term poor air quality day is simply referred to as exceedance day in this study. Similarly a high pollution event was identified for a site if 66% of the relevant standard was exceeded. Collectively, a Sydney exceedance day or high pollution event was designated for a specific pollutant if one of the 14 monitoring sites recorded an exceedance day or high pollution event for that pollutant.
Appendix S3. Air quality climatology for the 2007-2014 baseline period Figure S1 shows the mean pollution levels, the number of high pollution events and the number of exceedance days (as defined in Appendix S2) for the baseline (2007-2014) period by monitoring site for NEPH, PM10 and O3, respectively. In general, the across-site variability in mean pollution levels is small, with slight differences observed between the eastern and western parts of the basin. The main points are summarised below:
Higher O3 pollution occurred in the west and south-west, and secondarily the north-west, with generally lower O3 levels occurring in the east of the basin. This is consistent with the notion that O 3 is a secondary pollutant, often formed downwind of the onshore sea breezes in the west and south-west of the basin, due to reactions between NOx (emissions from major urban centres over the central east) and VOCs (emissions from anthropogenic sources near major urban centres in the central east and biogenic sources dominantly in the west and south-west of the basin) under high solar and temperature conditions (Hyde et al., 1997; DECCW, 2010).
Slightly higher NEPH levels were experienced within the central to north-west part of the basin, while relatively higher PM10 levels were experienced within the central east part of the basin. This is generally consistent with the relatively larger contribution of particle (PM 10 and PM2.5) emissions by domesticcommercial and mobile sources in (central east) Sydney (NSWEPA, 2012a), as is evidenced in local experience that brown haze can often be observed on fine mornings over Sydney CBD (central business district).
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Number of exceedance days or high pollution events follow similar spatial patterns to those of mean pollution levels, with bushfire smoke effects contributing significantly to the PM 10 (and certainly NEPH) exceedance but little to the O3 exceedance (as illustrated by the difference between Figure S1h and j, but little difference between Figure S1i and k).
Appendix S4. Properties of the warm-month synoptic catalogue Frequency of occurrence Figure S2a displays the frequency (%) of occurrence for each synoptic type on the SOM grid/plane. The frequency of occurrence of synoptic types varies considerably across the SOM continuum. It is clear that the high and low frequency values manifest in clusters on the SOM. The less frequent types occur near the top and left edges of the grid (Types 1-4, 5 and 9), mainly associated with the activity of low-latitude thermal low/easterly trough or high-latitude westerly trough/frontal systems. In contrast, relatively more frequent synoptic types are those on the bottom-right section (e.g., Types 6-8, 11 and 12), characterised by the dominance of a high system in the south over the Tasman Sea, near the Great Australian Bight, or over or near Tasmania.
Persistence Persistence of individual synoptic types was calculated as lifetime (Figure S2b), defined as the mean time that a synoptic type persists as an uninterrupted sequence (Jiang, 2011). Again, high and low lifetime values tend to occur in clusters on the SOM. Synoptic types on the top and right edges of the SOM, including the southern high types (Types 9, 11, and 12, i.e., blocking situations) and those associated with strong thermal lows/easterly troughs (Type 5) or frontal systems (Type 1), have relatively longer lifetimes (i.e., greater persistence). In contrast, the synoptic types on the bottom-left section of the grid, including those associated with activities of westerly systems, have relatively shorter lifetimes (i.e., less persistence). Overall, Type 6 is the least persistent and Type 5 is the most persistent. In general, these lifetime values are of similar amplitudes to those for the summer-prevailing synoptic types identified in Jiang et al. (2016).
Transition Knowledge of transitional patterns among synoptic weather patterns is of practical use for forecasting operations. Figure S3 shows the probability of transition by individual type, expressed as the percentage of changes from a source type to a destination type over the total number of changes from the source type. The transition pathways are complex, not as organised as those for winter shown by Jiang et al. (2016). However, the transitions are generally characterised by the change in the intensity of low-latitude thermal low/easterly trough systems and the eastward migration of high and low systems associated with subtropical high belt and
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westerly waves (with one example being the synoptic sequence: Type 5 -> Type 10 ->Type 11 -> Type 7 -> Type 3 -> Type 8). It is noteworthy that reversed transitions are possible, e.g., between Types 1 and 2. Jiang et al. (2016) noted that the complex transition pathways during summer (warm months) are associated with the weakened forcing (compared to winter) from the subtropical high pressure systems and the increased influence from the low-latitude easterly troughs/thermal low systems. In some sense, this finding highlights the complexity in Sydney’s weather and climate, primarily due to its subtropical location and coastal-basin environment.
Correlation Due to the continuity of atmospheric states, there exist significant correlations in the occurrence of synoptic types. The occurrence of synoptic types can be selective, i.e., some may be more likely than others, during a specific time period. Following Jiang (2011), this correlation is studied by performing a Varimax rotated Pmode principal component analysis (PCA) on the monthly frequencies of the 12 synoptic types. Given the difference in the number of days in each month, monthly frequencies were normalised by a factor of 30 divided by the actual number of days in that month. The PCA led to retention of two dominant PCs, accounting for about 45% of the total variance (rotated PC1 and PC2 explain 25% and 20% of the total variance, respectively). The loadings of monthly type frequencies on the two PCs are shown in Figures 2c and 2d. Again, of note is the clustered pattern of PC loadings on the SOM plane. PC1 reflects the subtropical high–easterly trough/thermal low relationship: increased occurrence of highs to the east of NSW over the Tasman Sea or near the Great Australian Bight (Types 3, 7, 8 and 12) are associated with reduced activity of low-latitude trough systems (Types 5, 9 and 10), and vice versa. PC2 indicates a negative correlation between the southern highs and westerly trough/frontal systems: more frequent activity of westerly troughs or frontal systems (Types 1, 2, and 4) is related to reduced prominence of southern highs to the south-east or south of the continent (Types 10-12), and vice versa. Type 6 has low loading on both PCs, indicating that this synoptic situation is not specifically in line with the above relationships but may act as a short-lived transitional type consistently, it has the shortest lifetime in Figure S2b.
Appendix S5. General air-mass characteristics by synoptic type General air-mass characteristics were examined by synoptic type using the surface and upper-air meteorological data from Sydney Airport, in order to provide a vertical context for the discussion in the main text. Figure S4 illustrates the air-mass characteristics by synoptic type on the SOM in terms of three types of variables: 1) 6am and 3pm gradient wind (850hPa), overcast probability (prob), MSLP, surface and upper air (850hPa) air temperature (T), surface and upper air dryness (Td-T, i.e., dew point minus air temperature at the same level), and mixing height; 2) change in MSLP or temperature from 6am to 3pm; 3) 24-hour rainfall, the
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probability for each synoptic type leading to rainfall ≥ 2mm and the probability for cloud overcast at both 6am and 3pm. The main points are summarised below:
Gradient flows (Figure S4a-b): the 850hPa winds approximate the gradient flow conditions influencing Sydney. Synoptic gradient flows are generally similar between 6am and 3pm (as expected). Types 1, 3 and 4 are associated with relatively high wind speed, implying relatively strong synoptic forcing and good upper-air ventilation over the regional airshed. In contrast, other synoptic types, in particular Types 5-7, 11 and 12, correspond to relatively low-speed gradient flows, indicating essentially weak synoptic forcing and poor (upper-air) ventilation over the region.
Warm/cool types (Figure S4k, l, n and o): mean surface and upper air temperatures tend to cluster together by gradient wind directions. Types 1-3, 5-7 and 9 are associated with warmer air masses over Sydney, due to advection of warm air over the basin by northerly to north-westerly gradient flows. In contrast, other synoptic states, due to advection of cooler air to the basin via south-westerly to southeasterly gradient flows, provide generally cooler air-masses over Sydney.
Of note are Types 1-3 and 7 (Figure S4h-t): these synoptic types are associated with relatively warm (surface and 850hPa) and dry (more clearly at 850hPa) conditions at Sydney Airport, featuring relatively large increase in surface temperature and decrease in MSLP (significant at the 0.05 level for a two-tailed z-score test) from 6am to 3pm due to enhanced solar heating to the land surface. As will be described in Section 5.2, this is consistent with local experience that these synoptic types favour the formation of local circulation patterns such as overnight/early morning drainage flows and afternoon sea breezes.
Mixing heights (Figure S4u-v): Types 1-3 and 7, together with other synoptic types except for Types 8 and 10, correspond to generally low mixing heights (~57-94m) in the morning (suggesting the existence of low-level radiation inversions). However, the mixing heights at 3pm vary significantly across synoptic types. Most synoptic types, including Types 2 and 7, correspond to relatively low (652-941m) mixing heights (suppressing dispersion). In contrast, Types 1, 3, 4 and 8 are related to relatively high (>1000m) mixing heights (enhancing dispersion). Of note are Types 4 and 8, which correspond to relatively large mixing heights at both 6am and 3pm, suggesting the presence of generally good boundary-layer dispersion conditions under these situations.
Rainfall and cloud cover (Figure S4c-g): Types 1-4, 7 and 12 are also associated with moderate to low mean 24-hour rainfall, and moderate to low chance of daily rainfall ≥ 2mm. Other synoptic types including Types 5, 6 and 9-11 correspond to moderate to high mean daily rainfall and high chance (~2542%) of rainfall ≥ 2mm. Types 4, 7, 8 and 12 tend to have relatively low chance of daytime overcast, but Types 6 and 10 have relatively high chance of daytime overcast conditions.
In summary, high pressure systems, e.g., under Types 11 and 12 (Figure S4h-i), are not necessarily related to hot and/or dry air-mass conditions in Sydney. Particularly of note are Types 1-3 and 7 which correspond
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locally to relatively low MSLP, high temperature, low humidity and low rainfall conditions, distinguishing them clearly from other synoptic types on the SOM grid.
References Jiang N. 2011. A new objective procedure for classifying New Zealand synoptic weather types during 1958–2008. Int. J. Climatol. 31: 863–879, doi: 10.1002/joc.2126. Jiang N, Riley ML. 2015. Exploring the utility of the random forest method for forecasting ozone pollution in Sydney. J. Environ. Prot. Sustain. Dev. 1: 245–254. NEPC. 2003. National Environment Protection (Ambient Air Quality)Measure – As varied 7 July 2003. Adelaide, Australia: Environment Protection & Heritage Council
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Figure S1. Air quality climatology for the 2007-2014 baseline period. (a)-(c): mean pollution levels; (d)-(f): total number of high pollution events; (g)-(i): total number of exceedance days; (j)-(k): total number of non-smoky exceedance days. The colour scale is used to highlight high (red) and low (green) values.
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Figure S2. Frequency (%) and lifetime (day) of synoptic types, along with loadings of the first two Varimaxrotated PCs from PCA on the monthly frequency time series of 12 synoptic types (as described in Appendix S4), displayed on the SOM grid as in Figure 2 (main text). The colour scale is used to highlight high (red) and low (green) values.
Figure S3. Probability (%) of synoptic type transitions, expressed as percentage of changes from a given synoptic type (row) to a different synoptic type (column) on the following day over the total number of changes from the source type. The colour scale is used to highlight high (red) and low (green) values.
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Figure S4. Mean air-mass characteristics by synoptic type, expressed in meteorological variables at Sydney Airport for Nov-Mar 2007-2014, on the SOM grid as in Figure 2. Data for 1994-2006 show similar patterns. Changes in MSLP and temperature are in bold if significant at the 0.05 level for one-sample t-test. Prob: probability in %. Wind vectors are in m/s. Units for other variables are given in Section 2.2. Colour scale is used to highlight high (brown) and low (yellow) values.
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Figure S5. Example maps: deviations (%) of mean NEPH, PM10 and O3 levels under Types 2, 3 and 10 for non-smoky days in 2007-2014 compared to the climatological means (mean values for all nonsmoky day in 2007-2014, rather than the baseline climatology defined in Figure S1). The colour scale is used to highlight high (red) and low (green) values.
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Figure S6. Example maps: deviations (%) of mean NEPH, PM 10 and O3 levels under Types 2, 3 and 10 for all days in 1994-2014, compared to the 2007-2014 baseline climatology. The colour scale is used to highlight high (red) and low (green) values.
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Figure S7. Probability (%) for high pollution event in Sydney under the occurrence of each synoptic type for all days and non-smoky days in 2007-2014 versus 1994-2014, respectively, on the SOM grid as in Figure 2. The colour scale is used to highlight high (brown) and low (yellow) values.
