Seasonal and Annual Trends in Australian Minimum/Maximum Daily Temperatures



W. A. van Wijngaarden*, A. Mouraviev
Physics Department, Petrie Building, York University, 4700 Keele St., Toronto, ON Canada


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© van Wijngaarden and Mouraviev; Licensee Bentham Open

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/legalcode), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Centre for Physics Department, Petrie Building, York University, 4700 Keele St., Toronto, ON Canada; Tel/Fax: 416-736-2100 x77750; E-mail: wlaser@yorku.ca


Abstract

Seasonal and annual trends in Australian minimum and maximum temperatures were studied. Records of daily minimum and maximum temperatures averaged over each month, extending as far back as 1856 were examined. Over 1/2 million monthly temperature values were retrieved from the Australian Bureau of Meteorology for 299 stations. Each station had an average of 89 years of observations. Significant step discontinuities affected the maximum temperature data in the 19th century when Stevenson screens were installed. The temperature trends were found after such spurious data were removed and averaged over all stations. The resulting trend in the minimum (maximum) daily temperature was 0.67 ± 0.19 (0.58 ± 0.26) oC per century for the period 1907-2014. Decadal fluctuations were evident in the maximum daily temperature with most of the increase occurring in the late 20th century. The minimum and maximum daily temperature trends were also found for the various seasons. The minimum daily temperature trend exceeded the maximum daily temperature trend for all seasons except during June to August. The largest increases in minimum temperature as well as the smallest maximum temperature increases were found for the region north of 30 oS latitude and east of 140 oE longitude. There was also evidence that urban stations had greater increases in maximum daily temperature than those located in a rural environment.

Keywords: Minimum/Maximum daily temperatures, Seasonal effects, Step inhomogeneity detection, Trends.



INTRODUCTION

Archival records of temperature provide invaluable information for studying climate change [1]. Stations with the longest data records are predominantly located in the Northern Hemisphere, especially in Western Europe and the United States [2]. For the Southern Hemisphere, Australia has records extending as far back as 1856 [3]. Its meteorological instruments and procedures are likely to be more uniform than in the case of Europe or South America which have many national jurisdictions. This is important as changes in apparatus or observation methodology can cause inhomogeneities that if not taken properly into account can significantly perturb temperature trends [4-9].

A second reason to study Australia is to examine whether there is any difference between trends in minimum and maximum daily temperatures. Some studies have found the minimum daily temperature appears to be increasing more than the maximum daily temperature due to turbulent mixing of warmer air to the surface caused by changes in the intensity of the stable boundary layer, for example, due to urbanization and other local and regional land uses [10-13]. It has also been suggested that changing cloud cover may be partially responsible for decreasing the daily temperature range [14]. One study did find evidence of urban heat island features in southeast Australian towns [15]. It is also interesting to see if the trends exhibit a seasonal dependence. Previous work has found that Arctic and North American temperatures have increased more during December to May than during June to November [6, 16].

A number of studies have analyzed Australian temperatures [17-19]. In particular, the Australian Bureau of Meteorology (BOM) provides the monthly averaged minimum and maximum daily temperatures, hereafter just referred to as the minimum and maximum temperatures, at a number of stations [3]. This includes data that is a composite of observations made at different sites near a given location. The resulting time series at many stations have obvious inhomogeneities. Torok and Nicholls [17] examined data from 224 stations covering the period 1910-1994. Some of these time series were created by making a composite of data observed at nearby stations. Various statistical analyses were used to compare each time series to nearby stations to detect and correct discontinuities. The mean number of adjustments to minimum (maximum) temperature was 6.6 (6.1) per station. Minimum and maximum temperatures were found to have both increased since 1950, with minimum temperatures increasing more than the maximum temperatures.

Daily minimum and maximum temperatures have been studied for 38 stations in southeastern Australia for the period 1860-2009 [20]. The data were examined for inhomogeneities and reference series were created using neighbouring station data. Over half of the nearly 200 adjustments made to each of the minimum and maximum temperature data during 1860-1950 were supported by changes recorded in the station histories. The minimum temperature appears to have steadily increased by about 1 oC during the last century and a half. The maximum temperature appears to have been about stable until 2000 when it increased abruptly by about 0.5 oC.

A homogenized daily minimum and maximum temperature dataset has been generated by the Australian Climate Observations Reference Network (ACORN) [19, 21-23]. This network consists of 112 stations and covers the period from 1910 onwards. Sixty of the stations have data for every month in all years during 1910-2014. Inhomogeneities arising from changes in instruments and the station site location were detected using a combination of metadata and statistical methods. A so called percentile matching algorithm was developed to make adjustments to correct the data. The adjusted ACORN data of daily minimum and maximum temperatures is available from the Australian Bureau of Meteorology [3]. Australian data has also been analyzed by NOAA’s National Climate Data Center as part of the Global Historical Climate Network (GHCN) analysis and both raw and adjusted monthly averaged minimum and maximum daily temperatures are available from the Royal Dutch Meteorological Institute and NOAA’s National Center for Environmental Information [2, 24]. The adjustments made to station data by ACORN and GHCN ranged from a few hundredths of a degree to nearly 2 oC and do not always agree with each other. A very small annual temperature adjustment may result from positive and negative adjustments made in different seasons. The inhomogeneities are not always apparent nor supported by station history metadata and concerns have been raised about their accuracy [25]. The ACORN analysis unlike that of GHCN did have access to metadata. ACORN has made a valiant effort to record the history of each station [22, 23]. Unfortunately, the available documentation does not adequately describe all pertinent changes to the measurement site and/or relocation of meteorological instruments. The station data have also been used to develop gridded datasets with a resolution of 0.05o x 0.05o [18].

This study examined the minimum and maximum temperatures observed at 299 stations. Each time series was first checked for inhomogeneities. Spurious data were removed rather than attempting to make corrective adjustments. The change in temperature relative to the 20th century was computed and averaged over all stations. The minimum and maximum temperature trends were then found. The results agreed closely with the ACORN and GHCN analyses that independently adjusted data for inhomogeneities, but the present work considered a longer time interval of 1880-2015 and considerably more stations. The trends did exhibit decadal variations. Seasonal trends were also found. The trend dependences on latitude and longitude as well as whether the station was located in either a rural or urban environment were also examined.

DATA ANALYSIS

Monthly averaged daily minimum and maximum values were downloaded from the Australian Bureau of Meteorology [3]. Stations were considered that had at least 50 years of data or were in close proximity to an ACORN station or a part of the ACORN network. The annual and seasonal averages were only computed if all monthly values in a year or season were present. The 299 stations listed in the Appendix A had an average of 89 years of observations. Fig. (1) shows the station locations. The longest records of observations were found for Melbourne and Sydney where data extend back to the 1850s. A significant increase in the number of operating stations occurred in 1907. A number of stations closed in recent decades. Annual averaged minimum and maximum temperatures could be found for 88% of the years that stations were operational. Stations having daily data for the ACORN network as well as monthly adjusted data from the GHCN archive are listed in the Appendix B.

The temperature relative to the 20th century was computed. This extended reference period, as opposed to the period 1961-1990, was chosen since a number of stations closed before 1961. The different reference periods changed the trends by considerably less than the trend uncertainty. It was evident that a number of stations had spurious data as is

Fig. (1). Stations examined in this study. A red (blue) circle denotes a station where data was taken for less (more) than 100 years.

Fig. (2). The average minimum and maximum annual temperature data are plotted for various stations. The red points are suspect as is discussed in the text.

illustrated in Fig. (2). Some data values were very distant from the average minimum or maximum temperatures. This occurred most often when a station began operation in the late 1800s as is shown for Deniliquin, Gabo Island and Inverell. The same stations also had missing observations for a number of years during their first two decades of operation. Step discontinuities were also evident in some time series. These frequently preceded a gap in the data which may indicate that either two different time series were appended or a change in instruments had occurred. Most of these steps occurred in the late 1800s as shown by the data for Deniliquin, Moruya Heads and White Cliffs. However, the data for a few stations show a sudden upward jump during the 20th century such as occurred in the maximum temperature for Cape Naturaliste in 1994. These temperature discontinuities were not exhibited at nearby stations indicating that they are not the result of a climate fluctuation.

This study adopted two criteria for determining whether a data point was spurious and required removal. The first criterion was to keep the first two decades of data only if data were present for at least two thirds of those years. Many time series of stations starting operation in the late 19th century exhibited missing data that cast doubt on the reliability of the observations. The second criterion was to detect step discontinuities [5, 7-9]. The data in each time series was examined as follows. For a minimum or maximum temperature occurring at year td, the average and standard deviation were found immediately before and after, over intervals [td - Ns + 1, td] and [td + 1, td + Ns]. The means (standard deviations) evaluated in the intervals extending over Ns years before and after year td were denoted by µL and µRL and σR), respectively. The shorter of the time series occurring either before or after year td was removed if |µL - µR|> σL + σR. A station time series was discarded entirely if a second discontinuity was detected.

Application of these criteria is shown in Fig. (3). It shows plots of the minimum and maximum daily temperature relative to the 20th century averaged over all stations for the raw data as well as when data having discontinuities were removed. The procedure to detect discontinuities was considered for the cases of Ns = 5, 6, 7, 8, 9, 10 years. Fig. (3) shows the amount of data discarded increased as Ns decreased from 10 to 5, as well as the result of discarding station time series in their entirety if a single discontinuity was found. However, the differences between the 5 year running

Fig. (3). Plot of (a) Minimum and (b) Maximum Temperature relative to the 20th century. Raw data are displayed as black circles. Red squares (blue diamonds) are data filtered using Ns = 10 (5) points to determine whether a discontinuity occurred as discussed in the text. Green triangles show data for stations where no discontinuities were found. The solid curves are the corresponding running 5 year averages which closely overlap in many years. The dashed curves show the corresponding number of stations.

average change in temperature for raw data and the cases of Ns = 10 - 5 were negligible for the minimum temperature data after 1880 and maximum temperatures following 1907. The differences between the curves shown in Fig. (3) are largest for the data in the 19th century when there are relatively few stations.

RESULTS

Table 1 shows the trends for the minimum and maximum daily temperatures for the intervals 1880-2014 and 1907-2014. The uncertainty of each trend in this study was defined by the 95% confidence interval found using the statistical t-test. The possibility of correlation effects was examined by binning the data over multi-year intervals. This had negligible effect on the trends. The 1907-2014 trends for both minimum and maximum temperature change vary little depending on the value of Ns used to determine the presence of a discontinuity. The maximum temperature trend for 1880-2014 evaluated using raw data is much smaller than the trends obtained after inhomogeneous data were discarded using Ns = 5 - 10. Table 1 also lists the trends found when time series were entirely removed if a single discontinuity was found. The time series shown in Fig. (3) were relatively insensitive to inhomogeneities that affect different stations at random times. Only systemic changes such as for example, the installation of Stevenson screens at all stations around 1900 will affect the averaged time series. This can be expected to have affected measurements of maximum temperatures more than minimum temperatures as the Stevenson screen prevents the exposure of the thermometer to direct sunlight.

Fig. (4). Trends at stations having data present for at least 50% of all years during 1907-2014 for (a) Minimum and (b) Maximum Annual Temperature. Red (blue) dots represent increasing (decreasing) trends significant at the 5% level. Yellow (green) dots represent increasing (decreasing) trends that are not statistically significant.

Fig. (3) shows the minimum temperature increased steadily throughout the 20th century while the maximum temperature remained approximately constant until suddenly increasing by approximately 0.5 oC around 2000. Considering the period 1907-2000, the minimum (maximum) temperature trend was 0.64 ± 0.24 (0.18 ± 0.29) oC per century. It should be noted that a number of Australian stations either ceased to operate or data was otherwise unavailable after 2000 as is the case worldwide [11]. Considering only stations operating after 2000, the minimum (maximum) temperature trend for 1907-2014 was 0.82 ± 0.19 (0.62 ± 0.26) oC per century. These values are compatible with those listed in Table 1.

Table 1. Minimum and maximum annual temperature trends. See the text for a discussion of how inhomogeneous data were removed.
Temperature Description Trend (oC per Century)
1880-2014 1907-2014
Minimum Temperature Raw Data 0.64 ± 0.14 0.73 ± 0.20
NS = 10 0.52 ± 0.14 0.70 ± 0.19
NS = 5 0.55 ± 0.13 0.67 ± 0.19
Inhomogeneous Data Removed 0.70 ± 0.14 0.73 ± 0.20
Maximum Temperature Raw Data 0.06 ± 0.20 0.56 ± 0.26
NS = 10 0.17 ± 0.19 0.57 ± 0.25
Ns = 5 0.28 ± 0.19 0.58 ± 0.26
Inhomogeneous Data Removed 0.35 ± 0.19 0.58 ± 0.27

Table 2 shows the seasonal trends for the minimum and maximum temperatures for the interval 1907-2014. These trends were found after inhomogeneous data was removed using the procedure discussed previously with Ns = 5. The minimum and maximum temperatures increased in all seasons. The minimum temperature trend is smallest during June to August while the maximum temperature increased the least during December to February. The minimum temperature trend exceeded the maximum temperature trend for all seasons except during June to August.

Table 2. Seasonal dependence of minimum/maximum temperature trends.
Temperature Season Period Trend
(oC per Century)
Minimum Temperature Dec. – Feb. 1908-2015 0.79 ± 0.28
March – May 1907-2015 0.80 ± 0.35
June – Aug. 1907-2015 0.50 ± 0.34
Sept. – Nov. 1907-2015 0.65 ± 0.28
Maximum Temperature Dec. – Feb. 1908-2015 0.46 ± 0.37
March – May 1907-2015 0.60 ± 0.33
June – Aug. 1907-2015 0.78 ± 0.29
Sept. – Nov. 1907-2015 0.58 ± 0.50

Figs. (4-6) show the trends for stations having at least 50% of data for all years during 1907-2015. The trend significance was found using a t-test at the 5% level. It appears that especially in southeastern Australia that the maximum temperatures are increasing more for stations near the coast but some cooling is evident at inland stations. The reason for this is not clear although a number of these coastal stations are in an urban environment. Table 3 shows 70% (55%) of all stations exhibited statistically significant increasing annual minimum (maximum) temperature trends. Only 6% (12%) of stations exhibited statistically significant decreasing annual minimum (maximum) temperature trends. Less than 14% of stations exhibited statistically significant decreasing minimum or maximum temperature trends in any season. The percentage of stations experiencing increasing/decreasing temperature trends did not drastically change if stations having at least 75% of data for all years during 1907-2015 were considered; only the total number of stations decreases by about half. For example, the percentage of stations exhibiting statistically significant increasing annual minimum (maximum) temperature trends was 80% (54%) whereas 7% (14%) of stations exhibited statistically significant decreasing annual minimum (maximum) temperature trends.

Fig. (5). Minimum Temperature trends at stations having data present for at least 50% of all years during 1907-2015 for (a) December – February, (b) March - May, (c) June - August and (d) September - November. Red (blue) dots represent increasing (decreasing) trends significant at the 5% level. Yellow (green) dots represent increasing (decreasing) trends that are not statistically significant.

Table 3. Number of stations experiencing statistically significant increasing/decreasing temperature trends in Figs. (4-6). The number of trends that is not statistically significant is in brackets.
Temperature Time Period Number Stations
with Increasing Trend
Number Stations
with Decreasing Trend
Total Station Number
Minimum Annual 175 (41) 14 (19) 249
Dec. – Feb. 169 (53) 7 (24) 253
March – May 159 (70) 7 (21) 257
June – Aug. 113 (81) 16 (48) 258
Sept. – Nov. 144 (62) 15 (37) 258
Maximum Annual 135 (45) 29 (35) 244
Dec. – Feb. 84 (80) 34 (54) 252
March – May 123 (78) 13 (40) 258
June – Aug. 163 (61) 6 (28) 258
Sept. – Nov. 95 (67) 33 (62) 257
Fig. (6). Maximum Temperature trends at stations having data present for at least 50% of all years during 1907-2015 for (a) December - February, (b) March – May, (c) June - August and (d) September - November. Red (blue) dots represent increasing (decreasing) trends significant at the 5% level. Yellow (green) dots represent increasing (decreasing) trends that are not statistically significant.

Table 4. Latitude/longitudinal dependence of annual temperature trends for 1907-2014.
Temperature Latitude Longitude Trend
(oC per Century)
Number Stations
Minimum Temperature North of 30 oS East of 140 oE 0.98 ± 0.26 77
West of 140 oE 0.61 ± 0.23 37
South of 30 oS East of 140 oE 0.30 ± 0.22 128
West of 140 oE 0.77 ± 0.21 57
Maximum Temperature North of 30 oS East of 140 oE 0.30 ± 0.22 77
West of 140 oE 0.68 ± 0.29 37
South of 30 oS East of 140 oE 0.57 ± 0.34 128
West of 140 oE 0.69 ± 0.28 57

The latitudinal and longitudinal trend dependence is shown in Table 4. The minimum temperature trend increased much more than the maximum temperature trend for the region north of 30 oS latitude and east of 140 oE longitude. The difference between minimum and maximum temperature trends was not significant for other parts of Australia. Table 5 also shows evidence that urban stations experienced a somewhat greater increase in maximum temperature than those located in a rural environment defined as a region having a population under 10,000. The change in minimum temperature was comparable for both urban and rural stations. This larger increase in maximum temperature in urban areas is believed to be caused by the replacement of the natural ground cover by black asphalt, concrete etc. [26]. Removal of trees and vegetation eliminates cooling shade and reduces evapotranspiration [27].

Fig. (7). GHCN and ACORN Data for (a) Gabo Island (b) Moruya Heads and (c) Sydney showing minimum (blue dots) and maximum (red dots) annual temperatures obtained by adding the adjustments (crosses) to the raw data.

The results were compared to the homogenized ACORN and GHCN data. The average temperature value was computed for each month using the homogenized ACORN data if not more than 3 daily values were missing. These monthly minimum and maximum temperature values were then treated as described in the previous section. An average of 67 (70) years of minimum (maximum) temperature data were available for the 112 ACORN stations. Fig. (7) shows the ACORN and GHCN homogenized data and adjustments for three stations. The temperature adjustments were found using the unadjusted monthly average temperatures [2, 3, 24]. The adjustment corrections are as small as a few hundredths of a degree Centigrade which is considerably less than the measurement accuracy. Corrections as large as nearly 2 oC, affect the monthly mean data at some stations in the late 19th and early 20th centuries. This can greatly alter the resulting trend. For the case of Sydney, which has the second longest record of data available, the maximum temperature trend found using the raw data is 1.1 ± 0.2 oC per century while the GHCN adjusted data yields a trend of 1.8 ± 0.2 oC per century. The ACORN and GHCN adjustments sometimes disagree. For Sydney, the ACORN adjustment is +1 oC for the years 1910-1914 while the GHCN adjustment is -0.6 oC. Moreover, some homogenized time series still exhibit suspicious points such as the dip in the minimum temperature at Moruya Heads in the 1890s.

Table 5. Rural/urban dependence of annual temperature trends for 1907-2014. A rural station is defined as having a population under 10,000. There were 247 rural and 52 urban stations.
Temperature Type Trend
(oC per Century)
Minimum Temperature Rural 0.68 ± 0.19
Urban 0.67 ± 0.18
Maximum Temperature Rural 0.54 ± 0.27
Urban 0.77 ± 0.24

Fig. (8) plots the minimum and maximum temperatures relative to the 20th century for the time series found by averaging the adjusted ACORN and GHCN data over available stations given in Appendices A and B with that obtained using the procedure for discarding inhomogeneous data discussed in this paper. The data is only plotted in Fig. (8) if at least 5 stations report data in a given year. The 5 year averaged curves of the ACORN analysis and this work nearly overlap in all years. There are some differences with the GHCN analysis. The GHCN minimum and maximum temperatures are somewhat lower than this work during the 1870s and 1880s when there were comparatively few stations. The GHCN maximum temperatures are higher than both the ACORN analysis and this work after 2000. These differences are not surprising given that the three analyses used different station datasets. Indeed, the same results are obtained for the period after 2000 if one only considers the subset of stations common to all three datasets. Closely overlapping minimum and maximum temperatures were obtained during the 20th century when the three datasets were comprised of the largest number of stations. The three independent analyses show similar decadal fluctuations in the minimum/maximum temperatures. Most notably, all indicate an increase in maximum temperature of about 0.5 oC occurred around 2000. This agrees with a previous study that examined data obtained for stations located in southeastern Australia [20].

CONCLUSION

There are significant downward step discontinuities affecting Australian temperature data in the 19th century. This is not altogether surprising given that Stevenson screens were not universally installed until around 1900. These would have the effect of reducing the observed maximum temperature. The maximum temperature trend for Australia therefore is significantly different if raw or adjusted data is used. In contrast, the minimum temperature trends found using the raw and adjusted data are comparable.

This study found trends after inhomogeneous data were removed. The resulting time series found by averaging the temperatures relative to the 20th century for 299 stations agreed closely with the ACORN and GHCN analysis. In contrast to the present work, the ACORN and GHCN datasets each made multiple adjustments to the minimum and maximum temperatures observed at each station, and their adjustments frequently disagreed. For the ACORN dataset comprised of 112 stations, over 1,000 adjustments were made to the minimum and maximum temperatures. Many adjustments did not correspond with evident inhomogeneities in the original time series nor did they coincide with changes recorded in the station metadata history. Climate studies frequently wish to determine the temperature change occurring over a large geographical region. This work shows that averaging the temperature anomalies over many stations after large inhomogeneities have been discarded, gives a reliable estimate of the temperature trends. The reason is that small random temperature adjustments, which may be difficult if not impossible to accurately estimate at a given station, do not dramatically affect the average temperature trend.

Minimum daily temperatures over Australia have increased steadily throughout the 20th century. Maximum temperatures remained nearly constant until abruptly increasing around 2000. Similar temporal temperature changes have also been noted elsewhere such as in Europe and the Arctic (van Wijngaarden, 2014). For the period 1907-2014, the Australian minimum (maximum) temperature trend is 0.67 ± 0.19 (0.58 ± 0.26) oC per century. The mean temperature trend was found by plotting the average of the maximum and minimum temperatures for each year during 1907-2014. This yielded a mean temperature trend of 0.63 ± 0.19 oC per century. The minimum temperature increased least during June to August while the maximum temperature increase was smallest during December to February. Northeastern Australia experienced the largest minimum and the smallest increasing maximum temperature trend. Urban stations exhibited maximum temperature trends that were about 30% larger than those experienced in rural areas. Hence, the actual Australian mean temperature change during 1907-2014 may be a bit lower if this urban heat island effect is considered. Nevertheless, this estimate is comparable to the global average temperature change observed during the 20th century [1].

Fig. (8). Comparison of adjusted GHCN and ACORN data as well as this work relative to the 20th century for (a) Minimum and (b) Maximum Annual Temperature. The red triangles are the GHCN results, blue squares are the ACORN results and the black dots are from this work. The solid red, green and black curves are the 5 year running averages which closely overlap in many years. The dashed lines give the corresponding number of stations.

APPENDIX

Appendix A. Australian bureau of meteorology station list. + denotes stations where monthly data was retrieved from the corresponding GHCN dataset as discussed in the text and 1 represents a station in an urban environment having a population exceeding 10,000.
Station BOM Number Latitude
oS
Longitude
oE
Station BOM Number Latitude
oS
Longitude
oE
Adelaide West1 23000 34.93 138.59 Cairns1 31011 16.88 145.75
Adelaide Airport1 23034 34.93 138.52 Cambooya 41011 27.72 151.87
Adelong 72000 35.32 148.07 Camooweal+ 37010 19.92 138.12
Albany+ 9500 34.94 117.8 Camperdown 90011 38.22 143.15
Alexandra 88001 37.2 145.72 Canberra1 70351 35.3 149.18
Alice Springs1 15540 23.71 133.87 Canowindra 65006 33.57 148.67
Alice Springs Airport1 15590 23.8 133.88 Cape Borda 22801 33.75 136.6
Amberley 40004 27.63 152.72 Cape Bruny+ 94010 43.5 147.15
Armidale1+ 56002 30.52 151.65 Cape Leeuwin+ 9518 34.37 115.12
Balladonia 11017 32.45 123.87 Cape Moreton+ 40043 27.03 153.47
Ballarat1+ 89002 37.52 143.77 Cape Naturaliste+ 9519 33.53 115
Balranald 49002 34.63 143.55 Cape Otway+ 90015 38.85 143.5
Barcaldine 36007 23.55 145.28 Cape Schanck 86017 38.5 144.88
Bathurst1+ 63005 33.43 149.57 Cardwell+ 32004 18.25 146.02
Beechworth 82001 36.32 146.67 Carnamah 8025 29.68 115.88
Bega 69002 36.67 149.82 Carnarvon 6011 27.87 113.67
Belair1 23703 35 138.62 Casino 58063 28.88 153.03
Benalla+ 82002 36.55 145.98 Cassilis 62009 32 149.98
Berrigan 74009 35.67 145.82 Ceduna 18012 32.12 133.7
Beulah 77004 35.95 142.42 Charleville 44021 26.4 146.27
Biloela 39006 24.37 150.52 Charters Towers 34002 20.08 146.27
Birchip 77007 35.98 142.9 Childers 39025 25.23 152.28
Birdsville 38002 25.9 139.35 Clare 21014 33.83 138.62
Bollon+ 44010 28.02 147.47 Clermont 35019 22.82 147.63
Bombala 77005 36.92 149.23 Cleve 18014 33.7 136.48
Boulia+ 38003 22.92 139.9 Cloncurry 29008 20.7 140.5
Bourke+ 48013 30.08 145.93 Clunes+ 88015 37.3 143.78
Bowen 33007 20.02 148.25 Cobar 48027 31.48 145.82
Braidwood 69010 35.45 150 Coen 27005 13.93 143.2
Brewarrina 48015 29.97 146.87 Coffs Harbour1 59040 30.32 153.12
Bridgetown+ 9510 33.95 116.13 Collarenebri 48031 29.53 148.57
Brisbane1+ 40214 27.48 153.03 Collie 9628 33.37 116.15
Brisbane Eagle1 40842 27.4 153.1 Condobolin 50014 33.08 147.15
Broken Hill1+ 47007 31.97 141.47 Cook 18110 30.62 130.4
Broome 3003 17.95 122.22 Cooktown 31016 15.43 145.18
Bundaberg1+ 39015 24.87 152.35 Coolgardei 12018 30.95 121.17
Burdekin 33001 19.57 147.4 Coonabarabran+ 64008 31.27 149.27
Burketown+ 29004 17.73 139.53 Coonamble 51010 30.95 148.38
Bushy Park 95003 42.72 146.9 Cootamundra 73009 34.63 148.02
Busselton 9515 33.65 115.35 Corowa 74034 36 146.35
Butlers Gorge 96003 42.28 146.28 Crohamhurst 40062 26.82 152.85
Cabramurra 72091 35.94 148.38 Crookwell 70025 34.47 149.47
Croydon 29012 18.2 142.23 Innisfail+ 32025 17.52 146.02
Cue+ 7017 27.43 117.9 Inverell+ 56017 29.77 151.1
Cunderdin 10286 31.62 117.22 Ipswich1 40101 27.62 152.75
Cunnamulla+ 44026 28.07 145.73 Isisford 36026 24.27 144.43
Currie 98001 39.93 143.85 Ivanhoe 49019 32.9 144.28
Dalwallinu 8039 30.28 116.66 Jenolan Caves 63036 33.82 150.03
Darwin1 14015 12.4 130.87 Jerrys Plains+ 61086 32.48 150.9
Deniliquin+ 74128 35.55 144.93 Jervis Bay+ 68034 35.08 150.8
Derby+ 3007 17.3 123.62 Junee 73019 34.85 147.57
Donnybrook+ 9534 33.57 115.82 Kalamunda1 9058 31.98 116.07
Dubbo1+ 65012 32.2 148.57 Kalgoorlie-Boulder1 12038 30.78 121.45
Dwellingup 9538 32.72 116.05 Kalumburu 1021 14.28 126.63
East Sale1 85072 38.1 147.13 Kapunda 23307 34.35 138.92
Echuca+ 80015 36.15 144.75 Katanning+ 10579 33.68 117.55
Eddystone 92045 40.99 148.35 Katoomba+ 63039 33.72 150.28
Emerald+ 35027 23.53 148.17 Kellerberrin+ 10073 31.63 117.72
Esperance 9789 33.83 121.89 Kempsey 59017 31.05 252.82
Eucla+ 11003 31.68 128.9 Kerang+ 80023 35.73 143.92
Euroa 82016 36.77 145.55 Killarney 41056 28.33 152.3
Farina 17024 30.07 138.27 Kingscote 22807 35.65 137.63
Forbes+ 65016 33.38 148 Kyancutta 18044 33.12 135.55
Forrest 11052 30.38 128.12 Lady Elliot 39059 24.1 152.7
Gabo Island+ 84016 37.57 149.9 Lake Cargelligo 75039 33.28 146.37
Gatton-Lawes 40082 27.55 152.33 Lameroo 25509 35.33 140.52
Gayndah+ 39039 25.63 151.62 Launceston1 91104 41.53 147.2
Georgetown, NSW 21020 33.37 138.4 Laverton, VIC1 87031 37.85 144.73
Georgetown, QLD+ 30018 18.3 143.55 Laverton, WA 12045 28.62 122.42
Geraldton1 8051 28.78 114.7 Learmonth 5007 22.24 114.1
Giles 13017 25.03 128.28 Lismore1+ 58037 28.8 153.25
Gilgandra 51018 31.72 148.67 Lithgow1 63224 33.48 150.13
Gladstone1 39123 23.85 151.25 Longerenong 79028 36.67 142.3
Glen Innes 56011 29.73 151.73 Longreach 36031 23.44 144.28
Goondiwindi+ 41038 28.55 150.3 Low Head+ 91057 41.05 146.8
Grenfell 73014 33.9 148.17 Maatsuyker 94041 43.65 146.27
Grove 94069 42.99 147.07 Macedon 87036 37.42 144.55
Gunnedah 55023 30.98 150.25 Mackay1 33119 21.12 149.22
Guyra 56016 30.22 151.67 Maffra 85055 37.97 146.98
Gympie 40093 26.15 152.63 Mandurah1 9572 32.5 115.77
Halls Creek 2012 18.22 127.65 Manjimup 9573 34.23 116.15
Hamelin Pool+ 6025 26.4 114.17 Marble Bar+ 4020 21.17 119.75
Harrington 60023 31.87 152.7 Marree 17031 29.65 138.05
Hay Miller+ 75031 34.52 144.82 Maryborough+ 40126 37.05 143.73
Herberton+ 31029 17.38 145.38 Meekathara 7045 26.6 118.53
Hillston 75032 33.48 145.53 Melbourne1+ 86071 37.82 144.97
Hobart1+ 94029 42.88 147.33 Menindee 47019 32.4 142.42
Horn Island 27058 10.58 142.29 Menzies+ 12052 29.68 121.02
Horsham+ 79023 36.65 142.1 Merredin 10092 31.48 118.28
Hughenden+ 30024 20.83 144.2 Mildura1 76031 34.22 142.08
Hume Reservoir 72023 36.1 147.02 Miles+ 42033 26.67 150.18
Milthorpe 63053 33.45 149.18 Rabbit Flat 15548 20.18 130.01
Mitchell 43020 26.48 147.97 Richmond, NSW 67021 33.62 150.75
Molong 65023 33.1 148.87 Richmond RAAF 67033 33.6 150.78
Morawa 8093 29.22 116 Richmond, QLD+ 30045 20.72 143.13
Moree 53048 29.49 149.85 Robe 26026 37.16 139.76
Moruya Heads+ 69018 35.9 150.15 Rockhampton1 39083 23.38 150.47
Moss Vale 68045 34.55 150.37 Roebourne 4035 20.78 117.12
Mount Barker, SA+ 23733 35.07 138.85 Roma+ 43030 26.57 148.78
Mount Barker, WA 9581 34.62 117.65 Roseworthy+ 23020 34.53 138.68
Mount Gambier1 26021 37.73 140.78 Rottnest Island+ 9038 32 115.5
Mount Hope 49032 32.85 145.88 Rutherglen 82039 36.1 146.51
Mount Surprise 30036 18.15 144.32 Sale 85072 38.12 147.13
Mount Victoria 63056 33.6 150.25 Sandgate1 40242 27.32 153.07
Mudgee+ 62021 32.58 149.58 Scone 61069 32.05 150.87
Mungindi 52020 28.97 148.98 Seymour 88053 37.02 145.17
Murrurrundi 61051 31.77 150.83 Snowtown 21046 33.78 138.22
Nanango 40158 26.67 152 Southern Cross+ 12074 31.22 119.32
Narrabri 54120 30.32 149.77 Southport1 40190 27.95 153.4
Newcastle1+ 61055 32.92 151.78 St. Arnaud+ 79040 36.62 143.27
Nhill+ 78015 36.33 141.63 St. George 43109 28.03 148.58
Nimmitabel 70067 36.52 149.28 St. Lawrence 33065 22.33 149.53
Normanton+ 29041 17.67 141.08 Stanley+ 91094 40.77 145.3
Northam 10111 31.65 116.67 Stanthorpe 41095 28.65 151.93
Nowra1 68076 34.95 150.53 Strathalbyn+ 23747 35.25 138.88
Nullagine+ 4027 21.88 120.12 Streaky Bay 18079 32.8 134.22
Nuriootpa 23321 34.47 139 Swan Hill 77042 35.35 143.57
Nungan 51039 31.55 147.18 Sydney1+ 66062 33.85 151.2
Omeo+ 83025 37.08 147.6 Sydney Airport1 66037 33.95 151.18
Onslow+ 5016 21.63 115.1 Tambo 35069 24.88 146.25
Oodnadatta 17043 27.56 135.45 Tamworth1+ 55054 31.07 150.83
Orbost 84030 37.68 148.45 Tarcoola+ 16044 30.72 134.57
Ouyen 76047 35.07 142.32 Taree1+ 60030 31.9 152.48
Palmerville+ 28004 16.98 144.07 Tennant Creek 15135 19.64 134.18
Parkes Macarthur++ 65026 33.13 148.15 Tenterfield 56032 29.05 152.02
Pemberton 9592 34.45 116.03 Terang 90077 38.25 142.92
Perth1+ 9097 31.95 115.85 Thargomindah 45017 27.98 143.82
Perth Airport1 9021 31.9 116 Tibooburra+ 46037 29.42 142.02
Picton 68052 34.18 150.62 Toowoomba1 41103 27.58 151.92
Pittsworth 41082 27.72 151.63 Townsville1 32040 19.25 146.75
Port Douglas 31052 16.48 145.47 Trangie 51048 32.03 147.98
Port Hedland1 4032 20.1 119.57 Urana 74110 35.33 146.27
Port Lincoln1 18070 34.72 135.87 Urandangie 37043 21.6 138.3
Port Macquarie1+ 60026 31.43 152.92 Victor Harbour 23751 35.55 138.62
Port Pirie1 21043 33.18 138.02 Victoria River 14825 16.4 131.01
Portland 90070 38.35 141.6 Wagga1 72151 35.13 147.37
Quambone 51042 30.93 147.87 Wagga1 72150 35.16 147.46
Queenscliff 87054 38.27 144.67 Walgett+ 52026 30.02 148.31
Quilpie 45015 26.62 144.27 Walterhall 39069 23.63 150.39
Quirindi 55049 31.5 150.67 Wandering+ 10648 32.67 116.67
Waratah 97014 41.45 145.53 Winton 37051 22.38 143.03
Warialda 54029 29.55 150.58 Wittenoom 5026 22.23 118.33
Weipa 27045 12.68 141.92 Woomera 16001 31.13 136.82
Wellington 65034 32.55 148.93 Wyalong 73054 33.93 147.24
Wentworth 47053 34.12 141.92 Wyndham 1005 15.47 128.1
White Cliffs+ 46042 30.85 143.08 Yalgoo 7091 28.35 116.68
Wilcannia+ 46043 31.55 143.37 Yamba+ 58012 29.43 153.35
Williamtown+ 61078 32.78 151.82 Yongala 19062 33.02 138.7
WilsonsPromontory+ 85096 29.12 146.42 York+ 10144 31.88 116.75
Wiluna+ 13012 26.58 120.22 Young 73056 34.32 148.3
Windorah 38024 25.42 142.65

Appendix B. Australian climate observations reference network (ACORN) station list.
Site Name Site Number Latitude
oS
Longitude
oE
Station BOM Number Latitude
oS
Longitude
oE
Adelaide 23090 34.92 138.62 Gunnedah 55024 31.03 150.27
Albany 9741 34.94 117.80 Halls Creek 2012 18.23 127.66
Alice Springs Airport 15590 23.80 133.89 Hobart 94029 42.89 147.33
Amberley 4004 27.63 152.71 Horn Island 27058 10.58 142.29
Barcaldine 36007 23.55 145.29 Inverell 56242 29.78 151.11
Bathurst 63005 33.43 149.56 Kalgoorlie-Boulder 12038 30.78 121.45
Birdsville 38026 25.90 139.35 Kalumburu 1019 14.30 126.65
Boulia 38003 22.91 139.90 Katanning 10579 33.69 117.56
Bourke 48245 30.04 145.95 Kerang 80023 35.72 143.92
Bridgetown 9510 33.96 116.14 Kyancutta 18044 33.13 135.56
Brisbane Airport 40842 27.39 153.12 Launceston Airport 91311 41.55 147.21
Broome Airport 3003 17.95 122.24 Laverton RAAF 87031 37.86 144.76
Bundaberg 39128 24.91 152.32 Learmonth 5007 22.24 114.10
Burketown 29077 17.75 139.54 Longreach 36031 23.44 144.28
Butlers Gorge 96003 42.28 146.28 Low Head 91293 41.05 146.79
Cabramurra 72161 35.94 148.38 Mackay 33119 21.12 149.22
Cairns 31011 16.87 145.75 Marble Bar 4106 21.18 119.75
Camooweal 37010 19.92 138.12 Marree 17031 29.65 138.06
Canberra 70351 35.30 149.20 Meekatharra 7045 26.61 118.54
Cape Borda 22823 35.75 136.60 Melbourne 86071 37.81 144.97
Cape Bruny 94010 43.49 147.15 Merredin 10092 31.48 118.28
Cape Leeuwin 9518 34.37 115.14 Mildura 76031 34.24 142.09
Cape Moreton 40043 27.03 153.47 Miles 42112 26.66 150.18
Cape Otway 90015 38.86 143.51 Morawa 8296 29.20 116.02
Carnarvon 6011 24.89 113.67 Moree 53115 29.49 149.85
Ceduna 18012 32.13 133.70 Moruya Heads 69018 35.91 150.15
Charleville 44021 26.41 146.26 Mount Gambier 26021 37.75 140.77
Charters Towers 34084 20.05 146.27 Nhill 78015 36.31 141.65
Cobar 48027 31.48 145.83 Normanton 29063 17.69 141.07
Coffs Harbour 59040 30.31 153.12 Nowra 68072 34.95 150.54
Cunderdin 10286 31.62 117.22 Nuriootpa 23373 34.48 139.01
Calwallinu 8039 30.28 116.66 Oodnadatta 17043 27.56 135.45
Darwin 14015 12.42 130.89 Orbost 84145 37.69 148.47
Deniliquin 74258 35.56 144.95 Palmerville 28004 16 144.08
Dubbo 65070 32.22 148.58 Perth 9021 31.93 115.98
Eddystone Point 92045 40.99 148.35 Point Perpendicular 68151 35.09 150.80
Esperance 9789 33.83 121.89 Port Hedland Airport 4032 20.37 118.63
Eucla 11003 31.68 128.88 Port Lincoln 18192 34.60 135.88
Forrest 11052 30.85 128.11 Port Macquarie 60139 31.43 152.87
Gabo Island 84016 37.57 149.92 Rabbit Flat 15666 20.18 130.01
Gayndah 39066 25.62 151.62 Richmond (NSW) 67105 33.60 150.78
Georgetown 30124 18.30 143.53 Richmond (QLD) 30045 20.73 143.14
Geraldton 8051 28.80 114.70 Robe 26026 37.16 139.76
Giles 13017 25.03 128.30 Rockhampton 39083 23.38 150.48
Grove 94220 42.99 147.07 Rutherglen 82039 36.10 146.51
Sale 85072 38.12 147.13 Wagga Wagga 72150 35.16 147.46
Scone 61363 32.03 150.83 Walgett 52088 30.04 148.12
Snowtown 21133 33.77 138.22 Wandering 10917 32.67 116.67
St. George 43109 28.05 148.59 Weipa 27045 12.68 141.92
Sydney 66062 33.86 151.21 Wilcannia 46043 31.56 143.37
Tarcoola 16098 30.71 134.58 Williamtown 61078 32.789 151.84
Tennant Creek 15135 19.64 134.18 Wilsons Promontory 85096 39.13 146.42
Thargomindah 45025 27.99 143.81 Wittenoom 5026 22.24 118.34
Tibooburra 46037 29.43 142.01 Woomera 16001 31.16 136.81
Townsville 32040 19.25 146.77 Wyalong 73054 33.93 147.24
Victoria River Downs 14825 16.40 131.01 Yamba 58012 29.43 153.36

CONFLICT OF INTEREST

The authors confirm that this article content has no conflict of interest.

ACKNOWLEDGEMENTS

The financial assistance of the Canadian Natural Science and Engineering Research Council is gratefully acknowledged.

REFERENCES

[1] Stocker TF, Qin D, Plattner GK, et al. Climate Change 2013: The Physical Science Basis, Contribution of Working Group I to the 5th Assessment Report of the Intergovernmental Panel on Climate Change. In: Cambridge, UK and New York, NY, USA: Cambridge University Press 2013.
[2] National Centers for Environmental Information. Available at: www.ncdc.noaa.gov [Accessed on: 15 June 2016]
[3] Australian Government, Climate Data Online. Available at: www.bom.gov.au/climate/data [Accessed on: 15 June 2016]
[4] van Wijngaarden WA, Vincent LA. Examination of trends in hourly surface relative humidity in canada during 1953-2003. J Geophys Res 2005; 110: D22102.
[5] Vincent LA, van Wijngaarden WA, Hopkinson R. Surface temperature and humidity trends in Canada for 1953-2005. J Clim 2007; 20: 5100-13.
[6] Isaac V, van Wijngaarden WA. Surface water vapor pressure and temperature trends in north america during 1948-2010. J Clim 2012; 25: 3599-609.
[7] Verbesselt J, Hyndman R, Newnham G, Culvenor D. Detecting trend and seasonal changes in satellite image time series. Remote Sens Environ 2010; 114: 106-15.
[8] Verbesselt J, Hyndman R, Zeileis A, Culvenor D. Phenological change detection while accounting for abrupt and gradual trends in satellite image time series. Remote Sens Environ 2010; 114: 2970-80.
[9] Verbesselt J, Zeileis A, Herold M. Near real-time disturbance detection using satellite image time series. Remote Sens Environ 2013; 123: 98-108.
[10] Klotzbach PJ, Pielke RA Sr, Pielke RA Jr, Christy JR, McNider RT. An alternative explanation for differential temperature trends at the surface and in the lower troposphere. J Geophys Res 2009; 114: D21102.
[11] McNider RT, Christy JR, Biazar A. A Stable Boundary Layer Perspective on Global Temperature Trends. Earth Environ Sci 2010; 13: 012003.
[12] McNider RT, Steeneveld GJ. Response and sensitivity of the nocturnal boundary layer over land to added longwave radiative forcing. J Geophys Res 2012; 117(D14)
[13] Salamanca F, Georgescu M, Mahalov A, Moustaoui M. Summertime response of temperature and cooling energy demand to urban expansion in a semiarid environment. J Appl Meteorol Climatol 2015; 54: 1756-72.
[14] Karl TR, Jones PD, Knight RW, et al. A new perspective on recent global warming: Asymmetric trends of daily maximum and minimum temperature. Bull Am Meteor 1993; 74: 1007: 1023.
[15] Torok SJ, Morris CJ, Skinner C, Plummer N. Urban heat island features of southeast Australian towns. Aust Met Mag 2001; 50: 1-13.
[16] van Wijngaarden WA. Arctic temperature trends from the early nineteenth century to the present. Theor Appl Climatol 2015; 122: 567-80.
[17] Torok SJ, Nicholls N. A historical annual temperature dataset for Australia. Aust Met Mag 1996; 45: 251-60.
[18] Jones DA, Wang W, Fawcett R. High-quality spatial climate datasets or Australia. Aust Met Ocean J 2009; 58: 233-48.
[19] Trewin B. A daily homogenized temperature dataset for Australia, Int. J Clim 2013; 33: 1510-29.
[20] Ashcroft L, Karoly DJ, Gergis J. Southeastern Australian climate variability 1860-2009: a multivariate analysis. Int J Clim 2014; 34: 1928-44.
[21] Trewin B. Techniques involved in developing the Australian Climate Observations Reference Network – Surface Air Temperature dataset, Center for Australian Weather and Climate Technical Report No 049. 2012. ISBN: 978 0 643 10815 8
[22] Fawcett RJ, Trewin BC, Braganza K, et al. On the sensitivity of Australian temperature trends and variability to analysis methods and observation networks, Center for Australian Weather and Climate Research Technical Report No 050. 2012. ISBN: 978 0 643 10819 6
[23] Australian Climate Observations Reference Network - Surface Air Temperature Dataset. Available at: https://data.gov.au/dataset/acorn-sat. [Accessed on: 15 June 2016]
[24] KNMI Climate Explorer. Available at: https://climexp.knmi.nl [Accessed on: 15 June 2016]
[25] Quirk T. The australian temperature anomaly, 1910-2000. Energy Environ 2009; 20: 97-100.
[26] Oke TR. The energetic basis of the urban heat island. Q J R Meteorol Soc 1982; 108: 1-24.
[27] Santos F. Trees – Natural Air Conditioners. Scientific Scribbles University of Melbourne [Accessed on: 24 August 2016]; Available at: https://blogs.unimelb.edu.au/sciencecommunication/2013/08/23/