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INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 34: 2585–2603 (2014) Published online 22 November 2013 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/joc.3861

Trends and variability of temperature extremes in the tropical Western Pacific K. Whan,a,b* L. V. Alexander,a,b A. Imielska,a,c S. McGree,c D. Jones,c E. Ene,d S. Finaulahi,e K. Inape,f L. Jacklick,g R. Kumar,h V. Laurent,i H. Malala,j P. Malsale,k R. Pulehetoa-Mitiepo,l M. Ngemaes,m A. Peltier,n A. Porteous,o S. Seuseu,p E. Skilling,q L. Tahani,r U. Tooruas and M. Vaiimenet a

b

Climate Change Research Centre, The University of New South Wales, Sydney, New South Wales, Australia ARC Centre of Excellence for Climate Systems Science, The University of New South Wales, Sydney, New South Wales, Australia c The Centre of Australian Weather and Climate Research, The Australian Bureau of Meteorology, Melbourne, Australia d Tuvalu Meteorological Service, Funafuti, Tuvalu e Tonga Meteorological Service, Fua’amotu Airport, Tonga f Papua New Guinea National Weather Service, Port Moresby, Papua New Guinea g National Weather Service, Majuro Weather Service Office, Marshall Islands h Fiji Meteorological Service, Nadi Airport, Fiji i M´ etéo-France Polynésie Fran¸caise, French Polynesia National Weather Service, Pagopago, Tahiti, American Samoa j National Weather Service, Pagopago Weather Service Office, Pagopago, American Samoa k Vanuatu Meteorological Service, Port Vila, Vanuatu l Niue Meteorological Service, Hanan Airport, Niue m National Weather Service, Koror Weather Service Office, Koror, Palau n Météo-France Nouvelle-Calédonie, Noumea, New Caledonia o National Institute of Water and Atmospheric Research, Wellington, New Zealand p Samoa Meteorological Division, Ministry of National Resources, Environment and Meteorology, Apia, Samoa q National Weather Service, Pohnpei Weather Service Office, Federated States of Micronesia r Solomon Islands Meteorological Service, Honiara, Solomon Islands s Kiribati Meteorological Service, Tarawa, Kiribati t Cook Islands Meteorological Service, Rarotonga, Cook Islands

ABSTRACT: A new high-quality daily and monthly temperature station dataset was prepared for the tropical Western Pacific through a quality control and homogenization process. The homogeneity of 46 temperature stations, collected at a workshop conducted as part of the Pacific-Australia Climate Change Science and Adaptation Planning program, was assessed and the non-climatic step changes were removed. Here we present trends in mean and extreme temperature for the Western Pacific, covering an extended time period and larger geographical area compared with previous analyses. We discuss five main conclusions: (1) There is a significant warming trend in annual mean temperature over the past 50 years (1961–2011), of between 0.05 and 0.34 ◦ C per decade. (2) Significant and spatially homogeneous warming trends are evident at the station level over 1961–2011 for the warm and cool extremes of both maximum and minimum temperatures. (3) Sub-regional trends, over the period 1951–2011, are spatially coherent, with the largest warming trends in the hottest day and night of the year and the coolest night of the year. (4) This analysis highlights the role of decadal variability in the number of days exceeding extreme temperature thresholds, with the upper (lower) tails of the distribution warming more (less) in recent decades. (5) We show that strong relationships exist between local and remote sea-surface temperature anomalies and all indices of extreme temperature, particularly with minimum temperature extremes. KEY WORDS

climate extremes; extreme indices; homogenization; temperature; Western Pacific

Received 17 June 2013; Revised 17 September 2013; Accepted 5 October 2013

1. Introduction The tropical Pacific is highly vulnerable to the effects of climate variability and change and to the influence of climate extremes (Mimura et al., 2007). * Correspondence to: K. Whan, Pacific Climate Impacts Consortium, University House 1, University of Victoria, Victoria BC V8W2Y2, Canada. E–mail: [email protected]

Changes in climate extremes are likely to impact many sectors including human health (Singh et al., 2001; McMichael et al., 2003; Barnett, 2011), agriculture, fisheries and water resource management (Field et al., 2012). Despite the known impacts, relatively little is known about more recent trends in climate extremes in the region. Here we focus on temperature extremes in the tropical Western Pacific updating recent analyses produced for mean temperature

 2013 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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(Jones et al., 2013). See McGree et al., 2013 for a discussion of rainfall extremes. The need to rescue data in the South Pacific region has been recognized (Page et al., 2004) and a global effort has focused on increasing the availability of daily data records for the analysis of climate extremes. Some datarescue efforts have been specifically targeted at the South Pacific region, for example, data digitization as part of the Pacific-Australian Climate Change Science and Adaptation Planning (PACCSAP, Power et al., 2011) program. The two most common approaches to the study of climate extremes are those based on indices, such as the frequency above thresholds or percentiles (e.g. Manton et al., 2001), and those using extreme value theory (e.g. Wang et al., 2013). Indices have the most value for studying those extremes that might happen once or a few times a year, while extreme value theory allows the analysis of those extremes that are much rarer and hence more extreme, such as events with an average recurrence interval of a few years or longer. We have followed the World Meteorological Organization’s Expert Team on Climate Change Detection and Indices (ETCCDI) to calculate 8 extreme indices from daily temperature data, which allows analysis of climate extremes and comparison across different regions (Peterson and Manton, 2008). The ETCCDI is jointly sponsored by the World Meteorological Organisation Commission for Climatology, the Joint Commission for Oceanography and Marine Meteorology and the Research Programme on Climate Variability and Predictability. Using the ETCCDI code, temperature extremes can be characterized by several types of indices. These include absolute indices that capture the hottest and coldest day and night of the year, percentile indices that examine changes in the tails of the distribution and threshold indices that quantify the number of days per year above or below particular temperature values (see Zhang et al. (2011) for definition of the indices). Absolute indices occur only once per year while the percentile indices typically occur several times per year (Peterson and Manton, 2008). The absolute and percentile indices are most valuable in the tropical Western Pacific region. Indices calculated from daily data allow information about extreme weather conditions to be more easily shared than the raw daily data alone (Zhang et al., 2011). A series of workshops have been conducted that have filled in data gaps in some data sparse regions globally (Peterson et al., 2002; Page et al., 2004; Aguilar et al., 2005; Vincent et al., 2005; Zhang et al., 2005; Klein Tank et al., 2006; New et al., 2006; Aguilar et al., 2009; Caesar et al., 2011) and these have considerably contributed to global datasets and analyses (Alexander et al., 2006; Donat et al., 2013). Global analyses have consistently found spatially coherent warming trends in temperature extremes over the latter half of the 20th century (Frich et al., 2002; Alexander et al., 2006; Donat et al., 2013); however, the tropical Western Pacific is generally missing from or under-represented in global studies of climate extremes.

The region was included in the Asia-Pacific Network (APN) workshops (Manton et al., 2001, Page et al., 2004, Griffiths et al., 2005, Choi et al., 2009) with some stations more recently updated (Caesar et al., 2011). Significant increases (decreases) in the number of hot days and warm nights (cool days and cold nights) are consistent across stations from mainland Australia, Fiji, French Polynesia, New Caledonia, New Zealand and the Solomon Islands, from 1961 to 1998 (Manton et al., 2001). Increases in mean temperature (maximum and minimum) and warm nights, and decreases in cold nights and cool days have been reported over the period 1961–2003 (Griffiths et al., 2005). Analysis of temperature distributions in non-urban stations suggests that warming is associated with significant increases in both the mean and extremes, but with little change in the variability, while urban stations have tended to experience increased variance. A strong correlation between mean and extreme temperature suggests that changes in mean climate could be a useful predictor of extremes (Griffiths et al., 2005). Significant warming trends in percentile indices have been reported in the most recent analysis (1971–2005) for the Asia-Pacific region that included stations from mainland Australia and Fiji. However, no significant trends in the hottest and coolest day and night of the year was found for the South Pacific region (Caesar et al., 2011). Yet, no thorough analysis exists of trends in extreme temperature over recent years, leaving a considerable knowledge gap as to trends in the Western Pacific including the last decade. This is important as recent analyses have shown that the relationship between the occurrence of extremes and global warming may be nonlinear (Coumou and Rahmstorf, 2012). Homogenization of climate data is crucial for the monitoring of climate and assessment of trends (Trewin, 2013). The first step is the detection of non-climatic changepoints (Reeves et al., 2007) using a variety of statistical techniques (Venema et al., 2012), often followed by their adjustment (Peterson et al., 1998). Some stations in the South-West Pacific have been previously homogenized (Salinger, 1995; Jones et al., 2013), though the focus has been on monthly or annual data. Here we have been able to extend previous regional analyses of climate extremes (Manton et al 2001; Griffiths et al., 2005; Caesar et al., 2011) both spatially and temporally using an updated high-quality dataset. In the following section we provide a description of the data, the homogenization process and analysis methods used (Section 2). Results and discussion are presented in Section 3 followed by summary and conclusions in Section 4.

2. Data and methods 2.1. Station and sub-regional data Data availability and data collection are a major challenge in analysing climate extremes that requires daily temperature data. This is particularly an issue in the Pacific as

 2013 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society.

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TEMPERATURE EXTREMES IN THE WESTERN PACIFIC

much of the data only sits in national (local) databases, with only a fraction having been exchanged internationally and incorporated into global datasets such as the Global Historical Climatology Network (Menne et al., 2012). To facilitate data collation for this project, representatives from 18 Pacific Island countries participated in a workshop run in the style of previous ETCCDI and APN workshops, held in Nouméa, New Caledonia, in May 2012. In total, 40 meteorological stations in the tropical Western Pacific with daily temperature records were collated and during rigorous post-workshop analysis each station was assessed for homogeneity and adjustments made if required (Table 1 and Figure 1). In some cases rescued data was bought to the workshop but some stations data was also added after the workshop. In addition to this number, six stations from New Caledonia that were previously homogenized (Cavarero et al., 2012) have been included in the analysis, taking the total number of stations to 46. The process of data collation was supported by the recent introduction of a climate database in many of the countries as part of PACCSAP (Power et al., 2011). Four sub-regions (Table 1 and Figure 1) are defined on the basis of station location and the influence of largescale climate features (McGree et al., 2013). These subregions were used to explore regional differences in the trends and variability of temperature extremes. One subregion is comprised of stations located to the south-west of the mean South Pacific Convergence Zone position (swSPCZ). Another is comprised of stations in the subtropics (ST) tending to lie under the influence of the trade winds and subtropical ridge. Stations north of the Intertropical Convergence Zone (nITCZ) are combined, as are stations under the influence on the West Pacific Monsoon (WPM). Finally, all stations were combined to assess trends and variability over the whole region. To account for a differing number of stations with data available in each year, the variance of each regional mean is adjusted in the subsequent analyses, taking into account the correlation between stations (Jones et al., 2001; Brunet et al., 2007; Caesar et al., 2011). See Jones et al. (2001) and Brunet et al. (2007) for further discussion of how the variance of the sub-regional time series was adjusted. 2.2. Quality control and homogenization Quality control, to detect and remove spurious daily temperature data, was carried out using the ‘RClimDex’ package developed by the ETCCDI (http://etccdi.pacificclimate.org/software.shtml), which runs in the ‘R’ statistical computing environment (R Development Core Team, 2012). In the quality control process, RClimDex flagged errors and suspect observations in the daily data (e.g. when maximum and minimum temperatures are equal, minimum temperature is greater than maximum temperature or a physically impossible value exists) and suspicious values, i.e.

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values that are greater than four standard deviations (SDs) from the mean. Erroneous (suspicious) values were then manually checked by local National Weather Service staff, where possible, and adjusted or removed if required. In some cases data were cross-checked against other variables and paper files. The number of erroneous daily values removed varied across stations but did not exceed 1%. ‘Homogenization’ is the process of removing artificial changes in the time series that are not related to natural variations in climate. Inhomogeneities can be step changes (e.g. station relocation, instrument changes) or trends in a time series (e.g. slow change in the environmental conditions influencing measurements at an observing site over time). As such, a complete metadata record (i.e. information about station history and recording practices) is vital for confident detection and removal of these artificial changes (Trewin, 2013). Metadata availability varies between countries and stations. For some countries (Kiribati, Tuvalu, Samoa, Tonga, Cook Islands, Niue and Fiji) near complete station histories are available from the beginning of the records until the 1980s–1990s when metadata quality declines. The majority of countries have incomplete metadata records that contain information available about a limited number of site moves but lacking details and information about the whole record. Many stations have little metadata available, including those in Vanuatu, the Solomon Islands and Papua New Guinea. For all stations it is likely that additional metadata can be found in colonial archives and in-country on paper records. A station catalogue is being developed and will be available from the Pacific Climate Change Data Portal (www.bom.gov.au/climate/pccsp) in the near future. We focus on the types of inhomogeneities that manifest as step changes, as assessing other types would be an extremely complex task and difficult to do well (Aguilar et al., 2003). In this study, homogenization is a twostep process; first is the detection of step changes, followed by adjustment of the time series if step changes are considered to be of a non-climatic origin. Step changes were identified using the monthly series because additional noise in the daily series makes testing for step changes more difficult (Wang and Feng, 2010) and computationally expensive. As a precaution, the set of changepoints identified in the monthly series were tested for significance in the daily series, using RHTestsV3. This was done to confirm that the changepoints identified as significant in the monthly series were also significant in the daily series, but was not an essential part of the methodology as the monthly and daily series were in agreement. The monthly and daily series were adjusted where appropriate (see Table A1 in Appendix A for a list of the adjustments). ‘RHtestsV3’ was the primary software package used for detecting and adjusting for step changes in the homogenization process (Wang and Feng, 2010; http://etccdi.pacificclimate.org/software.shtml). Step changes are detected with multiple tests to increase


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Table 1. The location, elevation and data availability for stations used in this study and the sub-regions to which the stations have been allocated. Country a

FSM FSMa FSMa Marshall Islandsa Marshall Islandsa Palaua American Samoa Cook Islandsa Fijia Fijia Fijia Fijia New Caledonia New Caledonia New Caledonia New Caledonia New Caledonia New Caledonia Niuea Samoaa French-Polynesia Tongaa Tongaa Tongaa Tongaa Tongaa Vanuatua Vanuatua Australia Australia New Zealand Australia PNGa PNGa PNGa PNGa PNGa PNGa Solomon Islandsa Solomon Islandsa Solomon Islandsa Solomon Islandsa Solomon Islandsa Solomon Islandsa Kiribatia Tuvalua

Station name 1

Chuuk Pohnpei2 Yap3 Kwajalein4 Majuro5 Koror6 Pago Pago7 Rarotonga8 Laucala Bay (Suva)9 Nabouwalu10 Nadi Airport11 Vunisea12 Koumac13 La Tontouta14 Nessadiou15 Noumea16 Poindimie17 Yate18 Hanan Airport19 Apia20 Tahiti-Faaa21 Fuaamotu Airport 22 Haapai23 Keppel24 Lupepauu25 Niuafoou26 Aneityum27 Port Vila28 Lord Howe Island29 Norfolk Island30 Raoul Island31 Willis Island32 Kavieng33 Madang34 Misima35 Momote36 Port Moresby37 Wewak38 Auki39 Henderson Airport 40 Honiara41 Kirakira42 Munda43 Taro44 Tarawa45 Funafuti46

Lon (◦ E) Lat (◦ N) Elevation (m) Sub-region 151.83 158.22 138.08 167.73 171.38 134.48 189.29 200.2 178.45 178.7 177.45 178.17 164.28 166.22 165.48 166.45 165.33 167.23 190.07 188.22 210.38 184.85 185.65 186.23 186.03 184.38 169.77 168.32 159.08 167.94 182.08 149.98 150.82 145.8 152.83 147.42 147.22 143.67 160.73 160.05 159.97 161.92 157.27 156.38 172.98 179.22

7.45 6.97 9.48 8.73 7.08 7.33 −14.33 −21.2 −18.15 −17 −17.77 −19.05 −20.57 −22.02 −21.62 −22.28 −20.93 −20.78 −19.08 −13.8 −17.55 −21.23 −19.8 −15.95 −18.58 −15.57 −20.23 −17.74 −31.54 −29.04 −29.25 −16.3 −2.57 −5.22 −10.68 −2.05 −9.38 −3.58 −8.78 −9.42 −9.42 −10.42 −8.33 −6.7 1.35 −8.5

1.5 37.5 17 2.1 3 27.4 3.7 6.4 6 34 22 31 25 37 2 70 14 22 59 1 2 38 2 2 67 60 7 20.4 5 112 49 9.3 7 4.3 20.1 3.7 48 4.9 11.0 7.9 55.0 5.5 2.7 1.2 3 1

nITCZ nITCZ nITCZ nITCZ nITCZ nITCZ swSPCZ swSPCZ swSPCZ swSPCZ swSPCZ swSPCZ swSPCZ swSPCZ swSPCZ swSPCZ swSPCZ swSPCZ swSPCZ swSPCZ swSPCZ swSPCZ swSPCZ swSPCZ swSPCZ swSPCZ swSPCZ swSPCZ ST ST ST WPM WPM WPM WPM WPM WPM WPM WPM WPM WPM WPM WPM WPM NA NA

Data availability 1951–2012 1951–2011 1951–2012 1952–2012 1955–2012 1951–2012 TX: 1957–2012 TN: 1960–2012 1934–2012 1941–2011 1952–2012 1942–2011 1960–2012 1970–2009 1970–2009 1970–2009 1970–2009 1970–2009 1970–2009 1940–2012 1957–2012 1961–2011 1979–2012 1950–2012 1949–2009 1956–2012 1978–2012 1948–2012 1969–2012 1939–2012 1939–2012 1940–2011 1939–2012 1962–2011 1951–2011 1975–2012 1950–2012 1939–2011 1957–2012 1962–2012 1975–2012 1951–2012 1965–2012 1962–2012 1975–2012 1950–2011 1961–2012

See Section 3.1 for sub-region definitions. Superscripts indicate station locations in Figure 1. FSM, Federated States of Micronesia; PNG, Papua New Guinea. Data availability refers to the homogenized daily series and is for both maximum (TX) and minimum (TN) temperatures, unless otherwise specified. a PACCSAP partner countries.

confidence in each adjustment. The majority of these tests are various implementations of RHTestsV3, which are outlined below in more detail. To begin with, RHTestV3 was used without a reference series to locate step changes in the record that are significant at the 95% confidence level with or without metadata support. Subsequently, five changepoint detection tests, including four tests using RHTestsV3, were used as additional lines of evidence in support of step changes. These

tests are: (1) Step changes in the record were identified using the closest neighbouring homogenous temperature station (up to 2000 km away) as reference series, where available. These stations were used as ‘additional lines of evidence’ in combination with the following techniques, rather than relying solely on these neighbouring stations, as the sparse data network means that correlations did not always exceed 0.7 (Stephenson et al., 2008). (2 and 3) There is a strong relationship between small island


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4 6

3

1

10N

5

2

nITCZ 45 36 38

33

0

WPM

34 44 35 37

43

41 39 42 40

46 −10S

20 7

swSPCZ

26 24

32 28 13

17

18 27

21

10 9

11

25 23 22

12

19 8

−20S

15 16 14 30 29 140E

160E

31 ST

−30S 180E

200E

Figure 1. Station locations with sub-regions marked in solid black lines. Station numbers relate to the superscripts in Table 1. Stations 45 and 46 are not included in a sub-region.

temperatures and both local and remote sea surface temperature (SST) anomalies (Kenyon and Hegerl, 2008; Alexander et al., 2009; Jones et al., 2013), and SST reference series have been used elsewhere in the homogenization of sparse island networks (Stephenson et al., 2008). SST anomalies from the HadISST dataset (Rayner et al., 2003) were used to create a local SST time series (10◦ × 10◦ area-averaged SST series centred over each station) and an index of El Niño–Southern Oscillation (ENSO) variability from area-averaged SST anomalies in the Niño 3.4 region (170–120◦ W, 5◦ N–5◦ S). Both these SST series were used as reference series to detect step changes for each station; (4) Step changes in the diurnal temperature range were assessed as changes in measurement practice can preferentially alter either maximum or minimum temperature (Zhou and Ren, 2011); (5) A general likelihood-ratio-based approach that tests for mean changes in a time series (Killick and Eckley, 2011) was also used to identify changepoints. This approach calculates the maximum log-likelihood under the null (no changepoints) and alternative (changepoints) hypotheses. The null hypothesis is then rejected based on the finding whether the test statistic exceeds a specific criteria. The use of multiple tests allow for greater confidence in the ability to correctly detect homogeneities in the data. Changepoints were only adjusted if they were supported by metadata (for example, matching a documented change in the station environment) or if they were highly statistically significant and supported by a majority of detection tests. Adjustments were only made when it was considered absolutely necessary and well supported by evidence. Greatest confidence in adjustments stems from metadata and so, where available, it is given the highest

priority and none of the additional statistical tests were weighted as heavily as metadata support. Different step changes were often identified by each of the tests so, without metadata support, step changes were only considered real if they were identified by a majority of the statistical tests. Confidence in changes identified in the absence of metadata was increased when the additional tests were in universal agreement. For stations with little metadata there is a greater reliance on the statistical tests. See Appendix B for some examples of the homogenization process. The quantile-matching method in RHTestsV3 was used to adjust both daily and monthly records without a reference series, to ensure that the empirical distributions of all segments match (Wang and Feng, 2010). The sparse data network inhibited the use of neighbouring reference stations for the adjustment. SSTs were used to detect changepoints in the station records but were not used as reference series during the adjustment process in order to not bias the adjusted temperature records. SSTs provide a valuable independent datasets for comparison purposes, and it was felt important to preserve this independence by not using the SST data to provide adjustments. Many other homogenization methods exist (see Venema et al., 2012 for a review) but RHTestsV3 was selected because it is a freely available product and more importantly because it can be used without reference series. Our experience at the Noumea workshop and previously has been that the ease of use of RHTestsV3 allow countries to adopt it for future data updates. The inclusion of adjusted temperature records in the high-quality temperature dataset sets this study apart from most previous regional analyses (Manton et al., 2001; Peterson et al., 2002; Aguilar et al., 2005; Zhang et al.,


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Table 2. Indices of extreme temperature used in this study. Type

Name

Absolute

Definition

Hottest day: TXx

Monthly maximum value of daily maximum temperature Coolest day: TXn Monthly minimum value of daily maximum temperature Hottest night: TNx Monthly maximum value of daily minimum temperature Coolest night: TNn Monthly minimum value of daily minimum temperature Percentile Warm days: TX90p Number of days per year when maximum temperature is above the 90th percentile Cool days: TX10p Number of days per year when maximum temperature is below the 10th percentile Warm nights: TN90p Number of days per year when minimum temperature is above the 90th percentile Cool nights: TN10p Number of days per year when minimum temperature is below the 10th percentile The percentile indices have been modified from the original ETCCDI definitions (Zhang et al., 2011). Units for the absolute indices are ◦ C and the percentile indices are number of days per year.

2005; Rahimzadeh et al., 2009; Caesar et al., 2011), but this is not the first time that adjusted temperature records have been used in trends analysis in the region (Salinger, 1995, Jovanovic et al., 2012, Jones et al., 2013). 2.3.

Indices and trend calculation

Annual mean temperature was calculated for each station by averaging homogenized monthly maximum and minimum temperatures in each 12-month period between 1961 and 2011. A year with one missing monthly value in either maximum or minimum temperature resulted in a missing value for the annual average. Extreme temperature indices (Table 2) were calculated using the Fortran implementation of RClimDex (‘FClimDex’ – http://etccdi.pacificclimate.org/software. shtml), with the 1971–2000 base period used to calculate the percentile indices. Many of the ETCCDI temperature indices are not appropriate for tropical regions; e.g. numbers of frost, icing or summer days and growing season length, while others are not statistically robust, e.g. the heatwave duration index (Donat et al., 2013; Perkins and Alexander, 2013). Therefore, we focus on the absolute and percentile indices provided by FClimDex. The hottest and coldest day and night of the year (TXx, TXn, TNx and TNn) were calculated for all 46 stations. Insufficient data for Fuaamotu, Niuafoou, Port Vila, Henderson and Misima prevented the calculation of the maximum and minimum percentile indices for these stations. Taro and Norfolk Islands have too many days of missing minimum temperature data to calculate warm nights and cool nights.

Trends were calculated using the ordinary least squares (OLS) method and Kendall’s tau slope estimator (Sen, 1968; Zhang et al., 2005; Caesar et al., 2011). A comparison of the OLS and Kendall’s slope estimator methods showed that trends were broadly similar in direction and magnitude, although the OLS method consistently had larger trends when compared with the slope estimator method. The Kendall’s slope estimator is a nonparametric method that is better able to handle time series with more complex properties, as it does not assume any distribution of the residuals and is better able to deal with outliers and upper/lower bounds. Trend calculation using Kendall’s slope estimator accounted for lag-1 autocorrelation in the time series residuals due to the method’s strong dependency on autocorrelation in the series when calculating trends (for more information see Zhang et al., 2005). As such, the trends in mean and extreme temperatures presented here (with the 95% confidence intervals) are estimated using Kendall’s slope estimator accounting for autocorrelation in the time series, as per the analyses from previous studies of this type (Zhang et al., 2005). Individual station trends in mean and extreme temperatures were calculated over 1961–2011 as the majority of stations had data available for this time. Where necessary this 51-year period was split into two sub-periods, 1961–1985 and 1986–2010. In addition, the sub-region and full-region trends were calculated over the period 1951–2011, so that decadal variability over the later half of the 20th century could be assessed. This was split into two sub-periods 1951–1980 and 1981–2011, where required. See Table 1 for the data availability of each station. To assess the relationship between SSTs and temperature extremes, Pearson’s correlation coefficients were calculated between detrended sub-regional temperature extremes and detrended gridded monthly SST anomalies from the HadISST dataset over the Pacific region (40◦ –300◦ E, 50.5◦ N–49.5◦ S), in each calendar season over the period 1951–2011. Maximum and minimum daily temperature anomalies for the stations in each sub-region were used to calculate probability density functions (PDFs, bin width of 0.5 ◦ C) for two 25-year periods; 1961–1985 and 1986–2011, to examine changes in the whole temperature distribution. The SD and skewness in the two periods was also calculated with the differences reported in the following section.

3. Results and discussion 3.1. Trends in annual mean temperature The clearest signal in annual mean temperature is warming. Warming across the region is widespread and spatially homogeneous (Figure 2), and statistically significant at all but two stations. Mean temperature trends since 1961 range from 0.05 ◦ C per decade in Nadi to 0.34 ◦ C per decade in Tahiti-Faaa (time series shown in Figure 3), with mean warming of 0.18 ◦ C


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10

0.35

Lat

0

0.25 0.15 0.05 −0.05

−10

−0.15 −0.25 −0.35

−20

−30 140

160

180

200

Lon Figure 2. Annual average mean temperature trends over 1961–2011 (◦ C decade−1 ). Filled triangles indicate trends that are significant at the 95% confidence level. 1960

1980

3.2.

2000

Trends and variability in temperature extremes

3.2.1. Trends in absolute temperature extremes

28

27

26

25 1960

1980

2000

1960

1980

2000

Figure 3. Annual average mean temperature (◦ C) for Nadi (left), Port Moresby (middle) and Tahiti-Faaa (right) over the period 1961–2011, with lines of best fit for the periods 1961–1985 and 1986–2011.

per decade averaged across all stations. This estimate of the mean warming compares favourably with the analysis of Jones et al. (2013) who found a warming trend of 0.16 ◦ C per decade based on monthly/annual temperature. The mean trend represents an average total warming of 0.9 ◦ C for the 50 years since 1961, ranging from 0.25 ◦ C in Nadi to 1.7 ◦ C in Tahiti-Faaa. The magnitude and rate of warming at Port Moresby since 1961 (0.18 ◦ C per decade) is representative of mean warming across the region (Figure 3). A comparison of annual average mean warming trends, over two periods (1961–1985 and 1986–2010) at the three stations mentioned above, that span the range of regional variability (Figure 3) shows that the mean temperature has increased steadily. There is little decadal variability in the magnitude of the trends between the early and latter periods at two of the three stations, consistent with the earlier analysis of Jones et al. (2013). The warmest years for these three stations occurred in 1998 in Nadi and Port Moresby and 2009 in Tahiti-Faaa, highlighting the interaction between the background warming of the climate and natural variability, likely driven by ENSO.

Trends over the period 1951–2011 were calculated for all sub-regions (nITCZ, ST, swSPCZ and WPM - Table 1), spatially and temporally extending previous results for the tropical Western Pacific region (Manton et al., 2001; Caesar et al., 2011). In the full-region analysis, the strongest (weakest) trends are found for the hottest day of the year, 0.16 ◦ C per decade (coolest day of the year, 0.13 ◦ C per decade), over the period 1951–2011. There are significant warming trends in the hottest day and night of the year (TXx, TNx) and coolest day of the year (TXn) in all sub-regions, from 1951 to 2011 (Table 3). In all sub-regions except the WPM (swSPCZ and WPM), the largest warming trends are found in the hottest day (night) of the year with weaker warming trends in the coolest day (night) of the year. Increases in the hottest day of the year (TXx, Table 3) range from 0.10 ◦ C per decade (WPM) to 0.18 ◦ C per decade (nITCZ) from 1951 to 2011; this is equivalent to an absolute increase of between 0.60 and 1.08 ◦ C in the hottest day of the year since 1951. Warming in the hottest night of the year (TNx) is rather less with absolute increases since 1951 ranging between 0.42 ◦ C (nITCZ) and 0.78 ◦ C (swSPCZ and WPM). Warming in the coolest day of the year (TXn) ranges from 0.42 ◦ C (swSPCZ) to 1.02 ◦ C (WPM) since 1951. Two of the largest warming trends across all sub-regions and indices are found in the coolest night of the year (TNn). Trends of 1.14 and 1.26 ◦ C are present in two regions, swSPCZ and WPM respectively, where a majority of stations are located (Table 1). The insignificant warming trend for nITCZ is likely related to the cooling minimum temperature trend in Yap. While the lack of a warming trend in the ST region may be related to a drying trend in the region (McGree et al., 2013).


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2592 1951–2011 0.03 (−0.02 to 0.09) 0.19 (0.12 to 0.25) −0.01 (−0.04 to 0.02) 0.21 (0.11 to 0.32) 0.15 (0.07 to 0.23)

3.2.2. Trends in percentile temperature extremes

Significant trends in bold. 95% confidence intervals in brackets.

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1951–2011 0.11 (0.03 to 0.18) 0.07 (0.01 to 0.13) 0.08 (0.05 to 0.12) 0.17 (0.12 to 0.20) 0.13 (0.06 to 0.19)

1951–1980 1981–2010 0.04 (−0.03 to 0.11) 0.05 (−0.03 to 0.11) 0.06 (−0.05 to 0.19) 0.18 (0.06 to 0.28) 0.11 (0.00 to 0.21) 0.04 (−0.06 to 0.16) 0.09 (−0.01 to 0.23) 0.16 (0.02 to 0.27) 0.11 (−0.01 to 0.20) 0.19 (0.09 to 0.28) Coolest night of the year (TNn) 1951–1980 1981–2010 −0.08 (−0.17 to 0.01) 0.00 (−0.18 to 0.18) −0.03 (−0.22 to 0.17) 0.22 (0.00 to 0.41) 0.06 (0.00 to 0.13) −0.06 (−0.19 to 0.07) 0.09 (−0.09 to 0.25) 0.38 (0.09 to 0.65) 0.05 (−0.06 to 0.15) 0.19 (0.00 to 0.34) 1951–1980 1981–2010 0.03 (−0.19 to 0.25) 0.33 (0.23 to 0.41) 0.18 (0.04 to 0.28) 0.10 (−0.04 to 0.21) 0.10 (0.03 to 0.17) 0.24 (0.01 to 0.46) 0.08 (−0.07 to 0.20) 0.08 (0.02 to 0.16) 0.12 (−0.03 to 0.26) 0.19 (0.12 to 0.28) Coolest day of the year (TXn) 1951–1980 1981–2010 0.14 (0.02 to 0.22) 0.10 (0.03 to 0.17) 0.02 (−0.12 to 0.21) 0.09 (−0.12 to 0.24) 0.09 (0.00 to 0.19) 0.05 (−0.02 to 0.15) 0.19 (−0.03 to 0.36) 0.12 (−0.01 to 0.25) 0.15 (0.03 to 0.21) 0.12 (0.00 to 0.24) nITCZ swSPCZ ST WPM All stations

1951–2011 0.18 (0.11 to 0.25) 0.14 (0.09 to 0.18) 0.14 (0.09 to 0.18) 0.10 (0.07 to 0.13) 0.16 (0.12 to 0.20)

Hottest night of the year (TNx) Hottest day of the year (TXx)

Table 3. Annual trends in absolute indices of extreme temperature (◦ C per decade).

1951–2011 0.07 (0.04 to 0.10) 0.13 (0.09 to 0.17) 0.10 (0.07 to 0.14) 0.13 (0.10 to 0.16) 0.15 (0.12 to 0.18)

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Warming trends in extreme maximum and minimum temperatures, at the station-level, are spatially homogeneous and largely statistically significant from 1961 to 2011 (Figure 4). These trends are consistent with previous research but more spatially coherent (Manton et al., 2001, Griffiths et al., 2005, Caesar et al., 2011), likely due to the use of a homogenized high-quality dataset, which allowed the availability of more stations and longer records in this analysis. Yap is the only station with a significant cooling trend (for extreme minimum temperatures), that likely stems from issues with the homogenization process and the method’s inability to adjust for gradual changes. Additional metadata are required to increase confidence in the homogenization of Yap’s minimum temperature record noting the existence of a sequence of small inhomogeneities followed by a large one near the end of the series (see Appendix B, Figure B2). Sub-region and full-region trends, over the 1951–2011 period, in the frequency of temperatures above and below the 90th and 10th percentile are significant across all regions and all variables (Table 4), highlighting a remarkably consistent influence of warming on the occurrence of these extreme events. These numbers reveal a dramatic decline in the occurrence of cool extremes, and an increase in the occurrence of hot extremes. For example, the full-regional mean shows that the frequency of warm days and nights has increased by more than three-fold over all stations (Figure 5). Indicating once rare extremes, that occurred approximately 20 days per year, are happening much more frequently (between 45 and 80 days per year). Warming appears to be intensifying after the 1970s, likely related to a combination of mean warming and interaction with the Inter-decadal Pacific Oscillation (IPO, Power et al., 1999). On a sub-regional level, over the latter half of the 20th century, the number of warm days (nights) per year is increasing at a rate of between 4 and 9 (3–10) days per decade, which equates to an additional 24–54 (18–60) days per year, over the whole study period (1951–2011), when maximum (minimum) temperatures are above the 90th percentile of the 1971–2000 climatological period. Over the same period, the increase across the full-region in the number of warm days (nights) is 54 (42) days per year (Table 4). The decrease in the number of cool days (nights) per year equates to a 24–54 (12–78) day reduction in the number of days when maximum (minimum) temperatures are in the bottom decile. On average, the percentile indices occur 36.5 days per year (i.e. 10% of the time), so an additional 54 of days per year above the 90th percentile (warm days in WPM) is almost doubling the long-term mean number of warm days compared to the base rate for the 1971–2000 period. While cool and warm extremes in maximum temperature are changing at a similar rate, the cool extremes of minimum temperature are warming faster than the warm extremes. This asymmetric change in the warm and cool extremes is consistent with previous research (Choi et al.,


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Figure 4. Trends in the number of days per year with the maximum (left) and minimum (right) temperature above the 90th percentile (top) and below the 10th percentile (bottom) over the period 1961–2011. Units are number of days per decade exceeding the threshold. Filled triangles indicate trends that are significant at the 95% confidence level.

Table 4. Annual trends in percentile indices of extreme temperature (number of days per decade exceeding threshold value). Warm days (TX90p) Region nITCZ swSPCZ ST WPM All stations

1951–1980 2 (0 to 3) 3 (−1 to 5) 1 (−4 to 6) 4 (1 to 7) 3 (0 to 5) Cool days (TX10p) Region 1951–1980 nITCZ −22 (−29 to −13) swSPCZ −4 (−8 to 2) ST −3 (−9 to 4) WPM −8 (−16 to −2) All stations −9 (−14 to −4)

Warm nights (TN90p) 1981–2010 15 (11 to 20) 6 (0 to 12) 8 (2 to 12) 13 (−1 to 36) 17 (2 to 31)

1951–2011 8 (5 to 10) 6 (5 to 8) 4 (2 to 7) 9 (8 to 11) 9 (7 to 10)

1951–1980 1981–2010 1951–2011 −6 (−10 to −2) 7 (1 to 12) 3 (0 to 6) 1 (−1 to 4) 14 (4 to 27) 8 (6 to 9) 2 (−1 to 6) 4 (0 to 7) 3 (1 to 5) 5 (0 to 9) 19 (7 to 29) 10 (8 to 12) 0 (−2 to 3) 17 (6 to 26) 7 (5 to 9) Cool nights (TN10p) 1981–2010 1951–2011 1951–1980 1981–2010 1951–2011 −2 (−4 to 0) −9 (−13 to −6) −6 (−11 to −1) 2 (−2 to 6) −3 (−6 to −1) −3 (−7 to 0) −6 (−7 to −4) −2 (−8 to 4) −6 (−9 to −2) −7 (−8 to −5) −5 (−10 to 0) −4 (−6 to −1) −7 (−13 to 0) 1 (−3 to 4) −2 (−4 to 0) −4 (−7 to −1) −7 (−13 to −1) −13 (−23 to −4) −8 (−14 to −2) −12 (−14 to −10) −4 (−7 to −1) −8 (−13 to −2) −6 (−11 to −1) −5 (−9 to −1) −7 (−9 to −2)

Significant trends in bold. 95% confidence intervals in brackets.

2009). A similar rate of warming for the upper and lower tails of the maximum temperature distribution suggests that a shift in the mean is primarily responsible for the changes in the extremes. The trends are consistent with those reported in previous studies for the Pacific region (Manton et al., 2001; Choi et al., 2009; Caesar et al., 2011) and from other tropical regions (Peterson et al., 2002). For example, over 1971–2005 warming trends in

warm days (nights) of 5 (7) days per decade have been reported for stations in the South Pacific (Caesar et al., 2011). This suggests that the warming evident in the earlier studies has continued up to the most recent period. Considerable decadal variability is found in the number of days per year exceeding the percentile thresholds (Table 4), compared to the absolute indices of extremes (Table 3). In the full region, for both maximum and


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Figure 5. Full-region trends in the number of days per year with the maximum (left) and minimum (right) temperature above the 90th percentile (top) and 10th percentile (bottom) over the period 1951–2011. Units are number of days per decade exceeding the threshold.

minimum temperatures, the lower tail of the distribution warmed at a faster rate than the upper tail during the 1951–1980 period. This effect was compensated for the 1981–2010 period as the upper tail of the distribution warmed more rapidly. This is reflected in the sub-regional analysis where most regions (all except ST cool days and swSPCZ cool nights and warm days) experienced warming of the 90th (10th) percentile during in the 1981–2010 (1951–1980) period. For example, in the WPM region, the trend in warm days (cool days) was 4 (−8) days per decade in 1951–1980 and 13 (−4) days per decade in 1981–2010. The large changes in warm extremes in recent decades are likely related to mean warming in a region with very low variability. 3.2.3. Assessment of variability changes The sub-regional analysis highlighted considerable decadal variability in the number of days exceeding extreme temperature thresholds that is not evident in the absolute extreme indices and mean temperature trends. Warming in the absolute indices shows no consistent pattern of decadal variability and time series plots (not shown) suggest rather more monotonic trends in line with the changes in the mean temperature through the same period (Jones et al., 2013). To further explore decadal variability in the percentile indices, seasonal trends in warm days are shown for two

regions that display small (swSPCZ) and large (nITCZ) decadal variability (Figure 6). There is an increase in warm days from the late 1990s in the nITCZ, with the largest changes in MAM and DJF (the north Pacific cool season). In comparison, warm days in the swSPCZ region increase more steadily since the 1950s, but still shows seasonal differences between the warm and cool seasons with the greatest increases tending to occur during the warmer and wetter seasons possibly related to shifts in the South-Pacific Convergence Zone (McGree et al., 2013). In addition, the full-region analysis shows additional decadal variability in maximum temperature extremes compared with minimum temperature, likely related to changes in cloud cover. A comparison of the PDFs of stations in each subregion over the periods 1961–1985 and 1986–2010 (Figure 7) confirms an increase in mean daily maximum and minimum temperature anomalies in all sub-regions. In all cases the PDF has moved to warmer temperatures, with a small reduction in the peak densities in the nITCZ and WPM regions. These results suggest that the impact of global warming on extremes temperatures in the tropical Western Pacific is largely a result of a simple shift in the whole PDF to the right. As such, changes in variability and skewness are less consistent across the four sub-regions. Here we discuss the difference in the SD and skewness between the two periods. It should


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Figure 6. Seasonal trends in warm days (TX90p) over the periods 1951–1980 and 1981–2010 for swSPCZ (top) and nITCZ (bottom) during MAM, JJA, SON and DJF (left to right). Units are percentage of days per year when maximum temperature exceeds the 90th percentile.

be noted that although skewness is reported as being positive, that means that it is ‘more positive’ in the later period even though it is often negative in both periods. The largest impact on warm (cool) extreme events would be in the case where the SD increases and/or the skew is more negative (positive), so these cases will be the focus of the following discussion. In nITCZ and swSPCZ the SD of both daily maximum and minimum temperature anomalies increases in the latter period. Daily maximum temperature anomalies in nITCZ, ST and WPM are more positively skewed in the later period (0.04, 0.02 and 0.13), while swSPCZ is more negatively skewed (−0.04). The higher order moments of daily minimum temperatures do not always change in the same ways. In nITCZ daily minimum temperatures are more positively skewed (0.28) in the recent period, while swSPCZ, ST and WPM minimum temperature anomalies are more negatively skewed (−0.08, −0.01 and −0.02). It is possible that increases in variance are related to the urbanization of stations (Griffiths et al., 2005), which has not been assessed in this study. Strong decadal variability is expected in a region that is so closely coupled to ENSO (Mantua et al., 1997; Power et al., 1999), but the lack of strong decadal variability in the absolute indices (despite a strong correlation with ENSO) suggests more complex mechanisms. The role of the IPO (Power et al., 1999) has not been assessed, but it is likely that it plays a role through interactions between SSTs, rainfall and temperature variability and in the position of the SPCZ.

3.3. Relationships between temperature extremes and sea surface temperature anomalies Globally ENSO is the most significant driver of climate variability (Ropelewski and Halpert 1987, Nicholls et al., 1997, Risbey et al., 2009) and is known to influence temperatures (Jones et al., 2013) and temperature extremes around the Pacific region (Kenyon and Hegerl 2008; Alexander et al., 2009). There are significant correlations between local SST anomalies and extreme temperature indices in all seasons and with the relationship with ENSO variability peaking in September-OctoberNovember (SON, not shown). We focus on SON and show that temperature extremes in the tropical Western Pacific regions have strong relationships with local and remote SST anomalies in this season over the latter half of the 20th century. A clear ENSO-like pattern is evident in the correlation between warm nights and SST anomalies (Figure 8), with the strongest signal in nITCZ, swSPCZ and ST. We note that the pattern of correlations indicates that the frequency of warm nights tends to be higher for the swSPCZ, WPM and ST during La Niña events that tend to have warmer than average SST anomalies west of the dateline. The nITCZ region is unique among the sub-regions in that the correlation with the ENSO tends to reverse. The positive correlation between SST anomalies and the frequency of warm nights in SON in the swSPCZ, ST and WPM regions is consistent with previous research (Caesar et al., 2011). This suggests that unusually warm nights are favoured when the local ocean temperatures


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Stepaniak, 2001) and the tendency for Eastern/Western Pacific SSTs to vary out of phase associated with El Niño and La Niña events. The relationship between extreme minimum temperature indices (TNx, TNn) and SST anomalies are more extensive, consistent with the modulating effect of spatially coherent ocean temperature on minimum temperature (Wu and Newell, 1998). Tropical tropospheric air temperature is closely related to SST variability in the tropical Eastern Pacific. A local heat source near the equator can warm the entire tropical troposphere. Latent heat release warms the central equatorial pacific and adiabatic subsidence warms the rest of the tropical atmosphere (Wu and Newell, 1998).

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Figure 7. Probability density functions of maximum (left) and minimum (right) daily temperature anomalies (◦ C) for stations in (a) nITCZ, (b) swSPCZ, (c) ST and (d) WPM over the periods 1961–1985 (blue) and 1986–2010 (red).

are higher than average. Jones et al. (2013) has highlighted remarkable agreement between sites temperatures and SSTs across the Western Pacific, with these results suggesting that this covariability holds at the regional level and for temperature extremes. The close coupling between eastern and western SST anomalies (Trenberth and Stepaniak 2001) is the likely cause of the ENSO-like patterns evident in correlation between sub-regional warm nights and SST anomalies, noting that La Niña events tend to have a warm/cool structure across the Western/Eastern tropical Pacific SSTs (with the reverse pattern during El Niño events). The strong correlations between absolute indices of temperature extremes in the swSPCZ region and SST anomalies (Figure 9) suggest that SST variability is related to the absolute values of extreme temperature, in addition to the number of days that temperature thresholds are exceeded (Figure 8). The correlation between both local and remote SST anomalies is stronger and more widespread for the minimum temperature extremes (i.e. hottest and coolest nights of the year – TNx and TNn) compared to the maximum temperature extremes, although the coolest day of the year (TXn) has a moderate relationship with SST variability (Figure 9). The relationship between maximum temperature extremes and ENSO is consistent with the close coupling between eastern and western SSTs (Trenberth and

4. Summary and conclusions The homogenization of climate data ensures that variability in climate records reflects changes in the background climate state and is not due to changes in measurement practice or other non-climatic artefacts. All countries have issues related to data quality and the collection and preservation of metadata, but these problems are exacerbated in countries with more limited economic resources. An additional factor in the Pacific is that many stations outside of the major populated islands are difficult to reach, usually requiring boat or plane. Small island states are among the most vulnerable nations to the impacts of climate change, yet relatively little is known about historical and recent trends in the observational record, owing to the limited availability of high-quality data (Field et al., 2012). In this study, we worked closely with representatives from 18 countries to compile a high-quality daily dataset for stations in the tropical Western Pacific region, so that we can increase our understanding of trends and variability in climate extremes over the observational record. These data have been updated to the present, and it is hoped that the maintenance of this new dataset becomes an operational practice in the region. The homogenization of daily temperature records was a two-stage process that removed inhomogeneities detected through the analysis of metadata records and the statistical detection of significant changepoints in the record. No systematic biases were introduced into the daily temperature records as some adjustments resulted in higher temperatures while some resulted in lower temperatures (see examples in Appendix B). Incomplete metadata records mean it is likely that not all inhomogeneities were resolved in this analysis, with particular uncertainty attached to the station at Yap which appears to have a sequence of small and large inhomogeneities. The adjustment without the use of a neighbouring reference station is not ideal; however, this dataset is a considerable improvement on the raw data record with the removal of many non-climatic changepoints. The importance of data rescue and the maintenance of complete metadata records is emphasized (Page et al., 2004) so remaining


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Figure 8. Correlations between detrended SON sub-regional warm nights and gridded SST anomalies, over the period 1951–2011. nITCZ (top left), swSPCZ (top right), ST (bottom left), WPM (bottom right). Stippling indicates correlations significant at the 95% confidence level.

inhomogeneities can be resolved. While time consuming, the effort to homogenize data has led to high spatial and temporal coherency between stations as spurious artefacts are removed. The resulting temperature trends, as shown previously, are remarkably consistent, suggesting that the homogenization process has significantly enhanced the reliability of results. This research has three key differences from previous studies of tropical Western Pacific temperature extremes. Firstly, the homogenization process and subsequent analysis of a high-quality dataset are important. Trends presented here are generally more spatially coherent (e.g. fewer ‘spurious’ cooling trends in 90th percentile indices, for example) and consistent with previous work (Manton et al., 2001; Choi et al 2009; Caesar et al., 2011), suggesting that trends can be captured with the inclusion of adjusted temperature records. Differences may stem from the inclusion of additional years and stations in this analysis, a more robust warming signal in recent years, interaction with natural variability or a combination of these factors. Secondly, we extend the station-based analysis to 1961–2011 and the regional means to 1951–2011. The extension of regional means, in particular, to cover the latter half of the 20th century is a substantial improvement compared with previous research and allows an examination of decadal variability. Finally, more stations from the tropical Western Pacific are included in this

analysis (36–46 stations, depending on data availability for each index) compared with earlier studies, including some stations that have not been reported before in the scientific literature. From this extended high-quality dataset we show that warming in mean and extreme temperatures is significant and spatially homogeneous and we reach five main conclusions. Firstly, mean warming trends are spatially homogenous and dominant across the region with mean warming of between 0.05 and 0.34 ◦ C per decade. This is consistent with previous research (Jones et al. 2013) and equates to an absolute increase of mean temperature between 0.25◦ and 1.7 ◦ C over 1961–2011. Secondly, stationlevel extreme temperature warming trends are spatially homogenous (1961–2011). Thirdly, we find spatially coherent warming trends in maximum and minimum temperature extremes over the full-region and sub-regional means for the period 1951–2011. Larger warming trends are generally found in the hottest day and night of the year compared with the coolest day and night of the year. Warming trends are found in all percentile indices, although there are fewer differences between the number of days and nights per year where maximum or minimum exceeded percentile thresholds. Fourthly, this analysis highlights the role of decadal variability in extreme temperature trends of the percentile indices only; so that steady increases in the value of extreme temperatures are


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Figure 9. SON correlation between detrended SST anomalies and swSPCZ sub-regional mean TXx (top left), TNx (top right), TXn (bottom left) and TNn (bottom right) over the period 1951–2011. Stippling indicates correlations significant at the 95% confidence level.

combined with considerable variability in the number of days per year that exceed extreme temperature thresholds. This shows the complex nature of temperature extremes in a region with low temperature variability (e.g. Perkins 2011) which could complicate planning and adaptation to extreme temperature as the effects may not be felt in some decades, only to experience large effects in other decades. This analysis emphasizes how small shifts in temperature distributions (mean or higher order statistics) can produce a large signal in the occurrence of extremes, particularly in a climate of low variability. Analysis of the daily maximum and minimum temperature distributions that confirms mean warming with higher order statistics shows more regional variability (Donat and Alexander, 2012). Further research is required to assess the role of urbanization, particularly over the last decade (Griffiths et al., 2005) and the role of the IPO (Power et al., 1999). Understanding the mechanisms behind the decadal variability in maximum and minimum extreme temperature trends is important as different industries will require different information about the impacts of these extremes. Finally, we show that significant relationships exist between SST anomalies and indices of extreme temperature, consistent with previous research (Nicholls et al., 2005). Future research could assess the relationship between proximity to the ocean and the magnitude of trends in extreme temperatures.

Western Pacific mean and extreme warming need to be understood in the context of low temperature variability. A small temperature rise in ecosystems that are adapted to a specific temperature range can have greater impact than a similar rise in an ecosystem adapted to greater temperature variability (Deutsch et al., 2008, Tewksbury et al., 2008). So, while these extreme temperature increases may seem small in an already hot climate, the impacts could be significant. While the literature on impacts is sparse, the Pacific Island co-authors to this project reported changes in the local plants (for example, coconuts growing a higher elevations), timing of fruit (bread fruit and mangoes were mentioned most frequently), human comfort (for example, reduced frequency of uncomfortably cool nights) and the occurrence of coral bleaching which are all consistent with the warming trends we reported here.

Acknowledgements This study was fully funded by the Australian government’s International Climate Change Adaptation Initiative, and delivered through the Pacific Climate Change Science and Pacific-Australia Climate Change Science and Adaptation Planning Programs. It has only been made possible through the contributions of historical data and


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station history information from meteorological and climate organizations located in American Samoa, Cook Islands, East Timor, Federated States of Micronesia, Fiji, French Polynesia, Kiribati, Marshall Islands, Nauru, New Caledonia, New Zealand, Niue, Palau, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, the USA and Vanuatu. The authors thank the heads of these agencies for permitting access to their data and appreciate the assistance of employees of these organizations. LVA is also supported by Australian Research Council

grant CE110001028. The authors are particularly grateful to Jim Salinger for metadata and personal knowledge about several Pacific Island stations, and Blair Trewin, Xiaolan Wang and Yang Feng for assistance in training Partner Country representatives in data homogenization and for providing the authors software with due advice. The raw and homogenized daily and monthly data used in this analysis can be viewed online at the Pacific Climate Change Data Portal (www.bom.gov.au/ climate/pccsp), which is updated as required.

Appendix A Table A1. The date of maximum and minimum temperature adjustments for stations used in this analysis. Country

Station name

T MAX adjustments

T MIN adjustments

Federated States of Micronesia Federated States of Micronesia Federated States of Micronesia Marshall Islands Marshall Islands Palau American Samoa Cook Islands Fiji Fiji Fiji Fiji Niue Samoa Tahiti Tonga Tonga Tonga Tonga Tonga Vanuatu Vanuatu Australia Australia New Zealand Australia Papua New Guinea Papua New Guinea Papua New Guinea Papua New Guinea Papua New Guinea Papua New Guinea Solomon Islands Solomon Islands Solomon Islands Solomon Islands Solomon Islands Solomon Islands Kiribati Tuvalu

Chuuk1 Pohnpei2 Yap3 Kwajalein4 Majuro5 Koror6 Pago Pago7 Rarotonga8 Laucala Bay9 Nabouwalu10 Nadi Airport11 Vunisea12 Hanan Airport19 Apia20 Tahiti-Faaa21 Fuaamotu22 Haapai23 Keppel24 Lupepauu25 Niuafoou26 Aneityum27 Port Vila28 Lord Howe Island29 Norfolk Island30 Raoul Island31 Willis Island32 Kavieng33 Madang34 Misima35 Momote36 Port Moresby37 Wewak38 Auki39 Henderson Airport40 Honiara41 Kirakira42 Munda43 Taro44 Tarawa45 Funafuti46

1987 (S), 2001 (U), 2009 (S) No adjustment required 1960 (U), 2008 (U), 2010 (M) 1954 (U), 1991 (U) No adjustment required 2008 (S) 1967 (U), 1995 (U), 2002 (U) No adjustment required 1954 (S), 1995 (S) No adjustment required 1948 (S), 1971 (S), 1985 (S) No adjustment required 1943 (S), 1996 (S) No adjustment required No adjustment required No adjustment required No adjustment required No adjustment required 1987 (S) No adjustment required 1965 (S), 1984 (U), 2007 (U) 1972 (U), 2008 (U) 1954 (U) No adjustment required 1996 (S) 1966 (S), 1987 (S) No adjustment required No adjustment required No adjustment required No adjustment required 1945 (U), 1966 (U) No adjustment required 1964 (U), 1981 (U) No adjustment required 1986 (S) 1985 (U) No adjustment required 1991 (U) No adjustment required No adjustment required

1996 (S), 1999 (U), 2004 (U). 1988 (S), 1975 (U), 1989 (U), 2003 (S), 2008 (S) 1961 (U) 1975 (S), 1996 (U) 2001 (S) No adjustment required No adjustment required 1995 (S) No adjustment required 1965 (S), 1998 (S) 1979 (S) 1971 (S), 1976 (S) 1990 (S), 2010 (S) No adjustment required No adjustment required No adjustment required 1968 (S), 1969 (S) 1987 (S) No adjustment required 2003 (U), 2007 (U) 1986 (S), 2000 (U) No adjustment required 1953 (S) 1996 (S) No adjustment required No adjustment required No adjustment required No adjustment required No adjustment required 1945 (U), 1962 (U) No adjustment required No adjustment required No adjustment required 1974 (S), 1987 (S) No adjustment required No adjustment required No adjustment required No adjustment required No adjustment required

Superscripts indicate station locations in Figure 1. Changepoints marked with ‘S’ (‘U’) were supported (unsupported) by metadata. Stations that were considered homogenous without adjustment are noted.


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Appendix B The following are some examples of how inhomogeneities were detected and adjustments were made to time series with and without metadata using the RHTestV3 software.

(a) −1 −2 −3

Example 1. Kira Kira maximum temperature (Solomon Islands)

−4 −5

There was no metadata available for Kira Kira, but a single step change in 1985 was adjusted in the maximum temperature series. The step change was considered to be of non-climatic origins despite the lack of metadata (a)

−6 1960

1970

1980

1990

2000

2010

1960

1970

1980

1990

2000

2010

1960

1970

1980

1990

2000

2010

1960

1970

1980

1990

2000

2010

(b) 2 0 −2

1

−4 0

−6 −8

−1

−2 1970

1980

1990

(c)

2000

2 1 0 −1

(b) 4 2

−3

0

(d)

−2

30

−4

28

−6

26

1970

1980

1990

2000 24

(c) 22

1.5 0.5

20

−0.5 −1.5 1965

1970

1975

1980

1985

1990

1995

2000

(d) 34

Figure B2. Yap minimum temperature series. The (a) monthly and (b) daily unadjusted series with adjusted step changes marked. (c) Detected step changes when local SST anomalies were used as a reference for the monthly series (base–reference series). (d) The adjusted series.

32 30 28 26 24 1970

1980

1990

2000

Figure B1. Kira Kira maximum temperature series. The (a) monthly and (b) daily unadjusted series with adjusted step change marked. (c) Detected step changes when local SST anomalies were used as a reference for the monthly series (base to reference series). (d) The adjusted series.

because it was detected with a number of techniques. Initially, the changepoint was detected with the ‘FindU’ function in RHTestsV3, which means that it is significant even without metadata (Wang and Feng, 2010). The changepoint was also found in the DTR

and when local SST anomalies, the Niño 3.4 index and homogenous maximum temperature from Honiara were used as reference series. In addition, the changepoint was not considered to be associated with ENSO activity and it caused a large step change (0.47 ◦ C). The effect of the adjustment is to decrease the warming trend. Example 2. Yap minimum temperature (FSM) The homogenization of Yap minimum temperature was difficult and four step changes were adjusted. Although some metadata existed for the station (e.g. information about a site move in 2003) and some metadata was transferable from neighbouring American affiliates (i.e. information about instrument changes in Chuuk), the metadata record was incomplete, particularly before 2000. The 1975 (‘Find-UD’) and 1989 (‘Find-U’) changepoints were not supported by metadata, but were identified when local SST, Niño 3.4, and Chuuk


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the cooling trend in minimum temperature is real, noting that SSTs have warmed as have nearby stations.

(a) 2

0

Example 3. Keppel minimum temperature (Tonga)

−2

−4

−6

1950

1960

1970

1980

1990

2000

1980

1990

2000

(b) 5

0

−5

−10 1950

1960

1970

Two-step changes were adjusted in the Keppel minimum temperature series. Both changepoints were supported by metadata (the instrument were replaced in 1965 and 1969) and were identified when local SST, the Niño 3.4 index and Laucala Bay (Suva) minimum temperature was used as reference series and with the alternative ‘changepoints’ package in R. This adjustment had no significant impact of the overall trend in the series as the trend decreased slightly from 0.00053 to 0.00026 ◦ C per year. A step change in 2004 was detected by RHTestsV3 but was considered significant only if documented. There was no metadata to support this step change, and it was not detected with the other tests, e.g. when SST, Niño 3.4 or a neighbouring station was used as a reference series, or when the DTR was tested for homogeneity.

(c) 2

References

0 −2 −4 −6 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

(d) 30

25

20

15

1950

1960

1970

1980

1990

2000

Figure B3. Keppel minimum temperature series. The (a) monthly and (b) daily unadjusted series with adjusted step changes marked. (c) Detected step changes when local SST anomalies were used as a reference for the monthly series (base–reference series). (d) The adjusted series.

maximum temperature were used as reference series and by the alternative ‘changepoints’ package in R. The 2003 and 2008 step changes were supported by metadata; a site move in 2003 and instrument changes likely to have occurred in 2008. They were also detected with local SST, Niño 3.4, Chuuk maximum temperature was used as reference series, with the ‘changepoints’ package in R, and in the DTR. The adjustment changed the sign of the trend in minimum temperature – from positive to negative – primarily due to the 2008 adjustment. There is less confidence in the adjustment of the Yap minimum temperature record, like due to issues with the adjustment and interactions with ENSO variability. The step change in 2008 is large (2.4 ◦ C) and is thus prone to large uncertainty. It is possible that but unlikely that

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