HESSHydrology and Earth System SciencesHESSHydrol. Earth Syst. Sci.1607-7938Copernicus PublicationsGöttingen, Germany10.5194/hess-20-1703-2016An ice core derived 1013-year catchment-scale annual rainfall reconstruction
in subtropical eastern AustraliaTozerCarly R.carly.tozer@utas.edu.auVanceTessa R.https://orcid.org/0000-0001-6970-8646RobertsJason L.KiemAnthony S.https://orcid.org/0000-0002-3994-6958CurranMark A. J.MoyAndrew D.https://orcid.org/0000-0002-7664-9960Antarctic Climate & Ecosystems Cooperative Research
Centre, University of Tasmania, Hobart, Tasmania 7001,
AustraliaCentre for Water, Climate and Land Use, University of
Newcastle, Callaghan, NSW 2308, AustraliaAustralian Antarctic Division, Kingston, Tasmania 7050,
AustraliaCarly R. Tozer (carly.tozer@utas.edu.au)11May20162051703171723October20153December201524March201611April2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://hess.copernicus.org/articles/20/1703/2016/hess-20-1703-2016.htmlThe full text article is available as a PDF file from https://hess.copernicus.org/articles/20/1703/2016/hess-20-1703-2016.pdf
Paleoclimate research indicates that the Australian instrumental climate
record (∼100 years) does not cover the full range of hydroclimatic
variability that is possible. To better understand the implications of this
on catchment-scale water resources management, a 1013-year
(1000–2012 common era (CE)) annual rainfall reconstruction was produced for
the Williams River catchment in coastal eastern Australia. No high-resolution
paleoclimate proxies are located in the region and so a teleconnection
between summer sea salt deposition recorded in ice cores from East Antarctica
and rainfall variability in eastern Australia was exploited to reconstruct
the catchment-scale rainfall record. The reconstruction shows that
significantly longer and more frequent wet and dry periods were experienced
in the preinstrumental compared to the instrumental period. This suggests
that existing drought and flood risk assessments underestimate the true risks
due to the reliance on data and statistics obtained from only the
instrumental record. This raises questions about the robustness of existing
water security and flood protection measures and has serious implications for
water resources management, infrastructure design and catchment planning. The
method used in this proof of concept study is transferable and enables
similar insights into the true risk of flood/drought to be gained for other
paleoclimate proxy poor regions for which suitable remote teleconnected
proxies exist. This will lead to improved understanding and ability to deal
with the impacts of multi-decadal to centennial hydroclimatic variability.
Location of Law Dome in relation to Australia with insets indicating
the Great Dividing Range, WR catchment boundary and the location of the 61010
high-quality rainfall gauge, Newcastle and Sydney.
Introduction
Water and catchment management systems (e.g., drought and flood mitigation
strategies) and water resources infrastructure have traditionally been
designed based on the trends, patterns and statistics revealed in relatively
short instrumental climate records (i.e., for Australia usually less than 100 years
of data recorded post-1900) (Verdon-Kidd and Kiem, 2010; Ho et al.,
2014; Cosgrove and Loucks, 2015; Razavi et al., 2015). This is a concern as
Australian paleoclimate research suggests that instrumental climate records
are not representative of the true range of hydroclimatic variability
possible (Verdon-Kidd and Kiem, 2010; Gallant and Gergis, 2011; Kiem and
Verdon-Kidd, 2011; Ho et al., 2014, 2015a, b; Razavi et al.,
2015; Vance et al., 2015). For example, paleoclimate archives show evidence
of droughts of longer duration than the three major droughts that have
affected eastern Australia over the instrumental period – the Federation
drought (∼ 1895–1902), World War II drought (∼ 1937–1945)
and Millennium or “Big Dry” drought (∼ 1997–2009)
(Gergis et al., 2012; Vance et al., 2013; Allen et al., 2015; Vance et
al., 2015).
Sources for paleoclimate proxy data include tree rings, coral skeletons, ice
cores, speleothems (cave deposits), sediments and documentary evidence (Ho et
al., 2014). Ideally, the climate proxy archives are located in the region of
interest but in areas where proxy records are sparse or of low
resolution, remote proxies are a viable
alternative (Ho et al., 2014). Remote proxies exploit circulation
teleconnections that link one region to another and are calibrated over the
instrumental period, to develop paleoclimate reconstructions (e.g., rainfall,
streamflow) for the target region (e.g., Verdon and Franks, 2007; McGowan et
al., 2009; van Ommen and Morgan, 2010; Vance et al., 2013, 2015). When using
remote proxies the assumption is that large-scale climate processes driving
climate variability at the location of the paleoclimate proxy also drive a
high proportion of climate variability at the region of interest (Gallant and
Gergis, 2011). For example, van Ommen and Morgan (2010) identified a
relationship between precipitation (snowfall) recorded in ice cores from
coastal Antarctica and rainfall in southwest Western Australia over the instrumental period, inferring
rainfall variability in the region for the past 750 years. Similarly,
Lough (2011) found significant correlations between coral luminescence
intensity recorded in coral cores from the Great Barrier Reef and summer
rainfall variability in northeast Queensland, which enabled the multi-century coral record to be
used to reconstruct Queensland summer rainfall back to the 18th century.
Another option is to use the link between large-scale ocean–atmospheric
climate processes and climate variability in the region of interest to
develop a paleoclimate reconstruction based on a paleoclimate proxy of the
climate process. For example, McGowan et al. (2009) reconstructed annual
inflows in the Murray River back to 1474 common era (CE) from a
reconstruction of the Pacific Decadal Oscillation (PDO) based on the previously identified relationship
between the PDO and streamflow in southeastern Australia (e.g., Power et al.,
1999a, b; Kiem et al., 2003; Kiem and Franks, 2004; Verdon et al., 2004). A
similar approach was also followed by Verdon and Franks (2006, 2007) and
Henley et al. (2011).
Vance et al. (2013, 2015) used a hybrid of the approaches discussed above.
Vance et al. (2013) developed a millennial length rainfall reconstruction for
subtropical eastern Australia by exploiting a relationship between the
region's annual rainfall and the summer sea salt record (see Sect. 3) from
the Law Dome ice core, East Antarctica (Fig. 1). Of key importance is that
the strength of the relationship during the instrumental period (in this case
1889–2009) varies synchronously with the Interdecadal Pacific Oscillation
(IPO) (Power et al., 1999a, b), the basin-wide expression of the PDO, with
increased correlations found during IPO positive phases (Vance et al., 2013,
2015). The IPO represents decadal sea surface temperature (SST) variability
across the Pacific Ocean whereby a positive IPO phase is associated with
warming across the tropical Pacific and cooling of the north and south
Pacific; the opposite occurs during the negative phase (Power et al., 1999a).
The most recent defined complete IPO phases are two positive phases
(∼ 1924–1941, ∼ 1979–1997) and one negative phase
(∼ 1947–1975) (Power et al., 1999a; Kiem et al., 2003; Kiem and
Franks, 2004; Verdon et al., 2004).
Vance et al. (2015) demonstrated that during the IPO negative phase there is
a predominantly zonal pressure pattern across the high to mid-latitudes,
which switches to a more meridional pattern in IPO positive. Folland et
al. (2002) also found that during the IPO positive phase, the mean position
of the South Pacific Convergence Zone (SPCZ) (usually bounded by Samoa and
Fiji) is displaced to the northeast. This northeastern displacement is
associated with a more meridional circulation pattern and enhances the link
between eastern Australia and mid- to high-latitude climate variability and
hence explains the stronger relationship between sea salt recorded at Law
Dome and rainfall in eastern Australia during the IPO positive phase. Based
on their reconstruction of the IPO, Vance et al. (2015) could therefore
identify periods in time (i.e., positive IPO phases) where they had greater
confidence in the rainfall reconstruction. A key finding from Vance et al.
(2015) was the identification of a century of IPO positive aridity
(1102–1212 CE), including evidence of a 39-year drought in
southeast Queensland, which is
well outside the bounds of instrumental drought duration. This illustrates
the importance of investigating climate variability over millennial
timescales, particularly in the Southern Hemisphere where many paleoclimate
records only span the last 200–500 years (Neukom and Gergis, 2012). Indeed,
it is evident that (a) instrumental data are not long enough to allow for
meaningful planning for climate variability; (b) paleodata, particularly at
the millennial timescale, offers an important insight into the climate beyond
the instrumental period; and (c) there is a need to incorporate insights from
paleodata into water resources planning and management.
Further work is also required to assess the robustness of the relationship
between climate variability in East Antarctica, large-scale climate processes
and eastern Australia, a region with limited local paleoclimate proxy data
(Vance et al., 2013; Ho et al., 2014). Practical usefulness of the insights
provided by the paleoclimate reconstructions for water resources management
at the catchment scale also requires investigation. Therefore, the links
between the Law Dome sea salt record, eastern Australian rainfall and the IPO
are further explored in this paper through the development of a millennial
length, annual resolution and catchment-scale rainfall reconstruction for the
Williams River (WR) catchment (Fig. 1). The WR catchment is located on the
eastern seaboard of New South Wales, east of the Great Dividing Range
(Fig. 1). The eastern seaboard contains about half of Australia's population,
and a proportionate amount of economic infrastructure and activity. The
region has hydroclimate features that are distinct from the rest of Australia
(e.g., Verdon and Franks, 2005; Timbal, 2010) and lacks high-resolution
paleoclimate proxies (Ho et al., 2014). This means there is significant
vulnerability, uncertainty and knowledge gaps relating to flood and drought
risk in eastern Australia. This recognition has recently motivated the
development of the Eastern Seaboard Climate Change Initiative (ESCCI), to
better understand the causes and impacts of current and future climate-related risk in the region
(http://www.climatechange.environment.nsw.gov.au/About-climate-change-in-NSW/Evidence-of-climate-change/Eastern-seaboard-climate-change-initiative).
The WR catchment is of particular regional importance because it forms part
of the conjunctive-use headworks scheme for potable water supply to
∼ 600 000 people in Newcastle, the sixth largest residential region in
Australia (Kiem and Franks, 2004; Mortazavi-Naeini et al., 2015).
Climatology of WR catchment rainfall. Shown is the mean and standard
deviation of monthly rainfall recorded at the 61010 gauge and for the AWAP
catchment average.
In the following sections we present a description of the WR catchment
location and relevant climate data, including a discussion of the link
between Law Dome, East Antarctica and eastern Australia. We proceed with an
investigation into the relationship between summer sea salts from Law Dome
and rainfall in the WR catchment and follow with the development of a
1013-year catchment-scale rainfall reconstruction (based on the Law Dome sea
salt record) and discussion of the insights and implications emerging from
this reconstruction.
Rainfall variability in the Williams River catchment
For the calibration data in this study we used daily 5km×5 km gridded rainfall data obtained from the Australian Water Availability
Project (AWAP) (Jones et al., 2009) for the period 1900–2010. The AWAP grid
cells overlapping the WR catchment were extracted and used to calculate
catchment average monthly rainfall totals for the WR catchment. Due to known
biases and uncertainty associated with gridded climate data (e.g., Tozer et
al., 2012), the AWAP-based information was ground-truthed with data from a
high-quality (Lavery et al., 1997) rainfall gauge (61010) located within the
WR catchment. The highest and most variable rainfall in the WR catchment is
received from December to May (summer and autumn) (Fig. 2) and the
hydrological water year for the WR catchment is therefore defined as October
to September in order to encompass this high rainfall period (pers. comm.,
Brendan Berghout, Senior Water Resources Engineer, Hunter Water Commission).
Rainfall variability in the WR catchment is influenced by the Great Dividing
Range to the west (Fig. 1), which provides orographic enhancement and the
Tasman Sea to the east, which brings moisture to the region (Pepler et al.,
2014). Synoptic scale influences known as East Coast Lows (ECLs), marine or
continental low-pressure systems, which tend to develop in the Tasman Sea,
are responsible for much of the extreme weather (e.g., heavy rainfall, high
winds) recorded in eastern New South Wales (Speer et al., 2009; Browning and
Goodwin, 2013; Pepler et al., 2014b; Ji et al., 2015; Kiem et al., 2015;
Twomey and Kiem, 2015). Indeed, ECLs have been found to contribute
20–30 % of annual rainfall in the WR region (Pepler et al., 2014a). In
addition to these local influences several large-scale ocean–atmospheric
processes influence rainfall in the WR catchment (e.g., Kiem and Franks,
2001, 2004; Risbey et al., 2009). The El Niño–Southern Oscillation
(ENSO) and IPO have been related to interannual to multi-decadal variability
in both WR rainfall and runoff (Kiem and Franks, 2001, 2004). Drier (wetter)
catchment conditions typically occur during El Niño (La Niña) events
and the IPO modulates both the frequency and magnitude of ENSO impacts such
that drought risk is increased during IPO positive phases and flood risk is
increased during IPO negative phases (Kiem and Franks, 2001, 2004; Kiem et
al., 2003; Kiem and Verdon-Kidd, 2013). Indian Ocean SSTs are also known to
influence eastern Australian rainfall during winter and spring (Verdon and
Franks, 2005; Risbey et al., 2009).
In addition, the subtropical ridge (STR) and Southern Annular Mode (SAM)
impact rainfall variability in the eastern seaboard (e.g., Risbey et al.,
2009; Ho et al., 2012; Whan et al., 2013). A positive SAM phase has been
related to increased daily rainfall in summer and spring (Hendon et al.,
2007; Risbey et al., 2009) while variability in the position of the STR is
significantly correlated with rainfall in the eastern seaboard. That is, a
shift south of the STR is associated with increased rainfall in the region
(Timbal, 2010; Whan et al., 2013). Variability in the intensity of the STR
is also related to rainfall variability in the eastern seaboard though to a
lesser extent than variability in the STR position (Timbal, 2010).
The Law Dome–eastern Australia rainfall proxyLaw Dome ice core site details
Law Dome is a small, coastal icecap located in Wilkes Land, East Antarctica
(Fig. 1), and the site of the Dome Summit South
(DSS) ice core, which spans around 90 000 years (Roberts et al., 2015). DSS
has high annual snowfall of around 0.63 m (water equivalent), which allows
for a monthly resolution record in the upper portion of the core and highly
accurate dating (van Ommen and Morgan, 1997; Vance et al., 2013; Roberts et
al., 2015). The ice core was dated by counting annual layers with known
volcanic horizons used to establish dating accuracy (Plummer et al., 2012).
As a result, the Law Dome record was dated with absolute accuracy from
1807 to 2009 CE and with ±1-year error from 894 to 1807 CE.
The sea salt record used here was produced using trace ion chromatography
from 2.5 to 5 cm sub-samples of the ice cores (Curran et al., 1998; Palmer et
al., 2001). The Law Dome summer (December–March) sea salts (LDSSS) were
extracted from the full record and used by Vance et al. (2013) and Vance et
al. (2015) as a rainfall proxy for eastern Australia. Here we use a slightly
extended LDSSS record to cover the epoch 1000–2012 CE using the
improved composite record of Roberts et al. (2015).
The link between sea salt deposition at Law Dome and large-scale
ocean–atmospheric processes
The climate signals recorded in the Law Dome ice core are driven by
large-scale ocean–atmospheric processes rather than local factors (Bromwich,
1988; Delmotte et al., 2000; Masson-Delmotte et al., 2003; Vance et al.,
2013). The southern Indian Ocean is the main source of moisture delivered to
Law Dome (Delmotte et al., 2000; Masson-Delmotte et al., 2003) and sea salt
deposition is related to the mid-latitude westerly winds (associated with
the SAM) in the Indian and Pacific sectors of the Southern Ocean (Goodwin et
al., 2004; Vance et al., 2015). Seasonal to annual scale SST anomalies in
the equatorial Pacific are known to propagate to high southern latitudes
(Karoly, 1989; Mo and Higgins, 1998; Ding et al., 2012). The resulting
circumpolar geopotential height and zonal wind anomalies influence the SAM
(L'Heureux and Thompson, 2006), and ultimately deliver sea salt aerosols to
coastal Antarctica (Vance et al., 2013). Indeed, Vance et al. (2013) found a
significant correlation between ENSO-related SST variability in the
central-western equatorial Pacific and LDSSS, with low summer sea salt
years associated with El Niño events over the period 1889-2009.
Furthermore, spectral analysis of the 1010-year LDSSS record found
significant (p< 0.01) spectral features in the 2–7-year ENSO band.
Similar to the LDSSS rainfall proxy discussed previously, the
LDSSS ENSO proxy varies decadally, coherent with the IPO, with a
stronger relationship during IPO positive phases (Vance et al., 2013, 2015).
It is thus clear that the ocean–atmospheric processes associated with sea
salt deposition at Law Dome (e.g., IPO, ENSO, SAM and variability in the
Indian Ocean) are the same as those that influence rainfall variability in
the WR catchment (discussed in Sect. 2). We can
therefore expect LDSSS to explain some variability in the rainfall in
the WR catchment.
Correlations between (a) 12-month average
(October–September) AWAP rainfall and LDSSS for the 1900–2010
period with inset showing correlations between annual AWAP rainfall
calculated from January–December and LDSSS for 1900–2010 period,
(b) as in (a) but for the combined IPO positive phases
(1924–1941, 1979–1997), (c) as in (a) but for the IPO
negative phase (1947–1975), (d) as in (a) but for the
first IPO positive (1924–1941) phase (e) as in (a) but for
the second IPO positive (1979–1997) phase and (f) 13-year moving
window correlations between 12-month average (October–September) rainfall
recorded at gauge 61010 and the AWAP WR catchment average and LDSSS
with shading indicating IPO positive (yellow) and IPO negative (purple)
phases (red line shown indicates 13-year smoothed instrumental IPO record).
Note that for (a–e) the star represents the location of the WR
catchment centroid, dashed pink line
shows 99 % significance level.
Investigating the relationship between LDSSS and rainfall in the
Williams River catchment
Vance et al. (2013) found a relationship between LDSSS and the
prior January–December rainfall west of the Great Dividing Range. The region
of interest in this study is further south and east of the Great Dividing
Range, so we needed to re-evaluate if this temporal offset was appropriate.
This re-evaluation was via a damped least-squares regression between AWAP
grid-cell data and the LDSSS record using the Marquardt–Levenberg
method, a method capable of multi-variate and non-linear regression. Although
it was only used for uni-variate linear regression here, the method was
selected for compatibility with planned future work. For every AWAP grid cell
in New South Wales, we performed linear least-squares regression between the
LDSSS record and 12-month-averaged rainfall over a 24 month
lead/lag range centered about the summer sea salt period (December–March).
The regression coefficients for each lead/lag were used to generate an
estimated spatial rainfall time series. The Pearson correlation coefficient
between the estimated rainfall and AWAP rainfall for each grid cell was then
assessed for each lead/lag. This process allowed us to determine the seasonal
window for rainfall that optimized the WR rainfall–LDSSS
relationship in order to optimize the utility of the LDSSS record.
Pearson correlation values between LDSSS and 12-month
average (October–September) rainfall recorded at gauge 61010 and the AWAP WR
catchment average for the 1900–2010 period and IPO phases. Bootstrap
95 % confidence intervals are also indicated (Mudelsee, 2003). Bold
values are significant at 95 %.
From the lead/lag analysis, October–September and November–October annual
rainfall in the region encompassing the WR catchment was found to have the
highest and most spatially coherent relationship with LDSSS. We present
the October–September rainfall / LDSSS correlations
(Fig. 3) as this period also corresponds to the
water year in the Newcastle region (discussed in Sect. 2) and hence all further analysis is based on the
12-month rainfall totals calculated from October–September.
Figure 3 shows spatial maps of the magnitude of the correlations between
October–September WR rainfall and LDSSS for the 1900–2010 (i.e.,
October 1900–September 2010) period as well as subsets for the different IPO
phases. For comparison Fig. 3a–e are inset with maps for the
January–December rainfall / LDSSS correlations, the analysis
period used in Vance et al. (2013) and Vance et al. (2015). Figure 3f
indicates the 13-year moving window correlations between LDSSS and
October–September for rainfall recorded at gauge 61010 and the AWAP WR
catchment average in order to identify low frequency variability associated
with the IPO. The Pearson correlation coefficients between LDSSS
and October–September annual rainfall recorded at gauge 61010 and the AWAP WR
catchment average for the full record and IPO phases are presented in
Table 1. The significance of the relationships are confirmed using bootstrap
confidence intervals based on the method of Mudelsee (2003).
The insets of Fig. 3a–e reveal low correlations in the WR catchment region.
The highest correlations occur in inland New South Wales and into
southeastern
Queensland, the focus region of Vance et al. (2013) and Vance et al. (2015).
However, when the correlation is aligned with the WR catchment water year
(October–September) we see a shift in the region of significant correlation
(Fig. 3a) to coastal New South Wales and, in particular, large parts of the
eastern seaboard. Importantly, correlations significant at the 99 % level
are seen over the WR catchment region. Rainfall at gauge 61010 and AWAP
catchment average show significant correlations with LDSSS (Pearson
correlation coefficients of 0.29 and 0.28 respectively) over the 1900–2010
period (Table 1).
As expected, based on the results of Vance et al. (2013, 2015) (discussed in
Sect. 3), the strength of the correlation between October–September rainfall
and LDSSS varies decadally. Figure 3b–c indicate that the
relationship between the variables is stronger during the IPO positive phases
relative to the negative phase. Figure 3d–e and the results in Table 1,
however, suggest that although the relationship between October–September
rainfall and LDSSS is stronger in IPO positive phase, this increase
in strength relative to IPO negative and the full record (1900–2010) is
primarily due to the very high correlation in the second IPO positive phase
(1979–1997). In fact, the correlation between rainfall recorded at gauge
61010 and LDSSS during the IPO negative phase is greater than the
correlation in the first IPO positive phase (Table 1). Figure 3f further
highlights that an increase (decrease) in the strength of the
LDSSS–WR rainfall relationship is not always synchronous with IPO
positive (negative) phases.
This result appears to be in contrast to Vance et al. (2013, 2015), who found
a clear link between IPO phase, LDSSS and January–December
rainfall in southeast Queensland
and northeastern New South Wales (west of the Great Dividing Range). That is,
the correlation between these variables was poor during the IPO negative
phase, yet was significant for both positive phases. Vance et al. (2013,
2015) used calendar year rainfall as opposed to a more catchment specific
analysis period used in this study. Another key difference is that the focus
region here is further south, on the coast and under the orographic influence
of the Great Dividing Range. Furthermore, although interdecadal and
interannual tropical Pacific Ocean variability (e.g., ENSO and IPO) has been
found to impact the whole of Australia at various times of the year (e.g.,
Power et al., 1999a; Risbey et al., 2009), the amount of rainfall variability
explained by these processes reduces to the south while climate mechanisms
stemming from the mid- to high latitudes (e.g., SAM and the STR, discussed in
Sect. 2) increase their influence on rainfall variability (Risbey et al.,
2009).
In addition, as mentioned previously, around one-quarter of annual rainfall
received in the WR catchment results from ECLs (Pepler et al., 2014a). In
1950 and 1955, the Newcastle region experienced heavy rainfall and floods as
a result of severe ECLs (Callaghan and Helman, 2008; Callaghan and Power,
2014) and indeed eastern Australia in general was subject to an increase in
intense storm activity between the 1940s and 1970s (Power and Callaghan,
2015). The relationship between LDSSS and rainfall in the WR
catchment could not be expected to hold during these short duration but
intense local-scale weather events and remote proxy records in general are
usually incapable of resolving events like these. As such, this period of
elevated intense storm activity may explain the marked reduction (and change
of sign) in the correlation between LDSSS and rainfall in the WR
catchment in the early 1950s (Fig. 3f). ECL variability has been related to
the IPO, with Speer (2008) finding that during the second IPO positive phase
(i.e., 1979–1997) there was a decrease in ECLs relative to IPO negative.
This would correspond to a reduction in ECL-related rainfall over New South
Wales in the most recent IPO positive phase and is further evidence that
these short duration, chaotic events affect the relationship between
LDSSS and rainfall in the WR catchment.
Indeed, a better understanding of the role of ECLs and also the relative
influence of ENSO, IOD, SAM, STR and other large-scale processes on rainfall
in the WR catchment (as is currently being investigated as part of the ESCCI
project) will undoubtedly improve our understanding of the variability in
the strength of the LDSSS–WR rainfall teleconnection. It should also be
noted that one of the key difficulties in understanding the non-stationarity
in the climate of the Southern Hemisphere is the lack of quality
atmospheric/oceanic data in the Southern Ocean in the pre-1979 satellite
era, particularly in the Indian/West Pacific sector. It is likely that more
high-resolution ice core records from the Indian Ocean sector of East
Antarctica will assist in filling this data gap (Vance et al., 2016).
Underpinning the above issue is that variability in the Australian climate
record can be up to centennial scale, which cannot be resolved using
relatively short instrumental data sets (Gallant et al., 2013). Ultimately,
for the purposes of this initial reconstruction, we have assumed
stationarity in the LDSSS–Williams River rainfall relationship.
Reconstructing rainfall in the Williams River catchmentDevelopment of the Williams River rainfall reconstruction
The linear regression coefficients determined for the full 1900–2010
instrumental calibration period (Sect. 4) were applied to the
1000–2012 CE LDSSS data to produce 1013 years of rainfall data
for each AWAP grid cell in the WR catchment. This grid-cell data were then
spatially averaged to produce a WR catchment average rainfall reconstruction
time series.
Comparing the catchment-average rainfall reconstruction with instrumental
(AWAP) data
A comparison between the AWAP catchment average and reconstructed WR
catchment rainfall over the instrumental period (1900–2010) is presented in
Fig. 4. The instrumental mean and pattern of peaks and troughs in the
recorded rainfall is well represented in the reconstruction but the range of
variability is underestimated. While the magnitude of the extremes is
important, the key focus is that the reconstruction captures the duration
and timing of wet and dry periods. The thinking behind this is that a short,
but extreme (in terms of rainfall deficit) drought, for example, will have
less severe implications on water security in a catchment than a drought of
long duration with consistently below average (but not necessarily extremely
below average) rainfall. Encouragingly, periods post-1900 that are known to
be associated with droughts and flooding in the WR catchment are identified
in the reconstruction, e.g., the World War II drought in the late 1930s, the
Millennium drought in the 1990s to 2000s and the flood dominated 1950s and
1960s (e.g., Verdon-Kidd and Kiem, 2009; Gallant et al., 2012; Callaghan and
Power, 2014).
Reconstructed rainfall (thick black), AWAP WR catchment average rainfall (grey) and reconstruction/AWAP mean rainfall (thin black). Shading indicates IPO positive (yellow) and IPO negative (purple) phases.
The rainfall reconstruction captures around 10 % of the rainfall
variability in the WR catchment for the full 1900–2010 instrumental period
(Table 1). In terms of IPO phases, it is clear that the reconstruction is in
better agreement with the instrumental record for the most recent IPO
positive phase (1979–1997) relative to the first IPO positive phase and the
IPO negative phase. This is no surprise given the higher correlation between
LDSSS and Williams River rainfall in the recent IPO positive
period,
i.e., LDSSS variability captures around 40 % of the Williams
River rainfall variability (Table 1). Influences on the stationarity of the
LDSSS–WR rainfall relationship were discussed in Sect. 4.
Root mean square error in mm year-1 (%) and reduction in
error between the rainfall reconstruction and 12-month average
(October–September) rainfall recorded at gauge 61010 and the AWAP WR
catchment average for the 1900–2010 period and IPO phases.
Time period61010 AWAP catchment average RMSE mm (%)RERMSE mm (%)REFull record (1900–2010)267 (25.1)0.07254 (23.1)0.08IPO positive (1924–1941, 1979–1997)239 (22.5)0.14202 (18.4)0.25IPO positive (1924–1941)254 (23.9)0.10187 (17.0)0.08IPO positive (1979–1997)223 (21.0)0.11216 (19.6)0.33IPO negative (1947–1975)254 (23.9)0.10306 (27.8)0.02
WR catchment rainfall reconstruction (grey line), 10-year Gaussian
smooth (bold black line) mean of the rainfall reconstruction for 1000–2012
period (red line) and 1900–2010 period (green line).
Table 2 presents the root mean square error (RMSE) and reduction in error
(RE) between the rainfall reconstruction and 12-month average
(October–September) rainfall recorded at gauge 61010 and the AWAP WR
catchment average for the 1900–2010 period and IPO phases. An RE value
greater than zero indicates that the reconstruction is skillful and has
better predictive skill than climatology (Cook, 1992). While improved RMSE
and RE statistics were recorded for the most recent IPO positive (1979–1997)
relative to the first IPO positive and IPO negative phases, it is clear that
the reconstruction has skill across the 1900–2010 instrumental period. For
the full instrumental record, the reconstruction has an RMSE of around
25 % of the annual instrumental rainfall with an RE value greater than
zero.
A millennial rainfall reconstruction for the WR catchment
Figure 5 presents the 1013-year rainfall
reconstruction produced for the WR catchment. From the 10-year smoothed
record it is evident that there have been multi-year periods of either above
or below average rainfall. A multi-century dry period is evident from around
1100 to 1250 CE, while two similarly persistent wet periods are seen from around
1400 to 1600 and 1800 to 1900 CE.
Duration of longest dry and wet periods for the AWAP and
reconstructed rainfall.
As mentioned previously, few rainfall proxy records exist in eastern
Australia. Those that do tend to be outside of the eastern seaboard region in
climate regimes that have significant differences, cover different time
periods or are at varying (lower) resolutions, which limits the ability to
compare them to the reconstruction provided here. However, broad
commonalities can be discussed. Heinrich et al. (2009) developed a 154-year
rainfall reconstruction for Brisbane, located near the northern boundary of
the eastern seaboard, from red cedar tree-ring analysis. Since the record
commences in 1854, which is within the instrumental period for the Brisbane
region (Heinrich et al., 2009), the utility of this record for comparison
here is limited. Nevertheless, the authors found drier periods in the 1880s,
1900–1920, most of 1940s and 1990s and wetter periods in the 1860s, 1890s,
1930s and 1970s, which fits with the results shown in Fig. 5 if the
reconstruction is compared with the instrumental mean as opposed to the full
1000–2012 mean. Although not a rainfall reconstruction, an aridity index of
wet and dry periods is available based on speleothems from the Wombeyan
Caves, located in the Sydney region (i.e., within the eastern seaboard) for
the 749 BCE to 2001 CE period (McDonald, 2005; McDonald et al., 2009,
2013). Dry epochs evident in the Wombeyan aridity index, for the period where
it overlaps with the WR reconstruction, include late 1100s, around 1500,
mid-1700s and early 1900s, which is consistent with the WR reconstruction
illustrated in Fig. 5. Similarly, the Wombeyan record indicates epochs that
were “not dry” include the early 1400s, 1510-1600, early 1700s, and
late 1800s, which is again consistent with the results presented in Fig. 5
(McDonald, 2005; McDonald et al., 2009, 2013; Ho et al., 2015a, b).
In addition, Gergis et al. (2012) produced a multi-proxy based annual
rainfall reconstruction for a broad southeastern portion of Australia for the
1783–1988 CE period, finding the 20th century to be drier during their
∼ 200-year analysis period. Similarly, Fig. 5 shows that the
recent era (1900–present) is relatively dry in the post-1783 time period and
also in the context of the last 1000 years, though it is not unprecedented.
For the 1685–1981 CE period, Lough (2011) found drier and less variable
summer rainfall in far northeastern Queensland between 1760 and 1850 and a tendency
for a wetter climate from the mid-1850s to 1900 from their coral-based
rainfall proxy. Likewise, the reconstruction shown here tends to indicate a
drier period post-1700, switching to a wetter regime from the late-1700s to
the beginning of the 20th century.
Ultimately we find good agreement with existing nearby rainfall
reconstructions. This further validates the rainfall reconstruction
presented here particularly in light of previously identified concerns with
comparing it to other reconstructions in the eastern Australia region.
Implications for water resources management
While Fig. 5 gives insights into periods of above
and below average rainfall, of particular interest for hydrological studies
and water resources management is not only whether a year or sequence of
years is above or below the long-term average but also whether a multi-year or
multi-decadal epoch is generally wet or dry even though some years within
that epoch may be slightly below or above the long-term average. For
example, a year that is only 0.1 standard deviations above the average is
unlikely to provide enough rainfall to break a drought or fill reservoirs.
To account for this, we define “wet” and “dry” years as (Eq. 1):
wet=years where rainfall>mean-x×SD,dry=years where rainfall<mean+x×SD.
Histograms of duration of above (blue) and below (pink) average
rainfall periods in each century since CE 1000. (a)–(j)
are centennial subsets and (k) is the CE 1000–2012 period (note
different axis scaling). Above/below average are defined using x=0 in
Eq. (1) (as per Table 2).
Histograms of duration of WET (blue) and DRY (pink) average periods
during each century since CE 1000. (a)–(j) are centennial
subsets and (k) is the CE 1000–2012 period (note different axis
scaling). WET/DRY are defined using x=0.3 in Eq. (1) (as per Table 2).
For example, for a standard deviation (SD) of 0.1 (and annual average of
1100 mm), the range is 1092.6–1107.4 mm. That is, a wet year will be
defined as any year with annual rainfall greater than 1092.6 mm and a dry
year as any year with annual rainfall less than 1107.4 mm. Some years will
be defined as both “wet” and “dry” but this methodology avoids a
situation where a consistently wet (or dry) period is broken by a single year
that is slightly below (or above) the mean.
Table 3 compares the persistence of the longest above and below average
rainfall periods (x=0 in Eq. 1), and “wet/dry” periods
(x=0.1, 0.2, 0.3, 0.4, 0.5 in Eq. 1) in the
AWAP catchment average rainfall and the reconstruction. Note that the time
periods identified in Table 3 should be considered in light of the ±1 year
dating uncertainty of the LDSSS record discussed in Sect. 3.1. As
shown in Table 3a and b, the reconstruction captures the dry
periods, in terms of duration and timing, of the AWAP instrumental record
well and also the duration of the longest wet periods. However, the timing
of the wettest periods detected by the reconstruction is different to that
seen in the AWAP record. As previously discussed this is likely due to the
inability of the LDSSS reconstruction to characterize extreme
local-scale synoptic activity in the WR region (i.e., ECLs). Importantly,
this also implies that the wettest epochs in the reconstruction may be an
underestimation, as the reconstruction is least accurate during wet periods
caused predominantly by local-scale influences (e.g., ECLs). In other words,
wet periods associated with increased ECL activity (e.g., similar to the
1950s) are possible and the magnitude of rainfall associated with these
events would be over and above the preinstrumental wet epochs suggested by
the LDSSS reconstruction.
Figure 6 shows the duration of above and below average rainfall periods
during each century since 1000 CE (and also for the whole 1013-year
reconstruction period, panel k). To easily visualize the results, Fig. 6
combines all durations > 15 years (information on all durations
is included in Table S1 in the Supplement). Figure 6 clearly shows that
(a) some centuries are drier (more pink) than others (more blue) and (b) the
most recent complete century (1900–1999, panel j), where the majority of our
instrumental record comes from, is not representative of either the duration
or frequency of periods of above or below average rainfall experienced
pre-1900.
While the results in Fig. 6 are important, of greater interest is the
identification of the persistence of wet or dry periods even though some
years within the otherwise dry (wet) regime were slightly wetter (drier) than
average. Table 3a and b show that for varying x values in Eq. (1) the duration of the
longest wet or dry periods in the instrumental period does not markedly change.
If dry and wet epochs, defined relative to the 1100.0 mm instrumental mean are
extracted from the preinstrumental reconstruction, a different story emerges (Table 3c).
For a standard deviation threshold of 0.3, for example, the results show that the
longest dry epochs persist for up to 12 years instead of a maximum of 8 years post-1900,
while wet epochs have lasted almost 5 times as long (maximum of 39 years
preinstrumental compared to a maximum of 8 years in the instrumental period). A similar
result is observed if the full reconstruction mean (1126.1 mm) is used to
indicate wet or dry (Table 3d), with both the dry and wet epochs persisting
up to twice as long in the preinstrumental compared to the instrumental
period. Figure 7 (and the associated Tables S2 and S3) further illustrates
this point (and the points made in relation to Fig. 6) by clearly showing
that the proportion, frequency and duration of wet/dry epochs in the
instrumental period (1900–1999) is not representative of either the overall
situation throughout the last 1000 years or the situation in any century
pre-1900. Also of interest is that some centuries tend to have short dry
periods compared with long dry periods and vice versa – e.g., the 15th and
16th century (Fig. 7e and f) compared to the 12th and 13th century (Fig. 7b
and c). The same can be said for wet periods. The variation in the
distribution of dry/wet-period duration between centuries further suggests
that water resources management and planning based on the statistics of 100
years of data (or less) is problematic.
Conclusions
This study produced a 1013-year rainfall reconstruction for the WR
catchment, a location without any local paleoclimate proxies. The strength
of the relationship between LDSSS and annual WR rainfall was found to
vary decadally but, unlike Vance et al. (2013, 2015), was
not always coherent with the IPO. This is likely due to the different
climate regime that the coastal WR catchment is subject to (e.g., likely more
influence from mid-latitude processes) compared to the previous studies,
which were located further north and predominantly west of the Great
Dividing Range. The WR catchment is also strongly influenced by local-scale
coastal storms such as ECLs, which may provide an explanation for the
different relationship to the IPO, as well as the breakdown in the East
Antarctic–WR teleconnection in periods associated with increased ECL
activity (e.g., the 1950s).
Despite the acknowledged non-stationarity in the relationship (which is being
further investigated in ongoing research) the relationship between
LDSSS and rainfall in the WR catchment is significant over the full
1900–2010 calibration period and indeed the reconstruction shows skill
across this period. The reconstruction was found to agree well with
identified dry/wet periods in other rainfall reconstructions in the eastern
Australia region providing further validation. Ultimately, the
LDSSS-based reconstruction shows that the instrumental period
(∼ 1900–2010) is not representative of the proportion,
frequency or duration of wet/dry epochs in any century in the
preinstrumental era. This is consistent with recent independent studies
focussed on Tasmania (Allen et al., 2015) and the Murray-Darling Basin (Ho
et al., 2015a, b).
These findings provide compelling evidence that existing hydroclimatic risk
assessment and associated water resources management, infrastructure design,
and catchment planning in the WR catchment is flawed given the reliance on
drought and flood statistics derived from post-1900 information. Figure 3
(and Fig. 4a in Vance et al., 2015) suggests that the same is true for most
of eastern Australia and indeed may also be the case for other regions in
Australia that are identified as (or yet to be identified as) having similar
teleconnections with East Antarctica, e.g., southwest Western Australia (van
Ommen and Morgan, 2010). Therefore, the robustness of existing flood and
drought risk quantification and management in eastern Australia is
questionable and the insights from paleoclimate data need to be incorporated
into catchment planning and management frameworks, especially given the
multi-decadal and centennial hydroclimatic variability demonstrated in this
study.
Acknowledgements
This work was supported by the Australian Government's Cooperative Research
Centres Programme through the Antarctic Climate and Ecosystems Cooperative
Research Centre (ACE CRC). The Australian Antarctic Division provided
funding and logistical support for the DSS ice cores (AAS projects 4061 and
4062). The Centre for Water, Climate and Land Use at the University of
Newcastle provided partial funding for Tozer's salary.
Edited by: D. Mazvimavi
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