Reconstructing El Niño Southern Oscillation using data from ships' logbooks, 1815- 1854. Part I: Methodology and Evaluation

Abstract

The meteorological information found within ships’ logbooks is a unique and fascinating source of data for historical climatology. This study uses wind observations from logbooks covering the period 1815 to 1854 to reconstruct an index of El Niño Southern Oscillation (ENSO) for boreal winter (DJF). Statistically-based reconstructions of the Southern Oscillation Index (SOI) are obtained using two methods: principal component regression (PCR) and composite-plus-scale (CPS). Calibration and validation are carried out over the modern period 1979–2014, assessing the relationship between re-gridded seasonal ERA-Interim reanalysis wind data and the instrumental SOI. The reconstruction skill of both the PCR and CPS methods is found to be high with reduction of error skill scores of 0.80 and 0.75, respectively. The relationships derived during the fitting period are then applied to the logbook wind data to reconstruct the historical SOI. We develop a new method to assess the sensitivity of the reconstructions to using a limited number of observations per season and find that the CPS method performs better than PCR with a limited number of observations. A difference in the distribution of wind force terms used by British and Dutch ships is found, and its impact on the reconstruction assessed. The logbook reconstructions agree well with a previous SOI reconstructed from Jakarta rain day counts, 1830–1850, adding robustness to our reconstructions. Comparisons to additional documentary and proxy data sources are provided in a companion paper.

Full text

Vol.:(0123456789)
1 3
Clim Dyn (2018) 50:845–862
DOI 10.1007/s00382-017-3644-7
Reconstructing El Niño Southern Oscillation using data
fromships’ logbooks, 1815–1854. PartI: methodology
andevaluation
HannahG.Barrett1 · JulieM.Jones1· GrantR.Bigg1
Received: 31 May 2016 / Accepted: 16 March 2017 / Published online: 8 April 2017
© The Author(s) 2017. This article is an open access publication
Comparisons to additional documentary and proxy data
sources are provided in a companion paper.
Keywords Ships’ logbooks· ENSO reconstruction· El
Niño· Climate change and variability
1 Introduction
The logbooks of ships which historically travelled the
world’s oceans contain a vast amount of meteorological
information useful for studies of historical climate. These
logbooks present a unique and exciting record of daily
weather observations including wind force and direction,
air temperature, pressure, and the state of the weather and
the sea from the pre-instrumental era (Garcia-Herrera etal.
2005). Initial studies using this data source focused on indi-
vidual events such as the reconstruction of daily synoptic
conditions during key nautical battles (Wheeler 2001) or of
sea ice cover during the summer of 1816 (Catchpole and
Faurer 1985). More recently, the scope of these investiga-
tions has expanded to reconstructions of larger scale pres-
sure fields (Gallego etal. 2005; Küttel etal. 2010), westerly
winds (Barriopedro etal. 2014), temperature (Brohan etal.
2012) and precipitation (Hannaford etal. 2015) over much
longer time periods. Jones and Salmon (2005) were the first
to attempt a reconstruction of El Niño Southern Oscilla-
tion (ENSO) using logbook observations from across the
Indian Ocean (16°N to 40°S). However, gaps in the spatial
and temporal coverage of the data were found to be a major
restriction to their reconstruction (Jones and Salmon 2005).
This paper builds on their initial attempt, by using the
increased amount of data available since their work, and by
focusing only on regions with strong ENSO signals.
Abstract The meteorological information found within
ships’ logbooks is a unique and fascinating source of data
for historical climatology. This study uses wind observa-
tions from logbooks covering the period 1815 to 1854
to reconstruct an index of El Niño Southern Oscillation
(ENSO) for boreal winter (DJF). Statistically-based recon-
structions of the Southern Oscillation Index (SOI) are
obtained using two methods: principal component regres-
sion (PCR) and composite-plus-scale (CPS). Calibration
and validation are carried out over the modern period
1979–2014, assessing the relationship between re-gridded
seasonal ERA-Interim reanalysis wind data and the instru-
mental SOI. The reconstruction skill of both the PCR and
CPS methods is found to be high with reduction of error
skill scores of 0.80 and 0.75, respectively. The relation-
ships derived during the fitting period are then applied to
the logbook wind data to reconstruct the historical SOI.
We develop a new method to assess the sensitivity of the
reconstructions to using a limited number of observations
per season and find that the CPS method performs better
than PCR with a limited number of observations. A differ-
ence in the distribution of wind force terms used by British
and Dutch ships is found, and its impact on the reconstruc-
tion assessed. The logbook reconstructions agree well with
a previous SOI reconstructed from Jakarta rain day counts,
1830–1850, adding robustness to our reconstructions.
Electronic supplementary material The online version of this
article (doi:10.1007/s00382-017-3644-7) contains supplementary
material, which is available to authorized users.
* Hannah G. Barrett
hbarrett2@sheffield.ac.uk
1 Department ofGeography, University ofSheffield, Winter
Street, SheffieldS102TN, UK
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846 H.G.Barrett et al.
1 3
El Niño Southern Oscillation is the dominant mode
of interannual climate variability in the tropics and is an
important part of the natural climate system (Diaz and
Markgraf 1992). ENSO is a coupled atmosphere–ocean
phenomenon associated with a see-saw pattern of atmos-
pheric pressure and sea surface temperatures (SSTs) across
the equatorial Pacific, with periodicities of 2 to 7 years.
Its two main phases, El Niño and La Niña, are associated
with regional changes in precipitation, sea level pressure
and temperature. These extremes result in hazards such
as flooding, drought and wildfires which have large socio-
economic consequences. Although ENSO is centred in the
equatorial Pacific, its teleconnections have a near global
extent making it a hugely important part of the climate sys-
tem on sub-decadal timescales.
The most reliable record of ENSO behaviour is from
instrumental data, which are obtained from in-situ measure-
ments of atmospheric and oceanic variables. The Southern
Oscillation Index (SOI), an atmospheric index of the pres-
sure difference between Tahiti and Darwin, extends back
to 1876 (Ropelewski and Jones 1987). This was extended
further using early pressure data from Jakarta and Tahiti
to 1841, and from rain-day counts from Jakarta back to
1829 (Können etal. 1998). The record of Jakarta rain-day
counts was used to create an SOI covering June–November,
1829–1850, and provides the most direct record to compare
to the logbook reconstructions. The Niño 3.4 index, based
on SST, extends back to 1877 (Bunge and Clarke 2009).
Understanding ENSO variability prior to these instrumental
records helps to place current ENSO variability in a longer
term context. This is important as it enables an assessment
of whether modern ENSO behaviour is the result of natural
climate variability or has been influenced by anthropogenic
climate change (Gergis etal. 2006). Current understand-
ing of ENSO behaviour during the early to mid-nineteenth
century comes from a range of data sources. Proxy records,
such as tree rings, corals and ice cores, are widely used as
indicators of past climate and environmental change but
have a range of uncertainties which are attached to them.
They are less direct measures of the climate than data from
ships’ logbooks. Recent multi-proxy studies have attempted
to bring together a range of reconstructions, however a lack
of consensus on ENSO behaviour on an interannual time
scale remains (Emile-Geay et al. 2013). Climate models
provide an alternative source of information on past ENSO
variability, but their ability to produce realistic ENSO sim-
ulations is still limited, and conflicts between models are
apparent in multi-model comparison projects (Bellenger
etal. 2014). The reconstructions using the data from log-
books presented in this paper provide a ‘bridge’ between
modern instrumental records and proxy records. Here,
we compare the logbook reconstructions to the early SOI
records from the Jakarta observations, as both records are
from direct, well-dated observations (Können etal. 1998).
Comparison between the logbook and multi-proxy recon-
structions will be discussed further in a companion paper
(Barrett etal. 2016).
Created as part of an international project completed in
2004, the Climatological Database for the World’s Ocean
(CLIWOC) provides access to a digital database of over
280,000 daily weather observations from mostly Dutch,
British, Spanish and French national archives (Können and
Koek 2005). The CLIWOC database spans 1662 to 1855,
however, over 99% of the observations occur in the period
1750 to 1855 (Küttel etal. 2010). They provide a signifi-
cant extension to modern instrumental records. However,
the process of digitization is expensive and time-consum-
ing so, following the CLIWOC project, there were still over
90% of European, mostly British, logbook collections yet
to be digitised (Garcia-Herrera etal. 2005). An additional
891 logbooks have since been digitised in the UK from
English East India Company (EEIC) records, providing a
further 273,000 observations from 1789 to 1834 (Brohan
etal. 2012). This paper considers the combined databases
in order to utilise the highest number of observations pos-
sible. Despite these two large digitisation projects there is
still a long way to go in order to fully exploit this resource
(Wheeler 2014).
This paper is organised as follows: Sect.2 describes the
data sources used to carry out the historical ENSO recon-
structions. Focus on spatial and temporal availability of the
logbook data is provided. Sect.3 explains the data selection
procedure and the reconstruction and validation method-
ologies. It also introduces a new method of assessing the
impact of changing the number of observations per sea-
son on reconstruction skill, which is particularly important
given this characteristic of logbook data. Sect.4 presents
the results of the logbook data analysis with a focus on the
wind force terms used. It also presents the results of an
evaluation of the reconstruction methods and presents the
historical ENSO reconstructions, along with comparison to
the early Jakarta rain-day SOI record. The reconstructions
and key findings are then discussed in Sect.5, while Sect.6
concludes this part of the study. A companion paper (Bar-
rett etal. 2016) compares the results of this paper to previ-
ous ENSO reconstructions, documentary and proxy-based,
and discusses the implications of these results.
2 Data
Wind direction and strength derived from ships’ logbooks
were used to reconstruct the Southern Oscillation Index.
Due to a lack of overlap between the logbook data and the
instrumental SOI, reanalysis data from the modern period
(1979–2014) were used to establish statistical relationships
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847
Reconstructing El Niño Southern Oscillation using data fromships’ logbooks, 1815–1854. Part…
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between zonal wind and the SOI. These relationships were
then applied to the logbook wind data to reconstruct the
historical SOI.
2.1 Reanalysis data
Sub-daily 10 m zonal wind values were obtained from
ERA-Interim Reanalysis data (Dee etal. 2011). These data
cover the period from 1979 to present, with 2013/2014
being the last DJF season used in this paper. Reanalysis
wind data were re-gridded to 7.5° × 8° latitude–longitude,
following previous studies using logbook data which found
the use of an 8° × 8° latitude–longitude grid provided a
good resolution to enable suitable logbook data density,
without compromising the climatology (Gallego et al.
2005; Jones and Salmon 2005; Küttel et al. 2010; Han-
naford etal. 2015). The re-gridding of the reanalysis data
was carried out using climate data operators (CDO, https://
code.zmaw.de/projects/cdo). ERA-Interim data are avail-
able at four synoptic time steps 00UTC, 06UTC, 12UTC
and 18UTC. In order to best match the logbook data,
which is taken at local noon, the daily value in each grid
box was taken as the one closest to its local noon. These
daily ‘noon’ values were then converted into monthly and
seasonal values, based on the four standard meteorologi-
cal seasons (DJF, MAM, JJA and SON). Only DJF values
are used in the paper as this is the season in which ENSO
events typically reach their peak intensity (Bellenger etal.
2014), but also in which a suitable amount of logbook data
availability was found.
2.2 Instrumental ENSO data
A monthly SOI was obtained from the Australian Bureau
of Meteorology (BoM) extending from 1876 to present
(Bureau of Meteorology 2015). The Troup SOI method
was used, which calculates the standardised anomaly of the
mean sea level pressure difference between observations
from Tahiti and Darwin. The seasonal SOI was calculated
from these monthly values. Other indices of ENSO are
available and preliminary investigations included a range of
indices, but the SOI was selected as it is an atmospheric
measure of ENSO and is therefore closely linked to winds
and circulation patterns, so had the strongest correlations
with zonal winds (not shown).
2.3 Ships’ logbook data
The focus of this paper is on the wind data obtained from
ships’ logbooks, combining the data from the Climatologi-
cal Database for the World’s Oceans (CLIWOC) and digit-
ised EEIC records. Wind direction was observed on-board
ships using a 32-point compass and reported with respect
to the direction from which wind was blowing (Wheeler
2005). For wind force, a range of descriptive terminology
was used to make observations, as quantitative measures
of wind speed had not yet been introduced. The Beaufort
Force scale was not introduced into the Royal Navy until
1838, however prior to this there is evidence to suggest a
fairly standardized vocabulary for recording wind force was
already in place, with terms passed down through genera-
tions of sailors (Wheeler 2005). The various wind force
terms found within the logbooks of the different countries
were recorded in CLIWOC’s ‘Multilingual Meteorologi-
cal Dictionary’ (Garcia-Herrera etal. 2003). This included
British, Dutch, Spanish and French terms, with translations
carried out on a country by country basis. This reference
guide was also used for transforming the wind force terms
found within the additional EEIC logbooks (Brohan etal.
2012). These descriptive wind force terms were then trans-
formed into Beaufort Force equivalents, and finally into
modern SI units of ms−1 (Können and Koek 2005). The
wind force and direction observations were used to calcu-
late zonal winds for use in the reconstructions.
When combined, the CLIWOC and EEIC databases pro-
vide over half a million records globally over the period
1750 to 1854. However, 43% of these do not include obser-
vations of both wind force and direction. As both of these
variables are required to calculate zonal wind, only those
records with both wind force and direction are used here.
The spatial and temporal distributions of these 283,943
wind observations are shown in Figs. 1 and 2. Figure1
shows that logbook observations are concentrated along the
tracks of key historical shipping routes, which has resulted
in their uneven global distribution. The highest concentra-
tions of data points are found in the Atlantic and Indian
Oceans, and around the tip of South Africa. Very few
observations are found over the Pacific Ocean as there were
no regular shipping routes in this area during this period
(Gallego etal. 2005).
Figure2 shows the interannual variation in the number
of wind observations globally from 1750 to 1854. Although
the number of records increases in the period of EEIC data
coverage, 1789 to 1834, the number of records with wind
observations does not increase to a similar extent. This is
a result of CLIWOC focusing on obtaining wind observa-
tions whereas, digitisation of EEIC logbooks prioritised
temperature and pressure measurements (Brohan et al.
2012).
Although the coverage of observations appears highly
concentrated in Fig.1, the data density on a given day
is extremely sparse and is a key limitation to studies
using this data source at present. To overcome this data
sparsity, daily observations were averaged and aggre-
gated into seasonal values, and seasonal reconstructions
were carried out. Wind observations were averaged and
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848 H.G.Barrett et al.
1 3
aggregated into a 7.5° by 8° latitude–longitude grid,
matching the resolution of the re-gridded ERA-Interim
zonal winds. As ships often travelled in convoy, obser-
vations taken on the same day from within a given grid
box were averaged into a daily value and counted as one
observation for that day. Given that the logbook obser-
vations were taken at local noon, the effect of averaging
observations from the same day is not influenced by the
effects of the diurnal cycle.
3 Method
3.1 Selection ofpredictor grid boxes
Ideally, logbook data from the equatorial Pacific would be
used as this is the centre of action for ENSO. However, the
lack of logbook data in this core region results in depend-
ence on regions with strong ENSO teleconnections. To
identify these regions, Pearson’s correlation coefficients
were calculated between the re-gridded, seasonal ‘noon’
zonal winds from ERA-Interim and the seasonal Southern
Oscillation Index, 1979/80 to 2013/14. Both data sets were
detrended prior to calculation of correlations. Grid boxes
whose correlations were significant at the 90% significance
level were considered as potential grid boxes suitable for
use in the reconstructions. The correlation coefficients dur-
ing the DJF season are shown in Fig.3. Strong positive cor-
relations are found during this season across much of the
central and Eastern Indian Ocean, and the Maritime conti-
nent, a region with strong ENSO teleconnections (Aldrian
and Susanto 2003). During El Niño events high pressure
anomalies are typical over the Western Pacific and negative
zonal wind anomalies are typical over the Maritime Conti-
nent (Wang and Fiedler 2006). The opposite is the case for
La Niña events, with positive zonal wind anomalies typi-
cally in this region. These patterns lead to the large area of
positive correlation between the SOI and zonal wind over
the Maritime Continent and into the Indian Ocean. Log-
book data from this region of strong teleconnections is used
Fig. 1 Location of data entries from ships’ logbooks with both wind force and wind direction observations from the digitised archives of CLI-
WOC and EEIC, 1750–1854
Fig. 2 Yearly distribution of all logbook data entries (grey) and those
with wind vector observations (black), 1750–1854
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849
Reconstructing El Niño Southern Oscillation using data fromships’ logbooks, 1815–1854. Part…
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for the reconstruction. A significant ENSO signal is also
found within the zonal winds in other regions of the globe,
which means that logbook data from more remote regions
can also be exploited.
After analysis of regions with significant correlations,
the main factor limiting use of individual grid boxes for
reconstruction is the availability of wind data from log-
books, also indicated in Fig.3. Following extensive analy-
sis of the availability of DJF observations per grid box per
season, the nine grid boxes indicated in Fig.3 were selected
for use in the reconstruction. These span the eastern and
central Indian Ocean (grid boxes 5–9), around the coast of
South Africa (grid boxes 2–4) and one from the mid-Atlan-
tic (grid box1). There are significant correlations between
zonal winds and the SOI during DJF seasons in these grid
boxes, with coefficients ranging from +0.86 to −0.60. The
use of observations from different regions follows recent
ENSO reconstructions which have used proxy data from
multiple teleconnection regions to provide a more robust
ENSO signal (Braganza etal. 2009).
The logbook data availability from these nine grid boxes
during DJF seasons 1815/16 to 1853/54 are shown in Fig.4,
and back to 1750 in Supplementary Fig.1 (S1). There is an
average of 13 observations per grid box per season, how-
ever there is large interannual variability in the availability
of data. In a given year, the amount of data across the nine
grid boxes is also non-uniform. Prior to 1815 data availabil-
ity in these grid boxes is reduced and missing data hinders
reconstruction. There are only four seasons between 1750
and 1815 where data are available in all nine grid boxes
(1788, 1791, 1802 and 1811), therefore the years after 1815
were the focus of the ENSO reconstructions, with SOI
reconstructions for these four additional years available in
Supplementary Table1 (TableS1).
In order to provide as continuous a historical reconstruc-
tion as possible, seasons in which individual grid boxes had
less than three observations per season were infilled via
longitudinal interpolation. Following methods of previous
studies (Küttel etal. 2010; Hannaford etal. 2015), where
available, wind observations from adjacent grid boxes
which exceeded the selected number threshold were used.
Interpolation was carried out longitudinally as the boxes
to the east/west had better data availability than those to
the north/south due to the historic shipping routes in the
regions used. For example, for a given grid box if there
were less than the minimum threshold of observations for
the DJF season in a given year, then the DJF value from
that year in the adjacent grid box which exceeds the thresh-
old would be used, and the difference between the ERA-
Interim reanalysis climatological means of the two grid
boxes in the reanalysis applied, to gain a value for the grid
box with low observations. If not enough observations were
available, then the year was deemed missing.
3.2 Reconstruction andvalidation methods
Two common approaches to past climate reconstruction
are composite-plus-scale (CPS) and climate field recon-
struction (CFR) (Mann et al. 2005). Principal component
regression (PCR) is a well-established method of CFR and
is used in previous studies containing logbook data (Jones
and Salmon 2005; Küttel et al. 2010; Hannaford et al.
2015). CPS and CFR are complementary methods, with
CFR providing reconstruction of spatial patterns based
Fig. 3 Pearson’s correlation coefficient between de-trended mean
local noon DJF zonal wind and DJF Southern Oscillation Index,
1979/80–2013/14. Only those significant at 90% are shown. Grid
boxes used in the reconstructions are outlined and are numbered 1
to 9 from West to East. Black dots indicate location of DJF logbook
wind observations 1815/16–1853/54
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850 H.G.Barrett et al.
1 3
on assumptions of stationarity between the predictor and
large scale patterns, and CPS involving a simpler procedure
based on local statistical relationships (Mann etal. 2008).
Recent ENSO reconstructions have used both composite-
plus-scale and climate field reconstruction methods (Wil-
son etal. 2010; Emile-Geay etal. 2013). Here we carry out
reconstructions using both PCR and CPS methodologies
(see Supplementary info for a detailed methodology).
For both methods, seasonal zonal wind anomalies
were calculated using detrended, re-gridded ERA-Interim
zonal wind, over the period 1979/80–2013/14. ERA-
Interim zonal winds and DJF Southern Oscillation Index
are detrended prior to model fitting in order to avoid an
influence from modern trends in the reconstruction method.
However, after fitting and validation, the non-detrended
data are used to produce the reconstruction. Uncertainties
were calculated using ± 1.96 standard deviations of the
residuals from the fitting as the 95% confidence intervals
(PAGES 2K Consortium 2013).
3.3 Assessing reconstruction skill
A number of statistics were used to assess the quality of
the reconstruction models following the example of pre-
vious ENSO-reconstructions studies (Emile-Geay et al.
2013). Pearson’s correlation coefficients and coefficient of
Fig. 4 Number of days with observations per DJF season in the nine predictor grid boxes, 1815/16–1853/54
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Reconstructing El Niño Southern Oscillation using data fromships’ logbooks, 1815–1854. Part…
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determination were calculated between the fitting recon-
struction and the instrumental SOI. Cross validation was
also carried out using the same period as the model fitting
(1979–2013) to test the regression relationships (Wilks
2005). This method has been used in previous PCR-based
reconstructions where the period over which regressions
are derived is too short for other validation methods (Jones
and Widmann 2003; Hannaford etal. 2015). Reduction of
error (RE) skill scores were also calculated as an objec-
tive measure to assessing the performance of the statistical
reconstruction models (Cook etal. 1994). RE scores range
from −∞ to +1, where one suggests complete agreement
with the predictand, zero indicates that the reconstruction
is of same value as the climatology, and negative scores
suggest no meaningful information can be gained from the
reconstruction.
3.4 Threshold tests
Due to the limited availability of logbook data, seasonal
averages must be calculated using only a restricted num-
ber of days of observations. Previous logbook studies have
used different thresholds for defining the minimum number
of observations that should be used in the calculation of
seasonal averages. Küttel etal. (2010) and Neukom etal.
(2013) only used seasons with observations for at least
3days in each 8° × 8° grid box, whereas Hannaford etal.
(2015) used a threshold of at least one-tenth of the num-
ber of days in the season. The key aim is to find a balance
between high spatial resolution and a good signal-to-noise
ratio from the observations (Küttel etal. 2010). Here, we
present a new methodology for quantifying the uncertainty
of individual years within a historical reconstruction based
upon the number of logbook observations available per
season.
A minimum observation threshold test was carried out
to assess the impact of using only a few observations per
season from modern reanalysis data to calculate the sea-
sonal means. These thresholds ranged from 1day of obser-
vations per season per grid box up to 10days of observa-
tions. This range was chosen as it covers both the threshold
used by Küttel etal. (2010) and Neukom etal. (2013), and
that used by Hannaford etal. (2015). At each threshold, the
PCR and CPS methods were carried out 100 times over
the fitting period using seasonal datasets composed of ran-
domly selected daily mean values from ERA-Interim rea-
nalysis data. The eigenvectors for PCR, and the correlation
weights for CPS, were those calculated with the full sea-
sonal data set, as these are what are used in the historical
reconstructions. The seasonal mean was calculated using
the dataset obtained from the selected threshold number of
observations in each grid box. At each threshold (1 to 10),
the mean correlation coefficients between the reconstructed
SOI and the instrumental SOI, and the RE skill scores,
were calculated.
The minimum data thresholds tests are based on a
‘worst-case scenario’, where each grid box contains only
the selected minimum threshold number of observations.
In reality the number of logbook observations per grid box
is non-uniform (Fig.4), with thresholds being exceeded in
most of the grid boxes. Therefore, an additional test was
carried out to directly replicate the data availability in each
of the years of the logbook reconstruction. For each year of
the logbook reconstruction, fitting and validation was car-
ried out using the data availability for that given year. For
example, the data availability in 1815/16 DJF season in the
predictor grid boxes was replicated for all seasons through-
out the fitting period (1979–2013), and the RE score calcu-
lated. Therefore, each year of the historical reconstruction
has its own RE score assigned to it based on a fitting which
replicates its data availability throughout a 35-year period.
The RE scores obtained from this analysis were then used
to assign a reconstruction skill to each of the years of the
reconstruction, flagging up the years that need to be treated
with more caution than others. This is a new method that
can be applied to logbook reconstructions so as to pro-
vide an uncertainty value for each year within a historical
reconstruction.
The initial threshold tests, which investigated the skill
of the reconstruction methods using observations ranging
from one per grid box per season up to 10 observations
(see Supplementary info and TableS2 for detailed results),
found that CPS performs better then PCR when a limited
number of observations are available. In reality, the num-
ber of observations in each grid box is not the same in a
given year and varies throughout the time period. There-
fore, when replicating the actual data availability from the
logbook years more directly applicable RE scores for the
historical reconstructions are obtained. The RE value for
the individual year relates to the skill of a reconstruction
that could be carried out assuming the data availability dur-
ing that given year is replicated over the entire reconstruc-
tion period, and the results are shown in Fig.5.
When fully replicating the data availability of the period
1815/16–1853/54 during the fitting period the mean RE
score obtained is 0.32 for the PCR method and 0.60 for
CPS. The PCR RE scores are lower than the CPS RE
scores for the same data availability, but all are positive
except for 1819/1820. Any positive RE score is considered
successful, making the difference between success and fail-
ure very small in some cases (Cook etal. 1994). Positive
values closer to zero have less skill than those closer to one,
however all are classed as ‘skilful’. In order to account for
the reduced skill of positive values in close proximity to
zero, a threshold was selected, below which additional cau-
tion in the reconstruction should be taken. From the results
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852 H.G.Barrett et al.
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in Fig. 5, an RE score of less than 0.30 was selected as
the value at which a year within the historical reconstruc-
tion which should be treated with caution. All of the CPS
scores are higher than this threshold, whereas 13 of the
PCR scores are lower than 0.3 and therefore are highlighted
as years with additional uncertainty in the historical PCR
reconstructions.
3.5 Data origin andwind force terms
Prior to carrying out the reconstructions, the wind data
from the logbooks were analysed. Previous analysis of
the wind force conversion terms highlighted that transla-
tions were carried out on a country by country basis, and
that improvements could still be made on the calibration
of wind force terms between countries (Koek and Können
2005). As a result of this, the country of origin of the obser-
vations used in this study has been analysed. The temporal
coverage of observations from different countries varies
throughout the CLIWOC period. Dutch ships dominate the
later portion of the CLIWOC period and contributions from
UK logbooks to CLIWOC end in 1829. The additional dig-
itised EEIC observations only span from 1787 to 1834. The
observations from the grid boxes used in the reconstruc-
tions have been divided into the countries from which the
ships originate. Figure6 shows the Dutch and EEIC contri-
butions, which together make up 96% of the observations
used during the period 1815–1854. An additional 3% of
observations are taken from UK CLIWOC ships and <1%
from Spanish ships, which are not included in this section
of analysis. The period 1815–1833 is dominated by EEIC
observations (68%), whereas after 1833 all of the observa-
tions are from Dutch ships. This distinct shift in origins of
the data source could be important when interpreting the
resulting seasonal zonal winds and the reconstructed SOI.
To examine the differences in wind force terms used per
country, the frequency of Beaufort Force equivalent wind
terms used by the Dutch and EEIC ships during the period
Fig. 5 RE skill scores obtained from PCR (dark grey) and CPS (light
grey) reconstructions carried out over the fitting period, replicating
the logbook data availability from each of the DJF seasons, 1815–
1854. 1829/30 and 1844/45 are missing data years. Dashed line indi-
cates RE = 0.30
Fig. 6 The number of DJF wind observations from the regions used
in the SOI reconstruction which come from Dutch and English East
India Company during DJF seasons 1815/16–1853/54
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of data overlap, 1815–1833, is assessed in Fig.7. The data
from the nine grid boxes were grouped together, based on
similar climatology, into three sub-regions. These sub-
regions consist of tropical grid boxes (1, 7, 8 and 9), those
from the trade wind region (5 and 6) and grid boxes from
around the southern tip of South Africa (2, 3 and 4). The
Beaufort Scale ranges from 0 to 12, with higher numbers
relating to stronger winds. Looking at both the Dutch and
EEIC observations, grid boxes located in the tropics have
a higher frequency of low Beaufort Force values, and some
of the most extreme values are found in the grid boxes
around southern Africa.
Although the Dutch observations come from a smaller
sample size, the general distributions from the two
countries can be compared. A main feature of the distri-
butions is the almost complete lack of Beaufort Force 3
recorded in the EEIC data. It was used less than 0.01%
in the EEIC observations, whereas it is the most common
Beaufort Force found in the Dutch data, being used in
32% of the records. The EEIC observations are generally
of higher wind forces than the Dutch data. A two sam-
pled Kolmogorov–Smirnov test (Wilks 2011) was used in
each of the three sub-regions to test the null hypothesis
that the wind force data from the Dutch and EEIC ships
come from the same distribution. In all three regions, the
null hypothesis can be rejected at 1% significance level,
indicating a statistically significant difference in the
Fig. 7 Frequency of Beaufort-equivalent wind force terms used by the Dutch and EEIC in the a Tropics (grid boxes 1 and 7–9) b trade wind
region (5–6) and c southern African grid boxes (2–4)
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854 H.G.Barrett et al.
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distributions of the wind force terms used by the Dutch
and EEIC from within the same regions.
These preliminary investigations highlighted the differ-
ence in wind force terms used by the different countries,
and the dominance of Dutch data in the later part of the
record. As a result, this was taken into account during the
ENSO reconstructions. Firstly, reconstructions were car-
ried out using a climatological mean calculated over the
entire period, 1815–1854 (known as variant ‘A’ later). Fol-
lowing this an alternative method was employed which
used two separate means, one for the period dominated by
British records, 1815–1833, and one for the later period,
dominated by Dutch records, 1834–1854 (variant ‘B’). This
second method aims to address the impact of using data
from countries with differences in the terminology for wind
force observations.
4 Results
4.1 Reconstruction skill
The reconstruction skill of both the PCR and CPS meth-
ods were assessed over the fitting period, 1979–2013,
using the re-gridded ERA-Interim data and instrumental
Southern Oscillation Index for the DJF season. Figure 8
shows the SOI reconstructions along with the instrumental
SOI for this period. Due to differences in the methodolo-
gies, the CPS method produces a normalised reconstruc-
tion, whereas the PCR method produces a reconstruction
with variability similar to the non-normalised instrumental
record.
For the PCR method, Principal Components 1, 2 and 6
were retained due to their high correlation with the instru-
mental SOI. The coefficient of multiple determination dur-
ing the fitting period is 0.89 (significant at the 1% level)
(Fig.8), thus 80% of the variance of the SOI is explained
by the PCR model. The correlation between the PCR-
reconstructed SOI and the detrended instrumental SOI
from cross validation is 0.86, with an RE score of 0.73. For
the CPS reconstruction, a correlation of 0.87 (significant at
the 1% level) was found during the fitting period, thus 75%
of the variance is explained by the CPS method (Fig.7).
For both methods, when comparing the non-detrended SOI
with the reconstructed SOI, high RE values are obtained,
with a RE score of 0.80 for the PCR method and 0.75 for
the CPS method. Overall, the PCR method has slightly
higher reconstruction skill than the CPS, however both per-
form well and are therefore both used to carry out historical
reconstructions.
4.2 Modern wind patterns duringstrong El Niño
andLa Niña events
To test these modern reconstructions further and to see if
the selection of predictor grid boxes is physically sensi-
ble, the wind patterns from some of the strongest ENSO
events in this record were analysed. The DJF zonal wind
anomalies in two recent strong El Niño events, 1982/83
and 1997/98, and two strong La Niña events, 1998/99
Fig. 8 Instrumental DJF Southern Oscillation Index and reconstructed DJF SOI using a principal component regression and b composite-plus-
scale (normalised values), 1979/80–2013/14. Dashed lines represent ± 1 standard deviation, the threshold used to define the main ENSO events
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and 2010/11 are shown in Figs.9 and 10, respectively.
Although the 1982/83 and 1997/98 El Niño events were
both strong events, the difference in the strength of the
wind anomalies in predictor grid boxes is clear from
Fig.9. The negative anomalies in the Indian Ocean are
stronger in 1997/98, whereas there is a much larger
region of positive anomalies around the tip of Southern
Africa in the 1982/83 event. Therefore, using grid boxes
from both these key regions in the historical reconstruc-
tions increased the chance of identifying key anomalous
years in the past. The same is true for La Niña events
(Fig.10), with much stronger positive anomalies in the
Fig. 9 DJF zonal wind anomalies (ms−1) during El Niño events in 1982/83 (left) and 1997/98 (right), relative to 1979–2013 climatology. Predic-
tor grid boxes used in the reconstruction are outlined in black
Fig. 10 DJF zonal wind anomalies (ms−1) during La Niña events in 1998/99 (left) and 2010/11 (right), relative to 1979–2013 climatology. Pre-
dictor grid boxes used in the reconstruction are outlined in black
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856 H.G.Barrett et al.
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eastern Indian Ocean in the 2010/11 event, compared to
the 1998/99 event.
Even though the expected anomalies are not found in all
of the grid boxes used in the four events, the reconstructed
SOI for these years show values very close to the true SOI
(Fig. 8). In most cases, the sign of the wind anomalies
in the predictor grid box fits with what is expected from
the correlations indicated in Fig. 3. Therefore, the use of
anomalies from different regions is beneficial for the recon-
struction approach and helps provide a more robust ENSO
signal.
4.3 Historical ENSO reconstructions
Following, fitting and validation of both reconstruction
methods, historical reconstructions were carried out,
using the model fitted over the whole period. Figure11
shows the DJF SOI reconstructions using PCR and CPS
over 1815–1854. Variant A reconstructions, (PCR A and
CPS A) were calculated using a climatological mean from
the entire period, 1815–1853, whereas, variant B (PCR
B and CPS B) were calculated using two climatological
values, one covering 1815 to 1833 and one for the later
period 1834 to 1854. This takes into account the change
in data source from mostly EEIC in the earlier period to
all Dutch records after 1833. Years with RE scores less
than 0.3 are indicated with the historical PCR reconstruc-
tions (light grey bars), taking into account the results of
the minimum data availability threshold testing.
Table1 shows the correlation coefficients between the
four historical reconstructions. The four reconstructions
are all significantly correlated and show a high degree
of similarity to each other. The strongest correlations
are found between the two CPS reconstructions, r = 0.88,
followed by the two PCR reconstructions, r = 0.86. How-
ever, correlations between the reconstructions using
the different methods remain high, suggesting good
agreement between the two reconstruction methodolo-
gies. Within the PCR reconstructions, some of the more
Fig. 11 Reconstructions of DJF SOI 1815/16–1853/54 using prin-
cipal component regression (PCR) and composite-plus-scale (CPS)
(normalised), with a the same mean throughout and b using 1815–
1833 and 1834–1853 means. Thin lines show the 95% confidence
intervals. Light grey bars indicate years with RE scores less than 0.3.
No data for 1829/30 and 1844/45
Table 1 Pearson’s correlation coefficients between historical recon-
structions of DJF SOI 1815/16–1853/54 using principal component
regression (PCR) and composite-plus-scale (CPS) (normalised), with
(a) the same mean throughout and (b) using 1815–1833 and 1834–
1853 means
All significant at 1% level
PCR A PCR B CPS A CPS B
PCR A –
PCR B 0.86 –
CPS A 0.81 0.65 –
CPS B 0.67 0.75 0.88 –
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extreme events are those with additional uncertainty
(i.e. RE <0.3). However, these events are still classified
as main ENSO events in the CPS reconstructions, where
reconstruction skill is higher. Therefore, similar signals
are being provided by the two methods.
In order to assess the differences between the reconstruc-
tions, additional statistical analysis was performed. Least
squares fitting was carried out to identify any statistically
significant trends in the four reconstructions. A negative
trend in SOI over the reconstruction period is suggested
from Fig.11, with the SOI generally decreasing during the
period 1815/16 to 1853/54. Results from linear regression
for the four reconstructions found that a statistically signifi-
cant (p < 0.05) negative trend is found when only one cli-
matology is used throughout the entire period, for both the
PCR and CPS methods (TableS3). When variant B is used,
to take into account the change in dominance from EEIC to
Dutch observations, this significant linear trend is no longer
present in either of the statistical reconstructions.
Analysis of the mean and two-sampled t tests was car-
ried out to assess the difference in SOI before and after
1833/34 from each of the four reconstructions (TableS4). It
was found that the reconstructions without the adjustments
in the mean (Variant A) have significantly different SOI in
the earlier period compared to the later period. In contrast,
for those reconstructions where adjustments to the mean
were made (Variant B), this statistically significant differ-
ence was not found. Therefore, the impact of using logbook
data from two periods dominated by countries which use
different wind force terminology is significant and should
be addressed.
To identify the main ENSO events from these recon-
structions, El Niño events were classified as seasons with
a negative SOI value more than one standard deviation
from the mean, and La Niña as a positive SOI more than
one standard deviation from the mean (Stahle etal. 1998).
Figure12 shows DJF seasons classified as El Niño and La
Niña in the four reconstructions. Two events are found in
all four of the reconstructions and have the most extreme
SOI values, an El Niño event in 1851/52, and a La Niña
event in 1836/37. These are named as high confidence
events. Attention is then given to those ENSO events found
in at least three of the four logbook reconstructions, which
are seen as medium confidence events. Between 1815/16
and 1853/54, five additional events fall into this category;
two La Niña events 1818/19 and 1819/20, and three El
Niño events 1835/36, 1842/43 and 1847/48. Taking into
account the significantly more positive SOI values found in
the reconstructions of variant A prior to 1834, the criteria
for medium confidence events is relaxed to only the two
reconstructions using variant B needed to classify El Niño
events prior to 1834. This results in two additional El Niño
events, 1824/25 and 1830/31. Grouping together these high
and medium confidence events, there are six main El Niño
events and three main La Niña events during the logbook
reconstruction period.
The zonal wind anomalies in the predictor grid boxes
in the two high confidence events are shown in Fig. 13.
There is a clear difference in the signal between El Niño,
1851/52 and La Niña, 1836/37. In 1851/52, negative zonal
wind anomalies are found in the three grid boxes closest to
Indonesia. This negative wind anomaly was also apparent
in the more recent El Niño 1997/98 and in a similar region
in 1982/83 (Fig.9). In contrast, strong positive zonal wind
anomalies are found in this region during the historical La
Niña 1836/37, as well as modern events in 2010/11 and
1998/99 (Fig.10).
The El Niño signal from the grid boxes around the
southern tip of Africa is of positive wind anomalies,
which is seen more strongly in the 1982/83, modern event
than the 1997/98 event, however such a signal fits with
that suggested from the modern correlation coefficients
between zonal wind and SOI in this region (Fig.3). The
signal from these grid boxes is less clear, in the 1836/37
La Niña event, as negative anomalies would be expected.
However, this raises the issue of inter-event diversity
of ENSO, which is returned to in PartII (Barrett etal.
2016). The spatial pattern of wind anomalies during
Fig. 12 Frequency of El Niño
(negative SOI events) and La
Niña (positive SOI events)
in logbook reconstructions,
1815/16–1853/54
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858 H.G.Barrett et al.
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different events are not always the same, therefore using
data from various regions helps to identify different sig-
nals. However, overall similarities are found in the typi-
cal spatial wind patterns from both historical and modern
events.
Additional caution is needed when analysing those years
with PCR RE scores less than 0.3. This additional level
of uncertainty will be discussed further in Barrett et al.
(2016). The limited logbook data availability from these
2years resulted in RE scores less than 0.3 when using the
PCR method, and are therefore flagged as more uncertain
years within the reconstruction. However, the CPS recon-
structions in these years also indicate strong ENSO events,
which supports the classification of these events within the
historical record.
5 Discussion
In the companion paper (Barrett etal. 2016) we examine
how our new logbook-based SOI reconstructions compare
to previous proxy and documentary ENSO reconstructions,
and discuss the implications of this comparison for the
nature of ENSO reconstruction methods. However, here we
discuss the limitations and strengths of our methodology,
and also compare our reconstruction with a previous SOI
reconstruction which is also based on direct meteorological
measurements (Können etal. 1998).
5.1 Difference inwind force terminology
An important finding of this paper is the statistically sig-
nificant difference between the distributions of wind force
terms used by the Dutch and EEIC ships. A shift from using
observations from mostly EEIC to entirely Dutch records
during the period of reconstruction is found, because of the
use of both EEIC and CLIWOC data, but in part influenced
by the spatial domain. The majority of previous logbook
studies focus on the North Atlantic and only use CLIWOC
data (Gallego et al. 2005; Küttel et al. 2010). Hannaford
etal. (2015) used both CLIWOC and EEIC data from grid
boxes around southern African region, but no clear shift
was identified in their study.
The major difference in the distributions of wind force
terms between the Dutch and EEIC observations is the very
limited appearance of Beaufort Force 3 (BF3) in the British
records but the common use of it in the Dutch records. Pre-
viously, Koek and Können (2005) highlighted a difference
in the frequency of Beaufort force values used by different
CLIWOC countries, especially in the range of Force 3 to 5.
Beaufort Force 3 is described as a ‘Gentle Breeze’ under
current British classification, and the CLIWOC multi-lin-
gual dictionary found the following historic terms to be rep-
resentative of equivalent wind force: ‘gentle breeze’, ‘gen-
tle gale’, ‘gentle trade’, feint gale’, ‘light gale’ and ‘easy
gale’, with the latter two uncommon after 1700 (Garcia-
Herrera etal. 2003). Of the 22,000 British wind force terms
analysed by CLIWOC, less than 0.5% were BF3 equivalent
Fig. 13 La Niña, 1836/37, (left) and El Niño, 1851/52, (right) zonal wind anomalies (ms−1), relative to 1834/35–1853/54 DJF climatology
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terms (Wheeler and Wilkinson 2005). Of the observations
used in the SOI reconstruction, BF3 was only recorded
twice in the British wind force records. The reason for this
limited use of BF3 by the British ships is unknown. As
the Beaufort terms are conversions of descriptive terms by
CLIWOC, it may either be because BF3 equivalents were
under recorded in the original logbook observations, or that
BF3 equivalents were under-converted by CLIWOC. Deter-
mination of which of these is true, is outside of the scope
of this paper. In contrast to the British records, Koek and
Können (2005) found that Beaufort Force 3 is over-repre-
sented in the Dutch records, and this is supported in the
results of this paper. The Beaufort Force 3 equivalent terms
in Dutch all include the term ‘bramzeilskoelte’ which refers
to the topgallant sail, the upper-most sail that could be used
at this wind speed (Koek and Können 2005).
It was also found that in general, the Beaufort Force
terms used by the EEIC were higher than those used by
the Dutch within the same geographical regions over the
same time period. One reason for this is that the method
of describing wind force used by the Dutch was distinctly
different than that used by EEIC and other countries from
within the CLIWOC database. The Dutch method of wind
force observations relied heavily on the wind’s influence
on the sails used, which provides a more explicit means of
determining the order of these terms, making them more
refined than is possible in the other languages (Garcia-
Herrera etal. 2003). In contrast the British relied more on
the state of the sea to describe wind force (Wheeler and
Wilkinson 2005). This different approach to wind determi-
nation is suggested as the likely reason for the differences
in the distribution of wind force terms used.
Another reason for this difference could be that some
of the Dutch CLIWOC data post 1826 are taken from
‘Extract Logbooks’, meteorological summaries of original
logbook data. These Extract Logs were not made until the
1860s, after the adoption of the Beaufort scale in the Neth-
erlands (Koek and Können 2005). The conversions from
wind force terms to Beaufort scales were carried out at the
time the Extract Logs were created, and these translations
were found to be of better quality than those done by the
CLIWOC team (Koek and Können 2005). Therefore, the
wind force terms taken from these logbooks could be more
reliable.
The implications of higher wind force terms from EEIC
than Dutch records is a bias towards stronger winds in the
period dominated by EEIC and weaker winds in the period
of Dutch observations. A tendency for stronger winds
in the first half of the record could promote an increased
frequency of La Niña events in the first part of the recon-
struction, as during La Niña events stronger westerly
winds are typical in the predictor grid boxes closest to the
Maritime continent. This was found in all of the historical
reconstructions carried out, but more so in those in which
the difference in wind force observations was not taken
into account. Seven of the eight La Niña events found in
the PCR A reconstruction were prior to 1831, thus within
the period dominated by stronger EEIC observations and
all four of the El Niño events in this reconstruction were
post 1835.
The impact of relying solely on Dutch logbooks post
1834 has not been investigated in previous papers which
carried out climate reconstructions using logbooks over
this period. Wheeler (2005) carried out an assessment of
the coherency of wind observations between vessels sail-
ing in convoy and found good agreement. However, a major
limitation in this analysis was the tendency for ships in con-
voy to be from the same country rather than from a mix of
nationalities and therefore a mix of recording approaches.
The difference between wind observations from different
countries has been highlighted here as a potential source
of inconsistency and an area that requires further investi-
gation. Less than 5% of British logbooks were exploited
by CLIWOC and none after 1828, therefore a vast amount
of records are available for future digitisation, as outlined
in a recent survey of logbooks in UK archives (Wilkinson
2009). Digitisation of these British records would result
in the ability to carry out reconstructions using data from
solely British sources. An analysis of wind force terms
and reconstructions using only British observations would
enable a fuller assessment of the impact of using a split of
Dutch and British records.
5.2 Difference inreconstruction methods: PCR
andCPS
Reconstructions using both PCR and CPS were carried out
to assess the impact of using different methodologies on
the resulting historical reconstructions. Recent multi-proxy
ENSO studies employed both techniques and found no
major differences between the ENSO reconstructions found
using CPS and PCR (Wilson etal. 2010). The two meth-
ods can be seen as complimentary approaches (Mann etal.
2008). Both methods assume stationarity in the statistical
relationships over time, a common limitation in studies of
the past (Gergis etal. 2006). Results from this paper sug-
gest good agreement between the two approaches and both
provided good reconstruction skill over the fitting period.
Nevertheless, it is worth pointing out that the PCR method
has slightly higher skill during the fitting period. Although
correlations are strong between reconstructions, the ENSO
event chronologies differ significantly between reconstruc-
tions. Only two ENSO events of high confidence were
found: La Niña 1836/37 and El Niño 1851/52. An addi-
tional, seven medium confidence events were found: five El
Niño (1824/25, 1830/31, 1835/36, 1842/43 and 1847/48)
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860 H.G.Barrett et al.
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and two La Niña events (1818/19 and 1819/20). A compan-
ion paper (Barrett etal. 2016) will analyse the discrepan-
cies in these events-based chronologies further.
5.3 Threshold tests onnumber ofobservations
perseason
A new method of threshold testing was carried out to assess
the reconstruction skill using a limited number of obser-
vations. Results showed that, as would be expected, the
higher the number of observations used per season, the bet-
ter the reconstruction skill. Nevertheless, these tests dem-
onstrated that even with very few observations reasonable
reconstruction skill can be obtained, with the CPS method
performing better than PCR with very limited data avail-
ability. Additional analysis which replicated the logbook
data available in the predictor grid boxes used in the his-
torical reconstruction, enabled the reconstruction skill in
each year to be evaluated, and identified years within the
PCR reconstructions where reconstruction skill is lower
than RE = 0.3. It is noteworthy, however, that the CPR
reconstruction for all years had an RE significantly greater
than 0.3 (Fig.5). Overall, the threshold results showed that
valuable reconstructions can be obtained even when using
limited data sources. However, the benefit of further dig-
itisation to increase data density of logbook wind observa-
tions is clear, as reconstruction skill can be increased if the
number of observations per season can be increased.
5.4 Comparison toJakarta based record oftheSOI
While PartII, focuses on a comprehensive comparison with
previous ENSO reconstructions, here we present a com-
parison of one of the logbook SOI reconstructions (CPS
B) with the Jakarta rain-day SOI (Können et al. 1998;
and single site pressure SOI, Fig S2; TableS5), as both
are from direct observations and can be used together to
obtain a more robust ENSO signal from this period. The
Jakarta-rain day SOI spans the Indonesian dry season,
June–November and the SOI was calculated from a con-
tinuous road maintenance record of dry days in Jakarta,
1830–1850. CPS B is focused on for comparison, as the
CPS methodology has the higher skill when using sparse
data availability (Fig.5) and variant B accounts for the dif-
ference in wind force terms between the earlier and later
part of the reconstruction period. Figure14 shows the log-
book CPS B DJF reconstruction along with the Jakarta
rain-day SOI, which are significantly correlated (95% sig-
nificance level). Over the period 1830 to 1850 the strong-
est El Niño event in CPS B is during 1833/34. The Jakarta
rain-day SOI also indicates 1833 to be the most extreme
negative SOI (El Niño) during this period. The strongest La
Niña in the CPS B reconstruction during this period is in
1836/37 (and extends into 1837/38). In 1837, the strongest
positive SOI (La Niña) is found in the Jakarta rain-day SOI.
Therefore, there is a good match between the two most
extreme ENSO events during this period. The strong agree-
ment between these records suggests that there is a robust
signal from these early instrumental based reconstruc-
tions of SOI, giving additional confidence to our logbook
reconstructions.
6 Conclusions
Here we present the development of ENSO reconstruc-
tions using wind observations from ships’ logbooks. Tem-
perature and pressure data from ships’ logbooks provide an
additional source of data, but are also subject to data availa-
bility issues, and additional sources of measurement uncer-
tainty. Therefore, they are not used in the reconstruction
approach presented here. Thus, we have built upon Jones
and Salmon’s (2005) preliminary logbook SOI reconstruc-
tion by using a larger amount of data, a focus on regions
with strong ENSO teleconnections and an analysis of
multiple methods of reconstructions. Two different recon-
struction methodologies were used, PCR and CPS and
both were found to be suitable methods for reconstruction
ENSO using data from ships’ logbooks.
We developed methodologies to assess the impact of
data availability on reconstruction quality. We found that
good reconstruction skill can be obtained with limited data
availability. As expected, the reconstruction skill increases
with the number of observations available. However, it
was found that statistically significant skill can be obtained
when using surprisingly few observations per season. We
expect that these results will be region specific, reflecting
Fig. 14 CPS B logbook DJF SOI (1830/31–1850/51) and Jakarta
rain-day June–Nov SOI (1830–1850). Pearson’s correlation coeffi-
cient (r) shown in top right
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the ability of few observations to capture seasonal climate
in a given region, and we recommend that this analysis is
repeated by researchers developing reconstructions using
logbook data from other geographical regions.
We identified a statistically significant difference in the
mean and distribution of the basic Dutch and EEIC wind
force observations, which leads to a potentially spuri-
ous trend in the reconstructions. We address this by split-
ting the study period into two, an earlier period domi-
nated by British records (1815/16–1833–34) and a later
period from which only Dutch observations are available
(1834/35–1853/54). For these two periods, different clima-
tological values were calculated and applied to the recon-
struction methods, thus producing two reconstructions for
each statistical reconstruction method. The resulting histor-
ical SOI time series had strong correlations with each other,
however some differences were found when converting this
into an events-based chronology. The most robust CPS B
logbook reconstruction compares well to the Jakarta rain-
day SOI, (Können etal. 1998) which spans much of the
reconstruction period. The strong agreement between these
two records indicate a common signal from direct observa-
tions, which together can be used to obtain a more robust
understanding of ENSO during this period. In Barrett etal.
(2016) we compare both sets of reconstructions against
existing reconstructions, to determine which method pro-
duces a reconstruction with a higher agreement to existing
reconstructions.
Digitisation of additional logbook records would ena-
ble a higher number of observations to be used. The SOI
reconstruction skill largely depends on the data availabil-
ity during a given year, as it varies between grid boxes and
seasons. Therefore, the number of observations needed to
obtain good reconstruction skill may be different in other
seasons and/or geographical regions with different clima-
tological variability, and should therefore be assessed on an
individual basis. Fig. S1 shows, for example, that the cur-
rent SOI reconstruction could be greatly extended in tem-
poral length, in principal back to 1750, with targeted addi-
tional digitisation of existing logbooks. There thus remains
a potentially rich source of marine records for studies of
past climate variability.
Acknowledgements We thank Matthew Hannaford for initial assis-
tance regarding the logbook data and the University of Sheffield for
supporting this research.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict
of interest.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted
use, distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were
made.
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