CPClimate of the PastCPClim. Past1814-9332Copernicus PublicationsGöttingen, Germany10.5194/cp-13-267-2017Was the Little Ice Age more or less El Niño-like than the Medieval
Climate Anomaly? Evidence from hydrological and temperature proxy dataHenkeLilo M. K.LambertF. HugoCharmanDan J.Department of Geography, College of Life and Environmental Sciences,
University of Exeter, Amory Building, Rennes Drive, Exeter, EX4 4RJ, UKDepartment of Mathematics, College of Engineering, Mathematics and
Physical Sciences, Harrison Building, Streatham Campus, University of Exeter,
North Park Road, Exeter, EX4 4QF, UKLilo M. K. Henke (lmkh201@exeter.ac.uk)29March201713326730121October201526November201512February20173March2017This 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://cp.copernicus.org/articles/13/267/2017/cp-13-267-2017.htmlThe full text article is available as a PDF file from https://cp.copernicus.org/articles/13/267/2017/cp-13-267-2017.pdf
The El Niño–Southern Oscillation (ENSO) is the most important source of
global climate variability on interannual timescales and has substantial
environmental and socio-economic consequences. However, it is unclear how it
interacts with large-scale climate states over longer (decadal to centennial)
timescales. The instrumental ENSO record is too short for analysing long-term
trends and variability and climate models are unable to accurately simulate past ENSO
states. Proxy data are used to extend the record, but different
proxy sources have produced dissimilar reconstructions of long-term ENSO-like
climate change, with some evidence for a temperature–precipitation
divergence in ENSO-like climate over the past millennium, in particular
during the Medieval Climate Anomaly (MCA; AD ∼ 800–1300) and the Little
Ice Age (LIA; AD ∼ 1400–1850). This throws into question the stability
of the modern ENSO system and its links to the global climate, which has
implications for future projections. Here we use a new statistical approach
using weighting based on empirical orthogonal function (EOF) to create two new
large-scale reconstructions of ENSO-like climate change derived independently
from precipitation proxies and temperature proxies. The method
is developed and validated using model-derived pseudo-proxy experiments that
address the effects of proxy dating error, resolution, and noise to improve
uncertainty estimations. We find no evidence that temperature and
precipitation disagree over the ENSO-like state over the past millennium, but
neither do they agree strongly. There is no statistically significant
difference between the MCA and the LIA in either reconstruction. However, the
temperature reconstruction suffers from a lack of high-quality proxy records
located in ENSO-sensitive regions, which limits its ability to capture the
large-scale ENSO signal. Further expansion of the palaeo-database and
improvements to instrumental, satellite, and model representations of ENSO are
needed to fully resolve the discrepancies found among proxy records and
establish the long-term stability of this important mode of climatic
variability.
Introduction
The El Niño–Southern Oscillation (ENSO) is the most influential source of
interannual variability in the modern climate. The warm El Niño state is
characterised by a weaker sea surface temperature (SST) gradient across the
equatorial Pacific and a shift in precipitation from the western Pacific
toward the central Pacific, while the cool La Niña state is roughly the
opposite. Although ENSO originates in the tropical Pacific, it has
far-reaching effects through teleconnections on some regions in higher
latitudes, and El Niño years are generally anomalously warm on a global
scale. However, it is unclear whether there is a link between anomalously
warm or cool periods and the two ENSO states on decadal to centennial
timescales. Given the severe socio-economic consequences of ENSO events
, and a warmer future under
continued anthropogenic warming, it is important to understand the natural
long-term ENSO and its interaction with the climate. It allows for an
evaluation of the effects of anthropogenic impacts on recent and future ENSO
behaviour .
Recent multi-model studies of projected changes in ENSO under anthropogenic
warming suggest robust changes to ENSO-driven temperature and precipitation,
including an increase in extreme El Niño and La Niña
events and changes in the ENSO SST pattern and ENSO-driven
precipitation variability . However, most current general
circulation models (GCMs) cannot simulate many aspects of the modern-day ENSO
accurately, often overestimating the western extent of the Pacific cold
tongue and failing to correctly simulate central Pacific precipitation
anomalies, ENSO feedbacks, and ENSO amplitude .This translates into uncertainty over
simulations of past ENSO-like climate change, calling for alternative sources
of climatic information to supplement, complement, and corroborate the model
and instrumental data. This is done using proxy climate records such as tree
ring widths, tropical ice cores, sediment cores, and corals .
There are very few annually resolved proxy records available longer than
∼ 500 years . The issue with high-resolution proxies is
that they tend to be short in length; trees and corals, for example, rarely
live beyond a few centuries . Some such highly resolved
records are available for the more distant past, but these generally offer
snapshots rather than continuous records
. However, there are several long,
lower-resolution proxy records of ENSO variability on decadal to millennial
scales, often derived from lake sediments see, marine
sediments see, or speleothems see.
While these are unable to capture the interannual frequency and amplitude of
individual ENSO episodes, they provide an insight into longer-term ENSO-like
climate states and average ENSO behaviour. Although there are some endeavours
to combine some low-resolution proxies, often to capture spatial gradients
, there has not, to our knowledge,
been a comprehensive effort to systematically merge a large set of such
low-resolution records to create a long-term reconstruction of ENSO-like
climate variability. Doing this could shed light on the long-term stability
of ENSO and its links with the wider climate, for example by examining ENSO
behaviour under different dominant cool or warm climate states, which in turn
can inform our understanding of potential future ENSO-like changes in a
warmer world.
A number of proxy, instrumental, and modelling studies investigate links
between ENSO and global climate variability on interannual , decadal , centennial
,
and millennial timescales. A
wide range of proxy records and modelling studies point to substantial shifts
in the ENSO-like state of the climate linked to changes in solar variability
on orbital timescales , movement of the
Intertropical Convergence Zone ITCZ;, and changes in ocean circulation linked to sea level
rise . In the more recent past, changes in
solar irradiance and stratospheric aerosol loadings due to volcanic activity
have played significant roles in modulating the hemispheric to global scale
climate . The so-called Medieval Climate Anomaly (MCA) and
the Little Ice Age (LIA), generally defined to fall within ca. AD 900–1300
and AD 1300–1850, respectively , are two periods of climate
upheavals based on Northern Hemisphere (NH) climate variability. The MCA was a
period of anomalously warm conditions, while the LIA was anomalously cool, at
least in the Northern Hemisphere . These two
periods are often used for exploring past behaviour of climatic phenomena as
they represent relatively large and sustained excursions from the long-term
mean. However, a comparison of hemispheric temperature reconstruction
and continental-scale temperature reconstructions
finds no evidence of a globally coherent MCA and only
partial evidence for a global LIA. Neither of these two studies focus
specifically on the equatorial tropics, leaving open the question of the
strength of a potential MCA and/or LIA in these latitudes. This is an
important knowledge gap because proxy evidence for the expression of ENSO-like
climate change over these periods appears to be ambiguous. A range of proxies
point to a more northerly ITCZ
during the MCA, which is characteristic of La Niña-like conditions and is
in agreement with warming patterns found in multi-proxy reconstructions of
hemispheric and global-scale temperature see.
similarly infer a reduction in El Niño-like activity
during the MCA based on ocean basin ventilation changes in Indonesia. In
contrast, a Southern Oscillation Index reconstruction based on two proxy
records shows an El Niño-like state during the MCA
(defined as AD 800–1200) and a La Niña-like LIA (AD 1300–1850). This seems
to be supported by a number of other precipitation proxies from the West
Pacific and East Pacific . Other
precipitation proxies indicate a highly variable ENSO during the LIA,
including two multi-decadal droughts in Java , high-amplitude rainfall fluctuations in Madagascar , and three
southerly ITCZ excursions .
The ENSO-like state of the climate may be linked to the Interdecadal Pacific
Oscillation (IPO). The IPO is characterised by a Pacific SST anomaly pattern
resembling ENSO , which oscillates on decadal timescales. The
North Pacific section (20–45∘ N) is often referred to as
the Pacific Decadal Oscillation (PDO). The interactions between the PDO/IPO
and ENSO are still not well understood beyond a statistical relationship
, but there is evidence for interactions between ENSO
behaviour and PDO state on multi-decadal timescales over the
instrumental period and further
back in time . Some studies suggest that these
decadal oscillations are essentially integrated long-term expressions of ENSO
and that they can be explained by stochastic ENSO
fluctuations on decadal timescales . A phase change
analysis of palaeoclimate data over the past 400 years
finds a tendency for more frequent El Niño events during positive PDO
phases and more frequent La Niña events during negative PDO phases. Over
Australia, the IPO phase modulates the strength of ENSO influence on rainfall
extremes, with a more pronounced effect of La Niña during negative IPO
. The spatiotemporal robustness of the IPO is questionable
as South Pacific coral records find low spatially coherent
IPO related prior to the mid-1880s, suggesting that the spatial pattern may
have changed. Additionally, documentary rainfall data from China
indicate that while the PDO has existed for at least
∼ 530 years, its periodicity changed from 75–115 years pre-1850 to 50–70 years post-1850.
The discrepancies in long-term ENSO-like variations between proxy records
raise two important questions. The current dynamical understanding of ENSO is
underpinned by the strong relationship between temperature and rainfall
observed today and the relationships between the ENSO source region in the
tropical Pacific and teleconnected regions, which largely fall between
40∘ S and 40∘ N. As highlight, however,
temperature and precipitation proxies appear to disagree on the ENSO-like
states of the MCA and the LIA. Thus, to what extent does the modern-day
precipitation–temperature relationship in the source and teleconnected
regions continue to exist in the past? The second question concerns the
relation of ENSO to the wider climate; is there a link between global
temperatures and long-term ENSO state on multi-decadal to centennial
timescales? A comparison between the MCA and the LIA can give some insight
into this and may hold some clues to what we can expect under anthropogenic
climate change.
The use of proxy archives can contribute valuable insights on past climate
variability by extending the instrumental records back in time, but
substantial uncertainties remain. This is because all reconstructions have
inherent limitations and ambiguities that must be identified and dealt with
appropriately. These include resolution, dating errors, noise, limited and/or
skewed spatial coverage, and nonlinear responses to the climatic variable of
interest . Various statistical techniques have been employed
to create multi-proxy reconstructions of climatological phenomena, broadly
falling into the categories “composite plus scaling” (CPS) or “climate field
reconstruction” (CFR) . CPS encompasses any method that
involves combining standardised proxy records into a single reconstruction,
which is subsequently calibrated to a known time series of the target variable
(e.g. instrumental temperature record) to provide a quantitative estimate of
the variable. CFRs, on the other hand, aim to reconstruct large-scale spatial
patterns of climatic change using covariance between proxies and instrumental
data. Within both methods there is a wide variety of approaches; see
for detailed descriptions and examples of both CPS and a
range of CFR methods. The focus of this study – comparing the climate
signals in temperature and precipitation proxy records separately – calls
for a slightly different approach.
Here we create two new ENSO reconstructions, one derived from temperature
proxies and one from precipitation proxies, using a new method for assessing
the stability of the modern-day ENSO patterns in the source region and the
wider teleconnected regions. In a fashion similar to
, for example, proxy records are not tuned to instrumental data other
than a simple location-dependent weighting. While this precludes direct
quantitative comparisons, it removes the bias towards high-frequency trends
that stems from calibrating to the relatively short (∼ 150 year)
instrumental record or indeed any short
record;. The method amplifies the ENSO component of
proxy records and simultaneously attempts to quantify uncertainty related to
noise and incomplete spatio-temporal data coverage, whilst maximising the use
of a wide range of tropical proxies. With this, we aim to answer two
questions:
Do temperature and precipitation proxies show consistent long-term ENSO behaviour over the last millennium?
Do the LIA and the MCA differ significantly in their mean ENSO state?
Section provides a description of the proxy and
instrumental–reanalysis data used in this study, and a concise overview of the
methodology is given in Sect. . The results and discussion of
the findings are presented in Sects.
and respectively before revisiting the research
questions and making concluding remarks in Sect. .
Data descriptionProxy records
For this study, a comprehensive effort was made to collect all published
proxy precipitation and temperature records between 40∘ S and 40∘ N that cover the last 2000 years. The large majority of records
were accessed from the NOAA Paleoclimatology and Pangaea Databases
(https://www.ncdc.noaa.gov/data-access/paleoclimatology-data/datasets).
In addition, over 200 tree ring records were taken from the dataset used by
and hence were subject to their criteria, including length,
intra-site signal coherence, and sample density (see Appendix
for details). A set of coral records was taken from the dataset made
available by ; these were largely temperature proxies,
although some were assigned as precipitation proxies or excluded altogether
based on information in the original publications (see
Appendix for details).
After collection, all records were screened for a maximum dating error of 60 years. Although somewhat arbitrary, this cut-off was decided by taking double
the averaging bin width of 30 years that was applied to the data prior to
analysis (Appendix ). This is a step towards
addressing the issue of dating uncertainty while allowing a wider range of
proxies to be utilised. Proxies with larger dating errors generally have
lower (multi-annual to multi-decadal) resolution but are also usually much
longer and are arguably more useful for capturing long-term trends that may
be less evident or reliable in annual resolution proxies cf.on
low-frequency trends in tree rings. Other
quality judgements regarding temporal resolution, record length, and proxy
location are accounted for by the method set out in Sect.
and Appendix .
Modern climate datasets
Instrumental climate data are the best available in terms of dating accuracy,
calibration, and physical basis . However, their spatial
coverage is not complete and sharply decreases back in time. The nature of
the method used in this study calls for full spatial coverage over a long
period; therefore, reanalysis products are more suitable. These are
combinations of instrumental and satellite data interpolated using models.
The 20th Century Reanalysis Version 2c (20CRv2c) is the
longest global dataset of atmospheric circulation available, spanning AD
1851–2014. It is based on surface pressure, temperature, and sea ice
distribution data, filled in with a deterministic ensemble Kalman filter
(EKF). It has a spatial resolution of
2∘ latitude × 2∘ longitude × 24 vertical pressure levels and a temporal
resolution of up to 6 h. It has been demonstrated that the 20CRv2c is
competent at representing the global tropospheric circulation as well as the
mean state and variability of the hydroclimate; for a detailed description
and evaluation of the product see . Comparison with ENSO
indices indicates that 20CRv2c accurately represents the temporal evolution of
ENSO (Table ), and a recent comparison with other
reanalyses (ERA-20C, JRA-55, and ERA-Interim) indicates that 20CRv2c shows
similar variations in various climate indices, including NINO3.4 and SOI
. The monthly mean surface air temperature and precipitation
rate datasets were downloaded from the NOAA/ESRL/OAR-PSD website
(http://www.esrl.noaa.gov/psd/). The
20CRv2c data were regridded to 2∘× 3∘ to be comparable
to the model data described below (Sect. ). The climatology
(for the period 1851–2014) was removed to produce monthly anomalies, which
were then averaged to annual resolution.
As noted in Sect. , the ENSO-like temperature pattern is
similar to the IPO. Regardless of the
directionality of influence between the two modes, the long-term ENSO-like
state may thus be similar to the IPO/PDO phasing. The ENSO-like state (and
the associated spatial pattern, here derived from 20CRv2c) is here referred
to as ENSO-like.
General circulation model simulations
There are several comprehensive modelling projects with the aim of improving
comparability between GCMs produced by different
teams. GCMs taking part in these projects perform a set of simulations with
standardised forcings and boundary conditions. For this study, the
pre-industrial control (piControl; pre-1850 parameters, no external forcings)
and historical (AD 1850–2000) runs from the Coupled Model Intercomparison
Project 5 CMIP5; were used, in addition to the last
millennium (past1000; AD 850–1850) runs from the Paleoclimate Model
Intercomparison Project 3 PMIP3;. Of
the six GCMs that have all three runs available, two were chosen for their
similarity to 20CRv2c in terms of spatial ENSO representation (see
Appendix ) for precipitation (climate modelling groups
in brackets):
CCSM4 (National Center for Atmospheric Research),
GISS-E2-R (NASA Goddard Institute for Space Studies).
And two were chosen for temperature:
IPSL-CM5A-LR (Institute Pierre Simon Laplace),
BCC-CSM1-1 (Beijing Climate Center, China Meteorological
Administration).
All model datasets were regridded to 2∘× 3∘, converted to
anomalies, and degraded to annual resolution to enable comparison between
models and 20CRv2c.
Methodology
The method used in this study consists of two stages: first, a temperature
and a precipitation ensemble of proxy networks is created based on
GCM-derived pseudo-proxy experiments; then, these network ensembles are used
to produce two independent reconstructions of ENSO-like climate change using
the real proxy data weighted by spatial temperature and precipitation ENSO
patterns from 20CRv2c reanalysis data. This approach attempts to take into
account the effects of proxy selection, the temporal limitations of
individual proxies, and non-climatic noise. A brief overview of the method is
given here, with a more extensive description in Appendix .
Pseudo-proxy creation
Pseudo-proxies are simulated proxies that attempt to mimic various sources of
uncertainty inherent in the real proxy records. This ranges from adding white
Gaussian noise with a prescribed signal-to-noise (SNR) ratio approximating
non-climatic random noise to more sophisticated process-based additions that
take into account effects such as dating error, nonlinear and multivariate
responses of the proxy sensor to the climate variable, and sampling biases
. The utility of any pseudo-proxy exercise lies in
the fact that the answer to the question is known as it can be derived
directly from the original model dataset. By putting the “signal plus noise”
pseudo-proxies through a method to make a reconstruction of the signal, it
allows for inferences about the stability and limitations of the method,
estimates of uncertainties due to noise and other proxy record
characteristics, and, as highlighted by the method used here, it provides a
way of objectively and systematically selecting the most appropriate data
seefor an introduction to pseudoproxy experiments.
Ideally, all pseudo-proxies in this study would be created from proxy system
models PSMs; e.g.. Unfortunately, many proxies (e.g.
δ18O from corals or speleothems) would require isotope-enabled
GCMs,
which are not available for the model runs used in this study. Tree ring
widths (TRWs), however, can easily be simulated using the VS-Lite Model, which
is a freely available PSM designed to estimate TRW based on minimal climatic
input . It is a
simplified version of the full Vaganov–Shashkin model
and only requires
temperature, precipitation, and latitude information. All TRW records in the
proxy dataset used in this study were thus represented using VS-Lite-derived
pseudo-TRW series. For the rest of the proxies, the GCM raw temperature or
precipitation series were used.
Coral δ18O is known to be particularly vulnerable to showing
multivariate responses, depending on both the δ18O of the seawater
(which can be altered through changes in sea surface salinity (SSS) linked to
ocean circulation or precipitation amount) and SST, which is usually the
desired variable . Since there is no simple
method of determining the extent of this contamination of the SST signal
without an isotope-enabled GCM and coral PSM, the decision has been made here
to exclude all coral δ18O records. Other coral proxies such as
Mg / Ca or Sr / Ca have been retained as they are more directly
associated with SST variations. Exclusion of these coral records equates to
the loss of 12 potential proxies that meet the minimum length criterion.
Testing showed that the exclusion of these proxies did not alter the
conclusions of this paper (not shown).
White Gaussian noise was added to all model temperature, precipitation, and
TRW series. Where the original publications or in the case of the
coral records, provided an indication of the strength of the
proxy–climate relationship (e.g. R value), the SNR was calculated. For all
other records, a prescribed SNR of 0.4 (which
corresponds to an R value of ∼± 0.38) was used as this has been
shown to be a realistic average . The median calculated
SNR was 0.63 overall, 0.62 for precipitation, and 0.58 for temperature
proxies, suggesting that the prescribed SNR of 0.4 is a conservative estimate. The
pseudo-proxies were also degraded to reflect the length, time span, and
resolution of the real proxies.
Network ensemble creation
The creation of a network ensemble was done using a calibration–validation
scheme to assess the ability of various pseudo-proxy combinations to
reconstruct multi-decadal ENSO-like precipitation or temperature variations
over the past millennium. As 20CRv2c only covers ∼ 160 years, it is not
suitable for testing over these time spans and the past1000 runs from the GCMs
were used instead. To account for the fact that the ENSO patterns in the GCMs are
different from the pattern in 20CRv2c (and the real ENSO pattern is likely to
be different again), two different GCMs are used for the calibration and
validation stages. This means that the sensitivity of the networks to the shape of
the ENSO-like pattern is tested. GCM selection is described in
Appendix . The precipitation and temperature
calibration GCMs (GCMcal) were CCSM4 and ISPL-CM5A-LR respectively; the
corresponding validation GCMs (GCMval) were GISS-E2-R and BCC-CSM1-1.
Using the GCMcal past1000 dataset, 1000 proxy networks were
created via an “add-one-in” pseudo-proxy algorithm that automatically builds a
network based on how much each proxy improves the reconstructive power of the
network (Fig. ). Similar to a forward stepwise regression
procedure, GCMcal-derived pseudo-proxies are gradually added to a base
network of zero proxies, testing the quality of the network with each
addition, until all proxies have been incorporated (Fig. a, b).
The final optimal network is the one that performed best over all steps
(Fig. c). By repeating the process 1000 times, adding different
random noise series to the pseudo-proxies each iteration, it addresses the
influence of stochastic processes on the ability of a proxy network to
optimally reconstruct the large-scale ENSO pattern.
The reconstruction process itself is a weighted average approach, where the
proxy weights are based on the ENSO-like empirical orthogonal function (EOF) pattern of the GCMcal
past1000 run (for the add-one-in network building) or 20CRv2c (for the final
reconstruction). First, all (pseudo)-proxies in the network were normalised
to a common period of at least 100 years (see Sect. )
and transformed using an inverse transform sampling
ITS; to approach a normal distribution. The EOF
values w
at the proxy locations i were scaled such that their
absolute sum at each time step t is 1 (Eq. ). This EOF
scaling deals with the fact that the number of proxies available changes over
time, preventing more proxy-dense periods from being amplified. Each proxy
series pi was then weighted by its corresponding (scaled,
time variable) EOF value wi, and the n-weighted proxy series
were summed to create a single reconstruction series ENSOr
(Eq. ). This is essentially a sparse reconstruction of
the PC, which is the dot product of the full raw dataset and EOF. The quality
of a network was assessed by comparing ENSOr with the principal component (PC) using the
Pearson rank correlation R (see Appendix ).
Validation was performed in two steps. First, the 1000 networks produced with
the GCMcal were used to make reconstructions using GCMval past1000
data and the GCMcal EOF. Using data from a different GCM ensures
complete separation between the calibration and validation periods and tests
the sensitivity of the networks to the spatial stability of the EOF pattern
as the ENSO-like EOFs from each model and from 20CRv2c are different. The
switch from GCMcal to GCMval thus mirrors the switch between the
GCMs and 20CRv2c. Each network was reconstructed 1000 times using GCMval
pseudo-proxies, again adding different noise realisations for each iteration.
Validation test scores were calculated to check the quality of these
validation reconstructions compared to the validation PC (calculated using
EOFcal and GCMval past1000 data). Second, critical values for the
validation test scores were calculated by repeating the first validation step
but using the GCMval piControl run. If the validation R value of a
network failed to exceed Rcrit, its reconstruction was deemed to be no
better than random noise and was consequently discarded.
Network creation process diagram.
Overview of the network creation process. (a) A new network is created from
the base network (base) plus each pseudo-proxy individually and
is tested for its reconstructive power. This results in n test
scores; (b) the highest score (maxn) is selected and the associated proxy
is moved from the test proxies to base. This is repeated until all
test proxies are incorporated into base. (c) The optimal network
is selected by cutting base where maxn was highest.
The entire process is repeated 1000 times, with new noise realisations being
added to the pseudo-proxies at the start of each run.
Final reconstruction
The remaining networks were used to create an ensemble of ENSOrs using
the real proxy data. The proxy records were first normalised to account for
the different units (Eq. ) and were subjected to an ITS
transform before undergoing the same reconstruction process used in the
network creation. All ensemble members (ENSOr) were then re-normalised
to the reference period 0–650 yr BP to ensure comparability. The final ENSO
reconstruction was taken as the ensemble mean, while the ensemble range
represents part of its uncertainty envelope.
The last step in creating the reconstruction was calculating the final error
range. RMSEs were calculated for each network
during validation, providing 1000 error estimates for each ensemble member.
The 95th percentile for each member was calculated from this and added
and subtracted from the ENSOr series to find the maximum and minimum
error limits. The uncertainty envelope around the final ENSO reconstruction
(i.e. the ensemble mean) is thus a combination of the reconstruction ensemble
range and the error ranges for individual ensemble members.
LIA–MCA difference analysis
The absence of a known reference period to which the reconstructions can be
calibrated precludes any absolute comparison of the result with recent
trends. However, it is possible to ascertain whether the MCA and the LIA
differ significantly in how El Niño-like they are. Evaluating the LIA–MCA
difference also directly removes the bias introduced by taking any reference
period . To do this, the means over the two periods were
taken and the MCA mean was subtracted from the LIA mean. If the difference is
significantly greater than zero, the LIA is more El Niño-like than the MCA;
if the difference is significantly less than zero, the MCA is more El
Niño-like than the LIA.
Results: ENSO reconstructions
The final ENSO reconstructions for precipitation and temperature and their
proxy network density are shown in Figs.
and . The final number of networks included in the
precipitation ensemble is 1000, of which 999 are unique
(Fig. a). The total number of proxies used is 48, with a
maximum of 40 for a single network. Proxy availability increases steadily
throughout time, save a slight drop off in the most recent period
(Fig. b). Although there is spread in the ensemble,
there are clear peaks and troughs visible. The within-ensemble coherence was
tested by correlating 1000 randomly chosen pairs with each other. This
confirmed that there is generally good agreement over the full period
(100–1500 yr BP) as well as during the MCA and LIA individually
(Fig. ).
Precipitation ENSO ensembles. (a) Precipitation reconstruction of ENSO-like climate change
that has been 30-year averaged (black line). Individual network solutions are shown as orange lines,
with the uncertainty envelope in orange shading. (b) Number of proxies
included in the ensemble over time, with the median in black and the range
in blue. The pink and purple shaded periods are the MCA and LIA respectively.
Temperature ENSO ensembles.
As Fig. , but for
temperature.
Within-ensemble correlations. Correlations between 1000 pairs randomly chosen from the precipitation (blue)
and temperature (red) ensembles. Box plots encapsulate the space between the
first and third quartile with the median shown as a black line; whiskers indicate
the 95 % confidence interval of the median; points are values outside this
confidence interval (outliers). Statistical significance of the median value
is indicated at the bottom: ∗∗∗p<0.001, ∗∗p<0.01, ∗p<0.05, ^ p<0.1, p>0.1.
The temperature reconstruction ensemble consists of 182 optimal networks, all
of which are unique (Fig. a). The total number of proxies
is 211, with a maximum of 104 for a single network. Despite the higher number of
proxies available for temperature, the median proxy coverage (Fig. b
black line) is lower compared to the precipitation reconstruction; while roughly the most
recent 1000 years of the precipitation reconstruction are based on a median of eight or
more proxies, this is only true for the last ∼ 330 years of the temperature
reconstruction. Prior to ∼ 480 yr BP, half the temperature ensemble members rely
on a single proxy. This also accounts for the high within-ensemble correlations
for the full and MCA periods but more variable correlations during the
LIA (Fig. ). With few long temperature proxies,
most ensembles likely rely on the same data for the pre-LIA reconstruction.
The steep increase in Fig. b. reflects the high number
of tree ring and coral series, all but a handful of which are less than 600 years long see and most of which are clustered in
North America. The add-one-in method has mitigated some of the risk of
co-varying non-white noise in a subset of the proxies skewing the
resulting reconstruction; testing showed that when all North American
tree ring records were added to the reconstruction, a regional climate
trend obscured the ENSO-like signal (not shown). However, the relatively
poor spatial coverage elsewhere and the lack of long proxies leaves the
reconstruction prone to spurious noise-driven trends in the earlier
period. The behaviour of the temperature ensemble members is less
coherent and shows more spurious temporal variation compared to
precipitation, particularly in the early period (Fig. ).
Nevertheless, for both temperature and precipitation the
error from proxy noise is overshadowed by the uncertainty
associated with the choice of network – the ensemble
spread makes up the bulk of the uncertainty envelope.
Figures and show the proxy locations
plotted onto the precipitation and temperature EOF patterns respectively.
The proxies included in the precipitation ensemble members are
distributed well over the western and eastern side of the Pacific, though
missing good coverage of the central Pacific. The relatively uniform size of
the bubbles suggests that there is no immediate preference of any one proxy
over the others. The spatial distribution of the temperature proxy locations,
in contrast, is highly skewed towards North America, where most of the tree
ring records are located, and the central and equatorial East Pacific lack
coverage. The combination of this poor spatial coverage, low temporal
coverage (Fig. b), and wide ensemble range leads to the
expectation that the temperature reconstruction is of lower quality than the
precipitation reconstruction. This is further supported by the low fraction
of networks that passed the validation (182 compared to 1000 for
precipitation). There is no clear preference of any combination of proxies,
with most proxies being selected equally often (i.e. equal bubble sizes). The
fact that the similarity of the EOF patterns of GCMcal and GCMval
to the 20CRv2c EOF pattern was lower for temperature than for precipitation
(Appendix ) further reduces confidence in the
temperature reconstruction.
Precipitation EOF with proxy locations.
Background colours are scaled EOF values. Bubbles are individual proxies; size is
indicative of how often the proxy is included in the network ensemble, and shading
indicates relative weighting such that darker colours are more strongly positive or negative.
Temperature EOF with proxy locations.
As Fig. , but for temperature.
Figures and illustrate the benefit of using
the pseudo-proxy approach in creating the optimal networks. There is no direct
correlation between proxy weighting (indicated by the bubble colour) and frequency
of use, suggesting that other aspects such as resolution, length, and the relationship
to other proxy locations played a significant role in determining the usefulness of a
proxy that would be difficult to judge from the outset. The fact that the choice of
proxy network is the dominant source of error is further evidence of the utility
of the pseudo-proxy optimal network method. The high clustering of temperature
tree ring records in North America is an example of where the add-one-in method
has worked to reduce the risk that some co-varying non-white noise in a subset
of the proxies skews the resulting reconstruction; testing showed that when
all North American tree ring records were added to the reconstruction, a
regional non-ENSO trend obscured the ENSO signal (not shown).
Comparing precipitation and temperature
Figure shows the correlation between 1000 randomly chosen
combinations of temperature and precipitation ensemble members as an indication
of the agreement between the two climate variables. There is no correlation – positive
or negative – apparent between the temperature and the precipitation reconstructions,
neither over the entire 1500 years nor over the MCA or LIA individually. Whether this
is a true physical phenomenon or simply a reflection of the high uncertainty in the
reconstructions is difficult to separate. Therefore, it is not possible to
categorically determine a systematic difference between the ENSO signals
in temperature and precipitation proxies.
Precipitation–temperature correlations.
Correlations between the temperature and precipitation ensemble members based on 1000
randomly chosen pairs for the period 100–1500 yr BP (“All”) and the MCA and
LIA individually. See Fig. for explanation of the
box plots and significance.
The definitions of the MCA and the LIA used here are based on those given
by ; there are many alternative definitions,
however . To test the sensitivity of the results to the definition of
these periods, we recalculated the LIA–MCA difference using two widely used alternative
definitions: from (MCA = AD 950–1250, LIA = 1400–1700) and the
Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment
Report MCA = AD 950–1250, LIA = 1450–1850;.
Figure shows the range of LIA–MCA differences for the individual
members within the precipitation and temperature ensembles, using
the three definitions. Using the definition, the
precipitation interquartile range indicates that the LIA is more El Niño-like
than the MCA, though the difference is statistically insignificant
(p=0.22). For temperature, there is no evidence of any difference between
the ENSO-like state of the MCA and the LIA, with a median value very close to
0 (p=0.48). There are also many more outliers (i.e. values outside the
95 % confidence interval) compared to precipitation, again reflecting the
high uncertainty in the temperature reconstruction. Applying the definitions
used by or only makes minor, and
largely statistically insignificant, differences. For precipitation, the
difference between the two periods is more pronounced for the alternative
definitions, with a weakly significantly more El Niño-like LIA (p<0.1).
For temperature there is very little change; although the median is negative
for the alternative definitions, the interquartile range still encompasses
zero. The precipitation reconstruction thus qualitatively suggests that the
LIA was more El Niño-like than the MCA, but our conclusion that there is no
evidence for any precipitation–temperature correlation stands.
Difference between the means of the MCA and LIA for three
definitions. Difference calculated by subtracting μMCA
from μLIA for each ensemble member, using the three MCA and LIA definitions
listed in Sect. . A positive value indicates LIA is more
El Niño-like than MCA. Precipitation is on the left in blue, temperature
is on the right in red. “Yan” refers to the definition used in ,
“Mann” in , and “IPCC” in .
See Fig. for explanation of the box plots and significance.
Discussion
An important question addressed in this study is whether the modern-day links
between ENSO-like temperature and precipitation persist back in time. There
is no concrete evidence in this study of any correlation between the
precipitation and temperature reconstructions, whether positive (as seen in
the modern day) or negative as suggested by. This is
contrary to expectations based on instrumental and modelling data, which both
show a strong relationship between ENSO-like precipitation and temperature.
To test the robustness of the ENSO-like temperature–precipitation coupling
in GCMs, we calculated the correlation between the temperature and
precipitation ENSO-like EOF time series (PCs) in the six CMIP5 GCMs listed in
Appendix . Four of the six models show a significant
(p<0.05) positive precipitation–temperature correlation over the study
region (40∘ S–40∘ N) at annual and 30-year resolution for the
historical run (0.74≤R2≤0.98), and five out of six for the
past1000 run (0.30≤R2≤0.92; not shown). This is similar to
coupling in the 20CRv2c data (R2≥0.76). The fact that the palaeodata
apparently do not display this relationship over the past 2 millennia
seeand this study is thus interesting from a physical
dynamical point of view as it contradicts our conventional understanding of
long-term ENSO-like climate change.
There is also no evidence in the two reconstructions presented here that
there was any significant difference in the mean ENSO-like climate state
during the MCA and the LIA. This is also contrary to the findings of
. They create a SOI reconstruction (SOIpr) from two
precipitation proxies from the Galápagos and the
Indo-Pacific warm pool , weighting them according to the
relationship of local rainfall to the instrumental SOI. Interestingly, the
SOIpr shows broad trends opposite to the precipitation reconstruction
presented here, with a more La Niña-like LIA compared to the MCA. While the
two proxies used were considered for this study, they were both rejected due
to high dating errors (average around 100 years). Several other precipitation
as well as temperature
proxies supporting conclusions of the study were similarly
rejected due to high dating errors, as were proxies supporting the opposite
conclusion . Testing showed that applying the
method described here using only the two proxies used by
produced highly similar results to their SOIpr reconstruction,
suggesting that it is not a methodological difference but rather it is related to proxy
selection. A reconstruction based on only two (poorly dated) proxies is
likely to be more vulnerable to spurious noise or other climatic influences
distorting the signal; this is evidenced by the degradation back in time of
the reconstructions presented in this study as the number of proxies
declines. This highlights the need for more accurately dated proxy records,
which remains an issue for low-resolution but long proxy archives such as
marine sediments .
A number of other (non-temperature or precipitation) ENSO-sensitive proxies
that were not included in our reconstructions provide evidence for a more La
Niña-like climate state in the MCA compared to the LIA, although the mean
state of the LIA appears inconsistent. Sedimentary sterol concentrations in
marine sediment off the Peru coast suggest the MCA
coincides with a reduction in El Niño activity, with both El Niño and La
Niña activity increasing from the late MCA onwards. Based on a range of
North American proxies, conclude that the MCA was
characterised by arid conditions in western North America consistent with a
La Niña-like state, followed by a wetter LIA. A basin ventilation record
from the Western Pacific warm pool (WPWP) agrees
particularly well with the earlier part of our precipitation reconstruction.
It shows a peak in El Niño activity at ∼ 1150 yr BP and a distinctive
minimum during the MCA, followed by a more El Niño-like LIA characterised
by a steady decline in activity. This decline is not apparent in our
reconstruction, but it is reflected in some other multi-millennial proxy records
.
Most multi-proxy reconstructions of ENSO variability are temperature-based
and focus on NINO regions (El Niño regions in the equatorial Pacific). A NINO3 region (90–150∘ W and
5∘ S–5∘ N) temperature reconstruction by shows
a slow millennial-scale warming trend (to a more El Niño-like state) from
AD 1100 onwards, with relative cooling during the MCA compared to the LIA,
consistent with a La Niña-like state during the MCA. In contrast,
are unable to detect a systematic difference between
the MCA and LIA in their Boreal winter NINO3.4 (120–170∘ W
and 5∘ S–5∘ N) SST reconstruction, which is consistent with the
findings of this study. The discrepancy between the two ENSO reconstructions
may be due to the difference in proxy networks, particularly the use of
lower-resolution proxies here and by , which contribute a
substantial part of the signal due to the slightly different definition of
the NINO regions or the target season (Boreal winter versus annual). Other
reasons may be related to the methodology or target instrumental dataset,
particularly for low-frequency variability and amplitude. Work by
indicates that the results of many temperature
reconstruction methods are sensitive to the target SST dataset used for
calibration, and find that the La Niña-like pattern in the
MCA evident in is not a robust feature across CFR methods.
The fact that multi-proxy reconstructions are less likely to show strong
differences between the ENSO-like state of the MCA and the LIA again
highlights the potential sensitivity of individual records to non-physical
trends, and suggests that conclusions drawn from single proxy records must be
considered with caution.
The lack of a coherent and strong difference between the ENSO-like state of
the MCA and the LIA in the reconstructions presented here and among other
multi-proxy reconstructions may indicate that there is no direct relationship
between long-term regional to global temperature anomalies and ENSO-like
state of the climate. Conversely, this result may be an indication that these
periods indeed were not characterised by a significant climate anomaly in
ENSO-sensitive regions. An examination of separate NH and Southern Hemisphere (SH) temperature
reconstructions only indicates a global cool period
between AD 1594 and 1677, while the report no globally
synchronised multi-decadal climate shifts evident in their compilation of
continental-scale temperature reconstructions except for generally cool
conditions between AD 1580 and 1880. Both these cool periods coincide with the
definition of LIA used in this study (AD 1400–1850). Neither study finds
strong evidence for a global MCA, instead suggesting an initial NH warm
period during AD 800–1100 followed by warming in South
America and Australasia between AD 1160 and 1370 and the SH
overall for AD 1200–1350 . These continental–hemispheric
warm phases are also encapsulated in the MCA definition used here (AD
800–1300). The lack of MCA or LIA indications in ENSO-like behaviour is
partly consistent with the multi-proxy NINO3.4 reconstruction by
, which finds no systematic difference between the two
periods, and with the La Niña-like pattern during the MCA found in the
multi-proxy reconstruction by . Conversely, numerous
reconstructions based on one or two proxy records
see do report a significant
difference, although as highlight, the sign of the
difference varies. This is another indication of the possible vulnerability
of single-proxy reconstructions to over-interpretation, the danger of
over-extrapolation, and “cherry-picking” of supporting evidence. The fact
that multi-proxy reconstructions are less likely to show strong differences
between the ENSO-like state of the MCA and the LIA again highlights the
potential sensitivity of individual records to non-physical trends and
suggests that conclusions drawn from single-proxy records must be considered
with caution. Further research into the physical and mechanistic interactions
between global temperature and ENSO dynamics over long timescales is needed
to elucidate this problem. Meanwhile, comparative studies of the MCA and the
LIA must thus be approached with caution for low-latitude and SH study areas
as their definitions are not necessarily rooted in real climatic events
outside the NH.
Interactions with other climatic phenomena
There may be alternative interpretations of the reconstructions presented
here due to the fact that ENSO interacts with various other climatic
phenomena. The ITCZ is a zone of low-level atmospheric convergence that lies
over the warmest water and creates substantial positive precipitation
anomalies. It follows a seasonal migratory pattern, moving southwards towards
the Equator during the boreal winter and retreating northwards during the
summer months. As mentioned in the Introduction (Sect. ),
there is a significant link between the movement of the ITCZ and ENSO state
of the climate. This is thought to be true on interannual
to millennial timescales . On decadal to centennial
timescales, proxy and
modelling evidence points to a potential NH temperature
forcing on the ITCZ, causing it to shift southwards in response to cooler NH
temperatures. This hypothesis has been invoked to explain an apparent
southward shift of the ITCZ during the LIA
. Similarly, there is evidence for a
northward shift of the ITCZ during the MCA . A southward shift is analogous to an El
Niño-like state , suggesting that the
MCA was characterised by a La Niña-like climate state and the LIA by an El
Niño-like state. However, recent studies hint at an expansion and
contraction of the ITCZ in the West Pacific during the MCA and LIA
respectively , accompanied by a respective
weakening and strengthening of the Pacific Walker Circulation (PWC). El
Niño is characterised by a weakening in the PWC, which would imply a La
Niña-like state during the LIA.
The most frequently used precipitation proxies (Fig. ) are
located in the West Pacific, which is an important region for ENSO, partly
due to the relatively large latitudinal migration of the ITCZ. The
precipitation reconstruction presented here suggests a slightly El
Niño-like LIA (Fig. ), which appears congruous with a
southward shift of the ITCZ (and a weakening of the PWC), but not with a
contraction of the ITCZ. However, inspection of the West Pacific proxy
records indicates that all but two records are assigned negative EOF weights
(i.e. their locations experience drought during El Niño), including the
most frequently used and most heavily weighted proxies
(Fig. ). If the ITCZ did contract during the LIA, this
could show up in the ensemble as an El Niño-like shift as these heavily
weighted proxies would have experienced drought. Nevertheless, the ensemble
is not solely based on these Western Pacific proxies, instead incorporating
information from across the Pacific Basin. Thus, while it is possible that a
contraction of the ITCZ influenced the results, this is (at least partially)
mitigated by proxies in other locations. Further examination would be needed
to conclusively determine the extent to which the ITCZ plays a role in driving the
precipitation reconstruction here.
As mentioned in Sect. , the interpretation of the
reconstructions is potentially confounded by interactions between ENSO and
the PDO/IPO. The two (pseudo)-oscillations are known to share many
similarities in their spatial pattern , and the temperature
EOF used in this study (Fig. ) is indeed very similar to
the IPO pattern as reported in . Many proxy records used
here have decadal or lower resolution (Appendix ), and the
30-year averaging applied here brings the reconstructions into the realm of
IPO/PDO variability. Moreover, many records, for example the North American
tree rings, lie in PDO-sensitive North America . This
again raises the issue of how individual proxies are interpreted,
potentially changing proxy–climate relationships at different timescales.
Many proxy records of low resolution have been interpreted as ENSO
reconstructions see based on modern-day
climate–proxy interactions, just as the reconstructions presented in this
paper are based on modern-day (interannual) spatial ENSO patterns.
Comparison of the precipitation reconstruction in this study with a reconstruction of the PDO based on North
American tree rings over AD 993–1996
shows a slight tendency for the precipitation and PDO
series to have the same sign over the MCA and LIA separately, but the
relationships are not statistically significant. There is no indication of
any relationship between the PDO and the temperature reconstructions, despite
the fact that many of the temperature proxies are located in potentially
PDO-sensitive areas (most notably North America). find
a strongly negative PDO during the MCA (roughly equivalent to a La
Niña-like spatial pattern), which corresponds to the qualitatively La
Niña-like tendency of the precipitation reconstruction presented here.
find no significant correlation between Boreal winter
NINO3 SST and a 9-year smoothed reconstruction of the Asian expression of the
PDO based on East Asian tree rings as opposed to the North American
tree rings used by, for example,, suggesting that the ENSO–PDO
link may be spatially variable. There is also no statistically significant
correlation between the temperature or precipitation reconstructions and this
Asian PDO reconstruction. The lack of co-variability between the
reconstructions presented here and these PDO reconstructions may be due to
the fact that this study relies on a wider proxy network that takes into
account non-Pacific locations. Another possibility is that the IPO/PDO
oscillation and spatial pattern is not robust further back in time, as
suggested by and . The resolution of
proxy data employed in this study prevents a robust separation of variability
on ENSO and IPO/PDO timescales, leaving open the question of how these
oscillations influence each other.
Not addressed in this study is the role of different “flavours” of ENSO
patterns. A different type of ENSO pattern, first defined by
and dubbed ENSO Modoki, differs from the traditional
(canonical) ENSO pattern in the shift of positive SST anomalies from the
West Pacific (mainly in NINO3 and NINO3.4) to the central Pacific (NINO4;
170–120∘ W and 5∘ S–5∘ N) and its mid-latitude
teleconnections. It is sometimes defined as the EOF2 of detrended SST data
note that the data in this study were not detrended; hence, here it
would be EOF3; or a combination of EOF1 and
EOF2 . Some modelling studies suggest
that ENSO Modoki will increase in frequency compared to canonical ENSO as a
result of anthropogenic climate change
, and there is some model evidence
that ENSO Modoki was also more common in the mid-Holocene
. The difference in equatorial spatial pattern and
teleconnections has implications for the interpretation of the proxy
reconstructions in this study as ENSO-Modoki-like climate change may appear
here as a reduction in ENSO-like activity.
The poor quality of the temperature reconstruction, which limits the
statistical robustness of the precipitation–temperature comparison, is
likely due to the low number and unequal distribution of available data
locations. Most temperature proxies are located in teleconnected regions
outside the ENSO source region, which have been shown to be subject to more
temporal variability in precipitation–temperature relationships
. A multi-region tree
ring reconstruction of ENSO variability displays substantial variability in
the strength of ENSO teleconnections over time and space . The
authors find that the Pacific Northwest and Texas–Mexico regions show highly
unstable teleconnections (although there is no discussion on whether this is
related to the different ENSO flavours). This may explain the lack of signal
in the temperature reconstruction presented here as many of the temperature
proxies are located in these teleconnected regions
(Fig. ). If the strength of the teleconnection has indeed
changed over time, the weightings based on modern-day ENSO patterns would not
reflect this; thus, this reconstruction should be regarded as an indication of
change of the modern-day ENSO-like climate pattern only. Without proxies
located in the centre of action or more robustly teleconnected areas, the
loss of signal due to unstable teleconnections can be expected to be
substantial, as suggested by the results presented here.
An interesting observation of the EOF maps presented here
(Figs. and ) is the low to no
correlation between EOF weighting (bubble shading) and how often proxies are
used (bubble size). Correlations of temperature proxy frequency of occurrence
versus GCM-derived and 20CRv2c-derived EOF weighting are 0.23 and 0.20
respectively (p<0.05); there is no significant correlation for temperature
proxies (p>0.54). Two possible explanations for this are (i) that the climatic
noise in the high-occurrence but low-weighted areas is less spatially
correlated with the noise elsewhere than in the low-occurrence but higher-weighted areas or (ii) that the length and resolution of the proxy records have a
more important effect on a proxy's utility than its weighting. For instance,
the proxies off the Australian coast and in the western Pacific Islands are
mostly short (<500 years) coral records; while several of them have high
weighting, their frequency of occurrence is very low. The proxy at the
southern tip of Australia, in contrast, is a ∼3600-year-long tree ring
record and is the most frequently used temperature proxy. Overall, however,
temperature proxy length is only very weakly correlated to occurrence
frequency (R=0.17,p<0.05). The SNR assigned to the temperature
pseudo-proxies is similarly only very weakly correlated to frequency
(R=0.13,p<0.05). In the precipitation case, neither length nor SNR are
significantly correlated to how often the proxies occur in the ensemble. The
selection process is likely driven by a combination of these factors rather
than any single factor and is modulated by the number of proxies available.
More detailed analysis will be needed to elucidate this.
Reflections on the method
The method set out in this study is one of few that attempt to take into
account the effect of real spatial and temporal patterns of proxy
records, thus increasing our confidence in their ability to accurately
evaluate the effectiveness of the networks. To our knowledge, this is the
only study in which realistic temporal proxy resolution in addition to their length has been taken into
account by the pseudo-proxies. This is an
improvement of the pseudo-proxy design used by, for example, ,
who take into consideration the declining proxy availability back in time but
not their resolution. The authors find that this already significantly
impacts the quality of multi-proxy reconstructions; thus, the inclusion of proxy
resolution as done here is likely to have further impacts. More extensive
research is needed to quantify this however.
The optimal network creation still has scope for improvement. Although we
have screened for maximum dating errors, its effect on the included proxies
is not explicitly assessed. This issue is often neglected in (multi)-proxy
reconstructions but seefor a recent effort to address it
systematically. Moreover, the noise simulation used here is
relatively simplistic; the use of a wider noise spectrum (including red, and
possibly even blue, noise) may alter the composition of the networks
and references therein. However, the issue remains
that there is no easy way to determine the real noise spectrum of the
proxies. With the advent of more isotope-enabled GCM simulations, further
improvement could come from the use of more proxy system models to more accurately estimate
the proxy–climate relationships for all types of proxies
see.
The choice of dataset from which to derive an EOF is also a source of
uncertainty see as differences in the EOF pattern
will affect the weighting of the proxies. This is particularly pertinent for
the precipitation reconstruction as the modern-day ENSO precipitation
signature is much less well-established than for temperature due to less and
lower-quality instrumental data. This is partially tested by using different
GCMs to calibrate and validate the proxy networks. However, the true
ENSO-like pattern has been non-stationary over time, as has been shown to be
true in 20CRv2 for the North Atlantic Oscillation (NAO) and Pacific North
American pattern (PNA), for example . We tested the
stability of the 20CRv2c temperature EOF used in this study by recalculating
it for a running 30-year window and found substantial variability in the
spatial pattern and amount of variance captured by the EOF. Further
investigation is necessary to explore whether this result is an artefact of
internal variability, is due to uncertainties in the reanalysis dataset, or
reflects real changes in the nature of ENSO. Nevertheless, it highlights the
vulnerability of the majority of ENSO reconstructions (including ours) to the
assumption that the modern-day ENSO is a good analogue for the past.
Conclusions
Two reconstructions of
ENSO-like climate change are presented based on temperature- and
precipitation-sensitive proxies. The quality of the
reconstructions degrades further back in time as there is less proxy data
available, which is particularly detrimental to the temperature
reconstruction. The main implications of these reconstructions are that
we find no evidence that temperature and precipitation proxies disagree
over the ENSO-like state of the climate during the past 2 millennia. The two
reconstructions in fact show little to no correlation, which is surprising as
there is a strong relationship between temperature and precipitation ENSO behaviour
at interannual timescales in instrumental–reanalysis data and GCMs.
the precipitation reconstruction shows a tendency for a more El Niño-like LIA
compared to the MCA, but the difference is not statistically significant and is not
apparent in the temperature reconstruction. This result is insensitive to the choice
of definition for the MCA and LIA.
a major limitation on our ability to accurately reconstruct ENSO-like climate
change back in time is the lack of high-quality long proxy records in the
tropical and subtropical latitude bands, and we reiterate the need for
continued efforts to collect such data. The discrepancies between the two
series presented here and many other interannual and (multi)-decadal ENSO
reconstructions are more likely to be reconciled with denser proxy networks
in the ENSO source region, along with resampling of existing locations to
increase the signal-to-noise ratio . The pseudo-proxy
experiments described in this paper can quite easily be adapted to search for
optimal locations from which additional proxy information would be the most
beneficial, as previously done specifically for corals by see alsofor a recent endeavour.
continued improvements in the ability
of GCMs to accurately simulate and reproduce ENSO behaviour in conjunction with more
high-quality proxy data will give both the palaeo-community and the modelling
community an increasingly reliable foundation for creating, calibrating, and
evaluating palaeo-ENSO reconstructions.
The proxy data used in this study were uploaded to Figshare
(https://figshare.com/articles/Proxy_data/4787575/1, Henke et al.,
2017) and published with 10.6084/m9.figshare.4787575. The records were
all originally downloaded from the NOAA Paleoclimatology and PANGAEA
databases
(https://www.ncdc.noaa.gov/data-access/paleoclimatology-data/datasets),
including the Mann et al. (2008) tree ring dataset and the Tierney et
al. (2015) coral dataset.
The 20CRv2c monthly mean surface air temperature and precipitation rate
datasets are available on the NOAA/ESRL/OAR-PSD website
(https://www.esrl.noaa.gov/psd/data/gridded/data.20thC_ReanV2c.monolevel.mm.html,
NOAA/ESRL/OAR-PSD, 2014).
The CMIP5 and PMIP3 GCM datasets are available at the Earth System
Grid-Center for Enabling Technologies (ESG-CET) portal
(http://pcmdi9.llnl.gov/, ESG-CET, 2017).
Proxy data details
Precipitation proxy details. “SNR” gives the signal-to-noise ratio
used to make the pseudo-proxies. “EOF” gives the (unscaled) 20CRV2c EOF value
used to weight the proxy. “Start” and “End” are starting and ending years in
years BP. “Res” is proxy resolution, rounded to the nearest integer; sub-annual
proxies are listed as having a 1-year resolution. “Dating” refers to dating
error. “Included?” indicates whether the proxy contributed to the final ENSO
reconstruction; if not, the reason for exclusion is listed (“Dating error”
and “Length” are a priori conditions, which the proxies failed to
meet. “AOI” indicates that it passed preprocessing screening, but was not
selected during the add-one-in process).
ReferenceLongLatProxySNREOFStartEndResDatingIncluded?27510tree ringn/an/a50-5312No; length6829lake0.400.32> 1500-581450Yes7613speleothem0.40-0.0341322515Yes25532speleothemn/an/a> 1500-1517550No; dating error7810lake0.40-0.22> 15001271030Yes7611lake0.400.52> 1500-572060Yes35630pollenn/an/a> 1500023110No; dating error2919tree ring0.370.04920-5811Yes2629lake0.40-0.02960-521060Yes782tree ring0.350.48604-5211Yes2924tree ring0.400.42564-4311Yes C/N271-1laken/an/a> 1500-541070No; dating error clay271-1laken/an/a> 1500-541070No; dating error sand271-1laken/an/a> 1500-541070No; dating error silt271-1laken/an/a> 1500-541070No; dating error gastropod7322lake0.400.16> 1500-43650Yes ostracod7322lake0.400.16> 1500-43650Yes6210speleothem0.401.08> 1500-36130Yes6926tree ring1.100.32303-4211Yes6728tree ring0.460.26353-5811Yes6727tree ring0.400.31411-5811Yes Fe7919marine0.40-0.93> 1500110550Yes Ti7919marine0.40-0.93> 1500110550Yes408coral0.86-0.90338-3511Yes gastropod7322lake0.400.16> 1500-102560Yes ostracod7322lake0.400.16> 1500-102560Yes s7322lake0.400.16> 15001572560Yes7321speleothem0.400.00> 1500-54119Yesδ18O7518coral0.10-1.38242-3511Yes 10fc4312speleothem2.35-0.68527-6016Yes 5fc160-10speleothemn/an/a70-2615No; length7322speleothem0.790.161463-54210Yes6922lake0.40-0.16> 1500-51124Yes7516lake0.400.70> 1500-27160Yes6529lake0.400.24> 1500101560Yes
Continued.
ReferenceLongLatProxySNREOFStartEndResDatingIncluded?δ18Osw3213marine0.40-1.82> 1500-51150Yes1154speleothemn/an/a> 1500045300No; dating error459speleothem1.10-2.42393-55111Yes7024tree ring1.010.18168-5011Yes asm1518speleothem0.40-1.97121-5117Yes asm2518speleothem0.40-1.97190-5115Yes asm3190-19speleothemn/an/a76-51113No; length a283-6speleothemn/an/a47-5619No; length d7613speleothem0.40-0.038614319Yes old7516lake0.400.70> 1500802110No; AOI recent7516lake0.400.70662-27110Yes3112lake0.40-1.861290-595560Yesδ13Cwax3314lake0.40-1.67> 150063022057Yes TiO3314lake0.40-1.67> 150066922057No; AOI815lake0.40-0.831399-24310Yes7122tree ring0.790.001179-5811Yes7419lake0.40-0.61> 1500-54510Yes Castor6529lake1.430.241450-50560Yes2927speleothem0.42-0.27701-3338Yes c2an2427ice core0.40-0.16349-461050Yes120-4marinen/an/a> 1500-483065No; dating error bc1δDwax1229marine0.400.24322-50925Yes pδDwax1229marine0.400.24> 1500-302025Yes2028tree ring0.610.22950-4811Yes dy211317laken/an/a926-461570No; dating error dy411317laken/an/a926-462070No; dating error dy611317laken/an/a926-531070No; dating error3028tree ring0.40-0.29350-5011Yes
n/a: not
applicable.
Tables and provide an overview of the
proxy records collected for this study. Where a proxy was rejected, the
reason is given. AOI refers to a proxy not being selected for any networks by
the add-one-in algorithm. This could be due to poor ability to capture the
EOF pattern related to location or time resolution. Records from the NOAA
Paleoclimate Database are identified by original publications. Where there
are multiple time series from one publication, an identifier suffix is added.
The naming of this identifier is based on the naming in the original database
files or the proxy type (e.g. δ18O, Sr / Ca).
Most tree ring records were taken from the dataset used by ,
which is a reduced set derived from the International Tree Ring Data Bank
(ITRDB, version 5.03; www.ncdc.noaa.gov/paleo/treering.html). The naming for these
series has not changed from the original (an abbreviated location followed by
a core number). The tree ring series were subject to the following selection
criteria :
“(i) series must cover at least the interval 1750 to 1970, (ii) correlation
between individual cores for a given site must be 0.50 for this period,
(iii) there must be at least eight samples during the screened period 1800–1960
and for every year used. Series that were redundant with other compilations
[used in the study] were not included. Four other series
were not included because of site- specific problems […]. Of the remaining
series, [some] had to be replaced because of format errors in the chronology
file on the ITRDB […], or because sample depth data were missing from the
chronology file. […] When sample depth data were absent, the raw ring-width
data from ITRDB were used to recalculate the chronology using program ARSTAN
(Version 6.05P), with the following settings: (a) a single detrending fitting
a cubic spline of 50 % variance reduction function at 66.6 % the length of
each sample, no stabilisation of variance or autoregressive modelling, indices
computed as ratio, that is measurement divided by curve, and chronology
calculated as biweight robust mean estimate.”
The coral dataset is a comprehensive compilation of coral
data covering the last ∼ 400 years. As explained in
Sect. , only coral records other than
δ18O are included here. The remaining records in this database were
used in this study as temperature proxies, with the following exceptions:
were replaced by the coral-derived SST series as presented in original
publications.
and were replaced by the coral-derived SST series as presented in
.
δ18O and Sr / Ca were replaced by the coral-derived SST series as presented in the original
publication.
Sr / Ca, Sr / Ca, ,
Sr / Ca, and Sr / Ca were
excluded as their correlations with SST reported in were
of opposite sign to what is expected (i.e. positive when the physical
processes should lead to a negative correlation).
Additional tree ring and coral records were retrieved from the NOAA
Paleoclimatology Database
(http://www.ncdc.noaa.gov/data-access/paleoclimatology-data/datasets)
and were used as presented there. Where available, temperature or
precipitation reconstructions were used (i.e. where the raw proxy series has
already been converted). This was done to minimise biases due to
nonlinearities in the raw proxy data, which are accounted for in the
conversion process. In some cases, two precipitation series were available
for different seasons; these were summed (or averaged, depending on the type
of data) to get a better approximation of an annual signal. While this may not
be entirely accurate, annual signals are more desirable for the purpose of
this study. Moreover, summing the records as opposed to treating them as
individual records makes very little difference due to the nature of the
method (weighting and summing the series). For corals, spliced records were
used where available to maximise their length.
Proxy records with dating errors >60 years were excluded from the analysis.
The dating error was taken from the original publications where it was
reported; otherwise it was derived from the age model results in the raw data
files. In the latter case, the maximum error reported for the past 2000 years
was used; larger errors further back in time were thus not taken into account
as they are irrelevant for this study.
Precipitation proxy details. “SNR” gives the
signal-to-noise ratio used to make the pseudo-proxies. “EOF” gives the
(unscaled) 20CRV2c EOF value used to weight the proxy. “Start”
and “End” are starting and ending years in years BP. “Res” is proxy resolution, rounded to the
nearest integer; sub-annual proxies are listed as having a 1-year resolution. “Dating” refers
to dating error. “Included?” indicates whether the proxy contributed to the final ENSO
reconstruction; if not, the reason for exclusion is listed (“Dating error” and
“Length”
are a priori conditions, which the proxies failed to meet. “AOI” indicates it
passed preprocessing screening, but was not selected during the add-one-in process).
The method developed in this study was used to create separate temperature-
and precipitation-based reconstructions of ENSO-like climate change made from
weighted temperature and precipitation proxy records respectively. The
weights were based on empirical orthogonal function (EOF) patterns derived
from general circulation models (GCMs) and the Twentieth Century Reanalysis
Project Version 2c (20CRv2c). First, an ensemble of optimal proxy networks
is created using a GCM-based cross-validated add-one-in approach. These
networks are then applied to real proxy data to create a separate
precipitation and temperature reconstruction of ENSO-like climate change over
the past 2 millennia. Each step is described in more detail below.
Empirical orthogonal function analysis
Empirical Orthogonal Function (EOF) analysis decomposes a spatio-temporal
dataset into spatially stationary, time-varying coefficients. For a dataset
of spatial resolution x×y and n time steps, it produces
n maps (EOFs) of x×y. The first map (EOF1) captures the
largest fraction of variance of the original data. Each subsequent map
maximises the amount of remaining variance captured, whilst being completely
uncorrelated (orthogonal) to all preceding maps. Every EOF map is accompanied
by a principal component (PC) time series of length n, which
describes how the magnitude and sign of the EOF pattern varies over n. The
first few EOFs can usually be attributed to physical dynamical phenomena such
as seasonality or ENSO. By only retaining the leading EOFs, a dataset can be
cleaned of the (assumed) random noise captured by the lower-order EOFs. EOF
analysis was applied to the 20CRv2c annual mean surface temperature and
precipitation rate datasets to extract ENSO-like temperature and
precipitation patterns respectively. The EOF patterns were derived from
annual data because while this study does not attempt to reconstruct
interannual variability, it is concerned with climate changes that exhibit
the classic ENSO-like spatial pattern. The EOFs were selected on the basis of
their ability to capture the temporal evolution of ENSO, measured by
comparing the PC time series to the three ENSO indices
Correlations (Pearson product-moment correlation coefficient R)
between ENSO indices and 20CRv2c precipitation (P) and temperature (T)
PC2.
NINO3.4, calculated from SST anomalies in the equatorial Pacific
region of 5∘ N–5∘ S, 120–170∘ W
(http://www.esrl.noaa.gov/psd/gcos_wgsp/Timeseries/Nino34/);
Southern Oscillation Index (SOI), calculated from the
difference in sea level pressure (SLP) between Tahiti and Darwin,
and it is inversely linked to ENSO state (i.e. negative SOI during El Niño).
(http://www.cpc.ncep.noaa.gov/data/indices/soi);
Extended Multivariate ENSO Index (MEI.ext), calculated
from a combination of sea-level pressure and sea surface temperature (SST)
http://www.esrl.noaa.gov/psd/enso/mei.ext/table.ext.html.
Results for the highest-scoring PCs are shown in Table .
Correlations with all three indices were extremely high for PC2 (R2>0.62, p<0.001); thus, EOF2 was selected as a basis for weighting the
proxies. The precipitation and temperature EOF2 describe 11.57 and 9.75 %
of variance respectively. These EOFs were used for the final reconstructions
of ENSO-like climate change.
GCM data
For the creation of the network ensemble, GCM data were employed. The
objective of the add-one-in method is to create networks that accurately
reconstruct the long-term (30-year averaged) ENSO signal. 20CRv2c covers less
than 200 years, which is too short for meaningful evaluation of low-frequency
change, particularly if the dataset is to be partitioned into calibration and
validation sets. GCMs, meanwhile, offer much longer datasets. Although they
cannot simulate the real temporal climate change and variability of the past
1000 years, they are still useful for the building of proxy network
ensembles, which asks only that they simulate realistic modes of
spatio-temporal variability (i.e. EOF patterns). For the pseudo-proxy
experiment, the past1000 runs from two different GCMs were used for
calibration (GCMcal) and validation (GCMval).
The following six GCMs (followed by their climate modelling groups in
brackets) were considered as they have past1000 runs available:
CCSM4 (National Center for Atmospheric Research),
GISS-E2-R (NASA Goddard Institute for Space Studies),
MPI-ESM-P (Max-Planck-Institut für Meteorologie, Max Planck Institute for
Meteorology),
IPSL-CM5A-LR (Institute Pierre Simon Laplace),
BCC-CSM1-1 (Beijing Climate Center, China Meteorological
Administration),
MIROC-ESM (Atmosphere and Ocean Research Institute (The University of Tokyo),
National Institute for Environmental Studies, and Japan Agency for Marine–Earth Science and
Technology).
All model datasets were regridded to 2∘× 3∘.
Each GCM produces a different ENSO-like EOF pattern, with varying biases
; there is further variation among the
different model runs. For this study, the most accurate GCM past1000 runs
were selected in the sense that their EOF values at proxy locations were
most similar to the corresponding 20CRv2c values, which is the most realistic
EOF pattern of modern-day ENSO available here. The real modern ENSO pattern
will be different again, as will the real ENSO pattern of the past 1–2 millennia. By calibrating and validating the networks on datasets with
slightly varying realisations of this ENSO pattern, the sensitivity of the
networks to these variations is tested. EOF analysis was performed separately on the
piControl and past1000 runs of the six GCMs for precipitation and
temperature. For each analysis, the first three EOFs were retained
for comparison to the 20CRv2c ENSO-like EOF. The GCM EOF values of grid boxes
with proxies were compared to the corresponding 20CRv2c grid boxes via
Pearson rank correlation. The two GCMs with the highest correlations with
20CRv2c across the two runs were chosen separately as GCMcal and GCMval for
precipitation and temperature. As a result, CCSM4 (R≥0.79 in
piControl and past1000) and GISS-E2-R (R≥0.75) were chosen as
GCMcal and GCMval for precipitation; ISPL-CM5A-LR (R≥0.46)
and BCC-CSM1-1 (R≥0.44) were chosen as GCMcal and GCMval for
temperature.
Pseudo-proxies
Since there is no straightforward way of assessing the quality or relevance
of a proxy beyond the selection criteria already discussed, a pseudo-proxy
approach can aid in making a more objective and refined decision on how to
optimise the use of available proxy data . Since for
pseudo-proxies the real world is known (in this case the GCM-derived EOFs
and PCs), it is possible to quantify the skill of the reconstruction. While a
blanket approach (in which every available record is used) may sound
attractive, it increases the risk that some co-varying non-white noise in a
subset of the proxies skews the resulting reconstruction. This is, for
example, pertinent in North America where there is high clustering of tree
ring records. Testing showed that when all records were added to the
reconstruction, a regional trend obscured the ENSO-like signal (not shown).
The only case in which it is certain that using all available proxies is
preferred is when each grid box contains a (non-climatic) noise-free proxy
such that the network gives complete spatial and temporal coverage.
Pseudo-proxies are model or instrumental data series that are degraded by
applying transformations and/or adding noise to simulate the behaviour of a
real proxy . Proxy system models
attempt to characterise the mechanistic and forward processes connecting the
response of a proxy archive to an environmental signal and the subsequent
observation of this response; this includes accounting for nonlinearities,
multivariate responses, and measurement limitations. Many PSMs require an
isotope-enabled GCM or other data not available in the original sources of
the proxy data collected for this study, but the VS-Lite model is an easily
implemented PSM for simulating tree ring widths (TRW) that needs minimal
input . Using the R
package VS-LiteR available from GitHub
(https://github.com/suztolwinskiward/VSLiteR),
GCM precipitation and temperature data were combined to create pseudo-TRW
datasets, with which TRW proxies were represented in the pseudo-proxy
experiment here. The rest of the proxies were represented by the GCM raw
temperature or precipitation series.
These precipitation, temperature, and TRW series (taken from the real proxy
locations) were firstly degraded by adding white Gaussian noise. Information
on true signal-to-noise ratios (SNRs) of the various proxies is sparse in the
literature. However, where data were available on the relationship between the
proxy and the target climate variable in the form of R values, SNR can be
calculated via the following equation :
SNR=R1-R2.
Several original publications on individual publications provide such
information (see Tables and ), and
have conducted a systematic comparison of most coral
records included here against the Hadley Centre Global Sea Ice and Sea
Surface Temperature (HadISST) 1900–1990 dataset. Where no concrete
information was available, a value of SNR = 0.4 was prescribed. While this has
been shown to be a realistic average , comparison with the
calculated SNRs in this study suggests that it is in fact a relatively
conservative value.
After the addition of noise, the pseudo-proxies were degraded further to
reflect the length, time span, and resolution of the real proxies. Real
proxy temporal resolution between 0 and 1000 yr BP was applied; this period was
chosen as it is the focus of this study. The resolution was recreated by
assuming that each data point represents an average of the previous unsampled years; for example, in a proxy with a 10-year resolution, data point n was
recreated by taking the average over points (n-9):n.
Calibration: network creation
After screening proxy records, selecting GCMs, and creating pseudo-proxies, a
pseudo-proxy experiment was conducted to create an optimal network ensemble.
The calibration stage builds proxy networks in a stepwise manner by
incrementally adding the proxy that maximally improves the quality of the
network reconstruction. All proxies were first subjected to inverse transform
sampling (ITS) using the MATLAB script (translated to R) available
at https://github.com/CommonClimate/common-climate/blob/master/gaussianize.m.
ITS converts the distribution of a time series to a standard normal
distribution, thus reducing the bias introduced by nonlinearities inherent to
many proxy records . Lastly, the pseudo-proxies were
averaged to 30 years (using a simple average) to prevent high-resolution
records from dominating the signal. This averaging period was chosen since our
focus is on long-term ENSO state rather than inter-annual variability, and
it reflects the resolution of many individual low-frequency proxy
reconstructions
see. Then at
each stage (for each interim network), the pseudo-proxies in the network
(derived from GCMcal past1000) were first normalised to a base period
(n) where all proxies overlap via the equation
zt=ct-μnσn,
where zi is the normalised proxy value at time t, ct is the
original value at time t, μn is the mean of series c over
the base period n, and σn is the standard deviation over the same
base period. The length of n was ensured to be at least 100 years to reduce
the influence of noise on μ and σ. In some cases, this requirement
led to the rejection of one or more proxies because their length or position
in time were not compatible with the other records. This process of
normalisation is similar to the method used in . Proxies
that fall in the same grid box were averaged after normalisation. This
prevents over-representation of those locations and improves their SNR by
cancelling out some of the stochastic noise and amplifying their signal
.
Each normalised pseudo-proxy z at location [x,y] was multiplied by
a scaled version of the EOF value at location [x,y]. The scaling was such
that at each time step the absolute sum of the weights was 1, which accounts
for the fact that the number of locations with proxy data varies over the
reconstructed period (especially the beginning and end). The weight w at
proxy location i can thus be considered an indication of the relative
sensitivity of location i to the ENSO-like EOF pattern, which changes
depending on the number of proxies n available at each time step t and
their associated EOF values d:
wi,t=di,t∑i=1n|di,t|.
The resultant vector wi is thus the weighting series for proxy
location i. The n proxy series are multiplied by their corresponding
wi and are finally summed to create a single reconstruction
series (ENSOr):
ENSOr=∑i=1nziwi.
This is essentially a sparse reconstruction of the PC, which is the dot
product of the full raw dataset and EOF. The quality of a network was
assessed by comparing ENSOr with the PC using the Pearson rank
correlation R.
The entire calibration process was repeated 1000 times, each time using
pseudo-proxies with newly generated noise iterations, resulting in 1000 proxy
networks.
Validation: network evaluation
The validation stage tests the robustness of the networks using independent
data. Of each calibration network, 1000 pseudo-proxy versions were created
using GCMval pseudo-proxies and were tested for their ability to reconstruct
the target (the PC created using the GCMcal EOF and the GCMval
past1000 dataset) by calculating R. There are several measures of quality
for validation statistics, the most common of which are the coefficient of
determination R2, the reduction of error (RE) and the coefficient of
efficiency CE;. Discussions on the relative merits and
pitfalls of these measures can be found in the literature
see. Although CE is generally regarded as
the appropriate indicator for low-frequency reconstructions
, the nature of the method described here
reduces its effectiveness as a measure of quality. The past1000 and piControl
runs have little (or no) external forcing driving the simulation; hence, they
have very little low-frequency variability or very few trends. Moreover, the data are
z-normalised at various stages, removing any differences in means. As the CE
effectively tracks changes in the mean, this removal of the mean renders the CE
sensitive to spurious results. The diagnostic R was instead chosen as
comparison of the three indicators, showing that it was more effective at
picking high-quality reconstructions than the CE and RMSE, though generally a
high R value did correspond to high CE and low RMSE (not shown). R is
essentially equivalent to using R2, but it retains the ability to distinguish
between positive and negative correlations.
The R values from the 1000×1000 validation reconstructions
(Rval) were then compared to critical values Rcrit calculated for
each network. Again, 1000 reconstructions were made for each network, but
using GCMval piControl data instead of past1000. The piControl run
contains no external forcing and is therefore essentially noise, but it retains the
inherent climatological spatial correlations. From these reconstructions,
Rcrit for each network was determined by taking the 95th percentile
of the R values of the corresponding 1000 reconstructions. Where
Rval>Rcrit, the network was retained; where Rval<Rcrit, the
network was deemed unfit and was discarded. Networks sensitive to the choice
of dataset are thus weeded out.
The combination of using pseudo-proxies, the add-one-in approach and
Rcrit simultaneously accounts for proxy temporal resolution, spatial
distribution, and temporal coverage (i.e. proxy start and end dates) and
gives an estimate of the uncertainty due to proxy noise. However, an
important assumption is that the signal in all proxies is solely
temperature or precipitation, and it is thus still a best-case estimate.
Proxy ENSO ensemble
The optimal networks that passed the Rcrit test were used to create an
ensemble of real proxy reconstructions of ENSO-like climate change. The
remaining networks may not all be unique, further reducing the effective
number of networks. Presumably, networks that occur multiple times are more
effective proxy combinations; retaining the duplicates accordingly upweights
these networks in the final reconstruction. Error estimates for the
reconstructions were made using the 1000×1000 RMSE values
calculated at the validation stage. This was translated into ensemble member
uncertainty limits by adding and subtracting the 1000 error series from the
reconstruction time series (to get the maximum and minimum error limits
respectively) and taking the 5–95th percentile over their full range.
This error estimation explicitly takes into account the impact of network
choice as well as random error affecting the proxies.
Once proxy reconstructions and associated uncertainty estimates were
calculated for all ensemble members, they were renormalised to 100–650 yr BP
to make the trends and amplitudes comparable within and between temperature
and precipitation. The period 100–650 yr BP was chosen because it was common
to all reconstruction time series and only covers one of the two periods of
interest (the LIA). Although calibration to the instrumental period would
potentially allow us to quantify the absolute amplitude, this was not done
for two reasons. Firstly, the proxy data coverage during the instrumental
period and the preceding century was relatively low, reducing the confidence
in the reconstruction during that period; calibrating to this period would
thus increase the uncertainty on the rest of the reconstruction. Secondly,
any calibration to the instrumental data is necessarily biased towards
high-frequency trends . Within a 30-year-averaged series, the
number of comparison points with the instrumental period is extremely low.
The final proxy reconstruction was calculated as the ensemble mean. The
corresponding error estimate is a combination of the reconstruction ensemble
range and the error ranges for individual ensemble members.
The authors declare that they have no conflict of
interest.
Acknowledgements
Many thanks to the Databases of the NOAA World Data Center for
Paleoclimatology and Pangaea and all contributing authors for making
available the proxy data used in this study.
The GPCP and 20th Century Reanalysis V2 data were provided
by the NOAA/ESRL/OAR-PSD, Boulder, Colorado, USA, from their website at
http://www.esrl.noaa.gov/psd/.
Support for the Twentieth Century Reanalysis Project dataset is provided by
the US Department of Energy, Office of Science Innovative and Novel
Computational Impact on Theory and Experiment (DOE INCITE) program, and
the Office of Biological and Environmental Research (BER), and by the National
Oceanic and Atmospheric Administration Climate Program Office.
The GPCP combined precipitation data were developed and computed by the
NASA/Goddard Space Flight Center's Laboratory for Atmospheres as a
contribution to the GEWEX Global Precipitation Climatology Project.
We acknowledge the World Climate Research Programme's Working Group on
Coupled Modelling, which is responsible for CMIP, and we thank the climate
modelling groups listed in Sect. for producing and making
available their model output. For CMIP the US Department of Energy's
Program for Climate Model Diagnosis and Intercomparison provides coordinating
support and led development of software infrastructure in partnership with
the Global Organization for Earth System Science Portals.
L. Henke was supported by a University of Exeter Climate Change and
Sustainable Futures studentship.
Edited by: N. Abram
Reviewed by: two anonymous referees
ReferencesAbram, N. J., McGregor, H. V., Gagan, M. K., Hantoro, W. S., and Suwargadi,
B. W.: Oscillations in the southern extent of the Indo-Pacific Warm Pool
during the mid-Holocene, Quat. Sci. Rev., 28, 2794–2803,
10.1016/j.quascirev.2009.07.006, 2009.Alibert, C. and Kinsley, L.: A 170-year Sr/Ca and Ba/Ca coral record from the
western Pacific warm pool: 2. A window into variability of the New Ireland
Coastal Undercurrent, J. Geophys. Res.-Oceans, 113, 1–10,
10.1029/2007JC004263, 2008.Anchukaitis, K. J. and Evans, M. N.: Tropical cloud forest climate variability
and the demise of the Monteverde golden toad., P. Natl. Acad. Sci. USA, 107, 5036–5040, 10.1073/pnas.0908572107, 2010.Anchukaitis, K. J., Evans, M. N., Kaplan, A., Vaganov, E. A., Hughes, M. K.,
Grissino-Mayer, H. D., and Cane, M. A.: Forward modeling of regional scale
tree-ring patterns in the southeastern United States and the recent influence
of summer drought, Geophys. Res. Lett., 33, 2–5,
10.1029/2005GL025050, 2006.Anderson, L.: Holocene record of precipitation seasonality from lake calcite
δ18O in the central Rocky Mountains, United States, Geology, 39,
211–214, 10.1130/G31575.1, 2011.Anderson, L.: Rocky Mountain hydroclimate: Holocene variability and the role
of insolation, ENSO, and the North American Monsoon, Glob. Planet. Change,
92–93, 198–208, 10.1016/j.gloplacha.2012.05.012, 2012.Apaéstegui, J., Cruz, F. W., Sifeddine, A., Vuille, M., Espinoza, J. C.,
Guyot, J. L., Khodri, M., Strikis, N., Santos, R. V., Cheng, H., Edwards, L.,
Carvalho, E., and Santini, W.: Hydroclimate variability of the northwestern
Amazon Basin near the Andean foothills of Peru related to the South American
Monsoon System during the last 1600 years, Clim. Past, 10, 1967–1981,
10.5194/cp-10-1967-2014, 2014.Ashok, K., Behera, S. K., Rao, S. A., Weng, H., and Yamagata, T.: El Niño
Modoki and its possible teleconnection, J. Geophys. Res.-Oceans, 112, 1–27,
10.1029/2006JC003798, 2007.Asmerom, Y., Polyak, V., Burns, S. J., and Rassmussen, J.: Solar forcing of
Holocene climate: New insights from a speleothem record, southwestern United
States, Geology, 35, 1–4, 10.1130/G22865A.1, 2007.Badjeck, M.-C., Allison, E. H., Halls, A. S., and Dulvy, N. K.: Impacts of
climate variability and change on fishery-based livelihoods, Mar. Policy,
34, 375–383, 10.1016/j.marpol.2009.08.007, 2010.Baker, P. A., Fritz, S. C., Burns, S. J., Ekdahl, E., and Rigsby, C. A.: The
Nature and Origin of Decadal to Millennial Scale Climate Variability in the
Southern Tropics of South America: The Holocene Record of Lago Umayo, Peru,
in: Past Clim. Var. South Am. Surround. Reg. From Last Glacial Maximum to
Holocene, edited by: Vimeux, F., Sylvestre, F., and Khodri, M., chap. 13,
301–322, Springer Netherlands, Dordrecht, 14 edn.,
10.1007/978-90-481-2672-9_13, 2009.Barron, J. A.: High-resolution climatic evolution of coastal northern
California during the past 16,000 years, Paleoceanography, 18, 1020,
10.1029/2002PA000768, 2003.Barron, J. A. and Anderson, L.: Enhanced Late Holocene ENSO/PDO expression
along the margins of the eastern North Pacific, Quat. Int., 235, 3–12,
10.1016/j.quaint.2010.02.026, 2011.Bellenger, H., Guilyardi, E., Leloup, J., Lengaigne, M., and Vialard, J.: ENSO
representation in climate models: From CMIP3 to CMIP5, Clim. Dyn., 42,
1999–2018, 10.1007/s00382-013-1783-z, 2014.Berke, M. A., Johnson, T. C., Werne, J. P., Schouten, S., and Sinninghe
Damsté, J. S.: A mid-Holocene thermal maximum at the end of the
African Humid Period, Earth Planet. Sci. Lett., 351–352, 95–104,
10.1016/j.epsl.2012.07.008, 2012.Bird, B. W., Abbott, M. B., Rodbell, D. T., and Vuille, M.: Holocene tropical
South American hydroclimate revealed from a decadally resolved lake sediment
δ18O record, Earth Planet. Sci. Lett., 310, 192–202,
10.1016/j.epsl.2011.08.040, 2011.Black, D. E., Abahazi, M. A., Thunell, R. C., Kaplan, A., Tappa, E. J., and
Peterson, L. C.: An 8-century tropical Atlantic SST record from the Cariaco
Basin: Baseline variability, twentieth-century warming, and Atlantic
hurricane frequency, Paleoceanography, 22, 1–10,
10.1029/2007PA001427, 2007.Bonnefille, R. and Chalié, F.: Pollen-inferred precipitation time-series
from equatorial mountains, Africa, the last 40 kyr BP, Glob. Planet. Change,
26, 25–50, 10.1016/S0921-8181(00)00032-1, 2000.
Braconnot, P., Harrison, S. P., Otto-Bliesner, B. L., Abe-Ouchi, A., Jungclaus,
J. H., and Peterschmitt, J.-Y.: The Paleoclimate Modeling Intercomparison
Project contribution to CMIP5, CLIVAR Exch. Newsl., 16, 15–19, 2011.Braconnot, P., Harrison, S. P., Kageyama, M., Bartlein, P. J., Masson-Delmotte,
V., Abe-Ouchi, A., Otto-Bliesner, B., and Zhao, Y.: Evaluation of climate
models using palaeoclimatic data, Nat. Clim. Chang., 2, 417–424,
10.1038/nclimate1456, 2012.Braganza, K., Gergis, J. L., Power, S. B., Risbey, J. S., and Fowler, A. M.: A
multiproxy index of the El Niño-Southern Oscillation, A.D. 1525–1982,
J. Geophys. Res.-Atmos., 114, 1–17, 10.1029/2008JD010896, 2009.Breitenmoser, P., Brönnimann, S., and Frank, D.: Forward modelling of
tree-ring width and comparison with a global network of tree-ring
chronologies, Clim. Past, 10, 437–449, 10.5194/cp-10-437-2014, 2014.Broccoli, A. J., Dahl, K. A., and Stouffer, R. J.: Response of the ITCZ to
Northern Hemisphere cooling, Geophys. Res. Lett., 33, L01702,
10.1029/2005GL024546,
2006.Brook, G., Sheen, S.-W., Rafter, M., Railsback, L. B., and Lundberg, J.: A
high-resolution proxy record of rainfall and ENSO since AD 1550 from layering
in stalagmites from Anjohibe Cave, Madagascar, The Holocene, 9, 695–705,
10.1191/095968399677907790, 1999.Buckley, B. M., Anchukaitis, K. J., Penny, D., Fletcher, R., Cook, E. R., Sano,
M., Nam, L. C., Wichienkeeo, A., Minh, T. T., and Hong, T. M.: Climate as a
contributing factor in the demise of Angkor, Cambodia., P. Natl. Acad. Sci. USA, 107, 6748–6752, 10.1073/pnas.0910827107, 2010.Bürger, G.: On the verification of climate reconstructions, Clim. Past,
3, 397–409, 10.5194/cp-3-397-2007, 2007.Cai, W., Santoso, A., Wang, G., Yeh, S.-w., An, S.-i., Cobb, K. M., Collins,
M., Guilyardi, E., Jin, F.-f., Kug, J.-s., Lengaigne, M., and McPhaden,
M. J.: ENSO and greenhouse warming, Nat. Publ. Gr., 5, 849–859,
10.1038/nclimate2743, 2015a.Cai, W., Wang, G., Santoso, A., McPhaden, M. J., Wu, L., Jin, F.-F.,
Timmermann, A., Collins, M., Vecchi, G., Lengaigne, M., England, M. H.,
Dommenget, D., Takahashi, K., and Guilyardi, E.: Increased frequency of
extreme La Niña events under greenhouse warming, Nat. Clim. Chang., 5,
132–137, 10.1038/nclimate2492, 2015b.Cane, M. A.: The evolution of El Niño, past and future, Earth Planet.
Sci. Lett., 230, 227–240, 10.1016/j.epsl.2004.12.003, 2005.Carré, M., Bentaleb, I., Fontugne, M., and Lavallée, D.: Strong El
Niño events during the early Holocene: stable isotope evidence from
Peruvian sea shells, The Holocene, 15, 42–47,
10.1191/0959683605h1782rp, 2005.Chen, J., Chen, F., Zhang, E., Brooks, S. J., Zhou, A., and Zhang, J.: A
1000-year chironomid-based salinity reconstruction from varved sediments of
Sugan Lake, Qaidam Basin, arid Northwest China, and its palaeoclimatic
significance, Chinese Sci. Bull., 54, 3749–3759,
10.1007/s11434-009-0201-8, 2009.Christie, D. A., Boninsegna, J. A., Cleaveland, M. K., Lara, A., Le Quesne,
C., Morales, M. S., Mudelsee, M., Stahle, D. W., and Villalba, R.: Aridity
changes in the Temperate-Mediterranean transition of the Andes since AD 1346
reconstructed from tree-rings, Clim. Dyn., 36, 1505–1521,
10.1007/s00382-009-0723-4, 2011.Cleaveland, M. K., Stahle, D. W., Therrell, M. D., Villanueva-Diaz, J., and
Burns, B. T.: Tree-ring reconstructed winter precipitation and tropical
teleconnections in Durango, Mexico, Clim. Change, 59, 369–388,
10.1023/A:1024835630188, 2003.
Clement, A. C.: Orbital controls on ENSO and the tropical climate,
Paleoceanography, 14, 441–456, 1999.Clement, A. C., Cane, M. A., and Seager, R.: An orbitally driven tropical
source for abrupt climate change, J. Climate, 14, 2369–2375,
10.1175/1520-0442(2001)014<2369:AODTSF>2.0.CO;2, 2001.Coats, S., Smerdon, J. E., Cook, B. I., and Seager, R.: Stationarity of the
tropical pacific teleconnection to North America in CMIP5/PMIP3 model
simulations, Geophys. Res. Lett., 40, 4927–4932, 10.1002/grl.50938,
2013.Collins, M.: El Niño- or La Niña-like climate change?, Clim. Dyn., 24,
89–104, 10.1007/s00382-004-0478-x, 2005.Comboul, M., Emile-Geay, J., Evans, M. N., Mirnateghi, N., Cobb, K. M., and
Thompson, D. M.: A probabilistic model of chronological errors in
layer-counted climate proxies: applications to annually banded coral
archives, Clim. Past, 10, 825–841, 10.5194/cp-10-825-2014, 2014.
Comboul, M., Emile-Geay, J., Hakim, G. J., and Evans, M. N.: Paleoclimate
Sampling as a Sensor Placement Problem, J. Climate, 28, 7717–7740, 2015.Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J.,
Yin, X., Gleason, B. E., Vose, R. S., Rutledge, G., Bessemoulin, P.,
Bronnimann, S., Brunet, M., Crouthamel, R. I., Grant, A. N., Groisman, P. Y.,
Jones, P. D., Kruk, M. C., Kruger, A. C., Marshall, G. J., Maugeri, M., Mok,
H. Y., Nordli, O., Ross, T. F., Trigo, R. M., Wang, X. L., Woodruff, S. D.,
and Worley, S. J.: The Twentieth Century Reanalysis Project, Q. J. Roy. Meteor. Soc., 137, 1–28, 10.1002/qj.776, 2011.
Conroy, J., Overpeck, J. T., and Cole, J. E.: El Niño-Southern
Oscillation and changes in the zonal gradient of tropical Pacific sea surface
temperature over the last 1.2 ka, PAGES News, 18, 1–5, 2010.Conroy, J. L., Overpeck, J. T., Cole, J. E., Shanahan, T. M., and
Steinitz-Kannan, M.: Holocene changes in eastern tropical Pacific climate
inferred from a Galápagos lake sediment record, Quat. Sci. Rev., 27,
1166–1180, 10.1016/j.quascirev.2008.02.015, 2008.Conroy, J. L., Restrepo, A., Overpeck, J. T., Steinitz-Kannan, M., Cole, J. E.,
Bush, M. B., and Colinvaux, P. A.: Unprecedented recent warming of surface
temperatures in the eastern tropical Pacific Ocean, Nat. Geosci., 2, 46–50,
10.1038/ngeo390, 2009.Cook, E. R., Briffa, K. R., and Jones, P. D.: Spatial regression methods in
dendroclimatology: A review and comparison of two techniques, Int. J.
Climatol., 14, 379–402, 10.1002/joc.3370140404, 1994.
Cook, E. R., Briffa, K. R., Meko, D. M., Graybill, D. A., and Funkhouser, G.:
The “segment length curse” in long tree-ring chronology development for
palaeoclimatic studies, The Holocene, 5, 229–237, 1995.Cook, E. R., Buckley, B. M., D'Arrigo, R. D., and Peterson, M. J.: Warm-season
temperatures since 1600 BC reconstructed from Tasmanian tree rings and their
relationship to large-scale sea surface temperature anomalies, Clim. Dyn.,
16, 79–91, 10.1007/s003820050006, 2000.
Corrège, T., Delcroix, T., Récy, J., Beck, W., Cabioch, G., and Le
Cornec, F.: Evidence for stronger El Niño-Southern Oscillation (ENSO)
events in a mid-Holocene massive coral, Paleoceanography, 15, 465–470,
2000.Crausbay, S. D., Russell, J. M., and Schnurrenberger, D. W.: A ca. 800-year
lithologic record of drought from sub-annually laminated lake sediment, East
Java, J. Paleolimnol., 35, 641–659, 10.1007/s10933-005-4440-7, 2006.Cronin, T. M., Dwyer, G. S., Kamiya, T., Schwede, S., and Willard, D. A.:
Medieval Warm Period, Little Ice Age and 20th century temperature
variability from Chesapeake Bay, Glob. Planet. Change, 36, 17–29,
10.1016/S0921-8181(02)00161-3, 2003.Cronin, T. M., Thunell, R., Dwyer, G. S., Saenger, C., Mann, M. E., Vann, C.,
and Seal, I. R.: Multiproxy evidence of Holocene climate variability from
estuarine sediments, eastern North America, Paleoceanography, 20,
10.1029/2005PA001145, 2005.Curtis, J. H., Hodelle, D. A., and Brenner, M.: Climate variability on the
Yucatán Peninsula (Mexico) during the past 3500 years, and implications for
Maya cultural evolution, Quat. Res., 46, 37–47,
10.1006/qres.1996.0042, 1996.D'Arrigo, R. and Wilson, R.: On the Asian expression of the PDO, Int. J.
Climatol., 26, 1607–1617, 10.1002/joc.1326, 2006.Dee, S., Emile-Geay, J., Evans, M. N., Allam, A., Steig, E. J., and Thompson,
D. M.: PRYSM: An open-source framework for PRoxY System Modeling, with
applications to oxygen-isotope systems, J. Adv. Model. Earth Syst., 7,
1220–1247, 10.1002/2015MS000447, 2015.DeLong, K. L., Quinn, T. M., Taylor, F. W., Lin, K., and Shen, C.-C.: Sea
surface temperature variability in the southwest tropical Pacific since AD
1649, Nat. Clim. Chang., 2, 799–804, 10.1038/nclimate1583, 2012.DeLong, K. L., Quinn, T. M., Taylor, F. W., Shen, C. C., and Lin, K.:
Improving coral-base paleoclimate reconstructions by replicating 350years of
coral Sr/Ca variations, Palaeogeogr. Palaeoclimatol. Palaeoecol., 373,
6–24, 10.1016/j.palaeo.2012.08.019, 2013.DeLong, K. L., Flannery, J. A., Poore, R. Z., Quinn, T. M., Maupin, C. R., Lin,
K., and Shen, C. C.: A reconstruction of sea surface temperature variability
in the southeastern Gulf of Mexico from 1734 to 2008 C.E. using cross-dated
Sr/Ca records from the coral Siderastrea siderea, Paleoceanography, 29,
403–422, 10.1002/2013PA002524, 2014.Denniston, R. F., Villarini, G., Gonzales, A. N., Wyrwoll, K.-H., Polyak,
V. J., Ummenhofer, C. C., Lachniet, M. S., Wanamaker, A. D., Humphreys,
W. F., Woods, D., and Cugley, J.: Extreme rainfall activity in the
Australian tropics reflects changes in the El Niño/Southern Oscillation
over the last two millennia., P. Natl. Acad. Sci. USA, 112,
4576–4581, 10.1073/pnas.1422270112, 2015.Deser, C. and Wallace, J. M.: Large-Scale Atmospheric Circulation Features of
Warm and Cold Episodes in the Tropical Pacific, J. Climate, 3, 1254–1281,
10.1175/1520-0442(1990)003<1254:LSACFO>2.0.CO;2, 1990.Deser, C., Trenberth, K., and Staff, N. C. F. A. R.: The Climate Data Guide:
Pacific Decadal Oscillation (PDO): Definition and Indices, available at:
https://climatedataguide.ucar.edu/climate-data/pacific-decadal-oscillation-pdo-definition-and-indices (last access: 22 March 2017), 2016.Díaz, S. C., Therrell, M. D., Stahle, D. W., and Cleaveland, M. K.:
Chihuahua (Mexico) winter-spring precipitation reconstructed from
tree-rings, 1647-1992, Clim. Res., 22, 237–244, 10.3354/cr022237,
2002.Emile-Geay, J. and Tingley, M.: Inferring climate variability from nonlinear
proxies: application to palaeo-ENSO studies, Clim. Past, 12, 31–50,
10.5194/cp-12-31-2016, 2016.Emile-Geay, J., Cobb, K. M., Mann, M. E., and Wittenberg, A. T.: Estimating
central equatorial pacific SST variability over the past millennium. Part I:
Methodology and validation, J. Climate, 26, 2302–2328,
10.1175/JCLI-D-11-00510.1, 2013a.Emile-Geay, J., Cobb, K. M., Mann, M. E., and Wittenberg, A. T.: Estimating
central equatorial pacific SST variability over the past millennium. Part II:
Reconstructions and Implications, J. Climate, 26, 2329–2352,
10.1175/JCLI-D-11-00511.1, 2013b.ESG-CET: Earth System Grid-Center for Enabling Technologies, CMIP5 Project
Data, available at: https://esgf-node.llnl.gov, last access:
27 March 2017.Esper, J., Cook, E. R., and Schweingruber, F. H.: Low-frequency signals in
long tree-ring chronologies for reconstructing past temperature
variability., Science, 295, 2250–2253, 10.1126/science.1066208, 2002.Evans, M. N., Kaplan, A., and Cane, M. A.: Optimal sites for coral-based
reconstruction of global sea surface temperature, Paleoceanography, 13,
502–516, 10.1029/98PA02132, 1998.
Evans, M. N., Tolwinski-Ward, S. E., Thompson, D. M., and Anchukaitis, K. J.:
Applications of proxy system modeling in high resolution paleoclimatology,
Quat. Sci. Rev., 76, 16–28, 2013.Evans, M. N., Smerdon, J. E., Kaplan, A., Tolwinski-Ward, S. E., and
González-Rouco, J. F.: Climate field reconstruction uncertainty
arising from multivariate and nonlinear properties of predictors, Geophys.
Res. Lett., 41, 9127–9134, 10.1002/2014GL062063, 2014.Faulstich, H. L., Woodhouse, C. A., and Griffin, D.: Reconstructed cool- and
warm-season precipitation over the tribal lands of northeastern Arizona,
Clim. Change, 118, 457–468, 10.1007/s10584-012-0626-y, 2013.Felis, T., Suzuki, A., Kuhnert, H., Dima, M., Lohmann, G., and Kawahata, H.:
Subtropical coral reveals abrupt early-twentieth-century freshening in the
western North Pacific Ocean, Geology, 37, 527–530, 10.1130/G25581A.1,
2009.Felis, T., Suzuki, A., Kuhnert, H., Rimbu, N., and Kawahata, H.: Pacific
decadal oscillation documented in a coral record of North Pacific winter
temperature since 1873, Geophys. Res. Lett., 37, 2000–2005,
10.1029/2010GL043572, 2010.Gallant, A. J. E., Phipps, S. J., Karoly, D. J., Mullan, A. B., and Lorrey,
A. M.: Nonstationary Australasian teleconnections and implications for
paleoclimate reconstructions, J. Climate, 26, 8827–8849,
10.1175/JCLI-D-12-00338.1, 2013.Gomez, B., Carter, L., Trustrum, N. A., Palmer, A. S., and Roberts, A. P.: El
Niño-Southern Oscillation signal associated with middle Holocene
climate change in intercorrelated terrestrial and marine sediment cores,
North Island, New Zealand, Geology, 32, 653–656, 10.1130/G20720.1,
2004.Goni, M. A., Thunell, R. C., Woodwort, M. P., and Müller-Karger, F. E.:
Changes in wind-driven upwelling during the last three centuries: Interocean
teleconnections, Geophys. Res. Lett., 33, 3–6, 10.1029/2006GL026415,
2006.Goodkin, N. F., Hughen, K. A., Curry, W. B., Doney, S. C., and Ostermann,
D. R.: Sea surface temperature and salinity variability at Bermuda during
the end of the Little Ice Age, Paleoceanography, 23, 1–13,
10.1029/2007PA001532, 2008.Graham, N. E., Hughes, M. K., Ammann, C. M., Cobb, K. M., Hoerling, M. P.,
Kennett, D. J., Kennett, J. P., Rein, B., Stott, L., Wigand, P. E., and Xu,
T.: Tropical Pacific – Mid-latitude teleconnections in medieval times,
Clim. Change, 83, 241–285, 10.1007/s10584-007-9239-2, 2007.Griffin, D., Woodhouse, C. A., Meko, D. M., Stahle, D. W., Faulstich, H. L.,
Carrillo, C., Touchan, R., Castro, C. L., and Leavitt, S. W.: North American
monsoon precipitation reconstructed from tree-ring latewood, Geophys. Res.
Lett., 40, 954–958, 10.1002/grl.50184, 2013.Griffiths, M. L., Kimbrough, A. K., Gagan, M. K., Drysdale, R. N., Cole, J. E.,
Johnson, K. R., Zhao, J.-X., Cook, B. I., Hellstrom, J. C., and Hantoro,
W. S.: Western Pacific hydroclimate linked to global climate variability
over the past two millennia, Nat. Commun., 7, 11719,
10.1038/ncomms11719,
2016.Guilyardi, E., Wittenberg, A., Fedorov, A., Collins, M., Wang, C., Capotondi,
A., van Oldenborgh, G. J., and Stockdale, T.: Understanding El Niño in
ocean-atmosphere general circulation models: Progress and challenges, B. Am. Meteorol. Soc., 90, 325–340, 10.1175/2008BAMS2387.1, 2009.Haug, G. H., Hughen, K. A., Sigman, D. M., Peterson, L. C., and Röhl, U.:
Southward migration of the intertropical convergence zone through the
Holocene., Science, 293, 1304–1308, 10.1126/science.1059725, 2001.Hendy, E. J., Gagan, M. K., and Lough, J. M.: Chronological control of coral
records using luminescent lines and evidence for non-stationary ENSO
teleconnections in northeast Australia, The Holocene, 13, 187–199,
10.1191/0959683603hl606rp, 2003.Henke, L. M. K., Lambert, F. H., and Charman, D. J.: Proxy data, figshare,
10.6084/m9.figshare.4787575.v1, last access: 27 March 2017.Henley, B. J., Gergis, J., Karoly, D. J., Power, S., Kennedy, J., and Folland,
C. K.: A Tripole Index for the Interdecadal Pacific Oscillation, Clim.
Dyn., 45, 3077–3090, 10.1007/s00382-015-2525-1,
2015.Hetzinger, S., Pfeiffer, M., Dullo, W. C., Garbe-Schönberg, D., and Halfar,
J.: Rapid 20th century warming in the Caribbean and impact of remote forcing
on climate in the northern tropical Atlantic as recorded in a Guadeloupe
coral, Palaeogeogr. Palaeoclimatol. Palaeoecol., 296, 111–124,
10.1016/j.palaeo.2010.06.019, 2010.Hjelle, B. and Glass, G. E.: Outbreak of hantavirus infection in the Four
Corners region of the United States in the wake of the 1997-1998 El
Niño-Southern Oscillation., J. Infect. Dis., 181, 1569–1573,
10.1086/315467, 2000.Hodell, D. A., Curtis, J. H., and Brenner, M.: Possible role of climate in the
collapse of Classic Maya civilization, Nature, 375, 391–394,
10.1038/375391a0, 1995.Hodell, D. a., Brenner, M., Curtis, J. H., Medina-González, R.,
Ildefonso-Chan Can, E., Albornaz-Pat, A., and Guilderson, T. P.: Climate
change on the Yucatan Peninsula during the Little Ice Age, Quat. Res., 63,
109–121, 10.1016/j.yqres.2004.11.004,
2005.Ivanochko, T. S., Ganeshram, R. S., Brummer, G. J. a., Ganssen, G., Jung, S.
J. A., Moreton, S. G., and Kroon, D.: Variations in tropical convection as
an amplifier of global climate change at the millennial scale, Earth Planet.
Sci. Lett., 235, 302–314, 10.1016/j.epsl.2005.04.002, 2005.Jones, P., Briffa, K., Osborn, T., Lough, J., van Ommen, T., Vinther, B.,
Luterbacher, J., Wahl, E., Zwiers, F., Mann, M., Schmidt, G., Ammann, C.,
Buckley, B., Cobb, K., Esper, J., Goosse, H., Graham, N., Jansen, E., Kiefer,
T., Kull, C., Küttel, M., Mosley-Thompson, E., Overpeck, J., Riedwyl,
N., Schulz, M., Tudhope, A., Villalba, R., Wanner, H., Wolff, E. W., Xoplaki,
E., Kuttel, M., Mosley-Thompson, E., Overpeck, J., Riedwyl, N., Schulz, M.,
Tudhope, A., Villalba, R., Wanner, H., Wolff, E. W., and Xoplaki, E.:
High-resolution palaeoclimatology of the last millennium: a review of
current status and future prospects, The Holocene, 19, 3–49,
10.1177/0959683608098952, 2009.Jones, P. D. and Mann, M. E.: Climate over past millennia, Rev. Geophys., 42,
1–42, 10.1029/2003RG000143, 2004.Karamperidou, C., Di Nezio, P. N., Timmermann, A., Jin, F. F., and Cobb,
K. M.: The response of ENSO flavors to mid-Holocene climate: Implications
for proxy interpretation, Paleoceanography, 30, 527–547,
10.1002/2014PA002742, 2015.Keigwin, L. D.: The Little Ice Age and Medieval Warm Period in the Sargasso
Sea, Science, 274, 1504–1508, 10.1126/science.274.5292.1504, 1996.Kellerhals, T., Brütsch, S., Sigl, M., Knüsel, S., Gäggeler,
H. W., and Schwikowski, M.: Ammonium concentration in ice cores: A new proxy
for regional temperature reconstruction?, J. Geophys. Res.-Atmos., 115,
1–8, 10.1029/2009JD012603, 2010.Kennett, D., Breitenbach, S., Aquino, V., Asmerom, Y., Awe, J., Baldini, J.,
Bartlein, P., Culleton, B., Ebert, C., Jazwa, C., Macri, M., Marwan, N.,
Polyak, V., Prufer, K., Ridley, H., Sodemann, H., Winterhalder, B., and Haug,
G.: Development and disintegration of Maya political systems in response to
climate change., Science, 788, 788–791, 10.1126/science.1226299,
2012.Khider, D., Jackson, C. S., and Stott, L. D.: Assessing millennial-scale
variability during the Holocene: A perspective from the western tropical
Pacific, Paleoceanography, 29, 143–159, 10.1002/2013PA002534, 2014.Kilbourne, K. H., Quinn, T. M., Webb, R., Guilderson, T., Nyberg, J., and
Winter, A.: Paleoclimate proxy perspective on Caribbean climate since the
year 1751: Evidence of cooler temperatures and multidecadal variability,
Paleoceanography, 23, 1–14, 10.1029/2008PA001598, 2008.Kim, S. T. and Yu, J. Y.: The two types of ENSO in CMIP5 models, Geophys.
Res. Lett., 39, 1–6, 10.1029/2012GL052006, 2012.Klein, S. a., Soden, B. J., and Lau, N. C.: Remote sea surface temperature
variations during ENSO: Evidence for a tropical atmospheric bridge, J. Climate, 12, 917–932, 10.1175/1520-0442(1999)012<0917:RSSTVD>2.0.CO;2,
1999.
Kovats, R. S., Bouma, M. J., Hajat, S., Worrall, E., and Haines, A.: El
Niño and health, Lancet, 362, 1481–1489, 2003.Krusic, P. J., Cook, E. R., Dukpa, D., Putnam, A. E., Rupper, S., and Schaefer,
J.: Six hundred thirty-eight years of summer temperature variability over
the Bhutanese Himalaya, Geophys. Res. Lett., 42, 2988–2994,
10.1002/2015GL063566, 2015.Kuhnert, H., Pätzold, J., Hatcher, B., Wyrwoll, K. H., Eisenhauer, A.,
Collins, L. B., Zhu, Z. R., and Wefer, G.: A 200-year coral stable oxygen
isotope record from a high-latitude reef off Western Australia, Coral Reefs,
18, 1–12, 10.1007/s003380050147, 1999.Kuhnert, H., Crüger, T., and Pätzold, J.: NAO signature in a
Bermuda coral Sr/Ca record, Geochem. Geophy. Geosy., 6, Q04004,
10.1029/2004GC000786, 2005.Langton, S. J., Linsley, B. K., Robinson, R. S., Rosenthal, Y., Oppo, D. W.,
Eglinton, T. I., Howe, S. S., Djajadihardja, Y. S., and Syamsudin, F.: 3500
yr record of centennial-scale climate variability from the Western Pacific
Warm Pool, Geology, 36, 795–798, 10.1130/G24926A.1, 2008.Lewis, S. C. and LeGrande, A. N.: Stability of ENSO and its tropical Pacific
teleconnections over the Last Millennium, Clim. Past, 11, 1347–1360,
10.5194/cp-11-1347-2015, 2015.Li, J., Xie, S.-P., Cook, E. R., Morales, M. S., Christie, D. A., Johnson,
N. C., Chen, F., D'Arrigo, R., Fowler, A. M., Gou, X., and Fang, K.: El
Niño modulations over the past seven centuries, Nat. Clim. Chang., 3,
822–826, 10.1038/nclimate1936, 2013.Linsley, B. K., Dunbar, R. B., Wellington, G. M., and Mucciarone, D. A.: A
coral-based reconstruction of Intertropical Convergence Zone variability over
Central America since 1707, J. Geophys. Res., 99, 9977–9994,
10.1029/94JC00360, 1994.Linsley, B. K., Wellington, G. M., Schrag, D. P., Ren, L., Salinger, M. J., and
Tudhope, A. W.: Geochemical evidence from corals for changes in the
amplitude and spatial pattern of South Pacific interdecadal climate
variability over the last 300 years, Clim. Dyn., 22, 1–11,
10.1007/s00382-003-0364-y, 2004.Linsley, B. K., Kaplan, A., Gouriou, Y., Salinger, J., DeMenocal, P. B.,
Wellington, G. M., and Howe, S. S.: Tracking the extent of the South Pacific
Convergence Zone since the early 1600s, Geochem. Geophy. Geosy.,
7, 1–15, 10.1029/2005GC001115, 2006.MacDonald, G. M. and Case, R. A.: Variations in the Pacific Decadal
Oscillation over the past millennium, Geophys. Res. Lett., 32, 1–4,
10.1029/2005GL022478, 2005.Makou, M. C., Eglinton, T. I., Oppo, D. W., and Hughen, K. A.: Postglacial
changes in El Niño and La Niña behavior, Geology, 38, 43–46,
10.1130/G30366.1, 2010.Mann, M. E.: Climate reconstruction using “Pseudoproxies”, Geophys. Res.
Lett., 29, 10–13, 10.1029/2001GL014554, 2002.Mann, M. E., Cane, M. A., Zebiak, S. E., and Clement, A. C.: Volcanic and
solar forcing of the tropical Pacific over the past 1000 years, J. Climate,
18, 447–456, 10.1175/JCLI-3276.1, 2005.Mann, M. E., Zhang, Z., Hughes, M. K., Bradley, R. S., Miller, S. K.,
Rutherford, S., and Ni, F.: Proxy-based reconstructions of hemispheric and
global surface temperature variations over the past two millennia, P. Natl. Acad. Sci. USA, 105, 13252–13257,
10.1073/pnas.0805721105, 2008 (data available at: https://www.ncdc.noaa.gov/data-access/paleoclimatology-data/datasets).Mann, M. E., Zhang, Z., Rutherford, S., Bradley, R. S., Hughes, M. K.,
Shindell, D., Ammann, C., Faluvegi, G., and Ni, F.: Global signatures and
dynamical origins of the Little Ice Age and Medieval Climate Anomaly,
Science, 326, 1256–1260, 10.1126/science.1177303, 2009.Marchitto, T. M., Muscheler, R., Ortiz, J. D., Carriquiry, J. D., and van Geen,
A.: Dynamical response of the tropical Pacific Ocean to solar forcing during
the early Holocene, Science, 330, 1378–1381,
10.1126/science.1194887, 2010.Masson-Delmotte, V., Schulz, M., Abe-Ouchi, A., Beer, J., Ganopolski, A.,
González Rouco, J. F., Jansen, E., Lambeck, K., Luterbacher, J.,
Naish, T., Osborn, T., Otto-Bliesner, B., Quinn, T., Ramesh, R., Rojas, M.,
Shao, X., Timmermann, A., and Rouco, J. F. G.: Information from Paleoclimate
Archives, in: Clim. Chang. 2013 Phys. Sci. Basis. Contrib. Work. Gr. I to
Fifth Assess. Rep. Intergov. Panel Clim. Chang., edited by: Stocker, T., Qin,
D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia,
Y., Bex, V., and Midgley, P., 383–464, Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA,
10.1017/CBO9781107415324, 2013.Maupin, C. R., Partin, J. W., Shen, C.-C., Quinn, T. M., Lin, K., Taylor, F.
W., Banner, J. L., Thirumalai, K., and Sinclair, D. J.: Persistent
decadal-scale rainfall variability in the tropical South Pacific Convergence
Zone through the past six centuries, Clim. Past, 10, 1319–1332,
10.5194/cp-10-1319-2014, 2014.McCulloch, M., Mortimer, G., Esat, T., Xianhua, L., Pillans, B., and Chappell,
J.: High resolution windows into early Holocene climate: Sr/Ca coral records
from the Huon Peninsula, Earth Planet. Sci. Lett., 138, 169–178,
10.1016/0012-821X(95)00230-A, 1996.McGregor, H. V., Gagan, M. K., McCulloch, M. T., Hodge, E., and Mortimer, G.:
Mid-Holocene variability in the marine 14C reservoir age for northern
coastal Papua New Guinea, Quat. Geochronol., 3, 213–225,
10.1016/j.quageo.2007.11.002, 2008.McGregor, S., Timmermann, A., and Timm, O.: A unified proxy for ENSO and PDO
variability since 1650, Clim. Past, 6, 1–17, 10.5194/cp-6-1-2010, 2010.Medina-Elizalde, M., Burns, S. J., Lea, D. W., Asmerom, Y., von Gunten, L.,
Polyak, V., Vuille, M., and Karmalkar, A.: High resolution stalagmite
climate record from the Yucatán Peninsula spanning the Maya terminal
classic period, Earth Planet. Sci. Lett., 298, 255–262,
10.1016/j.epsl.2010.08.016, 2010.Metcalfe, S., Jones, M., Davies, S., Noren, A., and MacKenzie, A.: Climate
variability over the last two millenia in the North American Monsoon region,
recorded in laminated lake sediments from Labuna de Juanacatlán, Mexico,
The Holocene, 20, 1195–1206, 10.1177/0959683610371994, 2010.Moy, A. D., Seltzer, G. O., Rodbell, D. T., and Andersons, D. M.: Variability
of El Niño/Southern Oscillation activity at millennial timescales
during the Holocene epoch, Nature, 420, 162–165,
10.1038/nature01194, 2002.Nelson, D. B., Abbott, M. B., Steinman, B., Polissar, P. J., Stansell, N. D.,
Ortiz, J. D., Rosenmeier, M. F., Finney, B. P., and Riedel, J.: Drought
variability in the Pacific Northwest from a 6,000-yr lake sediment record,
P. Natl. Acad. Sci. USA, 108, 3870–3875,
10.1073/pnas.1009194108, 2011.Neukom, R., Gergis, J., and Karoly, D.: Inter-hemispheric temperature
variability over the past millennium, Nat. Clim. Chang., 4, 1–6,
10.1038/NCLIMATE2174, 2014.Newman, M., Compo, G. P., and Alexander, M. A.: ENSO-forced variability of the
Pacific Decadal Oscillation, J. Climate, 16, 3853–3857,
10.1175/1520-0442(2003)016<3853:EVOTPD>2.0.CO;2, 2003.NOAA/ESRL/OAR-PSD: NOAA-CIRES Twentieth Century Reanalysis (V2c): Monthly
Mean Single Level (Analyses and Forecasts), available at:
https://www.esrl.noaa.gov/psd/data/gridded/data.20thC_ReanV2c.monolevel.mm.html),
2014.Nurhati, I. S., Cobb, K. M., Charles, C. D., and Dunbar, R. B.: Late 20th
century warming and freshening in the central tropical Pacific, Geophys.
Res. Lett., 36, 1–4, 10.1029/2009GL040270, 2009.Oppo, D. W., Rosenthal, Y., and Linsley, B. K.: 2,000-year-long temperature
and hydrology reconstructions from the Indo-Pacific warm pool, Nature, 460,
1113–1116, 10.1038/nature08233, 2009.Page, S. E., Siegert, F., Rieley, J. O., Boehm, H.-D. V., Jaya, A., and Limin,
S.: The amount of carbon released from peat and forest fires in Indonesia
during 1997, Nature, 420, 61–65, 10.1038/nature01131, 2002.PAGES2k Consortium, T.: Continental-scale temperature variability during the
past two millennia, Nat. Geosci., 6, 503–503, 10.1038/ngeo1849,
2013.Partin, J. W., Cobb, K. M., Adkins, J. F., Clark, B., and Fernandez, D. P.:
Millennial-scale trends in west Pacific warm pool hydrology since the Last
Glacial Maximum, Nature, 449, 452–455, 10.1038/nature08125, 2007.Partin, J. W., Quinn, T. M., Shen, C. C., Emile-Geay, J., Taylor, F. W.,
Maupin, C. R., Lin, K., Jackson, C. S., Banner, J. L., Sinclair, D. J., and
Huh, C. A.: Multidecadal rainfall variability in south pacific convergence
zone as revealed by stalagmite geochemistry, Geology, 41, 1143–1146,
10.1130/G34718.1, 2013.
Pohl, K., Therrell, M. D., Blay, G. S., Ayotte, N., Hernandez, J. J. C.,
Castro, S. D., Oviedo, E. C., Elvir, J. A., Elizondo, M. G., Opland, D.,
Park, J., Pederson, G., Salazar, S. B., Selem, L. V., Villanueva-Diaz, J.,
and Stahle, D. W.: A cool season precipitation reconstruction for Satillo,
Mexico, Tree-ring Res., 95, 11–19, 2003.Poli, P., Hersbach, H., Dee, D. P., Berrisford, P., Simmons, A. J., Vitart, F.,
Laloyaux, P., Tan, D. G. H., Peubey, C., Thépaut, J.-N.,
Trémolet, Y., Hólm, E. V., Bonavita, M., Isaksen, L., and Fisher,
M.: ERA-20C: An Atmospheric Reanalysis of the Twentieth Century, J. Climate,
29, 4083–4097, 10.1175/JCLI-D-15-0556.1,
2016.Power, S. and Colman, R.: Multi-year predictability in a coupled general
circulation model, Clim. Dyn., 26, 247–272,
10.1007/s00382-005-0055-y,
2006.
Power, S., Tseitkin, F., Torok, S., Lavery, B., Dahni, R., and McAcaney, B.:
Australian tempertature, Australian rainfall and the Southern Oscillation,
1910-1992 - coherant variability and recent changes, Aust. Meterological
Mag., 47, 85–101, 1998.Power, S., Delage, F., Chung, C., Kociuba, G., and Keay, K.: Robust
twenty-first-century projections of El Niño and related precipitation
variability, Nature, 502, 541–5, 10.1038/nature12580, 2013.Powers, L. A., Johnson, T. C., Werne, J. P., Castañeda, I. S., Hopmans,
E. C., Sinninghe Damsté, J. S., and Schouten, S.: Organic
geochemical records of environmental variability in Lake Malawi during the
last 700 years, Part I: The TEX86 temperature record, Palaeogeogr.
Palaeoclimatol. Palaeoecol., 303, 133–139,
10.1016/j.palaeo.2010.09.006, 2011.Quinn, T. M., Crowley, T. J., and Taylor, F. W.: New stable isotope results
from a 173-year coral from Espiritu Santo, Vanuatu, Geophys. Res. Lett., 23,
3413–3416, 10.1029/96GL03169, 1996.Quinn, T. M., Taylor, F. W., and Crowley, T. J.: Coral-based climate
variability in the Western Pacific Warm Pool since 1867, J. Geophys. Res.,
111, 1–11, 10.1029/2005JC003243, 2006.Raible, C. C., Lehner, F., González-Rouco, J. F., and
Fernández-Donado, L.: Changing correlation structures of the Northern
Hemisphere atmospheric circulation from 1000 to 2100 AD, Clim. Past, 10,
537–550, 10.5194/cp-10-537-2014, 2014.Rasbury, M. and Aharon, P.: ENSO-controlled rainfall variability records
archived in tropical stalagmites from the mid-ocean island of Niue, South
Pacific, Geochem. Geophy. Geosy., 7, Q07010, 10.1029/2005GC001232,
2006.Reuter, J., Stott, L., Khider, D., Sinha, A., Cheng, H., and Edwards, R. L.: A
new perspective on the hydroclimate variability in northern South America
during the Little Ice Age, Geophys. Res. Lett., 36, 1–5,
10.1029/2009GL041051, 2009.Richey, J. N., Poore, R. Z., Flower, B. P., Quinn, T. M., and Hollander, D. J.:
Regionally coherent Little Ice Age cooling in the Atlantic Warm Pool,
Geophys. Res. Lett., 36, 3–7, 10.1029/2009GL040445, 2009.Rodbell, D. T.: An 15,000-Year Record of El Niño-Driven Alluviation in
Southwestern Ecuador, Science, 283, 516–520,
10.1126/science.283.5401.516, 1999.Rodgers, K. B., Friederichs, P., and Latif, M.: Tropical Pacific decadal
variability and its relation to decadal modulations of ENSO, J. Climate, 17,
3761–3774, 10.1175/1520-0442(2004)017<3761:TPDVAI>2.0.CO;2, 2004.Rodysill, J. R., Russell, J. M., Bijaksana, S., Brown, E. T., Safiuddin, L. O.,
and Eggermont, H.: A paleolimnological record of rainfall and drought from
East Java, Indonesia during the last 1,400 years, J. Paleolimnol., 47,
125–139, 10.1007/s10933-011-9564-3, 2012.Russell, J. M. and Johnson, T. C.: Little Ice Age drought in equatorial
Africa: Intertropical convergence zone migrations and El Niño-Southern
Oscillation variability, Geology, 35, 21–24, 10.1130/G23125A.1, 2007.Russell, J. M., Vogel, H., Konecky, B. L., Bijaksana, S., Huang, Y., Melles,
M., Wattrus, N., Costa, K., and King, J. W.: Glacial forcing of central
Indonesian hydroclimate since 60,000 y B.P., P. Natl. Acad. Sci. USA, 111, 5100–5105, 10.1073/pnas.1402373111, 2014.Russon, T., Tudhope, A. W., Hegerl, G. C., Collins, M., and Tindall, J.:
Inter-annual tropical Pacific climate variability in an isotope-enabled CGCM:
implications for interpreting coral stable oxygen isotope records of ENSO,
Clim. Past, 9, 1543–1557, 10.5194/cp-9-1543-2013, 2013.Rustic, G. T., Koutavas, A., Marchitto, T. M., and Linsley, B. K.: Dynamical
excitation of the tropical Pacific Ocean and ENSO variability by Little Ice
Age cooling, Science, 350, 1537–41, 10.1126/science.aac9937,
2015.Sachs, J. P., Sachse, D., Smittenberg, R. H., Zhang, Z., Battisti, D. S., and
Golubic, S.: Southward movement of the Pacific intertropical convergence
zone AD 1400–1850, Nat. Geosci., 2, 519–525, 10.1038/ngeo554, 2009.Saenger, C., Cohen, A. L., Oppo, D. W., Halley, R. B., and Carilli, J. E.:
Surface-temperature trends and variability in the low-latitude North
Atlantic since 1552, Nat. Geosci., 2, 492–495, 10.1038/ngeo552, 2009.Saenger, C., Came, R. E., Oppo, D. W., Keigwin, L. D., and Cohen, A. L.:
Regional climate variability in the western subtropical North Atlantic
during the past two millennia, Paleoceanography, 26, 1–12,
10.1029/2010PA002038, 2011.Schopf, P. S. and Burgman, R. J.: A simple mechanism for ENSO residuals and
asymmetry, J. Climate, 19, 3167–3179, 10.1175/JCLI3765.1, 2006.Shen, C., Wang, W.-C., Gong, W., and Hao, Z.: A Pacific Decadal Oscillation
record since 1470 AD reconstructed from proxy data of summer rainfall over
eastern China, Geophys. Res. Lett., 33, L03702,
10.1029/2005GL024804,
2006.Shin, S. I., Sardeshmukh, P. D., Webb, R. S., Oglesby, R. J., and Barsugli,
J. J.: Understanding the mid-Holocene climate, J. Climate, 19, 2801–2817,
10.1175/JCLI3733.1, 2006.Smerdon, J. E.: Climate models as a test bed for climate reconstruction
methods: Pseudoproxy experiments, Wiley Interdiscip. Rev. Clim. Chang., 3,
63–77, 10.1002/wcc.149, 2012.Stahle, D. W., Diaz, J. V., Burnette, D. J., Paredes, J. C., Heim, R. R., Fye,
F. K., Soto, R. A., Therrell, M. D., and Cleaveland, M. K.: Major
Mesoamerican droughts of the past millennium, Geophys. Res. Lett., 38, 2–5,
10.1029/2010GL046472, 2011.Stansell, N. D., Steinman, B. A., Abbott, M. B., Rubinov, M., and Roman-Lacayo,
M.: Lacustrine stable isotope record of precipitation changes in Nicaragua
during the Little Ice Age and Medieval Climate Anomaly, Geology, 41,
151–154, 10.1130/G33736.1, 2013.Steinman, B. A., Abbott, M. B., Mann, M. E., Stansell, N. D., and Finney,
B. P.: 1,500 year quantitative reconstruction of winter precipitation in the
Pacific Northwest, P. Natl. Acad. Sci. USA, 109, 11619–11623,
10.1073/pnas.1201083109, 2012.Stott, L., Poulsen, C., Lund, S., and Thunell, R.: Super ENSO and global
climate oscillations at millennial time scales, Science, 297, 222–226,
10.1126/science.1071627, 2002.Stott, L., Cannariato, K., Thunell, R., Haug, G. H., Koutavas, A., and Lund,
S.: Decline of surface temperature and salinity in the western tropical
Pacific Ocean in the Holocene epoch, Nature, 431, 56–59,
10.1038/nature02903, 2004.Sturm, C., Zhang, Q., and Noone, D.: An introduction to stable water isotopes
in climate models: benefits of forward proxy modelling for paleoclimatology,
Clim. Past, 6, 115–129, 10.5194/cp-6-115-2010, 2010.Sundqvist, H. S., Holmgren, K., Fohlmeister, J., Zhang, Q., Matthews, M. B.,
Spötl, C., and Körnich, H.: Evidence of a large cooling between
1690 and 1740 AD in southern Africa, Sci. Rep., 3, 1–6,
10.1038/srep01767, 2013.Takahashi, K., Montecinos, A., Goubanova, K., and Dewitte, B.: ENSO regimes:
Reinterpreting the canonical and Modoki El Niño, Geophys. Res. Lett., 38,
1–5, 10.1029/2011GL047364, 2011.Tan, L., Cai, Y., Cheng, H., An, Z., and Edwards, R. L.: Summer monsoon
precipitation variations in central China over the past 750 years derived
from a high-resolution absolute-dated stalagmite, Palaeogeogr.
Palaeoclimatol. Palaeoecol., 280, 432–439,
10.1016/j.palaeo.2009.06.030, 2009.Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and the
experiment design, B. Am. Meteorol. Soc., 93, 485–498,
10.1175/BAMS-D-11-00094.1, 2012.Thompson, D. M., Ault, T. R., Evans, M. N., Cole, J. E., and Emile-Geay, J.:
Comparison of observed and simulated tropical climate trends using a forward
model of coral δ18O, Geophys. Res. Lett., 38, 1–6,
10.1029/2011GL048224, 2011.Thompson, L. G., Yao, T., Davis, M. E., Henderson, K. A., Mosley-Thompson,
E., Lin, P.-N., Beer, J., Synal, H.-A., Cole-Dai, J., and Bolzan, J. F.:
Tropical climate instability: the last glacial
cycle from a Qinghai – Tibetan ice core, Science, 276, 1821–1825,
10.1126/science.276.5320.1821, 1997.Thompson, L. G., Mosley-Thompson, E., Davis, M. E., Lin, P.-N. P.-N.,
Henderson, K. A., Cole-Dai, J., Bolzan, J. F., and Liu, K.-B.: Late Glacial
Stage and Holocene Tropical Ice Core Records from Huascaran, Peru, Science,
269, 46–50, 10.1017/CBO9781107415324.004, 1995.Thompson, L. G., Mosley-Thompson, E., Davis, M. E., Henderson, K. A., Brecher,
H. H., Zagorodnov, V. S., Mashiotta, T. A., Lin, P.-n., Mikhalenko, V. N.,
Hardy, D. R., and Beer, J.: Kilimanjaro ice core records: evidence of
holocene climate change in tropical Africa, Science, 298, 589–593,
10.1126/science.1073198, 2002.
Thompson, L. G., Mosley-Thompson, E., Davis, M. E., Lin, P., Henderson, K., and
Mashiotta, T. A.: Tropical glacier and ice core evidence of climate change
on annual to millenial time scales, Clim. Change, 59, 137–155, 2003.Thompson, L. G., Yao, T., Davis, M. E., Mosley-Thompson, E., Mashiotta, T. A.,
Lin, P. N., Mikhalenko, V. N., and Zagorodnov, V. S.: Holocene climate
variability archived in the Puruogangri ice cap on the central Tibetan
Plateau, Ann. Glaciol., 43, 61–69, 10.3189/172756406781812357, 2006.Thompson, L. G., Mosley-Thompson, E., Davis, M. E., Zagorodnov, V. S., Howat,
I. M., Mikhalenko, V. N., and Lin, P.-N.: Annually resolved ice core records
of tropical climate variability over the past ∼1800 years, Science,
340, 945–950, 10.1126/science.1234210, 2013.Tierney, J. E., Oppo, D. W., Rosenthal, Y., Russell, J. M., and Linsley, B. K.:
Coordinated hydrological regimes in the Indo-Pacific region during the past
two millennia, Paleoceanography, 25, 1–7, 10.1029/2009PA001871, 2010.Tierney, J. E., Lewis, S. C., Cook, B. I., LeGrande, A. N., and Schmidt, G. A.:
Model, proxy and isotopic perspectives on the East African Humid Period,
Earth Planet. Sci. Lett., 307, 103–112, 10.1016/j.epsl.2011.04.038,
2011.Tierney, J. E., Abram, N. J., Anchukaitis, K. J., Evans, M. N., Giry, C.,
Kilbourne, K. H., Saenger, C. P., Wu, H. C., and Zinke, J.: Tropical sea
surface temperatures for the past four centuries reconstructed from coral
archives, Paleoceanography, 30, 226–252, 10.1002/2014PA002717, 2015 (data available at: https://www.ncdc.noaa.gov/data-access/paleoclimatology-data/datasets).Tiwari, M., Nagoji, S. S., and Ganeshram, R. S.: Multi-centennial scale SST
and Indian summer monsoon precipitation variability since mid-Holocene and
its nonlinear response to solar activity, The Holocene, 25, 1415–1424,
10.1177/0959683615585840, 2015.Tolwinski-Ward, S. E., Evans, M. N., Hughes, M. K., and Anchukaitis, K. J.: An
efficient forward model of the climate controls on interannual variation in
tree-ring width, Clim. Dyn., 36, 2419–2439,
10.1007/s00382-010-0945-5, 2011.Treydte, K. S., Schleser, G. H., Helle, G., Frank, D. C., Winiger, M., Haug,
G. H., and Esper, J.: The twentieth century was the wettest period in
northern Pakistan over the past millennium, Nature, 440, 1179–1182,
10.1038/nature04743, 2006.Trouet, V., Scourse, J. D., and Raible, C. C.: North Atlantic storminess and
Atlantic Meridional Overturning Circulation during the last Millennium:
Reconciling contradictory proxy records of NAO variability, Glob. Planet.
Change, 84-85, 48–55, 10.1016/j.gloplacha.2011.10.003, 2012.Vaganov, E. A., Hughes, M. K., and Shashkin, A. V.: Growth Dynamics of Conifer
Tree Rings: Images of Past and Future Environments, Springer-Verlag Berlin
Heidelberg, New York, 1 edn., 10.1007/3-540-31298-6, 2006.Vaganov, E. A., Anchukaitis, K. J., and Evans, M. N.: How well understood are
the processes that create dendroclimatic records? A mechanistic model of the
climatic control on conifer tree-ring growth dynamics, in: Dendroclimatology
Prog. Prospect., edited by: Hughes, M. K., Swetnam, T. W., and Diaz, H. F.,
11, 37–75, Springer Netherlands, 10.1007/978-1-4020-5725-0,
2011.van Hengstum, P. J., Donnelly, J. P., Kingston, A. W., Williams, B. E., Scott,
D. B., Reinhardt, E. G., Little, S. N., and Patterson, W. P.: Low-frequency
storminess signal at Bermuda linked to cooling events in the North Atlantic
region, Paleoceanography, 30, 52–76, 10.1002/2014PA002662, 2015.van Oldenborgh, G. J., Philip, S. Y., and Collins, M.: El Niño in a
changing climate: a multi-model study, Ocean Sci., 1, 81–95,
10.5194/os-1-81-2005, 2005.Vasquez-Bedoya, L. F., Cohen, A. L., Oppo, D. W., and Blanchon, P.: Corals
record persistent multidecadal SST variability in the Atlantic Warm Pool
since 1775 AD, Paleoceanography, 27, 1–9, 10.1029/2012PA002313, 2012.Vecchi, G. A. and Wittenberg, A. T.: El Niño and our future climate: Where
do we stand?, Wiley Interdiscip. Rev. Clim. Chang., 1, 260–270,
10.1002/wcc.33, 2010.Verdon, D. C. and Franks, S. W.: Long-term behaviour of ENSO: Interactions
with the PDO over the past 400 years inferred from paleoclimate records,
Geophys. Res. Lett., 33, 1–5, 10.1029/2005GL025052, 2006.Wang, H. J., Zhang, R. H., Cole, J. E., and Chavez, F.: El Niño and the
related phenomenon Southern Oscillation (ENSO): the largest signal in
interannual climate variation., P. Natl. Acad. Sci. USA, 96,
11071–11072, 10.1073/pnas.96.20.11071, 1999.Wang, J., Emile-Geay, J., Guillot, D., Smerdon, J. E., and Rajaratnam, B.:
Evaluating climate field reconstruction techniques using improved emulations
of real-world conditions, Clim. Past, 10, 1–19, 10.5194/cp-10-1-2014,
2014a.Wang, J., Emile-Geay, J., Guillot, D., McKay, N. P., and Rajaratnam, B.:
Fragility of reconstructed temperature patterns over the Common Era:
Implications for model evaluation, Geophys. Res. Lett., 42, 7162–7170,
10.1002/2015GL065265, 2015.Wang, S., Huang, J., He, Y., and Guan, Y.: Combined effects of the Pacific
Decadal Oscillation and El Niño-Southern Oscillation on Global Land
Dry-Wet Changes, Sci. Rep., 4, 6651, 10.1038/srep06651,
2014b.Wang, Y., Cheng, H., Edwards, R. L., He, Y., Kong, X., An, Z., Wu, J., Kelly,
M. J., Dykoski, C. A., and Li, X.: The Holocene Asian Monsoon: Links to
Solar Changes and North Atlantic Climate, Science, 308, 854–857,
10.1126/science.1106296,
2005.Wanner, H., Beer, J., Bütikofer, J., Crowley, T. J., Cubasch, U.,
Flückiger, J., Goosse, H., Grosjean, M., Joos, F., Kaplan, J. O.,
Küttel, M., Müller, S. A., Prentice, I. C., Solomina, O.,
Stocker, T. F., Tarasov, P., Wagner, M., and Widmann, M.: Mid- to Late
Holocene climate change: an overview, Quat. Sci. Rev., 27, 1791–1828,
10.1016/j.quascirev.2008.06.013, 2008.Wilson, R., Cook, E. R., D'Arrigo, R. D., Riedwyl, N., Evans, M. N., Tudhope,
A. W., and Allan, R.: Reconstructing ENSO: the influence of method, proxy
data, climate forcing and teleconnections, J. Quat. Sci., 25, 62–78,
10.1002/jqs.1297, 2010.Wolter, K. and Timlin, M. S.: El Niño/Southern Oscillation behaviour since
1871 as diagnosed in an extended multivariate ENSO index (MEI.ext), Int. J.
Climatol., 31, 1074–1087, 10.1002/joc.2336, 2011.Wörheide, G.: The reef cave dwelling ultraconservative coralline
demosponge Astrosclera willeyana Lister 1900 from the Indo-Pacific, Facies,
38, 1–88, 10.1007/BF02537358, 1998.Wu, H. C., Linsley, B. K., Dassié, E. P., Schiraldi, B., and Demenocal,
P. B.: Oceanographic variability in the south pacific convergence zone
region over the last 210 years from multi-site coral Sr/Ca records,
Geochem. Geophy. Geosy., 14, 1435–1453,
10.1029/2012GC004293, 2013.Wurtzel, J. B., Black, D. E., Thunell, R. C., Peterson, L. C., Tappa, E. J.,
and Rahman, S.: Mechanisms of southern Caribbean SST variability over the
last two millennia, Geophys. Res. Lett., 40, 5954–5958,
10.1002/2013GL058458, 2013.Yan, H., Sun, L., Oppo, D. W., Wang, Y., Liu, Z., Xie, Z., Liu, X., and Cheng,
W.: South China Sea hydrological changes and Pacific Walker Circulation
variations over the last millennium, Nat. Commun., 2, 293,
10.1038/ncomms1297, 2011a.Yan, H., Sun, L., Wang, Y., Huang, W., Qiu, S., and Yang, C.: A record of the
Southern Oscillation Index for the past 2,000 years from precipitation
proxies, Nat. Geosci., 4, 611–614, 10.1038/ngeo1231,
2011b.Yan, H., Wei, W., Soon, W., An, Z., Zhou, W., Liu, Z., Wang, Y., and Carter,
R. M.: Dynamics of the intertropical convergence zone over the western
Pacific during the Little Ice Age, Nat. Geosci., 8, 8–13,
10.1038/ngeo2375,
2015.Yeh, S.-W., Kug, J.-S., Dewitte, B., Kwon, M.-H., Kirtman, B. P., and Jin,
F.-F.: El Niño in a changing climate, Nature, 461, 511–514,
10.1038/nature08316, 2009.Yi, L., Yu, H., Ge, J., Lai, Z., Xu, X., Qin, L., and Peng, S.:
Reconstructions of annual summer precipitation and temperature in
north-central China since 1470 AD based on drought/flood index and tree-ring
records, Clim. Change, 110, 469–498, 10.1007/s10584-011-0052-6, 2012.Yuan Zhang, Wallace, J. M., and Battisti, D. S.: ENSO-like interdecadal
variability: 1900-93, J. Climate, 10, 1004–1020,
10.1175/1520-0442(1997)010<1004:ELIV>2.0.CO;2, 1997.Zhang, P., Cheng, H., Edwards, R. L., Chen, F., Wang, Y., Yang, X., Liu, J.,
Tan, M., Wang, X., Liu, J., An, C., Dai, Z., Zhou, J., Zhang, D., Jia, J.,
Jin, L., and Johnson, K. R.: A Test of Climate, Sun, and Culture
Relationships from an 1810-Year Chinese Cave Record, Science, 322,
940–942, 10.1126/science.1163965,
2008.Zhao, M., Read, G., and Schimmelmann, A.: An alkenone (U37K′)
quasi-annual sea surface temperature record (A.D. 1440 to 1940) using varved
sediments from the Santa Barbara Basin, Org. Geochem., 31, 903–917, 2000.Zinke, J., Dullo, W. C., Heiss, G. A., and Eisenhauer, A.: ENSO and Indian
Ocean subtropical dipole variability is recorded in a coral record off
southwest Madagascar for the period 1659 to 1995, Earth Planet. Sci. Lett.,
228, 177–194, 10.1016/j.epsl.2004.09.028, 2004.
Zinke, J., Loveday, B. R., Reason, C. J. C., Dullo, W.-C., and Kroon, D.:
Madagascar corals track sea surface temperature variability in the Agulhas
Current core region over the past 334 years, Sci. Rep., 4, 4393,
10.1038/srep04393, 2014.
Zinke, J., Hoell, a., Lough, J. M., Feng, M., Kuret, a. J., Clarke, H., Ricca,
V., Rankenburg, K., and McCulloch, M. T.: Coral record of southeast Indian
Ocean marine heatwaves with intensified Western Pacific temperature
gradient, Nat. Commun., 6, 8562, 10.1038/ncomms9562, 2015.