Introduction
Climate variability in the North Atlantic region can, to a large extent, be
explained by different modes of atmospheric circulation. This is particularly
true for the variability during winter. The dominant mode is the North
Atlantic Oscillation (NAO), which is a measure of the strength and position
of the westerly winds across the North Atlantic. Traditionally, the NAO is
defined as the pressure difference between Iceland and the Azores
. Using gridded data, the NAO can be defined as the
first principal component (PC1) of sea level pressure (SLP) in the North
Atlantic region . The second most important pattern, PC2
of SLP, is often referred to as the Eastern Atlantic Pattern
. Modes of circulation can also be identified using
cluster analysis, and the secondary modes include Atlantic ridge- and
trough-type variability, characterized by mid-Atlantic and Scandinavian
blockings, respectively . These different modes, for
example, determine the severity of European winters . In
paleoclimate studies, climate variability and
changes in atmospheric circulation are
often attributed to external forcing related to solar variability or volcanic
eruptions e.g.,. Using reanalysis of weather
observations (1871–2008) it has been shown that a positive phase of the NAO
occurs in the winters following major tropical volcanic eruptions, while
climate models generally fail to reproduce this dynamical response to the
forcing . For the past
millennium, found a positive NAO response in the second
winter following the 11 largest tropical eruptions in their reconstructed
NAO. However, more persistent climate effects of volcanic eruptions were
found by who inferred cooler European summer temperatures
lasting up to 10 years following major tropical eruptions during the past
2500 years, raising the question if a more persistent impact on winter
circulation also can be expected. The observed NAO does not show a consistent
correlation to the solar forcing . However, anomalies in SLP
during 1950–2010 exhibit a NAO-like pattern correlated to the 11-year solar
cycle . It has been hypothesized that
solar-induced anomalies in the stratosphere can propagate to the troposphere
possibly synchronizing NAO variability to the
11-year solar cycle with a maximum response lagging
2–4 years due to ocean memory effects . Several
paleoclimate studies have indicated solar influences on climate in the North
Atlantic region on centennial timescales , and it has been suggested that
the climate conditions during the Little Ice Age was linked to negative NAO
forced by low solar activity . However,
the nature of the solar influence in terms of mechanisms and dynamical
response is debated.
Our understanding of past climate dynamics and the impact of external
forcings relies heavily on analysis of past changes. Gridded data sets of
climate variables based on meteorological observations have been developed
for these purposes . Such reanalysis data sets are
constrained in quality and coverage back in time due to sparse instrumental
data, and we rely on climate reconstructions based on proxy data to go beyond
the instrumental era. Previously, reconstructions of climate indices (such
as, for example, the NAO) have been site-based, with the reconstruction essentially done
by extrapolating observed empirical relationships back in time. This approach
results in a wide spread of reconstructions of past atmospheric circulation
modes . Recently, efforts have been done to develop
reanalysis-type climate reconstructions based on climate proxy data. However,
those reconstructions are focused on annual mean data, not taking into
account the migration of circulation patterns from summer to winter
, and have low skill for atmospheric circulation over the
North Atlantic .
Stable water isotope ratios in ice cores carry quantitative information about
past climate . For Greenland ice cores, seasonal isotope
variability is attainable. In particular, the winter isotope signal has been
shown to be highly correlated to atmospheric circulation and temperature
. However, the strength of the relation
between the main patterns of variability of the ice core isotope signal and
the NAO has been suggested to vary in strength ,
indicating that a simple (regression-based) relation between the ice core
records and NAO bears large uncertainties for reconstruction purposes.
Here we present a climate reconstruction for the North Atlantic region for
winter covering 1241–1970 CE, and analyze the impact of solar and volcanic
forcing. For our reconstruction we combine a simulation using a coupled
atmosphere–ocean model with stable isotope diagnostics embedded in the
hydrological cycle with eight seasonally resolved isotope records from
Greenland ice cores. We do not calibrate the reconstructed meteorological
variables to observations, as we solely rely on matching the modeled isotopic
composition to the ice core data. Testing the reconstruction against
reanalysis data and observations, the reconstruction has good skill not only
for the NAO but also for secondary circulation modes. We find the average
response to major tropical volcanic eruptions to be a positive NAO for the
five consecutive winters after eruptions, which is more persistent than
previous studies have shown. However, we find no persistent relationship
between solar forcing and the NAO. On the other hand, we find a strong impact
of solar forcing on the secondary modes of circulation represented by PC2 of
reconstructed SLP. We achieve the strongest correspondence between the solar
forcing and reconstructed PC2 of SLP with a time lag of 5 years, indicating
that an atmosphere–ocean feedback is in play. Taking this time lag into
account, we find a consistent relationship between PC2 of the reconstructed SLP
and solar forcing on decadal to centennial timescales.
Data and methods
Model simulation
We use the isotope-enabled version of the atmosphere–ocean model
ECHAM5/MPI-OM to simulate the period 800–2000 CE forced
by greenhouse gases, volcanic aerosols, total solar irradiance, land use and
orbital forcing (see Table 1). For our study, ECHAM5 is run with a T31
spectral resolution (3.75∘×3.75∘) with 19 vertical
layers, 5 of which are in the stratosphere. Our simulation uses a similar
setup as the E1 COSMOS ensemble by , except for an
updated solar forcing (see Sect. 2.2) and fully prescribed CO2,
where the E1 simulations incorporate a carbon cycle module. We apply the
identical physical general circulation models (GCMs) (ECHAM5/MPI-OM) as the
E1 ensemble, as well as similar forcings, and our simulation generally yields
a very similar climate as the E1 ensemble. The performance of the atmospheric
component of the model used in this study, ECHAM5-wiso, was evaluated for the
Arctic region and Antarctica using different configurations of spatial
resolution by . For the configuration used in this study
(T31), the model has a warm bias and is not depleted enough in
δ18O; however, the climatological relation between
δ18O and temperature compares well to observations, despite
the relatively course resolution. Greenland is represented by 50 grid points
in the simulation.
List of forcings for the ECHAM5/MPI-OM model simulation.
Greenhouse gases
(CO2, CH4, N2O)
Greenhouse gases
(historical, anthropogenic)
Ozone
Climatology of
Volcanic aerosols
Aerosol forcing
Background from
and post-1850 variations
by
Total solar irradiance
Based on
(see also the Methods section)
Land use
with vegetation
from
E1 ensemble member mil0010
Orbital forcing
Variation Seculaires des Orbites
Planetaires (VSOP) analytical solution
by
Solar forcing
The solar forcing record employed in this study is based on the solar
modulation record inferred from the combined neutron monitor and tree-ring
14C data . In contrast to
it covers the last 2000 years and
consistently uses the solar modulation record for the complete period, while
in the 14C-based record is
combined with sunspot-based data for the period after 1850 CE. Therefore,
our approach employs an internally self-consistent forcing record for the
last 2000 years, and agrees well with the latest recommended solar forcing
reconstruction for the past millennium . The 11-year
solar cycle is based on the neutron monitor and 14C data for the
last 500 years where the underlying data has a sufficiently high temporal
resolution. For the period before 1500 CE an artificial 10.5-year cycle was
added and the phasing was adjusted to allow for a smooth connection to the
subsequent start of the data-based solar cycle. The amplitude of the solar
cycle modulation was inferred from the data-based part of the record, i.e., by
employing the relationship between the solar cycle amplitude and the
longer-term solar modulation levels (11-year averages) during last 500 years.
This solar modulation record was scaled to the total solar irradiance (TSI)
record by including longer-term trends in
TSI (see “MEA (back)”, Fig. 8 in ). This was done by
linearly transforming the solar modulation to a TSI record in order to
reproduce the long-term changes (11-year average) in TSI from the Maunder
minimum to the most recent 50 years, i.e., leading to a similar range of
long-term TSI changes as suggested by .
Climate reconstruction
Spatial patterns of the three main modes of variability in the
δ18O ice core records used in this study and the modeled modes
of variability at these sites. The pattern of the loadings on ice core
δ18O PCs are shown in (a, b, c) and modeled loadings
on δ18O PCs at the ice core sites are shown in (d, e, f).
We use winter seasonal means (November–April) for eight Greenland ice cores
for the period 1241–1970 CE (Table 2). All ice cores
are synchronized via volcanic reference horizons, and the dating uncertainty
is estimated to 1 year for the oldest parts of the records used here
. Under the assumption that δ18O in
precipitation is a result of a number of processes mainly determined by
atmospheric variability, we treat each year of the model run (see above) as a
sample in a sampling space relating δ18O in precipitation with
circulation, temperature, etc. By extracting precipitation-weighted winter
seasonal means (November–April) of δ18O from the model at the
eight ice core sites, we can find the model years best matching the isotope
pattern of each winter in the ice core data. In order not to over-fit noise
in the ice core data (post-depositional processes etc. ),
we first perform a principal component analysis of the ice core
δ18O and the model δ18O from grid cells
covering the investigated ice core sites. We retain the first three PCs
explaining a total of 60 % and 97 % of variability in the ice core and
model δ18O, respectively. The loadings of the 3 PCs for the
model data match the loading of the ice core data well (Fig. 1). Notice that
this step is done without performing any selection of the model data, meaning
that the modeled spatiotemporal variability in the δ18O in
precipitation corresponds well to that of the ice core data. As we only
explain part of the variability in the δ18O data using the
first 3 PCs, we use an ensemble approach to take into account that the
matching of a given ice core δ18O pattern will result in a
suite of well-matching model years. To match each year in the ice core data,
the model data are evaluated using a χ2 measure between the 3 PCs of
Greenland ice core δ18O and the 3 PCs of the modeled
δ18O:
χMatch2=13∑k=13(PC(k)model-PC(k)icecore)2,
where PC(k)model and PC(k)icecore
are the values from a given year of the normalized time series of model and
ice core δ18O PCs, respectively. Each model year is evaluated
against each year of the ice core data and then sorted in ascending order of
the quality of the fit. This creates 1201 (number of model years, see above)
fits of model output for each year of the ice core data, i.e., 1201 resampled
(with replacement) and sorted model years for the entire length of the ice
core data (1241–1970 CE). We define the sorted model output as ensemble
members, such that the best fitting model year, for each year of the ice core
data, belongs to ensemble member 1, the second best fitting model years
belong to ensemble member 2, and so forth. Using a Chi-square goodness-of-fit
test, with respect to the measure in Eq. (1), we evaluate the ensemble
members against the PCs of Greenland ice core δ18O and reject
model fits with likelihood p>0.01 of not fitting the ice core data. This
leaves us with 39 time series of reshuffled model data fitted to the ice core
data. The temporal succession of the reshuffled model output does not
resemble the order of the years in the original model run, and there are no
systematic preferences of the method to pick certain time periods of the
model run to match certain time periods of the reconstruction. For example if
we exclude the years 1851–2000 from the model run and perform the
reconstruction using the remaining years, the reconstruction is almost
identical to the reconstruction using all model years. This also means that
the timing of the forcing used for the model run has no relation to the
timing or impact of specific forcings in the reconstruction, i.e., the
forcings of the model run effectively serve to produce enough range in order
to match the simulation to the isotope variability in the ice cores. Since
the timing of the forcings used for the model simulation is not a factor
influencing the reconstruction, the model performance for the response to
forcings does not influence the reconstruction. We treat the 39 model fits as
an ensemble solution fitting the model output to the ice cores
δ18O, and we calculate the ensemble mean reconstructed
δ18O and the standard deviation showing the ensemble spread.
For the target climate variables (SLP, T2m) we extract the DJF ensemble mean
corresponding to the reconstructed δ18O constituting the
reconstruction of these variables. Note that using this approach the method
is optimized to fit modeled δ18O to ice core
δ18O records and, in contrast to presently existing
reconstructions, it is not calibrated to match observations of any of the
target variables of the reconstruction (i.e., SLP or T2m).
Site details of ice core records used for the reconstruction
. BC* indicates the at the core was drilled to bedrock.
Drill site
Lat.
Long.
Elevation
Acc. rate
Time span
(∘ N)
(∘ W)
(m a.s.l.)
(m ice yr-1)
Crete
71.12
37.32
3172
0.289
551–1974
DYE-3 71
65.18
43.83
2480
0.56
1239–1971
DYE-3 79
65.18
43.83
2480
0.56
BC*–1979
GRIP 89-1
72.58
37.64
3238
0.23
918–1989
GRIP 89-3
72.58
37.64
3238
0.23
BC*–1989
GRIP 93
72.58
37.64
3238
0.23
1062–1993
Milcent
70.30
44.50
2410
0.54
1173–1973
Renland
71.27
26.73
2350
0.50
BC*–1988
As a first test we correlate the reconstructed δ18O to winter
means of ice core data from 20 cores covering the last 200 years of the
reconstruction, i.e., including ice core data not part of the reconstruction.
The correlation shows a spread between 0.4 and 0.9 with the highest
correlations found for the high accumulation sites, and where multiple ice
cores for the same site are used for the reconstruction. This supports the
idea that the skill of the model fit to the seasonal ice core data is largely
limited by the signal-to-noise ratio of the ice core data (Fig. S1 in the
Supplement). High accumulation sites are generally less sensitive to wind
scoring and post-depositional diffusion. Despite using the variability of the
PCs to fit the model output to the ice core data, the reconstructed range of
δ18O matches the ice core data well (Fig. S2). Furthermore we
test the fit of the reshuffled model δ18O to the ice core data
of the eight sites to investigate if any time periods stand out, as well as the
mean fit of the method across the whole period of the reconstruction. We find
no trends in the performance in terms of fitting the modeled PCs to the ice
core data PCs. As the method performs equally well during any period as
during the instrumental period (post-1850) in terms of matching the ice core
isotope variability, it is likely that our reconstruction of SLP and T2m is
equally valid for any period as it is for the instrumental period. This
conclusion can be drawn as the reconstruction is not calibrated to either SLP
or T2m, and is only constrained by the ice core isotope variability.
Statistical tests and filtering of data
We test the significance for anomalies of climate field variables with a
two-tailed Student's t test. For low-pass and band-pass filtering of data
series, we use a fast Fourier transform approach if data is used for
correlation analysis. In Fig. 2b we use a “loess” filter to smooth the data
for visualization of the multidecadal variability. When calculating
significance for correlations of filtered data we use the method by
to take autocorrelation into account.
Evaluation of the winter circulation reconstruction. Grid point
correlation between reconstructed DJF SLP (a) and T2m (b),
and reanalysis data (1851–1970) interpolated to the model
grid (lat. × long. ∼3.75∘×3.75∘). The
white stippling indicates significance p<0.05, and black stippling
indicates significance p<0.1. (c) Ensemble mean reconstructed
DJF NAO (PC1 of reconstructed DJF SLP ) with root mean
square error (RMSE) compared to observed DJF NAO , NAOcc and
NAOmc by . (d) Ensemble mean PC2 of reconstructed
DJF SLP with RMSE compared to PC2 of reanalysis DJF SLP
.
Results
Evaluation of climate reconstruction
Comparing to the 20th Century Reanalysis (; 20CR), our
reconstruction shows skill for SLP and T2m in the North Atlantic region
(Fig. 2a, b), the main mode of atmospheric circulation (NAO) as well as
secondary circulation modes (Fig. 2c, d and Table 3). This is a completely
independent test of the reconstruction as the reconstruction has not been
calibrated to reanalysis data or observations. For T2m, the pattern of
significant correlations with the 20CR data can be associated with the main
circulation modes (Figs. S3 and S4), albeit with decreasing skill with the
distance to the ice cores. We interpret the high skill near Greenland as
being due to the direct physical connection between the local temperature in
Greenland and the temperature along the path of the vapor, and the isotopic
signal in Greenland ice cores. Contrary to previous millennial-scale
reconstructions, our reconstructed NAO shows similar strength of year-to-year
variability as the observed NAO indicating that the reconstruction preserves
the known characteristic variability in the NAO (Figs. 2d, e and 3a). In
addition to capturing the NAO, our reconstruction has skill in representing
Atlantic ridge- or trough-type variability as projected on PC2 of SLP over the
North Atlantic. The correlation of the reconstructed SLP PC2 and PC2 of the
20CR SLP is 0.24 (p<0.01) and increases to 0.53 (p<0.01) on decadal
timescales (Table 3). However, comparing the SLP patterns of PC2 in our
reconstruction and reanalysis (Fig. S3) implies that the variability captured
by PC2 of the reconstruction is likely split between PC2 and PC3 in the
reanalysis. Indeed, the reconstructed PC2 of SLP is also correlated to PC3 of
the reanalysis SLP (corr.=0.19, p<0.01, Table 3).
Correlating the reconstructed PC2 of SLP against the sum of the reanalysis
PC2 and PC3 shows increased correlations between the reconstruction and
reanalysis, in particularly on decadal and bi-decadal timescales (Table 3).
This indicates that the variability projected on PC2 and PC3 of the
reanalysis data is partly summarized in PC2 of the reconstruction. The
difference in distribution of the variability on the PCs in the
reconstruction and reanalysis is likely due to (i) the intrinsic variability
of the model used for the reconstruction and (ii) the reconstruction only
capturing the North Atlantic variability as recorded in the isotopic
composition of the Greenland ice core data.
Comparison of instrumental NAO and proxy-based NAO reconstructions.
(a) Moving 31-point correlation between reconstructed NAO from this
study and NAOcc (magenta), NAOmc (green; ) and observed NAO
(yellow; ). Only significant correlations are plotted (p<0.05). (a) Ensemble mean reconstructed NAO (PC1 of reconstructed
SLP ) with error estimated by ensemble spread and RMSE,
compared to observed NAO and NAO reconstructions by
. The amplitude of all time series are scaled to fit the
decadal variability in the observed NAO. (c) Same as (b),
except filtered with a 30 point “loess” filter.
Correlations of reconstructed NAO and PC2 of reconstructed SLP, and
observed NAO, 20CR PC2 and PC3 of SLP, as well as NAO reconstructions by
and . All correlations are for
detrended data, and p values are calculated with the random-phase test by
to take into account autocorrelation.
Annual (DJF)
10-year low pass
20-year low pass
Corr(NAOrecon, HadCRU) 1824–1970
0.52 (p<0.01)
0.68 (p<0.01)
0.70 (p<0.01)
Corr(NAOrecon, NAO20CR) 1851–1970
0.44 (p<0.01)
0.44 (p<0.01)
0.46 (p<0.01)
Corr(SLPPC2-recon, SLPPC2-20CR) 1851–1970
0.24 (p<0.01)
0.53 (p<0.01)
0.58 (p<0.01)
Corr(SLPPC2-recon, SLPPC3-20CR) 1851–1970
0.19 (p<0.01)
0.57 (p<0.01)
0.66 (p<0.01)
Corr(SLPPC2-recon, SLPPC2+PC3-20CR) 1851–1970
0.30 (p<0.01)
0.67 (p<0.01)
0.84 (p<0.01)
Corr(NAOrecon, Ortega et al. MC) 1241–1969
0.49 (p<0.01)
0.43 (p<0.01)
0.37 (p<0.05)
Corr(NAOrecon, Ortega et al. MC) 1241–1820
0.47 (p<0.01)
0.36 (p<0.05)
0.15 (p<0.2)
Corr(NAOrecon, Ortega et al. CC) 1241–1969
0.36 (p<0.01)
0.35 (p<0.01)
0.40 (p<0.01)
Corr(NAOrecon, Ortega et al. CC) 1241–1820
0.29 (p<0.01)
0.21 (p<0.05)
0.12 (p<0.2)
Corr(NAOrecon, Luterbacher) 1659–1970
0.34 (p<0.01)
0.39 (p<0.01)
0.40 (p<0.01)
The reconstructed NAO shows strong multidecadal variability, while no major
trends are found on centennial timescales, as opposed to the reconstruction
by , and in agreement with the NAO reconstructions by
(Fig. 3). The NAO reconstructions by
consists of two multi-proxy NAO reconstructions. Of these reconstructions, one
is calibration-constrained (NAOcc) and the other model-constrained (NAOmc)
(Fig. 3b), where NAOmc only uses proxies from sites that were estimated from
model simulations to have a stable relation to the NAO. It should be noted
that both the reconstruction by and this study use some of
the same Greenland ice core data (Crete, DYE-3, GRIP), which obviously could
lead to a correspondence in variability. We find the NAO in our SLP
reconstruction has best correspondence with NAOmc for interannual
variability, while for multidecadal timescales prior to the instrumental
period, there is little coherency between our reconstruction and both NAOmc
and NAOcc (Table 3). This lack of coherence of multidecadal variability is
similar to the aforementioned divergence between previous NAO
reconstructions. For an independent comparison we used reconstructed NAO and
gridded reconstructed SLP over Europe , as well as
gridded reconstructed temperature over Europe.
However, we restricted the comparison to 1659–1970 due to the methodological
differences in the reconstructions for Europe prior to and post-1659, and the
use of Greenland ice core data for the European temperature reconstruction
covering 1500–1658. For the NAO reconstruction by ,
our reconstruction shows slightly lower correlation on interannual
timescales compared to the correlation to NAOmc, but similar correlation on
decadal to multidecadal timescales (Table 3). The comparison between our
reconstruction and these reconstructions for Europe shows similar pattern and
correlation levels for SLP as with the 20CR data, and moderate, but
significant, correlations for temperature (Fig. S5).
Superimposed epoch analysis of the mean response in atmospheric
circulation to the 12 largest tropical volcanic eruptions
(; Table S1). The response in SLP and T2m is normalized to
the mean fields of the 10 years preceding the eruption. (a) Mean DJF
SLP anomalies (Pa) for the first five post-eruption years. (b) Mean
DJF T2m anomalies (∘C) for the first five post-eruption years. The
white stippling indicates significant anomalies p<0.01, and black
stippling indicates significant anomalies p<0.05 (two-tailed Student's
t test). (c) Mean
response in reconstructed NAO (blue) with the time series normalized to the
mean NAO of the 10 years preceding the eruption. For comparison the same
analysis is carried out for the NAOmc reconstruction (magenta) by
. The significance levels in (c) are estimated
from 100 000 random samples of 12 years drawn from the reconstructed NAO.
See Fig. S6 for significance levels for NAOmc.
Our reconstructed NAO shows higher correlation (corr.=0.52, p<0.01) to the observed NAO (DJF, 1824–1970) than NAOmc and
NAOcc, which have correlations to the observed NAO of 0.46 and 0.47,
respectively. Even more important, the skill of our reconstructed NAO is
achieved without calibrating to the observed NAO. In summary, we think that
our reconstruction is the most suitable for analyzing the influence of
volcanic eruptions and solar activity on circulation, because (i) our
reconstruction not only has good skill for the NAO but also for the
secondary modes of circulation, which, as we will show later, is crucial for
investigating the impact of solar forcing; (ii) high-frequency variability is
preserved, making it possibly better to detect rapid shifts in circulation
after volcanic eruptions; and finally (iii) our reconstruction is not
calibrated to observed SLP or T2m, making it free of biases that could arise
from tuning a reconstruction to observations during the instrumental era.
Response of atmospheric circulation to external forcing
In this section we investigate the reconstructed response in SLP and
temperature to major tropical volcanic eruptions and solar variability. The
mean post-eruption SLP and T2m anomalies in response to 12 major tropical
eruptions show the characteristics of a positive NAO (Fig. 4a, b). On
average, we find a significant positive NAO response during the five
consecutive winters following the eruptions (Fig. 4c). Performing the same
analysis on NAOmc yields a similar response, while not reaching as high
significance levels and persistence as our reconstruction, likely due to the
attenuated year-to-year variability in NAOmc (Figs. 2c, 4c and S6). Due to
the short time span between some of the volcanic eruptions, it is not possible
to consistently analyze any longer-term response than 5 years for single
eruptions, since it would limit the number of eruptions and, hence, the
robustness of the statistical analysis. We analyzed the decadal to
multi-decadal response to volcanic forcing by calculating the correlation
between reconstructed NAO and volcanic forcing from tropical eruptions
after filtering both data series with a band-pass
filter. The correlation analysis of filtered data estimates the combined
effect of the eruptions during the reconstructed time frame. Due to the very
abrupt nature of the volcanic forcing, heavy filtering can introduce
artificial forcing prior to the onset of the actual forcing. We find that
smoothing the volcanic forcing data using a low-pass filter with no less than
1/10 cycle per year only has negligible effects on the analysis. Performing a
time-lag correlation analysis on the band-pass filtered data (1/10 to 1/100
cycles per year) we obtain significant correlations (p<0.01) with the
reconstructed NAO lagging the volcanic forcing 1 to 6 years (Fig. S7). This
corresponds well to the results of the analysis of the mean NAO response for
the 12 major tropical eruptions. Maximum correlation is reached at time lags
of 3 to 4 years with a correlation of 0.33. This shows the cumulative effect
of several volcanic eruptions which can cause trends in the NAO on longer
timescales than single eruptions.
Reconstructed atmospheric response to solar forcing.
(a) DJF SLP anomalies (Pa) in response to the 11-year solar cycle
(solar min. minus solar max. defined in Fig. S10). (b) DJF SLP
anomalies (Pa) in response to the 5-year lagged 11-year solar cycle (solar
min. minus solar max.). (c) DJF SLP anomalies (Pa) in response to
the long-term solar forcing (solar min. minus solar max. defined in
Fig. S11). (d, e, f) corresponding figures to (a, b, c),
but for T2m (∘C). The white stippling indicates significant anomalies
p<0.05, and black stippling indicates significant anomalies p<0.1
(two-tailed Student's t test).
For the analysis of solar influences on circulation, we analyzed the average
response to the 11-year solar cycle and the multidecadal to centennial solar
variability. We calculated the difference between reconstructed SLP and T2m
for years of low and high solar activity using the annual sunspot number
(; 1700–1970) and a 14C-based solar
reconstruction (1241–1970; see Sect. ) for the short-term
and long-term cycles, respectively. The response to the 11-year solar
cycle (solar low minus high) is a high pressure over Scandinavia
corresponding well to the pattern found for reanalysis data (Figs. 5a, d and
6a, c). This anomalous high pressure could be due to increased frequency
in Scandinavian blockings, which has been shown to impact Greenland
δ18O . Investigating the time-lagged response
to the 11-year solar cycle, we find the strongest response in
reconstructed SLP and T2m when lagging the solar forcing with 5 years, which
also matches the time-lagged pattern found in reanalysis data (Figs. 5b, e
and 6b, d). This pattern projects on PC2 of the reconstructed SLP, also with
the strongest correlation between forcing and response when lagging the
sunspot data by 5 years. Taking this time lag into account yields a
consistent relationship between solar forcing and PC2 of reconstructed SLP on
decadal to multidecadal timescales (Fig. 7, Table 4). The 5-year lagged
response in circulation is not simply due to the response to the solar
maximum approximately half a cycle later in the 11-year solar cycle, but a
reinforced response. This is most clearly seen in the stronger correlations
for the time-lagged response, both for the original data and the filtered
data (Table 4, Figs. 7 and S8). The relation between PC2 of reconstructed SLP
and solar forcing persists also for centennial variability, which is seen by
comparing the circulation response to the 14C-based solar
reconstruction (Table 4, Fig. S9). The pattern of the response in SLP to the
long-term solar minima is an Atlantic ridge-type pattern (anomalous high
south of Greenland), which also projects on PC2 of SLP, with an associated
cooling pattern for the western North Atlantic (Fig. 5c, f). Compared to the
5-year lagged response to the 11-year cycle this pattern has the strongest
response in SLP south of Greenland, with a similar, but more widespread
cooling in the eastern North Atlantic. Even though the SLP response looks
slightly different for short- and long-term solar forcing variations, the main
feature, a wave structure over the North Atlantic and Scandinavia, is
consistent (Fig. 5b, c). This similarity in the 5-year lagged and long-term
response can also be seen in the patterns of the temperature anomalies
(Fig. 5e, f). The temperature response to the long-term solar minima is a
cooling across Greenland, Iceland and western Europe during solar minima
(Fig. 5f). This cooling pattern corresponds well to the suggested cooling
during the Little Ice Age in proxy records from Greenland
, Iceland and Europe
. A NAO-type response to long-term solar forcing would
give opposing temperature responses in Greenland and Europe, which is not the
case. We find no consistent relation between our reconstructed NAO and solar
forcing. Instead we would like to stress the importance of the connection
between solar activity and the secondary circulation patterns, which possibly
shows the main response to solar forcing on decadal to centennial timescales,
with correlations of 0.29 (p<0.01) and 0.6 (p<0.01),
respectively.
20CR (1948–2010) atmospheric response to solar forcing.
(a) DJF SLP anomalies (Pa) in response to the 11-year solar cycle
(solar min. minus solar max. defined in Fig. S12). (b) DJF SLP
anomalies (Pa) in response to the 5-year lagged 11-year solar cycle (solar
min. minus solar max.). (c, d) corresponding figures to (a, b), but for T2m (∘C). The white stippling indicates significant
anomalies p<0.05, and black stippling indicates significant anomalies p<0.1 (two-tailed Student's t test). The time interval for this analysis in limited to 1948–2010 due
to limitation of the data quality prior to this, although similar results can
be achieved for the period 1851–2010.
Correlation between solar forcing and PC2 of SLP, with and without
time lag. The first column indicates which data are used, and if any filtering
is done to the data. The second column is correlation coefficients between solar
forcing and PC2 of reconstructed SLP, with solar forcing either being
represented by sunspot number or 14C data. The third column is
correlation coefficients between solar forcing and PC2 of reconstructed SLP,
with solar forcing represented by sunspot number shifted for a lag of
5 years. All correlations are for detrended data, and p values are
calculated with the random-phase test by to take into
account autocorrelation.
No time lag
5-year time lag
Corr(Recon. PC2 SLP, sunspots) 1700–1970
-0.06 (p>0.1)
0.20 (p<0.01)
5-year low-pass filtered data: Corr(Recon. PC2 SLP, sunspots) 1700–1970
-0.07 (p>0.1)
0.29 (p<0.01)
20-year low-pass filtered data: Corr(Recon. PC2 SLP, sunspots) 1700–1970
0.30 (p<0.05)
0.53 (p<0.01)
20–500 year band-pass filtered data: Corr(Recon. PC2 SLP, solar activity (14C)) 1241–1970
0.30 (p<0.01)
–
60–500 year band-pass filtered data: Corr(Recon. PC2 SLP, solar activity (14C)) 1241–1970
0.60 (p<0.01)
–
Discussion and conclusions
The model simulation used for our reconstruction translates the climate variability recorded in the Greenland ice
cores to climate variability in the North Atlantic region. In the initial
test of the isotope variability, it is shown that the spatiotemporal
δ18O variability in the ice cores is well represented by the
model (Fig. 1). This is a fundamental prerequisite which allows us to match
the modeled δ18O to the ice core δ18O
year-by-year. While the skill of the reconstruction is higher in the vicinity
around Greenland, the reconstruction shows significant correlations to
reanalysis data wide spread across the North Atlantic region. This skill
depends on (i) the integrative nature of the δ18O as recorded
in the ice cores, and represented by the modeled δ18O;
(ii) the modeled atmospheric teleconnection patterns in terms of temperature
and circulation; and (iii) how these patterns are connected to modeled
δ18O for Greenland. Clearly, the reconstruction is strongly
dependent on the climate model when it comes to whether or not it is possible
at all to use our method, and when it comes to the skill of reconstructed
spatial patterns. The resolution of our model simulation is relatively course
and using a higher-resolution simulation could improve the representation of
several processes. For example, vapor transport to dry polar regions is often
inhibited in models with courser resolution, resulting in too little
precipitation in the interior of ice sheets and a positive bias in
δ18O . This is related
to cloud parameterizations and course-resolution models having difficulties
in explicitly representing frontal zones in connection with synoptic weather
systems. The orography in course-resolution models is also more smooth,
loosing orographical features such as the southern dome of the Greenland ice
sheet, which also affects atmospheric circulation and small-scale spatial
variability. In our approach we match the modeled PCs of δ18O,
meaning that we are matching regional-scale patterns in δ18O,
which partly addresses the problem of matching course model output to
site-specific proxy data. However, having a higher-resolution model
simulation could for example improve the spatiotemporal representation of
Greenland δ18O, allowing more than 3 PCs to be fitted, and
generally giving a better representation of temperature, pressure and
precipitation in the reconstruction. For reasons discussed above, it would be
desirable using different GCMs to test for model dependencies of the
reconstruction, as well as testing for added value of ensemble
reconstructions with several different GCMs. Doing these tests is presently
limited by the availability of millennium length simulations using
isotope-enabled GCMs.
Reconstructed PC2 of SLP plotted with the 5-year lagged sunspot
number. (a) Moving 61-point correlation between reconstructed PC2 of
SLP and sunspot number shifted for a 5-year time lag. (b) Time
series of sunspot number shifted for a 5-year time lag,
PC2+PC3 of 20CR SLP (see text and Table 4) and PC2 of
reconstructed SLP. (c) Same as (b), except filtered with a
5-year low-pass filter. (d) Same as (b), except filtered
with a 20-year low-pass filter.
We selected the proxy records for this study based on the criterion of having
seasonal resolution, small dating uncertainty, a long time span and a wide
regional spread. In order to provide a quantitative link to the
isotope-enabled GCM we selected only isotope-based proxies. For the time
being, this leaves us with the eight Greenland ice cores used in this study.
Other seasonal resolution ice cores from Greenland are available, but only
covering a limited time span, and comparing to these cores shows that the
reconstructed δ18O also compares well to the isotopic
variability at these sites (Fig. S2). However, including more Greenland ice
cores of similar quality would generally improve the signal to noise ratio of
the reconstruction, and such records should be included if available for
subsequent studies. Obtaining seasonal resolution in ice core data is mainly
limited by the accumulation rate and seasonality of precipitation, which
depends on the regional climate setting of the drill site . Including other archives than ice cores would give a more
widespread regional coverage, potentially providing better constraints on
circulation patterns and climate trends. Some oxygen isotope records from
tree rings in Sweden e.g., and speleothems from the
European Alps e.g.,
covering the past millennium primarily reflect winter climate conditions.
Both records in these examples have 5-year resolution, and the speleothem
record has hiatuses, which reflect some of the challenges in using these
proxy records. However, there could be benefits to using a larger selection
of data, despite the different temporal resolution .
The comparison between the response to volcanic forcing between our
reconstruction and NAOmc (Fig. 3c) shows that the mean response look
qualitatively very similar in the two reconstructions. However, as already
mentioned, due to the preserved high-frequency variability, our reconstruction
shows both a more immediate and persistent NAO response to volcanoes. This
underlines the importance of producing climate reconstructions that do
preserve high-frequency variability, in particular if the reconstruction is
used as baseline for model evaluation. In our analysis of the reconstructed
response to volcanic eruptions we choose eruptions larger than or of similar
magnitude to the 1991 Pinatubo eruption (<-6Wm-2). As
discussed by , climate effects of smaller eruptions can
be difficult to detect due to stochastic climate variability. We find that we
can detect an impact on reconstructed NAO from tropical eruptions
selected in the range from -4 to -8Wm-2 ,
yielding a significant positive NAO 1 year after the eruptions, on average.
This appears to be the limit of detection for our reconstruction, possibly
owing both to the partly stochastic variability in the NAO and to noise in
the reconstruction.
Model studies of the volcanic response to major tropical eruptions during the
past millennium show a large spread in the modeled NAO response, with either
no consistent response or 1–2 years of significant response
. In contrast to this we find a clear
tendency for positive NAO for the five consecutive winters following the year
of eruption as an average response to the 12 largest tropical eruptions
during 1241–1970 CE. An immediate strengthening of the polar vortex
following eruptions is in agreement with the observed response of atmospheric
circulation , which then translates to a positive NAO as
the stratospheric anomaly propagates to the troposphere. The presence of
volcanic aerosols gradually tails off during the first 2–3 years
and a more sustained positive NAO for up to 5 years could
be explained via a positive ocean feedback through a tri-pole sea surface
temperature (SST) response to the strongly anomalous positive NAO
. Ongoing efforts to improve the simulation of volcanic
forcing and response could help close the gap between models and observations
as well as reconstructions .
It has been suggested that the observed increase in blocking frequency over
the North Atlantic in response to solar minima is
coupled to a weakening of the polar night jet in response to a weaker
stratospheric equator–pole temperature gradient. This mechanism could be in
play on both decadal and centennial timescales. A recent study investigated
the response of circulation to solar activity using a regression-based
analysis between sunspot data and gridded observed SLP and SST data
. The authors analyzed the time-lagged response to solar
forcing, and found that the solar response could be explained via two
mechanisms . One involving the aforementioned
stratosphere–troposphere coupling acting on time lags of 0–2 years, and one
for time lags of 3–4 years involving ocean temperature anomalies being
stored beneath the mixed layer and reinforced from the previous winter
. The reinforcement of SST anomalies from year-to-year has
also been shown in a simulation of the response to the solar 11-year cycle
. Such a mechanism could be the cause of the time lag we
see in the reconstructed response to solar forcing, although we get the
maximum response at 5-year time lag, compared to the 3–4 year time lag found
in observations . However, this difference could be due to
differences in the methodologies of the analysis of the response to solar
forcing, and that the aforementioned study is focusing on
the NAO-like response seen in their analysis. A possible mechanism based on
our findings is as follows. An initial increase in atmospheric blockings
weakens the subpolar gyre (SPG; ) , thereby decreasing
the heat transport to the north-western North Atlantic giving favorable
conditions for mid-Atlantic blocking. This pattern is reinforced year-by-year
and the main atmospheric response shifts to the node of PC2 of SLP south of
Greenland under sustained forcing conditions on longer timescales (Fig. 5c).
A recent model study suggested that the cooling
during the Little Ice Age was connected to a weakening in the SPG, sustained
by way of atmosphere–ocean feedbacks. Although the authors do not relate
this to solar forcing, but with preconditioned initial model variability, the
anomalies in SLP and temperature associated with the weakening of the SPG are
very similar to the reconstructed pattern of the response to long-term solar
forcing. One explanation could be that low solar activity is the
preconditioning factor in reality, causing the response to solar forcing seen
in our reconstruction, while the climate response to solar forcing might not
be fully captured by the MPI-ESM used by
. Our study of the reconstructed North Atlantic
winter circulation shows a complex response to solar forcing which is, in
contrast to a prevalent hypothesis, not directly linked to the NAO. The
complexity is also reflected in a nonuniform temperature response to solar
forcing, with both regional warming and cooling. This also means that part of
this signal will be smoothed out if such analysis is carried out on
hemispherical mean temperature e.g.,. In our study we
do not exclude that there could be an influence of the solar 11-year cycle on
NAO. However, unlike for PC2 of reconstructed SLP, we find no consistent
relationship between reconstructed NAO and solar forcing across multiple timescales.
Furthermore, the results suggest that sustained longer-term solar
forcing leads to a shift in the atmospheric circulation response compared to
the response to the short-term forcing, possibly due to feedback processes
involving the ocean integrating the long-term effects of anomalous
atmospheric circulation.
Our study presents a new climate reconstruction of SLP and temperature for
the North Atlantic region. The reconstruction not only resolves the first
mode of atmospheric circulation (PC1), the NAO, but also captures the second
mode (PC2), referred to as the Eastern Atlantic Pattern. In the analysis of
our reconstruction we find that solar and volcanic forcing impacts different
modes of the atmospheric circulation during winter, which can aid to separate
the regional effects of forcings and understand the underlying mechanisms.
The reconstructed response to forcings can also serve as a baseline for
climate model evaluation. Although atmospheric variability to a large extent
is a stochastic process, the variability in our reconstruction also shows an
overall significant impact of forcings. The squared correlation coefficient
can provide an estimate for explained variance in the external forcings.
Using this approach, tropical volcanic forcing accounts for about 10 % of
the decadal to multidecadal variability in the reconstructed NAO, while
solar forcing accounts for about 40 % of the variability in PC2 of
reconstructed SLP on centennial timescales.