NACLIM Deliverable D12.18
Deliverable title
Report on the identification of NA/Arctic ocean surface state changes that most affect
the atmosphere
This is a report on the influence of the ocean on the atmosphere, which processes and mechanisms
are involved and which observations need to be taken to represent those processes best
WP No.
1.2
Lead beneficiary:
WP title
Predictability of the atmosphere related to the North Atlantic/Arctic
Work duration1) 18
X
Due delivery deadline:
UHAM
30 April 2014
Actual delivery date:
30 April 2014
R= report
Nature of the
P= prototype
deliverable
D= demonstrator
O= Other
PU = public
X
PP= restricted to other programme participants, including the Commission services
Dissemination
RE= restricted to a group specified by the consortium, including the Commission
level
services
CO= confidential, only for members of the consortium, including the Commission
services
1) Work duration = project month
Lead beneficiary:
UHAM
Other contributing
partners:
UPMC
Detlef Stammer, Armin Köhl, Ion Matei, Reema
Agarwal, Xueyuan Liu, Nikolay Koldunov,
Andrey Vlasenko
Group of Claude Frankignoul, Guillaume
Gastineau, Javier Garcia Serrano
Yongqi Gao, Helene Langehaug
NERSC
NACLIM project is financed by the European Commission
through the 7th Framework Programme for Research
Theme 6 Environment
Grant Agreement 308299
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Index
1. Executive summary .................................................................................................................................... 3
2. Project objectives .......................................................................................................................................... 4
2. Detailed report on the deliverable ............................................................................................................... 4
4. References .................................................................................................................................................. 18
5. Dissemination and uptake ........................................................................................................................... 19
5.1 Dissemination ........................................................................................................................................... 19
5.2 Uptake by the targeted audience .......................................................................................................... 20
6. The delivery is delayed:  Yes  No ....................................................................................................... 20
7. Changes made and difficulties encountered, if any ...................................................................................... 20
8. Efforts for this deliverable ............................................................................................................................ 20
9. Sustainability ............................................................................................................................................... 20
10. Dissemination activities ............................................................................................................................. 20
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1. Executive summary
The space-time patterns of sea surface temperature (SST), sea ice concentration (SIC), northern snow cover,
and western boundary current changes that most impact the atmospheric circulation, the surface conditions,
and the storm track in the North Atlantic/European sector on seasonal to decadal time scale have been
identified and their seasonality established, based on a statistical analysis of the observations and atmospheric
reanalyses. The EU FP7 THOR adjoint assimilation system has been used to identify sensitivities of
predictable air temperature over northern Europe on SST in the North Atlantic, and sensitivity studies with an
atmospheric GCM have been made to investigate the influence of the predicted autumn ice loss in the Arctic.
Two modes of atmospheric response to North Atlantic SST anomalies were found at the seasonal scale: a
summer response resembling the East Atlantic Pattern, and an early winter response resembling the NAO,
albeit shifted south. Both modes have a significant impact on temperature and precipitation in Europe and
North America. On interannual to decadal timescales, a SST anomaly that closely resembles the Atlantic
Multidecadal Oscillation (AMO) precedes a negative NAO-like atmospheric signal. The variability of the
western boundary current extensions in the North Pacific also has an impact on the North Atlantic/European
sector. In particular, the decadal variability of the Kuroshio Extension influences the atmospheric circulation
over the North Pacific sector in fall/early winter, followed one month later via upper tropospheric/stratospheric
wave propagation by a signal over Europe.
The influence of cold season SIC changes was investigated. The NAO drives a SIC seesaw between the
Labrador Sea and the Greenland-Barents Sea and in return the seesaw precedes a mid-winter/spring NAO-like
signal of the opposite polarity. In addition, a late winter/spring pattern resembling a combination of the NAO
response to the Atlantic SIC seesaw and the Aleutian-Icelandic Low seesaw response to in-phase SIC
changes in the Bering and Okhotsk Seas lags the SIC anomalies by 8 weeks. Cold season North Pacific SST
anomalies also precede a NAO/AO-like signal by a few weeks.
October SIC anomalies in the Arctic were found to precede the winter NAO, a SIC reduction in the northern
Barents-eastern Kara Seas preceding a negative NAO. A negative winter NAO also follows a faster
progression of the Eurasian snow cover during October. Both atmospheric responses are mediated by
absorption of vertical wave activity flux into the stratosphere, which weakens the polar vortex and thereafter
affects the tropospheric circulation.
Ensemble numerical simulations with an atmospheric GCM were used to investigate the influence of the icefree Arctic during autumn that is predicted to occur by the mid 21th century. The loss of the sea ice and the
associated increase in Arctic SST should substantially warm (by up to 10°C) the overlying atmosphere over the
Arctic Ocean, the North Pacific and North Atlantic, and large parts of high-latitude continents during autumn
and early winter, while the predicted changes are smaller in mid winter.
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Finally, it was shown that the sensitivity of the atmosphere to SST can be estimated using a coupled
atmospheric-ocean model and its adjoint, which was developed further during NACLIM. The SST affects the
atmosphere in Europe on the daily time scale mainly kinematically via heating or cooling the air temperature
above. On longer time scales of several days, dynamic effects become more relevant. The optimal patterns
resemble the regression patterns on North Atlantic SST, suggesting that SST most efficiently affects the
atmospheric circulation by triggering an NAO-type response.
2. Project objectives
With this deliverable, the project has contributed to the achievement of the following objectives (see DOW
Section B.1.1):
No.
Objective
Assessing the predictability and quantifying the uncertainty in forecasts of the North
Atlantic/Arctic Ocean surface state
2. Assessing the atmospheric predictability related to the North Atlantic/Arctic Ocean
surface state
3. Monitoring of volume, heat and fresh water transports across key sections in the
North Atlantic
4. Quantifying the benefit of the different ocean observing system components for the
initialization of decadal climate predictions
5. Establishing the impact of an Arctic initialization on the forecast skill in the North
Atlantic/European sector
6. Quantifying the impact of predicted North Atlantic upper ocean state changes on the
oceanic ecosystem
7. Quantifying the impact of predicted North Atlantic upper ocean state changes on
socioeconomic systems in European urban societies
8. Providing recommendations for observational and prediction systems
9. Providing recommendations for predictions of the oceanic ecosystem
10. Disseminating the key results to the climate service community and relevant
endusers/stakeholders
11. Constructing a dataset for sea surface and sea ice surface temperatures in the Arctic
12.
1.
Yes
X
No
X
x
2. Detailed report on the deliverable
The space-time patterns of SST, sea ice concentration, northern snow cover, and western boundary current
changes that most impact the atmospheric circulation, the polar amplification of climate change, the surface
conditions, and the storm track activity in the North Atlantic/European sector on seasonal to decadal time scale
have been identified and their seasonality established, based on a statistical analysis of the observations and
atmospheric reanalyses.
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1. SST influence on the North Atlantic/European sector
The SST influence onto the atmospheric circulation was investigated using the 20th century reanalysis that only
assimilates surface pressure and uses HadISST SST and sea ice extension as boundary forcing. The quality
of 20CR over Europe has been shown to be poor before 1930, better afterward, and improved further after
1966. The SST patterns that have the largest influence onto the atmosphere were studied using maximum
covariance analysis (MCA), namely an MCA of seasonal anomalies of the 500-hPa geopotential height (Z500)
and the SST in the North Atlantic sector. The MCA estimates the main modes of covariability between two
fields while making no a-priori assumption on the spatial patterns. We have also used rotated MCA (RMCA) to
verify the stability of the patterns and used the RMCA modes when they showed large differences with the
unrotated modes. As the intrinsic atmospheric memory is ≤1 month, a significant covariability when the SST
leads the atmosphere by one season or more indicates that the SST might be the cause of the atmospheric
pattern, provided there are no other sources of atmospheric persistence. Hence, the teleconnections with the
tropical SST and the global warming trend were first removed, as both introduce a persistent component in the
atmosphere. The MCA analysis of the 1930-2010 period or the better sampled 1966-2010 period revealed two
significant modes of atmospheric response at the seasonal scale, one in summer around JJA (June-JulyAugust) and one in early winter around NDJ (November-December-January). The spatial pattern of the two
modes is shown in Fig. 1 for Z500 in JJA (left) and NDJ (right) when SST leads by 3 months. The summer
mode reveals that a spring SST anomaly tripole precedes an anticyclone over the whole subpolar region,
which resembles the East Atlantic Pattern. The early winter mode shows that a warming over the subpolar and
eastern North Atlantic precedes a geopotential height dipole over the North Atlantic/Europe sector that
resembles a negative phase of the North Atlantic Oscillation (NAO), albeit shifted south. Both modes are
robust and have a significant impact on temperature and precipitation in Europe and North America.
Figure 1. First RMCA mode homogeneous pattern for SST (in K) and heterogeneous pattern for Z500 (contour in m) of
the first RMCA mode when the JJA (left) and DJF (right) atmosphere lags SST by 3 months. The square covariance SC,
the correlation R and the SC fraction SCF of the RMCA modes is indicated at the top of each map.
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The interannual to decadal variability was emphasized without affecting seasonality by using a small temporal
smoothing for each atmospheric and SST yearly time series, so that lags ≥ 3 yr could unambiguously show a
SST influence. A significant mode was found in winter for Z500 in JFM (January-February-March). As shown in
Fig. 2, the SST pattern resembles that found at the seasonal scale with Z500 in NDJ, but for a larger subpolar
warming and a smaller one in the subtropics, and it also precedes a dipole of geopotential height resembling a
negative phase of the NAO. Interestingly, the SST anomaly closely resembles, and is highly correlated with,
the Atlantic Multidecadal Oscillation (AMO), which is the main mode of North Atlantic SST variability at the
decadal time scale. The climatic impact of the AMO-like mode in winter is illustrated in Fig. 3. Since the North
Atlantic subpolar region corresponds to one of the few regions where there is substantial potential SST
predictability at the decadal scale in the CMIP5 simulations, air temperature and precipitation might be in part
predictable at low frequency in the North Atlantic/Europe sector. For details, see Gastineau and Frankignoul (in
preparation).
Figure 2. As in Fig. 1 for low-pass filtered fields when SST leads Z500 by 3 years.
Figure 3. Climate impact of the SST low frequency variability in winter (JFM): (Left panel) atmospheric temperature at
850-hPa, in K (Right panel) rainfall, in mm d-1, regressed onto the MCA SST time series 3 year earlier. The color shades
are masked if the significance is above 10%.
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The mechanisms controlling the atmospheric response were discussed using a wide range of diagnostics and
lagged regressions, as illustrated for two cases in Fig. 4. The summer atmospheric response (Fig. 4, top) is in
part driven by ascending motion (left) over the tropical North Atlantic, as shown by the velocity potential
anomalies at 200-hPa that are in phase with the JJA atmospheric response, although SST forcing in the
subpolar North Atlantic also contributes. The wave activity flux (right), which points to the source of the
atmospheric waves that are responding to SST, indicates that the summer response is embedded into a global
teleconnection pattern with a wavenumber 5. As shown in the bottom panels, the early atmospheric winter
response seems to be mainly driven locally by the subpolar SST, since there is little signal in the velocity
potential (left) but the wave activity (right) indicates a vertical wave activity flux over the Labrador Sea and the
western subpolar gyre. We did not find any stratospheric precursors of the atmospheric signal, so that the
atmospheric response seems solely controlled by the surface conditions below and does not involve a
downward propagation from the stratosphere where the intensity of the polar vortex would play a role, as in
Omrani and Keenlyside (2012).
Figure 4. Lagged regression onto the SST time series associated to the rotated MCA using the JJA atmosphere (upper
panels) and the NDJ atmosphere (lower panels) of the velocity potential in 10-6 s-1 (left panels), and the wave activity flux
in m2 s-2 and geopotential height (right panels). In the left panels, colors indicate the 5% significance. In the right panels,
colors indicate the vertical component of the wave acticity flux at 500-hPa, in Pa m s-2.
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2. Influence of the western boundary currents
The hemispheric influence of the observed variability of the western boundary current extensions in
the North Pacific has been investigated, using indices of their main mode of variability and
atmospheric reanalyses. Frankignoul et al. (2011) have shown that the meridional shifts of the
Oyashio Extension (OE), as derived from high-resolution monthly sea surface temperature (SST)
anomalies in 1982-2008, had a significant influence on the North Pacific Oscillation/Western Pacific
teleconnection pattern, in a positive phase for a northward OE displacement, with an equivalent
barotropic high over western Europe and lows over southeastern Europe and Greenland. The signal
was most significant in winter.
Using a new index of the monthly variability of Kuroshio Extension (KE) derived by Qiu et al. (2013)
from altimetric observations and a high-resolution oceanic hindcast, we have shown that the decadal
variability of the KE during 1979-2012 also has a significant influence on the northern hemisphere
atmospheric circulation in the cold season. The KE modulates the SST (Fig. 5), which affects the heat
exchanges with the atmosphere. The largest response over the North Pacific sector in found from
October to January (ONDJ), but significant teleconnections occur the North Atlantic sector slightly
later in the season, persisting thru late winter, as shown by the lagged regression of monthly SLP
(after a delay of 2 months) and temperature anomalies at 900 hPa in DJFM (after a delay or 4
months) onto the KE index in ASON. The atmospheric temperature pattern following a northward shift
and an intensification of the KE (together with a reduction in mesoscale eddy activity) shows some
resemblance with a positive phase of the Arctic Oscillation, with a significant warming over northern
Europe. The associated potential predictability is limited, however. Details in Révelard et al. (in prep.)
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Figure 5. Top: Time series of the KE index (in red, after removing a cubic trend) and regression of the associated monthly
SST anomalies in NDJFM. Bottom: Lagged regression of SLP in ONDJ (left) and air temperature at 900 hPa in DJFM
(righ) onto the normalized KE index in ASON. The black curves indicate10% significant areas.
3. Arctic sea-ice influence
To identify the space-time patterns of Arctic sea-ice concentration (SIC) anomalies that most impact
the atmospheric circulation during North Atlantic/European winter and spring, we have used lag MCA.
The relation between weekly Arctic SIC from December to April and sea level pressure (SLP) during
1979-2007 was first investigated. In the North Atlantic sector, the interaction between the NAO and a
SIC seesaw between the Labrador Sea and the Greenland-Barents Sea dominates. The NAO drives
the seesaw and in return the seesaw precedes a mid-winter/spring NAO-like signal of the opposite
polarity, but with a strengthened northern lobe, thus acting as a negative feedback, with maximum
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squared covariance at a lag of 6 weeks, which corresponds to an atmospheric signal in midwinter/spring (Fig. 6).
Figure 6. Covariance map for SIC (left, in %) and SLP (right, in hPa) in the North Atlantic sector when SLP leads SIC by
1 week (top) and lags SIC by 6 weeks (bottom). The time series were normalized so that the maps show typical amplitudes.
The SC, correlation R, and the SCVF are indicated, with the estimated significance level.
Statistical significance increases when North Pacific SIC is included in the analysis. A pattern resembling a
combination of the NAO response to the Atlantic SIC seesaw and an Aleutian-Icelandic Low seesaw-like
response to in-phase SIC changes in the Bering and Okhotsk Seas lag the North Pacific SIC by 8 weeks (Fig.
7). Adding SST anomalies to the SIC anomalies in the MCA leads to a loss of significance when the MCA is
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limited to the North Atlantic sector, and a slight degradation in the Pacific and hemispheric cases, suggesting
that SIC is the driver of the mid-winter/spring atmospheric signal. However, North Pacific cold season SST
anomalies also precede a NAO/AO-like SLP signal by 3 to 4 weeks. The relative influence of the two boundary
variables was difficult to disentangle in the short period when high resolution SIC data are available. In any
case, this study stresses that boundary forcing in the whole northern hemisphere need to be considered to
investigate climatic impacts in the North Atlantic/Europe sector (Frankignoul et al. 2014)
Figure 7. As in Fig. 6, but covariance map for hemispheric SIC leading SLP by 8 weeks.
A second study focused on the influence of SIC onto climate earlier in the cold season. We have considered
Arctic SIC during autumnal months, September throughout November, and winter Euro-Atlantic SLP in the
1979/80-2012/13 period. There is no significant mode with September SIC. The most significant relation in
October and November is obtained when limiting the SIC to the eastern Arctic, consistent with the literature.
Figure 8 shows the leading MCA mode for eastern Arctic SIC in October, which explains 79% of the SCF. It
yields a rather large correlation (0.56) between the expansion coefficients of SIC and SLP, although the crossvalidated hindcast skill for the NAO is limited (0.31), even though the MCA mode is statistically significant. The
MCA patterns display negative anomalies for SIC (a sea-ice reduction corresponding to a retreat of the sea-ice
edge) over the Barents- Kara Seas (Fig. 8a) and a negative phase of the NAO for SLP (Fig. 8b), which means
that a sea-ice reduction in the Barents-Kara Seas precedes by two months a negative NAO phase in winter.
The SIC anomaly persists in the fall while following the climatological advance of the ice edge, and the MCA
results are similar, with a slightly reduced NAO hindcast skill of 0.29. Yet, it suggests that sea-ice information
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should be incorporated in dynamical predictions systems for improving their skill at forecasting the surface
winter climate in Europe. Analysis is underway to understand how the SIC influences the NAO, but preliminary
results suggest that it involves stationary Rossby wave activity flux from the eastern North Atlantic towards
central-eastern Eurasia, an amplification of the meridional eddy heat flux, and injection of tropospheric wave
activity into the stratosphere, which is a precursor of the weakened polar vortex associated with a negative
phase of the NAO/AO during winter. Transient-eddy momentum transport may play a role (Garcia-Serrano and
Frankignoul 2014; Garcia-Serrano et al., in prep.)
Figure 8. Leading MCA covariability mode between detrended October SIC in the eastern Arctic (a) and winter SLP (b)
anomalies over the North Atlantic-European sector. Statistically significant areas at 95% confidence level are contoured,
and the SC, correlation R, and the SCVF are indicated
4. Atmospheric response to autumn Arctic sea ice-free conditions
Future projections indicate that the Arctic can likely be sea ice free during the autumn at mid-21th century (Liu
et al., 2013a) or even earlier (Overland and Wang, 2013). However, the anomalous forcing of the climate
system by an ice free Arctic is not clear. To investigate the possible response of the autumn and winter
atmosphere to an ice free Arctic in autumn, we have performed ensemble numerical simulations with an
atmosphere general circulation model. In the simulations, the set up of the sea ice-free conditions represent
the combination of the fully removed autumn Arctic sea ice and the associated SST changes. The results
showed that the loss of the sea ice and the associated increase in SST in the Arctic Ocean can substantially
warm the overlying atmosphere over the Arctic Ocean, the North Pacific and North Atlantic, and large parts of
high-latitude continents (Fig.9). The increased surface air temperature can reach above 10 degree. Meanwhile
the SLP changes show wave patterns with anomalous low covering the Arctic Ocean and the North Pacific,
which imply the modulation of the planetary waves (Fig. 9). Three weak but significant positive anomalous SLP
centers are found over northwest America, northern Europe and the northern part of the East Asia. This does
Page 12
not resemble a typical negative AO or NAO pattern. The zonal mean significant SAT changes can reach the
regions north of 40N during the autumn and the early winter but cannot persist after December (Fig. 9). The
winter SLP changes are weaker than the autumn ones and show more regional wave patterns (not shown).
The relatively weaker responses may reflect the stronger internal variability. The internal variability and the
detection ability of the winter SLP response need to be further investigated (Suo et al., in preparation).
Figure 9. Oct-Nov mean (top) SAT and (middle) SLP changes in response to the autumn ice free in the Arctic.
(bottom) the zonal mean SAT responses. The green X indicate where the response is significant at the 95%
confidence level
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5. Eurasian snow cover influence
Despite much research on the influence of autumn Eurasian snow cover extent (SCE) on the winter NAO, only
little investigation has been devoted to the spatial distribution of those SCE anomalies. We have further
documented the lagged relationship between autumn Eurasian SCE and the winter NAO, taking into account
the recent finding of Cohen and Jones (2011), who showed that the progression of October SCE over the
continent is a better NAO predictor than the October-mean anomaly. Hence, Extended-MCA, which introduces
more than one time lag in the predictor field, was used to represent the SCE progression in October. The
leading EMCA covariability mode between weekly October SCE anomalies and winter Euro-Atlantic SLP
anomalies explains 50% of the SCF, and the correlation between expansion coefficients is 0.72. The leading
time-evolving SCE pattern first shows a westward advance of snow cover over eastern Siberia, and then a
southward extension to central Eurasia (Fig. 8c). Except in the third October week, statistically significant
negative SCE anomalies to the west accompany the SCE evolution, depicting a dipole-like pattern. As the SLP
expansion coefficient correlates at -0.99 with the winter NAO index, the time-evolving SCE pattern in Fig. 8c is
associated with a negative NAO phase (Fig. 8d). Analysis is under way to test its statistical significance and its
possible links with Arctic SIC.
6. Sensitivity of the European temperature to SST changes
The main goal of this work package is to identify the North Atlantic/Artic sea surface temperature changes that
most efficiently affect the atmosphere over Europe. The computation of sensitivity of the atmosphere to SST
was done on the basis of the coupled atmospheric-ocean model, called CENAM (previously THOR), where the
MIT general circulation model (Marshall et al. 1997) is used for the ocean part and the Planet Simulator
(PLASIM, Fraedrich et al., 2005) is used for the atmospheric part. The adjoint of PLASIM were obtained with
the help of TAF (Giering and Kaminski 2003), a special software that automatically generates the adjoint model
from the source code.
The experimental setup was as follows: the ocean model has a resolution of 4 degree with 90 × 44 grid points
and with 15 vertical levels. The atmospheric part was resolved on T21 horizontal grid and split into 15 levels.
The model was initiated with the cold start conditions (zero atmospheric/ocean currents, only zonal
temperature distribution is given). The climatology for the experiment was formed after CENAM had been spun
up for 200 years: The model run continued until the Meridional Overturning Circulation (MOC) became stable
and the seasonal climatology did not change any further. For the spin up and the experiment described below,
the time step in ocean was set to 8 hours while the time step in the atmosphere was 48 minutes.
After the climatology for the experiment had been computed, the tests with different cost functions for
measuring the averaged near-surface atmospheric temperature were performed. The task of the tests was to
find a form of the cost function that is affected minimally by numerical errors in the atmospheric part of the
adjoint. Note that the atmospheric model component of CENAM and the corresponding adjoint are computed in
the discrete Fourier space (DFS). In contrast to continuous Fourier space the DFS has only finite number of
frequencies. This means that part of the spectrum of the transformed into DFS function is not resolved
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(especially, its high frequency part). Thus a fraction of high frequency in the functional spectrum controls the
amount of error that appears in corresponding adjoints after they are transformed in DFS. To minimize the
error, a special cost functional was chosen, such that the resulting adjoint has a small fraction of high
frequencies. The formula for the functional is as follows:
2
𝐽(𝑇) = −𝑒 −𝜎(𝒊−𝒓) 𝑤 ∑ 𝑇𝑗𝑘
𝑖,𝑘
Here, 𝑇 is temperature, 𝑖 = 1, 𝑁 stands for spatial coordinates, where N is the total number of grid points, and
𝑘 = 1, 𝜏 corresponds to the time steps, where 𝜏 = 8 hours 𝜎 and 𝒓 are set in such a way, that temperature
values in grid points outside Europe have negligible impact in 𝐽.
As a next step, the numerical errors appearing at the interpolation stage from the atmosphere grid to the ocean
grid were removed. This error was several orders less than the minimal nonzero value of the sensitivity.
However, due to their accumulation the model “exploded” after a number of time steps. The distribution of the
noise, appearing at the interpolation stage, was almost constant in time and space (see Fig.11), except 3-4
points where the continents have sharp edges. The removal of that noise was done in two phases. At first, the
noise was stored during a special “idle” run of the CENAM adjoint with zero atmospheric adjoint. At the time
when the sensitivities were calculated, this noise was subtracted from the sensitivity estimates during each
time step. The remaining couple of outliers were removed by histogram filter (Raffel et. al., 2007).
The computations of the atmospheric sensitivities to SST were carried out on the basis of a so-called
“minimum configuration” in which processes associated with humidity were switched off. The results of the
experiment are presented in Figs. 12-14. They correspond to the 5-day evolution of the sensitivity of the nearland atmospheric temperature in Europe with respect to oceanic SST. The target period for the atmospheric
temperature was an 8 hour mean on the 15th of February. Figure 12 shows the sensitivity corresponding to 16
hours before the end of the target period. Note that the sensitivity with respect to air temperature during the
target day basically reflects the region over which the atmospheric temperature was averaged to define the
cost function. As the time progresses backward, the sensitivity is advected upstream, and once it reaches
ocean-covered areas it affects sensitivities to SST. This explains most of the development of the sensitivities
during the initial phase. Two advective pathways become apparent in Fig. 12, one towards the northwest of
Scandinavia and one west of the Biscaya. At this stage sensitivities remain mainly kinematic, which means that
SST changes do not change the atmospheric flow but just lead to changes in air temperature that are passively
advected towards Europe. Sensitivities remain therefore exclusively positive in Fig. 12.
Figure 13 corresponds to 56 hours backward in time. In addition to the passive advection described above, the
structure of the sensitivity starts to form a tripole structure, in which the axis of southern dipole is similar to the
NAO. This SST pattern is very similar to the SST/NAO regression pattern shown by e.g. Rodwell et al. (1999)
and Czaja and Frankignoul (2002). This suggests that SST perturbations most efficiently affect the
atmospheric temperature by changing the dynamics, if circulation changes similar to the NAO pattern are
Page 15
triggered. Figure 14 shows the pattern of sensitivity 150 hours before the target day. At that stage, after more
than 6 days, which is several times the typical time scale of the atmosphere, the chaotic dynamics of the
atmospheric model start to affect the patterns. We see that the pattern starts to further break up it into a
quadrupole with alternating min-max zones.
Figure 11. The noise pattern related to the re-gridding process necessary for the coupling.
Figure 12. Sensitivity of near surface atmospheric temperature on 15-th of February in Northern Europe to SST
16 hours before the end of the target period.
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Figure 13. Sensitivity of near surface atmospheric temperature on 15-th of February in Northern Europe to SST 56 hours
before the end of the target period.
Figure 14. Sensitivity of near surface atmospheric temperature on 15-th of February in Northern Europe to SST 150
hours before the end of the target period.
Summary
In this experiment, we showed that the sensitivity of the atmosphere to SST can be estimated. From the results
of the estimation, we see that SST affects the atmosphere in Europe on short time scales of about 1 day
mainly kinematically via heating or cooling the air temperature above. On longer time scales of about a few
days, dynamic effects become more relevant. The optimal patterns resemble the regression patterns
SST/NAO, suggesting that SST most efficiently affects the atmospheric circulation by triggering an NAO type
response. The computed pattern of sensitivities might be used as a guideline for optimal place of deployment
of an observational and prediction system, since the sensitivities indicate the regions that have the largest
impact on the formation of the near- land temperature anomalies in Europe.
Further Development
•
To obtain the atmospheric response to SST in a framework of maximum configuration where all
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processes associated with moisture are included.
•
To estimate the atmospheric sensitivity to other oceanic and atmospheric state variables, in particular
geopotential height, surface pressure, and sea ice concentration.
4. References
Cohen, J., and J. Jones, 2011: A new index for more accurate winter predictions. Geophys. Res. Lett., 38,
L21701, doi:10.1029/2011GL049626.
Czaja, A., and C. Frankignoul, 2002: Observed impact of Atlantic SST anomalies on the North Atlantic
Oscillation. Journal of Climate 15, 606-623.
Fraedrich, K., H. Jansen, E. Kirk, F. Lunkeit, 2005: The Planet Simulator: Green planet and desert world,
Meteorologische Zeitschrift, 14, 305–314.
Frankignoul, C., N. Sennéchael, Y.-O. Kwon, and M.A. Alexander, 2011: Influence of the meridional shifts of
the Kuroshio and the Oyashio Extensions on the atmospheric circulation. J. Climate, 24, 762-777.
Frankignoul, C., N. Sennéchael, and P. Cauchy, 2014: Observed atmospheric response to cold season sea ice
variability in the Arctic. J. Climate, 27, 1243-1254
Garcia-Serrano, J. and C. Frankignoul, 2014: High predictability of the winter Euro-Atlantic climate from
cryospheric variability. Nature Geoscience, 10.1038/NGEO2118.
Giering R., and T. Kaminski, 2003: Applying TAF to generate efficient derivative code of Fortran 77-95
programs. PAMM, 2(1): 54-57.
Liu, J., M. Song, R. M. Horton, Y. Hu, 2013 : Reducing spread in climate model projections of a September icefree Arctic. Proceedings of the National Academy of Sciences. doi:10.1073/pnas.1219716110.
Marshall, J., Hill, C., Perelman, L., & Adcroft, A., 1997: Hydrostatic, quasi‐hydrostatic, and nonhydrostatic
ocean modeling. Journal of Geophysical Research: Oceans (1978–2012), 102(C3), 5733-5752.
Omrani, N. E., Keenlyside, N. S., Bader, J., & Manzini, E., 2013: Stratosphere key for wintertime atmospheric
response to warm Atlantic decadal conditions. Climate Dynamics, 1-15.
Overland, J. E., and M. Wang, 2013 : When will the summer Arctic be nearly sea ice free? Geophys. Res.
Lett., 40, 2097–2101, doi:10.1002/grl.50316.
Qiu, B, S. Chen, N. Schneider, and B. Taguchi, 2013: A coupled decadal prediction of the dynamic state of the
Kuroshio Extension. J. Climate, 27, 1751-1764.
Raffel, M., C.E. Willert, S.T. Wereley, and J. Komphenhas, 2007: Particle Image Velocimetry – A Practical
Guide. Springer.
Rodwell, M. J., D. P. Rowell, and C. K. Folland, (1999). Oceanic forcing of the wintertime North Atlantic
Oscillation and European climate, Nature, 398.6725 320-323.
Page 18
5. Dissemination and uptake
5.1 Dissemination
Peer reviewed articles:
Title
Main
author All
authors
Observed
atmospheri
c response
to cold
season sea
ice
variability in
the Arctic
High
predictabilit
y of the
winter
EuroAtlantic
climate
from
cryospheric
variability
Frankigno
ul, C, N.
Sennécha
el, P.
Cauchy
GarciaSerrano J,
Frankigno
ul C.
Title of
the
periodi
cal or
the
series
Journal
of
Climate
Number,
date or
frequenc
y
Publish
er
Vol 27
Nature
Geosci
ence
Vol 7
Place
of
public
ation
Year of
publicati
on
Permanent
identifiers[1]
DOI
Is/Will open
access[2]
provided to this
publication?
Americ
an
Meteor
ological
Society
2014
doi.org/10.1
175/JCLI-D13-00189.1
yes
Nature
Publish
ing
Group
(NPG)
2014
DOI:
10.1038/NG
EO2118
Self-archiving
to ERC
Plan for future publication:
In
preparation
OR
submitted?
Title
Main author
All authors
Title of the
periodical or the
series
Is/Will open
access[2]
provided to this
publication?
In
preparation
Influence of the North
Atlantic SST variability on
the atmospheric circulation
during the 20th century
Gastineau, G,
Frankignoul, C
Journal of Climate
yes
In
preparation
On the influence of autumn
Arctic sea-ice and Eurasian
snow on the winter NAO
J. García-Serrano, C.
Frankignoul, G.
Gastineau, A. de la
Cámara
Journal of Climate
yes
In
preparation
Influence of the decadal
variability of the Kuroshio
Extension on the
atmospheric circulation in
the cold season
Sensitivity of atmospheric
near-land temperature in
Europe to SST
Révelard, A., C.
Frankignoul, N.
Sennéchael, and Y-O
Kwon
Journal of Climate
yes
Vlasenko. A, Köhl,
A., Stammer, D.
Monthly Weather
Review
yes
In
preparation
Page 19
5.2 Uptake by the targeted audience
The general public (PU)
X
The project partners, including the Commission services (PP)
A group specified by the consortium, including the Commission services (RE)
This reports is confidential, only for members of the consortium, including the Commission services (CO)
Results will be published in scientific open access journals.
6. The delivery is delayed:
 Yes
 No
7. Changes made and difficulties encountered, if any
8. Efforts for this deliverable
Partner
UPMC
UHAM
NERSC
Person-months
Period covered
15
November 2012-April 2014
25
November 2012-April 2014
6
November 2012-April 2014
46
Total
Total estimated effort for this deliverable (DOW) was 46 person-months.
9. Sustainability
This deliverable provides input that will be used in deliverable D12.37 and D12.48, and it will also contribute to
D13.50
WP 1.1 and in particular to Task 1.1.1 In addition to the chaotic dynamics of atmospheric models, lack of
differentiability associated with certain parameterizations, particularly related to processes changing humidity,
severely hinder a straightforward application of atmospheric models with the adjoint method for the calculation
of sensitivities or for preforming data assimilation. Calculated sensitivities remain therefore approximate i.e.
suboptimal for the decision on designing observing systems.
10. Dissemination activities
The complete list of dissemination activitites is available on the NACLIM website
http://naclim.zmaw.de/Dissemination.2509.0.html and on the ECAS portal, section “List of Dissemination
Activities”.
Page 20