NACLIM Deliverable D32.10
Assessment of model build-up, storage and release of Arctic Ocean
freshwater pools
Deliverable title
(Assessment of model build-up, storage and release of Arctic Ocean
freshwater pools: This deliverable is a process oriented assessment of model
variability of Arctic Ocean freshwater storage in terms of location, magnitude
and statistics incorporating data on observed Arctic marine climate variability)
WP No.
3.2
WP title
Impact of Arctic initialization on forecast skill
Work duration1)
Lead beneficiary:
DMI /Steffen Olsen
12
15 October
Due delivery deadline:
2013
X
R= report
Nature of the
P= prototype
deliverable
D= demonstrator
Actual delivery
date:
15
October
2013
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
level
Commission services
CO= confidential, only for members of the consortium, including the
Commission services
1) Work duration = project month
Lead beneficiary:
DMI
Steffen M. Olsen
Other contributing
partners:
UHAM
Detlef Stammer, Armin Köhl, Ion Meti, Reema
Agarwal, Xueyuan Liu
DMI
Cecilie D. Aakjær
DMI
Torben Schmith
Page 1
Index
1. Executive summary ........................................................................................................... 3
2. Project objectives .............................................................................................................. 3
3. Detailed report on the deliverable ...................................................................................... 4
4. References ...................................................................................................................... 15
5. Dissemination and uptake ............................................................................................... 17
5.1 Dissemination ................................................................................................................ 17
5.2 Uptake by the targeted audience .................................................................................... 18
6. The delivery is delayed:  Yes  No ........................................................................... 18
7. Changes made and difficulties encountered, if any.......................................................... 18
8. Efforts for this deliverable ................................................................................................ 19
9. Sustainability ................................................................................................................... 19
10. Dissemination activities ................................................................................................. 20
Description of the work package in the Description of Work (DOW) for reference .......... 21
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1. Executive summary
The upper ocean gyres of the Arctic Ocean constitute a reservoir of freshwater. Accumulated
liquid freshwater has exhibited a strong increase during the last decade, potentially linked to
climate change. Sudden release of excess freshwater from the arctic may influence the subpolar ocean circulation, the rate and properties of the North Atlantic deep water formation. Here
we report variability of comparable magnitude in a free, multi-centennial pre-industrial control
simulation with the coupled climate model EC-Earth. It is shown that the volume of liquid
freshwater in the Arctic Ocean is largely unconstrained in the model and associated with a multidecadal de-correlation time-scale. This may be explained by absorption of uncorrelated
fluctuations in modelled exchanges with the neighbouring ocean regions and, between liquid
and solid phases. In contrast, the volume of sea-ice in the Arctic Ocean is highly constrained
explained by a positive relation between the magnitude of storage and export. It is
demonstrated that this coupling does not exist in the model for the storage of liquid freshwater
controlled by multiple exchange systems. A simple approach is used to diagnose the changes in
storage from the exchanges through individual ocean gateways. Multi-decadal variability in
freshwater storage can be inferred from all exchange systems, though the largest amplitude is
associated with the Fram Strait liquid freshwater export. Considering liquid and solid
components independently reveals an important role of transfer between phases in forming the
decadal to centennial variability in the liquid freshwater storage.
An inverse relation between freshwater storage in the Arctic Ocean and Sub-polar region can be
established whereby sub-polar decrease aligns with periods of accumulation in the Arctic
Ocean. The opposite relation exists but shows less robust. Parallel to these changes are high
amplitude interannual to decadal anomalies in the intensity of the sub-polar gyre circulation of 510 Sv (1Sv = 106 m3/s). Intensification is found to link to the accumulating phases of the Arctic
Ocean freshwater inventory. Indeed also changes in the Atlantic Meridional Overturning
Circulation (AMOC) locks only to this phase and exhibits an increase of up to 1 Sv in low and
mid-latitude intensity.
2. Project objectives
With this deliverable, the project has contributed to the achievement of the following objectives
(see DOW Section B.1.1):
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Nr.
1.
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 end-users/stakeholders
11. Constructing a dataset for sea surface and sea ice surface
temperatures in the Arctic
Yes
No
X
X
X
X
X
X
X
X
X
X
X
3. Detailed report on the deliverable
Please write a detailed report on the work done.
The scientific context and motivation
Liquid freshwater is an integral part of the vertical stratification in the Arctic Ocean halocline
between the warm, saline Atlantic Water and the cold, fresh surface layer (Aagaard and
Carmack [1989]; Rudels et al. [2004]). Within the Arctic, the unique stratification strongly
constrains the upward heat transfer influencing formation and melt of sea-ice. The sources of
liquid freshwater relative to the mean Arctic salinity of 34.8 (Serreze et al. [2006]) include import
of low saline Pacific waters through the Bering Strait, river input, ice melt and net precipitation
partly balanced on annual time-scales by sea-ice formation and liquid net export through Fram
Strait and the Canadian Arctic Archipelago. Reoccurring salinity anomalies in the northern north
Atlantic affecting the thermohaline properties have been attributed to episodic release of
freshwater from the Arctic Ocean (Dickson et al. [1988]; Curry and Mauritzen [2005]). Modeling
studies have suggested that such release is remotely excited in the Arctic, rather than by local
atmospheric forcing over the sub-polar Atlantic (Haak et al. [2003]; Jahn et al. [2010]). Here, the
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controlling process is the formation and propagation of sea ice thickness anomalies that are
communicated via Fram Strait into the North Atlantic. These export events led to the Great
Salinity Anomalies (GSA) of the 1970s, 1980s and 1990s in the Labrador Sea. Only the
observed anomaly of the ’90s may primarily be a result of liquid export anomalies (Karcher et al.
[2005]; Haak et al. [2003]). Sea ice and fresh water exports through the Canadian Archipelago
(CAA) are found to be only of minor importance, except for the 1990s GSA. Future anomalies
will likely be linked to the liquid phase considering the ongoing reduction of the arctic sea-ice
cover and near elimination of thick multiyear ice in the Eurasian Arctic.
A remarkable freshening of parts of the Arctic Ocean has been observed since 2003 (McPhee
et al. [2009], Proshutinsky et al. [2009]), predominantly within the Beaufort Gyre (BG), an
anticyclonic upper-ocean circulation pattern of the Canadian Basin (Aagaard and Carmack
[1989]). The large reservoir capacity of the BG is explained by a convergent upper ocean
Ekman transport and deepening of isopycnals associated with the atmospheric high–arctic anticyclonic circulation centred in Beaufort Sea (Proshutinsky et al. [2009]; Giles et al. [2012]).
Preceding this, prevailing cyclonic circulation in the 1990s caused freshwater in the Beaufort
Sea to increase significantly but a relaxation to near pre-1990 climatology was observed in early
the 2000s attributed to a decline in the Arctic Oscillation (AO). Although the recent dilution which
amounts to an equivalent of 8500 km3 of freshwater (McPhee et al. [2009]; Rabe et al. [2011];
Giles et al. [2012]) appears to have come to a halt, the majority of the anomalous storrage has
not been released from the Arctic (de Steur et al. [2013]). The attractive concept of a ’Beaufort
Gyre Flywheel’ (Morison et al. [2006]) has been put forward whereby variability of the intensity
of the BG FW content is forced by weakening and strengthening of the anti-cyclonic
atmospheric circulation regime. This concept has shown some skill in explaining central Arctic
variability and basin scale redistribution over the twenties century Morison et al. [2006]; Steele
and Ermold [2007]; Polyakov et al. [2008]). Also recent relaxation of the anti-cyclonic circulation
regime has been coupled to observed redistribution within the Canadian Basin and between the
Canadian and Eurasian Basin (de Steur et al. [2013]; Timmermans et al. [2011]; Morison et al.
[2012]). Key characteristics of the studies variability include an out-of-phase variability between
the Eurasian and central Arctic basin (Morison et al. [2012]) and between the shelves and
central basin where anomalies exceed those on the shelf by an order of magnitude (Polyakov et
al. [2008]). Consistently, a strong relation has yet not been established from observations
between the strength of the Arctic Ocean freshwater storage and the export of freshwater to the
sup-polar ocean (Polyakov et al. [2008]). Moreover, even in the present configuration
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characterized as ’charged’ (Steele and Ermold [2007]), recent observational evidence indicates
only an intermittent and modest anomalous liquid export through the Fram Strait and Nares
Strait in response to the relaxing anti-cyclonicity associated with a weakening of the positive
mode of the Arctic Oscillation (de Steur et al. [2013]). This qualitatively agrees with the results of
model experiments where transitions between idealized Arctic Oscillation positive and negative
phases only force 12% variation in liquid freshwater export from the Arctic (Zhang et al. [2003]).
The EC-Earth model and simulations
We detail here the process oriented assessment of model variability of Arctic Ocean freshwater
storage in the EC-Earth climate model in terms of location, magnitude and statistics referencing
data on observed Arctic marine climate variability. The version (V2.2) of EC-Earth is a fully
coupled Atmosphere Ocean General Circulation Model (AOGCM), with oceanic (Nucleus for
European Modelling of the Ocean, NEMO), sea ice (LIM2) and land surface (HTESSEL)
components having been coupled to the IFS atmospheric forecast model through the OASIS3
coupler (Sterl et al. [2012]). The ocean configuration of NEMO has a resolution of 1°×1° with a
meridional refinement to 1/3° at the equator, referred to as the ORCA1 grid. Here, the singularity
at the North Pole is avoided by use of a tri-polar grid with poles over land (Siberia, Canada,
Antarctica). Using 42 vertical z-layers, vertical ocean resolution increases from 10m at the
surface to 300m at depth and reaches down to 5,500m.
A set of three simulations using the EC-Earth climate models participating in CMIP5 is
considered (Figure 1): a 600 year control simulation using fixed preindustrial forcing, a historic
simulation using time-varying forcing 1850-2006 and, a future simulation based on the RCP8.5
emission scenario for the period 2006-2100 (RCP stands for representative concentration
pathway, the number 8.5 represents the net Top Of Atmosphere imbalance at the year 2100 in
W/m2, cf. Moss et al. [2010]).
Assessment
A strong multi-decadal signal dominates the variability of the Arctic Ocean FW content in the
free, multi-centennial pre-industrial control simulation (Figure 1). The magnitude of the variability
is comparable to the recent freshening of 8.500 km3 since 2003 (McPhee et al. [2009]; Rabe et
al. [2011]; Giles et al. [2012]). A long term trend characterizes the evolution in the historical
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simulation and this is in particular the case for the future scenario. However, in both cases this
trend is overlaid by a few multi-decadal fluctuations resembling the variability in the free control
simulation. It is concluded that such variability is an inherent feature of the model under a wide
range of climatic forcing. Indeed, characteristics of modelled variability may serve to guide
interpretations of observed variability and illuminate the potential impact in the sub-polar region
and on the AMOC.
Figure 1 Multi-decadal variability of liquid FW content in the Arctic Ocean in EC-Earth simulations under three forcing
scenarios, a 600 year control simulation using fixed preindustrial forcing (black), a historic simulation using time-varying
forcing 1850-2006 (green) and a future scenario based on the RCP8.5 emission scenario (red) for the period 2006 to 2100.
Characteristics of the multi-decadal variability in FW content are best analysed and described
using the long time-series of the control simulation representing the free model evolution (Figure
2 to Figure 6). In addition to the Arctic Ocean FW content in liquid and solid (sea-ice) phases
respectively, we have calculated the export rates though the main exchange systems of FW
with the rest of the World Ocean. This includes the Fram Strait, the Bering Strait and two
gateways west of Greenland resembling the Nares Strait and the Canadian Arctic Archipelago
(CAA). In the latter case, this is in the model grid represented by a single channel exiting in the
Lancaster Sound of the Northern Baffin Bay. Both the liquid FW content and exchange rates of
liquid FW are calculated relative to an ocean mean salinity of 34.8 (Aagaard and Carmack
[1989]). Average values of the inventories and exchanges are found given in
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Tabel 1 together with the best available observational estimates. Reasonable model
performance includes the size of the liquid and solid FW inventories and a realistic strength of
the Bering Strait liquid FW inflow and the total outflows west of Greenland. Though the total FW
export in the Fram Stait is realistic, it is in this preindustrial model simulation biased towards iceexport.
Normalized liquid and solid Arctic Ocean FW content is compared with liquid and solid export
through the Fram Strait in Figure 2 (a,e). This comparison illustrates the small amplitude of
liquid Fram Strait export variability relative to ice export and, the contrasting strong variations in
the liquid FW content relative to the ice volume. Characteristics of the variability of the liquid and
solid pools are vastly different as illustrated in Figure 2 panels. In particular the non-Gaussian
distribution of the liquid FW content show that in the model, the volume of liquid freshwater in
the Arctic Ocean is largely unconstrained and associated with a multi-decadal de-correlation
time-scale. This may be explained by absorption or filtering of uncorrelated fluctuations in
modelled exchanges with the neighbouring ocean regions and, between liquid and solid phases.
This will be verified below. In contrast, the volume of sea-ice in the Arctic Ocean is highly
constrained (near Gaussian distribution, Figure 2g) explained in part by a positive (lagged)
relation between the magnitude of storage and export (Figure 2f). For the liquid storage, some
correlation between transport and storage also exists but both the transport time-series (here
the Fram Stait) and the storage show significant autocorrelation. This is may partly result from
the approximations associated with estimating the FW content. The approach is not robust
considering long term variations in the mean salinity of the Arctic Ocean and/or of the inflow.
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Figure 2 Normalized liquid (a, black) and solid (e, grey) Arctic Ocean FW content compared with liquid (a, red) and solid
(e, blue) export through the Fram Strait. FW content and ice volume series are normalized against the Arctic Ocean icevolume variability and transports against variability of the Fram Strait ice export. The autocorrelations are shown in (b)
and (f) together with the lagged correlation (dash-dot) for lags up to 25 years using de-trended time-series. Colour coded
histograms present the distribution of the normalized time series of volume (c,g) and transports (d,h).
A set of pseudo time-series of the FW inventories has been constructed by integrating individual
exchanges for solid and liquid fractions separately. By this approach we aim to verify that the
multi-decadal variability results from absorption or filtering of uncorrelated fluctuations in
modelled exchanges with the neighbouring ocean regions and, between liquid and solid phases.
The resulting time-series are presented in Figure 3a,c and document the explained variability of
the FW inventory from individual exchange branches. The constructed time-series use the initial
value of the FW content and incorporate a time-independent source or sink term representing
the average values of all other contributions. This is achieved by choosing a magnitude which
matches the inventory size at year 600. Such pseudo time-series of the FW inventory have also
been constructed for the combined set of liquid exchanges and the sum of liquid and solid
exchanges (Figure 3b). Convincingly, the chosen set of exchanges account for modelled multidecadal variability in the total FW inventory, but only when considering the combined solid and
liquid exchanges (Figure 3b). This verifies that contributions from runoff, evaporation,
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precipitation and exchanges across the Barents Sea opening can be neglected in a description
of the variability of the FW content.
This simple approach show that multi-decadal variability in freshwater storage can be inferred
from all exchange systems (Figure 3a,c) with the exception of the weak sea-ice fluxes west of
Greenland. For the liquid storage, large amplitude variability can be associated with variability
the Fram Strait liquid freshwater export, which is also significantly correlated with the true liquid
FW content. A striking negative correlation is identified between the variability induced by export
anomalies east and west of Greenland, in particular between the variability attributed to the
Fram Strait and CAA export, respectively.
Considering liquid and solid components independently reveals an important role of transfer
between phases in forming the decadal to centennial variability in the liquid freshwater storage.
Indeed, liquid FW fluxes alone do not describe the evolution of the liquid FW content over the
600 year simulation (Figure 3b).
Observations of the variability of the solid FW content of the Arctic Ocean are only recently
becoming available from model synthesis (e.g. Schweiger et al. [2011]), and may not be
considered robust, independent estimates. Thus, it is of interest to disentangle to what extend
variability on shorter time-scales of individual pools can be attributed to anomalies in solid and
liquid exchanges independently. In Figure 4, the 600 year fitted projections of the FW content
(Figure 3b,c) is compared with the spread produced by comparable 10 year and 30 year
projections initialized annually throughout the model time-series. Evidently, if each pool and its
associated exchanges are considered separately, an estimate of its magnitude can be
associated with an error approaching the recent increase of 8.500 km3 on time-scales beyond
roughly 10 years.
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Figure 3 Modelled Arctic Ocean FW content variability for liquid (black, a,b) and solid (c, grey) fractions. In (a), the FW
content variability is compared with the explained variability from individual branches of exchange (T ocean, TIce) applying
a constant source term (see text for details), Fram Strait (red), Nares Strait (blue), Bering Strait (green) and the CAA
(Lancaster Sound, orange) and Fram Strait ice export (grey). Arctic Ocean sea-ice volume variability is compared
separately in (c) with the variability explained by the Fram Strait (red) and Nares Strait (blue), respectively. The
explained variability of the Arctic Ocean FW inventory by the combined liquid FW transports (red) and total (liquid +
solid, blue) transports is compared in (b) with the true modelled inventory.
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11
Figure 4 Arctic Ocean FW variability for solid (a, black) and liquid (b, black) components individually and the spread in
repeated 10 year (blue) and 30 year (green) fitted projections using solid (a) and liquid (b) transports only. Also shown is
the fit from Figure (2 b,c) using the full transport time series, only tied to the end points.
Coupled Arctic Ocean and sub-polar variability
Model experiments have generally indicated only a minor impact of a single GSA event on the
strength of the North Atlantic Thermohaline circulation (e.g. Haak et al. [2003]). Figure 5
summarizes the model evidence for a coupling between Arctic Ocean and sub-polar variability
including variations in the intensity of the AMOC at mid latitude. It is clear that direct correlations
are weak due to the dynamics of the sub-polar region which is dominated by interannual to
decadal variability. Also a near Gaussian characteristics (not shown) of the variability of all ckey
diagnostic time-series here contrast the bi-modal distributions of the unconstrained Arctic FW
inventory. However, an inverse relation can be identified between freshwater storage in the
Arctic Ocean and Sub-polar region whereby sub-polar decrease aligns with periods of
accumulation in the Arctic Ocean. The opposite relation exists but shows less robust. These
central relations are detailed in Figure 6 by presenting segments of the diagnostics of Figure 5
for the control integration defined by continuous decreasing or increasing tendencies,
respectively, of total Arctic Ocean (liquid and solid) FW content. It is seen that the tendency of
both liquid and solid inventories have the same sign as the tendency of the total FW content
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12
(Figure 6a,b) and, that the opposing sub-polar response is most pronounced in the
accumulating phase of the Arctic Ocean.
Parallel to these changes are high amplitude interannual to decadal anomalies in the intensity of
the sub-polar gyre (SPG) circulation of 5-10 Sv (1Sv = 106 m3/s) (Figure 5). The SPG strength is
here defined as the absolute intensity of the closed barotropic circulation cell of the sub-polar
region. Intensification is found to link to the accumulating phases of the Arctic Ocean freshwater
inventory, but not exclusively (Figure 6c,d). In fact, also the segments of net export of FW from
the Arctic are primarily associated with SPG intensification.
The contrasts in the sub-polar response to Arctic Ocean FW accumulation versus release
phases becomes even more pronounced when considering changes in the AMOC (Figure 6e,f).
Whereas no consistent relation can be identified for segments of Arctic Ocean FW release, the
AMOC response locks to the accumulating phase. In these segments the AMOC intensity
exhibits a gradual increase which in many cases yields a 1 Sv increase in low and mid-latitude
intensity.
Figure 5 Coupled Arctic Ocean and sub-polar variability: the absolute strength of the SPG (green) and sub-polar liquid
freshwater storage (red), note the different axis and the reversed scale for the fresh water content (right). Anomalies are
shown for the intensity of the AMOC at 36N (blue). Background colours depict the variability of the total, liquid and solid
Arctic Ocean FW reservoir. A low-pass, phase conserving filter has been applied to all annual mean time-series with a
cut-off of 8 years.
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13
Figure 6 Segments of the control integration are defined by a continuous decreasing (left) and increasing (right) tendency
of total Arctic Ocean liquid and solid freshwater volume. Shown are segments where the tendency in total FW content
exceeds a +/- 1 std. dev. criteria. In (a,b) the evolution of liquid (black) and solid (grey) Arctic Ocean FW content is
compared with the liquid storage in the sub-polar Atlantic (red). Anomalies in the absolute strength of the SPG (green,
c,d) and the intensity of the AMOC at 36N (blue, e,f) are calculated for the same segments referenced to the initial value.
A low-pass, phase conserving filter has been applied to all annual mean time-series with a cut-off of 8 years.
Observed
Modelled
𝑭𝑾𝑶𝒄𝒆𝒂𝒏
74.000a
68.495
𝑭𝑾𝑰𝒄𝒆
28.700b
35.464
𝑻𝑶𝒄𝒆𝒂𝒏
𝑭𝒓𝒂𝒎
2.400a
491
𝑻𝑶𝒄𝒆𝒂𝒏
𝑵𝒂𝒓𝒆𝒔
883c
1192
𝑻𝑶𝒄𝒆𝒂𝒏
𝑪𝑨𝑨
1.500d
730
𝑻𝑶𝒄𝒆𝒂𝒏
𝑩𝒆𝒓𝒊𝒏𝒈
2.500a
2.587
𝑻𝑰𝒄𝒆
𝑭𝒓𝒂𝒎
2.300a
4.523
𝑻𝑰𝒄𝒆
𝑵𝒂𝒓𝒆𝒔
18c
13
𝑻𝑰𝒄𝒆
𝑪𝑨𝑨
116d
0
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14
Tabel 1 Modelled Arctic Ocean liquid and solid FW volume (JFM average in km3) and annual mean exchanges (T)
(km3/year). Observations are taken from a(Serreze et al. [2006]), b(PIOMAS Schweiger et al. [2011]), c(Rabe et al. [2012])
and d(Melling et al. [2008]). Sea-ice export assumes a uniform thickness of 2m. For storage, winter values are given for
both observations and model results.
4. References
Please quote references here for this deliverable
Author, (year), “Title”, doi:
Aagaard, K., and E. C. Carmack (1989), The role of sea ice and other fresh water in the arctic
circulation, Journal of Geophysical Research: Oceans, 94 (C10), 14,485–14,498,
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Curry, R., and C. Mauritzen (2005), Dilution of the northern north atlantic ocean in recent
decades, Science, 308 (5729), 1772–1774, doi:10.1126/science.1109477.
de Steur, L., M. Steele, E. Hansen, J. Morison, I. Polyakov, S. Olsen, H. Melling, F. McLaughlin,
and R. Kwok (2013), Hydrographic changes in the Lincoln Sea in the Arctic Ocean with focus on
an upper ocean freshwater anomaly between 2007-2010, Journal of Geophysical Research:
Oceans. doi: 10.1002/jgrc.20341
Dickson, R. R., J. Meincke, S.-A. Malmberg, and A. J. Lee (1988), The great salinity anomaly in
the northern north atlantic 1968-1982, Progress in Oceanography, 20 (2), 103 – 151,
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ocean freshwater storage increased by wind-driven spin-up of the beaufort gyre, Nature Geosci,
5 (3), n/a–n/a, doi:10.1038/ngeo1379.
Haak, H., J. Jungclaus, U. Mikolajewicz, and M. Latif (2003), Formation and propagation of
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Melling, H., K. K. Falkner, R. A. Woodgate, S. Prinsenberg, A. Muenchow, D. Greenberg, T.
Agnew, R. Samelson, C. Lee, and B. Petrie (2008), Freshwater fluxes via pacific and arctic
outflows across the canadian polar shelf, in Arctic-Subarctic Ocean Fluxes: Defining the role of
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Morison, J., M. Steele, T. Kikuchi, K. Falkner, and W. Smethie (2006), Relaxation of central
arctic ocean hydrography to pre-1990s climatology, Geophysical Research Letters, 33 (17),
doi:10.1029/2006GL026826.
Morison, J., R. Kwok, C. Peralta-Ferriz, M. Alkire, R. Andersen, and M. Steele (2012), Changing
arctic ocean freshwater pathways, Nature, 481, doi: 10.1038/nature10705.
Moss, R.H. et al., 2010. The next generation of scenarios for climate change research and
assessment. Nature 463: 747-756, doi: 10.1038/nature08823.
Polyakov, I. V., V. A. Alexeev, G. I. Belchansky, I. A. Dmitrenko, V. V. Ivanov, S. A. Kirillov, M.
Steele, L. A. Timokhov, and I. Yashayaev (2008), Arctic ocean freshwater changes over the
past 100 years and their causes, J. Climate, 21 (2), 364–384, doi:10.1175/2007JCLI1748.1.
Proshutinsky, A., R. Krishfield, M.-L. Timmermans, J. Toole, E. Carmack, F. McLaughlin, W. J.
Williams, S. Zimmermann, M. Itoh, and K. Shimada (2009), Beaufort gyre freshwater reservoir:
State and variability from observations, Journal of Geophysical Research: Oceans, 114 (C1),
n/a–n/a, doi:10.1029/2008JC005104.
Rabe, B., M. Karcher, U. Schauer, J. M. Toole, R. A. Krishfield, S. Pisarev, F. Kauker, R.
Gerdes, and T. Kikuchi (2011), An assessment of arctic ocean freshwater content changes from
the 1990s to the 20062008 period, Deep Sea Research Part I: Oceanographic Research
Papers, 58 (2), 173 – 185, doi:http://dx.doi.org/10.1016/j.dsr.2010.12.002.
Rabe, B., H. L. , Johnson, A. Münchow, and H. Melling (2012), Geostrophic ocean currents and
freshwater fluxes across the canadian polar shelf via nares strait, Journal of Marine Research,
70, 603–640.
Rudels, B., E. P. Jones, U. Schauer, and P. Eriksson (2004), Atlantic sources of the arctic
ocean surface and halocline waters, Polar Research, 23 (2), 181–208, doi:10.1111/j.17518369.2004.tb00007.x.
Schweiger, A., R. Lindsay, J. Zhang, M. Steele, H. Stern, and R. Kwok (2011), Uncertainty in
modeled arctic sea ice volume, Journal of Geophysical Research: Oceans, 116 (C8),
doi:10.1029/2011JC007084.
Page
16
Serreze, M. C., A. P. Barrett, A. G. Slater, R. A. Woodgate, K. Aagaard, R. B. Lammers, M.
Steele, R. Moritz, M. Meredith, and C. M. Lee (2006), The large-scale freshwater cycle of the
arctic, Journal of Geophysical Research: Oceans, 111 (C11), doi:10.1029/2005JC003424.
Steele, M., and W. Ermold (2007), Steric sea level change in the northern seas, Journal of
Climate, 20 (3), 403–417, doi:doi: 10.1175/JCLI4022.1.
Sterl, A. et al., 2012. A look at the ocean in the EC-Earth climate model. Climate Dynamics
39:11, 2631-2657.
Timmermans, M.-L., A. Proshutinsky, R. A. Krishfield, D. K. Perovich, J. A. Richter-Menge, T. P.
Stanton, and J. M. Toole (2011), Surface freshening in the arctic ocean’s eurasian basin: An
apparent consequence of recent change in the winddriven circulation, Journal of Geophysical
Research: Oceans, 116 (C8), doi:10.1029/2011JC006975.
Zhang, X., M. Ikeda, and J. E. Walsh (2003), Arctic sea ice and freshwater changes driven by
the atmospheric leading mode in a coupled sea iceocean model, Journal of Climate,
16 (13), 2159–2177, doi: 10.1175/2758.1.
5. Dissemination and uptake
5.1 Dissemination
Add the publications related to this deliverable. Please fill in the table below in ALL its parts.
These are inputs we must upload in the European Commission database SESAM.
Peer reviewed articles:
Title
Main author
All authors
Title of
the
periodica
l or the
series
Hydrographi
c changes in
the Lincoln
Sea in the
Arctic Ocean
with focus on
an upper
ocean
freshwater
anomaly
between
2007-2010
de Steur,
L., M.
Steele, E.
Hansen, J.
Morison, I.
Polyakov,
S. Olsen,
H. Melling,
F.
McLaughlin
, and R.
Kwok
Journal
Number,
date or
frequency
Publisher
Place of
publicat
ion
AGU
of
Geophy
sical
Researc
h:
Oceans.
Check the NACLIM „Dissemination Plan“ on the open access requirements:
http://naclim.zmaw.de/Deliverables.2224.0.html
1
Page
17
Year of
publication
Permanent
identifiers[1]
DOI
2013
doi:
10.1002/jgrc.
20341
Is/Will open
access
1provided to
this
publication?
Access to
postprint
version is
provided.
Publications in preparation OR submitted
Is there any publication in plan or already submitted. Add lines if needed.
In preparation
OR submitted?
Title
All authors
Title of the periodical
or the series
Is/Will open access be provided
to this publication?
In preparation
Variability of the Arctic
Ocean freshwater
storage in a coupled
climate model
S. M. Olsen, Torben
Schmith, Shuting
Yang, Cecilie Aakjær
Journal of
Geophysical
Research - Oceans
Yes
5.2 Uptake by the targeted audience
According to the DOW, your audience for this deliverable is:
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)
How are you going to ensure the uptake of the deliverables by the targeted audience?
Publication of results in peer reviewed journal ensures the uptake of the present assessment by
the targeted audience. In addition, results will be presented by S. M. Olsen at an international
workshop November 2013.
6. The delivery is delayed:  Yes
 No
7. Changes made and difficulties encountered, if any
If you have encountered difficulties or made changes, please write four lines of explanation.
The assessment has been performed as planned at the Danish Meteorological Institute.
It was chosen to base the assessment predominantly on a multi-centennial pre-industrial control
simulation with the EC-Earth climate model. Consequently, the direct comparison with
observations has been kept at a qualitative level. Instead, we have fully exploited the length of
the model data series to reach a set of strong scientific results and conclusions.
Page
18
8. Efforts for this deliverable
How many person-months have been used up for this deliverable?
Partner
Person-months
DMI
UHAM
Total
Period covered
8
01/01/2013-15/09/2013
5
13
01/01/2013-15/09/2013
Total estimated effort for this deliverable (DOW) was 13 person-months.
9. Sustainability
 Lessons learnt: both positive and negative that can be drawn from the experiences of the
work to date and
 Links built with other deliverables, WPs, and synergies created with other projects
The assessment has established a set of arctic sub-polar linkages in a model system which
should be used to explain potential predictive skill resulting from initialization of the Arctic Ocean
(CT3, WP3.2, D32.40). Some of these results require further investigation including the weak
control exerted from the arctic on sub-polar marine climate variability during phases of net
export of freshwater from the Arctic. The methodology used in this assessment also addresses
the value of flux monitoring time-series for confining the state of the upper Arctic Ocean. This
will be detailed later in the project with focus on the Greenland Scotland Ridge (D 32.51) but
has
been
initialized
in
collaboration
with
Page
19
CT2
(Østerhus
et
al.
In
prep.).
10. Dissemination activities
Add the dissemination activities (starting from November 2012) related to this deliverable.
Fill in the table below in all its parts.
[3] Indicate here which type of activities from the following list: Publications, conferences, workshops, web, press releases, flyers, articles published
in the popular press, videos, media briefings, presentations, exhibitions, thesis, interviews, films, TV clips, posters, Other.
[4] Indicate here which type of audience: Scientific Community (higher education, Research), Industry, Civil Society, Policy makers, Medias ('multiple
choices' is possible.
Type of
activities[3]
Main leader
Title (+website
reference)
Date
Place
Type of
audience[4]
Size of
audience
Countries
addressed
Have you
sent a copy
to project
office via
mail?
Presentations
DMI
2nd FAMOS
meeting
http://www.whoi.edu
/page.do?pid=1231
56
October 22-25,
2013
Woods Hole
Oceanographic
Institution, Woods
Hole, Massachusetts
(USA)
Scientific
Community
(higher
education,
Research)
150
USA,
Europe
Yes
Presentation
DMI
http://roskildesejlklub.klubmodul.dk/cms/Clubr
oskildesejlklub/ClubI
mages/130302_hav
etunderisen.pdf
March 20, 2013
Herslev Strand
Sejlklub
Civil Society
30
Danish
Yes
Page
20
Description of the work package in the Description of Work (DOW) for
reference
Objectives WP 3.2
• To establish the impact of Arctic data and initialization of the Arctic region on forecast skill for the North
Atlantic/European sector.
• To construct a 15-year dataset of combined satellite sea surface and sea ice surface temperatures (SST and
IST) covering the entire Arctic Ocean and demonstrate the data impact on forecast skill.
• To explore the potential to constrain the state of the Arctic Ocean by integrating flux monitoring time series at
the Greenland Scotland Ridge (GSR), which have been established in previous projects (incl. EU FP7 THOR
project).
Description of work and role of the partners
The new and innovative IST product will be used with SST and the EUMETSAT sea ice climate record to
improve the initialization of the Arctic Ocean and to better constrain the heat fluxes associated with the interface.
We will verify and quantify the potential predictability linked to initialization of the Arctic region in an ideal model
experiment using the EC-Earth coupled climate model. Sources of forecast skill will be identified by comparing
the potential predictive skill across a perfect model ensemble with data withholding experiments. Using a
long pre-industrial control simulation and forming a perfect model ensemble will allow us to focus on the most
predictable signals. In parallel, we will exploit the sparse Arctic observations using the EU FP7 THOR adjoint
assimilation system approach. Model skill will be improved by optimizing uncertain model parameters.
Task 3.2.1 Model assessment
a) Assess the ability of the EC-Earth model in building up, sustaining and releasing realistically Arctic Ocean
freshwater pools [DMI]. This leads to the deliverable D10
b) Establish the potential to diagnose the upper Arctic freshwater reservoir from time series of ocean transports
across the GSR in coupled climate models, and determine the potential impact of assimilating anomalous
transport characteristics at the Ridge. [DMI, UHAM]. This leads to the deliverable D51
c) Identify model components or parameterizations potentially limiting the predictive skill. [DMI].
Task 3.2.2 Improve the skill of the EU FP7 THOR adjoint assimilation system
Climate observations obtained over the Arctic Sector will be used to better constrain uncertain model parameters
in the adjoint model. This step will help to better estimate initial conditions for a coupled climate model. [UHAM]
This leads to the deliverable D27
Task 3.2.3 Data set of sea surface and ice surface temperatures
a) Construct a 15-year dataset of combined satellite sea surface (SST) and ice surface temperatures (IST) for
the entire Arctic Ocean from the NOAA AVHRR satellite record, building on new processing algorithms of remote
sensing data developed for operational purposes. [DMI]
b) Complement the surface temperature data set with the EUMETSAT sea ice climate record, including spatially
and temporally varying uncertainty estimates; this will also be made available for improving the skill of the adjoint
assimilation system. [DMI]
This task leads to deliverable D28.
Task 3.2.4 Generation of perfect and real model ensembles
a) Perform a perfect model ensemble generated by applying perturbations to independent initial states selected
from the control simulation. The ensemble size should be at least 3, each with 10 members and of 10 years long
constituting 300 years of simulations (DMI).
b) Perform an assimilated ensemble initializing the integrations by relaxing the ocean and sea ice state towards
the time varying control state for an initial period. Five ensemble members should be considered yielding 3x5
integrations, 10 years long, in total 150 years. [DMI, UHAM]
This task leads to deliverable D40.
Task 3.2.5 Evaluation of predictive skill
a) Evaluate the predictive skill of the initialized ensemble against the perfect model ensemble. [DMI]. This leads
to deliverable D40.
b) Repeat the assimilated ensemble simulation by withholding Arctic Ocean data and by using model climatology
(150 years of simulations). [DMI]. This leads to deliverable D40.
c) Consider additional cases where only sea-ice or ocean stratification is withheld (2x150 years of simulations).
[DMI, UHAM]
Page
21
Interaction with other work packages
This oceanic focus in this work package (Task 3.2.1 and 3.2.2) complements the assessments detailed in WP
1.1, 1.2 and 1.3 addressing variability of the ocean surface state, its impact on predictability and atmospheric
patters. The synergy will be exploited by close coordination and dissemination of scientific progress throughout
the project.
New datasets (SST/IST) developed within WP3.2 (Task 3.2.3) will be utilized across several work packages (WP
1.2 and WP 1.3) and integrated in the joint data comparison (WP 2.3, Task 2.3.2).
Also the assessment of Arctic Ocean freshwater dynamics and model derived coherent large scale fields (Task
3.2.1) will contribute to the joint comparison (WP 2.3, Task 2.3.3).
Data on exchanges across the GSR from WP 2.1 is required as input to Task 3.2.1.
The role of the Arctic (e.g., sea ice thickness) for AMOC prediction will be investigated together with WP.
3.2. WP3.1will provide to WP 3.2 with information about the potential skill increase by assimilating sea ice
information.Results from 3.2.4 and 3.2.5 on the impact of the state of the Arctic Ocean on predictive skill will
support and guide the experiments outlined in WP 3.1, Task 3.1.3.
List of deliverables
D32.10) Assessment of model build-up, storage and release of Arctic Ocean freshwater pools: This deliverable
is a process oriented assessment of model variability of Arctic Ocean freshwater storage in terms of location,
magnitude and statistics incorporating data on observed Arctic marine climate variability. [month 12] Who is in charge:
DMI
D32.27) Report on the documentation and description of improved model parameters: This deliverable is a
report on improving estimates of model parameters through data assimilation, which lead to a better predictive
skill of the model. [month 24] Who is in charge: UHAM
D32.28) Report on the documentation and description of the new Arctic Ocean dataset combining SST and IST:
This deliverable is a report documenting the applied data, developed methods and algorithms used to construct
the SST/IST dataset. Error estimates are supplied and the report will describe the mean climatology, seasonality
and interannual variability based on a 15 year period. [month 24] Who is in charge: DMI
D32.40) Report on establisment of impact of Arctic region initialization+identification sources pred. skills: The
full title of this deliverable is: Report on the establishment of impact of the Arctic region initialization, and on
the identification of sources of predictive skill from data withholding experiments. This deliverable is a report
synthesizing results from a suite of model experiments tailored in combination to decipherer the potential impact
of Arctic Ocean initialization on forecasts skill. [month 36] Who is in charge: DMI
D32.51) Assessment of value of GSR flux monitoring time series for confining initial state of upper Arctic O: The
full title of this deliverable is: Assessment of the value of the GSR flux monitoring time series for confining the
initial state of the upper Arctic Ocean. This deliverable is an assessment of exploratory approaches to link the
state of the Arctic Ocean to remote flux transport variability at the GSR building on the combination of coupled
models and near two decade of direct observations. [month 44] Who is in charge: DMI
Page
22
Person-months per participant
Person-months (PM)
Deliverable title
Lead
benef
iciary
Tot
PM
UH
AM
DS
Assessment of
model build-up,
storage and release
of Arctic Ocean
freshwater pools
Report on the
documentation and
description of
improved model
parameters
Report on the
documentation and
description of the
new Arctic Ocean
dataset combining
SST and IST
Report on the
establisment of
impact of the Arctic
region initialization,
and on the
identification of
sources of predictive
skill from data
withholding
experiments
Assessment of the
value of the GSR
flux monitoring time
series for confining
the initial state of the
upper Arctic Ocean
DMI
13
5
UH
AM
25
25
DMI
8
DMI
34
DMI
8
MPG
UPMC
UiB
UniRE
S
GEOM
AR
DMI
8
8
10
24
8
Page
23
HAV
FMI
MRI
NIOZ
SAMS
NER
C/
ICPO
NERS
C
VITO
GIM
DTU
MSS