NACLIM Deliverable D32.10
Colour code:
Your inputs to be provided in the parts in green
Template prefilled by project office in the grey sections
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
Due delivery deadline:
X
R= report
Nature of the
P= prototype
deliverable
D= demonstrator
15 Sept. 2013
Actual delivery date:
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 Olsen
Other contributing
partners:
UHAM
Detlef Stammer group
Institute
Please list the names of the scientist(s) involved
Institute
Please list the names of the scientist(s) involved
Institute
Please list the names of the scientist(s) involved
Institute
Please list the names of the scientist(s) involved
Page 1
Index
1. Executive summary ........................................................................................................... 3
2. Project objectives .............................................................................................................. 3
3. Detailed report on the deliverable ...................................................................................... 3
4. References ........................................................................................................................ 4
5. List of publications ............................................................................................................. 4
6. The delivery is delayed:  Yes  No ............................................................................. 5
7. Changes made and difficulties encountered, if any............................................................ 6
8. Efforts for this deliverable .................................................................................................. 6
9. Sustainability ..................................................................................................................... 6
Page 2
1. Executive summary
Please provide in 1 page:
-
a brief summary of the work performed in this deliverable
-
a list of the main results achieved
Input requested here.
2. Project objectives
With this deliverable, the project has contributed to the achievement of the following objectives
(see DOW Section B.1.1):
Please put an X where appropriate
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
3. Detailed report on the deliverable
Please write a detailed report on the work done.
Input requested
Page 3
Yes
No
X
X
X
X
X
X
X
X
X
X
X
4. References
Please quote references here for this deliverable
Author, (year), “Title”, doi:
Input requested
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
Example
Systematic
Estimates of
Initial-Value
Decadal
Predictability
for Six
AOGCMs
Branstator,
G., H. Teng,
G.A. Meehl,
M. Kimoto,
J.R. Knight,
M. Latif, and
A. Rosati
Title of
the
periodica
l or the
series
JOURNA
L OF
CLIMAT
E
Number,
date or
frequency
Publisher
VOLUME
25
America
n
Meteorol
ogical
Society
Place of
publicat
ion
Check the NACLIM „Dissemination Plan“ on the open access requirements:
http://naclim.zmaw.de/Deliverables.2224.0.html
1
Page 4
Year of
publication
Permanent
identifiers[1]
DOI
2012
DOI:
10.1175/JCL
I-D-1100227.1
Is/Will open
access
1provided to
this
publication?
Yes
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
Example: In
preparation
Decadal Predictability
Branstator, G., H. Teng,
G.A. Meehl, M. Kimoto
Is/Will open access be provided
to this publication?
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?
Input requested
6. The delivery is delayed:  Yes
 No
If yes, please write four lines for justifying the delay and estimate risks for the project.
Input requested
Page 5
7. Changes made and difficulties encountered, if any
If you have encountered difficulties or made changes, please write four lines of explanation.
Input requested
8. Efforts for this deliverable
How many person-months have been used up for this deliverable?
Partner
Person-months
DMI
UHAM
Period covered
8
From dd/mm/yyyy- to dd/mm/yyyy
5
13
From dd/mm/yyyy- to dd/mm/yyyy
Total
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
Input requested
Page 6
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 Chiara
(project
office) via
mail?
Presentations
UPMC
16th AOMIP and 1st
FAMOS meetings
http://www.whoi.edu
/page.do?pid=1094
56
23-26 October
2012
Woods Hole
Oceanographic
Institution, Woods
Hole, Massachusetts
(USA)
Scientific
Community
(higher
education,
Research)
150
USA,
Europe
Yes
Page 7
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 8
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 9
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
10
HAV
FMI
MRI
NIOZ
SAMS
NER
C/
ICPO
NERS
C
VITO
GIM
DTU
MSS