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CT3 Hamburg 22-23/4 2013
GEOMAR (6)
• Mojib Latif (CT/WP lead)
• Wonsun Park
• Thomas Martin
MPG (2)
• Johann Jungclaus
• Katja Lohmann
UHAM (1)
• Detlef Stammer
• Armin Köhl
DMI (7)
• Steffen M. Olsen (CT/WP lead)
• Jacob L. Høyer
• Rasmus T. Tonboe
• Torben Schmith (tbc)
Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK
Initialization of prediction systems
with ocean observations
WP 3.1
Mojib Latif
WP 3.2
Steffen M. Olsen
Suitability of the ocean
observing system
components for
initialization
Impact of Arctic
initialization on forecast
skill
Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK
WP 3.1
Suitability of the ocean
observing system
components for
initialization
Objectives
• Investigate and quantify the benefit of
different components of the ocean observing
system for prediction systems (decadal)
• Identify necessary enhancements and
potential reductions in the present system
Methodology
• Ideal model World hindcast experiments (using the adjoint assimilation
system of UHAM - an environment for climate model initialization ?)
• Re-start simulations with truncated ocean initial conditions corresponding
to different ocean regions and observing systems
Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK
Deliverables
D9 (GEOMAR, month 12): Report on the setup of coupled model and hindcasts
conducted with initial conditions corresponding to ARGO-like sampling
D 26 (GEOMAR, month 24): Report on hindcasts conducted with initial
conditions extended to include ”RAPID”, and on the feasibility of decadal
forecasts with the current ocean observing system
D 39 (GEOMAR, month 36): Report on hindcasts conducted with satellite
information
D 58 (GEOMAR, month 44): Report on the identifications of potential needs
that are not captured by the present ocean observing system for enhancing
decadal predictions.
Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK
WP 3.2
Impact of Arctic
initialization on forecast
skill
Methodology
Objectives
• Establish the impact of Arctic data and initialization of the Arctic region on forecast skill
• Construct a 15 year combined SST/IST
dataset for the Arctic Ocean
• Explore the potential to constrain the state
of the Arctic Ocean by remote observations –
flux monitoring system at the GSR.
• This WP address in detail the Arctic region of sparse data coverage.
• Work is organized along three parallel tracks including
- ideal model experiments (data withholding, potential predictability)
- improving data availability and
- explore the use of remote transport measurements.
Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK
Deliverables
D10 (DMI, month 12): Assessment of model build-up, storage and release of
Arctic Ocean freshwater pools.
D27 (UHAM, month 24): Report on the documentation and description of
improved model parameters.
D28 (DMI, month 24): Report on the documentation and description of the new
Arctic Ocean dataset combining SST and IST.
D40 (DMI, month 36): Report on the establishment of impact of the Arctic
region initialization, and on the sources of predictive skill from data withholding
experiments.
D51 (DMI, month 44): Assessment of the value of the GSR flux monitoring time
series for confining the initial state of the upper Arctic Ocean.
Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK
Verify the list of CT3 people and their involvement – done!
Explore overlapping synergies with CT1 ongoing – ongoing!
Decide on the level of internal coordination and WP specific
meetings in addition to the annual meetings – done!
- joint activities with CT1 on overarching themes
CT2 work includes a complete work package on joint
model-observational data comparison (WP 2.3,
UHAM+FMI). Possibilities for involvement.
Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK
Combined satellite SST and IST for the Arctic Ocean
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DMI is experienced with SST and Ice Surface Temperature data
processing through Eumetsat (OSI-SAF), ESA (CCI) and EU
(MyOcean) projects.
Arctic SST reanalysis product (1985-present) will be available
from end of the year (within other project)
15 years combined SST and Ice Surface Temperature data record
will be developed within NACLIM, based upon AVHRR
observations.
Both Level 3 (with gaps) and level 4 (gap-free) fields will be
produced.
Special attention will be on error characterization and
uncertainties
Objective: to demonstrate the impact of improved data on the
forecast skills.
Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK
Combined satellite SST and IST for the Arctic Ocean
Level 3 example of Ice, Sea and
Marginal Ice Zone – Surface
Temperatures from METOP AVHRR
References:
Tonboe, R. T., Dybkjær, G. and Høyer, J. L.Simulations of the snow covered sea ice surface
temperature and microwave effective temperature, Tellus , 63A, 1028–1037, 2011
Høyer, J. L., Ioanna Karagali, Gorm Dybkjær, Rasmus Tonboe, Multi sensor validation and error
characteristics of Arctic satellite sea surface temperature observations, Remote Sensing of
Environment, Volume 121, June 2012, Pages 335-346, ISSN 0034-4257,
10.1016/j.rse.2012.01.013.
Dybkjær, G., Høyer, J., Tonboe, R., 2012. Arctic surface temperatures from Metop AVHRR
compared to in situ ocean and land data. In press, Ocean Sci., 9, doi:10.5194/osd-9-1009-2012,
2012.
Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK
Combined satellite SST and IST for the Arctic Ocean
Long term satellite datasets with uncertainties for model validation and
assimilation: ice surface temperature and ice concentration
Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK
Preindustrial
de Steur et al. 2012 (in prep)
Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK
Freshwater content relative to S=34.8
Arctic Ocean+Baffin
Subpolar North Atlantic
GIN Seas
8000 km3
Historical
Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK
RCP8.5
Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK
FWC (103 km3)
The Arctic FW reservoir appears weakly constrained
Distributions may suggest two modes?
- no significant atmospheric mode identified
Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK
No relation – no concern !
Difference in SLP between high and
low anomalies in FWC changes
• Changes in FWC are driven by multi annual variations in AO
• Results are consistent with the concept of a cumulative process of
uncorrelated variability with AO constituting the signal.
• If so, the autocorrelation of the FWC is practically unlimited and predictable
• but this was not what we expected to establish…
Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK
The research leading to these results has
received funding from the European
Union 7th Framework Programme (FP7
2007-2013), under grant agreement
n.308299
NACLIM www.naclim.eu
Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK