Download - Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK

Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK

CT3 Barcelona 6-8/10 2012

NACLIM Core Theme 3:People

GEOMAR (6)• Mojib Latif (CT/WP lead)• Wonsun Park• Thomas Martin

MPG (2)• Johann Jungclaus (tbc)• Katja Lohmann (tbc)

UHAM (1)• Detlef Stammer• Armin Köhl

DMI (7)• Steffen M. Olsen (CT/WP lead)• Jacob L. Høyer• Rasmus T. Tonboe• Torben Schmith



Suitability of the ocean observing system components for

initialization

Impact of Arctic initialization on forecast

skill

Initialization of prediction systems with ocean observations

WP 3.1Mojib Latif

WP 3.2Steffen M. Olsen

NACLIM Core Theme 3:Structure



Suitability of the ocean observing system components for

initialization

WP 3.1

NACLIM Core Theme 3:WP 3.1

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




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.



Impact of Arctic initialization on forecast

skill

WP 3.2


Objectives• Establish the impact of Arctic data and ini-

tialization 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.Methodology

• 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.




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.




Combined satellite SST and IST for the Arctic Ocean

• 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.




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.




Combined satellite SST and IST for the Arctic OceanLong term satellite datasets with uncertainties for model validation and assimilation: ice surface temperature and ice concentration



NACLIM Core Theme 3:Open questions

Verify the list if people and expected level of involvement

Explore overlapping synergies with CT1

Decide on the level of internal coordination and WP specific meetings in addition to the annual meetings

- joint activities with CT1 on overarching themes may be more constructive scientifically.


Barcelona 6-8/10 2012

End



Forced response to GIS mass lossOutline

• Model experiment• Large scale ocean response to enhanced GIS mass loss

- present and future sensitivity• Impact on the Arctic and ocean freshwater anomalies

Conclusions- Strong anthropogenic warming (RCP8.5) result in a decoupling of the abyss and reduced impact of hosing.

- Ventilation in the Arctic Ocean may contribute a new source of dense water production aiding to maintain an ongoing overturning.

- Using the FWC as a diagnostic aid, that the Arctic Ocean response to hosing is partly masked by internal ocean variability and dominated by global warming effects.

- with contributions from Thor CT2 scientists



Forced response to GIS mass lossEC-Earth

Experimental setup

Two parallel model experiments forced with 0.1 Sv additional runoff

Runoff is distributed equally around Greenland in two periods

1965-2005 (Historical)2050-2090 (RCP8.5)

RCP8.5 forcing used over the period 2006-2100

In Thor CT2, five models contributed: EC-Earth, MPI-ESM, HadCM3, IPSLCM5A-LR and BCM (Swingedouw et al. in prep).



Forced response to GIS mass lossEC-Earth RCP8.5

AMOC26N 36N

45N 60N



Forced response to GIS mass lossRCP8.5 vs histocical

AMOC – decade 4 20501965

Changes are confined to the main thermocline in the 2050/RCP8.5 experiment



Forced response to GIS mass lossRCP8.5 vs historical

1965 2050

<- Control ->

<- Hosing->

MLD – decade 4



Forced response to GIS mass lossRCP8.5 vs Historical

20501965

Annual maximum mixed layer depth




20501965

0-800m mean temperature




20501965

0-800m mean salinity



Forced response to GIS mass lossFW content - preindustrial

de Steur et al. 2012 (in prep)

Preindustrial


Forced response to GIS mass loss Historical to RCP8.5

Freshwater content relative to S=34.8

Arctic Ocean+Baffin Subpolar North Atlantic GIN Seas


8000 km3

Historical RCP8.5


Forced response to GIS mass loss Preindustrial control climate



Forced response to GIS mass loss Preindustrial control climate

The Arctic FW reservoir appears weakly constrainedDistributions may suggest two modes? - no significant atmospheric mode identified


FWC (103 km3)



Forced response to GIS mass lossPreindustrial control climate

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 simple white noise integration

- AO defining the noise • If so, the autocorrelation of the FWC is practically unlimited and

predictable• but this was not what we wanted to establish…

No relation – no concern !



Forced response to GIS mass lossEnd


Forced response to GIS mass lossFWC anomaly




Forced response to GIS mass lossAO (Morison et al. 2012)



Forced response to GIS mass lossHistorical climate conditions

MO

C (S

v)

Heat

tran

spor

t (PW

)

Outf

low

/Infl

ow (

-)

Download - Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK

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