meteorologisk institutt met.no. spar : rasmus e. benestad, yvan orsolini, ina t. kindem, arne melsom...
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Meteorologisk institutt met.no
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SPARSPAR : :
Rasmus E. Benestad, Yvan Orsolini, Ina T. Kindem, Rasmus E. Benestad, Yvan Orsolini, Ina T. Kindem, Arne MelsomArne Melsom
Seasonal Predictability over the Arctic Region – exploring the role of boundary conditions
Norwegian Norwegian Institute for Institute for Air ResearchAir Research
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Motivation
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Motivation
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• SPAR
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Motivation
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Motivation
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• SPAR
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Motivation
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Courtesy of ClimateExplorer, Geert Jan van Oldenborgh, KNMI
Ensemble mean correlation
http://climexp.knmi.nl/forecast_verification.cgi
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Courtesy of ClimateExplorer, Geert Jan van Oldenborgh, KNMI
Brier Skill Score w.r.t. climatology
http://climexp.knmi.nl/forecast_verification.cgi
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Seasonal predictability 'high' in the Tropics (ENSO). Notoriously low in higher-latitudes
Forecast models Initial conditions in the Tropics Sea-ice, snow, SST, role of stratosphere not well
represented
Climate change & media pressure International Polar Year
New observations
Fundamental science How does predictability vary with latitude?
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Objectives
Identify signals in the Arctic. Examining predictability in northern
Europe
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Means
Numerical experiments ECMWF IFS model
Analysis Model data Observations (IPY?)
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Seasonal forecast models apply prescribed sea ice, alternatively, initial fields are strongly relaxed towards climatology.
…but sea ice undergo interannual fluctuations, e.g. Deser et al., 2000
SPAR WP1: Sea-ice
Is the sea-ice in seasonal forecast models a limiting factor for the quality of these forecasts at high latitudes?
WP1.1: Winter sea ice patterns and summer ice extent extremes
WP1.2: A summertime “blue Arctic”
WP1.3: Limits of seasonal predictions with “perfect” sea ice data
N.H. September ice extent,CCSM/SRES-A1B
(Holland et al., GRL ’06)
NOAA oceanexplorer
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Eurasian snow cover has been linked to the forcing of large-scale NH teleconnections. Extensive fall snow cover has been linked to a negative NAO phase in the following winter.
(e.g. Cohen et al., 2004; Saito et al., 2001)
SPAR WP2: The role of snow cover
Recent 20-year ensemble simulations with an AGCM forced by global satellite snow cover observations indicates a better representation of the year-to-year variability in the Icelandic and Aleutian Lows, and their out-of-phase coupling, compared to control simulations.(Orsolini, Kvamstø and Sorteberg, to be submitted, 2007)
We aim at improving the use of snow variables in initialisation of seasonal forecasting, and assess the impact of snow cover inter-annual variability onto the NH high latitude circulation.
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Impact on 500mb wind speed : note
Negative NAO-like signal
SPAR WP3: The stratosphereSSW composite
We aim at examining three issues:
1: SSWs and Arctic storm track changes: While stratospheric extreme events influence mid-latitude storm tracks (see figure to the left), do they specifically influence the path of cyclones into the Arctic ?
2: Predictability of the spring onset: Can the occurrence of the stratospheric final warming lead to improved predictability in the troposphere (like SSWs in mid winter) ?
3: Predictability of SSWs: we will carry out ensemble medium-time-scale forecasts during the IPY to better understand precursors of SSWs, and synoptic conditions during their downward influence.
Simulations with Arpege GCM (T106-60lev)
Kindem, Orsolini and Kwamstø, to be submitted, 2007.
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SST patterns are associated withthe large-scale atmospheric circulation,not only in the tropical region (El Niño), but also at mid-latitudes(e.g. the NAO/SST tri-pole pattern.)
(Rodwell et al., 1999; Benestad & Melsom, 2002)
SPAR WP4: SST
Examine how seasonal forecasts are affected by perturbations of anomalies on a large scale, withan emphasis on SST and sea ice consistency
WP4.1: The NAO-tripolar SSTA pattern
WP4.2: Arctic SSTA patterns
WP4.3: Simulations with different polar SSTAs
N. Norway
Svalbardarchipelago
Barents Sea75N
30E
SST from IR:
+ Data coverage+ Front resolved- Resolution- Coastal data
SST from IR:
SST from microwave:
Winter data
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StratosphereSST
Sea-iceSnow
cover
Picture from ozonewatch.gsfc.nasa.gov
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Meteorologisk institutt met.no
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Meteorologisk institutt met.no
Extra slides
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Figure copyright:
David W. J. Thompson
The troposphere responds to changes in the stratospheric state.
(e.g. Wallace and Thompson, 1998, Baldwin and Dunkerton, 2001)
SPAR WP3: The stratosphere
NH winter While numerical simulations with troposphere-stratosphere AGCM indicates downward influence of extreme stratospheric events (such strong vortex episodes, or stratospheric sudden warmings), there is mild impact on winter seasonal forecasts. Rather the influence is on the medium time scale.
We aim at improving practical use of the S-T coupling in sub-seasonal and seasonal forecasts
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SST data in SPAR:
• SST from existing seasonal forecasts(control experiment)
• SST from IR measurements(sensivity to ocean model trends & errors)
• SST from microwave data(spatio-temporal data coverage and instrumentation)
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Life-cycle of stratospheric
sudden warmings:
geopotential composites
Winter
Results•Precursory High anomalies over northern Europe, akin to Scandinavian blocking
•Lingering negative NAO anomalies near surface (as shown by Baldwin et al., or Limpasuvan et al)
ONSET
GROWTH
MATURE
DECLINE
DECAY
100mb 1000mb
Nao negative
30mb 500mb 1000mb
Precursory Blocking
Lingering NAO-
Simulations with Arpege GCM (T106-60lev)
Kindem, Orsolini and Kwamstø, to be submitted, 2007.
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SST data in SPAR:
• SST from existing seasonal forecasts(control experiment)
• SST from IR measurements(sensivity to ocean model trends & errors)
• SST from microwave data(spatio-temporal data coverage and instrumentation)
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SST from IR measurements(traditional method)
vs.SST from microwave data(experimental method);
winter data
N. Norway
Svalbardarchipelago
Barents Sea75N
30EC
+ data coverage+ front resolved- resolution- coastal data
K
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IR measurements vs. microwave data;summer data
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C K
• similar features• microwave data are warmer in the southern Barents Sea
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IR measurements vs. microwave data;summer data trends over an 8 day period
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• obvious differences, e.g. in the central Barents Sea• ‘noisy’ trends in the high-resolution IR measurements
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Collaboration with EC-Earth Complimentary studies
Use experimental set-up for comparisons Share data Use EC-Earth data as input? Different focus but overlapping interests
Share experience Modelling Data processing
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Time schedule:July 2007: Hire postdoc.
Start setting up modelAnalysing input data
2008-2009: experimental runs
2010: analysis and reporting.
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