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Vertikalblanding i den marginale issonen i Barentshavet Arild Sundfjord

Vertikalblanding i den marginale issonen i Barentshavet Arild Sundfjord. OPNet-møte, Geilo, 06 Nov 2007. Motivasjon - turbulens Hvordan skal man representere turbulens i numeriske havmodeller?

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Vertikalblanding i den marginale issonen i Barentshavet Arild Sundfjord

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  1. Vertikalblanding i den marginale issonen i Barentshavet • Arild Sundfjord OPNet-møte, Geilo, 06 Nov 2007

  2. Motivasjon - turbulens • Hvordan skal man representere turbulens i numeriske havmodeller? • Dissipasjon av turbulent kinetisk energi foregår på mindre skala enn det som er oppløst i vanlige 3D sirkulasjonsmodeller. • De turbulente blandingsprosessene må derfor representeres gjennom parameterisering, basert på fenomener på større skala som ER med i modellen. • Det er mange ulike parameteriseringer i bruk, med ulik grad av kompleksitet. Strømskjær, stratifisering, og gjerne en form for lengdeskala, brukes ofte til å beregne blandingskoeffisientene. • Hvor godt er turbulens representert i DIN modell? • Hva er effekter og konsekvenser av (den dårlige?) representasjonen? • Hvordan kan det forbedres? • Min motivasjon i CABANERA-prosjektet: effekt på biologi (prim.prod.)

  3. SINMOD 3D baroclinic hydrodynamical model z-level model in regular Arakawa C-grid Standard setup uses Ri-number scheme 20 km large-scale model 4 km model (black) nested into 20 km 4 km/800 m grid (red) nested into 4 km 5-year spin-up period before simulation of the three project years 2003-2005. Model grid for experiments with two different vertical mixing schemes, and with increased horizontal and vertical resolution.

  4. Sesongvariasjon i stratifisering og vertikal diffusjon

  5. end of ice covered period Vinddrevet blanding

  6. Tidevannsdrevet blanding

  7. Også geografiske forskjeller (pga vannmasser og tidevann) Sesongutvikling Diffusjon i ”pyknoklinen” April 2004 August 2004

  8. measured modelled mod. “pre-history” Sammenligning med observert turbulens/diffusjon Diffusivity Density St X • “Problemer” som ble identifisert: • For sterk og for dyp miksing i overflatelaget. • For svak turbulens i pyknoklinen. • For sterk blanding i ”dypvannet” og BBL. St XI St XIII

  9. Sammenligning mellom Ri-tall-skjema og Mellor-Yamada Level 2.5

  10. Numerical ocean modelling – possible improvements? Richardson number scheme: Adjustment of minimum and maximum diffusivities + initiation threshold Incorporate bottom boundary parameterization (e.g. KPP, Durski et al. 2004) Add wind-enhancement and under ice/ice-keel effect (Timmermann and Beckmann, 2004) Mellor-Yamada scheme: Length scale parameter adjustment (Burchard, 2001) Add wind-wave effect (Qiao et al., 2004)

  11. ice edge South North Simuleringer med 800×800 m2 grid (=moro!) ice cover Warm Atlantic Water meets ice and cold Arctic Water “Jet” current flows westward along the ice edge Alternating up- and downwelling below ice edge Enhanced diffusivities down to >100 m depth

  12. Oppsummering • The modelled development of diffusivity shows significant seasonal changes; high diffusivities during winter, minimum during the melting period, increasing in the ice-free season. • The model is able to reproduce individual episodes of strong wind and tides, as well as calm conditions, reasonably well. • Over time, the effect of surface mixing can extend too deep, the pycnocline may be too strong, and near-bottom mixing often homogenizes the deep part of the water column too effectively. • Both the Ri-number mixing scheme and the Mellor-Yamada scheme reproduce the general water mass distributions and seasonal development from winter to summer, but neither is found to reproduce the observed MIZ hydrography optimally. • Increasing the horizontal resolution from 4 km to 800 m allows for important ice edge processes to be resolved. • STORE effekter på primærproduksjon – f.eks. timing på våroppblomstring, effekter av vindepisoder (“pumping”) og total produksjon i løpet av året (regenereringssyklus).

  13. Referanse A. Sundfjord, I. Ellingsen, D. Slagstad and H. Svendsen. Vertical mixing in the marginal ice zone of the Barents Sea – results from numerical model experiments. Deep Sea Research-II, in press.

  14. Numerical ocean modelling- large scale features Ice cover at times of CABANERA cruises: July 2003 July 2004 May 2005 Satellite obs. Model What controls the variability of the large-scale heat budget – advection of water, or ice?

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