modélisation couplée (océan-atmosphère- biogéochimie) …...4ièmes journées scientifiques...

4ièmes Journées Scientifiques Equip@Meso : Sciences de l’Univers, Toulouse, CALMIP 26&27 Novembre 2015

Modélisation couplée (océan-atmosphère-biogéochimie) haute-résolution du système de

courant de Humboldt (Pérou/Chili)

Boris Dewitte (LEGOS) Séréna Illig, Véronique Garçon, Joel Sudre, Aurélien Paulmier, Oscar Vergara Katerina Goubanova (CERFACS) Ivonne Montes, Ken Takahashi (IGP, Pérou)

• General scientific motivations • Extreme El Niño dynamics • Regional air-sea interactions in the Peru upwelling system • Oxygen Minimum zone dynamics off Peru • Conclusions/Perspectives

Outline

Problématiques

Winds

Zone de Minimum d’Oxygène dans le Pacifique Sud-Est

Chlorophyl-a (Satellite)

Concentration en Oxygène (in situ) à 400m Rouge= concentration < à 0.5 ml/L)

Data= CARS Data =SeaWIFS

P=1020 HPa

Strato-Cumulus Cloud Deck

Problématiques

Evénements Extrême

Bilan radiatif

Pêches

Société

Climat

Biais en température de surface dans les modèles CMIP5

K. Goubanova

Besoin de recourir à la modélisation régionale haute résolution

Dynamique des événements Extrême El Niño (Modélisation océanique)

Halloween, 2015, Lima

1997

2009

2015

Eastern Pacific El Niño versus Modoki El Niño (or Central Pacific El Niño)

ENSO diversity (Obs.)

Observations are indicative of two regimes of variability (Takahashi and Dewitte, 2015)

Warmer in the far eastern Pac.

War

mer

in th

e ce

ntra

l Pac

ific

Data: SST (HadISST) (1950-2014)

Observed* nonlinear Bjerknes

feedback (SST/rain/wind)

The response in convection and wind stress to SST is more than 3 times for E > 1.5, i.e. strong eastern

Pacific warming -> stronger Bjerknes feedback

Piecewise linear fit: Multivariate adaptive regression splines

Takahashi and Dewitte, 2015

Linear regression of SST (colors), OLR (contours) and wind stress on the E index in observations

* Similar in CM2.1 but shifted westwards

Data: OLR (1974-2013), WAS-Wind (1950-2010)

(Dewitte et al., 2003, JGR)

Evolution of the 1997/98 El Niño

OGCM experiments

• TROP-LR (1/4°): Forced by Mercator as OBCs (S,T,U, SSH); ECMWF 6h forcing • TROPEAST-HR (1/12°) : Forced by TROP-LR (1/4°) as OBCs; ECMWF 6h forcings - 2 experiments: with and without non-linear advection included in the momentum equations (filter out TIWs activity)

Tropical Instability wave activity

Model validation Sea Level (JASON)

Mean thermocline (TAO)

Westerly Wind Burst during early 1997 and 2014

(Menkes et al., 2014, GRL)

CR CR - LIN

Evolution of SST and SL anomalies (model)

CR: Control Run simulation LIN: without non-linear advection included in the momentum equation from December 2013 -> Equatorial waves dissipate less

Regional air-sea interaction in the eastern Pacific (modélisation couplée océan-atmosphère)

The Humboldt region: a complex ocean-atmosphere coupled system

Schematic of the Hadley-Walker Cell in the Pacific

Atmospheric boundary

Layer ~ 1km

Surface winds

Stratocumulus

~ 10 km

12°S 14°S 18°S 22°S 26°S 30°S 34°S

95°W 90°W 85°W 80°W 75°W 70°W

~10 km

Wind profile

(Illig et al., 2015) Surface Ekman layer

SST gradient OMZ

upwelling

Merged Satellite Product (1km resolution) (Vasquez,

Dewitte et al., 2013)

Spatial scales to be resolved for coupled studies..

~30 km

Regional coupled model

S. Illig

Oceanic model configuration (ROMS) : South East Pacific Parent domain (ℙ): [22°S-12°N ; 88°W-70°W] at 1/12° Embedded Coastal Peru Zoom domain (ℤ) with AGRIF: [17°S-4.5°S ; 85°W-70°W] at 1/36° 37 (sigma) vertical levels Bathy GEBCO_08

Atmospheric model configuration (WRF) : South East Pacific Parent domain (ℙ): [22°S-12°N ; 88°W-62°W] at 1/6° Embedded Coastal Peru Zoom domain (ℤ) with AGRIF: [17°S-4.5°S ; 85°W-70°W] at 1/18° 40 vertical levels

Simulating the Peruvian Upwelling System -Atmosphere

Differences between WRF Parent and Zoom

dist in km (km)

10-2

N/m

2

Simulating the Peruvian Upwelling System - PCE

Increasing ROMS & WRF resolution More realistic bottom topo Upwelling trapped at topo SST at the coast Marked drop-off PCC and PCUC off shore SST SST cross-shore gradient

Zoom

dist in km dist in km dist in km

Dynamique de la zone du Minimum d’Oxyègne dans le Pacifique Sud-Est

(modélisation couplée océan-biogéochimie)

OMZ are expanding over the last 5 decades

Stramma et al. (2008), Science Years

Evolution of the OMZ in Eq. Pacific

Mean Oxygen concentration at 400m depth

This “expansion” of the OMZs is quantified as a negative trend in

oxygen concentrations.

∂O2/∂t < 0

Deficiency of the OGCM in simulating the OMZs

Global medium resolution coupled model have severe biases in simulating the OMZs. This is due to:

Biases in the equatorial circulation (in

particular the EUC extension) The too low resolution that leads to an

unrealistic amplitude of EKE Biases in remineralization due to

inappropriate parametrization

Obs.

Oxygen Trend (global zonal average at 300db)

Forced Global Simulations Stramma et al. (2012)

Tropical Oceans

Typical pathways of the feeding sources of the Peru Undercurrent in high-resolution oceanic model

Montes et al. 2010 (1/9°)

Dewitte et al. 2012 (1/12°)

Along-shore currents at 12°S

Regional modeling allows overcoming some of the global model biases. (Indian: Resplandy et al. (2012); Benguela: Gutknecht et al. (2013); Peru: Montes et al. (2014)).

Montes et al. (2014)

Regional modeling of the OMZs: State of the Art

Simulations Observations

OMZ thickness (in meters)

Bettencourt et al., (2015), Nature Geoscience

Map of backward FSLE Frontal position

45µM

Mean [O2] at 60 and 200m, inside 45µM and mean frontal position at 410m

The OMZ boundaries are maintained by mesoscale activity (i.e. mean O2 transport is much weaker than eddy-induced transport) Can the changes in eddy flux explain the variability of the OMZ?

Regional modeling of the OMZ: Role of mesoscale dynamics

Biogeochemical coupled model Ocean model: ROMS_AGRIF. Biogeochemical module: BioEBUS

(N2P2Z2D2, Koné et al, 2005; Gutknecht et al., 2013a).

Resolution: 1/12°. Period: 1958-2008 (15 years of spin-up). Atmospheric forcing: statistically

downscaled NCEP winds (Goubanova et al., 2011). Climatological bulk formula derived heat fluxes.

Oceanic forcing: 3-day SODA (0.25°x0.25°). Bathymetry: GEBCO 30 arc-second grid. (Dewitte et al., 2015)

(Gutierrez et al., 2008)

Dissolved Oxygen off Callao (12°S)

22 µM

Dissolved Oxygen off Callao (12°S) (1958-2008)

2000-2008 mean 1958-2008 mean

Evolution of composite anomalies of dissolved oxygen at 200m (anomalies are normalized by their variance)

DJF MAM JJA

EP El Niño

CP El Niño

Significance at the 95% level

Time

Conclusions • L’étude des bords Est des océans (en particulier système du Pérou/Chili) requiert une modélisation haute-résolution pour:

• Résoudre les échelles typiques de l’upwelling et de la circulation atmosphérique côtière (e.g. wind drop-off) • Mieux prendre en compte les mécanismes d’intéraction air-mer à fine échelle • Comprendre la dynamique non-linéaire des événements El Niño extrêmes (connexion entre la dynamique équatoriale et côtière) • Mieux comprendre la dynamique des zones de minimum d’Oxygène (effet d’upscalling sur le climat) • Pour comprendre et interpréter les biais dans les modèles globaux à « basse résolution » (modèles du GIEC)

Modélisation régionale intégrée (e.g. regional Earth Modeling system) – océan-atmosphère-biogéochimie-chimie atmosphérique-dynamique de population