Modelled dust cycle of the largest desert dust source in the world, the Bodélé depression (Chad)

Transcription

1 Modelled dust cycle of the largest desert dust source in the world, the Bodélé depression (Chad) Christel BOUET (1), Guy CAUTENET (1), Benoit LAURENT (2), Béatrice MARTICORENA (3), Gilles BERGAMETTI (3), and Frédéric LASSERRE (1) (1) Laboratoire de Météorologie Physique, Clermont-Ferrand, FRANCE (2) Institut für Troposphärenforschung, Leipzig, GERMANY (3) Laboratoire Inter-universitaire des Systèmes Atmosphériques, Créteil, FRANCE July, IUGG 2007 Perugia Italy

2 Outline 1. Scientific context 2.Numerical tool 3.Validation of the numerical tool 4.Presentation of the simulation 5.Results 6.Conclusions

3 Scientific context IPCC 2001 Mineral dust radiative effect poorly understood

4 Scientific context Mineral dust global emissions (Mt) Reference Peterson and Junge [1971] Hidy and Brock [1971] SMIC [1971] Judson [1968] Joseph et al. [1973] D Almeida [1986] Schütz [1897] Jaenicke and Matthias-Maser [1992] Tegen and Fung [1995] Andreae [1994] Duce [1994] Houghton et al. [2001] Zender et al. [2004] From Duce [1994] completed Great uncertainty on the emitted quantities

5 Scientific context High intra-annual variability of dust emission and transport Dust cycle difficult to model NDJ MJJ Why focusing on the Bodélé depression? The Bodélé depression is the greatest source of mineral dust in the world Particularly difficult to model this emissive area (complex topography) Engelstaedter et al. [2006]

6 Numerical tool Regional Atmospheric Modeling System (RAMS, Cotton et al. [2003]) coupled online with the Dust Production Model (DPM, Marticorena and Bergametti [1995], Laurent [2005]) Initialization and nudging data: P, T, wind (u, v, w), relative humidity from ECMWF Mesoscale model: RAMS Version paralleled wind at 10 m emission flux Transport Model of mineral dust production: Dust Production Model [Marticorena and Bergametti, 1995] Model outputs: Concentrations Deposit Size distribution Optical depth

7 Validation of the numerical tool: the case of BoDEx 2005 (Washington et al. [2006]; Todd et al. [2007]) Horizontal resolution: 10 x 10 km² Tibesti 11 mars, 10TU Tibesti MODI S (Terra) Ennedi Ennedi RAMS Dust optical thickness e AOT at 440 nm + AOT at 670 nm March, 9 March, 10 March, 11 March, 12 March, 13 Daily mean flux (10-12 mars 2005) : Todd et al. (2007) : 1.2 Mt/jour Bouet et al. (GRL, 2007) : 1 Mt/jour CEPMMT : 0.4 Mt/jour Model able to reproduce the main features of the event (plume, concentrations) (Bouet et al., GRL, 2007)

8 Description of the simulation One year of simulation (2001) x = y = 50 km nx = 55 ny = 39 nz = 30 x = y = 20 km nx = 139 ny = 99 nz = 30 Validation data: -OMM stations -TOMS AI 7N N Hoggar Mali Benin Algeria E Nigeria Aïr N Guigmi Mao Tibesti The Bodélé depression N Djamena Faya Ennedi 12N 0E 26.5E Sudan Hoggar Aïr Bodélé Tibesti Ennedi 10N E

9 Results (1): dust mass flux density Bodélé Whole simulated area Mass flux density (µg/m²/s) Mass flux density (µg/m²/s) Calculations agree with observations of great dust events over the Bodélé depression Washington et al. [JGR, 2006]

10 Results (2): regional dust mass budget and a comparison with a GCM approach The same DPM uses either RAMS or ECMWF surface wind field RAMS ECMWF Dust mass flux (Tg/day) Dust mass flux (Tg/day) Julian day Julian day Difference in annual seasonality Difference in intensity Annual regional mass budget: - RAMS: 390 Tg (170 Tg for Bodélé only) -ECMWF: 150 Tg

11 Results (3): surface wind velocity RAMS vs. observations at Faya RAMS + Observations ECMWF vs. observations at Faya ECMWF + Observations Solid line: monthly average Surface wind velocity (m/s) Surface wind velocity (m/s) Monthly averaged surface wind velocity (m/s) RAMS vs. ECMWF vs. observations at Faya Vertical bar: monthly standard deviation of observations RAMS ECMWF Obs. Seasonality better retrieved with RAMS than with GCM in agreement with previous results (previous slide)

12 Results (4a): surface wind field 27N January, 3 27N July, 19 Tibesti Σ Tibesti Σ Bodélé Ennedi Bodélé Ennedi 12N 0E 26.5E 12N 0E 26.5E Different wind field pattern according to the season What effect on dust regime? Wind flux WF is calculated at the North of the defile between the Tibesti and the Ennedi across control surface Σ (about 250km x 50m) Froude number relative to the Tibesti is calculated at the green point (overpassing or bypassing the mountain?)

13 Results (4b): surface wind field Wind flux: r WF= V r. ndσ ( Σ ) -2E11-4E11-6E11 Froude number: with the Brünt-Vaïsälä frequency Monthly averaged surface wind velocity (m/s) at Bodélé Froude number*50 Froude number Surface wind flux (m 3 /s) Hourly and monthly averaged surface wind flux entering the defile 6E11 4E11 2E11 0 Fr= Nh U g N= θ θ z

14 Results (5): link between dust uptake and wind field Wind flux < 0 NE SW Wind flux > 0 SW NE Dust mass flux (kg/m 2 /s) 0-6E11-4E11-2E11 0 2E11 4E11 6E11 Surface wind flux (m 3 /s) Strong dust uptake is generally associated with northeasterly circulation (WF<0: wind bypasses the Tibesti and the Ennedi and is accelerated in the defile)

15 Conclusions A one-year simulation of the dust cycle over the Bodélé depression and its surroundings: -Annual dust cycle well retrieved by the model -Annual wind velocity cycle well retrieved by the model -Seasonality of these cycles explained by the regional atmospheric circulation -Strong dust uptake associated with northeasterly (Harmattan) circulation