West African Monsoon 

Modeling and Evaluation

  WAMME Initiative
  Experiment - I
   West  African Monsoon and its Modeling Special Issue
  WAMME-2 Documents
   WAMME-2 Models
   WAMME-2 Experiments
   WAMME-2 Forcing
   WAMME-2 Output List
   WAMME-2 Participants
  Working Group
  Highlights of new   African simulations
 WAMME Related:
  AMMA International
  UCAR Africa Initiative

Forcing data


The forcing data are posted on the AMMA website ftp.bddamma.ipsl.polytechnique.fr  under ‘WAMME-2 FORCING’


1). SST data

The monthly mean SST has 1 degree horizontal resolution.  The file name is the same as the Case name in the WAMME-2 Experiment.  For instance, the SST file for Case “SST50pasi” is named as “SST50pasi”   

The decadal mode of sea surface temperature (SST) associated with the long-term variation of Sahel rainfall was extracted based on singular vector decomposition (SVD) between rainfall over Sahel and global Hadley SST for 1950-2009. To remove interannual variation component, the El Nino component was first subtracted from the rainfall and SST data using a linear regression with NINO3 SST. The residual interannual component was further suppressed by a 13-month moving average.  The first SVD mode accounts for 67.25% of covariance between Sahel rainfall and global SST. The interdecadal component of SST used for forcing WAMME-2 was reconstructed based on the maximum values of the principal component in 1950's and minimum values in 1980's.


 To convert it to plain binary file, you may use GrADS and do following to save it to binary file.


sdfopen sstclim.nc

set gxout fwrite

set fwrite sstclim.binary

set x 1 360

set y 1 180

set t 1 12

d t

disable fwrite


2).   GOCART Dust data

The 3d-monthly climatology of the GOCART dust mixing ratio (Kg/Kg ) is available in 5 size bins (GOCART.mean.conc.nc). The 5 size bins are 0.1–1, 1–1.8, 1.8–3.0, 3.0-6.0, and 6.0–10 um. The data's horizontal resolution is 1.25x1.25 degree with 30 vertical layers. The file also contains surface pressure (Ps) climatology and two coefficients (hyma& hymb) to convert hybrid sigma level to pressure level (pressure= hyma*1013.25 + hymb * Ps). The first bin is the integration of four size bins (0.1-0.18, 0.18-0.3, 0.3-0.6, 0.6-1) in the GOCART model. For calculation of number concentration and/or optical properties, the first bin can be split into the original four size bins with mass fractions of 0.01053, 0.08421, 0.25263, 0.65263, following Tegen and Fung (1994). More information on GOCART aerosols is available in Ginoux et al., (2001) and Chin et al. (2002, 2009).


3).   MATCH Dust Data

The 3d-monthly mean mixing ratio distribution of the dust in 4 size bins from the MATCH runs are available at MATCH.mean.conc.nc. The data is in NETCDF format. The 4 size bins are 0.1–1, 1–2.5, 2.5–5.0, and 5.0–10 mm. The data' horizontal resolution is T62 with 28 vertical layers from surface to 10 mb (the same as the resolution of the NCEP reanalysis II).  More information on MATCH dust is available in Luo et al., (2003) and Mahowald et al (2003).





Chin, M., P. Ginoux, S. Kinne, B. N. Holben, B. N. Duncan, R. V. Martin, J. A. Logan, A. Higurashi, and T. Nakajima, Tropospheric aerosol optical thickness fromt he GOCART model and comparisons with satellite and sunphotometer measurements, J. Atmos. Sci. 59, 461-483, 2002.


Chin, M., Diehl, T., Dubovik, O., Eck, T. F., Holben, B. N., Sinyuk, A., and Streets, D. G.: Light absorption by pollution, dust, and biomass burning aerosols: a global model study and evaluation with AERONET measurements, Ann. Geophys., 27, 3439-3464, doi:10.5194/angeo-27-3439-2009, 2009.


Ginoux, P., M. Chin, I. Tegen, J. Prospero, B. Holben, O. Dubovik, and S.-J. Lin, Sources and global distributions of dust aerosols simulated with the GOCART model, J. Geophys. Res. 106, 20,255-20,273, 2001.


Luo, C., N. M. Mahowald, and J. del Corral, Sensitivity study of meteorological parameters on mineral aerosol mobilization, transport, and distribution, J. Geophys. Res., 108(D15), 4447, doi:10.1029/2003JD003483, 2003


Mahowald, N., Luo, C., Corral, J. d., and Zender, C.: Interannual variability in atmospheric mineral aerosols from a 22-year model simulation and observational data, J. Geophys. Res., 108, 4352, doi:4310.1029/2002JD002821, 2003.


Tegen, I., and I. Fung,  Modeling of mineral dust transport in the atmosphere: Sources, transport, and optical thickness. J. Geophys. Res., 99, 22897-22914, doi:10.1029/94JD01928, 1994.

Tegen, I., and I. Fung, Modeling of mineral dust transport in the atmosphere: Sources, transport, and optical thickness. J. Geophys. Res., 99, 22897-22914, doi:10.1029/94JD01928, 1994.