Atmospheric aerosols deliver terrestrial elements essential to the ocean ecosystem and ocean biogeochemical cycles and thus have an important impact on the global carbon cycle. Aerosol-borne nutrients that can limit the growth of phytoplankton include nitrate, ammonium, phosphate, silicate and iron. Iron deficiency limits the primary production in High-Nutrient Low-Chlorophyll (HNLC) ocean areas [Martin and Fitzwater, 1988] and most iron in the remote ocean comes from mineral dust deposition [Fung et al., 2000]. Unfortunately, aerosol deposition to the surface ocean is poorly quantified because of sparse measurements [Prospero, 1996]. Dissolved Al provides an independent estimate for atmospheric mineral dust aerosol deposition [Measures and Brown, 1996; Gehlen et al., 2003]. Direct measurements of oceanic Al or dust deposition are only available at a handful of remote islands for limited time periods [Duce et al., 1991; Prospero, 1996; Ginoux et al., 2001]. Estimates of ocean nutrient deposition are extrapolated from these observations or predicted by atmospheric models which have been evaluated against available concentration and optical depth data [Andersen et al., 1998; Mahowald et al., 1999; Ginoux et al., 2001; Zender et al., 2003]. The intermodel uncertainty in global deposition estimates is at least a factor of four [Zender et al., 2004]. Deposition uncertainty propagates into uncertainties in oceanic iron availability and in atmospheric aerosol direct and indirect radiative forcing. To better understand the role of aerosol deposition on ocean biogeochemistry and the global carbon cycle, dust input to the ocean must be better constrained.
 Aluminum is an ideal tracer for quantifying the dust deposition to the surface ocean because of its geochemical characteristics. First, Al is a major and relatively invariant component of continental materials [Wedephohl, 1995]. Al is the third most common element in continental materials accounting for about 8% of crustal mass. Second, the residence time of Al in the surface ocean is relatively short (∼6.5 years) [Jickells et al., 1994] which impedes transfer from the coastal areas to the open oceans. That means the atmospheric input is the main source for the surface ocean Al concentration in the remote oceans. Third, ocean Al chemistry is relatively simple compared to Fe because Al is not involved in complicated redox chemistry.
 The concentrations and distributions of trace metals in the ocean are controlled by processes including external input, removal, and internal cycling. It is likely that Al cycles in a manner similar to Fe in the oceans [Bruland and Lohan, 2003]. The most important source for iron and aluminum is partial dissolution from dust deposition. Al solubility estimates range widely from 0.5–86% with a mean of about 5% [Prospero et al., 1987; Sato, 2003; Baker et al., 2006]. Removal processes include active biological uptake by diatoms and passive scavenging onto particles. The removal rate is first-order dependent on the particle concentration [Moran et al., 1992]. There is some active uptake and incorporation into the frustules by diatoms [Gehlen et al., 2002]. According to the few deposition observations available, the dissolved Al distribution seems well correlated to the oceanic dust flux [Measures and Vink, 2000]. Typical surface Al concentrations range from 50 to 0.1 nM for high-dust areas and low-dust areas respectively [Measures and Vink, 2000]. Measures and Vink  used a simple model named MADCOW to invert observed Al concentrations to dust deposition fluxes. MADCOW assumes that dissolved Al is in steady state, that surface Al originates only from partial dissolution of deposited mineral dust, and that the Al loss occurs solely from biological particle scavenging. In addition, a constant mixed layer depth and residence time for dissolved Al are assumed. With these assumptions, Al scavenging balances the partial dissolution of deposited Al. MADCOW-inferred dust deposition from surface Al concentrations agrees fairly well with observations [Duce et al., 1991] over 4 orders of magnitude [Measures and Vink, 2000].
 Gehlen et al.  assembled a database for dissolved Al concentrations in oceanic water and used a geochemical ocean general circulation model coupled with the geochemical cycles of Al and Si to study the relationship between surface Al and total dust input. They provided an empirically corrected parameterization of the partition coefficient for Al removal by biogenic opal and they left the Al scavenging rate a free parameter. Their ocean model–calculated Al concentration in the surface ocean from two different modeled dust deposition fields [Andersen et al., 1998; Mahowald et al., 1999]. They obtained the best fit between predicted and measured Al concentrations using an Al solubility of 1.5%–3.0%. Their work demonstrates how ocean observations may be used to evaluate atmospheric model estimates of mineral aerosol deposition. The many uncertainties involved in inferring dust deposition from Al measurements include the choice of scavenging parameters, Al solubility, and the surface ocean biology, which itself through Fe addition may depend on dust deposition.
 We use an augmented Al observational database that is used to characterize, evaluate, and improve our understanding of ocean Al cycling as represented in a state-of-the-art ocean ecosystem-biogeochemical model. We have assembled all known, relevant oceanic dissolved Al observations into a single database. The database includes eighteen more cruise tracks and stations and approximately 3 times as much data (10,460 points) as previous studies [Gehlen et al., 2003]. The newer data significantly improves the characterization of the North Pacific and Southern Ocean Al cycle. Second, we develop a more realistic and complete prognostic global ocean Al cycle model which agrees well overall with the measurements. The model provides new insights on the timescale, solubility, and basin distributions of Al that are consistent with measurements.