DATABASE OF MINERAL CONTENT IN ARID SOILS GMINER30 Belgrade University, Serbia Contact: Ana Vukovic (anavuk@agrif.bg.ac.rs)

Why database of mineral content in arid soils is important? behavior of aerosol dust in its interactions with the environment on global and local scales is to a large extent determined by mineral composition of dust particle it is necessary to provide gridded global data set of significant minerals that have impact on environment in order to be able to use it as input for atmospheric-dust transport models that can improve understanding of the transport, interaction and impact processes after extensive study introduction into models of mineral transport and processes which censers them can improve forecast qualities and climate simulation results Claquin et al. (1999) – pioneer work: - silicates (quartz, feldspars, illite, kaolinite, and smectite), carbonates (calcite), sulfates (gypsum) and iron oxides (hematite) - global dataset masked with mask for arid regions, available on 0.25X0.25 resolution GMINER30 database: + additional content in several more soil types - added phosphorus - global dataset on 1km resolution without mask

Why modeling of mineral transport is important? Introduction of mineral dust transport and its interaction in numerical models: can improve skill of forecast and climate simulations can help to understand processes of mineral impact, considering the problem of data insufficiency, and to point out important parameters and areas that should be monitored Implementation in atmospheric models is important, in modeling sense, because of: nucleation process (Klein at al. 2010) → to improve modeling of cloud formation it directly affect atmospheric radiation and thereby atmospheric dynamics by modifying the incoming solar radiation and the outgoing infrared radiation….. Aqua MODIS, Dec 30th 2010 Dust storm, El Paso, Mexico

Benefit of mineral transport modeling for other science disciplines dust has a great influence on environment and health, which depends on its mineral composition output of models can serve scientists of other disciplines to understand processes from their field of research, such is ocean productivity (sea blooming) and human health issues Bloom of Trichodesmium Canary Islands August 2004 (Ramos et al., 2008)

+ Steps that lead to GMINER30 USGS land cover + FAO soil texture which soil types are dust productive/arid/erodible + mineral composition of selected soil types in silt and clay population global mineral database of dust productive soils GMINER30

Land cover types distribution in arid regions Mask of arid regions based on USGS land cover categories ● areas that are mostly bare and dry

Frequency of FAO soil types in arid regions Added soil types (light blue) Yermosols (Y) Podzoluvisols (D) Haplic Yermosols (Yh) Fluvisols (J) Need to include (dark blue) Luvic Kastanozems Halpic Kastanozems Solonchaks ….. orange + blue soil types cover more than 95% area in arid regions Claquin et al, 1999 Nickovic et al, 2011 no data on mineral composition

Mineral database http://www.seevccc.rs/GMINER30/ Mineral fractions are separated for silt and clay size populations silt clay silt & clay feldspar illite calcite gypsium kaolinite hematite smectite quartz + phosphorus data are organized in 27 tiles for each mineral 30-second resolution (~1km) 4800x6000 points in each tile

CLAY POPULATION SILT POPULATION CALCITE QUARTZ HEMATITE

ILLITE KAOLINITE SMECTITE FELDSPARS PHOSPHORUS QYPSUM

Model: DREAM-IRON (first results) Iron is transported with dust particles in dust model DREAM is added information about iron content of transported dust T=total Fe and S=soluble Fe Total Fe (T) = Soluble (S) + Non-soluble (N) during transport through atmosphere (N)  (S) Dust concentration (C): Total iron concentration (T): Soluble iron concentration (S):

Clay content masked with LC Silt content masked with LC Iron in clay minerals: illite, kaolinite, smectite, hematite Iron in silt minerals: feldspars, hematite OPTIANAL: Dust source mask in this case study are USGS arid regions (LC mask) Clay content masked with LC Silt content masked with LC DREAM-IRON knows where and how much Fe is available for uptake

Case study: Canary Islands (July-August 2004) Bloom of Trichodesmium Canary Islands August 2004 (Ramos et al., 2008) model domain CANARY ISLANDS when soluble iron is deposited to the ocean after ~10days (more or less) bloom of the sea can be expected at the end of the July 2004 large dust episode happened over west Africa and dust was transported over the Atlantic Ocean in August 2004 was recorded sea bloom near Canary Islands

Model results for June 24th 2004 12UTC Surface dust concentration Image: 2004/207 - 07/25 14 :20 UTC Saharan dust off West Africa Satellite: Aqua MODIS Dust Load

surface total iron (TS) surface soluble iron (SS) soluble iron load (TL)

Future plans include 4 more soil types (dark blue) with its mineral content in GMINER30: mineral composition needed! replace 4km FAO database with new 1km HWSD (Harmonized World Soil Database) first update of the model: improved iron dynamics test model for more marine response to Fe cases necessary collaboration with soil scientists, ocean scientists,….. implementation of deposited soluble iron in ocean models in order to transport soluble Fe by ocean dynamics and to estimate marine biology response (coupling DREAM Fe and ocean models) include in models impact of minerals on cloud formation and radiation ………… etc.