Quantification of Icelandic dust export: proposal of a combined measurement and modeling experiment

Transcription

1 Quantification of Icelandic dust export: proposal of a combined measurement and modeling experiment Konrad Kandler, Stephan Weinbruch, Kerstin Schepanski Technische Universität Darmstadt, Applied Geosciences TROPOS Leibniz Institute for Tropospheric Research

2 Questions with respect to dust How much dust leaves Iceland which way? What are the specific properties of Icelandic dust? What modulates the source? Where does the dust go? How much of the remotely deposited dust comes from Iceland? Can it by amount and composition impact on ocean processes? Are the properties modified by emission and transport processes? Is the a change observed in a changing atmosphere?

3 Dust cycle: challenges Modelling Representation of dust sources Soil characteristics Geographic location Temporal changes Atmospheric circulation Wind-driven emission fluxes Transport height and pathways Dust sinks Measurement Determination of relevant properties Large particle size range Unknown material properties Harsh environment Representativity Potential high variability with seasonal cycle Potential high emission in single events Local sources, but regional impact Inter-annual variability Long-term trends

4 Iceland dust export and potential measurement locations potential locations graphs from Arnalds, 2010 and 2016 Map:

5 Experiment structure Three levels of observation intensity Baseline monitoring (BLM) Automatic online instruments Intermittent automatic sampling / long exposure sampling Air quality network Remote sensing? Dust season monitoring (DSM; in addition at baseline stations) Automatic dust sampling Additional online instruments? Intensive observation periods (IOP; additional field stations) Manual dust sampling at temporal field stations Manual microphysics Online instruments at temporal field stations Third party contributions

6 Potential time plan IOP DSM BLM Iceland first year second year third year J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D remote Jan Mayen??????? Hornsund Greenland??????? Faröer????????????? IOPs Field sites close to dust hot spots Emission process assessment Source properties variability Regional dust emission simulation Derive dust emission parametrization DSM Dust transport and distribution Seasonal behavior Context for IOPs BLM Context for seasons Long-term variability not limited to 3 years

7 Microphysics and dust sampling Baseline monitoring Particle size distribution by optical spectrometer (0.5 to 20 µm) Intermittent passive dry / wet deposition sampling for composition analysis Dust season monitoring Continuous passive dry / wet deposition sampling for composition analysis Active sampling for additional analyses (e.g., trace metals)? Intensive observation periods Continuous stations Particle size distribution by impactor techniques: extension to giant particles? Additional size-resolved active sampling for composition analysis? Temporal field stations Particle size distribution by optical spectrometer (0.5 to 20 µm) Particle size distribution by impactor techniques (>> 20 µm) Size-resolved active sampling for composition analysis Deposition sampling around field size (i.e. small network of passive samplers)

8 Dust modelling: model setup Input Landuse Lateral boundaries Land surface properties GME Global Model COSMO Meso-scale Model 2-Moment cloud scheme Radiation scheme Coupling Meteorology Aerosol MUSCAT Transport Model Aerosol dynamics Mass-based aerosol scheme DES Dust emission scheme 4-D Output fields [Wolke et al., 2012]

9 Dust modelling: objectives Revisiting the description of dust sources in COSMO-MUSCAT Adaptation to the needs of high-latitude dust sources Simulating the atmospheric dust life-cycle including emission, transport and deposition Assessing the relevance of dust emitted from Iceland in order to improve the understanding of high-latitude dust and its controlling mechanism

10 Example: microphysics of SAMUM campaigns Time series of mass concentrations measured at Tinfou, Morocco. In addition, the relative abundance of quartz is shown as crosses. Individual events are identified. doi: /j x

11 Example: conclusions for emission Morocco Regime-dependent dependency of concentration on wind speed Cape Verde Size distributions, e.g. for model validation doi: /j x, /j x

12 Electron microscopy scheme for atmospheric aerosol Aerosol collection collection method particle size collection substrate particle damage particle loss SE-image Extended information mineralogy / crystallinity mixing state internal structure / coatings / element distribution crystalline, Fe-rich regions Preparation? conductive coating stabilizing of volatile substances Experiment? hygroscopicity freezing volatilization Basic information visual inspection image analysis elemental composition amorphous regions P S Si Analysis image acquisition electron diffraction X-ray (UV/optical) fluorescence signal localization Fe K

13 (Aged) Desert aerosol composition Cape Verde Morocco Time series of mass deposition rates at Gozo, Malta, for different components; can be linked to sources by modeling year 2017; red high, green low March April May June July August September Oct doi: /j x, /j x

14 Heterogeneity of mineral dust Composition of example dust samples in terms of major components Saharan dust, (Morocco) Saharan dust, (dust component, Cape Verde) Eyjafjell eruption dust, (onboard DLR Falcon) in the same sample, single particles have different composition and, thus, must possess different melting points 14

15 Strength of combined measurement & modeling approach Input from field measurements on dust sources and dust properties Relevance assessment of field measurement for total dust production Improve significance by specification of needed parameters Using detailed measurements for model physics validation Using long-term measurements for model stability validation Bringing measurements into a larger context Generalizing from local measurements to (trans-) regional properties Extrapolating on (trans-) regional impacts Towards a holistic interpretation and analysis of the measurement results: Merging modelling and measurements

16 Potential funding and fallback options Funding and contributions Basic personal by institution (Minor) institution funding for analyses German Science Foundation (DFG) Norwegian sources (Svalbard, Jan Mayen) Service contributions from interested bodies? Other sources? Fallback options in case of lacking resources (Determined by the project structure) Reduction on IOPs: number, instrumentation Shifting of IOPs (requirement: continuous funding for BLM / DSM) Reduction in BLM / DSM: less stations, less accuracy / cheaper instruments