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Improving dust characterization in climate models
 

Improving dust characterization in climate models

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  • Direct effect, indirect effect. CCN, IN
  • Sandblasting mass efficiency, CLM has DEAD
  • Key inputs for the dust models clay content, bare-soil fraction and soil moisture. 1 degree map, in the US it is
  • Subtle differences, reflects wind pattern and clay content mainly
  • Wealth of satellite/ground-based observs., at least two decades, plan to use these datasets in refining the dust mass flux
  • We have a long wind tunnel, gathered soil substrates and instruments for measuring dust and wind. Existing measure downwind.
  • Classified lanforms into 12 categories; agricultural areas don’t exist in current model, playa smallest but more intense,
  • JJA: effect of African monsoon,
  • 5 geomorphic types have highest erodibility in January. All derived within 16-26N. In the future, we will calculate average erodibility: by removing the effect of the environment, reclassifying the geomorphic map etc.

Improving dust characterization in climate models Improving dust characterization in climate models Presentation Transcript

  • Black Sunday On the 14th day of April of 1935, There struck the worst of dust storms that ever filled the sky. You could see that dust storm comin', the cloud looked deathlike black, ……….. From Oklahoma City to the Arizona line, Dakota and Nebraska to the lazy Rio Grande, It fell across our city like a curtain of black rolled down, We thought it was our judgement, we thought it was our doom. http://en.wikipedia.org/wiki/Dust_Bowl http://altereddimensions.net/20 12/the-dust-bowl-black-sunday
  • Improving dust emission characterization in climate models MODIS image on 03/19/2012 (Courtesy of NASA) MODIS image on 03/18/2012 (Courtesy of NASA) - Sagar Parajuli 11/21/2013
  • 3 Outline 1. Introduction 2. Proposed research 3. Methods 4. Preliminary results 5. Expected results and significance 6. Broader Impact 7. References
  • 4 Outline 1. Introduction 2. Proposed research 3. Methods 4. Preliminary results 5. Expected results and significance 6. Broader Impact 7. Research timeline 8. References
  • 5 Introduction 1Sokolik and Toon 1996 1. Introduction 2Rosenfeld et al., 2001 3Kellogg and Griffin 2006 1.1 Dust modeling 4Koren, et al., 2006
  • 6 Introduction • Dust affects the earth radiation budget 1 by scattering and absorbing shortwave and longwave radiation. • Dust modifies cloud microphysical properties by forming Ice Nuclei and Cloud Condensation Nuclei 2. • Dust storms affect daily life activities and hinders air/ground traffic operation. • Dust storms degrade air quality and transmit human/plant diseases 3. • Mineral dust acts as a fertilizer (P, Fe) for terrestrial and marine ecosystem 4. • Dust deposition affects efficiency of solar panels. 1Sokolik and Toon 1996 1. Introduction 2Rosenfeld et al., 2001 1.1 Key definitions 3Kellogg and Griffin 2006 1.2 Dust modeling 4Koren, et al., 2006
  • Key definitions 7 • 1. Introduction 1.1 Key definitions 1.2 Dust modeling
  • 8 Dust Emission modeling • Dust emission is initiated when the wind speed exceeds a dynamic threshold known as threshold friction speed. 1 • Major inputs required by the dust models are surface wind speed, soil types, soil moisture, roughness length, bare soil fraction etc. • Dust modeling is challenging because dust emission is affected both by geomorphic processes and atmospheric phenomena. 1Bagnold 1. Introduction 1941 1.1 Key definitions 1.1 Dust modeling
  • 9 Dust emission modeling F = Vertical dust mass flux DEAD 1 (Saltation model) GOCART 2 (non-saltation model) 1 Dust Entrainment and Deposition Model (Zender et al. 2003) 2 Global Ozone Chemistry Aerosol Radiation and Transport (Ginoux et al. 2001) 1. Introduction 1.1 Dust modeling
  • 10 Outline 1. Introduction 2. Proposed research 3. Methods 4. Preliminary results 5. Expected results and significance 6. Broader Impact 7. Research timeline 8. References
  • 11 Problem statement 1. There is the general unavailability of accurate and high resolution surface input data (mainly clay content, bare-soil fraction and soil moisture). % clay content map1 used in CLM2 1Post and Zobler 2000 2Community land model 2. Proposed Research 2.1 Problem Statement 2.2 Research Questions 2.3 Hypotheses
  • 12 Problem Statement 2. Static erodibility factor is used to constrain the simulated dust emission, but the factors determining the erodibility are not well understood. ERA-Interim wind NCEP wind = 13596 = 2881 9643 1603 Dust emission simulated by CLM under different reanalysis wind forcing (2003). Annual dust (Tg) 2. Proposed Research 2.1 Problem Statement 1Ginoux et al. 2001 2.2 Research Questions 2.3 Hypotheses
  • 13 Problem Statement 3. Bulk parameterizations derived from controlled wind tunnel experiments are used for calculating saltating flux and vertical dust mass flux, which do not adequately represent the range of soil types, geomorphology and environment found in dust sources regions. (Marticorena and Bergamatti 1995) 2. Proposed Research 2.1 Problem Statement (Gillette 1978) 2.2 Research Questions 2.3 Hypotheses
  • 14 Research Questions 1. What details of geomorphology are important for characterizing the dust sources and how can these be best represented in the dust models? 2. What factors determine the soil erodibility and how can we better quantify this parameter to represent the spatial and temporal dynamics of dust sources? 3. Why different parameterizations derived from wind tunnel experiments yield varying vertical dust mass flux and how can these be adapted to represent the wind-dust relationship for different geomorphic types? 2. Proposed Research 2.1 Problem Statement 2.2 Research Questions 2.3 Hypotheses
  • 15 Hypotheses 1. Threshold friction speed depends upon geomorphic types. Thus, representing the heterogeneity of landforms in sub-grid scale by using a geomorphic map can improve the vertical dust mass flux. 2. Erodibility has both spatial and temporal variability. The correlation between observed wind speed and aerosol optical depth (AOD) can be used as a proxy for representing the dynamics of erodibility. 2. Proposed Research 2.1 Problem Statement 2.2 Research Questions 2.3 Hypotheses
  • 16 Hypotheses 3. Difference in vertical mass flux observed in different wind tunnel experiments is because of the variation in sampling techniques. Therefore, location-independent dust measurement can provide more accurate relationship between wind and dust. These parameterizations can be adapted to different geomorphic types by constraining against wind-dust relationship near geomorphic types. 2. Proposed Research 2.1 Problem Statement 2.2 Research Questions 2.3 Hypotheses
  • 17 Outline 1. Introduction 2. Proposed research 3. Methods 4. Preliminary results 5. Expected results and significance 6. Broader Impact 7. Research timeline 8. References
  • The Study Area • The Middle East and North Africa (MENA), commonly known as the dust belt, as it contains more than 50% of global mineral dust sources 1. • To be expanded to global scale ultimately. Mean aerosol optical depth (AOD)2 Bodélé, Chad 1Shao 2http://gdata1.sci.gsfc.nasa.gov/daac-bin/G3/gui.cgi?instance_id=aerosol_daily 2008 3. Methods 3.1 Study area 3.2 Datasets 3.3 Geomorphic map 3.4 Erodibility 3.5 Paramaterization
  • 19 Datasets Datasets Hi-resolution images of Google Earth Pro and EsriBasemap MODIS Aqua level 3 aerosol product (MYD08_D3) MODIS Terra level 3 aerosol product (MOD08_D3) MODIS Aqua level 2 aerosol product (MYD04_L2) MODIS Terra level 2 aerosol product (MOD04_L2) AERONET aerosol data products Resolution Geomorphic features Aerosol properties Station (15 min) ERA-Interim reanalysis wind NCEP reanalysis 1 wind 3. Methods 3.1 Study area Wind speed 3.2 Datasets 3.3 Geomorphic map 3.4 Erodibility 3.5 Paramaterization
  • Geomorphic mapping Visually examine high resolution images and identify the type of landforms. Create polygons of unique landforms. Verify against secondary data and maps. Group the polygons into several geomorphic types and create gridded geomorphic map. 3. Methods 3.1 Study area 3.2 Datasets 3.3 Geomorphic map 3.4 Erodibility 3.5 Paramaterization
  • 21 Quantification of erodibility Identify the accurate surface wind and AOD data either from ground based observations or from satellite data. Calculate correlation between surface wind and AOD near known geomorphic types. Quantify the mean erodibility of a geomorphic type by removing the effect of the environment. 3. Methods 3.1 Study area 3.2 Datasets 3.3 Geomorphic map 3.4 Erodibility 3.5 Paramaterization
  • Parameterization improvement Measure the vertical dust mass emitted directly from the substrate bed in the wind tunnel. Establish wind-dust relationship. Tune with observed wind-dust relationship near geomorphic types. 3. Methods 3.1 Study area 3.2 Datasets 3.3 Geomorphic map 3.4 Erodibility 3.5 Paramaterization
  • 23 Outline 1. Introduction 2. Proposed research 3. Methods 4. Preliminary results 5. Expected results and significance 6. Broader Impact 7. Research timeline 8. References 4. Preliminary Results 4.1 Geomorphic map 4.2 Erodibility
  • 24 Geomorphic mapping 4. Preliminary Results 4.1 Geomorphic map 4.2 Erodibility
  • Erodibility Low wind but high AOD: Pollution? Transported dust? Strong saltation? AOD Vs. ERA-Interim wind at Bodélé 5 AOD = 0.047*WIND2 - 0.3*WIND + 1.2 R2 = 0.48 4 Deep Blue AOD550nm Deep Blue AOD550nm 5 3 2 1 0 0 2 4 6 8 10 1000 hPa ERA Wind (ms-1) 4. Preliminary Results 12 4.1 Geomorphic map 14 High wind but low AOD: Supply-limited case? High soil moisture? Absence of saltation? 25 AOD Vs. NCEP wind at Bodélé AOD = 0.032*WIND2 - 0.095*WIND + 1 R2 = 0.19 4 3 2 1 0 0 2 4 6 8 10 12 -1 1000 hPa NCEP Wind (ms ) 4.2 Erodibility 14
  • 26 Erodibility Developed erodibility map 1Ginoux et al. 2001. 4. Preliminary Results Topographic erodibility1 Proposed method emphasizes agricultural dust sources and eliminates fictitious sources involving long-range transport, pollution and biomass burning. 4.1 Geomorphic map 4.2 Erodibility
  • 27 Erodibility • Erodibility is highly dynamic spatially and temporally. • Dust source intensity and distribution is maximum in the winter and spring. • Dust sources less active during summer and fall (effect of west African monsoon). Correlation between annual cycles of ERA-Interim wind and MODIS AOD (2003-2012). 4. Preliminary Results 4.1 Geomorphic map 4.2 Erodibility
  • Erodibility of geomorphic types Geomorphic types Bedrock, with sediments Sand deposit Sand deposit, on bedrock Sand deposits, stabilized Land Use (Agriculture) Fluvial system 28 Derived location 24N, 8E Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec 0.26 0.15 0.12 0.10 0.10 24.4N, 13E 18.4N, 26E 0.22 0.24 0.1 0.11 0.08 0.21 0.01 0 0 0.14 0.19 0.09 0.14 0.24 0.24 0.24 0.30 0.25 0.12 0.15 0.13 0.33 0.15 0.10 25.4N, 49E 0.22 0.33 0.25 0.20 0.22 0.11 0.36 0.04 17.3N, 2W 0.51 0.35 0.17 0.01 0.2 0.02 0.25 0.16 0.04 0.03 0.04 0.28 21.4N, 15W 0.42 Stony surface 19.4N, 57E 0 Playa/Sabkha 16.4N, 17E 0.71 0 0.1 0 0.12 0.07 0.12 0 0 0 0 0 0 0 0 0 0 0 0.19 0.06 0.22 0.1 0.10 0.17 0.12 0.13 0.34 0.21 0.11 0.23 0.11 0.08 0.08 0.12 0.67 0.54 0.42 0.34 0.28 0.30 0.17 0.28 0.32 0.60 0.65 Erodibility is highest to lowest for playa, land use, fluvial system, sand Deposits: stabilized, sand deposits: on bedrock, sand deposits: with sediments and sand deposits in order. 4. Preliminary Results 4.1 Geomorphic map 4.2 Erodibility
  • 29 Expected results and significance • The proposed work will more accurately map the intensity and distribution of mineral dust sources around the world. • Inclusion of the proposed changes in the dust model should improve the simulated vertical dust mass flux that should better match with the remote sensing and ground-based observations. • Improved dust source characterization will ultimately help to reduce the large uncertainty in the sign and magnitude of radiative forcing of dust aerosols that exists presently (IPCC, 2007). 5. Expected results and significance 6. Broader Impact 7. References
  • 30 Broader Impact • Since dust storms are common phenomena in many parts of the world, this work is directly relevant to the public health and safety. • Proposed changes will be implemented in the community land model (CLM), which enhances community participation in scientific research. • Proposed work will also aid in developing dust storm monitoring and forecasting tools. • Proposed work is an interdisciplinary research and seeks collaboration among: 5. Expected results and significance 6. Broader Impact 7. References
  • 31 References Bullard, J. E., Harrison, S. P., Baddock, M. C., Drake, N., Gill, T. E., McTainsh, G., & Sun, Y. (2011). Preferential dust sources: a geomorphological classification designed for use in global dustcycle models. Journal of Geophysical Research, 116(F4), F04034. Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., … Vitart, F. (2011). The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Ginoux, P., Chin, M., Tegen, I., Prospero, J. M., Holben, B., Dubovik, O., & Lin, S. J. (2001). Sources and distributions of dust aerosols simulated with the GOCART model. Journal of Geophysical Research, 106(D17), 20255–20. Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., … others. (1996). The NCEP/NCAR 40-year reanalysis project. Bulletin of the American meteorological Society, 77(3), 437–471. Oleson, K. W., Lawrence, D. M., Gordon, B., Flanner, M. G., Kluzek, E., Peter, J., … others. (2010). Technical description of version 4.0 of the Community Land Model (CLM). Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.172.7769 Prospero, J. M., Ginoux, P., Torres, O., Nicholson, S. E., & Gill, T. E. (2002). Environmental characterization of global sources of atmospheric soil dust identified with the Nimbus 7 Total Ozone Mapping Spectrometer (TOMS) absorbing aerosol product. Rev. Geophys, 40(1), 1002. Zender, C. S., Bian, H., & Newman, D. (2003). Mineral Dust Entrainment and Deposition (DEAD) model: Description and 1990s dust climatology. J. Geophys. Res, 108(D14), 4416. 5. Expected results and significance 6. Broader Impact 7. References
  • 32 Thank you!