Figure 2: Schematic of cloud types (Roman numerals: (i) water, (ii) mixed-phase, (iii) cirrus, (iv) COLD_HET, (v) WARM_HET, (vi) clear sky) and cloud formation processes (Arabic numerals: (1) WATER, (2) MPC’, (3) WARM_HET, (4) MPC, (5) CIRRUS’, (6) COLD_HET, (7) CIRRUS) possible along the air parcel trajectories, depending on a schematically indicated initial moisture content (‘wet’, ‘average’, ‘dry’). Straight line containing (1) and (5) approximates RH w = 100% (for simplicity extrapolated to T > 273 K). Straight line separating colored regions (iii) and (iv) follows the parameterization of Koop et al. [2000] for 200 nm solution droplets. Bottom panel shows the distribution of water saturation temperatures of individual trajectories, given their actual initial moisture content. See Table 1 for summary of cloud definitions. Characterizing mineral dust aerosol in the upper troposphere : : 12th Symposium of the International Commission on Atmospheric Chemistry and Global Pollution (iCACGP) : : 11th Science Conference of the International Global Atmosphere Chemistry (IGAC) Project : : Dalhousie University, Halifax, Canada, July 11-16, 2010 : : Contact: : : Department of Physics and Atmospheric Science : : Aldona Wiacek 1, Randall V. Martin 1, Adam E. Bourassa 2, N.D. Lloyd 2, Doug. A. Degenstein 2, Thomas Peter 3 and Ulrike Lohmann 3 1 Dalhousie University, Halifax, Nova Scotia, Canada 2 University of Saskatchewan, Saskatoon, Saskatchewan, Canada 3 ETH Zürich, Zürich, Switzerland Introduction Naturally occurring mineral dust is, on a mass basis, the most abundant aerosol in the atmosphere, emitted every few days in large quantities from sources in the Sahara (summer peak), and a little less frequently from sources in Asia (spring peak). Dust travels thousands of kilometers across the Atlantic and Pacific oceans, fertilizing them with nutrients and thus producing feedbacks on the carbon cycle. Dust plumes also cause strong direct radiative aerosol effects, which feed back on surface temperatures and winds, as well as on climate. One of the biggest uncertainties related to aerosols in general and mineral dust in particular, is their indirect radiative effect via changes to clouds and precipitation. Mineral dust is an efficient ice nucleus with a strong potential for altering the properties of upper tropospheric cirrus clouds and, consequently, the Earth’s radiation budget. Given that it is the most abundant aerosol in the atmosphere, potentially set to increase in the future due to human activities, is important to improve our ability to detect and predict mineral dust concentrations, particularly in the sensitive upper tropospheric regions where small amounts of dust can lead to large changes in cirrus clouds. To this effect, we performed an extensive trajectory modeling study to explore the availability of mineral dust particles as ice nuclei for cirrus clouds, also tracking the particles' history of processing by water and mixed-phase clouds. At present, observations of upper tropospheric mineral dust are sparse, coming primarily from aircraft campaigns. While nadir- viewing remote sounders provide global coverage of optically thicker lower tropospheric dust plumes, the limb-scatter satellite geometry is well suited to detecting low-concentration mineral dust in the upper troposphere due to its long observation path length (~ km), high vertical resolution (~1-2 km) and good geographic coverage. In a complementary study, we used the fully three-dimensional radiative transfer code SASKTRAN, to simulate the sensitivity of limb-viewing Odin/OSIRIS measurements to upper tropospheric mineral dust. OSIRIS includes a grating spectrometer launched in 2001 on board Odin Records limb-scattered sunlight between 280 – 810 nm (~1 nm resol’n) ~ km observation path length, ~1-2 km vertical resolution, and good geographic coverage all give the potential for detecting small quantities of mineral dust (AOT<<1) in the upper troposphere Small dust and ice particles can potentially be discriminated based on different scattering and absorption properties in near-UV/vis/near-IR We simulated the sensitivity of Odin/OSIRIS satellite measurements to mineral dust and ice using the fully three-dimensional radiative transfer code SASKTRAN [Bourassa et al., 2008] Particles were simulated in 1-km thick layers either at 7 km or 14 km Trajectory Modelling CONCLUSIONS & OUTLOOK References & Acknowledgements Bourassa, A. E., D. A. Degenstein, and E. J. Llewellyn (2008), SASKTRAN: A spherical geometry radiative transfer code for efficient estimation of limb scattered sunlight, J. Quant. Spectrosc. Rad. Transfer, 109, Tegen, I. (2003), Modeling the mineral dust aerosol cycle in the climate system, Quat. Sci. Rev., 22, 1821–1834. Wernli, H. and H. C. Davies (1997), A Lagrangian-based analysis of extratropical cyclones. I: The method and some applications, Q. J. Royal Meteorol. Soc., 123, Wiacek, A., and T. Peter (2009), On the availability of uncoated mineral dust ice nuclei in cold cloud regions, GRL, 36, L Wiacek, A., T. Peter, and U. Lohmann (2010), The potential influence of Asian and African mineral dust on ice, mixed-phase and liquid water clouds, Atmos. Chem. Phys. Discuss., 10, 4027–4077, This work, in part supported by a Marie Curie Incoming International Fellowship awarded by the EC under FP6 and CFCAS, makes extensive use of ECMWF analyses and the LAGRANTO trajectory analysis tool developed by Prof. Wernli at ETH Zurich. ETH Eidgenössische Technische Hochschule Zürich Swiss Federal Institute of Technology Zurich Figure 1: Circles show the locations of Asian (left) and African (right) trajectory starting points, superimposed on a map (adapted from Tegen [2003]) of preferential dust source areas (blue shades). Asian Taklimakan desert and West African dust sources are shown in pink; Asian western Gobi desert and African Bodélé Depression are shown in yellow; eastern Gobi in orange. Only western Gobi results are reported in this study as they are very similar to eastern Gobi results. Potential Influence of Dust on Clouds (Trajectory Modelling) Odin/OSIRIS Sensitivity to Mineral Dust in the Upper Troposphere Figure 5: The effect of a 1-km-thick-layer perturbation of r=1.0 μm dust (squares), r=1.0 μm ice (diamonds) and non-spherical D eff =10 μm ice (pentagrams) as seen by OSIRIS inside and just below the aerosol layer height. SZA is constant and relatively high (65°). SSA varies between 80° (light shades) and 60° (dark shades). Albedo = 0.0. Figure 6: Response below (blue), inside (green) and above (red) a 1-km dust layer at 14 km at a various imaginary indeces of refraction (n). Real index constant (m=1.55), SZA=65°, SSA=100°, albedo=0. Figure 3: The breakdown of only those trajectory points classified as cloudy in each geographic region: Taklimakan (T), Gobi (G), West Africa (W) and Bodélé (B). Note that cloud types CIRRUS, COLD_HET and COLD_HET_OSC are not visible on this scale. See Wiacek et al. [2010] for details of oscillatory cloud types (suffix ‘_OSC’). Cloud TypeDescriptionTGWB WATERRH w > 100%, T > 0°C MPCRH w > 100%, -40°C < T < 0°C WARM_HETRH i > 100%, RH w < 100%, -40°C < T < 0°C COLD_HET100% < RH i < homogeneous nucleation limit, T < -40°C0.00 CIRRUSRH i > homogeneous nucleation limit, T < -40°C CIRRUS’Like CIRRUS, but experienced MPC conditions first CIRRUS’’Like CIRRUS, but experienced WATER & MPC conditions first Table 1: Breakdown of cloudy trajectory points in each region as a percent fraction of all trajectory points in each region (~1.8x10 6 ). Oscillatory clouds (Figure 3) are not shown. Figure 4: Distribution of temperature at water saturation for trajectory points classified as MPC (A, B, C, D) and CIRRUS’ (E, F, G, H) clouds. Frequency of occurrence of WARM_HET cloud points (I, J, K, L) in the RH i vs. T space, whose integral over RH i is shown in (E, F, G, H) using the T sat axis for brevity. Each geographic region is shown in a separate column, as labeled Figure 2 outlines cloud formation processes and pathways possible for ascending ‘wet’, ‘average’ and ‘dry’ trajectories, while Table 1 summarizes the cloud definitions used in our study. Applying this classification to the ~1.8 million trajectory points from each geographic region we find that the occurrence of cloud types CIRRUS and COLD_HET (homogeneously and heterogeneously formed pure ice clouds, respectively) is everywhere negligible (Figure 3). The potential for mineral dust interactions with the remainder of cold cloud types is greater from Asian than from African dust sources by roughly a factor of 2 (except for MPC’). By far, the overall potential influence of mineral dust is greatest via MPC clouds, however, the details of these interactions will depend on the efficiency of specific ice formation mechanisms in operation in a given relative humidity / temperature environment. This is also the case for CIRRUS’ clouds, as well as the small but non-negligible number of trajectory points which passed through ice-saturated (but water- subsaturated) regions too warm for homogeneous nucleation, where “warm” cirrus clouds (WARM_HET) may be able to form. Figure 4 shows the RH i vs. T location of WARM_HET clouds and the distribution of water saturation temperatures for MPC and CIRRUS’ clouds. It is such that dust could indeed remain interstitial through WARM_HET and MPC cloud processing and remain available for CIRRUS’ formation. In our trajectory modeling study we found that dust emissions from Asian deserts have a higher potential for interactions with high clouds, despite being climatologically much smaller than African emissions. Moreover, for "classical" cirrus-forming temperatures, our results show that only mineral dust IN that previously underwent mixed-phase cloud-processing are likely to be relevant. By far the largest fraction of cloud forming trajectories entered conditions of mixed-phase clouds, where mineral dust will potentially exert the biggest influence. In the complementary satellite simulation study we demonstrated the complex wavelength dependence of dust and ice sensitivity on observation geometry, aerosol layer height, and index of refraction. Work is ongoing to characterize the effect of surface albedo, dust/ice optical thickness, particle size, non-sphericity, and multi-layer spatial distribution in limb-scatter spectra. Profiles of single and multiple scattering contributions are a useful tool in accounting for the complex effects of background and aerosol scattering and absorption effects. Canadian Foundation for Climate and Atmospheric Sciences (CFCAS) Fondation canadienne pour les sciences du climat et de l’atmosphère (FCFCA) Figure 7: As in Figure 6, also including variable real index of refraction (m). Figure 8: Single and multiple scattering components at 450 and 800 nm due to background (blue), and a 1-km dust (red) or ice (black) layer at 14 km. Realistic indeces of refraction were used, SZA=65°, SSA=100°, albedo=0. Small dust/ice size and concentration: r=1.0 μm; σ=1.6 μm; ρ=2.0 cm -3 ; AOT ~0.025 Non-spherical ice size and concentration: D effective ~ 10 μm; AOT ~0.25 Treatment of phase function: Mie theory for small dust/ice; modified bulk scattering climatology for non-spherical ice ( Figure 5 shows the relative response to aerosol layers under two different Solar Scattering Angle (SSA) conditions and a constant Solar Zenith Angle (SZA). The response BELOW and INSIDE the aerosol layer is generally NOT similar, depending both on viewing geometry and layer height (not shown). Figures 6 and 7 show the influence of wavelength-independent imaginary and real indeces of refraction for dust, while Figure 8 compares profiles of single and multiple scattering signal components due to 1.0 μm dust and ice at 14 km against those from the background atmosphere (incl. air, O 3, NO 2, sulphates). Multiple scattering is indeed the source of in- layer signal enhancements due to dust or ice. Used 42 trajectory starting points per each dust source region (Figure 1) Four trajectories per point per each day of 2007 (63120 / region) One-week trajectories depart at 00Z, 06Z, 12Z, and 18Z from 770 hPa High-resolution ECMWF analyzed fields (T799, ~25km, ~200m vertical) Trajectory pressure (p), temperature (T), specific humidity (Q), latitude and longitude saved every 6 hours (~1.8 x 10 6 points / region) Trajectories calculated using the LAGRangian trajectory ANalysis TOol (LAGRANTO) developed by Wernli and Davies [1997] Specific humidity at each trajectory’s starting point transported in a Lagrangian manner and used to calculate relative humidities with respect to water (RH w ) and ice (RH i ) in 6-hr steps downstream Dust-bearing trajectories assumed to coincide with known dust emission seasons, without explicitly modelling dust emission and deposition A limitation of our approach is that trajectories, though high in resolution, do not fully resolve vigorous boundary layer convection or deep convection, which may underestimate dust transport to the upper troposphere.