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Comparison of Model-Derived Aerosol Properties with Satellite Products over the South Asian Region S. N. Tripathi Indian Institute of Technology, Kanpur.

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Presentation on theme: "Comparison of Model-Derived Aerosol Properties with Satellite Products over the South Asian Region S. N. Tripathi Indian Institute of Technology, Kanpur."— Presentation transcript:

1 Comparison of Model-Derived Aerosol Properties with Satellite Products over the South Asian Region S. N. Tripathi Indian Institute of Technology, Kanpur Kanpur, India

2 Objectives (only those in which IITK is committed to contribute) To test the representation of aerosol burden in the new generation of climate models and assess the role of (global) historical aerosol forcing in South Asian monsoon simulations in the CMIP5 multi-model ensemble and in HadGEM3-AO. To quantify the relative role of changes in aerosol emissions/burdens/properties on variability of the South Asian monsoon with respect to Indo-Pacific drivers such as ENSO, Modoki and the IOD. To assess the role of the direct and indirect effects of aerosols on future South Asian precipitation (mean and variability) relative to greenhouse gas forcing. South Asian PRecIpitaton: A SEamless Assessment – SAPRISE Co-PI- S. N. Tripathi Indian Institute of Technology Kanpur Collaborating Organisations The University of Reading Methodology 1.Decide on the aerosol properties (e.g. Aerosol Optical Depth, Single Scattering Albedo (SSA); columnar or profiles) that will be used in validation/assimilation. 2.Select the best available data source for aerosols from ground, aircraft and space-based sensors 3.Use compositing techniques to create gridded aerosol data ready to be assimilated in the models 4.Creating AOD for various aerosol species (e.g. Black Carbon, Dust) for model evaluation and assimilation 5. Creating a mechanism for evaluating modeled-aerosol forcing using Aeronet and satellite data Before we proceed ……

3 Satellite Data Validation over Indian Region Quick comparisons with aeronet AOD Kanpur 26 N, 80 E (2001-continued) Jaipur 28 N, 75 E (2009-continued) Gandhicollege 25N, 84 E (2006-continued) Others ((Nainital, Pantnagar) with smaller set of data -monthly mean modeled and aeronet AOD -monthly mean modeled and aeronet single scattering albedo -absorbing angstrom exponent (spectral nature of absorption) - Larger region using MODIS/MISR (opposite bias) Eck, Tripathi et al., 2010 Next step? Vertical profiles? Chaudhry et al., Annales Geophysicae, 2012

4 Regression results of the total data, shows a very poor correlation between MODIS and AERONET retrieved AOD values, worst for Terra. Aqua : R 2 decreases with increase in wavelength (λ) Terra : R 2 improves (even though poor) with increase in wavelength (λ). Choudhry et al, Ann Geophys, 2012

5 The results from Kanpur show consistency with earlier studies; Terra has better performance. m≈1 in all cases for Terra ; intercept (representative of surface reflectance contribution to AOD) is very small and as predicted by the MODIS team before the latest algorithm version was used; Aqua higher intercept, thus daytime reflectance poses more problem in urban environment Choudhry et al, Ann Geophys, 2012

6 Terra better performance; Winter poor correlation issues with respect to cloud screening and snow masking Correlation improves with λ ; m≈2 in post monsoon for Terra; error due to reflectance decreases with λ Choudhry et al, Ann Geophys, 2012

7 Mishra, Tripathi et al., 2012, J. Atmos. Ocean Tech. MPLnet Climatology at Kanpur Vertical profiling (since 2009) Kanpur 26 N, 80 E (2009-continued, 24x7) -Can model reproduce pre monsoon maxima; models face difficulty -Magnitude of maxima -Extent to which it is distributed in troposphere -The enhanced absorbing layer using vertical extinction and columnar SSA Next step: Compare with Calipso measured profiles for larger region How good is Calipso over Kanpur (representative of IGP)?

8 Validation of CALIPSO derived backscatter with MPLNET data Due to insufficient models in the algorithm, CALIPSO yields improper results when more than one feature is present, for example, aerosol and cloud 24 hour backtrajectory shows both instruments are measuring the same air parcel  Space based observation of aerosol and cloud from Cloud-Aerosol LIDAR with Orthogonal Polarization (CALIOP ).  Launched aboard the Cloud- Aerosol LIDAR and Infrared Pathfinder Satellite Observation (CALIPSO) in April  Vertical Profiles of backscatter, extinction, optical depth, layer height and thickness are provided.  Comparison made with backscatter coefficient derived from Micro Pulse LIDAR (MPL) over Kanpur for May 2009 to September  Constraints: Distance < 130 Km Time Difference < 3hrs  7 out of 16 available cases compare well with R 2 greater than 0.5, and slope between 0.4 and 2.5  Cases of poor comparison indicate confusion between cloud and dust aerosol by CALIPSO. A representative case of poor comparison between the two datasets (10 June 2009) A representative case of good comparison between the two datasets (16 October 2009) Mishra, Tripathi et al., 2012, J. Atmos. Ocean Tech.

9 Even for cases of poor comparison, the aerosol type is properly identified by CALIPSO – as seen from AERONET data and backtrajectory analysis Mishra, Tripathi et al., 2012, J. Atmos. Ocean Tech.

10 Objectives and Outline To evaluate the model simulations of aerosol and cloud properties over the Indian region. Parameters to be examined are aerosol optical depth, vertical extinction profile, and cloud optical depth. Satellite derived products will be used for the assessment of models. Satellites considered:- MODIS, MISR, CALIPSO, CLOUDSAT.

11 CMIP5 ModelLongitudeLatitude CSIRO-BOM-ACCESS1.0 CSIRO-BOM-ACCESS1.3 CISRO-Mk3.6.0 IPSL-CM5A-LR IPSL-CM5A-MR IPSL-CM5B-LR MIROC-ESM MIROC-ESM-CHEM MIROC-MIROC4h MIROC-MIROC5 MOHC-HadGEM2-CC MOHC-HadGEM2-ES MRI-CGCM3 NASA-GISS-E2-H NASA-GISS-E2-R NCC-NorESM1-M NCC-NorESM1-ME NOAA-GFDL-CM3 NOAA-GFDL-ESM2G NOAA-GFDL-ESM2M NSF-DOE-NCAR-CESM1-CAM vanni Level 3 gridded data Product CMIP5 models data Source: AOD: MODIS vs MISR In the next step of analysis, Level 2 data would be used All CMIP5 models providing information on AOD have been included

12 Good Moderate Poor * *** ** * * * * * ** * * * *** AOD: MODIS and MISR vs CMIP5 models

13 MODIS and MISR vs CMIP5 models * *** ** * * * * * ** * * MODIS vs CMIP5 MISR vs CMIP5 Gridwise correlation analysis is required

14 MODIS and MISR vs CMIP5 models: A correlation study CMIP5 ModelsMODISMISR MODIS0.54 CSIRO-BOM-ACCESS CSIRO-BOM-ACCESS CSIRO-Mk IPSL-CM5A-LR IPSL-CM5A-MR IPSL-CM5B-LR0.47 MIROC-ESM MIROC-ESM-CHEM MIROC-MIROC4h MIROC-MIROC MOHC-HadGEM2-CC MOHC-HadGEM2-ES MRI-CGCM NASA-GISS-E2-H NASA-GISS-E2-R NCC-NorESM1-M NCC-NorESM1-ME NOAA-GFDL-CM NOAA-GFDL-ESM2G NOAA-GFDL-ESM2M NSF-DOE-NCAR-CESM1-CAM Whenever the correlation is good, MISR has even better correlation

15 Zonal means of AOD over longitude o E MOHC stand out clearly – likely due to dust dominated model. Generally, MISR-MODIS match very well with most models but AOD magnitude differs a lot. MISR and MODIS AOD peak value differ approximately by a factor of 1.5 or more. So need a better product (composite?)

16 Zonal means of AOD over longitude o E Only those models which follow general trend observed in satellite AOD are highlighted

17 Seasonal AOD variability over IG Basin Not all models are able to capture the two AOD peaks (summer:- dust dominated, and winter:- anthropogenic)

18 Seasonal AOD variability over IG Basin Only those models which follow general trend observed in satellite AOD are highlighted

19 MODIS-MISR composite AOD product Creating a satellite database of sufficient accuracy for validation of models and other aerosol-related studies MODIS: Large swath, near-daily global coverage, extensively studied and validated MISR: More view angles, more number of aerosol types Step 1: Create gridded data product from MODIS and MISR swath data. For each grid cell, have the average AOD and the median of QA parameter. Step 2: Start with the MODIS (gridded in step 1 above) AOD product. For the grid cells having low QA parameter from MODIS, replace the AOD from MISR AOD, provided the latter is of high QA. Step 3: Fill in the missing values that are still remaining by following the statistical approach outlined by Xavier et al (GRL, 2010). Comments and suggestions are welcome!

20 Thanks


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