Comparison of Model-Derived Aerosol Properties with Satellite Products over

the South Asian Region

S. N. TripathiIndian Institute of Technology, Kanpur

Kanpur, India

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 – SAPRISECo-PI- S. N. Tripathi

Indian Institute of Technology KanpurCollaborating OrganisationsThe 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 sensors3. Use compositing techniques to create gridded aerosol data ready to be assimilated in the models4. 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 ……

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)

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AERONET Climatology - Kanpur, India

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Next step? Vertical profiles?0 12 24 36 48 60 72 84 96 108 1200

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Chaudhry et al., Annales Geophysicae, 2012

Regression results of the total data, shows a very poor correlation between MODIS and AERONET retrieved AOD values, worst for Terra. Aqua : R2 decreases with increase in wavelength (λ) Terra : R2 improves (even though poor) with increase in wavelength (λ) . Choudhry et al, Ann Geophys, 2012

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

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

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)?

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 2006.

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 2010.

Constraints: Distance < 130 Km Time Difference < 3hrs 7 out of 16 available cases compare well with R2 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.

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.

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.

CMIP5 Model Longitude Latitude1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.1920.21.

CSIRO-BOM-ACCESS1.0CSIRO-BOM-ACCESS1.3CISRO-Mk3.6.0IPSL-CM5A-LRIPSL-CM5A-MRIPSL-CM5B-LRMIROC-ESMMIROC-ESM-CHEMMIROC-MIROC4hMIROC-MIROC5MOHC-HadGEM2-CCMOHC-HadGEM2-ESMRI-CGCM3NASA-GISS-E2-HNASA-GISS-E2-RNCC-NorESM1-MNCC-NorESM1-MENOAA-GFDL-CM3NOAA-GFDL-ESM2GNOAA-GFDL-ESM2MNSF-DOE-NCAR-CESM1-CAM5

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1.2413791.2413791.8452541.8751.2587411.8752.7457442.7457440.5598131.3894961.2413791.2413791.1142691.9777781.9777781.8751.8751.9777781.9887641.9887640.9375

http://disc.sci.gsfc.nasa.gov/giovanniLevel 3 gridded data Product

CMIP5 models data Source: http://badc.nerc.ac.uk/home/

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

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AOD: MODIS and MISR vs CMIP5 models

MODIS and MISR vs CMIP5 models*

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MODIS vs CMIP5MISR vs CMIP5

Gridwise correlation analysis is required

MODIS and MISR vs CMIP5 models: A correlation study

CMIP5 Models MODIS MISR

MODIS 0.54

CSIRO-BOM-ACCESS1.0 0.16 0.41

CSIRO-BOM-ACCESS1.3 0.14 0.35

CSIRO-Mk3.6.0 0.35 0.66

IPSL-CM5A-LR 0.47 0.50

IPSL-CM5A-MR 0.44 0.61

IPSL-CM5B-LR 0.47 0.47

MIROC-ESM 0.11 0.31

MIROC-ESM-CHEM 0.14 0.34

MIROC-MIROC4h 0.07 0.39

MIROC-MIROC5 0.40 0.32

MOHC-HadGEM2-CC 0.22 0.44

MOHC-HadGEM2-ES 0.20 0.47

MRI-CGCM3 0.41 0.62

NASA-GISS-E2-H 039 0.49

NASA-GISS-E2-R 0.33 0.47

NCC-NorESM1-M 0.43 0.45

NCC-NorESM1-ME 0.43 0.46

NOAA-GFDL-CM3 0.03 0.32

NOAA-GFDL-ESM2G 0.24 0.39

NOAA-GFDL-ESM2M 0.24 0.38

NSF-DOE-NCAR-CESM1-CAM5 0.43 0.56

Whenever the correlation is good,

MISR has even better correlation

Zonal means of AOD over longitude 75-88oE

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?)

Zonal means of AOD over longitude 75-88oE

Only those models which follow general trend observed in satellite AOD are highlighted

Seasonal AOD variability over IG Basin

Not all models are able to capture the two AOD peaks (summer:- dust dominated, and winter:- anthropogenic)

Seasonal AOD variability over IG Basin

Only those models which follow general trend observed in satellite AOD are highlighted

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 validatedMISR: 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!

Thanks