# Presentation: 363816

## Multi-year global aerosol distributions from MODIS (and MISR) and GOCART

*Mian Chin, Thomas Diehl, Lorraine Remer, Robert Levy, Ralph Kahn, David Giles, Brent Holben, Hongbin Yu, Huisheng Bian, ****et al.***

- Special thanks to Giovanni and AERONET websites that make data access easy
- Thanks to NASA EOS program for funding

## Introduction

- MODIS has now provided almost 7 years of global aerosol products
- MODIS has simulated aerosol research exponentially, especially has helped advance global aerosol chemistry transport models, including GOCART

## Global aerosol distributions

## Comparisons between MODIS and GOCART

| MODIS | GOCART |
| --- | --- |
| First launched on Terra in 1999 | First version developed in 1999 |
| Remotely sensing aerosol from space | Theoretically (or ideally) calculating aerosol from computer |
| Taking measurements 2 times/day (Terra & Aqua) for clear skies | Saving model output every 3 hours for all skies |
| Seeing aerosol “top down”: measuring “radiance” from space | Simulating aerosol “bottom up”: starting with surface emissions |
| Converting “radiance” to “aerosol optical thickness” | Converting “mass concentrations” to “aerosol optical thickness” |

## How does MODIS get AOT?

- Over land:
- Finding a cloud-free scene
- Knowing/guessing surface reflectance
- Knowing/guessing aerosol type
- Choosing an AOT number from the LUT
- Over ocean:
- Allowing the algorithm to find its own aerosol model by matching 6 MODIS reflectance with LUT values
- Better constrained

- Radius (μm)

## How does GOCART get AOT?

- Obviously MODIS and GOCART have very different ways to get the AOT – are we “seeing” the same thing?
- What we have learned from the comparisons among MODIS, MISR, AERONET, and GOCART (2000 – 2005)
- Thoughts and questions regarding using data and model

## GOCART, MODIS, MISR time series

- MODIS: Terra, C004, 1x1   MISR: Vx(?), 1x1  GOCART: v4.0, 2.5x2

- MODIS and MISR data downloaded from Giovanni website

## Looking closer and using AERONET as “ground truth”

- AERONET measures AOT directly
- Data have been widely used for satellite validation and model evaluation
- Limitations:
- Sites mainly over land
- “point” measurements
- Even “truth” has different versions – we are now using the new version, 2

## MODIS, MISR, GOCART, AERONET: 200009

_**Feature: SH biomass burning**_
- GOCART: biomass burning emissions seem too low
- MODIS: too high over North America, gap over bright surfaces
- MISR: covering desert, but gap over tropical land surfaces
- GOCART: AOT lower than satellite data over SH ocean

## MODIS, MISR, GOCART, AERONET: 200104

_**Feature: Asian dust transport**_
- MISR: AOT over Asia seem too low, gap over tropical land surfaces
- MODIS: too high over North America, gap over bright surfaces
- GOCART: AOT lower than satellite data over SH ocean, much higher than MISR over Sahara

## MODIS, MISR, GOCART, AERONET: 200403

_**Feature: African dust storm**_
- MISR: AOT over Asia seem too low, gap over tropical land surfaces
- MODIS: too high over SH land (South America and Australia, gap over bright surfaces
- GOCART: SE Asia biomass burning too low

## MODIS, MISR, GOCART, AERONET: 200407

_**Feature: North America Boreal fire**_
- GOCART: Boreal fire emission (or injection height) maybe too low so fire did not go far enough
- MISR: Excessive AOT over Greenland
- MODIS: AOT too high over North America boreal

## MODIS vs AERONET, 2000 – 2005

- Relatively higher correlations, but overall biased high (~30 – 67%)

## MISR vs AERONET, 2000 – 2005

- Little systematic bias globally, but less tight correlations

## GOCART vs AERONET, 2000 – 2005

- Little systematic bias globally, but less tight correlations

## These comparisons have revealed:

- MODIS (collection 4):
- In general biased high over land
- No coverage over deserts and snow/ice
**Collection 5** will have less bias and include over desert retrieval (deep blue)
- MISR (version x):
- No systematic bias over land
- Data coverage limited over regions where clouds often in presence due to its sparse spatial coverage
- Over Asia the AOT seem too low – “hit or miss” problem

## These comparisons have revealed:

- GOCART (version 4.0):
- Biomass burning emissions seem too low in most cases (GFED v2) – currently several different methods being investigated
- Dust is higher than MISR in the source regions
- Maritime AOT lower than MODIS and MISR, but difficult to verify since no AERONET data
- No systematic bias compared with AERONET

## Questions from me

- Data fusion: is “MODIS over ocean + MISR over land” the best combination to represent the global aerosol picture?
- Data assimilation: What to assimilate?  How to maintain the consistency between optical measurements with aerosol composition and mass concentrations?
- Inversion: If satellite data are biased and model processes are uncertain, how can we get the source right through inversion?

## Last but not least: AOT is only a part of the problem...

- While satellite data (from passive sensors) have “forced” models to improve their simulations of AOT, their provide little constraints on 3 major quantities that diversify the modeling community (e.g. AEROCOM results):
- Vertical distributions
- Aerosol composition
- Aerosol hygroscopic property (water up take)

## Looking forward to next year – 

- Vertical data from CALIPSO
- Absorbing AOT from OMI
- Improved version(s) from MODIS and MISR
- Field measurement data
- Chemical composition
- RH effects
- Wet removal (very little data!!!)
- Model improvements (as always)
- Emission, emission, emission (biomass burning, sea-salt, dust)
- Higher resolution simulations (1.25° x 1°)
- Hemispheric Transport of Atmospheric Pollutants (HTAP) experiments – Aerosol + CO