# Presentation: 239042

## Using Visibility Observations as a proxy for dust emissions: Questions and Challenges

- Karen E. Kohfeld
- NOW AT: School of Earth and Environmental Sciences,
- Queens College, City University of New York
- Ina Tegen
- NOW AT: Institute for Tropospheric Research, Leipzig Germany
- Marith Reheis
- USGS, Denver, Colorado
- Grant McTainsh
- Griffith University, Australia

- Special thanks to: Gerhard Boenisch (MPI-Biogeochemistry); Ajay Rambaran (Queens College); Natalie Mahowald (NCAR)
- Funding: SEES at Queens College, Max Planck Institute for Biogeochemistry, PSC-CUNY grant

## How will dust emissions change in future?

*Tegen et al., 2004*

- Changes for natural soils
- between 1990 and 2050

- NCAR Results:
- 60% REDUCTION
- by 2090
*(Mahowald and Luo, 2003)*

- Results are model-dependent

## Global contribution of anthropogenically-disturbed soils to dust emissions

| IPCC, 2001 | Up to 50% | tuned to AVHRR AOT |
| --- | --- | --- |
| Prospero et al. 2002 | small | Natural sources dominant |
| Luo et al. 2003 | 0-50% | New desert sources |
| Tegen et al., 2004 | <10% | Comparing simulated and observed dust storm frequencies |
| Mahowald et al. subm. | 0-50% | Comparing simulated and observed dust storm frequencies |

- Large Uncertainties!

## Key Scientific Questions:

- What controls global dust emissions?
- How will global dust emissions change in the future?
- What is the anthropogenic component of these dust emissions?

- Nuts and Bolts Questions:
- How do we estimate global dust emissions?
- How have they changed through time?

## Visibility Data  Dust Storms  Emissions

- Visibility Data  Dust Storms  Emissions

- Dust storm frequencies (DSF)
- Used for decades for regional studies!
- International Station Meteorological Climate Summary (ISMCS)  GLOBAL COVERAGE

*Engelstaedter et al., 2003*

## ISMCS Dust Storm Frequencies

- Dust Storm = event when visibility is reduced to < 1km due to presence of dust
- Pre-processed climatology
- 15-25 years average (variable, but >8 years)
- ~1970-1990
- Screened to eliminate impact of transport, urban contamination

## Estimating Impact of Cultivation

- KG01: Klein Goldewijk, 2001
- RF99: Ramankutty and Foley, 1999

- Cultivation maps

- Compare observed dust storm frequencies with natural vegetation and cultivated areas

## Observed differences between dust storm frequencies in different source regions 

*Tegen et al., 2004*

- Observed differences between dust storm frequencies in different source regions

- Locations grouped according to vegetation cover
- Small but significant increase in DSF
- in cultivated areas with >10% vegatation cover

## Model without enhanced dust from agricultural areas underestimates DSFs

- MODEL requires <10% increase in dust events in cultivated areas to match observations
- (lowering the surface wind stress threshold by 0.93 and 0.77 in rangelands or croplands, respectively)

## EITHER THAT OR: 0-50% anthropogenic contributions give statistically indistinguishable agreement with DSFs

- ANALYSIS:
- 		Calculate visibility reduction for direct comparison
- 		Vary global emission factors for natural / 	cultivation sources
- METEOROLOGY: Different winds data
- MODEL: Different emission, transport model scheme
- DATA: Different subsets of data used in comparison

*Mahowald et al., submitted*

**Notes:**
Provides innovative means of comparing dsf with modeled visibility reduction
Primary differences are the use of an entirely different modeling scheme, and use of data: Tegen et al. exclude DSF in regions with <10% veg cover because cultivated regions have LOWER DSFs in this area.

## “If you torture the data long enough, it will confess to anything...”					- attributed to Mark Cane, LDEO

- PROBLEM
- Amount of data too limited to produce statistically significant results:
- Regional comparisons not possible

- N

## Using historical visibility data instead of a climatology

- Integrated Surface Hourly (ISH) Data
- 1900-2004

- Fill in the gaps temporally?
- Temporal variability in DSF
- Regional
- Global

- Annual dust storm frequency

- Australia, 1960-2002

## Applications to the SW USA

- Simulated Natural Vegetation, 1990-1999

- (LPJ model)

- Simulated Increase in Vegetation Cover,
- 1990-1999 minus 1955-1965

## Key Issues:

- Transport
- How to eliminate “false positives”
- Humidity
- Water absorption by aerosols impacts light scattering coefficents
- Contamination
- Visibility impacted by smoke, organic aerosols, sulfates
- Quantification or Calibration
- How does visibility reduction due to dust compare with actual dust concentration data?

**Notes:**
Transport: (1) screening, (2) innovative modeling of fine scale
Humidity: applying humidity filters?  Will this differ depending on components of PM2.5? Previous studies applied a vegetation cutoff. Other studies eliminate visibility disturbance when other events (precip?)
Contamination: smoke codes? Either the stations need to be filtered for locations / time of year when it is known that the main contribution is from dust, or take other sources of information into account, like IMPROVE 
Quantification: see next slide

## Southwest USA

- Interagency Monitoring of Protected Visual Environments (IMPROVE) Stations that have >7 years data, and neighboring ISH meteorological station

**Gila Wilderness (NM) Lat. 33.2204 Long. -108.2351 Elev. 1776.0**

- 0

- 5

- 10

- 15

- 20

- 25

- 30

- 1/4/1995

- 5/4/1995

- 9/4/1995

- 1/4/1996

- 5/4/1996

- 9/4/1996

- 1/4/1997

- 5/4/1997

- 9/4/1997

- 1/4/1998

- 5/4/1998

- 9/4/1998

- 1/4/1999

- 5/4/1999

- 9/4/1999

- 1/4/2000

- 5/4/2000

- 9/4/2000

- 1/4/2001

- 5/4/2001

- 9/4/2001

- 1/4/2002

- 5/4/2002

- 9/4/2002

- 1/4/2003

- 5/4/2003

**Date**

**ug/m3**

- PM 2.5

- PM 2.5-10

- PM 10

- 22 per. Mov. Avg. (PM 10)

- Red: IMPROVE
- Blue: ISH

- (figure courtesy of A. Rambaran)

**Notes:**
IMPROVE: PM2.5, PM10 measurements, 19 sites have data from 1988-2003, 8 sites have data from 1995-2003
Measurements currently taken every third day.  Soil mass concentrations estimated by summing the elements predominantly associated with soil (Al, Si, Ca, Fe, Ti).  Malm and colleagues have estimated contributions to visibility reduction on spatial scales.  Perhaps we can compare the soil contribution temporally with the actual visibility reduction.

## Work in Progress!

- Mundane Logistic Issues
- Coding changes
- 1995 change to automation
- 1960 weather code changes in Australia
- Spatial differences in weather code reporting
- NCDC coding changes
- 7 years of ISH data = 100 Gigabytes
- “Potential surprises”

## Slide 17

## Dust model developed at BGC-Jena: 

**Dust model developed at BGC-Jena:**
- Emission depending on
- preferential source areas (‘hot spots’)
- vegetation cover
- surface wind speed
- soil particle size
- soil moisture, snow cover
- 0.5ox0.5o resolution, 6h timestep
- Transport: TM3 model
- Deposition: Dry and wet

## Regional Dust Emission Changes: Projection for 2050

- Tegen et al., 2004:
-           HADCM, ECHAM
- Mahowald and Luo, 2003:
-          NCAR

## What Controls Dust Emissions

- _MODELED NATURAL EMISSIONS:_
- Preferential sources
- Vegetation cover / type
- Surface wind speed
- Soil particle size
- Soil moisture

- _HUMAN IMPACTS:_
- Soil surfaces
- Cultivation
- Overgrazing
- Deforestation
- Roads, construction, military activity
- Climate
- Meteorology (winds, precip)
- Natural vegetation