aerosol modeling using the giss modele

17
Second ICAP Workshop Aerosol Modeling using Aerosol Modeling using the GISS modelE the GISS modelE Sophia Zhang, Dorothy Koch, Susanna Bauer, Reha Cakmur, Ron Miller, Jan Perlwitz Nadine Bell NASA/GISS, New York

Upload: liang

Post on 11-Jan-2016

41 views

Category:

Documents


0 download

DESCRIPTION

Aerosol Modeling using the GISS modelE. Sophia Zhang, Dorothy Koch, Susanna Bauer, Reha Cakmur, Ron Miller, Jan Perlwitz Nadine Bell NASA/GISS, New York. GISS Aerosol Transport Model. The new GISS model version “modelE” ( Schmidt et al., submitted to J. Clim.). - PowerPoint PPT Presentation

TRANSCRIPT

Page 1: Aerosol Modeling using the GISS modelE

Second ICAP Workshop

Aerosol Modeling using the Aerosol Modeling using the GISS modelEGISS modelE

Sophia Zhang, Dorothy Koch, Susanna Bauer, Reha Cakmur,

Ron Miller, Jan Perlwitz

Nadine Bell

NASA/GISS, New York

Page 2: Aerosol Modeling using the GISS modelE

GISS Aerosol Transport ModelGISS Aerosol Transport Model

The new GISS model version “modelE” (Schmidt et al., submitted to J. Clim.).

Improved radiative treatment, including relative-humidity dependence of optical properties for sulfate, sea salt, and OC.

Implementation of dissolved species budget (DSB) with stratiform cloud (Koch et al., JGR 2003, Koch et al., in prep).

Closer coupling between species and the BL. Resolution: 4 o lat x 5 o long, 20 vertical layers with 10

in the troposphere. The model top is 0.1 mb.

Page 3: Aerosol Modeling using the GISS modelE

Emissions

Emission (Tg/yr)

References:

Industrial SO2 52.9 IIASA, Dentener, in prep

Biomass SO2 2.3 Sprio et al., 1992

Volcanic SO2 10.7 GEIA (Andres and Kasgnoc, 1998) scaled by 1.5 Halmer et al., 2002; Graf et al., 1997

DMS 21.2 Kettle et al., 1999; Nightingale et al., 2000

Biomass Burning BC+POM

Cooke and Wilson, 1996

Fossil fuel and biofuel BC+POM

Bond et al., 2004

Aircraft BC 0.01 Baughcum et al.,1993

Dust (4 bins) 1500 Cakmur et al, in prep

Sea Salt (2 bins) 1734.4 Monahan et al., 1986

Page 4: Aerosol Modeling using the GISS modelE

Comparison with observations

Remote sites EMEP IMPROVE

Implementation of DSB results in lower loadings for soluble species, especially for sulfate due to reduced aqueous-phase-produced amount.

Heterogeneous processes may correct the deficient sulfate amounts.

Page 5: Aerosol Modeling using the GISS modelE

The direct radiative forcing for anthropogenic sulfate is reduced to only -0.25 W/m2 because of a lower anthropogenic sulfate loading in this model.

Direct Radiative Forcing

Page 6: Aerosol Modeling using the GISS modelE

The Tropospheric Sulfur CycleThe Tropospheric Sulfur Cycle

DMS

SO2

OtherSO+4

Cloud

OH

NO3 OH

S+4 S+6

H2O2

SO4

OtherSO+6

DUSTO3

LandOcean

The Tropospheric Sulfur CycleThe Tropospheric Sulfur Cycle

Page 7: Aerosol Modeling using the GISS modelE

SO2 is irreversibly absorbed on the dust surface and then oxidized by ozone and forms stable sulfate on the dust surface.

HET assumes 1.e-4 uptaking rate for SO2 on dust.

Heterogeneous Reaction on Dust

SO4 concentrations

CTR (g/m3) HET (g/m3) HET- CTR (g/m3)

Sulfate surface concentrations

Page 8: Aerosol Modeling using the GISS modelE

Comparison with observations.

EMEPEurope

1995-2000

IMPROVEUSA

1995-2003

Winter DJFAnnual MeanEUROPE EUROPE

USAUSA

SO4 concentrations [ppbv]

- CONTROL

- HETERO. CHEM

- HETERO. CHEM - CONTROL

Page 9: Aerosol Modeling using the GISS modelE

Impact on Dust

Heterogeneous reaction can change the dust loading and lifetime. In addition, sulfate, when coated on dust, can change the optical properties of dust.

CTR (mg/m2) HET-CTR (mg/m2)

Page 10: Aerosol Modeling using the GISS modelE

BC Emissions by regionsBC Emissions by regions

Region* Tg yr−1 % burden(Tg) %

Globe 10.68 100 0.222 100.0

Biomass S of 40 oN 5.68 53 0.128 57.7

Biomass N of 40 oN 0.32 3 0.007 3.2

Far East 2.08 19 0.046 20.7

Europe 0.47 4 0.008 3.6

North America 0.39 4 0.007 3.2

Russia 0.21 2 0.005 2.2

Aircraft 0.01 0 0.003 1.3

Rest of World 1.53 15 0.017 7.7

* Region names are not exactly correspond to their geographic labels.

Page 11: Aerosol Modeling using the GISS modelE

Contributions to BC optical depth based on regional source experiments.

Page 12: Aerosol Modeling using the GISS modelE

Climate effects of BC

BC absorbs solar radiation and is thought to warm the climate through aerosol direct effect, and other enhancing mechanisms (semi-direct effect and snow albedo effect).

The climate effect of BC is highly uncertain due the uncertainties in aerosol loading, vertical distribution, and how cloud changes due to BC.

Page 13: Aerosol Modeling using the GISS modelE

Results of BC in each model Layer

Layer Fa (w/m2)

EfficacyChange of Cloud Cover (%)

low mid high

1 0.38 5.56 -1.44 -0.40 0.16

2 0.52 3.09 -1.06 -0.26 0.21

3 0.86 2.29 -1.06 -0.43 0.16

4 1.23 0.82 -0.26 -0.16 0.20

5 1.44 0.47 0.49 -0.29 0.14

6 1.63 0.53 0.62 -0.83 0.15

7 1.91 0.40 1.13 -0.94 -0.41

8 2.25 0.32 1.30 0.27 -1.35

The efficacy is 0.82 for BCI and 0.60 for BCB.

Page 14: Aerosol Modeling using the GISS modelE

Change of Low Cloud Cover

When BC is put in the layer 1, the reduction of low cloud cover over land is smaller than that over ocean on average.

When BC is put in the layer 4, the reduction of low cloud cover occurs mostly overland and the coastal region near source, while most of the increase of low cloud cover occurs over ocean.

BC in Layer 4 (847mb)BC in Layer 1(959mb)

Page 15: Aerosol Modeling using the GISS modelE

Correlation of anomalies of ISCCP low cloud amount and TOMS AI (1983-1993).

The TOMS AI points used for calculating anomalies are those with reflectivity ≤ 15%.

The correlation for low cloud amount is slightly positive for dust and biomass burning region.

Relationship between the modeled ISCCP low cloud amount and AI needs to be studied.

TOMS AI vs. Low Cloud Amount TOMS AI vs. Total Cloud Amount

Page 16: Aerosol Modeling using the GISS modelE

Optimizing Dust Emissions

An optimal global value of dust emission and the aerosol load are calculated by minimizing the difference between the model and multiple observational data sets. (Cakmur et al., in prep)

No single data set is sufficient to constrain the emission. The optimal value represents the influence of all data sets.

The optimal dust emission ranges from 1150 to 2850 Tg/yr using different a priori sources.

The result is sensitive to the a priori source, datasets, and location used

Page 17: Aerosol Modeling using the GISS modelE

Other on-going aerosol projects at GISS Fully interactive tropospheric gas-phase coupling

between sulfur and chemistry has been implemented in the GISS modelE (Bell et al., submitted to JGR).

On annual and global scales, the differences of sulfate burden between the coupled and off-line simulations are small but larger deviations do occur on regional and seasonal scales.

The chemistry-aerosol coupling leads increases in surface sulfate over source regions in the NH summer, with compensating decreases downwind and in the upper troposphere due to depleted SO2.