Comparisons to Aeronet

For a local comparison to AERONET-site statistics, the monthly averages of models at the 4 grid-points closest to each AERONET-site have been interpolated (with inverse distance weights). Deviations of these interpolated aerosol optical depth averages on a quarterly basis with respect to the AERONET statistics are summarized from all five models in Table 7. Table 8 extends comparisons involving particular ECHAM4 and GOCART versions. Deviations of Tables 7 and 8 are supported by more detailed model comparisons of monthly (rather than quarterly) averages, however only for the four sites of Figures 2, in Figures 5. Vertical bars over symbols in Figures 5 display the uncertainty of AERONET monthly averages based on year-to-year variability (see Figures 2 and 3).

The AERONET vs. model comparisons assumed (despite linear interpolation from the four closest grid-points) that local monthly averages are comparable to regional averages. The validity of this assumption was tested in comparing satellite retrieved optical depths for different spatial resolutions near each site (see Table 3). In that comparison, trends remained largely inconclusive. Thus, only deviations of mid-visible aerosol optical depths that exceeded +/-0.1 should be discussed (solid arrows in Tables 7 and 8).

Table 7. Model based deviations for quarterly (1: J,F,M / 2: A,M,J / 3: J,A,S / 4: 0,N,D) averaged aerosol optical depths with respect to AERONET statistics. Results of five different models

Model

ECHAM 4

MIRAGE

GOCART

GISS

CCSR

yearly quarter

1

2

3

4

1

2

3

4

1

2

3

4

1

2

3

4

1

2

3

4

Mongu

6

6

K

K

6

6

K

K

6

6

3

3

6

-

3

K

6

-

-

6

Sevileua

-

=

6

=

5

=

=

=

S

5

5

S

6

6

6

6

5

5

5

5

Cm aba

-

6

K

K

-

5

K

K

-

6

K

K

-

-

K

K

-

6

K

K

Bamzoumbou

K

K

K

K

K

K

K

K

#

3

S

3

3

K

K

K

a

K

K

6

Waskesiu

-

3

6

=

-

3

6

5

-

6

=

5

-

3

6

-

-

K

3

6

Goddard

5

6

3

5

#

#

#

5

5

-

6

5

=

6

3

5

6

6

3

-

tspra

3

3

6

6

3

3

6

3

3

6

5

6

3

6

=

3

K.

3

3

3

Cart-si ic

=

6

3

5

5

S

6

5

5

6

3

5

=

-

6

5

6

3

3

6

Cape Verde

K

K

K

K

K

K

K

K

5

-

6

-

3

B

K

K

#

3

K.

3

Bermuda

5

6

6

=

5

S

6

=

6

-

-

=

6

6

6

6

b

6

6

6

Dry Tonugas

6

3

3

-

=

6

6

--

-

6

3

6

-

6

3

6

6

3

3

6

Lanai

=

6

-

-

5

-

6

6

m

=

6

6

6

6

Dakar

K

K

K

6

K

K

K

6

3

K

K

#

K

K

K.

6

5

K

K

=

Kaashidhoo

B

3

6

6

3

3

6

6

K

6

5

6

3

3

6

6

3

3

=

6

Bahrain

3

3

6

6

3

3

3

3

#

#

#

#

6

5

5

6

6

b

3

3

Barbados

6

3

3

6

6

3

3

6

5

6

S

=

-

3

3

6

6

3

3

6

(Kc-0.2, -0.2<3<-0.1, -0.1<6<-0.02, -0.02< = <0.02, 0.02<5<0.1,0.1<#<0.2,0.2<S / - : no data)

(Kc-0.2, -0.2<3<-0.1, -0.1<6<-0.02, -0.02< = <0.02, 0.02<5<0.1,0.1<#<0.2,0.2<S / - : no data)

Table 8. Model based deviations for quarterly (I: J,F,M / 2: A,M,J / 3; J,A,S / 4: O.N.D) averaged aerosol optical depths with respect to AERONET data. Deviations of modified ECHAM4 and GOCART versions. For GOCART5 the horizontal resolution of GOCART was relaxed from 2*2.5deg to 5*5deg lat/longitudc. ECHAM4-old prescribes dust (Westphal 1988) and sea-salt (Monahan 1986) fields, rather than predicting the fields as in ECHAM4. ECHAM4-clr, a subset of ECHAM4, only considers grid-cells (ca. 300*300km) with low liquid water content (less than 33g/m2)____

Table 8. Model based deviations for quarterly (I: J,F,M / 2: A,M,J / 3; J,A,S / 4: O.N.D) averaged aerosol optical depths with respect to AERONET data. Deviations of modified ECHAM4 and GOCART versions. For GOCART5 the horizontal resolution of GOCART was relaxed from 2*2.5deg to 5*5deg lat/longitudc. ECHAM4-old prescribes dust (Westphal 1988) and sea-salt (Monahan 1986) fields, rather than predicting the fields as in ECHAM4. ECHAM4-clr, a subset of ECHAM4, only considers grid-cells (ca. 300*300km) with low liquid water content (less than 33g/m2)____

Mod.-version

ECI1AM4, old

F.CHAM4, lyr

ECHAM4, clr

GOCART, old

GOCART,

5

yearly quarter

1

2

3

4

1

2

3

4

1

2

3

4

1

2

3

4

1

2

3

4

Mongu

6

6

K

K

6

6

K.

K

6

6

3

3

6

S

a

3

6

5

#

3

Sevitetta

5

6

6

=

=

6

6

=

=

6

6

=

5

n

5

S

5

#

5

5

Cuiaba

-

6

K

K

-

3

K

K.

-

3

K

K

-

s

3

K

-

5

K

K

Banizoumbou

K

K.

K

K

K

K

K

K

K

K

K

K

S

6

#

9

S

3

5

a

Waskesiu

-

=

6

5

-

3

6

=

-

3

6

6

-

3

S

#

-

6

5

5

Goddard

#

5

6

5

5

6

3

S

=

3

3

=

5

5

6

#

5

-

6

#

Ispra

6

6

3

6

3

3

6

6

3

K

3

3

6

-

#

5

6

=

#

=

Cart-site

5

6

3

-

-

3

3

5

6

3

3

=

#

5

-

5

5

6

3

b

Cape Verde

K

K

K

K

K

K

K

K.

K

K

K

K

#

=

6

b

5

b

3

6

Bermuda

-

6

6

6

5

6

6

=

=

6

6

6

=

5

5

5

-

-

-

-

Dry Tortugas

6

3

3

6

6

3

3

=

6

3

3

6

=

6

6

-

=

6

6

Lanai

6

6

6

b

=

6

=

=

=

6

=

=

S

5

5

5

5

5

5

-

Dakar

K.

K

K

3

K

K.

K

6

K

K

K

6

5

K.

3

#

5

K

K

#

Kaashidhoo

K

3

6

6

K

3

6

6

K

3

6

6

3

6

5

6

3

b

5

6

Bahrain

3

K.

K

3

3

3

6

6

3

3

6

6

H

Ô

a

#

5

it

a

#

Barbados

6

3

3

6

6

3

3

6

6

3

3

6

5

6

-

tt

5

6

m

=

(Kc-0.2, -0.20C-0.1, -0.K6c-0.02, -0.02c = 0.02,0.02<5<0.1,0.lc#c0.2, 0.2<8 / - : no data)

(Kc-0.2, -0.20C-0.1, -0.K6c-0.02, -0.02c = 0.02,0.02<5<0.1,0.lc#c0.2, 0.2<8 / - : no data)

Figure 5a-d. Model deviations with respect to the statistics from AERONET for mid-visible aerosol optical depths monthly averages. Model data have been spatially interpolated (from the resolution of each models - see Table 6) Presented are model deviations to multi-year AERONET data (large symbols) and with respect to the range in AERONET averages (vertical bars) - unless AERONET monthly averages were not available (small dark symbol). For an improved comparison, deviation-distinguishing criteria [+/-0.02 and +/- 0.1] of Tables 7 and 8 are indicated (horizontal lines). Comparisons are presented for the four AERONET-sites of Figures 2 and 4

Figure 5a-d. Model deviations with respect to the statistics from AERONET for mid-visible aerosol optical depths monthly averages. Model data have been spatially interpolated (from the resolution of each models - see Table 6) Presented are model deviations to multi-year AERONET data (large symbols) and with respect to the range in AERONET averages (vertical bars) - unless AERONET monthly averages were not available (small dark symbol). For an improved comparison, deviation-distinguishing criteria [+/-0.02 and +/- 0.1] of Tables 7 and 8 are indicated (horizontal lines). Comparisons are presented for the four AERONET-sites of Figures 2 and 4

FIRST IMPRESSIONS : The tested models tend to underestimate aerosol optical depths with respect to the AERONET statistics. The largest model deviations are underestimates and also underestimates are much more frequent than overestimates with respect to AERONET. All models have difficulties to reproduce the large aerosol optical depths at sites near biomass burning and dust sources, in particular the ECHAM4, MIRAGE and GISS models. The CCSR model has the least underestimates at biomass burning sites, the GOCART model performs best near dust sources, and the Mirage model usually suggests the largest optical depths at urban industrial sites.

Differences among the five models are large, often exceeding half of the value suggested by AERONET. Differences also change from month to month. In contrast, differences are less significant from changes in grid-spacing, based on a GOCART [2-by-2.5deg] vs. GOCART5 [5-by-5 deg] comparison. Thus, differences in-grid spacing among of the five models seem less important. Also small in comparison to the model spread are differences between ECHAM4 and its cloud-free subset, ECHAM4-clr . Larger reductions to aerosol optical depths of ECHAM4 occur mainly at urban-industrial sites and mainly during the winter when ambient relative humidity is largest. Clear-sky data sub-sets, like ECHAM4-clr, were only available for the ECHAM4 model, although such subsets seems a better match to AERONET statistics, with its conservative cloud-screening. Thus, all-sky data of models are expected to exceed AERONET averages, at least at urban-industrial sites. For most models this would increase the differences to AERONET data.

Prescribed fields for sea-salt and dust in ECHAM4-old, rather than predicted fields in ECHAM4 provide on occasions a better match to AERONET data (see Table 8). This illustrates that an increased complexity will not necessarily reduce uncertainties, at least not initially, as physical processes and feedbacks need to be understood. An evaluation between ECHAM4 and ECHAM4-old reveals that better agreement for a prescribed treatment at some sites is created by compensating errors (e.g. underestimates in dustand carbon-aerosol are partially compensated by sea-salt overestimates). This illustrates, that an evaluation of the model performance has to be conducted on an aerosol sub-component basis.

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