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Seasonal and spatial variations of global aerosol optical depth: multi-year modelling with GEOS-Chem-APM and comparisons with multiple-platform observations Cover

Seasonal and spatial variations of global aerosol optical depth: multi-year modelling with GEOS-Chem-APM and comparisons with multiple-platform observations

By:  and    
Open Access
|Jan 2015

Figures & Tables

Fig. 1

(a) Time series of global mean AOD from the GEOS-Chem-APM, and MODIS, MISR and SeaWiFS from 2004 to 2012. The results from 16 AeroCom models are also marked in the figure; (b) time series of AOD over ocean; (c) over land; (d) time series of total AOD for clear sky and all sky, as well as the contributions of each aerosol components.

Table 1. Comparisons of AOD in clear sky from the GEOS-Chem-APM with satellite data MODIS, MISR and SeaWiFS, and comparisons in 72 AERONET sites having at least 36 months data available between the model and the observations. The AERONET sites with low quality and low spatial domain are also excluded

GEOS-Chem-APMMODISMISRSeaWiFSAERONETGlobal0.1020.1540.1680.130Land0.1350.1880.1980.188Ocean0.0880.1390.1570.110Low AOD (<0.1)0.0550.0690.0750.066Median AOD (0.1,0.3)0.1480.1560.1620.158Large AOD (>0.3)0.5060.4710.4600.45872 AERONET sites0.1680.1920.2060.1930.223
Fig. 2

Multi-year averaged AOD from MODIS, MISR, and SeaWiFS (a, b, c), and absolute differences between the GEOS-Chem-APM and MODIS, MISR and SeaWiFS in clear sky (d, e, f).

Fig. 3

Seasonal mean differences of AOD between the GEOS-Chem-APM and MODIS (a, d, g, j), MISR (b, e, h, k), and SeaWiFS (c, f, i, l) in DJF, MAM, JJA and SON.

Fig. 4

Locations of 72 AERONET sites used in the study.

Table 2. Comparisons of aerosol optical depth (AOD) between the GEOS-Chem-APM (τ1), and AERONET (τ2), MODIS/MISR (τ3), SeaWiFS (τ4) in North America, where d is the absolute difference between the model and observations, and R correlation coefficient and s standard derivation. The negative differences are marked in blue, and the positive are marked in red. The R in black denotes that the correlation coefficient pass the Student's t-test (α=0.01), otherwise in yellow. Mean values and standard deviation are also summarised in the bottom rows. The results are listed from low to high AOD observed from AERONET

APM vs. AERONETAPM vs. MODIS/MISRAPM vs. SeaWiFSτ1τ2dRsτ1τ3DRsτ1τ4dRs1. Frenchman0.050.07−0.020.570.030.050.24−0.190.880.050.050.18−0.140.820.112. Sevilleta0.040.07−0.020.750.020.040.12−0.080.760.030.040.050.000.440.043. White_San0.050.07−0.020.830.020.050.22−0.170.860.050.050.050.000.450.034. Trinidad_H0.080.09−0.010.410.050.080.12−0.040.380.040.080.080.000.680.035. Maricopa0.060.09−0.030.720.020.060.15−0.100.710.040.060.10−0.040.570.046. Halifax0.080.10−0.030.280.040.070.11−0.040.250.070.080.09−0.010.280.047. Bratts_Lake0.090.10−0.010.320.050.080.15−0.070.720.050.090.12−0.030.720.068. CARTEL0.090.12−0.030.520.050.090.10−0.010.440.070.100.12−0.020.480.079. Howland0.090.12−0.030.330.090.080.080.010.320.050.090.11−0.020.290.0710. KONZA_0.110.13−0.030.760.040.100.100.000.570.040.110.12−0.010.800.0511. Sioux_Fall0.110.13−0.030.690.050.110.110.000.440.050.110.110.000.590.0612. Cart_Site0.100.13−0.030.640.050.100.100.000.760.030.110.090.020.690.0413. Thompson0.100.15−0.050.470.070.100.11−0.010.560.050.100.110.000.630.0414. Ames0.130.15−0.020.750.050.120.110.010.440.040.130.14−0.010.780.0515. BONDVI0.130.17−0.040.820.060.130.13−0.010.520.060.130.16−0.030.670.0716. GSFC0.130.19−0.070.840.090.120.15−0.030.730.080.130.20−0.070.820.0517. SERC0.120.19−0.070.770.100.120.15−0.030.790.060.130.20−0.070.820.0518. MD_Scien0.130.20−0.080.820.090.120.14−0.020.750.060.130.19−0.060.690.10Mean0.090.130.030.630.050.090.130.040.600.050.100.120.030.620.06Std0.030.040.020.190.030.030.040.060.190.010.030.050.040.170.02
Fig. 5

Seasonal cycle (a and c) from the GEOS-Chem-APM, AERONET, MODIS and MISR in site BONDVILLE and GSFC over North America. The error bars over the curves denote the standard deviations. Time series of AOD contributed from each aerosol component and comparisons with AERONET from 2004 to 2012 (b and d).

Table 3. Same as Table 2, but for Europe

APM vs. AERONETAPM vs. MODIS/MISRAPM vs. SeaWiFSτ1τ2dRsτ1τ3dRsτ1τ4dRs1. Caceres0.130.120.010.530.060.130.090.040.520.060.140.110.020.330.072. Ersa0.150.130.020.540.050.160.17−0.010.510.060.170.130.030.570.053. Toulon0.150.140.010.700.050.150.100.050.730.040.160.120.040.640.054. Le_Fauga0.130.15−0.020.560.050.130.120.010.580.050.130.15−0.010.540.065. Minsk0.160.160.000.410.070.160.160.000.400.070.160.18−0.020.380.096. Munich_U0.160.17−0.010.470.060.170.140.030.570.050.190.130.060.510.057. Sevastopol0.180.19−0.010.430.070.180.150.030.470.060.190.170.010.590.068. IFT-Leipzi0.190.190.000.640.050.200.170.030.520.060.220.190.030.290.089. Kyiv0.170.20−0.040.290.080.170.170.000.330.060.180.20−0.020.280.0810. Moldova0.190.20−0.010.590.050.190.130.060.460.060.210.160.040.360.0611. Brussels0.170.22−0.040.540.080.190.180.020.370.100.210.190.010.470.0712. Thessalon0.170.24−0.070.320.100.180.19−0.010.570.070.190.180.000.570.08Mean0.160.180.010.500.060.170.150.020.500.060.180.160.020.460.07Std0.020.040.030.120.020.020.030.020.110.010.030.030.030.130.01
Fig. 6

Same as Fig. 5 but for AERONET site Ersa and Toulou over Europe.

Table 4. Same as Table 2, but for East Asia

APM vs. AERONETAPM vs. MODIS/MISRAPM vs. SeaWiFSτ1τ2dRsτ1τ3dRsτ1τ4dRs1. Shirahama0.200.29−0.090.580.090.190.26−0.070.590.080.200.21−0.020.640.072. Xinglong0.410.340.060.820.110.420.260.160.760.120.430.200.220.650.153. Gosan_SN0.340.35−0.010.310.150.320.33−0.020.510.110.320.310.010.620.114. SACOL0.080.38−0.300.410.100.080.25−0.170.590.060.080.12−0.050.600.085. Anmyon0.370.46−0.090.320.220.350.37−0.020.500.160.350.40−0.060.660.136. XiangHe0.580.79−0.210.670.240.570.470.100.720.190.560.430.130.240.247. Taihu0.550.84−0.290.350.260.530.64−0.100.310.220.570.500.060.320.17Mean0.360.490.130.490.170.350.370.020.570.130.360.310.040.530.14Std0.180.230.140.200.070.180.140.110.150.060.180.140.100.180.06
Fig. 7

Same as Fig. 5 but for AERONET site Xianghe and Anmyon over East Asia.

Table 5. Same as Table 2, but for North Africa

APM vs. AERONETAPM vs. MODIS/MISRAPM vs. SeaWiFSτ1τ2dRsτ1τ3dRsτ1τ4dRs1. Izana0.190.060.130.590.080.190.20−0.010.690.080.180.160.020.750.072. La_Laguna0.190.150.040.760.070.190.20−0.020.690.080.180.160.020.750.073. Santa_Cruz0.180.160.020.740.060.190.20−0.020.690.080.180.160.020.750.074. Danker0.570.440.120.530.270.550.480.070.510.290.570.440.130.560.255. DMN_Mai0.570.490.080.110.390.530.520.010.160.340.570.540.030.260.356. IER_Cinza0.450.49−0.030.520.260.460.450.010.280.330.510.510.000.460.277. Agoufou0.530.520.010.550.230.510.460.040.510.260.500.460.040.660.228. Banizoumb0.560.57−0.010.430.400.530.55−0.020.210.450.550.530.020.410.409. Ilorin0.360.66−0.300.650.440.520.490.030.370.600.550.74−0.190.380.56Mean0.400.390.010.540.240.410.390.010.460.280.420.410.010.550.25Std0.170.210.130.190.150.170.150.030.210.180.180.210.080.180.17
Fig. 8

Same as Fig. 4 but for AERONET site IER_Cinzana and Saada over North Africa.

Fig. 9

Same as Fig. 4 but for AERONET site Mongu and Skukuza over South Africa.

Table 6. Same as Table 2, but for South Africa

APM vs. AERONETAPM vs. MODIS/MISRτ1τ2dRsτ1τ3dRs1. Skukuza0.120.20−0.080.740.060.130.130.000.480.062. Mongu0.140.23−0.090.850.100.150.18−0.030.720.07Mean0.130.220.090.800.080.140.160.020.600.07Std0.010.020.010.080.030.010.040.020.170.01

Table 7. Same as Table 2, but for South America

APM vs. AERONETAPM vs. MODIS/MISRτ1τ2dRsτ1τ3dRs1. CEILAP-RG0.050.020.030.540.010.050.040.010.210.022. Trelew0.070.040.030.210.040.060.09−0.030.650.043. CEILAP-BA0.090.11−0.03−0.150.060.090.090.000.270.054. Campo_Grande_0.110.17−0.060.820.130.090.090.000.780.085. Rio_Branco0.080.24−0.160.700.250.130.26−0.130.810.246. CUIABA-MIRA0.150.27−0.120.880.220.120.15−0.030.850.127. Alta_Floresta0.180.34−0.160.850.290.190.27−0.140.730.24Mean0.100.170.070.550.140.100.140.050.610.11Std0.050.120.080.390.110.050.090.060.260.09
Fig. 10

Same as Fig. 4 but for AERONET site Alta_Floresta and Campo_Grande_SO over South America.

Language: English
Page range: 25115 - 25115
Submitted on: Jun 4, 2014
Accepted on: Jun 14, 2015
Published on: Jan 1, 2015
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2015 Xiaoyan Ma, Fangqun Yu, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.