Quantitative assessment of AOD from 17 CMIP5 models based on satellite-derived AOD over India

Misra, Amit; Kanawade, Vijay P.; Tripathi, Sachchida Nand

doi:10.5194/angeo-34-657-2016

Cited by 16 publications

(12 citation statements)

References 73 publications

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“…The larger MODIS AOD compared to MISR over India has been reported in many previous studies (e.g. Misra et al [10], Ramachandran and Kedia [41], David et al [42]). We show that AOD climatologies from MISR are also more stable than those from MODIS over these two regions.…”

Section: Discussionsupporting

confidence: 49%

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How Long should the MISR Record Be when Evaluating Aerosol Optical Depth Climatology in Climate Models?

et al. 2018

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This study used the nearly continuous 17-year observation record from the Multi- angle Imaging SpectroRadiometer (MISR) instrument on the National Aeronautics and Space Administration (NASA) Terra Earth Observing System satellite to determine which temporal subsets are long enough to define statistically stable speciated aerosol optical depth (AOD) climatologies (i.e., AOD by particle types) for purposes of climate model evaluation. A random subsampling of seasonally averaged total and speciated AOD retrievals was performed to quantitatively assess the statistical stability in the climatology, represented by the minimum record length required for the standard deviation of the subsampled mean AODs to be less than a certain threshold. Our results indicate that the multi-year mean speciated AOD from MISR is stable on a global scale; however, there is substantial regional variability in the assessed stability. This implies that in some regions, even 17 years may not provide a long enough sample to define regional mean total and speciated AOD climatologies. We further investigated the agreement between the statistical stability of total AOD retrievals from MISR and the Moderate Resolution Imaging Spectroradiometer (MODIS), also on the NASA Terra satellite. The difference in the minimum record lengths between MISR and MODIS climatologies of total AOD is less than three years for most of the globe, with the exception of certain regions. Finally, we compared the seasonal cycles in the MISR total and speciated AODs with those simulated by three global chemistry transport models in the regions of climatologically stable speciated AODs. We found that only one model reproduced the observed seasonal cycles of the total and non-absorbing AODs over East China, but the seasonal cycles in total and dust AODs in all models are similar to those from MISR in Western Africa. This work provides a new method for considering the statistical stability of satellite-derived climatologies and illustrates the value of MISR’s speciated AOD data record for evaluating aerosols in global models.

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Section: Discussionsupporting

confidence: 49%

“…Pan et al [4] used satellite-derived AOD averaged for the eight years between 2000 and 2007. The six-year mean AOD between 2000 and 2005 was compared with model simulations in Misra et al [10]. More recent studies define AOD climatologies as the mean of AOD over a longer period of time than 10 years.…”

Section: Introductionmentioning

confidence: 99%

How Long should the MISR Record Be when Evaluating Aerosol Optical Depth Climatology in Climate Models?

et al. 2018

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“…Recent surface and aircraft measurements reported similar high CCN concentration (2,000–6,000/cm 3 ) within the boundary layer during monsoon season over the GB (Bhattu & Tripathi, ; Dumka et al, ; Roy et al, ; Sarangi et al, ; Srivastava et al, ). Moreover, satellite observations and global modeling studies indicated similar spatial differences in aerosol loading over North India during monsoon season (Misra et al, , and references therein). Thus, this methodology of prescribing CCN emission fluxes as surface source in each grid box and allowing them to physically move around with the meteorological forcing is able to mimic reasonably well the spatial and vertical distribution of CCN over North India.…”

Section: Data Set and Model Detailsmentioning

confidence: 79%

Aerosol and Urban Land Use Effect on Rainfall Around Cities in Indo‐Gangetic Basin From Observations and Cloud Resolving Model Simulations

et al. 2018

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Coupling of urban land use land cover (LULC) and aerosol loading on rainfall around cities in the Gangetic Basin (GB) is examined here. Long‐term observations illustrate more rainfall at urban core and climatological downwind regions compared to the upwind regions of Kanpur, a metropolitan area located in central GB. In addition, analysis of a 15 day cloud resolving simulation using the Weather Research and Forecasting model also illustrated similar rainfall pattern around other major cities in the GB. Interestingly, the enhancement of downwind rainfall was greater than that over urban regions, and it was positively associated with both the urban area of the city and ambient aerosol loading during the propagating storm. Further, to gain a process‐level understanding, a typical storm that propagated northwestward across Kanpur was simulated using Weather Research and Forecasting under three different scenarios. Case 1 has realistic LULC representation of Kanpur, while the grids representing the Kanpur urban region were replaced by cropland LULC pattern in Case 2. Comparison illustrated that urban heat island effect caused convergence of winds and moisture in the lower troposphere, which enhances convection over urban region and induced more rainfall over the urban core compared to upwind regions. Case 3 is similar to Case 1 but lower aerosol concentration (by a factor of 100) over the storm region. Analysis shows that aerosol‐induced microphysical changes delay the initiation of warm rain (over the upwind region) but enhance ice phase particle formation in latter stages (over the urban and downwind regions) resulting in increase in downwind rainfall.

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“…The influences of anthropogenic land use and land cover change are also not included in the regression model. Anthropogenic land use and land cover change has been found to play an important role in long-term dust variability in some regions (e.g., Neff et al, 2005Neff et al, , 2008Moulin and Chiapello, 2006;McConnell et al, 2007), although previous modeling studies found its influences on future dust emissions to be minor compared to climate change . Thus the projection made by the regression model Figure 9.…”

Section: Regionmentioning

confidence: 99%

“…They found most CMIP5 models, except two HadGEM2 models, underestimated dust load over the Indo-Gangetic Plains and suggested the biases are due to a misrepresentation of 850 hPa winds in the models. Later, Misra et al (2016) also examined CMIP5-modeled AOD for India but did not specifically focus on dust. Shindell et al (2013) examined the output of 10 models from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) for 1 year 2000, among which eight models also participated in the CMIP5.…”

Section: Introductionmentioning

confidence: 99%

How reliable are CMIP5 models in simulating dust optical depth?

Ginoux

2018

Atmos. Chem. Phys.

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Abstract. Dust aerosol plays an important role in the climate system by affecting the radiative and energy balances. Biases in dust modeling may result in biases in simulating global energy budget and regional climate. It is thus very important to understand how well dust is simulated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) models. Here seven CMIP5 models using interactive dust emission schemes are examined against satellite-derived dust optical depth (DOD) during 2004–2016. It is found that multi-model mean can largely capture the global spatial pattern and zonal mean of DOD over land in present-day climatology in MAM and JJA. Global mean land DOD is underestimated by −25.2 % in MAM to −6.4 % in DJF. While seasonal cycle, magnitude, and spatial pattern are generally captured by the multi-model mean over major dust source regions such as North Africa and the Middle East, these variables are not so well represented by most of the models in South Africa and Australia. Interannual variations in DOD are not captured by most of the models or by the multi-model mean. Models also do not capture the observed connections between DOD and local controlling factors such as surface wind speed, bareness, and precipitation. The constraints from surface bareness are largely underestimated while the influences of surface wind and precipitation are overestimated. Projections of DOD change in the late half of the 21st century under the Representative Concentration Pathways 8.5 scenario in which the multi-model mean is compared with that projected by a regression model. Despite the uncertainties associated with both projections, results show some similarities between the two, e.g., DOD pattern over North Africa in DJF and JJA, an increase in DOD in the central Arabian Peninsula in all seasons, and a decrease over northern China from MAM to SON.

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Quantitative assessment of AOD from 17 CMIP5 models based on satellite-derived AOD over India

Cited by 16 publications

References 73 publications

How Long should the MISR Record Be when Evaluating Aerosol Optical Depth Climatology in Climate Models?

How Long should the MISR Record Be when Evaluating Aerosol Optical Depth Climatology in Climate Models?

Aerosol and Urban Land Use Effect on Rainfall Around Cities in Indo‐Gangetic Basin From Observations and Cloud Resolving Model Simulations

How reliable are CMIP5 models in simulating dust optical depth?

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