Assessment of the aerosol distribution over Indian subcontinent in CMIP5 models

Sanap, S. D.; Ayantika, D. C.; Pandithurai, G.; Niranjan, K.

doi:10.1016/j.atmosenv.2014.01.017

Cited by 47 publications

(33 citation statements)

References 62 publications

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“…and India27 during recent decades. A recent study has shown that the CMIP5 models perform significantly different in simulating atmospheric aerosols over India28. This is because the atmospheric aerosol loading was interactively calculated by each individual model10, although the same emission inventory29303132 was used for all of the CMIP5 models7.…”

Section: Resultsmentioning

confidence: 99%

Measurement Biases Explain Discrepancies between the Observed and Simulated Decadal Variability of Surface Incident Solar Radiation

Wang

2014

Sci Rep

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Observations have reported a widespread dimming of surface incident solar radiation (Rs) from the 1950s to the 1980s and a brightening afterwards. However, none of the state-of-the-art earth system models, including those from the Coupled Model Intercomparison Project phase 5 (CMIP5), could successfully reproduce the dimming/brightening rates over China. We find that the decadal variability of observed Rs may have important errors due to instrument sensitivity drifting and instrument replacement. While sunshine duration (SunDu), which is a robust measurement related to Rs, is nearly free from these problems. We estimate Rs from SunDu with a method calibrated by the observed Rs at each station. SunDu-derived Rs declined over China by −2.8 (with a 95% confidence interval of −1.9 to −3.7) W m−2 per decade from 1960 to 1989, while the observed Rs declined by −8.5 (with a 95% confidence interval of −7.3 to −9.8) W m−2 per decade. The former trend was duplicated by some high-quality CMIP5 models, but none reproduced the latter trend.

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Section: Resultsmentioning

confidence: 99%

Measurement Biases Explain Discrepancies between the Observed and Simulated Decadal Variability of Surface Incident Solar Radiation

Wang

2014

Sci Rep

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“…The aerosols originating from the anthropogenic emissions, biomass burning and transported dust in Indian subcontinent are recognized as the major source of pollution (Lau and Kim, ; Lau et al , ; Prasad and Singh, ; Ramanathan et al , , ; Ramanathan and Feng, ; Zhao et al , ; Sanap et al , ). Moreover, the long‐range transport of these pollutants (mainly dust along with other pollution aerosols such as sulphate, nitrate and carbonaceous particles) normally depends on the meteorological conditions, which affects the air quality over the downwind areas.…”

Section: Introductionmentioning

confidence: 99%

Inter‐annual variability of aerosols and its relationship with regional climate over Indian subcontinent

Sanap

Pandithurai

2014

Intl Journal of Climatology

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The spatio‐temporal variability of aerosols over Indian subcontinent is mainly due to transported dust from adjacent deserts (Thar and Middle East deserts), local emission due to anthropogenic activities and prevailing meteorological conditions. On large scale, the quantification of transported and locally emitted aerosol from these regions is complicated. Here, we used empirical orthogonal function (EOF) analysis to identify these regions and their variability by using 33 years of Total Ozone Monitoring Spectrometer (TOMS) satellite data. The maximum variability in aerosol is explained by first two EOF modes (70.31 and 20.57%) over Indian subcontinent. The major aerosol, i.e. transported dust from adjacent deserts confined to NW India and Pakistan, is observed in first leading mode, whereas biomass burning, industrial and dense populated region of southeast and eastern region of the Indo‐Gangetic Plain (IGP) are revealed in the second dominant mode. The EOF analysis is carried out specifically for the pre‐monsoon and monsoon seasons over Indian subcontinent as maximum aerosol loading is observed during this period. The region of NW India, IGP, Pakistan and northern Arabian Sea explains maximum variability in both seasons. The first three leading modes and their relationship with different atmospheric and surface variables are carried out for pre‐monsoon and monsoon seasons. This study explains the potential role of aerosols on reduction in cloudiness, increased shortwave at the ground, land‐surface tropospheric warming and its feedback to other related processes. This study strongly suggests that there is a need for further appropriate observational as well as modelling study on the role of semi‐direct aerosol effect over Indian subcontinent.

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“…While the sources of these emissions are relatively well-known, there are large uncertainties in emission estimates across inventories (e.g., Jena et al, 2015;Zhong et al, 2016). Indeed, correctly simulating the extremely high PM 2.5 abundances in the IGP has proved troublesome for current global chemistry models (Reddy et al, 2004;Mian Chin et al, 2009;Ganguly et al, 2009;Menon et al, 2010;Henriksson et al, 2011;Goto et al, 2011;Nair et al, 2012;Cherian et al, 2013;Moorthy et al, 2013;Sanap et al, 2014;Pan et al, 2015). A multi-model evaluation by Pan et al (2015) concluded that an underestimation of wintertime biofuel emissions was the dominant cause of the models' low biases.…”

Section: Introductionmentioning

confidence: 99%

Exploring the relationship between surface PM<sub>2.5</sub> and meteorology in Northern India

et al. 2018

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Abstract. Northern India (23)(24)(25)(26)(27)(28)(29)(30)(31) • N, 68-90 • E) is one of the most densely populated and polluted regions in world. Accurately modeling pollution in the region is difficult due to the extreme conditions with respect to emissions, meteorology, and topography, but it is paramount in order to understand how future changes in emissions and climate may alter the region's pollution regime. We evaluate the ability of a developmental version of the new-generation NOAA GFDL Atmospheric Model, version 4 (AM4) to simulate observed wintertime fine particulate matter (PM 2.5 ) and its relationship to meteorology over Northern India. We compare two simulations of GFDL-AM4 nudged to observed meteorology for the period 1980-2016 driven by pollutant emissions from two global inventories developed in support of the Coupled Model Intercomparison Project Phases 5 (CMIP5) and 6 (CMIP6), and compare results with ground-based observations from India's Central Pollution Control Board (CPCB) for the period 1 October 2015-31 March 2016. Overall, our results indicate that the simulation with CMIP6 emissions produces improved concentrations of pollutants over the region relative to the CMIP5-driven simulation.While the particulate concentrations simulated by AM4 are biased low overall, the model generally simulates the magnitude and daily variability of observed total PM 2.5 . Nitrate and organic matter are the primary components of PM 2.5 over Northern India in the model. On the basis of correlations of the individual model components with total observed PM 2.5 and correlations between the two simulations, meteorology is the primary driver of daily variability. The model correctly reproduces the shape and magnitude of the seasonal cycle of PM 2.5 , but the simulated diurnal cycle misses the early evening rise and secondary maximum found in the observations. Observed PM 2.5 abundances are by far the highest within the densely populated IndoGangetic Plain, where they are closely related to boundary layer meteorology, specifically relative humidity, wind speed, boundary layer height, and inversion strength. The GFDL AM4 model reproduces the overall observed pollution gradient over Northern India as well as the strength of the meteorology-PM 2.5 relationship in most locations.

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Assessment of the aerosol distribution over Indian subcontinent in CMIP5 models

Cited by 47 publications

References 62 publications

Measurement Biases Explain Discrepancies between the Observed and Simulated Decadal Variability of Surface Incident Solar Radiation

Measurement Biases Explain Discrepancies between the Observed and Simulated Decadal Variability of Surface Incident Solar Radiation

Inter‐annual variability of aerosols and its relationship with regional climate over Indian subcontinent

Exploring the relationship between surface PM<sub>2.5</sub> and meteorology in Northern India

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Assessment of the aerosol distribution over Indian subcontinent in CMIP5 models

Cited by 47 publications

References 62 publications

Measurement Biases Explain Discrepancies between the Observed and Simulated Decadal Variability of Surface Incident Solar Radiation

Measurement Biases Explain Discrepancies between the Observed and Simulated Decadal Variability of Surface Incident Solar Radiation

Inter‐annual variability of aerosols and its relationship with regional climate over Indian subcontinent

Exploring the relationship between surface PM&lt;sub&gt;2.5&lt;/sub&gt; and meteorology in Northern India

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Exploring the relationship between surface PM<sub>2.5</sub> and meteorology in Northern India