Pollution from China increases cloud droplet number, suppresses rain over the East China Sea

Bennartz, Ralf; Fan, Jiwen; Rausch, John; Leung, L. Ruby; Heidinger, Andrew K.

doi:10.1029/2011gl047235

Cited by 55 publications

(46 citation statements)

References 31 publications

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“…Panel (a) shows the mean CDNC over the period -2013 shows the mean relative spatial standard deviation of the CDNC estimates and panel (c) shows the fraction of months with missing data in the climatology. Areas BA1-BA7 are defined in Bennartz (2007), BB1 in Bennartz et al (2011), BC1 in this publication, K01-K09 in Klein and Hartmann (1993), R01-R06 in Rausch et al (2010), and X01-X21 in Zhao et al (2016). The corresponding latitude-longitude boundaries are also listed in Table 3. in Table 3.…”

Section: Regional Resultsmentioning

confidence: 99%

Global and regional estimates of warm cloud droplet number concentration based on 13 years of AQUA-MODIS observations

2017

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Abstract. We present and evaluate a climatology of cloud droplet number concentration (CDNC) based on 13 years of Aqua-MODIS observations. The climatology provides monthly mean 1 × 1 • CDNC values plus associated uncertainties over the global ice-free oceans. All values are incloud values, i.e. the reported CDNC value will be valid for the cloudy part of the grid box. Here, we provide an overview of how the climatology was generated and assess and quantify potential systematic error sources including effects of broken clouds, and remaining artefacts caused by the retrieval process or related to observation geometry. Retrievals and evaluations were performed at the scale of initial MODIS observations (in contrast to some earlier climatologies, which were created based on already gridded data). This allowed us to implement additional screening criteria, so that observations inconsistent with key assumptions made in the CDNC retrieval could be rejected. Application of these additional screening criteria led to significant changes in the annual cycle of CDNC in terms of both its phase and magnitude. After an optimal screening was established a final CDNC climatology was generated. Resulting CDNC uncertainties are reported as monthly-mean standard deviations of CDNC over each 1 × 1 • grid box. These uncertainties are of the order of 30 % in the stratocumulus regions and 60 to 80 % elsewhere.

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Section: Regional Resultsmentioning

confidence: 99%

Global and regional estimates of warm cloud droplet number concentration based on 13 years of AQUA-MODIS observations

2017

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“…The daily mean CDNC at 1 • × 1 • resolution is calculated using filtered level-2 swath data and only low (cloud tops below 3.2 km), liquid clouds were used to calculate CDNC. Only 1 • × 1 • regions where the cloud fraction exceeds 80 % are considered valid (Bennartz et al, 2011) and the CDNC is calculated using the 3.7 µm MODIS channel r e . In the remainder of this paper MERRA2 data are only considered for days and 1 • × 1 • regions when and where MODIS is able to perform a retrieval of CDNC within this set of criteria, notably in the comparison between predicted and observed CDNC in Sect.…”

Section: Methodsmentioning

confidence: 99%

Predicting decadal trends in cloud droplet number concentration using reanalysis and satellite data

et al. 2018

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Abstract. Cloud droplet number concentration (CDNC) is the key state variable that moderates the relationship between aerosol and the radiative forcing arising from aerosolcloud interactions. Uncertainty related to the effect of anthropogenic aerosol on cloud properties represents the largest uncertainty in total anthropogenic radiative forcing. Here we show that regionally averaged time series of the ModerateResolution Imaging Spectroradiometer (MODIS) observed CDNC of low, liquid-topped clouds is well predicted by the MERRA2 reanalysis near-surface sulfate mass concentration over decadal timescales. A multiple linear regression between MERRA2 reanalyses masses of sulfate (SO 4 ), black carbon (BC), organic carbon (OC), sea salt (SS), and dust (DU) shows that CDNC across many different regimes can be reproduced by a simple power-law fit to near-surface SO 4 , with smaller contributions from BC, OC, SS, and DU. This confirms previous work using a less sophisticated retrieval of CDNC on monthly timescales. The analysis is supported by an examination of remotely sensed sulfur dioxide (SO 2 ) over maritime volcanoes and the east coasts of North America and Asia, revealing that maritime CDNC responds to changes in SO 2 as observed by the ozone monitoring instrument (OMI). This investigation of aerosol reanalysis and top-down remote-sensing observations reveals that emission controls in Asia and North America have decreased CDNC in their maritime outflow on a decadal timescale.

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“…The different stratification may be a result of differing cloud dynamics in this region due to a weaker inversion strength, resulting in greater entrainment compared to the other regions which lie in subsidence zones. There is a strong seasonal response to aerosols visible in each channel coincident with the advection of aerosols by the Asian winter monsoon (Bennartz et al, 2011). …”

Section: Regional Common Pixel Comparisonmentioning

confidence: 98%

Differences in liquid cloud droplet effective radius and number concentration estimates between MODIS collections 5.1 and 6 over global oceans

et al. 2017

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Abstract. Differences in cloud droplet effective radius and cloud droplet number concentration (CDNC) estimates inferred from the Aqua-MODIS (Moderate Resolution Imaging Spectroradiometer) collections 5.1 (C5.1) and 6 (C6) cloud products (MYD06) are examined for warm clouds over global oceans for the year 2008. Individual pixel level retrievals for both collections are aggregated to 1 • × 1 • and compared globally and regionally for the three main spectral channel pairs used for MODIS cloud optical property retrievals. Comparisons between both collections are performed for cases in which all three effective radii retrievals are classified by the MODIS cloud product as valid. The contribution to the observed differences of several key MYD06 Collection 6 algorithm updates are also explored, with a focus on changes to the surface reflectance model, assumed solar irradiance, above-cloud emission, cloud-top pressure (CTP), and pixel registration. Global results show a neutral to positive (> 50 cm −3 ) change for C6-derived CDNC relative to C5.1 for the 1.6 and 2.1 µm channel retrievals, corresponding to a neutral to −2 µm difference in droplet effective radius (r e ). For 3.7 µm retrievals, CDNC results show a negative change in the tropics, with differences transitioning toward positive values with increasing latitude spanning −25 to +50 cm −3 related to a +2.5 to −1 µm transition in effective radius. Cloud optical thickness (τ ) differences were small relative to effective radius and found to not significantly impact CDNC estimates. Regionally, the magnitude and behavior of the annual CDNC cycle are compared for each effective radius retrieval. Results from this study indicate significant inter-collection differences in aggregated values of effective radius due to changes to the precomputed retrieval lookup tables (LUTs) for ocean scenes, changes to retrieved cloud-top pressure, solar irradiance, or above-cloud thermal emission, depending upon spectral channel. The observed differences between collections may have implications for existing MODIS-derived climatologies and validation studies of effective radius and CDNC.

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Pollution from China increases cloud droplet number, suppresses rain over the East China Sea

Cited by 55 publications

References 31 publications

Global and regional estimates of warm cloud droplet number concentration based on 13 years of AQUA-MODIS observations

Global and regional estimates of warm cloud droplet number concentration based on 13 years of AQUA-MODIS observations

Predicting decadal trends in cloud droplet number concentration using reanalysis and satellite data

Differences in liquid cloud droplet effective radius and number concentration estimates between MODIS collections 5.1 and 6 over global oceans

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