Strengthened Indian Summer Monsoon Precipitation Susceptibility Linked to Dust‐Induced Ice Cloud Modification

Patel, Piyushkumar N.; Gautam, Ritesh; Michibata, Takuro; Gadhavi, Harish

doi:10.1029/2018gl081634

Cited by 13 publications

(4 citation statements)

References 75 publications

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“…ACI studies often use satellite retrievals to take advantage of their global coverage, but satellites have been unable to measure the CCN. Nevertheless, the aerosol optical parameters such as aerosol optical depth (AOD) and aerosol index (AI) are commonly used as proxies for CCN in previous studies (Gryspeerdt & Stier, 2012;Patel et al, 2017Patel et al, , 2019Patel & Kumar, 2016;Quaas et al, 2008Quaas et al, , 2009Rosenfeld, 2008). However, all these proxies are crude tools and suffer from various issues such as aerosol swelling, lack of vertical information, cloud contamination, uncertainty in size distribution and solubility, and more (Rosenfeld et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Resolving Vertical Profile of Cloud Condensation Nuclei Concentrations from Spaceborne Lidar Measurements

Patel

Jiang²,

Gautam³

et al. 2022

Preprint

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Abstract. Cloud condensation nuclei (CCN) are mediators of aerosol-cloud interactions (ACI), contributing to the largest uncertainties in the understandings of global climate change. We present a novel remote sensing-based algorithm that quantifies the vertically-resolved CCN number concentrations (NCCN) using aerosol optical properties measured by a multiwavelength lidar. The algorithm considers five distinct aerosol subtypes with bimodal size distributions. The inversion used the look-up tables developed in this study, based on the observations from the Aerosol Robotic Network to efficiently retrieve optimal particle size distributions from lidar measurements. The method derives dry aerosol optical properties by implementing hygroscopic enhancement factors to lidar measurements. The retrieved optically equivalent particle size distributions and aerosol type dependent particle composition are utilized to calculate critical diameter using the κ-Köhler theory and NCCN at six supersaturations ranging from 0.07 % to 1.0 %. Sensitivity analyses indicate that uncertainties in extinction coefficients and relative humidity greatly influence the retrieval error in NCCN. The potential of this algorithm is further evaluated by retrieving NCCN using airborne lidar from the NASA ORACLES campaign and validated against simultaneous measurements from the CCN counter. The independent validation with robust correlation demonstrates promising results. Furthermore, the NCCN has been retrieved for the first time using a proposed algorithm from spaceborne lidar - Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) - measurements. The application of this new capability demonstrates the potential for constructing a 3D CCN climatology at a global scale, which help to better quantify ACI effects and thus reduce the uncertainty in aerosol climate forcing.

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Section: Introductionmentioning

confidence: 99%

Resolving Vertical Profile of Cloud Condensation Nuclei Concentrations from Spaceborne Lidar Measurements

Patel

Jiang²,

Gautam³

et al. 2022

Preprint

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“…Levin et al (2005) found that an increase in IN concentration induced by dust can reduce the collision efficiency between liquid droplets and further weaken cumulus precipitation in the Mediterranean region. Dust concentration within clouds, cloud type, cloud height and meteorological conditions lead to big changes and even mutations or reversals in dust-cloud interactions (Wielicki et al 1996, Zelinka et al 2014, Fan et al 2016, Wang et al 2018, Liu et al 2019, Patel et al 2019. Moreover, under certain water vapour conditions, an increase in dust aerosols can reduce the effective radius of cloud droplets, increase the cloud albedo, change the cloud lifetime, and indirectly lead to the enhancement of reflected radiation and a decrease in surface temperature (Han et al 2008, Sun et al 2012.…”

Section: Introductionmentioning

confidence: 99%

Impact of dust-cloud-radiation interactions on surface albedo: a case study of ‘Tiramisu’ snow in Urumqi, China

Chen

Zhang

et al. 2021

Environ. Res. Lett.

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Dust-cloud-surface radiation interactions (DCRI) is a complex nonlinear relation referring to the influences of both atmospheric dust and dust-on-snow on surface albedo. A “Tiramisu” snow event occurred on December 1st, 2018, in Urumqi, China, providing an excellent testbed for exploring the comprehensive effect induced by atmospheric dust and those deposited atop fresh snowpack on surface radiation. A detailed analysis indicates that the decrease of snow albedo by 0.17–0.26 (22–34%) is contributed by the effects both the dust-cloud interactions and dust-on-snow at synoptic scale in this case. In particular, dust well mixed with ice clouds at altitudes of 2.5–5.5 km disrupted the “seeder-feeder” structure of clouds and heterogeneous ice nucleation. Dust-induced changes in the low layer of ice cloud (3.3–5.5 km) under a low temperature of –20 °C resulted in a 31.8% increase in the ice particle radius and 84.6% in the ice water path, which acted to indirectly buffer the incident solar radiation reaching the surface. Dust particles deposited on the snow surface further caused snow darkening since the snow albedo was found to decrease by 11.8–23.3%. These findings underscore the importance of considering the comprehensive effect of dust-cloud-radiation interactions in the future.

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“…The current version of the simulator package comprises the ISCCP (Klein and Jakob, 1999;Webb et al, 2001), MODIS (Pincus et al, 2012), MISR (Marchand and Ackerman, 2010), PARASOL (Konsta et al, 2016), CloudSat (Haynes et al, 2007), and CALIPSO (Chepfer et al, 2008;Cesana and Chepfer, 2012) simulators. To effectively utilize these capabilities, there is a growing need for "processoriented" model diagnostics (Maloney et al, 2019), which have been recognized as essential to the community effort to advance climate modeling (Tsushima et al, 2017;Webb et al, 2017).…”

mentioning

confidence: 99%

“…To investigate microphysics at a fundamental process level, it is best to analyze the instantaneous output for the variables of interest rather than their monthly means (e.g., Konsta et al, 2016). This is because these processes typically occur over short timescales ("fast processes") and contribute to the regime dependency of important phenomena including aerosol-cloud-precipitation interactions (Michibata et al, 2016;Patel et al, 2019). This requires highfrequency data output ( ∼ 6 hourly) from COSP (see also Tsushima et al, 2017), which results in large amounts of data, particularly when subcolumn (pixel-scale) variables, such as a radar or lidar simulator, are involved.…”

mentioning

confidence: 99%

Incorporation of inline warm rain diagnostics into the COSP2 satellite simulator for process-oriented model evaluation

et al. 2019

Self Cite

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Abstract. The Cloud Feedback Model Intercomparison Project Observational Simulator Package (COSP) is used to diagnose model performance and physical processes via an apple-to-apple comparison to satellite measurements. Although the COSP provides useful information about clouds and their climatic impact, outputs that have a subcolumn dimension require large amounts of data. This can cause a bottleneck when conducting sets of sensitivity experiments or multiple model intercomparisons. Here, we incorporate two diagnostics for warm rain microphysical processes into the latest version of the simulator (COSP2). The first one is the occurrence frequency of warm rain regimes (i.e., non-precipitating, drizzling, and precipitating) classified according to CloudSat radar reflectivity, putting the warm rain process diagnostics into the context of the geographical distributions of precipitation. The second diagnostic is the probability density function of radar reflectivity profiles normalized by the in-cloud optical depth, the so-called contoured frequency by optical depth diagram (CFODD), which illustrates how the warm rain processes occur in the vertical dimension using statistics constructed from CloudSat and MODIS simulators. The new diagnostics are designed to produce statistics online along with subcolumn information during the COSP execution, eliminating the need to output subcolumn variables. Users can also readily conduct regional analysis tailored to their particular research interest (e.g., land–ocean differences) using an auxiliary post-process package after the COSP calculation. The inline diagnostics are applied to the MIROC6 general circulation model (GCM) to demonstrate how known biases common among multiple GCMs relative to satellite observations are revealed. The inline multi-sensor diagnostics are intended to serve as a tool that facilitates process-oriented model evaluations in a manner that reduces the burden on modelers for their diagnostics effort.

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Strengthened Indian Summer Monsoon Precipitation Susceptibility Linked to Dust‐Induced Ice Cloud Modification

Cited by 13 publications

References 75 publications

Resolving Vertical Profile of Cloud Condensation Nuclei Concentrations from Spaceborne Lidar Measurements

Resolving Vertical Profile of Cloud Condensation Nuclei Concentrations from Spaceborne Lidar Measurements

Impact of dust-cloud-radiation interactions on surface albedo: a case study of ‘Tiramisu’ snow in Urumqi, China

Incorporation of inline warm rain diagnostics into the COSP2 satellite simulator for process-oriented model evaluation

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