scholarly journals Time-dependent entrainment of smoke presents an observational challenge for assessing aerosol–cloud interactions over the southeast Atlantic Ocean

2018 ◽  
Author(s):  
Michael S. Diamond ◽  
Amie Dobracki ◽  
Steffen Freitag ◽  
Jennifer D. Small Griswold ◽  
Ashley Heikkila ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Atlantic Ocean ◽  
Marine Boundary Layer ◽  
Climate Modeling ◽  
Cloud Droplet ◽  
Number Concentration ◽  
Cloud Properties ◽  
History Of ◽  
Cloud Droplet Number Concentration ◽  
The Relationship

Abstract. The colocation of clouds and smoke over the southeast Atlantic Ocean during the southern African biomass burning season has numerous radiative implications, including microphysical modulation of the clouds if smoke is entrained into the marine boundary layer. NASA’s ObseRvtions of Aerosols above CLouds and their intEractionS (ORACLES) campaign is studying this system with aircraft in three field deployments between 2016 and 2018. Results from ORACLES-2016 show that the relationship between cloud droplet number concentration and smoke below cloud is consistent with previously reported values, whereas cloud droplet number concentration is only weakly associated with smoke immediately above cloud at the time of observation. Combining field observations, regional chemistry–climate modeling, and theoretical boundary layer aerosol budget equations, we show that the history of smoke entrainment (which has a characteristic mixing timescale on the order of days) helps explain variations in cloud properties for similar instantaneous above-cloud smoke environments. Precipitation processes are also expected to obscure the relationship between above-cloud smoke and cloud properties in parts of the southeast Atlantic, although marine boundary layer carbon monoxide concentrations for two case study flights suggest that smoke entrainment history drove the observed differences in cloud properties for those days. A Lagrangian framework following the clouds and accounting for the history of smoke entrainment and precipitation is likely necessary for quantitatively studying this system: an Eulerian framework (e.g., instantaneous correlation of A-train satellite observations) is unlikely to capture the true extent of smoke–cloud interaction in the southeast Atlantic.

scholarly journals Time-dependent entrainment of smoke presents an observational challenge for assessing aerosol–cloud interactions over the southeast Atlantic Ocean

2018 ◽  
Vol 18 (19) ◽  
pp. 14623-14636 ◽  
Author(s):  
Michael S. Diamond ◽  
Amie Dobracki ◽  
Steffen Freitag ◽  
Jennifer D. Small Griswold ◽  
Ashley Heikkila ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Atlantic Ocean ◽  
Marine Boundary Layer ◽  
Climate Modeling ◽  
Cloud Droplet ◽  
Number Concentration ◽  
Cloud Properties ◽  
History Of ◽  
Cloud Droplet Number Concentration ◽  
The Relationship

Abstract. The colocation of clouds and smoke over the southeast Atlantic Ocean during the southern African biomass burning season has numerous radiative implications, including microphysical modulation of the clouds if smoke is entrained into the marine boundary layer. NASA's ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign is studying this system with aircraft in three field deployments between 2016 and 2018. Results from ORACLES-2016 show that the relationship between cloud droplet number concentration and smoke below cloud is consistent with previously reported values, whereas cloud droplet number concentration is only weakly associated with smoke immediately above cloud at the time of observation. By combining field observations, regional chemistry–climate modeling, and theoretical boundary layer aerosol budget equations, we show that the history of smoke entrainment (which has a characteristic mixing timescale on the order of days) helps explain variations in cloud properties for similar instantaneous above-cloud smoke environments. Precipitation processes can obscure the relationship between above-cloud smoke and cloud properties in parts of the southeast Atlantic, but marine boundary layer carbon monoxide concentrations for two case study flights suggest that smoke entrainment history drove the observed differences in cloud properties for those days. A Lagrangian framework following the clouds and accounting for the history of smoke entrainment and precipitation is likely necessary for quantitatively studying this system; an Eulerian framework (e.g., instantaneous correlation of A-train satellite observations) is unlikely to capture the true extent of smoke–cloud interaction in the southeast Atlantic.

Quantification of aerosol effects on marine boundary layer clouds with machine learning

2021 ◽  
Author(s):  
Lukas Zipfel ◽  
Hendrik Andersen ◽  
Jan Cermak
Keyword(s):  
Machine Learning ◽  
Boundary Layer ◽  
Marine Boundary Layer ◽  
Cloud Droplet ◽  
Number Concentration ◽  
Satellite Observations ◽  
Radiative Properties ◽  
Cloud Base ◽  
Boundary Layer Clouds ◽  
Cloud Droplet Number Concentration

<p>Satellite observations are used in regional machine learning models to quantify sensitivities of marine boundary-layer clouds (MBLC) to aerosol changes.</p><p>MBLCs make up a large part of the global cloud coverage as they are persistently present over more than 20% of the Earth’s oceans in the annual mean.They play an important role in Earth’s energy budget by reflecting solar radiation and interacting with thermal radiation from the surface, leading to a net cooling effect. Cloud properties and their radiative characteristics such as cloud albedo, horizontal and vertical extent, lifetime and precipitation susceptibility are dependent on environmental conditions. Aerosols in their role as condensation nuclei affect these cloud radiative properties through changes in the cloud droplet number concentration and subsequent cloud adjustments to this perturbation. However, the magnitude and sign of these effects remain among the largest uncertainties in future climate predictions.</p><p>In an effort to help improve these predictions a machine learning approach in combination with observational data is pursued:</p><p>Satellite observations from the collocated A-Train dataset (C3M) for 2006-2011 are used in combination with ECMWF atmospheric reanalysis data (ERA5) to train regional Gradient Boosting Regression Tree (GBRT) models to predict changes in key physical and radiative properties of MBLCs. The cloud droplet number concentration (N<sub>d</sub>) and the liquid water path (LWP) are simulated for the eastern subtropical oceans, which are characterised by a high annual coverage of MBLC due to the occurrence of semi-permanent stratocumulus sheets. Relative humidity above cloud, cloud top height and temperature below the cloud base and at the surface are identified as important predictors for both N<sub>d</sub> and LWP.  The impact of each predictor variable on the GBRT model's output is analysed using Shapley values as a method of explainable machine learning, providing novel sensitivity estimates that will improve process understanding and help constrain the parameterization of MBLC processes in Global Climate Models.</p>

scholarly journals Improvement of airborne retrievals of cloud droplet number concentration of trade wind cumulus using a synergetic approach

2019 ◽  
Vol 12 (3) ◽  
pp. 1635-1658 ◽  
Author(s):  
Kevin Wolf ◽  
André Ehrlich ◽  
Marek Jacob ◽  
Susanne Crewell ◽  
Martin Wirth ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Cloud Droplet ◽  
Number Concentration ◽  
Cloud Base ◽  
Trade Wind ◽  
Liquid Water Path ◽  
Cloud Properties ◽  
Research Aircraft ◽  
Cloud Droplet Number Concentration

Abstract. In situ measurements of cloud droplet number concentration N are limited by the sampled cloud volume. Satellite retrievals of N suffer from inherent uncertainties, spatial averaging, and retrieval problems arising from the commonly assumed strictly adiabatic vertical profiles of cloud properties. To improve retrievals of N it is suggested in this paper to use a synergetic combination of passive and active airborne remote sensing measurement, to reduce the uncertainty of N retrievals, and to bridge the gap between in situ cloud sampling and global averaging. For this purpose, spectral solar radiation measurements above shallow trade wind cumulus were combined with passive microwave and active radar and lidar observations carried out during the second Next Generation Remote Sensing for Validation Studies (NARVAL-II) campaign with the High Altitude and Long Range Research Aircraft (HALO) in August 2016. The common technique to retrieve N is refined by including combined measurements and retrievals of cloud optical thickness τ, liquid water path (LWP), cloud droplet effective radius reff, and cloud base and top altitude. Three approaches are tested and applied to synthetic measurements and two cloud scenarios observed during NARVAL-II. Using the new combined retrieval technique, errors in N due to the adiabatic assumption have been reduced significantly.

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

2018 ◽  
Vol 18 (3) ◽  
pp. 2035-2047 ◽  
Author(s):  
Daniel T. McCoy ◽  
Frida A.-M. Bender ◽  
Daniel P. Grosvenor ◽  
Johannes K. Mohrmann ◽  
Dennis L. Hartmann ◽  
...  
Keyword(s):  
North America ◽  
Radiative Forcing ◽  
Cloud Droplet ◽  
Number Concentration ◽  
Ozone Monitoring Instrument ◽  
Anthropogenic Aerosol ◽  
Near Surface ◽  
Cloud Properties ◽  
Cloud Droplet Number Concentration ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Cloud droplet number concentration (CDNC) is the key state variable that moderates the relationship between aerosol and the radiative forcing arising from aerosol–cloud 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 Moderate-Resolution 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 (SO4), 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 SO4, 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 (SO2) over maritime volcanoes and the east coasts of North America and Asia, revealing that maritime CDNC responds to changes in SO2 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.

scholarly journals The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data

2021 ◽  
Author(s):  
Edward Gryspeerdt ◽  
Daniel T. McCoy ◽  
Ewan Crosbie ◽  
Richard H. Moore ◽  
Graeme J. Nott ◽  
...  
Keyword(s):  
Satellite Data ◽  
Radiative Forcing ◽  
Sampling Strategy ◽  
Cloud Droplet ◽  
Number Concentration ◽  
Cloud Properties ◽  
Systematic Biases ◽  
Cloud Droplet Number Concentration ◽  
Aerosol Cloud Interactions ◽  
The Impact

Abstract. Cloud droplet number concentration (Nd) is of central importance to observation-based estimates of aerosol indirect effects, being used to quantify both the cloud sensitivity to aerosol and the base state of the cloud. However, the derivation of Nd from satellite data depends on a number of assumptions about the cloud and the accuracy of the retrievals of the cloud properties from which it is derived, making it prone to systematic biases. A number of sampling strategies have been proposed to address these biases by selecting the most accurate Nd retrievals in the satellite data. This work compares the impact of these strategies on the accuracy of the satellite retrieved Nd, using a selection of insitu measurements. In stratocumulus regions, the MODIS Nd retrieval is able to achieve a high precision (r2 of 0.5–0.8). This is lower in other cloud regimes, but can be increased by appropriate sampling choices. Although the Nd sampling can have significant effects on the Nd climatology, it produces only a 20 % variation in the implied radiative forcing from aerosol-cloud interactions, with the choice of aerosol proxy driving the overall uncertainty. The results are summarised into recommendations for using MODIS Nd products and appropriate sampling.

scholarly journals A new OCO-2 cloud flagging and rapid retrieval of marine boundary layer cloud properties

2020 ◽  
Author(s):  
Mark Richardson ◽  
Matthew D. Lebsock ◽  
James McDuffie ◽  
Graeme L. Stephens
Keyword(s):  
Marine Boundary Layer ◽  
A Priori ◽  
Cloud Droplet ◽  
Lookup Table ◽  
Cloud Properties ◽  
Cloud Droplet Number Concentration ◽  
Boundary Layer Cloud ◽  
Priori Information ◽  
Heterogeneous Cloud ◽  
Cloud Top Pressure

Abstract. The Orbiting Carbon Observatory-2 (OCO-2) carries a hyperspectral A-band sensor that can obtain information about cloud geometric thickness (H). The OCO2CLD-LIDAR-AUX product retrieved H with the aid of collocated CALIPSO lidar data to identify suitable clouds and provide a priori cloud-top pressure (Ptop). This collocation is no longer possible since CALIPSO's coordination flying with OCO-2 has ended, so here we introduce a new cloud flagging and a priori assignment using only OCO-2 data, restricted to ocean footprints where solar zenith angle  1) for a valid retrieval, and agreement with MODIS-CALIPSO is 90.0 %. Secondly, we developed a lookup table to simultaneously retrieve cloud τ, effective radius (re) and Ptop from A-band and CO2 band radiances. Median Ptop difference versus CALIPSO is 12 hPa with interdecile range [−11,87] hPa, substantially better than the MODIS-CALIPSO [−83,81] hPa. The MODIS-OCO-2 τ difference is 0.8 (−3.8,6.9) and re is −0.3 [−2.8,2.1] μm. The tau difference is due to optically thick and horizontally heterogeneous cloud scenes. As well as an improved passive Ptop retrieval, this a priori information will allow a purely OCO-2 based Bayesian retrieval of cloud droplet number concentration (Nd). Finally, our cloud flagging procedure may also be useful for future partial column above-cloud CO2 abundance retrievals.

Exploring satellite-derived relationships between cloud droplet number concentration and liquid water path using a large-domain large-eddy simulation

2021 ◽  
Author(s):  
Sudhakar Dipu ◽  
Matthias Schwarz ◽  
Annica Ekman ◽  
Edward Gryspeerdt ◽  
Tom Goren ◽  
...  
Keyword(s):  
Liquid Water ◽  
Cloud Droplet ◽  
Number Concentration ◽  
Liquid Water Path ◽  
Large Domain ◽  
Water Path ◽  
Large Eddy ◽  
Cloud Droplet Number Concentration ◽  
Les Model ◽  
The Relationship

<div class="page" title="Page 1"> <div class="layoutArea"> <div class="column"> <p>Important aspects of the adjustments to aerosol-cloud interactions can be examined using the relationship between cloud droplet number concentration (Nd) and liquid water path (LWP). Specifically, this relation can constrain the role of aerosols in leading to thicker or thinner clouds in response to adjustment mechanisms. This study investigates the satellite retrieved relationship between Nd and LWP for a selected case of mid-latitude continental clouds using high-resolution Large-eddy simulations (LES) over a large domain in weather prediction mode. Since the satellite retrieval uses adiabatic assumption to derive the Nd (NAd), we have also considered NAd from the LES model for comparison. The NAd-LWP relationship in the satellite and the LES model show similar, generally positive, but non-monotonic relations. This case over continent thus behaves differently compared to previously-published analysis of oceanic clouds, and the analysis illustrates a regime dependency (marine and continental) in the NAd-LWP relation in the satellite retrievals. The study further explores the impact of the satellite retrieval assumptions on the Nd-LWP relationship. When considering the relationship of the actually simulated cloud-top Nd, rather than NAd, with LWP, the result shows a much more nonlinear relationship. The difference is much less pronounced, however, for shallow stratiform than for convective clouds. Comparing local vs large-scale statistics from satellite data shows that continental clouds exhibit only a weak nonlinear Nd-LWP relationship. Hence a regime based Nd-LWP analysis is even more relevant when it comes to continental clouds.</p> </div> </div> </div>

Sign in / Sign up

Export Citation Format

Share Document