scholarly journals Investigation of the adiabatic assumption for estimating cloud micro- and macrophysical properties from satellite and ground observations

2016 ◽  
Vol 16 (2) ◽  
pp. 933-952 ◽  
Author(s):  
D. Merk ◽  
H. Deneke ◽  
B. Pospichal ◽  
P. Seifert
Keyword(s):  
Optical Depth ◽  
Microwave Radiometer ◽  
Cloud Droplet ◽  
Satellite Observations ◽  
Data Set ◽  
Cloud Optical Depth ◽  
Cloud Radar ◽  
Aerosol Cloud ◽  
Droplet Concentration ◽  
Aerosol Cloud Interactions

Abstract. Cloud properties from both ground-based as well as from geostationary passive satellite observations have been used previously for diagnosing aerosol–cloud interactions. In this investigation, a 2-year data set together with four selected case studies are analyzed with the aim of evaluating the consistency and limitations of current ground-based and satellite-retrieved cloud property data sets. The typically applied adiabatic cloud profile is modified using a sub-adiabatic factor to account for entrainment within the cloud. Based on the adiabatic factor obtained from the combination of ground-based cloud radar, ceilometer and microwave radiometer, we demonstrate that neither the assumption of a completely adiabatic cloud nor the assumption of a constant sub-adiabatic factor is fulfilled (mean adiabatic factor 0.63 ± 0.22). As cloud adiabaticity is required to estimate the cloud droplet number concentration but is not available from passive satellite observations, an independent method to estimate the adiabatic factor, and thus the influence of mixing, would be highly desirable for global-scale analyses. Considering the radiative effect of a cloud described by the sub-adiabatic model, we focus on cloud optical depth and its sensitivities. Ground-based estimates are here compared vs. cloud optical depth retrieved from the Meteosat SEVIRI satellite instrument resulting in a bias of −4 and a root mean square difference of 16. While a synergistic approach based on the combination of ceilometer, cloud radar and microwave radiometer enables an estimate of the cloud droplet concentration, it is highly sensitive to radar calibration and to assumptions about the moments of the droplet size distribution. Similarly, satellite-based estimates of cloud droplet concentration are uncertain. We conclude that neither the ground-based nor satellite-based cloud retrievals applied here allow a robust estimate of cloud droplet concentration, which complicates its use for the study of aerosol–cloud interactions.

scholarly journals Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, and their application in aerosol-cloud interaction

2019 ◽  
Author(s):  
Giulia Saponaro ◽  
Moa K. Sporre ◽  
David Neubauer ◽  
Harri Kokkola ◽  
Pekka Kolmonen ◽  
...  
Keyword(s):  
Optical Depth ◽  
Climate Models ◽  
Cloud Droplet ◽  
Global Scale ◽  
Cloud Optical Depth ◽  
Aerosol Cloud ◽  
Cloud Properties ◽  
Direct Model ◽  
Land Surfaces ◽  
Aerosol Cloud Interactions

Abstract. The evaluation of modeling diagnostics with appropriate observations is an important task that establishes the capabilities and reliability of models. In this study we compare aerosol and cloud properties obtained from three different climate models ECHAM-HAM, ECHAM-HAM-SALSA, and NorESM with satellite observations using MOderate Resolution Imaging Spectrometer (MODIS) data. The simulator MODIS-COSP version 1.4 was implemented into the climate models to obtain MODIS-like cloud diagnostics, thus enabling model to model and model to satellite comparisons. Cloud droplet number concentrations (CDNC) are derived identically from MODIS-COSP simulated and MODIS-retrieved values of cloud optical depth and effective radius. For CDNC, the models capture the observed spatial distribution of higher values typically found near the coasts, downwind of the major continents, and lower values over the remote ocean and land areas. However, the COSP-simulated CDNC values are higher than those observed, whilst the direct model CDNC output is significantly lower than the MODIS-COSP diagnostics. NorESM produces large spatial biases for ice cloud properties and thick clouds over land. Despite having identical cloud modules, ECHAM-HAM and ECHAM-HAM-SALSA diverge in their representation of spatial and vertical distribution of clouds. From the spatial distributions of aerosol optical depth (AOD) and aerosol index (AI), we find that NorESM shows large biases for AOD over bright land surfaces, while discrepancies between ECHAM-HAM and ECHAM-HAM-SALSA can be observed mainly over oceans. Overall, the AIs from the different models are in good agreement globally, with higher negative biases on the Northern Hemisphere. We computed the aerosol-cloud interactions as the sensitivity of dln(CDNC)/dln(AI) on a global scale. However, one year of data may be considered not enough to assess the similarity or dissimilarities of the models due to large temporal variability in cloud properties. This study shows how simulators facilitate the evaluation of cloud properties and expose model deficiencies which are necessary steps to further improve the parametrization in climate models.

scholarly journals Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions

2020 ◽  
Vol 20 (3) ◽  
pp. 1607-1626 ◽  
Author(s):  
Giulia Saponaro ◽  
Moa K. Sporre ◽  
David Neubauer ◽  
Harri Kokkola ◽  
Pekka Kolmonen ◽  
...  
Keyword(s):  
Optical Depth ◽  
Climate Models ◽  
Cloud Droplet ◽  
Global Scale ◽  
Cloud Optical Depth ◽  
Aerosol Cloud ◽  
Cloud Properties ◽  
Direct Model ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Aerosol Cloud Interactions

Abstract. The evaluation of modelling diagnostics with appropriate observations is an important task that establishes the capabilities and reliability of models. In this study we compare aerosol and cloud properties obtained from three different climate models (ECHAM-HAM, ECHAM-HAM-SALSA, and NorESM) with satellite observations using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The simulator MODIS-COSP version 1.4 was implemented into the climate models to obtain MODIS-like cloud diagnostics, thus enabling model-to-model and model-to-satellite comparisons. Cloud droplet number concentrations (CDNCs) are derived identically from MODIS-COSP-simulated and MODIS-retrieved values of cloud optical depth and effective radius. For CDNC, the models capture the observed spatial distribution of higher values typically found near the coasts, downwind of the major continents, and lower values over the remote ocean and land areas. However, the COSP-simulated CDNC values are higher than those observed, whilst the direct model CDNC output is significantly lower than the MODIS-COSP diagnostics. NorESM produces large spatial biases for ice cloud properties and thick clouds over land. Despite having identical cloud modules, ECHAM-HAM and ECHAM-HAM-SALSA diverge in their representation of spatial and vertical distributions of clouds. From the spatial distributions of aerosol optical depth (AOD) and aerosol index (AI), we find that NorESM shows large biases for AOD over bright land surfaces, while discrepancies between ECHAM-HAM and ECHAM-HAM-SALSA can be observed mainly over oceans. Overall, the AIs from the different models are in good agreement globally, with higher negative biases in the Northern Hemisphere. We evaluate the aerosol–cloud interactions by computing the sensitivity parameter ACICDNC=dln⁡(CDNC)/dln⁡(AI) on a global scale. However, 1 year of data may be considered not enough to assess the similarity or dissimilarities of the models due to large temporal variability in cloud properties. This study shows how simulators facilitate the evaluation of cloud properties and expose model deficiencies, which are necessary steps to further improve the parameterisation in climate models.

scholarly journals Addressing the difficulties in quantifying the Twomey effect for marine warm clouds from multi-sensor satellite observations and reanalysis

2022 ◽  
Author(s):  
Hailing Jia ◽  
Johannes Quaas ◽  
Edward Gryspeerdt ◽  
Christoph Böhm ◽  
Odran Sourdeval
Keyword(s):  
Radiative Forcing ◽  
Cloud Droplet ◽  
Influential Factors ◽  
Satellite Observations ◽  
Cloud Base ◽  
Aerosol Cloud ◽  
Spatio Temporal ◽  
Observational Estimate ◽  
Aerosol Cloud Interactions ◽  
Aerosol Effects

Abstract. Aerosol–cloud interaction is the most uncertain component of the overall anthropogenic forcing of the climate, in which the Twomey effect plays a fundamental role. Satellite-based estimates of the Twomey effect are especially challenging, mainly due to the difficulty in disentangling aerosol effects on cloud droplet number concentration (Nd) from possible confounders. By combining multiple satellite observations and reanalysis, this study investigates the impacts of a) updraft, b) precipitation, c) retrieval errors, as well as (d) vertical co-location between aerosol and cloud, on the assessment of Nd-toaerosol sensitivity (S) in the context of marine warm (liquid) clouds. Our analysis suggests that S increases remarkably with both cloud base height and cloud geometric thickness (proxies for vertical velocity at cloud base), consistent with stronger aerosol-cloud interactions at larger updraft velocity. In turn, introducing the confounding effect of aerosol–precipitation interaction can artificially amplify S by an estimated 21 %, highlighting the necessity of removing precipitating clouds from analyses on the Twomey effect. It is noted that the retrieval biases in aerosol and cloud appear to underestimate S, in which cloud fraction acts as a key modulator, making it practically difficult to balance the accuracies of aerosol–cloud retrievals at aggregate scales (e.g., 1° × 1° grid). Moreover, we show that using column-integrated sulfate mass concentration (SO4C) to approximate sulfate concentration at cloud base (SO4B) can result in a degradation of correlation with Nd, along with a nearly twofold enhancement of S, mostly attributed to the inability of SO4C to capture the full spatio-temporal variability of SO4B. These findings point to several potential ways forward to account for the major influential factors practically by means of satellite observations and reanalysis, aiming at an optimal observational estimate of global radiative forcing due to the Twomey effect.

scholarly journals The Influence of Entrainment and Mixing Assumption on Aerosol–Cloud Interactions in Marine Stratocumulus

2009 ◽  
Vol 66 (5) ◽  
pp. 1450-1464 ◽  
Author(s):  
Adrian A. Hill ◽  
Graham Feingold ◽  
Hongli Jiang
Keyword(s):  
Optical Depth ◽  
Relative Sensitivity ◽  
Number Concentration ◽  
Liquid Water Path ◽  
Marine Stratocumulus ◽  
Cloud Optical Depth ◽  
Aerosol Cloud ◽  
Aerosol Cloud Interactions ◽  
Entrainment Effect ◽  
The Impact

Abstract This study uses large-eddy simulation with bin microphysics to investigate the influence of entrainment and mixing on aerosol–cloud interactions in the context of idealized, nocturnal, nondrizzling marine stratocumulus (Sc). Of particular interest are (i) an evaporation–entrainment effect and a sedimentation–entrainment effect that result from increasing aerosol concentrations and (ii) the nature of mixing between clear and cloudy air, where homogeneous and extreme inhomogeneous mixing represent the bounding mixing types. Simulations are performed at low resolution (Δz = 20 m; Δx, y = 40 m) and high resolution (Δz = 10 m; Δx, y = 20 m). It is demonstrated that an increase in aerosol from clean conditions (100 cm−3) to polluted conditions (1000 cm−3) produces both an evaporation–entrainment and a sedimentation–entrainment effect, which couple to cause about a 10% decrease in liquid water path (LWP) when all warm microphysical processes are included. These dynamical effects are insensitive to both the resolutions tested and the mixing assumption. Regardless of resolution, assuming extreme inhomogeneous rather than homogeneous mixing results in a small reduction in cloud-averaged drop number concentration, a small increase in cloud drop effective radius, and ∼1% decrease in cloud optical depth. For the case presented, these small changes play a negligible role when compared to the impact of increasing aerosol and the associated entrainment effects. Finally, it is demonstrated that although increasing resolution causes an increase in LWP and number concentration, the relative sensitivity of cloud optical depth to changes in aerosol is unaffected by resolution.

scholarly journals Ground-based remote sensing scheme for monitoring aerosol–cloud interactions

2016 ◽  
Vol 9 (3) ◽  
pp. 1039-1050 ◽  
Author(s):  
Karolina Sarna ◽  
Herman W. J. Russchenberg
Keyword(s):  
Remote Sensing ◽  
Cloud Droplet ◽  
Effective Radius ◽  
Aerosol Concentration ◽  
Backscatter Coefficient ◽  
Liquid Water Path ◽  
Data Set ◽  
Aerosol Cloud ◽  
Microphysical Properties ◽  
Aerosol Cloud Interactions

Abstract. A new method for continuous observation of aerosol–cloud interactions with ground-based remote sensing instruments is presented. The main goal of this method is to enable the monitoring of the change of the cloud droplet size due to the change in the aerosol concentration. We use high-resolution measurements from a lidar, a radar and a radiometer, which allow us to collect and compare data continuously. This method is based on a standardised data format from Cloudnet and can be implemented at any observatory where the Cloudnet data set is available. Two example case studies were chosen from the Atmospheric Radiation Measurement (ARM) Program deployment on Graciosa Island, Azores, Portugal, in 2009 to present the method. We use the cloud droplet effective radius (re) to represent cloud microphysical properties and an integrated value of the attenuated backscatter coefficient (ATB) below the cloud to represent the aerosol concentration. All data from each case study are divided into bins of the liquid water path (LWP), each 10 g m−2 wide. For every LWP bin we present the correlation coefficient between ln re and ln ATB, as well as ACIr (defined as ACIr = −d ln re∕d ln ATB, change in cloud droplet effective radius with aerosol concentration). Obtained values of ACIr are in the range 0.01–0.1. We show that ground-based remote sensing instruments used in synergy can efficiently and continuously monitor aerosol–cloud interactions.

scholarly journals A 12-year long global record of optical depth of absorbing aerosols above the clouds derived from the OMI/OMACA algorithm

2018 ◽  
Vol 11 (10) ◽  
pp. 5837-5864 ◽  
Author(s):  
Hiren Jethva ◽  
Omar Torres ◽  
Changwoo Ahn
Keyword(s):  
Atlantic Ocean ◽  
Optical Depth ◽  
Frequency Of Occurrence ◽  
Cloud Optical Depth ◽  
Low Level ◽  
Aerosol Cloud ◽  
Aerosol Absorption ◽  
The World ◽  
Absorbing Aerosols

Abstract. Aerosol–cloud interaction continues to be one of the leading uncertain components of climate models, primarily due to the lack of adequate knowledge of the complex microphysical and radiative processes of the aerosol–cloud system. Situations when light-absorbing aerosols such as carbonaceous particles and windblown dust overlay low-level cloud decks are commonly found in several regions of the world. Contrary to the known cooling effects of these aerosols in cloud-free scenario over darker surfaces, an overlapping situation of the absorbing aerosols over the cloud can lead to a significant level of atmospheric absorption exerting a positive radiative forcing (warming) at the top of the atmosphere. We contribute to this topic by introducing a new global product of above-cloud aerosol optical depth (ACAOD) of absorbing aerosols retrieved from the near-UV observations made by the Ozone Monitoring Instrument (OMI) onboard NASA's Aura platform. Physically based on an unambiguous “color ratio” effect in the near-UV caused by the aerosol absorption above the cloud, the OMACA (OMI above-cloud aerosols) algorithm simultaneously retrieves the optical depths of aerosols and clouds under a prescribed state of the atmosphere. The OMACA algorithm shares many similarities with the two-channel cloud-free OMAERUV algorithm, including the use of AIRS carbon monoxide for aerosol type identification, CALIOP-based aerosol layer height dataset, and an OMI-based surface albedo database. We present the algorithm architecture, inversion procedure, retrieval quality flags, initial validation results, and results from a 12-year long OMI record (2005–2016) including global climatology of the frequency of occurrence, ACAOD, and aerosol-corrected cloud optical depth. A comparative analysis of the OMACA-retrieved ACAOD, collocated with equivalent accurate measurements from the HSRL-2 lidar for the ORACLES Phase I operation (August–September 2016), revealed a good agreement (R = 0.77, RMSE = 0.10). The long-term OMACA record reveals several important regions of the world, where the carbonaceous aerosols from the seasonal biomass burning and mineral dust originated over the continents are found to overlie low-level cloud decks with moderate (0.3 < ACAOD < 0.5, away from the sources) to higher levels of ACAOD (> 0.8 in the proximity to the sources), including the southeastern Atlantic Ocean, southern Indian Ocean, Southeast Asia, the tropical Atlantic Ocean off the coast of western Africa, and northern Arabian sea. No significant long-term trend in the frequency of occurrence of aerosols above the clouds and ACAOD is noticed when OMI observations that are free from the “row anomaly” throughout the operation are considered. If not accounted for, the effects of aerosol absorption above the clouds introduce low bias in the retrieval of cloud optical depth with a profound impact on increasing ACAOD and cloud brightness. The OMACA aerosol product from OMI presented in this paper offers a crucial missing piece of information from the aerosol loading above cloud that will help us to quantify the radiative effects of clouds when overlaid with aerosols and their resultant impact on cloud properties and climate.

scholarly journals Implementation of aerosol–cloud interactions in the regional atmosphere–aerosol model COSMO-MUSCAT(5.0) and evaluation using satellite data

2017 ◽  
Vol 10 (6) ◽  
pp. 2231-2246 ◽  
Author(s):  
Sudhakar Dipu ◽  
Johannes Quaas ◽  
Ralf Wolke ◽  
Jens Stoll ◽  
Andreas Mühlbauer ◽  
...  
Keyword(s):  
Satellite Data ◽  
Transport Model ◽  
Cloud Condensation Nuclei ◽  
Cloud Droplet ◽  
Atmospheric Model ◽  
Horizontal Resolution ◽  
Small Scale ◽  
Aerosol Model ◽  
Aerosol Cloud ◽  
Aerosol Cloud Interactions

Abstract. The regional atmospheric model Consortium for Small-scale Modeling (COSMO) coupled to the Multi-Scale Chemistry Aerosol Transport model (MUSCAT) is extended in this work to represent aerosol–cloud interactions. Previously, only one-way interactions (scavenging of aerosol and in-cloud chemistry) and aerosol–radiation interactions were included in this model. The new version allows for a microphysical aerosol effect on clouds. For this, we use the optional two-moment cloud microphysical scheme in COSMO and the online-computed aerosol information for cloud condensation nuclei concentrations (Cccn), replacing the constant Cccn profile. In the radiation scheme, we have implemented a droplet-size-dependent cloud optical depth, allowing now for aerosol–cloud–radiation interactions. To evaluate the models with satellite data, the Cloud Feedback Model Intercomparison Project Observation Simulator Package (COSP) has been implemented. A case study has been carried out to understand the effects of the modifications, where the modified modeling system is applied over the European domain with a horizontal resolution of 0.25°  ×  0.25°. To reduce the complexity in aerosol–cloud interactions, only warm-phase clouds are considered. We found that the online-coupled aerosol introduces significant changes for some cloud microphysical properties. The cloud effective radius shows an increase of 9.5 %, and the cloud droplet number concentration is reduced by 21.5 %.

scholarly journals The source of discrepancies in aerosol–cloud–precipitation interactions between GCM and A-Train retrievals

2016 ◽  
Vol 16 (23) ◽  
pp. 15413-15424 ◽  
Author(s):  
Takuro Michibata ◽  
Kentaroh Suzuki ◽  
Yousuke Sato ◽  
Toshihiko Takemura
Keyword(s):  
General Circulation ◽  
Climate Models ◽  
Circulation Model ◽  
Cloud Droplet ◽  
Systematic Difference ◽  
Liquid Water Path ◽  
Light Rain ◽  
Aerosol Cloud ◽  
Nonlinear Complexity ◽  
Aerosol Cloud Interactions

Abstract. Aerosol–cloud interactions are one of the most uncertain processes in climate models due to their nonlinear complexity. A key complexity arises from the possibility that clouds can respond to perturbed aerosols in two opposite ways, as characterized by the traditional “cloud lifetime” hypothesis and more recent “buffered system” hypothesis. Their importance in climate simulations remains poorly understood. Here we investigate the response of the liquid water path (LWP) to aerosol perturbations for warm clouds from the perspective of general circulation model (GCM) and A-Train remote sensing, through process-oriented model evaluations. A systematic difference is found in the LWP response between the model results and observations. The model results indicate a near-global uniform increase of LWP with increasing aerosol loading, while the sign of the response of the LWP from the A-Train varies from region to region. The satellite-observed response of the LWP is closely related to meteorological and/or macrophysical factors, in addition to the microphysics. The model does not reproduce this variability of cloud susceptibility (i.e., sensitivity of LWP to perturbed aerosols) because the parameterization of the autoconversion process assumes only suppression of rain formation in response to increased cloud droplet number, and does not consider macrophysical aspects that serve as a mechanism for the negative responses of the LWP via enhancements of evaporation and precipitation. Model biases are also found in the precipitation microphysics, which suggests that the model generates rainwater readily even when little cloud water is present. This essentially causes projections of unrealistically frequent and light rain, with high cloud susceptibilities to aerosol perturbations.

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