scholarly journals Quantifying the structural uncertainty of the aerosol mixing state representation in a modal model

2021 ◽  
Vol 21 (23) ◽  
pp. 17727-17741
Author(s):  
Zhonghua Zheng ◽  
Matthew West ◽  
Lei Zhao ◽  
Po-Lun Ma ◽  
Xiaohong Liu ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Mixing State ◽  
Aerosol Model ◽  
Aerosol Radiative Forcing ◽  
Aerosol Cloud ◽  
Process Representation ◽  
Community Earth System Model ◽  
Aerosol Cloud Interactions ◽  
Aerosol Module ◽  
Aerosol Mixing State

Abstract. Aerosol mixing state is an important emergent property that affects aerosol radiative forcing and aerosol–cloud interactions, but it has not been easy to constrain this property globally. This study aims to verify the global distribution of aerosol mixing state represented by modal models. To quantify the aerosol mixing state, we used the aerosol mixing state indices for submicron aerosol based on the mixing of optically absorbing and non-absorbing species (χo), the mixing of primary carbonaceous and non-primary carbonaceous species (χc), and the mixing of hygroscopic and non-hygroscopic species (χh). To achieve a spatiotemporal comparison, we calculated the mixing state indices using output from the Community Earth System Model with the four-mode version of the Modal Aerosol Module (MAM4) and compared the results with the mixing state indices from a benchmark machine-learned model trained on high-detail particle-resolved simulations from the particle-resolved stochastic aerosol model PartMC-MOSAIC. The two methods yielded very different spatial patterns of the mixing state indices. In some regions, the yearly averaged χ value computed by the MAM4 model differed by up to 70 percentage points from the benchmark values. These errors tended to be zonally structured, with the MAM4 model predicting a more internally mixed aerosol at low latitudes and a more externally mixed aerosol at high latitudes compared to the benchmark. Our study quantifies potential model bias in simulating mixing state in different regions and provides insights into potential improvements to model process representation for a more realistic simulation of aerosols towards better quantification of radiative forcing and aerosol–cloud interactions.

scholarly journals Quantifying the structural uncertainty of the aerosol mixing state representation in a modal model

2021 ◽  
Author(s):  
Zhonghua Zheng ◽  
Matthew West ◽  
Lei Zhao ◽  
Po-Lun Ma ◽  
Xiaohong Liu ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Mixing State ◽  
Aerosol Model ◽  
Aerosol Radiative Forcing ◽  
Process Representation ◽  
Modal Model ◽  
Community Earth System Model ◽  
Aerosol Cloud Interactions ◽  
Aerosol Module ◽  
Aerosol Mixing State

Abstract. Aerosol mixing state is an important emergent property that affects aerosol radiative forcing and aerosol-cloud interactions, but it has not been easy to constrain this property globally. This study aims to verify the global distribution of aerosol mixing state represented by modal models. To quantify the aerosol mixing state, we used the aerosol mixing state indices for submicron aerosol based on the mixing of optically absorbing and non-absorbing species (χo), the mixing of primary carbonaceous and non-primary carbonaceous species (χc), and the mixing of hygroscopic and non-hygroscopic species (χh). To achieve a spatiotemporal comparison, we calculated the mixing state indices using output from the Community Earth System Model with the modal MAM4 aerosol module, and compared the results with the mixing state indices from a benchmark machine-learned model trained on high-detail particle-resolved simulations from the particle-resolved stochastic aerosol model PartMC-MOSAIC. The two methods yielded very different spatial patterns of the mixing state indices. In some regions, the yearly-averaged χ value computed by the MAM4 model differed by up to 70 percentage points from the benchmark values. These errors tended to be zonally structured, with the MAM4 model predicting a more internally mixed aerosol at low latitudes, and a more externally mixed aerosol at high latitudes, compared to the benchmark. Our study quantifies potential model bias in simulating mixing state in different regions, and provides insights into potential improvements to model process representation for a more realistic simulation of aerosols.

scholarly journals Deconvolution of Boundary Layer Depth and Aerosol Constraints on Cloud Water Path in Subtropical Stratocumuli

2019 ◽  
Author(s):  
Anna Possner ◽  
Ryan Eastman ◽  
Frida Bender ◽  
Franziska Glassmeier
Keyword(s):  
Radiative Forcing ◽  
Process Modelling ◽  
Liquid Water Path ◽  
Water Path ◽  
Aerosol Radiative Forcing ◽  
Aerosol Cloud ◽  
Modelling Studies ◽  
Aerosol Cloud Interactions ◽  
Marine Boundary Layers

Abstract. The liquid water path (LWP) adjustment due to aerosol-cloud interactions in marine stratocumuli remains a considerable source of uncertainty for climate sensitivity estimates. An unequivocal attribution of LWP changes to changes in aerosol concentration from climatology remains difficult due to the considerable covariance between meteorological conditions alongside changes in aerosol concentrations. Here, we show that LWP susceptibility in marine boundary layers (BLs) inferred from climatological relationships, triples in magnitude from −0.1 to −0.33 as the BL deepens. We further find deep BLs to be underrepresented in pollution track, process modelling and in-situ studies of aerosol-cloud interactions in marine stratocumuli. Susceptibility estimates based on these approaches are skewed towards shallow BLs of moderate LWP susceptibility. Therefore, extrapolating LWP susceptibility estimates from shallow BLs to the entire cloud climatology, may underestimate the true LWP adjustment within sub-tropical stratocumuli, and thus overestimate the effective aerosol radiative forcing in this region. Meanwhile, LWP susceptibility estimates inferred from climatology in deep BLs are still poorly constrained. While susceptibility estimates in shallow BLs are found to be consistent with process modelling studies, they are overestimated as compared to pollution track estimates.

scholarly journals Significant underestimation of radiative forcing by aerosol–cloud interactions derived from satellite-based methods

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hailing Jia ◽  
Xiaoyan Ma ◽  
Fangqun Yu ◽  
Johannes Quaas
Keyword(s):  
Radiative Forcing ◽  
Cloud Fraction ◽  
Future Climate Change ◽  
Satellite Measurements ◽  
Aerosol Cloud ◽  
Fine Mode ◽  
Sampling Biases ◽  
Aerosol Cloud Interactions ◽  
Significant Underestimation ◽  
Weak Satellite

AbstractSatellite-based estimates of radiative forcing by aerosol–cloud interactions (RFaci) are consistently smaller than those from global models, hampering accurate projections of future climate change. Here we show that the discrepancy can be substantially reduced by correcting sampling biases induced by inherent limitations of satellite measurements, which tend to artificially discard the clouds with high cloud fraction. Those missed clouds exert a stronger cooling effect, and are more sensitive to aerosol perturbations. By accounting for the sampling biases, the magnitude of RFaci (from −0.38 to −0.59 W m−2) increases by 55 % globally (133 % over land and 33 % over ocean). Notably, the RFaci further increases to −1.09 W m−2 when switching total aerosol optical depth (AOD) to fine-mode AOD that is a better proxy for CCN than AOD. In contrast to previous weak satellite-based RFaci, the improved one substantially increases (especially over land), resolving a major difference with models.

scholarly journals 100 Years of Progress in Cloud Physics, Aerosols, and Aerosol Chemistry Research

2019 ◽  
Vol 59 ◽  
pp. 11.1-11.72 ◽  
Author(s):  
Sonia M. Kreidenweis ◽  
Markus Petters ◽  
Ulrike Lohmann
Keyword(s):  
Radiative Forcing ◽  
Cloud Microphysics ◽  
Climate System ◽  
Mitigation Strategies ◽  
Climate Projections ◽  
Weather Modification ◽  
Theoretical Understanding ◽  
Aerosol Cloud ◽  
Chemistry Research ◽  
Aerosol Cloud Interactions

Abstract This chapter reviews the history of the discovery of cloud nuclei and their impacts on cloud microphysics and the climate system. Pioneers including John Aitken, Sir John Mason, Hilding Köhler, Christian Junge, Sean Twomey, and Kenneth Whitby laid the foundations of the field. Through their contributions and those of many others, rapid progress has been made in the last 100 years in understanding the sources, evolution, and composition of the atmospheric aerosol, the interactions of particles with atmospheric water vapor, and cloud microphysical processes. Major breakthroughs in measurement capabilities and in theoretical understanding have elucidated the characteristics of cloud condensation nuclei and ice nucleating particles and the role these play in shaping cloud microphysical properties and the formation of precipitation. Despite these advances, not all their impacts on cloud formation and evolution have been resolved. The resulting radiative forcing on the climate system due to aerosol–cloud interactions remains an unacceptably large uncertainty in future climate projections. Process-level understanding of aerosol–cloud interactions remains insufficient to support technological mitigation strategies such as intentional weather modification or geoengineering to accelerating Earth-system-wide changes in temperature and weather patterns.

scholarly journals Reply to “Comments on ‘Rethinking the Lower Bound on Aerosol Radiative Forcing’”

2018 ◽  
Vol 31 (22) ◽  
pp. 9413-9416
Author(s):  
Bjorn Stevens
Keyword(s):  
Lower Bound ◽  
Little Ice Age ◽  
Radiative Forcing ◽  
Ice Age ◽  
Top Down ◽  
Aerosol Radiative Forcing ◽  
Poor Understanding ◽  
Aerosol Cloud Interactions ◽  
Lines Of Evidence ◽  
Aerosol Radiative

This reply addresses a comment questioning one of the lines of evidence I used in a 2015 study (S15) to argue for a less negative aerosol radiative forcing. The comment raises four points of criticism. Two of these have been raised and addressed elsewhere; here I additionally show that even if they have merit the S15 lower bound remains substantially (0.5 W m–2) less negative than that given in the AR5. Regarding the two other points of criticism, one appears to be based on a poor understanding of the nature of S15’s argument; the other rests on speculation as to the nature of the uncertainty in historical SO2 estimates. In the spirit of finding possible flaws with the top-down constraints from S15, I instead hypothesize that an interesting—albeit unlikely—way S15 could be wrong is by inappropriately discounting the contribution of biomass burning to radiative forcing through aerosol–cloud interactions. This hypothesis is interesting as it opens the door for a role for the anthropogenic (biomass) aerosol in causing the Little Ice Age and again raises the specter of greater warming from ongoing reductions in SO2 emissions.

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 Machine learning uncovers aerosol size information from chemistry and meteorology to quantify potential cloud-forming particles

2021 ◽  
Author(s):  
Arshad Nair ◽  
Fangqun Yu ◽  
Pedro Campuzano Jost ◽  
Paul DeMott ◽  
Ezra Levin ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Radiative Forcing ◽  
Climate Models ◽  
Cloud Condensation Nuclei ◽  
Global Climate Models ◽  
Aerosol Composition ◽  
Airborne Measurements ◽  
Aerosol Cloud ◽  
Aerosol Cloud Interactions

Abstract Cloud condensation nuclei (CCN) are mediators of aerosol–cloud interactions, which contribute to the largest uncertainty in climate change prediction. Here, we present a machine learning/artificial intelligence model that quantifies CCN from variables of aerosol composition, atmospheric trace gases, and meteorology. Comprehensive multi-campaign airborne measurements, covering varied physicochemical regimes in the troposphere, confirm the validity of and help probe the inner workings of this machine learning model: revealing for the first time that different ranges of atmospheric aerosol composition and mass correspond to distinct aerosol number size distributions. Machine learning extracts this information, important for accurate quantification of CCN, additionally from both chemistry and meteorology. This can provide a physicochemically explainable, computationally efficient, robust machine learning pathway in global climate models that only resolve aerosol composition; potentially mitigating the uncertainty of effective radiative forcing due to aerosol–cloud interactions (ERFaci) and improving confidence in assessment of anthropogenic contributions and climate change projections.

scholarly journals Effective radiative forcing in the aerosol–climate model CAM5.3-MARC-ARG

2018 ◽  
Author(s):  
Benjamin S. Grandey ◽  
Daniel Rothenberg ◽  
Alexander Avramov ◽  
Qinjian Jin ◽  
Hsiang-He Lee ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Climate Model ◽  
Regional Distribution ◽  
Radiative Effect ◽  
Mixing State ◽  
Anthropogenic Aerosols ◽  
Effective Radiative Forcing ◽  
Aerosol Module ◽  
Aerosol Emissions ◽  
Global Mean

Abstract. We quantify the effective radiative forcing (ERF) of anthropogenic aerosols modelled by the aerosol–climate model CAM5.3-MARC-ARG. CAM5.3-MARC-ARG is a new configuration of the Community Atmosphere Model version 5.3 (CAM5.3) in which the default aerosol module has been replaced by the two-Moment, Multi-Modal, Mixing-state-resolving Aerosol model for Research of Climate (MARC). CAM5.3-MARC-ARG uses the default ARG aerosol activation scheme, consistent with the default configuration of CAM5.3. We compute differences between simulations using year-1850 aerosol emissions and simulations using year-2000 aerosol emissions in order to assess the radiative effects of anthropogenic aerosols. We compare the aerosol column burdens, cloud properties, and radiative effects produced by CAM5.3-MARC-ARG with those produced by the default configuration of CAM5.3, which uses the modal aerosol module with three log-normal modes (MAM3). Compared with MAM3, we find that MARC produces stronger cooling via the direct radiative effect, stronger cooling via the surface albedo radiative effect, and stronger warming via the cloud longwave radiative effect. The global mean cloud shortwave radiative effect is similar between MARC and MAM3, although the regional distributions differ. Overall, MARC produces a global mean net ERF of −1.75 ± 0.04 W m−2, which is stronger than the global mean net ERF of −1.57 ± 0.04 W m−2 produced by MAM3. The regional distribution of ERF also differs between MARC and MAM3, largely due to differences in the regional distribution of the cloud shortwave radiative effect. We conclude that the specific representation of aerosols in global climate models, including aerosol mixing state, has important implications for climate modelling.

scholarly journals Quantifying cloud adjustments and the radiative forcing due to aerosol–cloud interactions in satellite observations of warm marine clouds

2020 ◽  
Vol 20 (10) ◽  
pp. 6225-6241 ◽  
Author(s):  
Alyson Douglas ◽  
Tristan L'Ecuyer
Keyword(s):  
Radiative Forcing ◽  
Local Environment ◽  
Satellite Observations ◽  
Cloud Environment ◽  
Climate Scenarios ◽  
Liquid Water Path ◽  
Sources Of Uncertainty ◽  
Aerosol Cloud ◽  
Effective Radiative Forcing ◽  
Aerosol Cloud Interactions

Abstract. Aerosol–cloud interactions and their resultant forcing remains one of the largest sources of uncertainty in future climate scenarios. The effective radiative forcing due to aerosol–cloud interactions (ERFaci) is a combination of two different effects, namely how aerosols modify cloud brightness (RFaci, intrinsic) and how cloud extent reacts to aerosol (cloud adjustments CA; extrinsic). Using satellite observations of warm clouds from the NASA A-Train constellation from 2007 to 2010 along with MERRA-2 Reanalysis and aerosol from the SPRINTARS model, we evaluate the ERFaci in warm, marine clouds and its components, the RFaciwarm and CAwarm, while accounting for the liquid water path and local environment. We estimate the ERFaciwarm to be -0.32±0.16 Wm−2. The RFaciwarm dominates the ERFaciwarm contributing 80 % (-0.21±0.15 Wm−2), while the CAwarm enhances this cooling by 20 % (-0.05±0.03 Wm−2). Both the RFaciwarm and CAwarm vary in magnitude and sign regionally and can lead to opposite, negating effects under certain environmental conditions. Without considering the two terms separately and without constraining cloud–environment interactions, weak regional ERFaciwarm signals may be erroneously attributed to a damped susceptibility to aerosol.

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