Constraining the instantaneous aerosol influence on cloud albedo

Much of the uncertainty in estimates of the anthropogenic forcing of climate change comes from uncertainties in the instantaneous effect of aerosols on cloud albedo, known as the Twomey effect or the radiative forcing from aerosol–cloud interactions (RFaci), a component of the total or effective radiative forcing. Because aerosols serving as cloud condensation nuclei can have a strong influence on the cloud droplet number concentration (Nd), previous studies have used the sensitivity of theNdto aerosol properties as a constraint on the strength of the RFaci. However, recent studies have suggested that relationships between aerosol and cloud properties in the present-day climate may not be suitable for determining the sensitivity of theNdto anthropogenic aerosol perturbations. Using an ensemble of global aerosol–climate models, this study demonstrates how joint histograms betweenNdand aerosol properties can account for many of the issues raised by previous studies. It shows that if the anthropogenic contribution to the aerosol is known, the RFaci can be diagnosed to within 20% of its actual value. The accuracy of different aerosol proxies for diagnosing the RFaci is investigated, confirming that using the aerosol optical depth significantly underestimates the strength of the aerosol–cloud interactions in satellite data.

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Analysis of polarimetric satellite measurements suggests stronger cooling due to aerosol-cloud interactions

Nature Communications ◽

10.1038/s41467-019-13372-2 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 9

Author(s):

Otto P. Hasekamp ◽

Edward Gryspeerdt ◽

Johannes Quaas

Keyword(s):

Radiative Forcing ◽

Cloud Condensation Nuclei ◽

Cloud Droplet ◽

Cloud Formation ◽

Anthropogenic Aerosol ◽

Intergovernmental Panel ◽

Aerosol Cloud ◽

Cloud Properties ◽

Aerosol Cloud Interactions ◽

Aerosol Emissions

AbstractAnthropogenic aerosol emissions lead to an increase in the amount of cloud condensation nuclei and consequently an increase in cloud droplet number concentration and cloud albedo. The corresponding negative radiative forcing due to aerosol cloud interactions (RF$${}_{{\rm{aci}}}$$aci) is one of the most uncertain radiative forcing terms as reported in the 5th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). Here we show that previous observation-based studies underestimate aerosol-cloud interactions because they used measurements of aerosol optical properties that are not directly related to cloud formation and are hampered by measurement uncertainties. We have overcome this problem by the use of new polarimetric satellite retrievals of the relevant aerosol properties (aerosol number, size, shape). The resulting estimate of RF$${}_{{\rm{aci}}}$$aci = −1.14 Wm$${}^{{\rm{-2}}}$$-2 (range between −0.84 and −1.72 Wm$${}^{{\rm{-2}}}$$-2) is more than a factor 2 stronger than the IPCC estimate that includes also other aerosol induced changes in cloud properties.

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

10.21203/rs.3.rs-244416/v1 ◽

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.

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Incorporation of advanced aerosol activation treatments into CESM/CAM5: model evaluation and impacts on aerosol indirect effects

Atmospheric Chemistry and Physics ◽

10.5194/acp-14-7485-2014 ◽

2014 ◽

Vol 14 (14) ◽

pp. 7485-7497 ◽

Cited By ~ 33

Author(s):

B. Gantt ◽

J. He ◽

X. Zhang ◽

Y. Zhang ◽

A. Nenes

Keyword(s):

Indirect Effects ◽

Radiative Forcing ◽

Cloud Condensation Nuclei ◽

Condensation Nuclei ◽

Activation Kinetics ◽

Aerosol Cloud ◽

Model Version ◽

Cloud Properties ◽

Aerosol Cloud Interactions ◽

Aerosol Activation

Abstract. One of the greatest sources of uncertainty in the science of anthropogenic climate change is from aerosol–cloud interactions. The activation of aerosols into cloud droplets is a direct microphysical linkage between aerosols and clouds; parameterizations of this process link aerosol with cloud condensation nuclei (CCN) and the resulting indirect effects. Small differences between parameterizations can have a large impact on the spatiotemporal distributions of activated aerosols and the resulting cloud properties. In this work, we incorporate a series of aerosol activation schemes into the Community Atmosphere Model version 5.1.1 within the Community Earth System Model version 1.0.5 (CESM/CAM5) which include factors such as insoluble aerosol adsorption and giant cloud condensation nuclei (CCN) activation kinetics to understand their individual impacts on global-scale cloud droplet number concentration (CDNC). Compared to the existing activation scheme in CESM/CAM5, this series of activation schemes increase the computation time by ~10% but leads to predicted CDNC in better agreement with satellite-derived/in situ values in many regions with high CDNC but in worse agreement for some regions with low CDNC. Large percentage changes in predicted CDNC occur over desert and oceanic regions, owing to the enhanced activation of dust from insoluble aerosol adsorption and reduced activation of sea spray aerosol after accounting for giant CCN activation kinetics. Comparison of CESM/CAM5 predictions against satellite-derived cloud optical thickness and liquid water path shows that the updated activation schemes generally improve the low biases. Globally, the incorporation of all updated schemes leads to an average increase in column CDNC of 150% and an increase (more negative) in shortwave cloud forcing of 12%. With the improvement of model-predicted CDNCs and better agreement with most satellite-derived cloud properties in many regions, the inclusion of these aerosol activation processes should result in better predictions of radiative forcing from aerosol–cloud interactions.

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Reducing uncertainties in satellite estimates of aerosol-cloud interactions over the subtropical ocean by integrating vertically resolved aerosol observations

10.5194/acp-2019-892 ◽

2019 ◽

Author(s):

David Painemal ◽

Fu-Lung Chang ◽

Richard Ferrare ◽

Sharon Burton ◽

Zhujun Li ◽

...

Keyword(s):

Climate Models ◽

Global Climate ◽

Cloud Condensation Nuclei ◽

Cloud Droplet ◽

Global Climate Models ◽

Orthogonal Polarization ◽

Near Surface ◽

Aerosol Cloud ◽

Error Characterization ◽

Aerosol Cloud Interactions

Abstract. Satellite quantification of aerosol effects on clouds relies on aerosol optical depth (AOD) as a proxy for aerosol concentration or cloud condensation nuclei (CCN). However, the lack of error characterization of satellite-based results hampers their use for the evaluation and improvement of global climate models. We show that the use of AOD for assessing aerosol-cloud interactions (ACI) is inadequate over vast oceanic areas in the subtropics. Instead, we postulate that a more physical approach that consists of matching vertically resolved aerosol data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite at the cloud-layer height with Aqua Moderate-resolution Imaging Spectroradiometer (MODIS) cloud retrievals reduces uncertainties in satellite-based ACI estimates. Combined aerosol extinction coefficients (σ) below cloud-top (σBC) from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and cloud droplet number concentrations (Nd) from Aqua-MODIS yield high correlations across a broad range of σBC values, with σBC quartile correlations > 0.78. In contrast, CALIOP-based AOD yields correlations with MODIS Nd of less than 0.62 for the two lower AOD quartiles. Moreover, σBC explains 41 % of the spatial variance in MODIS Nd, whereas AOD only explains 17 %, primarily caused by the lack of spatial covariability in the eastern Pacific. Compared with σBC, near-surface σ weakly correlates in space with MODIS Nd, accounting for a 16 % variance. It is concluded that the linear regression calculated from ln(Nd)−ln(σBC) (the standard method for quantifying ACI) is more physically meaningful than that derived from the Nd−AOD pair.

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The hemispheric contrast in cloud microphysical properties constrains aerosol forcing

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1922502117 ◽

2020 ◽

Vol 117 (32) ◽

pp. 18998-19006 ◽

Cited By ~ 2

Author(s):

Isabel L. McCoy ◽

Daniel T. McCoy ◽

Robert Wood ◽

Leighton Regayre ◽

Duncan Watson-Parris ◽

...

Keyword(s):

Southern Ocean ◽

Northern Hemisphere ◽

Radiative Forcing ◽

Climate Models ◽

Cloud Droplet ◽

Number Concentration ◽

Global Climate Models ◽

Aerosol Cloud ◽

Cloud Droplet Number Concentration ◽

Aerosol Cloud Interactions

The change in planetary albedo due to aerosol−cloud interactions during the industrial era is the leading source of uncertainty in inferring Earth’s climate sensitivity to increased greenhouse gases from the historical record. The variable that controls aerosol−cloud interactions in warm clouds is droplet number concentration. Global climate models demonstrate that the present-day hemispheric contrast in cloud droplet number concentration between the pristine Southern Hemisphere and the polluted Northern Hemisphere oceans can be used as a proxy for anthropogenically driven change in cloud droplet number concentration. Remotely sensed estimates constrain this change in droplet number concentration to be between 8 cm−3and 24 cm−3. By extension, the radiative forcing since 1850 from aerosol−cloud interactions is constrained to be −1.2 W⋅m−2to −0.6 W⋅m−2. The robustness of this constraint depends upon the assumption that pristine Southern Ocean droplet number concentration is a suitable proxy for preindustrial concentrations. Droplet number concentrations calculated from satellite data over the Southern Ocean are high in austral summer. Near Antarctica, they reach values typical of Northern Hemisphere polluted outflows. These concentrations are found to agree with several in situ datasets. In contrast, climate models show systematic underpredictions of cloud droplet number concentration across the Southern Ocean. Near Antarctica, where precipitation sinks of aerosol are small, the underestimation by climate models is particularly large. This motivates the need for detailed process studies of aerosol production and aerosol−cloud interactions in pristine environments. The hemispheric difference in satellite estimated cloud droplet number concentration implies preindustrial aerosol concentrations were higher than estimated by most models.

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Global distribution and climate forcing of marine organic aerosol – Part 2: Effects on cloud properties and radiative forcing

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-12-7453-2012 ◽

2012 ◽

Vol 12 (3) ◽

pp. 7453-7474 ◽

Cited By ~ 2

Author(s):

B. Gantt ◽

J. Xu ◽

N. Meskhidze ◽

Y. Zhang ◽

A. Nenes ◽

...

Keyword(s):

Radiative Forcing ◽

Climate Models ◽

North Pacific Ocean ◽

Cloud Condensation Nuclei ◽

Organic Aerosols ◽

Organic Aerosol ◽

Liquid Water Path ◽

Anthropogenic Aerosol ◽

Low Level ◽

Cloud Properties

Abstract. In the first part of this paper series (Meskhidze et al., 2011), a treatment of marine organic aerosols (including primary organic aerosol, secondary organic aerosols, and methane sulfonate) was implemented into the Community Atmosphere Model version 5 (CAM5) with a 7-mode Modal Aerosol Module. A series of simulations was conducted to quantify the changes in aerosol and cloud condensation nuclei concentrations in the marine boundary layer. In this study, changes in the cloud microphysical properties and radiative forcing resulting from marine organic aerosols are assessed. Model simulations show that the anthropogenic aerosol indirect forcing (AIF) predicted by CAM5 is decreased in absolute magnitude by up to ~0.10 W m−2 (8%) when marine organic aerosols are included. Changes in the AIF from marine organic aerosols are associated with small global increases in low-level in-cloud droplet number concentration and liquid water path of ~1.3 cm−3 (~1.6%) and 0.2 g m−2 (0.5%), respectively. Areas especially sensitive to changes in cloud properties due to marine organic aerosol include the Southern Ocean, North Pacific Ocean, and North Atlantic Ocean, all of which are characterized by high marine organic emission rates. As climate models are particularly sensitive to the background aerosol concentration, this small but non-negligible change in the AIF due to marine organic aerosols provides a notable link for ocean-ecosystem marine low-level cloud interactions and may be a candidate for consideration in future earth system models.

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Reducing uncertainties in satellite estimates of aerosol–cloud interactions over the subtropical ocean by integrating vertically resolved aerosol observations

Atmospheric Chemistry and Physics ◽

10.5194/acp-20-7167-2020 ◽

2020 ◽

Vol 20 (12) ◽

pp. 7167-7177 ◽

Cited By ~ 1

Author(s):

David Painemal ◽

Fu-Lung Chang ◽

Richard Ferrare ◽

Sharon Burton ◽

Zhujun Li ◽

...

Keyword(s):

Climate Models ◽

Global Climate ◽

Cloud Condensation Nuclei ◽

Cloud Droplet ◽

Global Climate Models ◽

Orthogonal Polarization ◽

Near Surface ◽

Aerosol Cloud ◽

Error Characterization ◽

Aerosol Cloud Interactions

Abstract. Satellite quantification of aerosol effects on clouds relies on aerosol optical depth (AOD) as a proxy for aerosol concentration or cloud condensation nuclei (CCN). However, the lack of error characterization of satellite-based results hampers their use for the evaluation and improvement of global climate models. We show that the use of AOD for assessing aerosol–cloud interactions (ACIs) is inadequate over vast oceanic areas in the subtropics. Instead, we postulate that a more physical approach that consists of matching vertically resolved aerosol data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite at the cloud-layer height with Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua cloud retrievals reduces uncertainties in satellite-based ACI estimates. Combined aerosol extinction coefficients (σ) below cloud top (σBC) from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and cloud droplet number concentrations (Nd) from MODIS Aqua yield high correlations across a broad range of σBC values, with σBC quartile correlations ≥0.78. In contrast, CALIOP-based AOD yields correlations with MODIS Nd of 0.54–0.62 for the two lower AOD quartiles. Moreover, σBC explains 41 % of the spatial variance in MODIS Nd, whereas AOD only explains 17 %, primarily caused by the lack of spatial covariability in the eastern Pacific. Compared with σBC, near-surface σ weakly correlates in space with MODIS Nd, accounting for a 16 % variance. It is concluded that the linear regression calculated from ln(Nd)–ln(σBC) (the standard method for quantifying ACIs) is more physically meaningful than that derived from the Nd–AOD pair.

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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

10.5194/acp-2019-631 ◽

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.

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A new statistical approach to improve the satellite-based estimation of the radiative forcing by aerosol–cloud interactions

Atmospheric Chemistry and Physics ◽

10.5194/acp-17-3687-2017 ◽

2017 ◽

Vol 17 (5) ◽

pp. 3687-3698 ◽

Cited By ~ 2

Author(s):

Piyushkumar N. Patel ◽

Johannes Quaas ◽

Raj Kumar

Keyword(s):

Radiative Forcing ◽

Statistical Approach ◽

Least Square ◽

Multilinear Regression ◽

Anthropogenic Aerosol ◽

Aerosol Cloud ◽

Cloud Properties ◽

Planetary Albedo ◽

Aerosol Cloud Interactions ◽

The Relationship

Abstract. In a previous study of Quaas et al. (2008) the radiative forcing by anthropogenic aerosol due to aerosol–cloud interactions, RFaci, was obtained by a statistical analysis of satellite retrievals using a multilinear regression. Here we employ a new statistical approach to obtain the fitting parameters, determined using a nonlinear least square statistical approach for the relationship between planetary albedo and cloud properties and, further, for the relationship between cloud properties and aerosol optical depth. In order to verify the performance, the results from both statistical approaches (previous and present) were compared to the results from radiative transfer simulations over three regions for different seasons. We find that the results of the new statistical approach agree well with the simulated results both over land and ocean. The new statistical approach increases the correlation by 21–23 % and reduces the error compared to the previous approach.

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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

Atmospheric Chemistry and Physics ◽

10.5194/acp-20-1607-2020 ◽

2020 ◽

Vol 20 (3) ◽

pp. 1607-1626 ◽

Cited By ~ 1

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.

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