scholarly journals Overview: The CLoud-Aerosol-Radiation Interaction and Forcing: Year-2017 (CLARIFY-2017) measurement campaign

2020 ◽  
Author(s):  
Jim M. Haywood ◽  
Steven J. Abel ◽  
Paul A. Barrett ◽  
Nicolas Bellouin ◽  
Alan Blyth ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Climate Models ◽  
Numerical Models ◽  
Weather Prediction ◽  
Full Range ◽  
Radiative Properties ◽  
Radiation Budget ◽  
Atmospheric Measurements ◽  
Aerosol Cloud ◽  
Aerosol Cloud Interactions

Abstract. The representation of clouds, aerosols and cloud-aerosol-radiation impacts remain some of the largest uncertainties in climate change, limiting our ability to accurately reconstruct and predict future climate. The south-east Atlantic is a region where high atmospheric aerosol loadings and semi-permanent stratocumulus clouds are co-located, providing a natural laboratory for studying the full range of aerosol-radiation and aerosol-cloud interactions and their perturbations of the Earth’s radiation budget. While satellite measurements have provided some useful insights into aerosol-radiation and aerosol cloud interactions over the region, these observations do not have the spatial and temporal resolution, nor the required level of precision to allow for a process level assessment. Detailed measurements from high spatial and temporal resolution airborne atmospheric measurements in the region are very sparse, limiting their use in assessing the performance of aerosol modelling in numerical weather prediction and climate models. CLARIFY-2017 was a major consortium programme consisting of 5 principal UK universities with project partners from the UK Met Office and European and USA-based universities and research centres involved in the complementary ORACLES, LASIC and AEROCLO-sA projects. The aims of CLARIFY-2017 were four-fold; (1) to improve the representation and reduce uncertainty in model estimates of the direct, semi-direct and indirect radiative effect of absorbing biomass burning aerosols; (2) improve our knowledge and representation of the processes determining stratocumulus cloud microphysical and radiative properties and their transition to cumulus regimes; (3) challenge, validate and improve satellite retrievals of cloud and aerosol properties and their radiative impacts; (4) improve numerical models of cloud and aerosol and their impacts on radiation, weather and climate. This paper describes the modelling and measurement strategies central to the CLARIFY-2017 deployment of the FAAM BAe146 instrumented aircraft campaign, summarises the flight objectives and flight patterns, and highlights some key results from our initial analyses.

scholarly journals The CLoud–Aerosol–Radiation Interaction and Forcing: Year 2017 (CLARIFY-2017) measurement campaign

2021 ◽  
Vol 21 (2) ◽  
pp. 1049-1084 ◽  
Author(s):  
Jim M. Haywood ◽  
Steven J. Abel ◽  
Paul A. Barrett ◽  
Nicolas Bellouin ◽  
Alan Blyth ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Climate Models ◽  
Numerical Models ◽  
Weather Prediction ◽  
Full Range ◽  
Radiative Properties ◽  
Radiation Budget ◽  
Atmospheric Measurements ◽  
Aerosol Cloud ◽  
Aerosol Cloud Interactions

Abstract. The representations of clouds, aerosols, and cloud–aerosol–radiation impacts remain some of the largest uncertainties in climate change, limiting our ability to accurately reconstruct past climate and predict future climate. The south-east Atlantic is a region where high atmospheric aerosol loadings and semi-permanent stratocumulus clouds are co-located, providing an optimum region for studying the full range of aerosol–radiation and aerosol–cloud interactions and their perturbations of the Earth's radiation budget. While satellite measurements have provided some useful insights into aerosol–radiation and aerosol–cloud interactions over the region, these observations do not have the spatial and temporal resolution, nor the required level of precision to allow for a process-level assessment. Detailed measurements from high spatial and temporal resolution airborne atmospheric measurements in the region are very sparse, limiting their use in assessing the performance of aerosol modelling in numerical weather prediction and climate models. CLARIFY-2017 was a major consortium programme consisting of five principal UK universities with project partners from the UK Met Office and European- and USA-based universities and research centres involved in the complementary ORACLES, LASIC, and AEROCLO-sA projects. The aims of CLARIFY-2017 were fourfold: (1) to improve the representation and reduce uncertainty in model estimates of the direct, semi-direct, and indirect radiative effect of absorbing biomass burning aerosols; (2) to improve our knowledge and representation of the processes determining stratocumulus cloud microphysical and radiative properties and their transition to cumulus regimes; (3) to challenge, validate, and improve satellite retrievals of cloud and aerosol properties and their radiative impacts; (4) to improve the impacts of aerosols in weather and climate numerical models. This paper describes the modelling and measurement strategies central to the CLARIFY-2017 deployment of the FAAM BAe146 instrumented aircraft campaign, summarizes the flight objectives and flight patterns, and highlights some key results from our initial analyses.

scholarly journals Improving our fundamental understanding of the role of aerosol−cloud interactions in the climate system

2016 ◽  
Vol 113 (21) ◽  
pp. 5781-5790 ◽  
Author(s):  
John H. Seinfeld ◽  
Christopher Bretherton ◽  
Kenneth S. Carslaw ◽  
Hugh Coe ◽  
Paul J. DeMott ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiative Forcing ◽  
General Circulation ◽  
Climate Models ◽  
Large Range ◽  
General Circulation Models ◽  
Radiative Properties ◽  
Aerosol Cloud ◽  
Aerosol Cloud Interactions

The effect of an increase in atmospheric aerosol concentrations on the distribution and radiative properties of Earth’s clouds is the most uncertain component of the overall global radiative forcing from preindustrial time. General circulation models (GCMs) are the tool for predicting future climate, but the treatment of aerosols, clouds, and aerosol−cloud radiative effects carries large uncertainties that directly affect GCM predictions, such as climate sensitivity. Predictions are hampered by the large range of scales of interaction between various components that need to be captured. Observation systems (remote sensing, in situ) are increasingly being used to constrain predictions, but significant challenges exist, to some extent because of the large range of scales and the fact that the various measuring systems tend to address different scales. Fine-scale models represent clouds, aerosols, and aerosol−cloud interactions with high fidelity but do not include interactions with the larger scale and are therefore limited from a climatic point of view. We suggest strategies for improving estimates of aerosol−cloud relationships in climate models, for new remote sensing and in situ measurements, and for quantifying and reducing model uncertainty.

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 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.

scholarly journals An Evaluation of Radiative Transfer Simulations of Cloudy Scenes from a Numerical Weather Prediction Model at Sub-Millimetre Frequencies Using Airborne Observations

2020 ◽  
Vol 12 (17) ◽  
pp. 2758
Author(s):  
Stuart Fox
Keyword(s):  
Optical Properties ◽  
Radiative Transfer ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Full Range ◽  
Numerical Weather Prediction Model ◽  
Atmospheric Measurements ◽  
Numerical Weather ◽  
Nwp Model ◽  
Cloud Ice

The Ice Cloud Imager (ICI) will be launched on the next generation of EUMETSAT polar-orbiting weather satellites and make passive observations between 183 and 664 GHz which are sensitive to scattering from cloud ice. These observations have the potential to improve weather forecasts through direct assimilation using "all-sky" methods which have been successfully applied to microwave observations up to 200 GHz in current operational systems. This requires sufficiently accurate representations of cloud ice in both numerical weather prediction (NWP) and radiative transfer models. In this study, atmospheric fields from a high-resolution NWP model are used to drive radiative transfer simulations using the Atmospheric Radiative Transfer Simulator (ARTS) and a recently released database of cloud ice optical properties. The simulations are evaluated using measurements between 89 and 874 GHz from five case studies of ice and mixed-phase clouds observed by the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 research aircraft. The simulations are strongly sensitive to the assumed cloud ice optical properties, but by choosing an appropriate ice crystal model it is possible to simulate realistic brightness temperatures over the full range of sub-millimetre frequencies. This suggests that sub-millimetre observations have the potential to be assimilated into NWP models using the all-sky method.

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

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.

scholarly journals EuLerian Identification of Ascending air Streams (ELIAS 2.0) in Numerical Weather Prediction and Climate Models. Part II: Model application to different data sets

2021 ◽  
Author(s):  
Julian Francesco Quinting ◽  
Christian Michael Grams ◽  
Annika Oertel ◽  
Moritz Pickl
Keyword(s):  
Temporal Resolution ◽  
Numerical Weather Prediction ◽  
Climate Models ◽  
Weather Prediction ◽  
Future Research ◽  
Data Sets ◽  
Ensemble Forecasts ◽  
Numerical Weather ◽  
Spatio Temporal ◽  
Air Streams

Abstract. Warm conveyor belts (WCBs) affect the atmospheric dynamics in midlatitudes and are highly relevant for total and extreme precipitation in many parts of the extratropics. Thus, these air streams and their effect on midlatitude weather should be well represented in numerical weather prediction (NWP) and climate models. This study applies newly developed convolutional neural network (CNN) models which allow the identification of footprints of WCB inflow, ascent, and outflow from a limited number of predictor fields at comparably low spatio-temporal resolution. The goal of the study is to demonstrate the versatile applicability of the CNN models to different data sets and that their application yields qualitatively and quantitatively similar results as their trajectory-based counterpart which is most frequently used to objectively identify WCBs but requires data at higher spatio-temporal resolution which is often not available and is computationally more expensive. First, an application to reanalyses reveals that the well-known relationship between WCB ascent and extratropical cyclones as well as between WCB outflow and blocking anticyclones is also found for WCB footprints identified with the CNN models. Second, the application to Japanese 55-year reanalyses shows how the CNN models may be used to identify erroneous predictor fields that deteriorate the models' reliability. Third, a verification of WCBs in operational European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts for three Northern Hemisphere winters reveals systematic biases over the North Atlantic with both the trajectory-based approach and the CNN models. The ensemble forecasts' skill tends to be lower when being evaluated with the trajectory approach due to the fine-scale structure of WCB footprints in comparison to the rather smooth CNN-based WCB footprints. A final example demonstrates the applicability of the CNN models to a convection permitting simulation with the ICOsahedral Nonhydrostatic (ICON) NWP model. Our study illustrates that deep learning methods can be used efficiently to support process-oriented understanding of forecast error and model biases, and opens numerous directions for future research.

A synthesis of observations of aerosol-cloud interactions over the pristine, biologically active Southern Ocean and their implications for global climate model predictions

2021 ◽  
Author(s):  
Isabel L. McCoy ◽  
Daniel T. McCoy ◽  
Robert Wood ◽  
Christopher S. Bretherton ◽  
Leighton Regayre ◽  
...  
Keyword(s):  
Southern Ocean ◽  
Radiative Forcing ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Model ◽  
Global Climate Models ◽  
Biologically Active ◽  
Aerosol Cloud ◽  
Aerosol Cloud Interactions ◽  
Industrial State

<div> <p>The change in planetary albedo due to aerosol-cloud interactions (aci) during the industrial era is the leading source of uncertainty in inferring Earth's climate sensitivity to increased greenhouse gases from the historical record. Examining pristine environments such as the Southern Ocean (SO) helps us to understand the pre-industrial state and constrain the change in cloud brightness over the industrial period associated with aci. This study presents two methods of utilizing observations of pristine environments to examine climate models and our understanding of the pre-industrial state.</p> </div><div> <p>First, cloud droplet number concentration (<em>N<sub>d</sub></em>) is used as an indicator of aci. Global climate models (GCMs) show that the hemispheric contrast in liquid cloud <em>N<sub>d</sub></em> between the pristine SO and the polluted Northern Hemisphere observed in the present-day can be used<strong> </strong>as a proxy for the increase in <em>N<sub>d</sub></em> from the pre-industrial. A hemispheric difference constraint developed from MODIS satellite observations indicates that pre-industrial <em>N<sub>d</sub></em> may have been higher than previously thought and provides an estimate of radiative forcing associated with aci between -1.2 and -0.6 Wm<sup>-2</sup>. Comparisons with MODIS <em>N<sub>d  </sub></em>highlight significant GCM discrepancies in pristine, biologically active regions.</p> </div><div> <p>Second, aerosol and cloud microphysical observations from a recent SO aircraft campaign are used to identify two potentially important mechanisms that are incomplete or missing in GCMs: i) production of new aerosol particles through synoptic uplift, and ii) buffering of <em>N<sub>d</sub></em> against precipitation removal by small, Aitken mode aerosols entrained from the free troposphere. The latter may significantly contribute to the high, summertime SO <em>N<sub>d</sub></em> levels which persist despite precipitation depletion associated with mid-latitude storm systems. Observational comparisons with nudged Community Atmosphere Model version 6 (CAM6) hindcasts show low-biased SO <em>N<sub>d  </sub></em>is linked to under-production of free-tropospheric Aitken aerosol which drives low-biases in cloud condensation nuclei number and likely discrepancies in composition. These results have important implications for the ability of current GCMs to capture aci in pristine environments.</p> </div>

scholarly journals Improving the Temporal Resolution Problem by Localized Gridpoint Nudging in Regional Weather and Climate Models

2011 ◽  
Vol 139 (4) ◽  
pp. 1292-1304 ◽  
Author(s):  
Piet Termonia ◽  
Daan Degrauwe ◽  
Rafiq Hamdi
Keyword(s):  
Climate Models ◽  
Numerical Models ◽  
Weather Prediction ◽  
Regional Climate ◽  
Coupled Model ◽  
Regional Climate Models ◽  
Pass Filter ◽  
Atmospheric Sciences ◽  
Compact Region ◽  
Resolution Problem

Most regional numerical models in the atmospheric sciences use temporally interpolated data provided by other low-resolution models, either for a gridpoint coupling at their lateral boundaries or for a spectral nudging of the large scales in the entire domain. In some cases, such as fast-propagating storms, these interpolations can seriously corrupt the meteorological fields. This article shows how to use an operational high-pass filter of the surface pressure field to detect and to localize a propagating storm, and to use this information to locally reinject the available uncorrupted storm in the coupled model. This is achieved by applying a technique of gridpoint nudging in a subarea of the domain, limited to a compact region around the eye of the depression. As an application it is shown that this restores the strength of the storm, while leaving the model state in the rest of the domain quasi intact. It is then discussed how this can improve numerical weather prediction and regional climate models.

scholarly journals A long-term study of aerosol–cloud interactions and their radiative effect at a mid latitude continental site using ground-based measurements

2016 ◽  
Author(s):  
Elisa T. Sena ◽  
Allison McComiskey ◽  
Graham Feingold
Keyword(s):  
Great Plains ◽  
Climate Models ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Radiative Effect ◽  
Aerosol Cloud ◽  
Long Term Study ◽  
Cloud Radiative Effect ◽  
Aerosol Cloud Interactions

Abstract. Empirical estimates of the microphysical response of cloud droplet size distribution to aerosol perturbations are commonly used to constrain aerosol–cloud interactions in climate models. Instead of empirical microphysical estimates, here macroscopic variables are analyzed to address the influences of aerosol particles and meteorological descriptors on instantaneous cloud albedo and radiative effect of shallow liquid water clouds. Long-term ground-based measurements from the Atmospheric Radiation Measurement (ARM) Program over the Southern Great Plains are used. A broad statistical analysis was performed on 14-years of coincident measurements of low clouds, aerosol and meteorological properties. Two cases representing conflicting results regarding the relationship between the aerosol and the cloud radiative effect were selected and studied in greater detail. Microphysical estimates are shown to be very uncertain and to depend strongly on the methodology, retrieval technique, and averaging scale. For this continental site, the results indicate that the influence of aerosol on shallow cloud radiative effect and albedo is weak and that macroscopic cloud properties and dynamics play a much larger role in determining the instantaneous cloud radiative effect compared to microphysical effects.

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