Observed Southern Ocean Cloud Properties and Shortwave Reflection. Part II: Phase Changes and Low Cloud Feedback*

2014 ◽  
Vol 27 (23) ◽  
pp. 8858-8868 ◽  
Author(s):  
Daniel T. McCoy ◽  
Dennis L. Hartmann ◽  
Daniel P. Grosvenor
Keyword(s):  
Solar Radiation ◽  
Optical Depth ◽  
Climate Models ◽  
Phase Changes ◽  
Shortwave Radiation ◽  
Cloud Optical Depth ◽  
Cloud Properties ◽  
Low Cloud ◽  
Model Transition ◽  
Comparable Magnitude

Abstract Climate models produce an increase in cloud optical depth in midlatitudes associated with climate warming, but the magnitude of this increase and its impact on reflected solar radiation vary from model to model. Transition from ice to liquid in midlatitude clouds is thought to be one mechanism for producing increased cloud optical depth. Here observations of cloud properties are used from a suite of remote sensing instruments to estimate the effect of conversion of ice to liquid associated with warming on reflected solar radiation in the latitude band from 40° to 60°S. The calculated increase in upwelling shortwave radiation (SW↑) is found to be important and of comparable magnitude to the increase in SW↑ associated with warming-induced increases of optical depth in climate models. The region where the authors' estimate increases SW↑ extends farther equatorward than the region where optical depth increases with warming in models. This difference is likely caused by other mechanisms at work in the models but is also sensitive to the amount of ice present in climate models and its susceptibility to warming.

scholarly journals Low-cloud optical depth feedback in climate models

2014 ◽  
Vol 119 (10) ◽  
pp. 6052-6065 ◽  
Author(s):  
Neil D. Gordon ◽  
Stephen A. Klein
Keyword(s):  
Optical Depth ◽  
Climate Models ◽  
Cloud Optical Depth ◽  
Low Cloud

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 Origins of the Solar Radiation Biases over the Southern Ocean in CFMIP2 Models*

2014 ◽  
Vol 27 (1) ◽  
pp. 41-56 ◽  
Author(s):  
A. Bodas-Salcedo ◽  
K. D. Williams ◽  
M. A. Ringer ◽  
I. Beau ◽  
J. N. S. Cole ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Southern Ocean ◽  
Climate Models ◽  
Radiation Budget ◽  
Shortwave Radiation ◽  
Leading Role ◽  
Cloud Properties ◽  
Daily Data ◽  
Cloud Regime ◽  
Cloud Regimes

Abstract Current climate models generally reflect too little solar radiation over the Southern Ocean, which may be the leading cause of the prevalent sea surface temperature biases in climate models. The authors study the role of clouds on the radiation biases in atmosphere-only simulations of the Cloud Feedback Model Intercomparison Project phase 2 (CFMIP2), as clouds have a leading role in controlling the solar radiation absorbed at those latitudes. The authors composite daily data around cyclone centers in the latitude band between 40° and 70°S during the summer. They use cloud property estimates from satellite to classify clouds into different regimes, which allow them to relate the cloud regimes and their associated radiative biases to the meteorological conditions in which they occur. The cloud regimes are defined using cloud properties retrieved using passive sensors and may suffer from the errors associated with this type of retrievals. The authors use information from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar to investigate in more detail the properties of the “midlevel” cloud regime. Most of the model biases occur in the cold-air side of the cyclone composite, and the cyclone composite accounts for most of the climatological error in that latitudinal band. The midlevel regime is the main contributor to reflected shortwave radiation biases. CALIPSO data show that the midlevel cloud regime is dominated by two main cloud types: cloud with tops actually at midlevel and low-level cloud. Improving the simulation of these cloud types should help reduce the biases in the simulation of the solar radiation budget in the Southern Ocean in climate models.

scholarly journals The interdependence of continental warm cloud properties derived from unexploited solar background signals in ground-based lidar measurements

2014 ◽  
Vol 14 (16) ◽  
pp. 8389-8401 ◽  
Author(s):  
J. C. Chiu ◽  
J. A. Holmes ◽  
R. J. Hogan ◽  
E. J. O'Connor
Keyword(s):  
Optical Depth ◽  
Effective Radius ◽  
Liquid Water Path ◽  
Cloud Optical Depth ◽  
Cloud Properties ◽  
Warm Cloud ◽  
Lidar Measurements ◽  
Atmospheric Radiation Measurement ◽  
Ground Based Lidar ◽  
Geometric Thickness

Abstract. We have extensively analysed the interdependence between cloud optical depth, droplet effective radius, liquid water path (LWP) and geometric thickness for stratiform warm clouds using ground-based observations. In particular, this analysis uses cloud optical depths retrieved from untapped solar background signals that are previously unwanted and need to be removed in most lidar applications. Combining these new optical depth retrievals with radar and microwave observations at the Atmospheric Radiation Measurement (ARM) Climate Research Facility in Oklahoma during 2005–2007, we have found that LWP and geometric thickness increase and follow a power-law relationship with cloud optical depth regardless of the presence of drizzle; LWP and geometric thickness in drizzling clouds can be generally 20–40% and at least 10% higher than those in non-drizzling clouds, respectively. In contrast, droplet effective radius shows a negative correlation with optical depth in drizzling clouds and a positive correlation in non-drizzling clouds, where, for large optical depths, it asymptotes to 10 μm. This asymptotic behaviour in non-drizzling clouds is found in both the droplet effective radius and optical depth, making it possible to use simple thresholds of optical depth, droplet size, or a combination of these two variables for drizzle delineation. This paper demonstrates a new way to enhance ground-based cloud observations and drizzle delineations using existing lidar networks.

scholarly journals The interdependence of continental warm cloud properties derived from unexploited solar background signal in ground-based lidar measurements

2014 ◽  
Vol 14 (7) ◽  
pp. 8963-8996
Author(s):  
J. C. Chiu ◽  
J. A. Holmes ◽  
R. J. Hogan ◽  
E. J. O'Connor
Keyword(s):  
Optical Depth ◽  
Effective Radius ◽  
Liquid Water Path ◽  
Background Signal ◽  
Cloud Optical Depth ◽  
Cloud Properties ◽  
Lidar Measurements ◽  
Atmospheric Radiation Measurement ◽  
Ground Based Lidar ◽  
Geometric Thickness

Abstract. We have extensively analysed the interdependence between cloud optical depth, droplet effective radius, liquid water path (LWP) and geometric thickness for stratiform warm clouds using ground-based observations. In particular, this analysis uses cloud optical depths retrieved from untapped solar background signal that is previously unwanted and needs to be removed in most lidar applications. Combining these new optical depth retrievals with radar and microwave observations at the Atmospheric Radiation Measurement (ARM) Climate Research Facility in Oklahoma during 2005–2007, we have found that LWP and geometric thickness increase and follow a power-law relationship with cloud optical depth regardless of the presence of drizzle; LWP and geometric thickness in drizzling clouds can be generally 20–40% and at least 10% higher than those in non-drizzling clouds, respectively. In contrast, droplet effective radius shows a negative correlation with optical depth in drizzling clouds, while it increases with optical depth and reaches an asymptote of 10 μm in non-drizzling clouds. This asymptotic behaviour in non-drizzling clouds is found in both droplet effective radius and optical depth, making it possible to use simple thresholds of optical depth, droplet size, or a combination of these two variables for drizzle delineation. This paper demonstrates a new way to enhance ground-based cloud observations and drizzle delineations using existing lidar networks.

Observed Southern Ocean Cloud Properties and Shortwave Reflection. Part I: Calculation of SW Flux from Observed Cloud Properties*

2014 ◽  
Vol 27 (23) ◽  
pp. 8836-8857 ◽  
Author(s):  
Daniel T. McCoy ◽  
Dennis L. Hartmann ◽  
Daniel P. Grosvenor
Keyword(s):  
Seasonal Variation ◽  
Solar Radiation ◽  
Southern Ocean ◽  
Water Content ◽  
Liquid Water ◽  
Effective Radius ◽  
Shortwave Radiation ◽  
Liquid Water Path ◽  
Cloud Liquid Water ◽  
Cloud Properties

Abstract The sensitivity of the reflection of shortwave radiation over the Southern Ocean to the cloud properties there is estimated using observations from a suite of passive and active satellite instruments in combination with radiative transfer modeling. A composite cloud property observational data description is constructed that consistently incorporates mean cloud liquid water content, ice water content, liquid and ice particle radius information, vertical structure, vertical overlap, and spatial aggregation of cloud water as measured by optical depth versus cloud-top pressure histograms. The observational datasets used are Moderate Resolution Imaging Spectroradiometer (MODIS) effective radius filtered to mitigate solar zenith angle bias, the Multiangle Imaging Spectroradiometer (MISR) cloud-top height–optical depth (CTH–OD) histogram, the liquid water path from the University of Wisconsin dataset, and ice cloud properties from CloudSat. This cloud database is used to compute reflected shortwave radiation as a function of month and location over the ocean from 40° to 60°S, which compares well with observations of reflected shortwave radiation. This calculation is then used to test the sensitivity of the seasonal variation of shortwave reflection to the observed seasonal variation of cloud properties. Effective radius decreases during the summer season, which results in an increase in reflected solar radiation of 4–8 W m−2 during summer compared to what would be reflected if the effective radius remained constant at its annual-mean value. Summertime increases in low cloud fraction similarly increase the summertime reflection of solar radiation by 9–11 W m−2. In-cloud liquid water path is less in summertime, causing the reflected solar radiation to be 1–4 W m−2 less.

scholarly journals The impact of neglecting ice phase on cloud optical depth retrievals from AERONET cloud mode observations

2019 ◽  
Vol 12 (9) ◽  
pp. 5087-5099 ◽  
Author(s):  
Jonathan K. P. Shonk ◽  
Jui-Yuan Christine Chiu ◽  
Alexander Marshak ◽  
David M. Giles ◽  
Chiung-Huei Huang ◽  
...  
Keyword(s):  
Linear Equation ◽  
Optical Depth ◽  
Climate Models ◽  
Deep Convection ◽  
Surface Flux ◽  
Climate Modelling ◽  
Cloud Optical Depth ◽  
The Impact ◽  
Ice Fraction

Abstract. Clouds present many challenges to climate modelling. To develop and verify the parameterisations needed to allow climate models to represent cloud structure and processes, there is a need for high-quality observations of cloud optical depth from locations around the world. Retrievals of cloud optical depth are obtainable from radiances measured by Aerosol Robotic Network (AERONET) radiometers in “cloud mode” using a two-wavelength retrieval method. However, the method is unable to detect cloud phase, and hence assumes that all of the cloud in a profile is liquid. This assumption has the potential to introduce errors into long-term statistics of retrieved optical depth for clouds that also contain ice. Using a set of idealised cloud profiles we find that, for optical depths above 20, the fractional error in retrieved optical depth is a linear function of the fraction of the optical depth that is due to the presence of ice cloud (“ice fraction”). Clouds that are entirely ice have positive errors with magnitudes of the order of 55 % to 70 %. We derive a simple linear equation that can be used as a correction at AERONET sites where ice fraction can be independently estimated. Using this linear equation, we estimate the magnitude of the error for a set of cloud profiles from five sites of the Atmospheric Radiation Measurement programme. The dataset contains separate retrievals of ice and liquid retrievals; hence ice fraction can be estimated. The magnitude of the error at each location was related to the relative frequencies of occurrence in thick frontal cloud at the mid-latitude sites and of deep convection at the tropical sites – that is, of deep cloud containing both ice and liquid particles. The long-term mean optical depth error at the five locations spans the range 2–4, which we show to be small enough to allow calculation of top-of-atmosphere flux to within 10 % and surface flux to about 15 %.

scholarly journals Optical instruments synergy in determination of optical depth of thin clouds

2018 ◽  
Vol 176 ◽  
pp. 08008
Author(s):  
Daniela Viviana Vlăduţescu ◽  
Stephen E. Schwartz ◽  
Dong Huang
Keyword(s):  
Optical Depth ◽  
Rayleigh Scattering ◽  
Climate Models ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Doppler Lidar ◽  
Cloud Optical Depth ◽  
Cloud Structure ◽  
Optical Instruments

Optically thin clouds have a strong radiative effect and need to be represented accurately in climate models. Cloud optical depth of thin clouds was retrieved using high resolution digital photography, lidar, and a radiative transfer model. The Doppler Lidar was operated at 1.5 μm, minimizing return from Rayleigh scattering, emphasizing return from aerosols and clouds. This approach examined cloud structure on scales 3 to 5 orders of magnitude finer than satellite products, opening new avenues for examination of cloud structure and evolution.

scholarly journals A Simple Stochastic Model for Generating Broken Cloud Optical Depth and Cloud-Top Height Fields

2009 ◽  
Vol 66 (1) ◽  
pp. 92-104 ◽  
Author(s):  
Sergei M. Prigarin ◽  
Alexander Marshak
Keyword(s):  
Optical Depth ◽  
Distribution Functions ◽  
Shortwave Radiation ◽  
Cloud Optical Depth ◽  
Fourier Filtering ◽  
Spectral Models ◽  
Original Field ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Modeling ◽  
Height Fields

Abstract A simple and fast algorithm for generating two correlated stochastic two-dimensional (2D) cloud fields is described. The algorithm is illustrated with two broken cumulus cloud fields: cloud optical depth and cloud-top height retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS). Only two 2D fields are required as an input. The algorithm output is statistical realizations of these two fields with approximately the same correlation and joint distribution functions as the original ones. The major assumption of the algorithm is statistical isotropy of the fields. In contrast to fractals and the Fourier filtering methods frequently used for stochastic cloud modeling, the proposed method is based on spectral models of homogeneous random fields. To retain the same probability density function as the (first) original field, the method of inverse distribution function is used. When the spatial distribution of the first field has been generated, a realization of the correlated second field is simulated using a conditional distribution matrix. This paper serves as a theoretical justification of the publicly available software “Simulation of a two-component cloud field,” which has been recently released. Although 2D rather than full 3D, the stochastic realizations of two correlated cloud fields that mimic statistics of given fields have proven to be very useful to study 3D radiative transfer features of broken cumulus clouds for a better understanding of shortwave radiation and the interpretation of remote sensing retrievals.

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