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

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 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 An Estimate of Low-Cloud Feedbacks from Variations of Cloud Radiative and Physical Properties with Sea Surface Temperature on Interannual Time Scales

2011 ◽  
Vol 24 (4) ◽  
pp. 1106-1121 ◽  
Author(s):  
Zachary A. Eitzen ◽  
Kuan-Man Xu ◽  
Takmeng Wong
Keyword(s):  
Physical Properties ◽  
Optical Depth ◽  
Cloud Amount ◽  
Cloud Fraction ◽  
Radiative Properties ◽  
Radiative Effect ◽  
Cloud Optical Depth ◽  
Cloud Radiative Effect ◽  
Low Cloud ◽  
Sst Anomaly

Abstract Simulations of climate change have yet to reach a consensus on the sign and magnitude of the changes in physical properties of marine boundary layer clouds. In this study, the authors analyze how cloud and radiative properties vary with SST anomaly in low-cloud regions, based on five years (March 2000–February 2005) of Clouds and the Earth’s Radiant Energy System (CERES)–Terra monthly gridded data and matched European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological reanalaysis data. In particular, this study focuses on the changes in cloud radiative effect, cloud fraction, and cloud optical depth with SST anomaly. The major findings are as follows. First, the low-cloud amount (−1.9% to −3.4% K−1) and the logarithm of low-cloud optical depth (−0.085 to −0.100 K−1) tend to decrease while the net cloud radiative effect (3.86 W m−2 K−1) becomes less negative as SST anomalies increase. These results are broadly consistent with previous observational studies. Second, after the changes in cloud and radiative properties with SST anomaly are separated into dynamic, thermodynamic, and residual components, changes in the dynamic component (taken as the vertical velocity at 700 hPa) have relatively little effect on cloud and radiative properties. However, the estimated inversion strength decreases with increasing SST, accounting for a large portion of the measured decreases in cloud fraction and cloud optical depth. The residual positive change in net cloud radiative effect (1.48 W m−2 K−1) and small changes in low-cloud amount (−0.81% to 0.22% K−1) and decrease in the logarithm of optical depth (–0.035 to –0.046 K−1) with SST are interpreted as a positive cloud feedback, with cloud optical depth feedback being the dominant contributor. Last, the magnitudes of the residual changes differ greatly among the six low-cloud regions examined in this study, with the largest positive feedbacks (∼4 W m−2 K−1) in the southeast and northeast Atlantic regions and a slightly negative feedback (−0.2 W m−2 K−1) in the south-central Pacific region. Because the retrievals of cloud optical depth and/or cloud fraction are difficult in the presence of aerosols, the transport of heavy African continental aerosols may contribute to the large magnitudes of estimated cloud feedback in the two Atlantic regions.

scholarly journals Mechanisms Behind the Extratropical Stratiform Low‐Cloud Optical Depth Response to Temperature in ARM Site Observations

2019 ◽  
Vol 124 (4) ◽  
pp. 2127-2147 ◽  
Author(s):  
C. R. Terai ◽  
Y. Zhang ◽  
S. A. Klein ◽  
M. D. Zelinka ◽  
J. C. Chiu ◽  
...  
Keyword(s):  
Optical Depth ◽  
Cloud Optical Depth ◽  
Low Cloud ◽  
Response To Temperature ◽  
Depth Response

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 The Impact of Neglecting Ice Phase on Cloud Optical Depth Retrievals from AERONET Cloud Mode Observations

2019 ◽  
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 ◽  
Surface Flux ◽  
Error Statistics ◽  
Cloud Optical Depth ◽  
Atmospheric Radiation Measurement ◽  
Simple Linear Equation ◽  
The Impact ◽  
Ice Fraction

Abstract. Cloud optical depth remains a difficult variable to represent in climate models, and hence there is a need for high-quality observations of cloud optical depth from locations around the world. Such observations could be readily obtained from Aerosol Robotic Network (AERONET) radiometers using a two-wavelength retrieval method. However, the method requires an assumption that all of the cloud in a profile is liquid, and this has the potential to introduce errors into long-term statistics of retrieved optical depth. Using a set of idealised cloud profiles, we find that 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”), with a magnitude of order 55 % to 70 % for clouds that are entirely ice. We derive a simple linear equation that could potentially be used as a correction at AERONET sites where ice fraction can be independently estimated. The greatest contribution to error statistics arises from optically thick cloud that is either mostly or entirely ice. Using this linear equation, we estimate the magnitude of the error for a set of cloud profiles measured at five sites of the Atmospheric Radiation Measurement programme. Instances of such clouds are not frequent, with less than 15 % of cloud profiles at each location showing an error of greater than 10. However, differences in the frequency of such clouds from one location to another affect the magnitude of the overall mean error, with sites dominated by deep tropical convection and thick frontal mixed-phase cloud showing greater errors than sites where deep clouds are less frequent. The mean optical depth error at the five locations spans the range 2.5 to 5.5, which we show to be small enough to allow calculation of top-of-atmosphere flux to within 10 %, and surface flux to about 15 %.

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