Total cloud cover from satellite observations and climate models

The trade-cumulus cloud feedback in climate models is mostly driven by changes in cloud-base cloudiness, which can largely be attributed to model differences in the strength of lower-tropospheric mixing. Using observations from the recent EUREC4A field campaign, we test the hypothesis that enhanced lower-tropospheric mixing dries the lower cloud layer and reduces near-base cloudiness. The convective mass flux at cloud base is used as a proxy for the strength of convective mixing and is estimated as the residual of the subcloud layer mass budget, which is derived from dropsondes intensively launched along a circle of ~200 km diameter. The cloud-base cloud fraction is measured with horizontally-pointing lidar and radar from an aircraft flying near cloud base within the circle area. Additional airborne, ground- and ship-based radar, lidar and in-situ measurements are used to estimate the total cloud cover, the surface fluxes and to validate the consistency of the approach.Preliminary mass flux estimates have reasonable mean values of about 15 mm/s. 3- circle (i.e. 3h) averaged estimates range between 0-40 mm/s and reveal substantial day-to-day and daily variability. The day-to-day variability in the mass flux is mostly due to variability in the mesoscale vertical velocity, whereas the entrainment rate mostly explains variability on the daily timescale, consistent with previous large-eddy simulations. We find the mass flux to be positively correlated to both the cloud-base cloud fraction and the total cloud cover (R=0.55 and R~0.4, respectively). Other indicators of lower-tropospheric mixing due to convection and mesoscale circulations also suggest positive relationships between mixing and cloudiness. Implications of these analyses for testing the hypothesized mechanism of positive trade-cumulus cloud feedback will be discussed.

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Do aerosols influence surface and satellite observations of total cloud cover over China

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) ◽

10.1109/igarss.2015.7325910 ◽

2015 ◽

Author(s):

Yawen Zhang ◽

Hui Lu

Keyword(s):

Cloud Cover ◽

Satellite Observations ◽

Total Cloud Cover

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Exploring Perturbed Physics Ensembles in a Regional Climate Model

Journal of Climate ◽

10.1175/jcli-d-11-00275.1 ◽

2012 ◽

Vol 25 (13) ◽

pp. 4582-4599 ◽

Cited By ~ 42

Author(s):

Omar Bellprat ◽

Sven Kotlarski ◽

Daniel Lüthi ◽

Christoph Schär

Keyword(s):

Regional Climate Model ◽

Annual Cycle ◽

Cloud Cover ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Total Cloud Cover ◽

Observational Uncertainty ◽

Perturbed Physics ◽

Perturbed Physics Ensembles

Abstract Perturbed physics ensembles (PPEs) have been widely used to assess climate model uncertainties and have provided new estimates of climate sensitivity and parametric uncertainty in state-of-the-art climate models. So far, mainly global climate models were used to generate PPEs, and little work has been conducted with regional climate models. This paper discusses the parameter uncertainty in two PPEs of a regional climate model driven by reanalysis data for the present climate over Europe. The uncertainty is evaluated for the variables of 2-m temperature, precipitation, and total cloud cover, with a focus on the annual cycle, interannual variability, and selected extremes. The authors show that the simulated spread of the PPEs encompasses the observations at a regional scale in terms of the annual cycle and the interannual variability, provided observational uncertainty is taken into account. To rank the PPEs a new skill metric is proposed, which takes into account observational uncertainty and natural variability. The metric is a generalization of the climate prediction index (CPI) and is compared to metrics used in other studies. The consideration of observational uncertainty is particularly important for total cloud cover and reveals that current observations do not allow for a systematic evaluation of high precipitation intensities over the entire European domain. The skill framework is additionally used to identify important model parameters, which are of interest for an objective model calibration.

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Optically thin clouds in the trades

10.5194/acp-2021-453 ◽

2021 ◽

Author(s):

Theresa Mieslinger ◽

Bjorn Stevens ◽

Tobias Kölling ◽

Manfred Brath ◽

Martin Wirth ◽

...

Keyword(s):

Cloud Cover ◽

Climate Models ◽

Trade Wind ◽

Cumulus Cloud ◽

Total Cloud Cover ◽

Clear Sky ◽

Cloud Radiative Effect ◽

Large Eddy ◽

Lidar Measurements ◽

Cloud Mask

Abstract. We develop a new method to describe the total cloud cover including optically thin clouds in trade wind cumulus cloud fields. Climate models as well as Large Eddy Simulations commonly underestimate the cloud cover, while estimates from observations largely disagree on the cloud cover in the trades. Currently, trade wind clouds contribute significantly to the uncertainty in climate sensitivity estimates derived from model perturbation studies. To simulate clouds well and especially how they change in a future climate we have to know how cloudy it is. In this study we develop a method to quantify the cloud cover from a clear-sky perspective. Using well-known radiative transfer relations we retrieve the clear-sky contribution in high-resolution satellite observations of trade cumulus cloud fields during EUREC4A. Knowing the clear-sky part, we can investigate the remaining cloud-related contributions consisting of areas detected by common cloud masking algorithms and those undetected areas related to optically thin clouds. We find that the cloud-mask cloud cover underestimates the total cloud cover by a factor of 2. Lidar measurements on board the HALO aircraft support our findings by showing a high abundance of optically thin clouds during EUREC4A. Mixing the undetected optically thin clouds into the clear-sky signal can cause an underestimation of the cloud radiative effect of up to −32 %. We further discuss possible artificial correlations in aersol-cloud cover interaction studies that might arise from undetected optically thin clouds. Our analysis suggests that the known underestimation of trade wind cloud cover and simultaneous overestiamtion of cloud brightness in models is even higher than assumed so far.

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Total cloud cover from satellite observations and climate models

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-10-21023-2010 ◽

2010 ◽

Vol 10 (9) ◽

pp. 21023-21046 ◽

Cited By ~ 3

Author(s):

P. Probst ◽

R. Rizzi ◽

E. Tosi ◽

V. Lucarini ◽

T. Maestri

Keyword(s):

Seasonal Cycle ◽

Cloud Cover ◽

Climate Models ◽

Climate Model ◽

Coupled Model ◽

Time Frame ◽

Satellite Observations ◽

Latitudinal Belt ◽

Zonal Average ◽

Model Diagnosis

Abstract. Global and zonal monthly means of cloud cover fraction for total cloudiness (CF) from the ISCCP D2 dataset are compared to same quantity produced by the 20th century simulations of 21 climate models from the World Climate Research Programme's (WCRP's) Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset archived by the Program for Climate Model Diagnosis and Intercomparison (PCMDI). The comparison spans the time frame from January 1984 to December 1999 and the global and zonal average of CF are studied. The restriction to total cloudiness depends on the output of some models that does not include the 3D cloud structure. It is shown that the global mean of CF for the PCMDI/CMIP3 models, averaged over the whole period, exhibits a considerable variance and generally underestimates the ISCCP value. Very large discrepancies among models, and between models and observations, are found in the polar areas, where both models and satellite observations are less reliable, and especially near Antarctica. For this reason the zonal analysis is focused over the 60° S–60° N latitudinal belt, which includes the tropical area and mid latitudes. The two hemispheres are analyzed separately to show the variation of the amplitude of the seasonal cycle. Most models overestimate the yearly averaged values of CF over all of the analysed areas, while differences emerge in their ability to capture the amplitude of the seasonal cycle. The models represent, in a qualitatively correct way, the magnitude and the weak sign of the seasonal cycle over the whole geographical domain, but overestimate the strength of the signal in the tropical areas and at mid-latitudes, when taken separately. The interannual variability of the two yearly averages and of the amplitude of the seasonal cycle is greatly underestimated by all models in each area analysed. This work shows that the climate models have an heterogeneous behaviour in simulating the CF over different areas of the Globe, with a very wide span both with observed CF and among themselves. Some models agree quite well with the observations in one or more of the metrics employed in this analysis, but not a single model has a statistically significant agreement with the observational datasets on yearly averaged values of CF and on the amplitude of the seasonal cycle over all analysed areas.

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Evaluation and Bias Correction of Regional Climate Model Results Using Model Evaluation Measures

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-11-0161.1 ◽

2012 ◽

Vol 51 (9) ◽

pp. 1670-1684 ◽

Cited By ~ 36

Author(s):

Robert Schoetter ◽

Peter Hoffmann ◽

Diana Rechid ◽

K. Heinke Schlünzen

Keyword(s):

Wind Speed ◽

Bias Correction ◽

Model Evaluation ◽

Cloud Cover ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Total Cloud Cover ◽

Evaluation Measures

AbstractFor the assessment of regional climate change the reliability of the regional climate models needs to be known. The main goal of this paper is to evaluate the quality of climate model data that are used for impact research. Temperature, precipitation, total cloud cover, relative humidity, and wind speed simulated by the regional climate models Climate Local Model (CLM) and Regional Model (REMO) are evaluated for the metropolitan region of Hamburg in northern Germany for the period 1961–2000. The same evaluation is performed for the global climate model ECHAM5 that is used to force the regional climate models. The evaluation is based on comparison of the simulated and observed climatological annual cycles and probability density functions of daily averages. Several model evaluation measures are calculated to assure an objective model evaluation. As a very selective model evaluation measure, the hit rate of the percentiles is introduced for the evaluation of daily averages. The influence of interannual climate variability is considered by determining confidence intervals for the model evaluation measures by bootstrap resampling. Evaluation shows that, with some exceptions, temperature and wind speed are well simulated by the climate models; whereas considerable biases are found for relative humidity, total cloud cover, and precipitation, although not for all models in all seasons. It is shown that model evaluation measures can be used to decide for which meteorological parameters a bias correction is reasonable.

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The impact of atmospheric stability and wind shear on vertical cloud overlap over the Tibetan Plateau

Atmospheric Chemistry and Physics ◽

10.5194/acp-18-7329-2018 ◽

2018 ◽

Vol 18 (10) ◽

pp. 7329-7343 ◽

Cited By ~ 8

Author(s):

Jiming Li ◽

Qiaoyi Lv ◽

Bida Jian ◽

Min Zhang ◽

Chuanfeng Zhao ◽

...

Keyword(s):

Tibetan Plateau ◽

Wind Shear ◽

Cloud Cover ◽

Atmospheric Stability ◽

Length Scale ◽

The Tibetan Plateau ◽

Total Cloud Cover ◽

Atmospheric Models ◽

The Impact ◽

Overlap Parameter

Abstract. Studies have shown that changes in cloud cover are responsible for the rapid climate warming over the Tibetan Plateau (TP) in the past 3 decades. To simulate the total cloud cover, atmospheric models have to reasonably represent the characteristics of vertical overlap between cloud layers. Until now, however, this subject has received little attention due to the limited availability of observations, especially over the TP. Based on the above information, the main aim of this study is to examine the properties of cloud overlaps over the TP region and to build an empirical relationship between cloud overlap properties and large-scale atmospheric dynamics using 4 years (2007–2010) of data from the CloudSat cloud product and collocated ERA-Interim reanalysis data. To do this, the cloud overlap parameter α, which is an inverse exponential function of the cloud layer separation D and decorrelation length scale L, is calculated using CloudSat and is discussed. The parameters α and L are both widely used to characterize the transition from the maximum to random overlap assumption with increasing layer separations. For those non-adjacent layers without clear sky between them (that is, contiguous cloud layers), it is found that the overlap parameter α is sensitive to the unique thermodynamic and dynamic environment over the TP, i.e., the unstable atmospheric stratification and corresponding weak wind shear, which leads to maximum overlap (that is, greater α values). This finding agrees well with the previous studies. Finally, we parameterize the decorrelation length scale L as a function of the wind shear and atmospheric stability based on a multiple linear regression. Compared with previous parameterizations, this new scheme can improve the simulation of total cloud cover over the TP when the separations between cloud layers are greater than 1 km. This study thus suggests that the effects of both wind shear and atmospheric stability on cloud overlap should be taken into account in the parameterization of decorrelation length scale L in order to further improve the calculation of the radiative budget and the prediction of climate change over the TP in the atmospheric models.

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Common summertime total cloud cover and aerosol optical depth weekly variabilities over Europe: Sign of the aerosol indirect effects?

Atmospheric Research ◽

10.1016/j.atmosres.2014.07.031 ◽

2015 ◽

Vol 153 ◽

pp. 59-73 ◽

Cited By ~ 15

Author(s):

A.K. Georgoulias ◽

K.A. Kourtidis ◽

G. Alexandri ◽

S. Rapsomanikis ◽

A. Sanchez-Lorenzo

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Indirect Effects ◽

Cloud Cover ◽

Total Cloud Cover ◽

Aerosol Indirect Effects

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Assessment of Arctic Cloud Cover Anomalies in Atmospheric Reanalysis Products Using Satellite Data

Journal of Climate ◽

10.1175/jcli-d-15-0861.1 ◽

2016 ◽

Vol 29 (17) ◽

pp. 6065-6083 ◽

Cited By ~ 25

Author(s):

Yinghui Liu ◽

Jeffrey R. Key

Keyword(s):

Cloud Cover ◽

Climate Models ◽

Global Climate ◽

Correlation Coefficients ◽

Cloud Amount ◽

Satellite Observation ◽

Skill Score ◽

Global Climate Models ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer

Abstract Cloud cover is one of the largest uncertainties in model predictions of the future Arctic climate. Previous studies have shown that cloud amounts in global climate models and atmospheric reanalyses vary widely and may have large biases. However, many climate studies are based on anomalies rather than absolute values, for which biases are less important. This study examines the performance of five atmospheric reanalysis products—ERA-Interim, MERRA, MERRA-2, NCEP R1, and NCEP R2—in depicting monthly mean Arctic cloud amount anomalies against Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations from 2000 to 2014 and against Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations from 2006 to 2014. All five reanalysis products exhibit biases in the mean cloud amount, especially in winter. The Gerrity skill score (GSS) and correlation analysis are used to quantify their performance in terms of interannual variations. Results show that ERA-Interim, MERRA, MERRA-2, and NCEP R2 perform similarly, with annual mean GSSs of 0.36/0.22, 0.31/0.24, 0.32/0.23, and 0.32/0.23 and annual mean correlation coefficients of 0.50/0.51, 0.43/0.54, 0.44/0.53, and 0.50/0.52 against MODIS/CALIPSO, indicating that the reanalysis datasets do exhibit some capability for depicting the monthly mean cloud amount anomalies. There are no significant differences in the overall performance of reanalysis products. They all perform best in July, August, and September and worst in November, December, and January. All reanalysis datasets have better performance over land than over ocean. This study identifies the magnitudes of errors in Arctic mean cloud amounts and anomalies and provides a useful tool for evaluating future improvements in the cloud schemes of reanalysis products.

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Global Radiative Flux and Cloudiness Variability for the Period 1959–2010 in Belgium: A Comparison between Reanalyses and the Regional Climate Model MAR

Atmosphere ◽

10.3390/atmos9070262 ◽

2018 ◽

Vol 9 (7) ◽

pp. 262 ◽

Cited By ~ 4

Author(s):

Coraline Wyard ◽

Sébastien Doutreloup ◽

Alexandre Belleflamme ◽

Martin Wild ◽

Xavier Fettweis

Keyword(s):

Cloud Cover ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Horizontal Resolution ◽

Radiative Flux ◽

Added Value ◽

Historical Trends ◽

The Mean

The use of regional climate models (RCMs) can partly reduce the biases in global radiative flux (Eg↓) that are found in reanalysis products and global models, as they allow for a finer spatial resolution and a finer parametrisation of surface and atmospheric processes. In this study, we assess the ability of the MAR («Modèle Atmosphérique Régional») RCM to reproduce observed changes in Eg↓, and we investigate the added value of MAR with respect to reanalyses. Simulations were performed at a horizontal resolution of 5 km for the period 1959–2010 by forcing MAR with different reanalysis products: ERA40/ERA-interim, NCEP/NCAR-v1, ERA-20C, and 20CRV2C. Measurements of Eg↓ from the Global Energy Balance Archive (GEBA) and from the Royal Meteorological Institute of Belgium (RMIB), as well as cloud cover observations from Belgocontrol and RMIB, were used for the evaluation of the MAR model and the forcing reanalyses. Results show that MAR enables largely reducing the mean biases that are present in the reanalyses. The trend analysis shows that only MAR forced by ERA40/ERA-interim shows historical trends, which is probably because the ERA40/ERA-interim has a better horizontal resolution and assimilates more observations than the other reanalyses that are used in this study. The results suggest that the solar brightening observed since the 1980s in Belgium has mainly been due to decreasing cloud cover.

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