The June–September Low Cloud Cover in Western Central Africa: Mean Spatial Distribution and Diurnal Evolution, and Associated Atmospheric Dynamics

Western central Africa (WCA) was recently shown to be one of the cloudiest areas of the tropics. Analyzing an ensemble of satellite products and surface cloud observations, we show that in June–September, WCA cloud cover is dominated by single-layered low stratiform clouds. Despite an underestimation of low cloud frequency in satellite estimates at night, comparisons with surface observations bring insights into the spatial distribution and diurnal cycle of low clouds. Both appear strongly influenced by orography: to the west, the coastal plains and the ocean-facing valleys have the largest cloud cover and a lower-amplitude diurnal cycle with a maximum cloud phase at 0400 local time (LT). To the east, across the windward slopes, plateaus, and downwind slopes, the cloud cover becomes progressively reduced and the diurnal cycle has a larger amplitude with a maximum cloud phase at 1000 LT. In terms of atmospheric dynamics, the east/west gradient observed in low cloud frequency and amount is related to a foehn effect without substantial rainfall on windward slopes. The diurnal cycle of low clouds on the windward slopes and plateaus is related to the reversal, from mean subsidence at 0700 LT over the Atlantic and inland to rising motion inland at 1300 LT. In addition, the airmass stability in low levels prevents the vertical development of cloud cover. Last, we could not detect in the European reanalyses any nocturnal jet as observed in southern West Africa (SWA), suggesting different mechanisms triggering low cloud formation in WCA compare to SWA.

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The ERA5’s Diurnal Cycle of Low-Level Clouds Over Western Central Africa During June-September: Dynamic and Thermodynamic Processes.

10.21203/rs.3.rs-689885/v1 ◽

2021 ◽

Author(s):

Atanas Dommo ◽

Derbetini A. Vondou ◽

Nathalie Philippon ◽

Ryan Eastman ◽

Vincent Moron ◽

...

Keyword(s):

Diurnal Cycle ◽

Lower Layer ◽

Central Africa ◽

Cloud Formation ◽

Atmospheric Conditions ◽

Low Level ◽

Low Cloud ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Low Cloud Cover

Abstract This paper analyzes the diurnal cycle of low cloud cover (LCC) and the atmospheric conditions under which it grows over Western Central Africa during the cloudiest season (June-September). Moderate Resolution Imaging Spectroradiometer (MODIS) observations, Extended Edited Clouds Reports Archive (EECRA) and the fifth generation of reanalysis of the European Centre for Medium Range Weather Forecasts (ECMWF), i.e., ERA5 are used. LCC peaks between 04LT and 07LT and tends to be less dense during the afternoon. The associated dynamic and thermodynamic ERA5 conditions reveal different processes. The strong low level (below 1000 m) southwesterly flow in the evening supplies the region with humidity from the ocean and leads to cloud formation. Relative humidity (RH) tendencies show that temperature contributes to 100% of RH changes : the strong cooling observed after sunset at 19LT increases RH in the area of about 8%/h in the lower layer (below 1000m). The nighttime cooling shows strong cooling rates of about -1.4K/h after sunset till 22 LT, then rates decrease during the night to reach a value of about -0.3K/h between 22LT and 07LT. The cloud formation is mostly related to horizontal air advection, strong convergence in the lower layer and turbulent upwards mixing of moisture, while cooling at the cloud-top helps to maintain the cloud deck once it has formed. During daytime, solar radiation suppressed cooling at the cloud-top, thereafter strong turbulent kinetic energy acts to partly destroy the cloud deck and cloud fraction.

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Characterisation and surface radiative impact of Arctic low clouds from the IAOOS field experiment

10.5194/acp-2020-918 ◽

2020 ◽

Author(s):

Julia Maillard ◽

François Ravetta ◽

Jean-Christophe Raut ◽

Vincent Mariage ◽

Jacques Pelon

Keyword(s):

Field Experiment ◽

Arctic Ocean ◽

Cloud Cover ◽

Great Majority ◽

The North ◽

Low Clouds ◽

Radiative Impact ◽

Low Cloud ◽

Ocean Observing ◽

Layer Cloud

Abstract. The Ice, Atmosphere, Arctic Ocean Observing System (IAOOS) field experiment took place from 2014 to 2019. Over this period, more than 20 instrumented buoys were deployed at the North Pole. Once locked into the ice, the buoys drifted for periods of a month to more than a year. Some of these buoys were equipped with 808 nm wavelength lidars which acquired a total of 1805 profiles over the course of the campaign. This IAOOS lidar dataset is exploited to establish a novel statistic of cloud cover and of the geometrical and optical characteristics of the lowest cloud layer. Cloud frequency is globally at 75 %, and above 85 % from May to October. Single layers are thickest in October/November and thinnest in the summer. Meanwhile, their optical depth is maximum in October. On the whole, the cloud cover is very low, with the great majority of first layer bases beneath 120 m. In the shoulder seasons, surface temperatures are markedly warmer when the IAOOS profile contains at least one low cloud than when it does not. This temperature difference is statistically insignificant in the summer months. Indeed, summer clouds have a shortwave cooling effect which can reach −60 W m−2 and balance out their longwave warming effect.

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Characterisation and surface radiative impact of Arctic low clouds from the IAOOS field experiment

Atmospheric Chemistry and Physics ◽

10.5194/acp-21-4079-2021 ◽

2021 ◽

Vol 21 (5) ◽

pp. 4079-4101

Author(s):

Julia Maillard ◽

François Ravetta ◽

Jean-Christophe Raut ◽

Vincent Mariage ◽

Jacques Pelon

Keyword(s):

Field Experiment ◽

Cloud Cover ◽

Great Majority ◽

Cloud Base ◽

The North ◽

Low Clouds ◽

Radiative Impact ◽

Low Cloud ◽

Ocean Observing ◽

Cloud Base Height

Abstract. The Ice, Atmosphere, Arctic Ocean Observing System (IAOOS) field experiment took place from 2014 to 2019. Over this period, more than 20 instrumented buoys were deployed at the North Pole. Once locked into the ice, the buoys drifted for periods of a month to more than a year. Some of these buoys were equipped with 808 nm wavelength lidars which acquired a total of 1777 profiles over the course of the campaign. This IAOOS lidar dataset is exploited to establish a novel statistic of cloud cover and of the geometrical and optical characteristics of the lowest cloud layer. The average cloud frequency from April to December over the course of the campaign was 75 %. Cloud occurrence frequencies were above 85 % from May to October. Single layers are thickest in October/November and thinnest in the summer. Meanwhile, their optical depth is maximum in October. On the whole, the cloud base height is very low, with the great majority of first layer bases beneath 120 m. In April and October, surface temperatures are markedly warmer when the IAOOS profile contains at least one low cloud than when it does not. This temperature difference is statistically insignificant in the summer months. Indeed, summer clouds have a shortwave cooling effect which can reach −60 W m−2 and balance out their longwave warming effect.

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On the Relationship between Stratiform Low Cloud Cover and Lower-Tropospheric Stability

Journal of Climate ◽

10.1175/jcli3988.1 ◽

2006 ◽

Vol 19 (24) ◽

pp. 6425-6432 ◽

Cited By ~ 296

Author(s):

Robert Wood ◽

Christopher S. Bretherton

Keyword(s):

Boundary Layer ◽

Temperature Profile ◽

Cloud Cover ◽

Cloud Amount ◽

Lapse Rate ◽

Stratus Cloud ◽

Low Clouds ◽

Wide Range ◽

Low Cloud ◽

Low Cloud Cover

Abstract Observations in subtropical regions show that stratiform low cloud cover is well correlated with the lower-troposphere stability (LTS), defined as the difference in potential temperature θ between the 700-hPa level and the surface. The LTS can be regarded as a measure of the strength of the inversion that caps the planetary boundary layer (PBL). A stronger inversion is more effective at trapping moisture within the marine boundary layer (MBL), permitting greater cloud cover. This paper presents a new formulation, called the estimated inversion strength (EIS), to estimate the strength of the PBL inversion given the temperatures at 700 hPa and at the surface. The EIS accounts for the general observation that the free-tropospheric temperature profile is often close to a moist adiabat and its lapse rate is strongly temperature dependent. Therefore, for a given LTS, the EIS is greater at colder temperatures. It is demonstrated that while the seasonal cycles of LTS and low cloud cover fraction (CF) are strongly correlated in many regions, no single relationship between LTS and CF can be found that encompasses the wide range of temperatures occurring in the Tropics, subtropics, and midlatitudes. However, a single linear relationship between CF and EIS explains 83% of the regional/seasonal variance in stratus cloud amount, suggesting that EIS is a more regime-independent predictor of stratus cloud amount than is LTS under a wide range of climatological conditions. The result has some potentially important implications for how low clouds might behave in a changed climate. In contrast to Miller’s thermostat hypothesis that a reduction in the lapse rate (Clausius–Clapeyron) will lead to increased LTS and increased tropical low cloud cover in a warmer climate, the results here suggest that low clouds may be much less sensitive to changes in the temperature profile if the vertical profile of tropospheric warming follows a moist adiabat.

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Clouds and the Near-Earth Environment: Possible Links

Annals of West University of Timisoara - Physics ◽

10.1515/awutp-2015-0208 ◽

2015 ◽

Vol 58 (1) ◽

pp. 64-72

Author(s):

Simona Condurache-Bota ◽

Mirela Voiculescu ◽

Carmelia Dragomir

Keyword(s):

Solar Wind ◽

Cloud Cover ◽

Anthropogenic Activities ◽

Galactic Cosmic Rays ◽

Electrical Circuit ◽

Atmospheric Dynamics ◽

Radiation Budget ◽

Cloud Formation ◽

Solar Cycles ◽

Ap Index

Abstract Climate variability is a hot topic not only for scientists and policy-makers, but also for each and every one of us. The anthropogenic activities are considered to be responsible for most climate change, however there are large uncertainties about the magnitude of effects of solar variability and other extraterrestrial influences, such as galactic cosmic rays on terrestrial climate. Clouds play an important role due to feedbacks of the radiation budget: variation of cloud cover/composition affects climate, which, in turn, affects cloud cover via atmospheric dynamics and sea temperature variations. Cloud formation and evolution are still under scientific scrutiny, since their microphysics is still not understood. Besides atmospheric dynamics and other internal climatic parameters, extraterrestrial sources of cloud cover variation are considered. One of these is the solar wind, whose effect on cloud cover might be modulated by the global atmospheric electrical circuit. Clouds height and composition, their seasonal variation and latitudinal distribution should be considered when trying to identify possible mechanisms by which solar energy is transferred to clouds. The influence of the solar wind on cloud formation can be assessed also through the ap index - the geomagnetic storm index, which can be readily connected with interplanetary magnetic field, IMF structure. This paper proposes to assess the possible relationship between both cloud cover and solar wind proxies, as the ap index, function of cloud height and composition and also through seasonal studies. The data covers almost three solar cycles (1984-2009). Mechanisms are looked for by investigating observed trends or correlation at local/seasonal scale

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Evaluating the Lagrangian evolution of subtropical low clouds in GCMs using observations: Mean evolution, timescales, and responses to predictors

Journal of the Atmospheric Sciences ◽

10.1175/jas-d-20-0178.1 ◽

2020 ◽

Author(s):

Ryan Eastman ◽

Christopher R. Terai ◽

Daniel P. Grosvenor ◽

Robert Wood

Keyword(s):

Time Evolution ◽

General Circulation ◽

Cloud Cover ◽

General Circulation Models ◽

Liquid Water Path ◽

Trade Winds ◽

Frequency Distributions ◽

Lagrangian Framework ◽

Low Clouds ◽

Stratiform Clouds

AbstractA Lagrangian framework is developed to show the daily-scale time evolution of low clouds over the Eastern Subtropical Oceans. An identical framework is applied to two General Circulation Models (GCMs): the CAM5 and UKMET and a set of satellite observations. This approach follows thousands of parcels as they advect downwind in the subtropical trade winds, comparing cloud evolution in time and space. This study tracks cloud cover, in-cloud liquid water path (CLWP), droplet concentration (Nd), boundary layer (PBL) depth, and rain rate as clouds transition from regions with predominately stratiform clouds to regions containing mostly trade cumulus.The two models generate fewer clouds with greater Nd compared to observations. Models show stronger Lagrangian cloud cover decline and greater PBL deepening compared to observations. Comparing frequency distributions of cloud variables over time, models generate increasing frequencies of nearly-clear conditions at the expense of overcast conditions, while observations show transitions from overcast to cloud amounts between 50-90%. Lagrangian decorrelation timescales (e-folding time, τ) of cloud cover and CLWP are between 11 and 19 hours for models and observations, though a bit shorter for models. A Lagrangian framework applied here resolves and compares the time evolution of cloud systems as they adjust to environmental perturbations in models and observations. Increasing subsidence in the overlying troposphere leads to declining cloud cover, CLWP, PBL depth, and rain rates in models and observations. Modeled cloud responses to other meteorological variables are less consistent with observations, suggesting a need for continuing mechanical improvements in GCMs.

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Analyzing of Cloud Macroscopic Characteristics in the Shigatse Area of the Tibetan Plateau Using the Total-Sky Images

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-18-0095.1 ◽

2018 ◽

Vol 57 (9) ◽

pp. 1977-1987

Author(s):

Jun Yang ◽

Weitao Lyu ◽

Ying Ma ◽

Yijun Zhang ◽

Qingyong Li ◽

...

Keyword(s):

Tibetan Plateau ◽

Cloud Cover ◽

Global Climate ◽

Climatic Conditions ◽

The Tibetan Plateau ◽

Clear Sky ◽

Stratiform Clouds ◽

Low Cloud ◽

Low Cloud Cover ◽

Autumn And Winter

AbstractThe macroscopic characteristics of clouds in the Tibetan Plateau are crucial to understanding the local climatic conditions and their impact on the global climate and water vapor cycle. In this study, the variations of cloud cover and cloud types are analyzed by using total-sky images of two consecutive years in Shigatse, Tibetan Plateau. The results show that the cloud cover in Shigatse presents a distinct seasonal difference that is characterized by low cloud cover in autumn and winter and high cloud cover in summer and spring. July is the month with the largest cloud coverage, and its average cloud cover exceeds 75%. The probability of clouds in the sky is the lowest in November, with an average cloud cover of less than 20%. The diurnal variations of cloud cover in different months also have considerable differences. Specifically, cloud cover is higher in the afternoon than that in the morning in most months, whereas the cloud cover throughout the day varies little from July to September. The dominant cloud types in different months are also not the same. The proportion of clear sky is large in autumn and winter. Stratiform cloud occupies the highest percentage in March, April, July, and August. The probability of emergence of cirrus is highest in May and June. The Shigatse region has clear rainy and dry seasons, and correlation analysis between precipitation and clouds shows that the largest cumulative precipitation, the highest cloud cover, and the highest proportion of stratiform clouds occur simultaneously in July.

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Classification and Evaluation of Stable and Unstable Cloud Forecasts

Monthly Weather Review ◽

10.1175/mwr-d-21-0056.1 ◽

2021 ◽

Author(s):

Jason E. Nachamkin ◽

Adam Bienkowski ◽

Rich Bankert ◽

Krishna Pattipati ◽

David Sidoti ◽

...

Keyword(s):

Cloud Cover ◽

Cloud Formation ◽

Cloud Base ◽

Forecast Errors ◽

Short Term ◽

Coverage Bias ◽

Low Cloud ◽

Low Cloud Cover ◽

Cloud Tops ◽

Cloud Patterns

AbstractA physics-based cloud identification scheme, originally developed for a machine learning forecast system, was applied to verify cloud location and coverage bias errors from two years of 6-hour forecasts. The routine identifies stable and unstable environments based on the potential for buoyant versus stable cloud formation. The efficacy of the scheme is documented by investigating its ability to identify cloud patterns and systematic forecast errors. Results showed stable cloud forecasts contained widespread, persistent negative cloud cover biases most likely associated with turbulent, radiative and microphysical feedback processes. In contrast, unstable clouds were better predicted despite being poorly resolved. This suggests that scale aliasing, while energetically problematic, results in less severe short-term cloud cover errors.This study also evaluated Geostationary Operational Environmental Satellite (GOES) cloud base retrievals for their effectiveness at identifying regions of lower tropospheric cloud cover. Retrieved cloud base heights were sometimes too high with respect to their actual values in regions of deep-layered clouds, resulting in underestimates of the extent of low cloud cover in these areas. Sensitivity experiments indicate the most accurate cloud base estimates existed in regions with cloud tops at or below 8 km.

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Diurnal Cycles of Cumulus, Cumulonimbus, Stratus, Stratocumulus, and Fog from Surface Observations over Land and Ocean

Journal of Climate ◽

10.1175/jcli-d-13-00352.1 ◽

2014 ◽

Vol 27 (6) ◽

pp. 2386-2404 ◽

Cited By ~ 37

Author(s):

Ryan Eastman ◽

Stephen G. Warren

Keyword(s):

Diurnal Cycle ◽

Cloud Cover ◽

Cloud Amount ◽

Early Morning ◽

Local Climate ◽

Diurnal Cycles ◽

Low Cloud ◽

Weather Stations ◽

Low Cloud Cover ◽

Surface Observations

Abstract A worldwide climatology of the diurnal cycles of low clouds is obtained from surface observations made eight or four times daily at 3- or 6-h intervals from weather stations and ships. Harmonic fits to the daily cycle are made for 5388 weather stations with long periods of record, and for gridded data on a 5° × 5° or 10° × 10° latitude–longitude grid over land and ocean areas separately. For all cloud types, the diurnal cycle has larger amplitude over land than over ocean, on average by a factor of 2. Diurnal cycles of cloud amount appear to be proprietary to each low cloud type, showing the same cycle regardless of whether that type dominates the diurnal cycle of cloud cover. Stratiform cloud amounts tend to peak near sunrise, while cumuliform amounts peak in the afternoon; however, cumulonimbus amounts peak in the early morning over the ocean. Small latitudinal and seasonal variation is apparent in the phase and amplitude of the diurnal cycles of each type. Land areas show more seasonality compared to ocean areas with respect to which type dominates the diurnal cycle. Multidecadal trends in low cloud cover are small and agree between day and night regardless of the local climate. Diurnal cycles of base height are much larger over land than over the ocean. For most cloud types, the bases are highest in the midafternoon or early evening.

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Satellite- and station-based climatology of low-level cloud cover during the long dry season in western Central Africa

10.5194/egusphere-egu21-9552 ◽

2021 ◽

Author(s):

Raffael Aellig ◽

Judith Gerighausen ◽

Andreas Fink ◽

Peter Knippertz ◽

Nathalie Philippon

Keyword(s):

Cloud Cover ◽

Central Africa ◽

Reanalysis Data ◽

Dry Season ◽

Boreal Winter ◽

Validation Data ◽

Data Set ◽

Low Level ◽

Low Clouds ◽

Station Data

Low-level cloud cover (LCC) in western Central Africa is an important factor for the persistence of the dense evergreen tropical forest, as it keeps conditions cool, humid, and light-deficient. A quantitative understanding of the mechanisms controlling LCC is an important prerequisite to anticipate future changes, particularly as climate and weather models have been shown to struggle with a realistic representation of low clouds. This is a major goal of the French-German project Dynamics, Variability, and Bioclimatic Effects of Low Clouds in Western Central Africa (DYVALOCCA, ) launched in 2020.Here we present an analysis of historical station data from the database ISD (Integrated Surface Database) and MIDAS (Met Office Data Archive System), ERA-5 reanalysis, and satellite data from the Meteosat Second Generation (MSG) focusing on the country of Gabon and surroundings. Station data (ISD and MIDAS) show a higher LCC during the major dry season months of July, August, and September (JAS) compared to the two rainy seasons and the other shorter dry season in boreal winter. During typical days in JAS, the LCC that thickens at night tends to break up at daytime near the coast and over the interior plateau, while it remains overcast at the windward site and over the crests of the Crystal and Chaillu low mountain ranges. Thus, stations at the coast have a different diurnal LCC cycles compared to stations in the interior of Gabon. The diurnal amplitudes of LCCs in the interior are lower and the maximum and minimum LCC occurs later in the day compared to coastal stations.A comparison to the station data shows that LCC is generally underestimated in ERA-5. At the diurnal scale, LCC over the plateaus of eastern Gabon often does not dissolve as fast as in the ERA-5 reanalysis. Data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) satellite corroborates the underestimation by ERA-5. The RGB Night Microphysical Scheme (NMS) with SEVIRI data to determine LCC in the region shows an acceptable fit to the station data. Other satellite products such as CLAAS-2 do not deliver as good an estimate of the LCC in western Central Africa as the NMS.Future work will employ Cloudsat-Calipso data to enhance our understanding of the vertical distribution of clouds. The best suited data sets will then be used as validation data set for convection-permitting modeling studies of example nights and days.

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