Cloud climatology of the Indian Ocean based on ship observations

Surface cloud data based on synoptic observations made by Voluntary Observing Ships (VOS) during the period 1951-98 were used to prepare the seasonal and annual cloud climatology of the Indian Ocean. The analysis has been carried out by separating the long-term trends, decadal and inter-annual components from the monthly cloud anomaly time series at each 5° × 5° grids. Maximum zone of total and low cloud cover shifts from equator to northern parts of India during the monsoon season. During the monsoon season (June-September), maximum total cloud cover exceeding 70% and low cloud cover exceeding 50% are observed over north Bay of Bengal. Maximum standard deviation of total and low cloud cover is observed near the equator and in the southern hemisphere. Both total and low cloud cover over Arabian Sea and the equatorial Indian Ocean are observed to decrease during the ENSO events. However, cloud cover over Bay of Bengal is not modulated by the ENSO events. On inter-decadal scale, low cloud cover shifted from a "low regime" to a "high regime" after 1980 which may be associated with the corresponding inter-decadal changes of sea surface temperatures over north Indian Ocean observed during the late 1970s.

Variability and trends in low cloud cover over India during 1961-2010

Annual and seasonal variability and trends in low cloud cover over India were analyzed for the period 1961-2010. Taking all period into account, there is a general decrease in mean low cloud cover over most regions of India, but an increase in the Indo-Gangetic plains and northeast India. Long term mean low cloud cover over India has inter-annual variations with highest cloud cover (39.4%) in monsoon and lowest cloud cover (10.5%) in winter season. The annual mean low cloud cover shows significant decreasing trend of -0.45% per decade, mainly contributed by monsoon where declining rate is -1.22% per decade. Out of the total numbers of stations showing decreasing trends, 65%, 47%, 53%, 71% and 37% of the stations show significant decrease in low cloud cover for annual, winter, summer, monsoon and post monsoon respectively, with large trend magnitudes occurring in central India. Spatially, the seasonal patterns of trends in low cloud cover confirm the annual patterns in most cases. Data analyses show that low cloud cover is having a strong negative correlation with maximum temperature and diurnal temperature range and a strong positive correlation with numbers of rainy days during the period of study.

Classification and Evaluation of Stable and Unstable Cloud Forecasts

A 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.

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

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.

Potential Driving Factors on Surface Solar Radiation Trends over China in Recent Years

<p>The annual mean surface solar radiation (SSR) trends under all-sky, clear-sky, all-sky-no-aerosol, and clear-sky-no-aerosol conditions as well as their possible causes are analyzed during 2005-2018 over China based on different satellite-retrieved datasets to determine the likely drivers of the trends. The results confirm clouds and aerosols as the major contributors to such all-sky SSR trends over China but playing different roles over sub-regions. Aerosol variations during this period result in a widespread brightening, while cloud effects show opposite trends from south to north. Moreover, aerosols contribute more to the increasing all-sky SSR trends over northern China, while clouds dominate the SSR declines over southern China. A radiative transfer model is used to explore the relative contributions of cloud cover from different cloud types to the all-types-of-cloud-cover-induced (ACC-induced) SSR trends during this period in four typical sub-regions over China. The simulations point out that the decreases in low-cloud-cover (LCC) over the North China Plain are the largest positive contributor of all cloud types to the marked annual and seasonal ACC-induced SSR increases, and the positive contributions from both high-cloud-cover (HCC) and LCC declines in summer and winter greatly contribute to the ACC-induced SSR increases over East China. The contributions from medium-low-cloud-cover (mid-LCC) and LCC variations dominate the ACC-caused SSR trends over southwestern and South China all year round, except for the larger HCC contribution in summer.</p>

Long-term Trend Comparison of Planetary Boundary Layer Height in Observations and CMIP6 models over China

<p>The planetary boundary layer (PBL) plays an essential role in climate and air quality simulations. Large uncertainties remain in understanding the long-term trend of PBL height (PBLH) and its simulation. Here we use the radiosonde data and reanalysis datasets to analyze PBLH long-term trends over China, and to further evaluate the performance of CMIP6 climate models in simulating these trends. Results show that the observed long-term “positive to negative” trend shift of PBLH is related to the variation in the surface upward sensible heat flux (SHFLX) which is further controlled by the synergistic effect of low cloud cover (LCC) and soil moisture (SM) changes. Variabilities in low cloud cover and soil moisture directly influence the energy balance via surface net downward shortwave flux (SWF) and the latent heat flux (LHFLX), respectively. We have found that the CMIP6 climate models cannot reproduce the observed PBLH long-term trend shift over China. The CMIP6 results show an overwhelming continuous downward PBLH trend during the 1979-2014 period, which is caused by the poorly simulated long-term changes of cloud radiative effect. Our results reveal that the long-term cloud radiative effect simulation is critical for CMIP6 models in reproducing the PBLH long-term trends. This study highlights the importance of low cloud cover and soil moisture processes in modulating PBLH long-term variations and calls attentions to improve these processes in climate models in order to improve the PLBH long-term trend simulations.</p>

Potential Driving Factors on Surface Solar Radiation Trends over China in Recent Years

The annual mean surface solar radiation (SSR) trends under all-sky, clear-sky, all-sky-no-aerosol, and clear-sky-no-aerosol conditions as well as their possible causes are analyzed during 2005–2018 across China based on different satellite-retrieved datasets to determine the major drivers of the trends. The results confirm clouds and aerosols as the major contributors to such all-sky SSR trends over China but play differing roles over sub-regions. Aerosol variations during this period result in a widespread brightening, while cloud effects show opposite trends from south to north. Moreover, aerosols contribute more to the increasing all-sky SSR trends over northern China, while clouds dominate the SSR decline over southern China. A radiative transfer model is used to explore the relative contributions of cloud cover from different cloud types to the all-types-of-cloud-cover-induced (ACC-induced) SSR trends during this period in four typical sub-regions over China. The simulations point out that the decreases in low-cloud-cover (LCC) over the North China Plain are the largest positive contributor of all cloud types to the marked annual and seasonal ACC-induced SSR increases, and the positive contributions from both high-cloud-cover (HCC) and LCC declines in summer and winter greatly contribute to the ACC-induced SSR increases over East China. The contributions from medium-low-cloud-cover (mid-LCC) and LCC variations dominate the ACC-caused SSR trends over southwestern and South China all year round, except for the larger HCC contribution in summer.

Synoptic-scale controls of fog and low-cloud variability in the Namib Desert

Fog is a defining characteristic of the climate of the Namib Desert, and its water and nutrient input are important for local ecosystems. In part due to sparse observation data, the local mechanisms that lead to fog occurrence in the Namib are not yet fully understood, and to date, potential synoptic-scale controls have not been investigated. In this study, a recently established 14-year data set of satellite observations of fog and low clouds in the central Namib is analyzed in conjunction with reanalysis data in order to identify synoptic-scale patterns associated with fog and low-cloud variability in the central Namib during two seasons with different spatial fog occurrence patterns. It is found that during both seasons, mean sea level pressure and geopotential height at 500 hPa differ markedly between fog/low-cloud and clear days, with patterns indicating the presence of synoptic-scale disturbances on fog and low-cloud days. These regularly occurring disturbances increase the probability of fog and low-cloud occurrence in the central Namib in two main ways: (1) an anomalously dry free troposphere in the coastal region of the Namib leads to stronger longwave cooling of the marine boundary layer, increasing low-cloud cover, especially over the ocean where the anomaly is strongest; (2) local wind systems are modulated, leading to an onshore anomaly of marine boundary-layer air masses. This is consistent with air mass back trajectories and a principal component analysis of spatial wind patterns that point to advected marine boundary-layer air masses on fog and low-cloud days, whereas subsiding continental air masses dominate on clear days. Large-scale free-tropospheric moisture transport into southern Africa seems to be a key factor modulating the onshore advection of marine boundary-layer air masses during April, May, and June, as the associated increase in greenhouse gas warming and thus surface heating are observed to contribute to a continental heat low anomaly. A statistical model is trained to discriminate between fog/low-cloud and clear days based on information on large-scale dynamics. The model accurately predicts fog and low-cloud days, illustrating the importance of large-scale pressure modulation and advective processes. It can be concluded that regional fog in the Namib is predominantly of an advective nature and that fog and low-cloud cover is effectively maintained by increased cloud-top radiative cooling. Seasonally different manifestations of synoptic-scale disturbances act to modify its day-to-day variability and the balance of mechanisms leading to its formation and maintenance. The results are the basis for a new conceptual model of the synoptic-scale mechanisms that control fog and low-cloud variability in the Namib Desert and will guide future studies of coastal fog regimes.

Climate Resource Potential to Develop Solar Power in Belarus

The work analyses climate resources that can potentially be used to develop solar power in Belarus efficiently. The authors determine space-time variability of radiation regime including such parameters as solar irradiance, atmosphere transparency, sunshine duration, cloud cover patterns, etc. The efficiency of solar power generators is assessed by taking into account the number of clear days with low cloud cover per year, sunshine duration per month, and solar irradiance of a horizontal surface in the daytime.

Distinct Patterns of Cloud Changes Associated with Decadal Variability and Their Contribution to Observed Cloud Cover Trends

With the goal of understanding the relative roles of anthropogenic and natural factors in driving observed cloud trends, this study investigates cloud changes associated with decadal variability including the Pacific decadal oscillation (PDO) and the Atlantic multidecadal oscillation (AMO). In the preindustrial simulations of CMIP5 global climate models (GCMs), the spatial patterns and the vertical structures of the PDO-related cloud cover changes in the Pacific are consistent among models. Meanwhile, the models show consistent AMO impacts on high cloud cover in the tropical Atlantic, subtropical eastern Pacific, and equatorial central Pacific, and on low cloud cover in the North Atlantic and subtropical northeast Pacific. The cloud cover changes associated with the PDO and the AMO can be understood via the relationships between large-scale meteorological parameters and clouds on interannual time scales. When compared to the satellite records during the period of 1983–2009, the patterns of total and low cloud cover trends associated with decadal variability are significantly correlated with patterns of cloud cover trends in ISCCP observations. On the other hand, the pattern of the estimated greenhouse gas (GHG)-forced trends of total cloud cover differs from that related to decadal variability, and may explain the positive trends in the subtropical southeast Pacific, negative trends in the midlatitudes, and positive trends poleward of 50°N/S. In most models, the magnitude of the estimated decadal variability contribution to the observed cloud cover trends is larger than that contributed by GHG, suggesting the observed cloud cover trends are more closely related to decadal variability than to GHG-induced warming.