Small and optically thin clouds in the trades

The trades and the inherent trade cumulus clouds cover large parts of the tropical oceans. Trade cumulus clouds are ubiquitous but also very small in their horizontal and vertical extent posing huge challenges on observing systems such as satellite imagers. Climate models exhibit a significant spread in the response of trade cumulus clouds to global warming motivating their intense study in recent years. Within this thesis, I use high-resolution satellite images to gain new insights on small and optically thin clouds in the trades. The way trade wind clouds change with surface warming is decisive for their feedback, which defines whether clouds further amplify or dampen the warming of the climate system. Cloud feedback estimates can be investigated from so-called cloud-controlling factors, their relation to cloud properties in the current climate and their change with global warming. Results from my first study indicate a wind-speed driven boundary layer in the trades. The surface trade winds show the most powerful control on cloud properties such as cloud sizes, top heights or cloud clustering. Furthermore, the Bowen ratio was firstly tested from observations and emerges as a potential new control factor. Trade cumulus cloud properties also show a susceptibility to the sea surface temperature and the stability of the lower troposphere which are both projected to change in a warming climate and may thus impact cloud feedbacks. Investigating cloud-controlling factors is an ongoing task and seems to be within reach from extensive measurements of the recent field campaign EUREC4A. First analysis of cloud observations from multiple instruments indicate the frequent occurrence of not only small, but also optically thin clouds. Due to their low reflectance, such clouds are challenging to detect from passive imagers. High- resolution imagers are able to detect small clouds, but, do conventional satellite cloud products still miss optically thin clouds? Within another study, I follow a new approach for defining the total cloud cover consisting of clouds detected by conventional cloud masking schemes and of undetected optically thin clouds. By simulating the well-understood clear-sky signal I can extract clouds as a residual from the all-sky observation and circumvent conventional but problematic thresholding tests in cloud masking schemes. From evaluating a high-resolution satellite dataset collected during EUREC4A, I find that optically thin clouds contribute 45 % to the total cloud cover and reduces the average cloud reflectance by 29 %. Undetected optically thin clouds can have major implications for estimates of the radiative effect of clouds and thus, cloud feedbacks

Optically thin clouds in the trades

We develop a new method to describe the total cloud cover including optically thin clouds in trade wind cumulus cloud fields. Climate models and 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 significantly contribute to the uncertainty in climate sensitivity estimates derived from model perturbation studies. To simulate clouds well, 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 cloud-free perspective. Using well-known radiative transfer relations we retrieve the cloud-free contribution in high-resolution satellite observations of trade cumulus cloud fields during EUREC4A. Knowing the cloud-free part, we can investigate the remaining cloud-related contributions consisting of areas detected by common cloud-masking algorithms and undetected areas related to optically thin clouds. We find that the cloud-mask cloud cover underestimates the total cloud cover by 33 %. Aircraft lidar measurements support our findings by showing a high abundance of optically thin clouds during EUREC4A. Mixing the undetected optically thin clouds into the cloud-free signal can cause an underestimation of the cloud radiative effect of up to −7.5 %. We further discuss possible artificial correlations in aerosol–cloud cover interaction studies that might arise from undetected optically thin low clouds. Our analysis suggests that the known underestimation of trade wind cloud cover and simultaneous overestimation of cloud brightness in models are even higher than assumed so far

Optimal interpolation of satellite and ground data for irradiance nowcasting at city scales

We use a Bayesian method, optimal interpolation, to improve satellite derived irradiance estimates at city-scales using ground sensor data. Optimal interpolation requires error covariances in the satellite estimates and ground data, which define how information from the sensor locations is distributed across a large area. We describe three methods to choose such covariances, including a covariance parameterization that depends on the relative cloudiness between locations. Results are computed with ground data from 22 sensors over a 75×80 km area centered on Tucson, AZ, using two satellite derived irradiance models. The improvements in standard error metrics for both satellite models indicate that our approach is applicable to additional satellite derived irradiance models. We also show that optimal interpolation can nearly eliminate mean bias error and improve the root mean squared error by 50%

EUREC⁴A

The science guiding the EUREC⁴A campaign and its measurements is presented. EUREC⁴A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. Through its ability to characterize processes operating across a wide range of scales, EUREC⁴A marked a turning point in our ability to observationally study factors influencing clouds in the trades, how they will respond to warming, and their link to other components of the earth system, such as upper-ocean processes or the life cycle of particulate matter. This characterization was made possible by thousands (2500) of sondes distributed to measure circulations on meso- (200 km) and larger (500 km) scales, roughly 400 h of flight time by four heavily instrumented research aircraft; four global-class research vessels; an advanced ground-based cloud observatory; scores of autonomous observing platforms operating in the upper ocean (nearly 10 000 profiles), lower atmosphere (continuous profiling), and along the air–sea interface; a network of water stable isotopologue measurements; targeted tasking of satellite remote sensing; and modeling with a new generation of weather and climate models. In addition to providing an outline of the novel measurements and their composition into a unified and coordinated campaign, the six distinct scientific facets that EUREC⁴A explored – from North Brazil Current rings to turbulence-induced clustering of cloud droplets and its influence on warm-rain formation – are presented along with an overview of EUREC⁴A's outreach activities, environmental impact, and guidelines for scientific practice. Track data for all platforms are standardized and accessible at https://doi.org/10.25326/165 (Stevens, 2021), and a film documenting the campaign is provided as a video supplement

EUREC⁴A

The science guiding the EUREC⁴A campaign and its measurements is presented. EUREC⁴A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. Through its ability to characterize processes operating across a wide range of scales, EUREC⁴A marked a turning point in our ability to observationally study factors influencing clouds in the trades, how they will respond to warming, and their link to other components of the earth system, such as upper-ocean processes or the life cycle of particulate matter. This characterization was made possible by thousands (2500) of sondes distributed to measure circulations on meso- (200 km) and larger (500 km) scales, roughly 400 h of flight time by four heavily instrumented research aircraft; four global-class research vessels; an advanced ground-based cloud observatory; scores of autonomous observing platforms operating in the upper ocean (nearly 10 000 profiles), lower atmosphere (continuous profiling), and along the air–sea interface; a network of water stable isotopologue measurements; targeted tasking of satellite remote sensing; and modeling with a new generation of weather and climate models. In addition to providing an outline of the novel measurements and their composition into a unified and coordinated campaign, the six distinct scientific facets that EUREC⁴A explored – from North Brazil Current rings to turbulence-induced clustering of cloud droplets and its influence on warm-rain formation – are presented along with an overview of EUREC⁴A's outreach activities, environmental impact, and guidelines for scientific practice. Track data for all platforms are standardized and accessible at https://doi.org/10.25326/165 (Stevens, 2021), and a film documenting the campaign is provided as a video supplement

The dependence of shallow cumulus macrophysical properties on large-scale meteorology as observed in ASTER imagery

This study identifies meteorological variables that control the macrophysical properties of shallow cumulus cloud fields over the tropical ocean. We use 1,158 high-resolution Advanced Spaceborn Thermal Emission and Reflection Radiometer (ASTER) images to derive properties of shallow cumuli, such as their size distribution, cloud top heights, fractal dimensions, and spatial organization, as well as cloud amount. The large-scale meteorology is characterized by the lower-tropospheric stability, subsidence rate, sea surface temperature, total column water vapor, wind speed, wind shear, and Bowen ratio. The surface wind speed emerges as the most powerful control factor. With increasing wind speed the cloud amount and cloud top heights show a robust increase accompanied by a marked shift in the cloud size distribution toward larger clouds with smoother shapes. These results lend observational support to the deepening response of a wind-driven marine boundary layer as simulated by large-eddy models. The other control factors cause smaller changes in the cloud field properties. We find a robust increase in cloud amount with increasing stability and decreasing sea surface temperature, respectively, which confirms a well-known behavior of marine stratocumulus also for shallow cumulus clouds. Due to the high resolution of cloud images, we are able to study the lower end of the cloud size distribution and find a robust double power law behavior with a scale break at 590 m. We find a variation in the shape of the cloud size distribution with Bowen ratio, qualitatively consistent with modeling results and suggesting the Bowen ratio as a new potential control factor on shallow cumulus clouds. © 2019. The Authors

Integration of ground measurements with model-derived data

Bankable data for solar energy projects needs to ensure as much as possible the accuracy and general quality of solar radiation data to be used in the solar resource assessment studies for any site of interest in a project development. The term “site adaptation” is being used in the framework of solar energy projects to refer to the improvement that can be achieved in satellite-­‐derived (or more generally model-­‐derived) solar irradiance when short-­‐term local ground measurements are used to correct systematic errors and bias of the original dataset. This document presents a review of different techniques for correcting long-­‐term satellite-­‐ derived solar radiation data by using short-­‐term ground measurements. The collaborative work has been done within the framework of Task 46 “Solar Resource Assessment and Forecasting” of the International Energy Agency’s Solar Heating and Cooling Programme. Different approaches whose use depends on the origin and characteristics of the uncertainties of the modelled data are presented. Recommendations to the use of ground measurements and the results of several approaches to improve satellite-­‐derived data are shown through this report highlighting the importance of using site adaptation and the different degree of improvement that can be achieved depending on the climatological characteristics of the site

Preliminary survey on site-adaptation techniques for satellite-derived and reanalysis solar radiation datasets

At any site, the bankability of a projected solar power plant largely depends on the accuracy and general quality of the solar radiation data generated during the solar resource assessment phase. The term ‘‘site adaptation” has recently started to be used in the framework of solar energy projects to refer to the improvement that can be achieved in satellite-derived solar irradiance and model data when short-term local ground measurements are used to correct systematic errors and bias in the original dataset. This contribution presents a preliminary survey of different possible techniques that can improve long-term satellite-derived and model-derived solar radiation data through the use of short-term on-site ground measurements. The possible approaches that are reported here may be applied in differentways, depending on the origin and characteristics of the uncertainties in the modeled data. This work, which is the first step of a forthcoming in-depth assessment of methodologies for site adaptation, has been done within the framework of the International Energy Agency Solar Heating and Cooling Programme Task 46 ‘‘Solar Resource Assessment and Forecasting”

Integration of ground measurements to model- derived data : A report of IEA SHC Task 46 Solar Resource Assessment and Forecasting

Bankable data for solar energy projects needs to ensure as much as possible the accuracy and general quality of solar radiation data to be used in the solar resource assessment studies for any site of interest in a project development. The term “site adaptation” is being used in the framework of solar energy projects to refer to the improvement that can be achieved in satellite-derived (or more generally model-derived) solar irradiance when short-term local ground measurements are used to correct systematic errors and bias of the original dataset. This document presents a review of different techniques for correcting long-term satellite- derived solar radiation data by using short-term ground measurements. The collaborative work has been done within the framework of Task 46 “Solar Resource Assessment and Forecasting” of the International Energy Agency's Solar Heating and Cooling Programme. Different approaches whose use depends on the origin and characteristics of the uncertainties of the modelled data are presented. Recommendations to the use of ground measurements and the results of several approaches to improve satellite-derived data are shown through this report highlighting the importance of using site adaptation and the different degree of improvement that can be achieved depending on the climatological characteristics of the site