Towards an improved treatment of cloud–radiation interaction in weather and climate models: exploring the potential of the Tripleclouds method for various cloud types using libRadtran 2.0.4

The representation of unresolved clouds in radiation schemes of coarse-resolution weather and climate models has progressed noticeably over the past years. Nevertheless, a lot of room remains for improvement, as the current picture is by no means complete. The main objective of the present study is to advance the cloud–radiation interaction parameterization, focusing on the issues related to model misrepresentation of cloud horizontal inhomogeneity. This subject is addressed with the Tripleclouds radiative solver, the fundamental feature of which is the inclusion of the optically thicker and thinner cloud fraction. The research challenge is to optimally set the pair of cloud condensates characterizing the two cloudy regions and the corresponding geometrical split of layer cloudiness. A diverse cloud field data set was collected for the analysis, comprising case studies of stratocumulus, cirrus and cumulonimbus. The primary goal is to assess the validity of the global cloud variability estimate along with various condensate distribution assumptions. More sophisticated parameterizations are subsequently explored, optimizing the treatment of overcast as well as extremely heterogeneous cloudiness. The radiative diagnostics including atmospheric heating rate and net surface flux are consistently studied using the Tripleclouds method, evaluated against a three-dimensional radiation computation. The performance of Tripleclouds mostly significantly surpasses the calculation on horizontally homogeneous cloudiness. The effect of horizontal photon transport is further quantified. The overall conclusions are intrinsically different for each particular cloud type, encouraging endeavors to enhance the use of cloud-regime-dependent methodologies in next-generation atmospheric models. This study, highlighting the Tripleclouds potential for three essential cloud types, signifies the need for more research examining a broader spectrum of cloud morphologies.

Role of surface latent heat flux in shallow cloud transitions: A mechanism-denial LES study

Surface latent heat flux (LHF) has been considered as the determinant driver of the stratocumulus-to-cumulus transition (SCT). The distinct signature of the LHF in driving the SCT, however, has not been found in observations. This motivates us to ask: how determinant is the LHF to SCT? To answer it, we conduct large-eddy simulations in a Lagrangian setup in which the sea-surface temperature increases over time to mimic a low-level cold air advection. To isolate the role of LHF, we conduct a mechanism-denial experiment in which the LHF adjustment is turned off. The simulations confirm the indispensable roles of LHF in sustaining (although not initiating) the boundary layer decoupling (first stage of SCT) and driving the cloud regime transition (second stage of SCT). However, using theoretical arguments and LES results, we show that decoupling can happen without the need for LHF to increase as long as the capping inversion is weak enough to ensure high entrainment efficiency. The high entrainment efficiency alone cannot sustain the decoupled state without the help of LHF adjustment, leading to the recoupling of the boundary layer that eventually becomes cloud-free. Interestingly, the stratocumulus sheet is sustained longer without LHF adjustment. The mechanisms underlying the findings are explained from the perspectives of cloud-layer budgets of energy (first stage) and liquid water path (second stage).

Classifying planetary cloudiness with an updated set of MODIS Cloud Regimes

We present an updated Cloud Regime (CR) dataset based on Moderate resolution Imaging Spectroradiometer (MODIS) Collection 6.1 cloud products, specifically joint histograms that partition cloud fraction within distinct combinations of cloud top pressure and cloud optical thickness ranges. The paper focuses on an edition of the CR dataset derived from our own aggregation of MODIS pixel-level cloud retrievals on an equal area grid and pre-specified 3-hour UTC intervals that spatiotemporally match International Satellite Cloud Climatology Project (ISCCP) gridded cloud data. The other edition comes from the 1-degree daily aggregation provided by standard MODIS Level-3 data, as in previous versions of the MODIS CRs, for easier use with datasets mapped on equal angle grids. Both editions consist of 11 clusters whose centroids are nearly identical.We provide a physical interpretation of the new CRs and aspects of their climatology that have not been previously examined, such as seasonal and interannual variability of CR frequency of occurrence. We also examine the makeup and precipitation properties of the CRs assisted by independent datasets originating from active observations, and provide a first glimpse of how MODIS CRs relate to clouds as seen by ISCCP.

Development of a random forest cloud regime classification model based on surface radiation and cloud products

Various methods have been developed to characterize cloud type, otherwise referred to as cloud regime. These include manual sky observations, combining radiative and cloud vertical properties observed from satellite, surface-based remote sensing, and digital processing of sky imagers. While each methodology has inherent advantages and disadvantages, none of these cloud typing methods actually include measurements of surface shortwave or longwave radiative fluxes. Here, a methodology that relies upon detailed, surface-based radiation and cloud measurements and derived data products to train a random forest machine learning cloud classification model is introduced. Measurements from five years of data from the ARM Southern Great Plains site were compiled to train and independently evaluate the model classification performance. A cloud type accuracy of approximately 80% using the random forest classifier reveals the model is well suited to predict climatological cloud properties. Furthermore, an analysis of the cloud type misclassifications is performed. While physical cloud types may be misreported, the shortwave radiative signatures are similar between misclassified cloud types. From this, we assert the cloud regime model has the capacity to successfully differentiate clouds with comparable cloud-radiative interactions. Therefore, we conclude the model can provide useful cloud property information for fundamental cloud studies, inform renewable energy studies, a tool for numerical model evaluation and parameterization improvement, among many other applications.

Towards an improved treatment of cloud-radiation interaction in weather and climate models: exploring the potential of the Tripleclouds method for various cloud types using libRadtran 2.0.4

Although the representation of unresolved clouds in radiation schemes of coarse-resolution weather and climate models has progressed noticeably over the past years, a lot of room remains for improvement, as the current picture is by no means complete. The main objective of the present study is to advance the cloud-radiation interaction parameterization, focusing on the issues related to model misrepresentation of cloud horizontal inhomogeneity. This subject is addressed with the state-of-the-art Tripleclouds radiative solver, the fundamental feature of which is the inclusion of the optically thicker and thinner cloud fraction, where the thicker is associated with the presence of convective updraft elements. The research challenge is to optimally set the pair of cloud condensates characterizing the two cloudy regions and the corresponding geometrical split of layer cloudiness. A diverse cloud field data set was collected for the analysis, comprising case studies of stratocumulus, cirrus and cumulonimbus. The primary goal is to assess the validity of global cloud variability estimate along with various condensate distribution assumptions. More sophisticated parameterizations are subsequently explored, optimizing the treatment of overcast as well as extremely heterogeneous cloudiness. The radiative diagnostics including atmospheric heating rate and net surface flux are consistently studied using the Tripleclouds method, evaluated against a three-dimensional radiation computation. The performance of Tripleclouds mostly significantly surpasses the conventional calculation on horizontally homogeneous cloudiness. The effect of horizontal photon transport is further quantified. The overall conclusions are intrinsically different for each particular cloud type, encouraging endeavors to enhance the use of cloud regime dependent methodologies in next-generation atmospheric models. This study highlighting the Tripleclouds potential for three essential cloud types signifies the need for more research examining a broader spectrum of cloud morphologies.

A Cluster Approach to Cloud Cover Classification over South America and Adjacent Oceans Using a k-means/k-means++ Unsupervised Algorithm on GOES IR Imagery

An unsupervised k-means/k-means++ clustering algorithm was implemented on daily images of standardized anomalies of brightness temperature (Tb) derived from the Geostationary Operational Environmental Satellite (GOES)-13 infrared data for the period 1 December 2010 to 30 November 2016. The goal was to decompose each individual Tb image into four clusters that captures the characteristics of different cloud regimes. The extracted clusters were ordered by their mean value in an ascending fashion so that the lower the cluster order, the higher the clouds they represent. A linear regression between temperature and height with temperature used as the predictor was conducted to estimate cloud top heights (CTHs) from the Tb values. The analysis of the results was performed in two different ways: sample dates and seasonal features. Cluster 1 is the less dominant one, representing clouds with the highest tops and variabilities. Cluster 4 is the most dominant one and represents a cloud regime that spans the lowest 2 km of the troposphere. Clusters 2 and 3 are entangled in the sense that both have their CTHs spanning the middle troposphere. Correlations between the monthly time series of the number of pixels in each cluster and of the entropy with several circulation indices are also introduced. Additionally, a fractal-related analysis was carried out on cluster 1 in order to resolve cirrus and cumulonimbus.

A new classification of satellite-derived liquid water cloud regimes at cloud scale

Clouds are highly variable in time and space, affecting climate sensitivity and climate change. To study and distinguish the different influences of clouds on the climate system, it is useful to separate clouds into individual cloud regimes. In this work we present a new cloud classification for liquid water clouds at cloud scale defined using cloud parameters retrieved from combined satellite measurements from CloudSat and CALIPSO. The idea is that cloud heterogeneity is a measure that allows us to distinguish cumuliform and stratiform clouds, and cloud-base height is a measure to distinguish cloud altitude. The approach makes use of a newly developed cloud-base height retrieval. Using three cloud-base height intervals and two intervals of cloud-top variability as an inhomogeneity parameter provides six new liquid cloud classes. The results show a smooth transition between marine and continental clouds as well as between stratiform and cumuliform clouds in different latitudes at the high spatial resolution of about 20 km. Analysing the micro- and macrophysical cloud parameters from collocated combined MODIS, CloudSat and CALIPSO retrievals shows distinct characteristics for each cloud regime that are in agreement with expectation and literature. This demonstrates the usefulness of the classification.

The ice-nucleating ability of quartz immersed in water and its atmospheric importance compared to K-feldspar

Mineral dust particles are thought to be an important type of ice-nucleating particle (INP) in the mixed-phase cloud regime around the globe. While K-rich feldspar (K-feldspar) has been identified as being a particularly important component of mineral dust for ice nucleation, it has been shown that quartz is also relatively ice-nucleation active. Given quartz typically makes up a substantial proportion of atmospheric desert dust, it could potentially be important for cloud glaciation. Here, we survey the ice-nucleating ability of 10 α-quartz samples (the most common quartz polymorph) when immersed in microlitre supercooled water droplets. Despite all samples being α-quartz, the temperature at which they induce freezing varies by around 12 ∘C for a constant active site density. We find that some quartz samples are very sensitive to ageing in both aqueous suspension and air, resulting in a loss of ice-nucleating activity, while other samples are insensitive to exposure to air and water over many months. For example, the ice-nucleation temperatures for one quartz sample shift down by ∼2 ∘C in 1 h and 12 ∘C after 16 months in water. The sensitivity to water and air is perhaps surprising, as quartz is thought of as a chemically resistant mineral, but this observation suggests that the active sites responsible for nucleation are less stable than the bulk of the mineral. We find that the quartz group of minerals is generally less active than K-feldspars by roughly 7 ∘C, although the most active quartz samples are of a similar activity to some K-feldspars with an active site density, ns(T), of 1 cm−2 at −9 ∘C. We also find that the freshly milled quartz samples are generally more active by roughly 5 ∘C than the plagioclase feldspar group of minerals and the albite end member has an intermediate activity. Using both the new and literature data, active site density parameterizations have been proposed for freshly milled quartz, K-feldspar, plagioclase and albite. Combining these parameterizations with the typical atmospheric abundance of each mineral supports previous work that suggests that K-feldspar is the most important ice-nucleating mineral in airborne mineral dust.

Response of Arctic mixed-phase clouds to aerosol perturbations under different surface forcings

The formation and persistence of low-lying mixed-phase clouds (MPCs) in the Arctic depends on a multitude of processes, such as surface conditions, the environmental state, air mass advection, and the ambient aerosol concentration. In this study, we focus on the relative importance of different instantaneous aerosol perturbations (cloud condensation nuclei and ice-nucleating particles; CCN and INPs, respectively) on MPC properties in the European Arctic. To address this topic, we performed high-resolution large-eddy simulation (LES) experiments using the Consortium for Small-scale Modeling (COSMO) model and designed a case study for the Aerosol-Cloud Coupling and Climate Interactions in the Arctic (ACCACIA) campaign in March 2013. Motivated by ongoing sea ice retreat, we performed all sensitivity studies over open ocean and sea ice to investigate the effect of changing surface conditions. We find that surface conditions highly impact cloud dynamics, consistent with the ACCACIA observations: over sea ice, a rather homogeneous, optically thin, mixed-phase stratus cloud forms. In contrast, the MPC over the open ocean has a stratocumulus-like cloud structure. With cumuli feeding moisture into the stratus layer, the cloud over the open ocean features a higher liquid (LWP) and ice water path (IWP) and has a lifted cloud base and cloud top compared to the cloud over sea ice. Furthermore, we analyzed the aerosol impact on the sea ice and open ocean cloud regime. Perturbation aerosol concentrations relevant for CCN activation were increased to a range between 100 and 1000 cm−3 and ice-nucleating particle perturbations were increased by 100 % and 300 % compared to the background concentration (at every grid point and at all levels). The perturbations are prognostic to allow for fully interactive aerosol–cloud interactions. Perturbations in the INP concentration increase IWP and decrease LWP consistently in both regimes. The cloud microphysical response to potential CCN perturbations occurs faster in the stratocumulus regime over the ocean, where the increased moisture flux favors rapid cloud droplet formation and growth, leading to an increase in LWP following the aerosol injection. In addition, IWP increases through new ice crystal formation by increased immersion freezing, cloud top rise, and subsequent growth by deposition. Over sea ice, the maximum response in LWP and IWP is delayed and weakened compared to the response over the open ocean surface. Additionally, we find the long-term response to aerosol perturbations to be highly dependent on the cloud regime. Over the open ocean, LWP perturbations are efficiently buffered after 18 h simulation time. Increased ice and precipitation formation relax the LWP back to its unperturbed range. On the contrary, over sea ice the cloud evolution remains substantially perturbed with CCN perturbations ranging from 200 to 1000 CCN cm−3.