Cloud Vertical Distribution Research Articles

A Review of Research on Cloud Detection Methods for Hyperspectral Infrared Radiances

Cloud contamination is a critical source of errors in the data assimilation of hyperspectral infrared radiance (IR). Therefore, it is necessary to filter out cloudy observations. In this study, we review and summarize the principles and research progress of cloud detection methods for the hyperspectral IR in the past two decades. Based on the impact of IR data utilization on cloud detection results, cloud detection methods are categorized into five types, namely clear field-of-view (FOV) detection, clear channel detection, three-dimensional cloud detection, cloud-clearing and deep learning methods. Clear FOV methods and clear channel methods aim to identify the purely clear FOVs and spectral channels that are not affected by clouds, respectively. Cloud-clearing methods are used to reconstruct clear-column radiance for cloudy observations. Deep learning cloud detection methods can quickly learn the mapping relationship between infrared hyperspectral radiation characteristics and FOV cloud distribution from a large amount of infrared radiative information with known FOV cloud labels. In this paper, we discuss and provide an outlook on the key issues in current hyperspectral IR cloud detection. Specifically, we analyze and summarize the factors affecting cloud detection, such as surface background information, vertical cloud distribution, hyperspectral IR channel selection, improvements in cloud detection algorithms and model applicability. The results indicate the use of deep learning methods offer advantages in detection accuracy and algorithm efficiency of hyperspectral IR cloud detection.

Multi‐Layer Cloud Detection and Distributions Over the Asia–Pacific Region Based on Geostationary Satellite Imagers

AbstractA large portion of cloud scenes over the globe shows multiple layers composed of different phases, in general with ice clouds on the top and liquid water clouds beneath. Such multi‐layer (ML) clouds constitute major challenges in cloud observations and weather and climate modeling. This study improved a threshold algorithm for detecting ice‐over‐water ML clouds using geostationary satellites. Optimal thresholds were established for the spectral characteristics of the Advanced Himawari Imager (AHI) and the Advanced Geostationary Radiation Imager (AGRI), accounting for differences between land and ocean surfaces. Validation with collocated space radar and lidar measurements indicated the identification accuracies of approximately 82% over the land and 76% over the ocean. Annual distributions of ML clouds inferred by AHI and AGRI exhibited strong similarity. Furthermore, 6 years of hourly observations revealed distinct monthly and daily variations in ice‐over‐water clouds over the Asia–Pacific region. The ML cloud monthly variations were similar to those of the seasonal convection cycle, with occurrence frequencies over the typical regions higher in summer (maximum ∼27%) and lower (minimum 6%–10%) in winter. Regarding daily variations, ice‐over‐water clouds occurred more frequently around local noon over most of the six time zones (from UTC + 06 to UTC + 11) throughout all seasons. The refined spatiotemporal distribution of ML clouds, particularly the daily variations, is possible to improve our understanding of cloud vertical distributions and radiative effects, and has the potential to promote subsequent validation and parameterization of cloud overlapping in global climate modeling.

A method to assess the cloud-aerosol transition zone from ceilometer measurements

Cloud and aerosol contribution to the Earth's radiative budget constitutes one of the most significant uncertainties in future climate projections. To distinguish between clouds and cloud-free air, atmospheric scientists have been using a wide range of instruments, techniques, and algorithms with various detection thresholds. However, since the change from a cloudy to a cloud-free atmosphere may be gradual and, in some cases, far from obvious, recent research is questioning where the threshold between both phases should be established. These considerations lead to contemplate a transition zone (TZ) between cloud and cloud-free conditions. In the present study, backscatter profiles obtained by a Vaisala CL31 ceilometer were processed to assess the transition zone. First, two widely used cloud detection algorithms were applied and compared: the method provided by the ceilometer manufacturer (Vaisala) and Cloudnetpy, the algorithm from ACTRIS Cloudnet, a project devoted to aerosol, clouds, and trace gases research. Second, a sensitivity analysis was applied to the backscatter and signal-to-noise ratio thresholds used for cloud detection in Cloudnetpy. This methodology has allowed us to assess the vertical distribution of clouds, aerosols, the TZ, and its frequency of occurrence. Results indicate a gradual transition in backscatter retrievals from cloud to cloud-free, where particles detected near cloud boundaries induced higher backscatter values than those found further away. Depending on the thresholds used, we observed a 9.3% (with an estimated range of uncertainty of 5.420%) variation in cloud occurrence which can be attributed to TZ conditions. Analysing the whole backscatter profile, we found as many TZ conditions as cloudy values, which emphasises the importance of studying the vertical distribution of the TZ. Moreover, the analysis of TZ occurrence in height and time revealed that such conditions concentrate below 800m during night periods, although annual height-hour distributions involve a remarkable variability among seasons. These findings highlight the importance of either including an additional phase between ‘pure clouds’ and ‘pure aerosols’ or treating them as a continuum of suspended particles in the atmosphere.

A Deep Learning Lidar Denoising Approach for Improving Atmospheric Feature Detection

Space-based atmospheric backscatter lidars provide critical information about the vertical distribution of clouds and aerosols, thereby improving our understanding of the climate system. They are additionally useful for detecting hazards to aviation and human health, such as volcanic plumes and man-made pollution events. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP, 2006–2023), Cloud-Aerosol Transport System (CATS, 2015–2017), and Advanced Topographic Laser Altimeter System (ATLAS 2018–present) are three such lidars that operated within the past 20 years. The signal-to-noise ratio (SNR) for these lidars is significantly lower in daytime data compared with nighttime data due to the solar background signal increasing the detector response noise. Averaging horizontally across profiles has been the standard way to increase SNR, but this comes at the expense of resolution. Modern, deep learning-based denoising algorithms can be applied to improve the SNR without coarsening resolution. This paper describes how one such model architecture, Dense Dense U-Net (DDUNet), was trained to denoise CATS 1064 nm raw signal data (photon counts) using artificially noised nighttime data. Simulated CATS daytime 1064 nm data were then created to assess the model’s performance. The denoised simulated data increased the daytime SNR by a factor of 2.5 (on average) and decreased minimum detectable backscatter (MDB) to ~7.3×10−4 km−1sr−1, which is lower than the CALIOP 1064 nm night MDB value of 8.6×10−4 km−1sr−1. Layer detection was performed on simulated 2 km horizontal resolution denoised and 60 km averaged data. Despite the finer resolution input, the denoised layers had more true positives, fewer false positives, and an overall Jaccard Index of 0.54 versus 0.44 when compared to the layers detected on averaged data. Layer detection was also performed on a full month of denoised daytime CATS data (Aug. 2015) to detect layers for comparison with CATS standard Level 2 (L2) product layers. The detection on the denoised data yielded 2.33 times more, higher-quality bins within detected layers at 2.7–33 times finer resolution than the CATS L2 products.

Warming Climate‐Induced Changes in Cloud Vertical Distribution Possibly Exacerbate Intra‐Atmospheric Heating Over the Tibetan Plateau

AbstractThe complex and diverse cloud vertical distribution (CVD) largely impacts radiative and precipitation properties of clouds. Using 10‐year active satellite observations, we classified CVD over the Tibetan Plateau into 12 categories and found that overlapping clouds have less frequency but stronger radiative effect, heating rate and larger precipitation (partly reflecting the seeding effect) compared with single‐layer non‐strong convective clouds. Under a warming climate due to uniform sea surface temperature increase of 4K (quadrupling CO2 increase), extremely high (>10 km) ice clouds will increase, particularly those below the tropopause will increase slightly (largely), accompanied by clear (weak) increases in stratospheric clouds. Simultaneously, a moderate to rapid decrease will occur in clouds below 10 km. Such CVD changes could further exacerbate tropopause warming. The probability of cloud overlap is also likely to increase in warmer climates, thus possibly further causing non‐convective cloud systems with stronger intra‐atmospheric heating, larger precipitation intensity and proportion.

Convective Couplings With Equatorial Rossby Waves and Equatorial Kelvin Waves: 3. Variations of Clouds and Their Radiative Effects

AbstractUtilizing spaceborne cloud radar and lidar (CloudSat/CALIPSO) observation products, we examine vertical distributions of clouds and quantify their radiative effects associated with equatorial Rossby and Kelvin waves. The most important result is that the radiative heating substantially increased the generation of the eddy available potential energy by 19% and 40%, in Rossby and Kelvin waves, respectively, adding to the convective latent heating. Composite analyses indicate a simultaneous development between deep‐convective anvil clouds and stratiform clouds of mesoscale convective systems in the Rossby waves, and a transition from low‐level clouds, anvil clouds to stratiform clouds in the Kelvin waves. These are consistent to precipitation characteristics provided by precipitation radar observation, and thus the apparent heat source can be estimated by combining convective heating and radiative heating.

Effect of Single and Double Moment Microphysics Schemes and Change in Cloud Condensation Nuclei, Latent Heating Rate Structure Associated with Severe Convective System over Korean Peninsula

To investigate the impact of advanced microphysics schemes using single and double moment (WSM6/WDM6) schemes, numerical simulations are conducted using Weather Research and Forecasting (WRF) model for a severe mesoscale convective system (MCS) formed over the Korean Peninsula. Spatial rainfall distribution and pattern correlation linked with the convective system are improved in the WDM6 simulation. During the developing stage of the system, the distribution of total hydrometeors is larger in WDM6 compared to WSM6. Along with the mixing ratio of hydrometeors (cloud, rain, graupel, snow, and ice), the number concentration of cloud and rainwater are also predictable in WDM6. To understand the differences in the vertical representation of cloud hydrometeors between the schemes, rain number concentration (Nr) from WSM6 is also computed using particle density to compare with the Nr readily available in WDM6. Varied vertical distribution and large differences in rain number concentration and rain particle mass is evident between the schemes. Inclusion of the number concentration of rain and cloud, CCN, along with the mixing ratio of different hydrometers has improved the storm morphology in WDM6. Furthermore, the latent heating (LH) profiles of six major phase transformation processes (condensation, evaporation, freezing, melting, deposition, and sublimation) are also computed from microphysical production terms to deeply study the storm vertical structure. The main differences in condensation and evaporation terms are evident between the simulations due to the varied treatment of warm rain processes and the inclusion of CCN activation in WDM6. To investigate cloud–aerosol interactions, numerical simulation is conducted by increasing the CCN (aerosol) concentration in WDM6, which simulated comparatively improved pattern correlation for rainfall simulation along with intense hydrometer distribution. It can be inferred that the change in aerosol increased the LH of evaporation and freezing and affected the warming and cooling processes, cloud vertical distribution, and subsequent rainfall. Relatively, the WDM6 simulated latent heating profile distribution is more consistent with the ERA5 computed moisture source and sink terms due to the improved formulation of warm rain processes.

Retrieving cloud base height from passive radiometer observations via a systematic effective cloud water content table

Cloud base heights (CBHs) play an important role in weather and climate studies, but remain one of the most challenging properties to be inferred using passive satellite spectral instruments. This is because spectral signals detected from space are mainly from cloud tops and contain minimal information on cloud vertical structures owing to limited penetration. This paper presents a new algorithm for retrieving CBH from radiometer measurements, utilizing an improved characterization for mutable cloud water contents (CWCs). This was achieved by introducing a variable named effective CWC (ECWC) to represent the vertically varying CWC; and subsequently investigating the sensitivity of ECWC to both cloud and environmental properties. Considering these characteristics, a systematic ECWC lookup table (LUT) was created based on collocated active and passive satellite observations. We then converted the existing cloud water path (CWP; kg/m2) to cloud geometric thickness (CGT; km) via the developed LUT, and calculated CBH by subtracting the CGT estimates from conventional cloud top height (CTH) retrievals. The algorithm was tested using observations from Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Himawari Imager (AHI) as examples, and was evaluated using active measurements from the CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellites and a ground-based Ka-band radar. Overall, our CBH results agreed closely with the active observations, with mean biases of 0.11 ± 1.93 km for MODIS and − 0.12 ± 2.34 km for AHI in single-layer cloud cases. Despite the natural limitations on retrieving CBHs using passive radiometers, the proposed algorithm achieves retrieval accuracies comparable to those of operational CTH products. Moreover, this algorithm works for both single-layer and overlapping clouds, and can be extended to other passive radiometers by reconstructing the LUT. Such accurate CBH information derived from high-resolution passive radiometers would greatly improve our understanding of cloud vertical distributions; and could play an important role in studies of cloud radiative effects and the aerosol-cloud-precipitation interactions.

Uncertain role of clouds in shaping summertime atmosphere-sea ice connections in reanalyses and CMIP6 models

Downwelling longwave radiation (DLR) driven by the atmospheric and cloud conditions in the troposphere is suggested to be a dominant factor to determine the summertime net surface energy budget over the Arctic Ocean and thus plays a key role to shape the September sea ice. We use reanalyses and the self-organizing map (SOM) method to distinguish CMIP6 model performance in replicating the observed strong atmosphere-DLR connection. We find all models can reasonably simulate the linkage between key atmosphere variables and the clear sky DLR but behave differently in replicating the atmosphere-DLR connection due to cloud forcing. In ERA5 and strongly coupled models, tropospheric high pressure is associated with decreased clouds in the mid- and high-levels and increased clouds near the surface. This out-of-phase structure indicates that DLR cloud forcing is nearly neutral, making the clear sky DLR more important to bridge JJA circulation to late-summer sea ice. In MERRA-2 and weakly coupled models, tropospheric clouds display a vertically homogeneous reduction; the cloud DLR is thus strongly reduced due to the cooling effect, which partially cancels out the clear sky DLR and makes the total DLR less efficient to translate circulation forcing to sea ice. The differences of cloud vertical distribution in CMIP6 appear to be differentiated by circulation related relative humidity. Therefore, a better understanding of the discrepancy of different reanalyses and remote sensing products is critical to comprehensively evaluate simulated interactions among circulation, clouds, sea ice and energy budget at the surface in summer.

Tropical Tropopause Layer Cloud Properties from Spaceborne Active Observations

A significant part of clouds in the tropics appears over the tropopause due to intense convections and in situ condensation activity. These tropical tropopause layer (TTL) clouds not only play an important role in the radiation budget over the tropics, but also in water vapor and other chemical material transport from the troposphere to the stratosphere. This study quantifies and analyzes the properties of TTL clouds based on spaceborne active observations, which provide one of the most reliable sources of information on cloud vertical distributions. We use four years (2007–2010) of observations from the joint Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat and consider all cloudy pixels with top height above the tropopause as TTL clouds. The occurrence frequency of TTL clouds during the nighttime is found to be almost 13% and can reach ~50–60% in areas with frequent convections. The annual averages of tropical tropopause height, tropopause temperature, and cloud top height are 16.2 km, −80.7 °C, and 16.6 km, respectively, and the average cloud top exceeds tropopause by approximately 500 m. More importantly, the presence of TTL clouds causes tropopause temperature to be ~3–4 °C colder than in the all-sky condition. It also lifts the tropopause heights ~160 m during the nighttime and lowers the heights ~84 m during the daytime. From a cloud type aspect, ~91% and ~4% of the TTL clouds are high clouds and altostratus, and only ~5% of them are associated with convections (i.e., nimbostratus and deep convective clouds). Approximately 30% of the TTL clouds are single-layer clouds, and multi-layer clouds are dominated by those with 2–3 separated layers.

Diurnal cycles of cloud cover and its vertical distribution over the Tibetan Plateau revealed by satellite observations, reanalysis datasets, and CMIP6 outputs

Abstract. Diurnal variations in cloud cover and cloud vertical distribution are of great importance to Earth–atmosphere system radiative budgets and climate change. However, thus far these topics have received insufficient attention, especially on the Tibetan Plateau (TP). This study focuses on the diurnal variations in total cloud cover, cloud vertical distribution, and cirrus clouds and their relationship to meteorological factors over the TP based on active and passive satellite observations, reanalysis data, and CMIP6 outputs. Our results are consistent with previous studies but provide new insights. The results show that total cloud cover peaks at 06:00–09:00 UTC, especially over the eastern TP, but the spatial and temporal distributions of clouds from different datasets are inconsistent. This could to some extent be attributed to subvisible clouds missed by passive satellites and models. Compared with satellite observations, the amplitudes of the diurnal variations in total cloud cover obtained by the reanalysis and CMIP6 models are obviously smaller. CATS can capture the varying pattern of the vertical distribution of clouds and corresponding height of peak cloud cover at middle and high atmosphere levels, although it underestimates the cloud cover of low-level clouds, especially over the southern TP. Compared with CATS, ERA5 cannot capture the complete diurnal variations in vertical distribution of clouds and MERRA-2 has a poorer performance. We further find that cirrus clouds, which are widespread over the TP, show significant diurnal variations with averaged peak cloud cover over 0.35 at 15:00 UTC. Unlike in the tropics, where thin cirrus (0.03< optical depth <0.3) dominate, opaque cirrus clouds (0.3< optical depth <3) are the dominant cirrus clouds over the TP. The seasonal and regional averaged cloud cover of opaque cirrus reaches a daily maximum of 0.18 at 11:00 UTC, and its diurnal cycle is strong positive correlation with that of 250 hPa relative humidity and 250 hPa vertical velocity. Although subvisible clouds (optical depth <0.03), which have a potential impact on the radiation budget, are the fewest among cirrus clouds over the TP, the seasonal and regional averaged peak cloud cover can reach 0.09 at 22:00 UTC, and their diurnal cycle correlates with that of the 250 hPa relative humidity, 2 m temperature, and 250 hPa vertical velocity. Our results will be helpful to improve the simulation and retrieval of total cloud cover and cloud vertical distribution and further provide an observational constraint for simulations of the diurnal cycle of surface radiation budget and precipitation over the TP region.

The extreme precipitation events of August 2018 and 2019 over southern Western Ghats, India: A microphysical analysis using in-situ measurements

The extreme rainfall events observed at the High-Altitude Cloud Physics Observatory (HACPO) in Rajamallay, Munnar (10° 9′19.94″N, 77° 1′6.65″E; 1820 m above MSL) over the southern Western Ghats (India) during the floods in 2018 and 2019 monsoon periods are investigated. The observations from Micro Rain Radar (MRR) and Ceilometer are used to analyze precipitation microphysics and the vertical distribution of clouds during the intense rainfall episodes on 14 - 16th August 2018 and on 8th August 2019. The drop size distribution (DSD) spectra during the 2018 event is characterized by a large number of small to medium-sized drops resulting in maximum reflectivity of 48dBZ with mass-weighted mean diameter (Dm) value of 1.2 mm. At the same time, the 2019 event is characterized by larger drops and resulted in high reflectivity of 53dBZ and Dm value shifted to 1.4 mm. The consistent increment of Dm and σm with slight variation in Nt during the intensive rain hours on 8th August 2019 shows a mixed-phase microphysical process that can invigorate the production of convective rainfall from deep cloud bands (217 K of cloud top temperature) with enhanced rain water content (22 gm−3). The parameters of scaled raindrop size distributions corresponding to higher rain rates (>50 mmhr−1) suggest that the microphysical process that control the variations in DSD is strongly number controlled during these extreme rainfall events. The DSDs are evolved from a consistent, widespread rainfall supported by anomalous moisture advection from the Arabian Sea in 2018 monsoon period. The moisture convergence occurred on the elevated terrains leads to an intense spell of rainfall in two consecutive hours and satisfies the occurrence of a mini-cloud burst (MCB) event on 8th August 2019 causing flash flood in the region.

Cloud phase and macrophysical properties over the Southern Ocean during the MARCUS field campaign

Abstract. To investigate the cloud phase and macrophysical properties over the Southern Ocean (SO), the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF2) was installed on the Australian icebreaker research vessel (R/V) Aurora Australis during the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign (41 to 69∘ S, 60 to 160∘ E) from October 2017 to March 2018. To examine cloud properties over the midlatitude and polar regions, the study domain is separated into the northern (NSO) and southern (SSO) parts of the SO, with a demarcation line of 60∘ S. The total cloud fractions (CFs) were 77.9 %, 67.6 %, and 90.3 % for the entire domain, NSO and SSO, respectively, indicating that higher CFs were observed in the polar region. Low-level clouds and deep convective clouds are the two most common cloud types over the SO. A new method was developed to classify liquid, mixed-phase, and ice clouds in single-layered, low-level clouds (LOW), where mixed-phase clouds dominate with an occurrence frequency (Freq) of 54.5 %, while the Freqs of the liquid and ice clouds were 10.1 % (most drizzling) and 17.4 % (least drizzling). The meridional distributions of low-level cloud boundaries are nearly independent of latitude, whereas the cloud temperatures increased by ∼8 K, and atmospheric precipitable water vapor increased from ∼5 mm at 69∘ S to ∼18 mm at 43∘ S. The mean cloud liquid water paths over NSO were much larger than those over SSO. Most liquid clouds occurred over NSO, with very few over SSO, whereas more mixed-phase clouds occurred over SSO than over NSO. There were no significant differences for the ice cloud Freq between NSO and SSO. The ice particle sizes are comparable to cloud droplets and drizzle drops and well mixed in the cloud layer. These results will be valuable for advancing our understanding of the meridional and vertical distributions of clouds and can be used to improve model simulations over the SO.

The ICON Earth System Model Version 1.0

AbstractThis work documents the ICON‐Earth System Model (ICON‐ESM V1.0), the first coupled model based on the ICON (ICOsahedral Non‐hydrostatic) framework with its unstructured, icosahedral grid concept. The ICON‐A atmosphere uses a nonhydrostatic dynamical core and the ocean model ICON‐O builds on the same ICON infrastructure, but applies the Boussinesq and hydrostatic approximation and includes a sea‐ice model. The ICON‐Land module provides a new framework for the modeling of land processes and the terrestrial carbon cycle. The oceanic carbon cycle and biogeochemistry are represented by the Hamburg Ocean Carbon Cycle module. We describe the tuning and spin‐up of a base‐line version at a resolution typical for models participating in the Coupled Model Intercomparison Project (CMIP). The performance of ICON‐ESM is assessed by means of a set of standard CMIP6 simulations. Achievements are well‐balanced top‐of‐atmosphere radiation, stable key climate quantities in the control simulation, and a good representation of the historical surface temperature evolution. The model has overall biases, which are comparable to those of other CMIP models, but ICON‐ESM performs less well than its predecessor, the Max Planck Institute Earth System Model. Problematic biases are diagnosed in ICON‐ESM in the vertical cloud distribution and the mean zonal wind field. In the ocean, sub‐surface temperature and salinity biases are of concern as is a too strong seasonal cycle of the sea‐ice cover in both hemispheres. ICON‐ESM V1.0 serves as a basis for further developments that will take advantage of ICON‐specific properties such as spatially varying resolution, and configurations at very high resolution.

Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data

Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, (1) increased the 1064 nm SNR by 75%, (2) increased the number of layers detected (any resolution) by 30%, and (3) enabled detection of 40% more atmospheric features during daytime operations at a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS operational data products. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime.

Improved Convective Ice Microphysics Parameterization in the NCAR CAM Model

AbstractPartitioning deep convective cloud condensates into components that sediment and detrain, known to be a challenge for global climate models, is important for cloud vertical distribution and anvil cloud formation. In this study, we address this issue by improving the convective microphysics scheme in the National Center for Atmospheric Research Community Atmosphere Model version 5.3 (CAM5.3). The improvements include: (1) considering sedimentation for cloud ice crystals that do not fall in the original scheme, (2) applying a new terminal velocity parameterization that depends on the environmental conditions for convective snow, (3) adding a new hydrometeor category, “rimed ice,” to the original four‐class (cloud liquid, cloud ice, rain, and snow) scheme, and (4) allowing convective clouds to detrain snow particles into stratiform clouds. Results from the default and modified CAM5.3 models were evaluated against observations from the U.S. Department of Energy Tropical Warm Pool‐International Cloud Experiment (TWP‐ICE) field campaign. The default model overestimates ice amount, which is largely attributed to the underestimation of convective ice particle sedimentation. By considering cloud ice sedimentation and rimed ice particles and applying a new convective snow terminal velocity parameterization, the vertical distribution of ice amount is much improved in the midtroposphere and upper troposphere when compared to observations. The vertical distribution of ice condensate also agrees well with observational best estimates upon considering snow detrainment. Comparison with observed convective updrafts reveals that current bulk model fails to reproduce the observed updraft magnitude and occurrence frequency, suggesting spectral distributions be required to simulate the subgrid updraft heterogeneity.

Vertical structures of temperature inversions and clouds derived from high-resolution radiosonde measurements at Ny-Ålesund, Svalbard

The knowledge of the vertical atmospheric structures in the Arctic Region remains elusive, due largely to sparse long-term continuous profiling observations. Based on the temporally high-resolution (1 s) radiosonde measurements from April 2017 to September 2019 collected at the Ny-Ålesund (11.92°E, 78.92°N) station in the Arctic, we analyzed the characteristics of temperature inversion (TI) and clouds, including the diurnal and seasonal variabilities under different atmospheric circulations. Clouds mainly appear in the lower troposphere, with the largest contribution by double-layer clouds. The seasonal variation of vertical cloud distribution above 7 km seems is closely linked to the seasonal variability of tropopause height. Besides, the diurnal variation of TI frequency exhibits significant seasonality, with a bimodal distribution in the vertical, with stronger TI intensity in summer. The lowest temperatures at the top of bottom of the elevated inversion are observed in winter, whereas the lowest temperature of the surface-based inversion top is observed in spring, which may be related to the seasonal variation of sea surface temperature. The characteristics of cloud and TI are further analyzed under the five typical circulation patterns. It is found that the low-pressure system and southerly wind in front of the trough are favorable for cloud formation in the lower troposphere, while the impact of synoptic pattern on clouds in the upper troposphere seems negligible, likely due to the cold environment. The TI associated with cyclone systems tends to be much thinner and weaker, owning to the conditionally unstable conditions. These findings provide key reference for the vertical structure of the inversion and cloud in the Arctic, which is expected to help improve cloud parameterization in numeric model.

Observational Evidence for a Stability Iris Effect in the Tropics

AbstractAnvil clouds cover extensive areas of the tropics, and their response to global warming can affect cloud feedbacks and climate sensitivity. A growing number of models and theories suggest that when the tropical atmosphere warms, anvil clouds rise and their coverage decreases, but observational support for this behavior remains limited. Here we use 10 years of measurements from the space‐borne CALIPSO lidar to analyze the vertical distribution of clouds and isolate the behavior of anvil clouds. On the interannual time scale, we find a strong evidence for anvil rise and coverage decrease in response to tropical warming. Using meteorological reanalyses, we show that this is associated with an increase in static stability and with a reduction in clear‐sky radiatively driven mass convergence at the anvil height. These relationships hold over a large range of spatial scales. This is consistent with the stability Iris mechanism suggested by theory and modeling studies.

Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions

Abstract. The evaluation of modelling diagnostics with appropriate observations is an important task that establishes the capabilities and reliability of models. In this study we compare aerosol and cloud properties obtained from three different climate models (ECHAM-HAM, ECHAM-HAM-SALSA, and NorESM) with satellite observations using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The simulator MODIS-COSP version 1.4 was implemented into the climate models to obtain MODIS-like cloud diagnostics, thus enabling model-to-model and model-to-satellite comparisons. Cloud droplet number concentrations (CDNCs) are derived identically from MODIS-COSP-simulated and MODIS-retrieved values of cloud optical depth and effective radius. For CDNC, the models capture the observed spatial distribution of higher values typically found near the coasts, downwind of the major continents, and lower values over the remote ocean and land areas. However, the COSP-simulated CDNC values are higher than those observed, whilst the direct model CDNC output is significantly lower than the MODIS-COSP diagnostics. NorESM produces large spatial biases for ice cloud properties and thick clouds over land. Despite having identical cloud modules, ECHAM-HAM and ECHAM-HAM-SALSA diverge in their representation of spatial and vertical distributions of clouds. From the spatial distributions of aerosol optical depth (AOD) and aerosol index (AI), we find that NorESM shows large biases for AOD over bright land surfaces, while discrepancies between ECHAM-HAM and ECHAM-HAM-SALSA can be observed mainly over oceans. Overall, the AIs from the different models are in good agreement globally, with higher negative biases in the Northern Hemisphere. We evaluate the aerosol–cloud interactions by computing the sensitivity parameter ACICDNC=dln⁡(CDNC)/dln⁡(AI) on a global scale. However, 1 year of data may be considered not enough to assess the similarity or dissimilarities of the models due to large temporal variability in cloud properties. This study shows how simulators facilitate the evaluation of cloud properties and expose model deficiencies, which are necessary steps to further improve the parameterisation in climate models.

Analysis and quantification of ENSO-linked changes in the tropical Atlantic cloud vertical distribution using 14 years of MODIS observations

Abstract. A total of 14 years (September 2002 to September 2016) of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) monthly mean cloud data are used to quantify possible changes in the cloud vertical distribution over the tropical Atlantic. For the analysis multiple linear regression techniques are used. For the investigated time period significant linear changes were found in the domain-averaged cloud-top height (CTH) (−178 m per decade), the high-cloud fraction (HCF) (−0.0006 per decade), and the low-cloud amount (0.001 per decade). The interannual variability of the time series (especially CTH and HCF) is highly influenced by the El Niño–Southern Oscillation (ENSO). Separating the time series into two phases, we quantified the linear change associated with the transition from more La Niña-like conditions to a phase with El Niño conditions (Phase 2) and vice versa (Phase 1). The transition from negative to positive ENSO conditions was related to a decrease in total cloud fraction (TCF) (−0.018 per decade; not significant) due to a reduction in the high-cloud amount (−0.024 per decade; significant). Observed anomalies in the mean CTH were found to be mainly caused by changes in HCF rather than by anomalies in the height of cloud tops themselves. Using the large-scale vertical motion ω at 500 hPa (from ERA-Interim ECMWF reanalysis data), the observed anomalies were linked to ENSO-induced changes in the atmospheric large-scale dynamics. The most significant and largest changes were found in regions with strong large-scale upward movements near the Equator. Despite the fact that with passive imagers such as MODIS it is not possible to vertically resolve clouds, this study shows the great potential for large-scale analysis of possible changes in the cloud vertical distribution due to the changing climate by using vertically resolved cloud cover and linking those changes to large-scale dynamics using other observations or model data.