Cloud Radiative Effects Research Articles

Evaluating two diagnostic schemes of cloud-fraction parameterization using the CloudSat data

Cloud fraction affects both shortwave and longwave cloud radiative effects, strongly regulating the Earth's energy budget and thus climate change in both observation and model simulations. This study evaluates two diagnostic cloud-fraction parameterization schemes, namely the Xu-Randall scheme and the Sundqvist scheme, using a novel approach. Unlike previous evaluation studies that compared schemes embedded in a climate model and could not identify the exact sources for the biases, this study obtained all inputs to the schemes by upscaling the CloudSat (quasi) observations. Hence, the scheme-observation discrepancies can be mostly attributed to deficiencies of the cloud-fraction parameterization, providing clear insights for model improvement.Results show that both schemes predict total cloud fraction close to the observation, overestimate high-level cloud fraction over the tropics, and underestimate low- and middle-level cloud fraction around 60°S and 60°N. Overall, the Xu-Randall scheme is relatively closer to the observation than the Sundqvist scheme. The Xu-Randall scheme produces a relatively more-realistic seasonal variation of cloud fraction, while the Sundqvist scheme underestimates low-level cloud fraction at all latitudes in all seasons. It is further revealed that the observed cloud fraction tends to vary with relative humidity (RH) in a non-monotonous way for clouds of any phase. Neither scheme can capture this non-monotonicity. Both schemes tend to overestimate the change rate of cloud fraction with RH and predict excessive cloud fraction when RH approaches 100%, especially for the ice-phase clouds. In addition, the biases are exacerbated in both schemes when the horizontal/vertical grid sizes are decreased/increased, respectively. Implications for future cloud-fraction parameterization improvement are also discussed.

Cloud Radiative Effects on MJO Development in DYNAMO

Abstract Observed Madden–Julian oscillation (MJO) events are examined with the aid of regional model simulations to understand the role of cloud radiative effects in the MJO development. The importance of this role is demonstrated by the absence of the MJO in the model simulations that contain no cloud radiative effects. Comparisons of model simulations with and without the cloud radiative effects and observation help identify the major processes arising from those effects. Those processes develop essentially from heating in the upper troposphere due to shortwave absorption within anvil clouds in the upper troposphere and the convergence of longwave radiation in the middle to upper troposphere, with a peak at 300 hPa, during deep convection. First, that heating adds extra buoyancy and accelerates the rising motion in the upper troposphere in deep convection. The vertical acceleration in the upper troposphere creates a vacuum effect and demands for more deep convection to develop. Second, in response to that demand and required by mass balance arises the large-scale horizontal and vertical mass, moisture, and energy convergence. It strengthens deep convection and, with the feedback from continuing cloud radiative effect, creates conditions that can perpetuate deep convection and MJO development. That perpetuation does not occur however because those processes arising from the cloud radiative heating in the upper troposphere stabilize the troposphere until it supports no further deep convection. Weakening deep convection reduces cloud radiative effects. The subsequent reduction of the vacuum effect in the upper troposphere diminishes deep convection completing an MJO cycle. These results advance our understanding of the development of the MJO in the radiative–convective system over warm waters in the tropics. They show that while the embryo of intraseasonal oscillation may exist in the system its growth/development is largely dependent on cloud radiative effects and feedbacks.

Impacts of the ice-particle size distribution shape parameter on climate simulations with the Community Atmosphere Model Version 6 (CAM6)

Abstract. The impacts of the ice-crystal size distribution shape parameter (μi) were considered in the two-moment bulk cloud microphysics scheme of the Community Atmosphere Model Version 6 (CAM6). The μi's impact on the statistical mean radii of ice crystals can be analyzed based on their calculating formulas. Under the same mass (qi) and number (Ni), the ratios of the mass-weighted radius (Rqi, not related to μi) to other statistical mean radii (e.g., effective radiative radius) are completely determined by μi. Offline tests show that μi has a significant impact on the cloud microphysical processes owing to the μi-induced changes in ice-crystal size distribution and statistical mean radii (excluding Rqi). Climate simulations show that increasing μi would lead to higher qi and lower Ni in most regions, and these impacts can be explained by the changes in cloud microphysical processes. After increasing μi from 0 to 5, the longwave cloud radiative effect increases (stronger warming effect) by 5.58 W m−2 (25.11 %), and the convective precipitation rate decreases by −0.12 mm d−1 (7.64 %). In short, the impacts of μi on climate simulations are significant, and the main influence mechanisms are also clear. This suggests that the μi-related processes deserve to be parameterized in a more realistic manner.

Near-Cloud Aerosol Retrieval Using Machine Learning Techniques, and Implied Direct Radiative Effects.

There is a lack of satellite-based aerosol retrievals in the vicinity of low-topped clouds, mainly because reflectance from aerosols is overwhelmed by three-dimensional cloud radiative effects. To account for cloud radiative effects on reflectance observations, we develop a Convolutional Neural Network and retrieve aerosol optical depth (AOD) with 100-500m horizontal resolution for all cloud-free regions regardless of their distances to clouds. The retrieval uncertainty is 0.01+5%AOD, and the mean bias is approximately -2%. In an application to satellite observations, aerosol hygroscopic growth due to humidification near clouds enhances AOD by 100% in regions within 1km of cloud edges. The humidification effect leads to an overall 55% increase in the clear-sky aerosol direct radiative effect. Although this increase is based on a case study, it highlights the importance of aerosol retrievals in near-cloud regions, and the need to incorporate the humidification effect in radiative forcing estimates.

Projecting Stratocumulus Transitions on the Albedo—Cloud Fraction Relationship Reveals Linearity of Albedo to Droplet Concentrations

AbstractSatellite images show solid marine stratocumulus cloud decks (Sc) that break up over the remote oceans. The Sc breakup is initiated by precipitation and is accompanied by a strong reduction in the cloud radiative effect. Aerosol has been shown to delay the Sc breakup by postponing the onset of precipitation, however its climatic effect is uncertain. Here we introduce a new approach that allows us to re‐cast currently observed cloud cover and albedo to their counterfactual cleaner world, enabling the first estimate of the radiative effect due to delayed cloud breakup. Using simple radiative approximation, the radiative forcing with respect to pre‐industrial times due to delayed Sc breakup is −0.39 W m−2. The radiative effect changes nearly linearly with aerosol due to the droplet concentration control on the cloud cover, suggesting a potentially accelerated warming if the current trend of reduction in aerosol emissions continues.

Apportionment of the Pre‐Industrial to Present‐Day Climate Forcing by Methane Using UKESM1: The Role of the Cloud Radiative Effect

AbstractThe Year 1850 to 2014 increase in methane from 808 to 1831 ppb leads to an effective radiative forcing (ERF) of 0.97 ± 0.04 W m−2 in the United Kingdom's Earth System Model, UKESM1. The direct methane contribution is 0.54 ± 0.04 W m−2. It is better represented in UKESM1 than in its predecessor model HadGEM2 due to shortwave and longwave absorption improvements and the absence of an anomalous dust response in the UKESM1 simulations. An indirect ozone ERF of 0.13–0.20 W m−2 is due to the tropospheric ozone increase outweighing that of the stratospheric decrease. The indirect water vapor ERF of 0.02–0.07 W m−2 is consistent with previous estimates. The methane increase also leads to a cloud radiative effect of 0.12 ± 0.02 W m−2 from thermodynamic adjustments and aerosol‐cloud interactions (aci). Shortwave and longwave contributions of 0.23 and −0.35 W m−2 to the cloud forcing arise from radiative heating and stabilization of the upper troposphere, reducing convection and global cloud cover. The aerosol‐mediated contribution (0.28–0.30 W m−2) is due to changes in oxidants reducing new particle formation (−8%), shifting the aerosol size distribution toward fewer but larger particles. Cloud droplet number concentration decreases and cloud droplet effective radius increases. This reduction in the Twomey effect switches the cloud forcing sign (−0.14 to 0.12 W m−2) and is due to chemistry‐aerosol‐cloud coupling in UKESM1. Despite uncertainties in rapid adjustments and process representation in models, these results highlight the potential importance of chemistry‐aerosol‐cloud interactions and dynamical adjustments in climate forcing.

Compensating Errors in Cloud Radiative and Physical Properties over the Southern Ocean in the CMIP6 Climate Models

The Southern Ocean is covered by a large amount of clouds with high cloud albedo. However, as reported by previous climate model intercomparison projects, underestimated cloudiness and overestimated absorption of solar radiation (ASR) over the Southern Ocean lead to substantial biases in climate sensitivity. The present study revisits this long-standing issue and explores the uncertainty sources in the latest CMIP6 models. We employ 10-year satellite observations to evaluate cloud radiative effect (CRE) and cloud physical properties in five CMIP6 models that provide comprehensive output of cloud, radiation, and aerosol. The simulated longwave, shortwave, and net CRE at the top of atmosphere in CMIP6 are comparable with the CERES satellite observations. Total cloud fraction (CF) is also reasonably simulated in CMIP6, but the comparison of liquid cloud fraction (LCF) reveals marked biases in spatial pattern and seasonal variations. The discrepancies between the CMIP6 models and the MODIS satellite observations become even larger in other cloud macro- and micro-physical properties, including liquid water path (LWP), cloud optical depth (COD), and cloud effective radius, as well as aerosol optical depth (AOD). However, the large underestimation of both LWP and cloud effective radius (regional means ∼20% and 11%, respectively) results in relatively smaller bias in COD, and the impacts of the biases in COD and LCF also cancel out with each other, leaving CRE and ASR reasonably predicted in CMIP6. An error estimation framework is employed, and the different signs of the sensitivity errors and biases from CF and LWP corroborate the notions that there are compensating errors in the modeled shortwave CRE. Further correlation analyses of the geospatial patterns reveal that CF is the most relevant factor in determining CRE in observations, while the modeled CRE is too sensitive to LWP and COD. The relationships between cloud effective radius, LWP, and COD are also analyzed to explore the possible uncertainty sources in different models. Our study calls for more rigorous calibration of detailed cloud physical properties for future climate model development and climate projection.

Cirrus cloud thinning using a more physically based ice microphysics scheme in the ECHAM-HAM general circulation model

Abstract. Cirrus cloud thinning (CCT) is a relatively new radiation management proposal to counteract anthropogenic climate warming by targeting Earth's terrestrial radiation balance. The efficacy of this method was presented in several general circulation model (GCM) studies that showed widely varied radiative responses, originating in part from the differences in the representation of cirrus ice microphysics between the different GCMs. The recent implementation of a new, more physically based ice microphysics scheme (Predicted Particle Properties, P3) that abandons ice hydrometeor size class separation into the ECHAM-HAM GCM, coupled to a new approach for calculating cloud fractions that increases the relative humidity (RH) thresholds for cirrus cloud formation, motivated a reassessment of CCT efficacy. In this study, we first compared CCT sensitivity between the new cloud fraction approach and the original ECHAM-HAM cloud fraction approach. Consistent with previous approaches using ECHAM-HAM, with the P3 scheme and the higher RH thresholds for cirrus cloud formation, we do not find a significant cooling response in any of our simulations. The most notable response from our extreme case is the reduction in the maximum global-mean net top-of-atmosphere (TOA) radiative anomalies from overseeding by about 50 %, from 9.9 W m−2 with the original cloud fraction approach down to 4.9 W m−2 using the new cloud fraction RH thresholds that allow partial grid-box coverage of cirrus clouds above ice saturation, unlike the original approach. Even with this reduction with the updated cloud fraction approach, the TOA anomalies from overseeding far exceed those reported in previous studies. We attribute the large positive TOA anomalies to seeding particles overtaking both homogeneous nucleation and heterogeneous nucleation on mineral dust particles within cirrus clouds to produce more numerous and smaller ice crystals. This effect is amplified by longer ice residence times in clouds due to the slower removal of ice via sedimentation in the P3 scheme. In an effort to avoid this overtaking effect of seeding particles, we increased the default critical ice saturation ratio (Si,seed) for ice nucleation on seeding particles from the default value of 1.05 to 1.35 in a second sensitivity test. With the higher Si,seed we drastically reduce overseeding, which suggests that Si,seed is a key factor to consider for future CCT studies. However, the global-mean TOA anomalies contain high uncertainty. In response, we examined the TOA anomalies regionally and found that specific regions only show a small potential for targeted CCT, which is partially enhanced by using the larger Si,seed. Finally, in a seasonal analysis of TOA responses to CCT, we find that our results do not confirm the previous finding that high-latitude wintertime seeding is a feasible strategy to enhance CCT efficacy, as seeding in our model enhances the already positive cirrus longwave cloud radiative effect for most of our simulations. Our results also show feedbacks on lower-lying mixed-phase and liquid clouds through the reduction in ice crystal sedimentation that reduces cloud droplet depletion and results in stronger cloud albedo effects. However, this is outweighed by stronger longwave trapping from cirrus clouds with more numerous and smaller ice crystals. Therefore, we conclude that CCT is unlikely to act as a feasible climate intervention strategy on a global scale.

Bistability of the Atmospheric Circulation on TRAPPIST-1e

Using a 3D general circulation model, we demonstrate that a confirmed rocky exoplanet and a primary observational target, TRAPPIST-1e presents an interesting case of climate bistability. We find that the atmospheric circulation on TRAPPIST-1e can exist in two distinct regimes for a 1 bar nitrogen-dominated atmosphere. One is characterized by a single strong equatorial prograde jet and a large day–night temperature difference; the other is characterized by a pair of mid-latitude prograde jets and a relatively small day–night contrast. The circulation regime appears to be highly sensitive to the model setup, including initial and surface boundary conditions, as well as physical parameterizations of convection and cloud radiative effects. We focus on the emergence of the atmospheric circulation during the early stages of simulations and show that the regime bistability is associated with a delicate balance between the zonally asymmetric heating, mean overturning circulation, and mid-latitude baroclinic instability. The relative strength of these processes places the GCM simulations on different branches of the evolution of atmospheric dynamics. The resulting steady states of the two regimes have consistent differences in the amount of water content and clouds, affecting the water absorption bands as well as the continuum level in the transmission spectrum, although they are too small to be detected with current technology. Nevertheless, this regime bistability affects the surface temperature, especially on the night side of the planet, and presents an interesting case for understanding atmospheric dynamics and highlights uncertainty in 3D GCM results, motivating more multimodel studies.

The TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI). II. Moist Cases—The Two Waterworlds

To identify promising exoplanets for atmospheric characterization and to make the best use of observational data, a thorough understanding of their atmospheres is needed. Three-dimensional general circulation models (GCMs) are one of the most comprehensive tools available for this task and will be used to interpret observations of temperate rocky exoplanets. Due to parameterization choices made in GCMs, they can produce different results, even for the same planet. Employing four widely used exoplanetary GCMs—ExoCAM, LMD-G, ROCKE-3D, and the UM—we continue the TRAPPIST-1 Habitable Atmosphere Intercomparison by modeling aquaplanet climates of TRAPPIST-1e with a moist atmosphere dominated by either nitrogen or carbon dioxide. Although the GCMs disagree on the details of the simulated regimes, they all predict a temperate climate with neither of the two cases pushed out of the habitable state. Nevertheless, the intermodel spread in the global mean surface temperature is nonnegligible: 14 K and 24 K in the nitrogen- and carbon dioxide-dominated case, respectively. We find substantial intermodel differences in moist variables, with the smallest amount of clouds in LMD-Generic and the largest in ROCKE-3D. ExoCAM predicts the warmest climate for both cases and thus has the highest water vapor content and the largest amount and variability of cloud condensate. The UM tends to produce colder conditions, especially in the nitrogen-dominated case due to a strong negative cloud radiative effect on the day side of TRAPPIST-1e. Our study highlights various biases of GCMs and emphasizes the importance of not relying solely on one model to understand exoplanet climates.

Breakdown in precipitation–temperature scaling over India predominantly explained by cloud-driven cooling

Abstract. Climate models predict an intensification of precipitation extremes as a result of a warmer and moister atmosphere at the rate of 7 % K−1. However, observations in tropical regions show contrastingly negative precipitation–temperature scaling at temperatures above 23–25 ∘C. We use observations from India and show that this negative scaling can be explained by the radiative effects of clouds on surface temperatures. Cloud radiative cooling during precipitation events make observed temperatures covary with precipitation, with wetter periods and heavier precipitation having a stronger cooling effect. We remove this confounding effect of clouds from temperatures using a surface energy balance approach constrained by thermodynamics. We then find a diametric change in precipitation scaling with rates becoming positive and coming closer to the Clausius–Clapeyron (CC) scaling rate (7 % K−1). Our findings imply that the intensification of precipitation extremes with warmer temperatures expected with global warming is consistent with observations from tropical regions when the radiative effect of clouds on surface temperatures and the resulting covariation with precipitation is accounted for.

Reconstructing all-weather daytime land surface temperature based on energy balance considering the cloud radiative effect

Land surface temperature (LST) has been used in many applications as its strong relationships with land surface processes. However, the greatest limitation of the use of LST is the missing data caused by cloud contamination and weather conditions. In this study, we first used the XGBoost method to describe complex relationships of LST with surface characteristics from clear-sky pixels, and applied the model to retrieve hypothetical clear-sky LST under cloudy sky. Secondly, cloud radiative effect (CRE) on the LST was calculated based on energy balance using the reanalysis data. The models were applied to reconstruct the all-weather LST over the Tibetan Plateau (TP). The spatial patterns of reconstructed LST indicated that our model could produce completely spatial-seamless LST and depict the detailed information. The accuracy of the XGBoost LST was validated against the clear-sky MODIS LST (average R2 = 0.92, MAPE = 0.52, RMSE = 2.32 K). The CRE-EB LST was evaluated using data from six in-situ sites from the TP. The validation results were separated into three conditions: clear sky (RMSE = 3.01 K–3.52 K, R2 = 0.88–0.93, bias = −1.08 K-1.88 K), cloudy sky (RMSE = 3.31 K–4.06 K, R2 = 0.87–0.92, bias = −0.21 K-1.11 K), and overall (RMSE = 3.31 K–3.82 K, R2 = 0.88–0.93, bias = −0.42 K-1.24 K). Compared to existing all-weather LST datasets, the temporal variability of our LST data shows similar seasonal and daily changes, and the CRE-EB LST has advantages in terms of image quality and accuracies under cloudy condition. This study demonstrated the utility of proposed models to reconstruct all-weather LST.

Exploring relations between cloud morphology, cloud phase, and cloud radiative properties in Southern Ocean's stratocumulus clouds

Abstract. Marine stratocumuli are the most dominant cloud type by area coverage in the Southern Ocean (SO). They can be divided into different self-organized cellular morphological regimes known as open and closed mesoscale-cellular convective (MCC) clouds. Open and closed cells are the two most frequent types of organizational regimes in the SO. Using the liDAR-raDAR (DARDAR) version 2 retrievals, we quantify 59 % of all MCC clouds in this region as mixed-phase clouds (MPCs) during a 4-year time period from 2007 to 2010. The net radiative effect of SO MCC clouds is governed by changes in cloud albedo. Both cloud morphology and phase have previously been shown to impact cloud albedo individually, but their interactions and their combined impact on cloud albedo remain unclear. Here, we investigate the relationships between cloud phase, organizational patterns, and their differences regarding their cloud radiative properties in the SO. The mixed-phase fraction, which is defined as the number of MPCs divided by the sum of MPC and supercooled liquid cloud (SLC) pixels, of all MCC clouds at a given cloud-top temperature (CTT) varies considerably between austral summer and winter. We further find that seasonal changes in cloud phase at a given CTT across all latitudes are largely independent of cloud morphology and are thus seemingly constrained by other external factors. Overall, our results show a stronger dependence of cloud phase on cloud-top height (CTH) than CTT for clouds below 2.5 km in altitude. Preconditioning through ice-phase processes in MPCs has been observed to accelerate individual closed-to-open cell transitions in extratropical stratocumuli. The hypothesis of preconditioning has been further substantiated in large-eddy simulations of open and closed MPCs. In this study, we do not find preconditioning to primarily impact climatological cloud morphology statistics in the SO. Meanwhile, in-cloud albedo analysis reveals stronger changes in open and closed cell albedo in SLCs than in MPCs. In particular, few optically thick (cloud optical thickness >10) open cell stratocumuli are characterized as ice-free SLCs. These differences in in-cloud albedo are found to alter the cloud radiative effect in the SO by 21 to 39 W m−2 depending on season and cloud phase.

The potential impact of model horizontal resolution on the simulation of atmospheric cloud radiative effect in CMIP6 models

The simulations of atmospheric cloud-radiative effect (ACRE) from 54 Coupled Model Intercomparison Project phase 6 (CMIP6) models during the historical period of 2000/03–2014/12 are compared and evaluated against the satellite-based Clouds and the Earth’s Radiant Energy System (CERES) products. For ease of comparison, all CMIP6 models are divided into high-, medium-, and low-resolution groups to examine the potential impact of model horizontal resolution change on the simulations of ACRE distribution over the tropical oceans. The results show that ACRE is positive inside the ITCZs but negative in the subtropics and cold tongue areas, owing to the very different radiative forcing between deep and shallow clouds. Simulations of ACRE are sensitive to the model horizontal resolution used and the finer resolution models generally produce a better performance of ACRE simulations against the CERES observations. The reduced ACRE biases in finer resolution models are mainly contributed by the improved longwave ACRE (i.e., LWACRE) simulations, especially over the Pacific and Atlantic cold tongue areas where shallow stratocumulus clouds prevail.

Decreasing surface albedo signifies a growing importance of clouds for Greenland Ice Sheet meltwater production

Clouds regulate the Greenland Ice Sheet’s surface energy balance through the competing effects of shortwave radiation shading and longwave radiation trapping. However, the relative importance of these effects within Greenland’s narrow ablation zone, where nearly all meltwater runoff is produced, remains poorly quantified. Here we use machine learning to merge MODIS, CloudSat, and CALIPSO satellite observations to produce a high-resolution cloud radiative effect product. For the period 2003–2020, we find that a 1% change in cloudiness has little effect (±0.16 W m−2) on summer net radiative fluxes in the ablation zone because the warming and cooling effects of clouds compensate. However, by 2100 (SSP5-8.5 scenario), radiative fluxes in the ablation zone will become more than twice as sensitive (±0.39 W m−2) to changes in cloudiness due to reduced surface albedo. Accurate representation of clouds will therefore become increasingly important for forecasting the Greenland Ice Sheet’s contribution to global sea-level rise.

Radiative closure and cloud effects on the radiation budget based on satellite and shipborne observations during the Arctic summer research cruise, PS106

Abstract. For understanding Arctic climate change, it is critical to quantify and address uncertainties in climate data records on clouds and radiative fluxes derived from long-term passive satellite observations. A unique set of observations collected during the PS106 expedition of the research vessel Polarstern (28 May to 16 July 2017) by the OCEANET facility, is exploited here for this purpose and compared with the CERES SYN1deg ed. 4.1 satellite remote-sensing products. Mean cloud fraction (CF) of 86.7 % for CERES SYN1deg and 76.1 % for OCEANET were found for the entire cruise. The difference of CF between both data sets is due to different spatial resolution and momentary data gaps, which are a result of technical limitations of the set of shipborne instruments. A comparison of radiative fluxes during clear-sky (CS) conditions enables radiative closure (RC) for CERES SYN1deg products by means of independent radiative transfer simulations. Several challenges were encountered to accurately represent clouds in radiative transfer under cloudy conditions, especially for ice-containing clouds and low-level stratus (LLS) clouds. During LLS conditions, the OCEANET retrievals were particularly compromised by the altitude detection limit of 155 m of the cloud radar. Radiative fluxes from CERES SYN1deg show a good agreement with ship observations, having a bias (standard deviation) of −6.0 (14.6) and 23.1 (59.3) W m−2 for the downward longwave (LWD) and shortwave (SWD) fluxes, respectively. Based on CERES SYN1deg products, mean values of the radiation budget and the cloud radiative effect (CRE) were determined for the PS106 cruise track and the central Arctic region (70–90∘ N). For the period of study, the results indicate a strong influence of the SW flux in the radiation budget, which is reduced by clouds leading to a net surface CRE of −8.8 and −9.3 W m−2 along the PS106 cruise and for the entire Arctic, respectively. The similarity of local and regional CRE supports the consideration that the PS106 cloud observations can be representative of Arctic cloudiness during early summer.

An Investigation of the Ice Cloud Detection Sensitivity of Cloud Radars Using the Raman Lidar at the ARM SGP Site

The ice cloud detection sensitivity of the millimeter cloud radar (MMCR) and the Ka-band Zenith radar (KAZR) is investigated using a collocated Raman lidar (RL) at the Atmospheric Radiation Measurement Program Southern Great Plains site. Only profiles that are transparent to the RL with ice clouds only are considered in this study. The MMCR underestimates the RL ice cloud optical depth (COD) by 20%. The MMCR detects no ice clouds in 37% of the profiles. These profiles where ice cloud goes undetected by the MMCR typically contain very optically thin clouds, with a mean RL ice COD of 0.03. Higher ice cloud detection sensitivity is found for the KAZR, which underestimates the RL ice COD by 15%. The decrease in the ice COD bias for the KAZR compared to the MMCR is largely due to a decrease in the ice COD bias for the situation where the transparent profiles with ice clouds are detected by both the RL and cloud radar. The climatic net ice cloud radiative effects (CREs) from the RL at the top of the atmosphere (TOA) and the surface are 3.2 W m−2 and −0.6 W m−2, respectively. The ice CREs at the TOA and surface are underestimated for the MMCR by 0.7 W m−2 and 0.16 W m−2 (21% and 29%) and underestimated for the KAZR by 0.6 W m−2 and 0.14 W m−2 (17% and 24%). The ice clouds undetected by the cloud radars led to underestimating the climatic net cloud heating rates below 150 hPa by about 0–0.04 K day−1.

The Arctic sea ice-cloud radiative negative feedback in the Barents and Kara Sea region

Shortwave cloud radiative effect (SWCRE), known as the cooling effect triggered by cloud, plays a vital role in adjusting the global radiation budget. As the Arctic gets warmer, it may become a more indispensable factor curbing this warming tendency. Research has pointed out a significant relationship between sea ice cover (SIC) and SWCRE over the Arctic during summer (June–August). Although no evidence has been found on cloud response to SIC during summer on the average of the Arctic, this study regards cloud as an inter-connection which can regulate SIC and SWCRE in a particular place: Barents and Kara Sea region (15°E–85°E, 70°N–80°N). Its SWCRE and SIC vary significantly, with their trends being 5.85 w∙m−2 and − 5.87% per decade compared to those of the Arctic mean (2.93 w∙m−2 and − 4.65% per decade). In this area, we find that the growing number of low-level cloud which is resulted from the loss on SIC may be accountable for the increase in SWCRE, as is shown in the correlation coefficient between low-level cloud and SIC reaches − 0.4. The correlation coefficient between low-level cloud and SWCRE is 0.6. It reflects a SIC-cloud-SWCRE negative feedback. Moreover, a regression fitting model is being established to quantify the contribution of Arctic cloud in the process of slowing down the Arctic warming. It reveals that this specific region would turn into an ice-free region with sea surface temperature (SST) 1.5 °C higher than reality during 2001 if we stop the increase in SWCRE. This result presents how fascinating the contribution cloud has been making in its way slowing down the warming pace.

Surface shortwave cloud radiative effect of cumulus and stratocumulus-cumulus cloud types in the Caribbean area (Camagüey Cuba, 2010-2016)

The effects of cumulus (Cu) clouds and the combination of stratocumulus-cumulus (Sc-Cu) clouds on solar radiation at the Earth’s surface were evaluated at Camagüey, Cuba, during a 6-yr period (from June 2010 to May 2016). Two methods to calculate the cloud radiative effect (CRE) were employed. The first method (CREm) uses solar irradiances in cloudy conditions from actinometric observations, where cloud information was also reported by visual observation. In the second method (CRE0) surface solar irradiances were estimated for both cloudy and clear sky conditions using a 1-D radiative transfer model, and cloud optical depth (COD) retrieved from an AERONET sun-photometer as the main input. A temporal correspondence criterion between COD retrievals and actinometric observations was performed in order to classify the COD of each cloud type. After the application of this criterion, the COD belonging to the optically thin clouds was removed. Finally, 255 and 732 COD observations for Cu and Sc-Cu, respectively, were found. Results show a statistically significant difference at the 95% confidence level between CRE calculated for Sc-Cu and Cu, using both methods. Mean values of CREm and CRE0 for Cu (Sc-Cu) were −442 (−390) and −460 (−417) Wm–2, respectively. CRE0 shows a linear relation with ln(COD), with stronger correlation at a lower solar zenith angle. The shortwave cloud effect efficiency (CEE) for the two cloud types sharply decreases with the increase of the COD value up to 20. For larger COD, the CEE is less sensitive to the increase of COD.

The surface longwave cloud radiative effect derived from space lidar observations

Abstract. Clouds warm the surface in the longwave (LW), and this warming effect can be quantified through the surface LW cloud radiative effect (CRE). The global surface LW CRE has been estimated over more than 2 decades using space-based radiometers (2000–2021) and over the 5-year period ending in 2011 using the combination of radar, lidar and space-based radiometers. Previous work comparing these two types of retrievals has shown that the radiometer-based cloud amount has some bias over icy surfaces. Here we propose new estimates of the global surface LW CRE from space-based lidar observations over the 2008–2020 time period. We show from 1D atmospheric column radiative transfer calculations that surface LW CRE linearly decreases with increasing cloud altitude. These computations allow us to establish simple parameterizations between surface LW CRE and five cloud properties that are well observed by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) space-based lidar: opaque cloud cover and altitude and thin cloud cover, altitude, and emissivity. We evaluate this new surface LWCRE–LIDAR product by comparing it to existing satellite-derived products globally on instantaneous collocated data at footprint scale and on global averages as well as to ground-based observations at specific locations. This evaluation shows good correlations between this new product and other datasets. Our estimate appears to be an improvement over others as it appropriately captures the annual variability of the surface LW CRE over bright polar surfaces and it provides a dataset more than 13 years long.