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We first present an algorithm for deriving cloudy sky outgoing spectral flux through the entire longwave spectrum from the collocated Atmospheric Infrared Sounder (AIRS) and Cloud and the Earth's Radiant Energy System (CERES) measurements over the tropical oceans. The algorithm is similar to the one described in part 1 of this series of studies: spectral angular dependent models are developed to estimate the spectral flux of each AIRS channel, and then a multivariate linear prediction scheme is used to estimate spectral fluxes at frequencies not covered by the AIRS instrument. The entire algorithm is validated against synthetic spectra as well as the CERES outgoing longwave radiation (OLR) measurements. Mean difference between the OLR estimated in this way and the collocated CERES OLR is 2.15 W m−2 with a standard deviation of 5.51 W m−2. The algorithm behaves consistently well for different combinations of cloud fractions and cloud‐surface temperature difference, indicating the robustness of the algorithm for various cloudy scenes. Then, using the Geophysical Fluid Dynamics Laboratory AM2 model as a case study, we illustrate the merit of band‐by‐band cloud radiative forcings (CRFs) derived from this algorithm in model evaluation. The AM2 tropical annual mean band‐by‐band CRFs generally agree with the observed counterparts, but some systematic biases in the window bands and over the marine‐stratus regions can be clearly identified. An idealized model is used to interpret the results and to explain why the fractional contribution of each band to the broadband CRF is worthy for studying: it is sensitive to cloud height but largely insensitive to the cloud fraction.

The double-Intertropical Convergence Zone (ITCZ) problem, in which excessive precipitation is produced in the Southern Hemisphere tropics, which resembles a Southern Hemisphere counterpart to the strong Northern Hemisphere ITCZ, is perhaps the most significant and most persistent bias of global climate models. In this study, we look to the extratropics for possible causes of the double-ITCZ problem by performing a global energetic analysis with historical simulations from a suite of global climate models and comparing with satellite observations of the Earth’s energy budget. Our results show that models with more energy flux into the Southern Hemisphere atmosphere (at the top of the atmosphere and at the surface) tend to have a stronger double-ITCZ bias, consistent with recent theoretical studies that suggest that the ITCZ is drawn toward heating even outside the tropics. In particular, we find that cloud biases over the Southern Ocean explain most of the model-to-model differences in the amount of excessive precipitation in Southern Hemisphere tropics, and are suggested to be responsible for this aspect of the double-ITCZ problem in most global climate models.

As the dominant atmospheric circulation pattern over the western Tibetan Plateau (TP), the Western Tibetan Vortex (WTV) exerts substantial control on springtime 2 m surface air temperature (T2m). However, its underlying radiative processes remain unclear. This study integrates GEWEX satellite observations with ERA5 and MERRA‐2 reanalysis, applying surface energy balance diagnostics to quantify the WTV's radiative forcing on T2m variability. We find the WTV explains ∼66% of T2m variance (R = 0.81) across the western TP and the adjacent Southwest Asia. Downward shortwave radiation (DSW) emerges as the primarily radiative factor modulated by the WTV via cloud radiative forcing (CRF) processes. Specifically, anticyclonic WTV events reduce cloudiness, generating positive CRF anomalies that enhancing surface DSW and cause warming. Conversely, cyclonic events increase cloudiness, producing negative CRF anomalies that diminish DSW and induce cooling. These findings advance understanding of the radiative processes by which the upper circulations modulate the surface climate over the TP.

Cirrus (Ci) clouds have an important influence on Earth's radiation budget, and they remain one of the most significant uncertainties in predicting Earth's climate. In this study, we use ground-based radiometers along with a sky imager to monitor clouds and retrieve cloud properties (cloud fraction (CF), cloud optical depth (COD) and effective radii (Re)) over a rain shadow region in south peninsular India during September and October months, 2011. Lower part of Ci clouds are identified using the thresholds pertaining to COD and cloud base height (CBH). The optical and radiative properties of Ci clouds showed large variability on temporal and diurnal scales. The CF, COD and Re varied from 7 to 100%, 0.76 to 9.99, and 2.76 to 37.92 μm, respectively. The CBH and Cloud Base Temperature (CBT) are found to vary from 7.24 to 8.99 km and − 31.99 to − 13.75 °C. The Shortwave Cloud Radiative forcing (SWCRF) exerted by the lower part of Ci clouds over the region is observed to vary from − 435 to 148.87 W m−2 on a temporal scale with an average value of − 23.06 W m−2. The relationship between SWCRF and COD revealed radiative cooling effect with increase in COD with a dependency rate of − 18.53 W m−2/τ. SWCRF is found to be more sensitive to COD as compared to other cloud characteristics (CF, CBH and CBT). The case studies depict that the observed lower part of Ci clouds are advected from the ocean indicating the influence of large scale systems. Lower part of Ci optical and radiative properties showed wide variability depending up on the source of formation and evolution. This study also suggests that the high temporal variability of optical and radiative properties of Ci clouds needs to be well considered in climate models to reduce the uncertainty of cirrus radiative effects.

The improvement of the accuracy of simulated cloud-related variables, such as the cloud fraction, in global climate models (GCMs) is still a challenging problem in climate modeling. In this study, the influence of cloud microphysics schemes (one-moment versus two-moment schemes) and cloud overlap methods (observation-based versus a fixed vertical decorrelation length) on the simulated cloud fraction was assessed in the BCC_AGCM2.0_CUACE/Aero. Compared with the fixed decorrelation length method, the observation-based approach produced a significantly improved cloud fraction both globally and for four representative regions. The utilization of a two-moment cloud microphysics scheme, on the other hand, notably improved the simulated cloud fraction compared with the one-moment scheme; specifically, the relative bias in the global mean total cloud fraction decreased by 42.9%–84.8%. Furthermore, the total cloud fraction bias decreased by 6.6% in the boreal winter (DJF) and 1.64% in the boreal summer (JJA). Cloud radiative forcing globally and in the four regions improved by 0.3%–1.2% and 0.2%–2.0%, respectively. Thus, our results showed that the interaction between clouds and climate through microphysical and radiation processes is a key contributor to simulation uncertainty.

The northward migration of the intertropical convergence zone (ITCZ) is a significant feature of the West African (WA) monsoon. An accurate simulation of ITCZ migration is essential for the realistic representation of WA precipitation in global coupled models. In this study, we employ the energetics and dynamics framework with a subset of CMIP6 models to investigate the bias in the simulated WA precipitation. Models were found to simulate more local positive (negative) shortwave cloud radiative forcing (SWCRF) in the Southeastern Atlantic Ocean (over the African continent). The effect of the excess local SWCRF is linked to the stagnation of the ITCZ latitudinal migration and the associated biases in the asymmetry index of precipitation. In the models, there is more (less) moist static energy in the lower (mid and upper) troposphere than in the reanalysis. The worst models have a stronger bias, especially over land. The vertical transport of moisture is confined to the boundary layer in the worst model ensemble. In most cases, the high-resolution coupled models show substantial northward migration of the ITCZ compared to the low-resolution models. Furthermore, the best-performing models capture local circulation and energetic processes more accurately than the worst-performing models.

Substantial biases in shortwave cloud forcing (SWCF) of up to ±30 W m−2are found in the midlatitudes of the Southern Hemisphere in the historical simulations of 34 CMIP5 coupled general circulation models. The SWCF biases are shown to induce surface temperature anomalies localized in the midlatitudes, and are significantly correlated with the mean latitude of the eddy‐driven jet, with a negative SWCF bias corresponding to an equatorward jet latitude bias. Aquaplanet model experiments are performed to demonstrate that the jet latitude biases are primarily induced by the midlatitude SWCF anomalies, such that the jet moves toward (away from) regions of enhanced (reduced) temperature gradients. The results underline the necessity of accurately representing cloud radiative forcings in state‐of‐the‐art coupled models. Key Points CMIP5 models exhibit large biases in midlatitude cloud shortwave forcing Biases in cloud shortwave forcing are linked to biases in mean jet latitude Jet latitude biases are caused by changes in midlatitude baroclinicity

Accurately representing mixed‐phase clouds (MPCs) in global climate models (GCMs) is critical for capturing climate sensitivity and Arctic amplification. Secondary ice production (SIP), can significantly increase ice crystal number concentration (ICNC) in MPCs, affecting cloud properties and processes. Here, we introduce a machine‐learning (ML) approach, called Random Forest SIP (RaFSIP), to parameterize SIP in stratiform MPCs. RaFSIP is trained on 16 grid points with 10‐km horizontal spacing derived from a 2‐year simulation with the Weather Research and Forecasting (WRF) model, including explicit SIP microphysics. Designed for a temperature range of 0 to −25°C, RaFSIP simplifies the description of rime splintering, ice‐ice collisional break‐up, and droplet‐shattering using only a limited set of inputs. RaFSIP was evaluated offline before being integrated into WRF, demonstrating its stable online performance in a 1‐year simulation keeping the same model setup as during training. Even when coupled with the 50‐km grid spacing domain of WRF, RaFSIP reproduces ICNC predictions within a factor of 3 when compared to simulations with explicit SIP microphysics. The coupled WRF‐RaFSIP scheme replicates regions of enhanced SIP and accurately maps ICNCs and liquid water content, particularly at temperatures above −10°C. Uncertainties in RaFSIP minimally impact surface cloud radiative forcing in the Arctic, resulting in radiative biases under 3 Wm−2 compared to simulations with detailed microphysics. Although the performance of RaFSIP in convective clouds remains untested, its adaptable nature allows for data set augmentation to address this aspect. This framework opens possibilities for GCM simplification and process description through physics‐guided ML algorithms. Plain Language Summary Being able to correctly simulate the amount of ice and liquid in clouds is essential for accurate predictions of the cloud radiative forcing in the climatologically sensitive polar regions. A number of collisional processes between ice and liquid particles in clouds, known as secondary ice production, can significantly enhance the ice crystal number concentrations contained in them. This enhancement is often accompanied by a decrease in the cloud liquid water content, resulting in less opaque clouds to incoming solar radiation, which, in turn, can cause a cloud‐induced warming at the surface. Currently most global climate models are missing the description of the most important secondary ice production processes, which can lead to a biased radiative impact of clouds at the surface. To address this, we propose using a machine learning algorithm trained on high‐resolution model outputs to include the effect of ice multiplication in large‐scale climate models. The machine learning framework effectively captures the physical processes underlying secondary ice production in stratiform clouds using only a few inputs readily available in model frameworks. This approach has the potential to improve model predictions bringing them closer to the observed cloud phase partitioning. Key Points A random‐forest parameterization for secondary ice production is developed using outputs from a 10‐km horizontal grid spacing simulation Cloud phase partitioning agrees within a factor of 3, with radiative biases below 3 Wm−2 compared to the detailed microphysics simulation The scheme can be adjusted to coarser resolutions typical of climate models without losing computational efficiency and numerical stability

Dusty cloud properties and radiative forcing over northwestern China (source region) are compared to the same quantities over the northwestern Pacific (downwind region) during the Pacific Dust Experiment (PACDEX; April 2007 to May 2007) using collocated data from three satellites in the A‐Train constellation: CALIPSO (Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations), the Clouds and Earth Radiant Energy System on Aqua, and CloudSat. Dusty clouds are defined as clouds extant in a dust plume environment (i.e., dust aerosols observed within 50 m of the cloud), while pure clouds are those in dust‐free conditions. CALIPSO lidar and CloudSat radar measurements are used to discriminate between dusty and pure clouds in both study regions. It was found that dust aerosols change the microphysical characteristics of clouds, reducing the cloud optical depth, liquid and ice water path, and effective droplet size. The decreased cloud optical depths and water paths diminish the cloud cooling effect, leading to a greater warming effect. The dust aerosols cause an instantaneous net cloud cooling effect of 43.4% and 16.7% in the source and downwind regions, respectively. The dust aerosol effects appear to be greater for ice clouds than for liquid water clouds in the downwind region. These results are consistent with PACDEX aircraft observations.

In this study, the annual and seasonal climatology of cloud fraction (CF) and cloud type simulated by the Canadian Environmental System Models (CanESMs) version 5 (CanESM5) and version 2 (CanESM2) at their fully coupled and AMIP configurations were validated against the CALIPSO-GOCCP-based CF. The CFs produced using the CALIPSO-COSP simulator based on the CanESMs data at their atmospheric (AMIP) configuration are also evaluated. The simulated shortwave, longwave, and net cloud radiative forcing using the AMIP version of the CanESM5 were also validated against satellite observations based on the recent CERES radiation satellite products. On average, all models have a negative bias in the total CF with global mean biases (MBs) of 2%, 2.4%, 3.9%, 6.4%, 5.6%, and 7.1% for the coupled-CanESM5, AMIP-CanESM5, COSP-AMIP-CanESM5, coupled-CanESM2, AMIP-CanESM2, and COSP-AMIP-CanESM2, respectively, indicating that the CanESM5 has a smaller MB. There were no significant differences between AMIP and coupled versions of the model, but the COSP-based model-simulated data showed larger biases. Although the models captured well the climatological features of CF, they also exhibited a significant bias in CF reaching up to 40% over some geographical locations. This is particularly prevalent over the low level (LL) marine stratocumulus/cumulus, convectively active tropical latitudes that are normally dominated by high level (HL) clouds and at the polar regions where all models showed negative, positive, and positive bias corresponding to these locations, respectively. The AMIP-CanESM5 model performed reasonably well simulating the global mean cloud radiative forcing (CRF) with slight negative biases in the NetCRF at the TOA and surface that would be expected if the model has a positive bias in CF. This inconsistent result may be attributed to the parameterization of the optical properties in the model. The geographical distributions of the model bias in the NetCRF, however, can be significant reaching up to ±40 Wm−2 depending on the location and atmospheric level. The Pearson correlation showed that there is a strong correlation between the global distribution of model bias in NetCRF and CF and it is significantly influenced by the LL and HL clouds.