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The study of climate and climate change is hindered by a lack of information on the effect of clouds on the radiation balance of the earth, referred to as the cloud-radiative forcing. Quantitative estimates of the global distributions of cloud-radiative forcing have been obtained from the spaceborne Earth Radiation Budget Experiment (ERBE) launched in 1984. For the April 1985 period, the global shortwave cloud forcing [-44.5 watts per square meter (W/m2)] due to the enhancement of planetary albedo, exceeded in magnitude the longwave cloud forcing (31.3 W/m2) resulting from the greenhouse effect of clouds. Thus, clouds had a net cooling effect on the earth. This cooling effect is large over the mid-and high-latitude oceans, with values reaching -100 W/m2. The monthly averaged longwave cloud forcing reached maximum values of 50 to 100 W/m2 over the convectively disturbed regions of the tropics. However, this heating effect is nearly canceled by a correspondingly large negative shortwave cloud forcing, which indicates the delicately balanced state of the tropics. The size of the observed net cloud forcing is about four times as large as the expected value of radiative forcing from a doubling of CO2. The shortwave and longwave components of cloud forcing are about ten times as large as those for a CO2 doubling. Hence, small changes in the cloud-radiative forcing fields can play a significant role as a climate feedback mechanism. For example, during past glaciations a migration toward the equator of the field of strong, negative cloud-radiative forcing, in response to a similar migration of cooler waters, could have significantly amplified oceanic cooling and continental glaciation.
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... The study of radiative forcing by the cloud has been undergoing many research groups since several years ago [1][2][3][4]. The main reason for the focus on clouds is that it is considered the largest source of uncertainty in the climate model simulation of the radiation budget of the atmosphere of the earth [5]. ...
... A previous study showed that CRF is the impact of clouds on the radiation balance relative to a clear sky, which is quantified by the difference between radiative fluxes under clear-sky (cloud-free) and all-sky conditions [1,35,36]. In this study, we have evaluated the sensitivities of CRF at LW, SW, and net CRF to SSTs changes. ...
Article
This study presents a numerical sensitivity experiment, where the regional climate model is used over the El Niño 3.4 region to understand how changes in Sea Surface Temperature (SST) affect cloud properties and the atmospheric radiation budget. The Spatio-temporal variation of cloud radiative forcing response to control (CNTRL), warmer (SST+2K), and colder (SST-2K) SSTs are analyzed. It is shown that the SST+2K experiment led to a reduced low-level cloud fraction, while the SST-2K experiment caused an enhancement of cloud fraction in the lower troposphere. The decreased low-level cloud coverage in the SST+2K experiment acted to compensate for the improvement of high-cloud. The opposite occurred in the SST-2K experiment. Consistent with previous studies, both surface temperature and precipitation increase (decrease) as SST increases (decreases). Besides, changes in radiation on the top of the atmosphere are simulated, primarily because of changes in cloud coverage. The results have ramifications for the improvement of convective, radiative schemes, and regional climate simulations.
... Clouds play a pivotal role in the global energy budget by reflecting solar radiation back to space, which is known as a cloud cooling effect (Schneider, 1972;Ramanathan et al., 1989), absorbing longwave radiation as a warming effect (Zhao and Garrett, 2015), as well as releasing latent heat to the atmosphere (Wang et al., 2014;Pan et al., 2020). Nearly 80% of the Southern Ocean (SO) region (defined as 40°-60°S) is covered by cloud (Dolinar et al., 2015) but has been underexplored due to lack of observations. ...
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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.
... Clouds play a remarkable role in the Earth's radiation budget (Ramanathan et al., 1989). Cloud properties are modulated by atmospheric aerosols, as almost all the liquid cloud droplets form on an aerosol particle that can act as cloud condensation nuclei (Charlson et al., 1992;Twomey, 1974). ...
... Cirrus clouds play a significant role in global heat balance. They reflect a significant portion of the incident solar flux back into space which results in net cooling of the Earth's surface [25] and also absorb infrared radiation emitted from the surface. The cirrus cloud transmission spectrum (Fig. 3) shows two prominent peaks at 961 cm -1 and 3506 cm -1 . ...
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Clouds and aerosols play a vital role in the Earth's climate. Detecting polar mesospheric clouds, polar stratospheric clouds and aerosols is useful for monitoring climate change and atmospheric chemistry. ACE (Atmospheric Chemistry Experiment) satellite data are used to provide an infrared spectral atlas of polar mesospheric clouds, three types of polar stratospheric clouds (nitric acid trihydrate, sulfuric/nitric acid ternary solutions, and ice), cirrus clouds, stratospheric smoke from fires and sulfate aerosols. Most of the example spectra have been modeled using the appropriate optical constants and the calculated extinction of sunlight by the particles.
... Surface solar radiation (SSR) provides essential energy for Earth and the climate system (Ramanathan et al., 1989;Valipour, 2015;Renner et al., 2019). Although the amount of SSR at a given location is overall determined by the geographical and astronomical factors, it would be attenuated by absorption and scattering of aerosol particles, and hydrometeor particles (Liou, 2002). ...
Article
Using surface solar radiation (SSR) during 2015–2019 and the corresponding meteorological variables, pollution observations and temporal code, random forest models were built for the seven stations in North China Plain. The permutation feature importance (PFI) and partial dependence plot (PDP) reveal that, besides temporal code, total cloud cover (TCC) and aerosol optical depth (AOD) are the most important influencing feature on global radiation (GR) and diffuse radiation (DR), respectively. Single feature PDP visualized how the input variables affect the estimated SSR. The different order in PFI of boundary layer height (BLH) between urban and farmland stations and the distinct variations in PDP of BLH among urban stations suggest the interaction of BLH and aerosols has substantial impact on the SSR in metropolis areas. The joint PDP displayed the nonlinear relationships between SSR and the input feature pairs. The change range of SSR with the corresponding input feature and feature pairs is also investigated, with the maximum variation of GR from 63.4 Wm⁻² to 231.9 Wm⁻² under the influence of TCC − BLH and of DR from 51.8 Wm⁻² to 104.9 Wm⁻² under the impact of TCC − AOD in Zhengzhou, respectively. These findings provide insight into the simulation and prediction of SSR, temperature and subsequent weather conditions in urban areas.
... The radiative fluxes at the surface are linked to the surface energy balance: The contribution of clouds and aerosols to the surface energy balance are quantified by the Cloud Radiative Effect (CRE) and the Aerosol Radiative Effect (ARE), respectively, following Ramanathan et al. (1989): where, for any radiative flux F, F clear-sky is the cloud free F and F clean-sky the cloud and aerosol free F. ...
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A severe heatwave occurred in April 2010 over West Africa. It was characterised by a particularly high daily minimum temperature reaching more than 35°C locally and a high water vapour content. In this study we analyse the ability of a mesoscale limited area model to represent such an event and investigate the advantage of using an explicit representation of deep convection for such a case associated with very limited precipitation amounts. Two high‐resolution simulations (5 km x 5 km horizontal grid) have been performed from 10 to 19 April 2010; they are identical except that one uses a deep convection parameterization (simulation PARAM) and the other does not (simulation EXPL). These simulations are evaluated with different observational datasets including gridded products as well as local meteorological measurements and radiosoundings. Overall, both simulations display a negative temperature bias in the low levels but this bias is much more pronounced in PARAM, mainly due to evaporative cooling of spurious precipitation. Indeed, in PARAM, precipitation is too frequently triggered (around mid‐day, i.e. several hours too early) and too strong; the Inter‐Tropical Discontinuity (ITD) propagates too far north during this 10‐day sequence. Conversely, in EXPL, the observed northward shift of the ITD is correctly simulated and precipitation displays a better timing, variability, intensity and latitudinal extent. It thus appears that the representation of deep convection affects the atmospheric circulation associated with the heatwave event. The mechanisms involved in this humid heatwave are further investigated with thermodynamic and dynamic budgets which also underline the main differences between the two simulations. A proper representation of deep convection on sub‐diurnal time scale turns out to be necessary for the simulation of this heatwave episode, which points to the interest of convection‐permitting simulations for the study of heatwaves even though they are generally characterised by very little precipitation. This article is protected by copyright. All rights reserved.
... Clouds play a pivotal role in the earth's radiation balance, mainly by interacting with the incoming short-wave radiation and outgoing long-wave radiation (Ramanathan et al. 1989;Kiehl and Trenberth 1997). Radiative properties of clouds mainly depend on the type of clouds (Chen et al. 2000). ...
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The deep convection and associated moist processes have a major role in regulating the circulation and precipitation characteristics of the Indian summer monsoon. This aspect is examined by conducting sensitivity experiments with the Weather Research and Forecast model. Three active monsoon cases during the periods 16–25 June 2015, 20–29 July 2010 and 1–9 August 2007 are selected for the study. Control simulations using reanalysis data as initial and lateral boundary conditions reveal that the model could simulate mean features of the precipitation and circulation pattern during those active monsoon periods. In sensitivity experiments, microphysical latent heat release in the model is switched off and all other conditions are kept same as that of control simulations. The removal of latent heat release in the model suppresses development of deep convection over the monsoon domain and causes substantial reduction in precipitation. A large-scale descending motion appears in the mid-troposphere and vertical growth of clouds is hampered. As a result, thick cloud bands form in the lower atmosphere, which reduces the short-wave radiation reaching the surface and leading to a reduction in land surface temperature over the Indian region. The cessation of deep convection also affects the strength and position of monsoon low-level circulation. The lack of convective heating shifts the low-level jet core over the Arabian Sea towards north. Consequently, the low-level jet gets strengthened over the north-west India and weakens over the peninsular India. The present study unambiguously established the fact that organized deep convection and concomitant vertical heating over the monsoon domain have a prominent role in regulating monsoon dynamics.
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In this study, the decomposed fast and slow responses of clouds to an abruptly quadrupled CO2 concentration (approximately 1139 ppmv) in East Asia (EA) are obtained quantitatively by using a general circulation model, BCC–AGCM2.0. Our results show that in the total response, the total cloud cover (TCC), low cloud cover (LCC), and high cloud cover (HCC) all increased north of 40°N and decreased south of 40°N except in the Tibetan Plateau (TP). The mean changes of the TCC, LCC, and HCC in EA were −0.74%, 0.38%, and −0.38% in the total response, respectively; 1.05%, −0.03%, and 1.63% in the fast response, respectively; and −1.79%, 0.41%, and −2.01% in the slow response, respectively. By comparison, we found that changes in cloud cover were dominated by the slow response in most areas in EA due to the changes in atmospheric temperature, circulation, and water vapor supply together. Overall, the changes in the cloud forcing over EA related to the fast and slow responses were opposite to each other, and the final cloud forcing was dominated by the slow response. The mean net cloud forcing (NCF) in the total response over EA was −1.80 W m−2, indicating a cooling effect which partially offset the warming effect caused by the quadrupled CO2. The total responses of NCF in the TP, south China (SC), and northeast China (NE) were −6.74 W m−2, 6.11 W m−2, and −7.49 W m−2, respectively. Thus, the local effects of offsetting or amplifying warming were particularly obvious.
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The inception of a moored buoy network in the northern Indian Ocean in 1997 paved the way for systematic collection of long-term time series observations of meteorological and oceanographic parameters. This buoy network was revamped in 2011 with Ocean Moored buoy Network for north Indian Ocean (OMNI) buoys fitted with additional sensors to better quantify the air–sea fluxes. An intercomparison of OMNI buoy measurements with the nearby Woods Hole Oceanographic Institution (WHOI) mooring during the year 2015 revealed an overestimation of downwelling longwave radiation (LWR↓). Analysis of the OMNI and WHOI radiation sensors at a test station at National Institute of Ocean Technology (NIOT) during 2019 revealed that the accurate and stable amplification of the thermopile voltage records along with the customized datalogger in the WHOI system results in better estimations of LWR↓. The offset in NIOT measured LWR↓ is estimated first by segregating the LWR↓ during clear-sky conditions identified using the downwelling shortwave radiation measurements from the same test station, and second, finding the offset by taking the difference with expected theoretical clear-sky LWR↓. The corrected LWR↓ exhibited good agreement with that of collocated WHOI measurements, with a correlation of 0.93. This method is applied to the OMNI field measurements and again compared with the nearby WHOI mooring measurements, exhibiting a better correlation of 0.95. This work has led to the revamping of radiation measurements in OMNI buoys and provides a reliable method to correct past measurements and improve estimation of air–sea fluxes in the Indian Ocean. Significance Statement Downwelling longwave radiation (LWR↓) is an important climate variable for calculating air–sea heat exchange and quantifying Earth’s energy budget. An intercomparison of LWR↓ measurements between ocean observing platforms in the north Indian Ocean revealed a systematic offset in National Institute of Ocean Technology (NIOT) Ocean Moored buoy Network for north Indian Ocean (OMNI) buoys. The observed offset limited our capability to accurately estimate air–sea fluxes in the Indian Ocean. The sensor measurements were compared with a standard reference system, which revealed problems in thermopile amplifier as the root cause of the offset. This work led to the development of a reliable method to correct the offset in LWR↓ and revamping of radiation measurements in NIOT-OMNI buoys. The correction is being applied to the past measurements from 12 OMNI buoys over 8 years to improve the estimation of air–sea fluxes in the Indian Ocean.
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A simple slab ocean of 50 m depth, which allows for seasonal ocean heat storage but no ocean heat transport, is coupled to a global spectral general circulation model with global domain, realistic geography, and computed clouds. The paper first describes the atmospheric and oceanic aspects of the model. Following that there is a general discussion of the model control experiment and comparison with observed data. The next two sections describe the zonal mean and geographical responses to a doubling of CO//2 concentration. Following those there is a section with a discussion of the swamp model results compared to the present mixed-layer model results. Finally, the last section draws conclusions from the experiments.