Diurnal variations of global clouds observed from the CATS spaceborne lidar and their links to large-scale meteorological factors

Ge, Jinming; Wang, Zhenquan; Wang, Chen; Yang, Xuan; Dong, Zixiang; Wang, Meihua

doi:10.1007/s00382-021-05829-2

Cited by 14 publications

(10 citation statements)

References 61 publications

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“…(2018) and Ge et al. (2021) illustrate that occurrence and development of continental clouds mainly occur during the daytime, which is consistent with the observation in Figure 3b that cloud occurrences and development mainly concentrate from 7: 00 a.m. to 5: 00 p.m. In contrast, our WRF simulation does not show a significant cloud occurrence decrease from 4: 00 p.m. to 5: 00 p.m.…”

Section: Resultssupporting

confidence: 92%

“…The simulated CTCs are cirrus clouds rather than convective clouds since the simulated CWPs are generally lower than 0.5 kg/m 2 (Figure 6c) which is much smaller than the typical CWPs (e.g., 2 kg/m 2 ) of deep convective clouds (Tian et al., 2016). Note that the maintenance and development of cirrus clouds heavily depend on large‐scale ascend air motions (Ge et al., 2021; Starr & Cox, 1985), suggesting that the vertical velocity at the upper troposphere layers may be overestimated, which leads to an overestimation of cirrus‐type cloud occurrence (Zhang et al., 2008; Zhao et al., 2017).…”

Section: Resultsmentioning

confidence: 99%

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Revealing Bias of Cloud Radiative Effect in WRF Simulation: Bias Quantification and Source Attribution

et al. 2022

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The solar irradiance is an important parameter for the entire earth climate system since it governs the physical processes at the land surface (i.e., glacier melting, potential evapotranspiration, and diurnal cycling development of the planetary boundary layer (PBL)), and impacts on water and energy balance in the earth atmosphere (Ramanathan et al., 2001;Rosenfeld, 2006aRosenfeld, , 2006b. One objective of the studies focusing on man-made climate change is to quantify the variation of surface solar irradiance directly and indirectly caused by air pollutants from anthropogenic emissions (Jing & Suzuki, 2018;Powers et al., 2017). Additionally, solar irradiance forecast plays a key role in accelerating the ongoing global transition from conventional energy to renewable energy (Jimenez et al., 2016). Therefore, representation of physical processes controlling solar irradiance in the atmospheric models is important to estimate the radiation budget of the earth system, assess climate change, and accelerate the common application of green energy.The accuracy of simulating solar irradiance damping by aerosols, clouds, and light-absorbing gases during the transport from the top of the atmosphere to the earth's surface is usually unsatisfactory (Feng & Wang, 2019). The uncertainty of the solar irradiance simulation in mesoscale and global models is believed to intimately link to cloud radiative effect (CRE) since clouds usually show much higher solar irradiance extinct efficiency and sharper evolution than the gases and aerosols (Paquin-Ricard et al., 2010;Ruiz-Arias et al., 2016). Simulation of cloud-solar irradiance effect consists of two parts: (a) simulating cloud properties and (b) quantifying the solar irradiance extinction in the cloudy atmosphere. Therefore, solar irradiance forecast is determined by three

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Section: Resultssupporting

confidence: 92%

Section: Resultsmentioning

confidence: 99%

Revealing Bias of Cloud Radiative Effect in WRF Simulation: Bias Quantification and Source Attribution

et al. 2022

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“…Therefore, evaluating the simulated CDV of certain cloud types can provide valuable insights on model parameterizations of the related physical processes. Given these, the investigation of CDV has received increasing attention in recent years in both observational (e.g., Chepfer et al., 2019; Eastman & Warren, 2014; Ge et al., 2021; Noel et al., 2018; Shang et al., 2018) and modeling studies (e.g., Chen & Wang, 2016a, 2016b; Hannak et al., 2017; Yin & Porporato, 2017, 2020).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Simulated Cloud Diurnal Variation in CMIP6 Climate Models

et al. 2022

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Cloud diurnal variation (CDV) affects cloud radiative effects significantly as clouds reflect shortwave radiation only during the daytime but trap outgoing longwave radiation in both daytime and nighttime. Meanwhile, CDV also rectifies atmospheric variations of longer time scales via interactions with other physical and dynamic processes. These make CDV a valuable aspect for diagnosing climate model performance. Here, we evaluate the accuracy of simulated CDV in state‐of‐the‐art global climate models (GCMs) by comparing CDV in the historical simulation of 32 GCMs from 20 institutes participating the Coupled Model Intercomparison Project Phase 6 (CMIP6) with observations from the International Satellite and Cloud Climatology Project‐H product. While good agreement is found over the oceans, significant biases exist over land (notably deserts and plateaus), where the models simulate excessive nighttime clouds and insufficient daytime clouds and miss the observed peak of cloud fraction in the early afternoon. These biases persist throughout the year. It is illustrated that correcting the CDV biases tends to reduce the known model biases of smaller shortwave cloud radiative effect over the midlatitude Africa‐Europe‐Asia continent, South America, and vast ocean areas. Inter‐model comparisons show that the CDV biases vary significantly among models from different institutes and present similar characteristics among models from the same institutes, and suggests that the biases are more likely to be attributed to deficiencies in cloud‐related physical parameterizations rather than the model treatment of resolution, ocean, and chemistry. The improvement of CMIP6 models against their CMIP5 counterparts in simulating CDV is also discussed.

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“…At the same time, low-power spaceborne lidar tends to be commercialized and low cost and has a short development cycle. A small team can rapidly and cost-effectively design, build, test, and deploy a space lidar capable of hundreds and thousands of hours of operation [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Simulation of Compact Spaceborne Lidar with High-Repetition-Rate Laser for Cloud and Aerosol Detection under Different Atmospheric Conditions

et al. 2023

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To provide references for the design of the lab’s upcoming prototype of the compact spaceborne lidar with a high-repetition-rate laser (CSLHRL), in this paper, the detection signal of spaceborne lidar was simulated by the measured signal of ground-based lidar, and then, the detection capability of spaceborne lidar under different atmospheric conditions was evaluated by means of the signal-to-noise ratio (SNR), volume depolarization ratio (VDR) and attenuated color ratio (ACR). Firstly, the Fernald method was used to invert the optical parameters of cloud and aerosol with the measured signal of ground-based lidar. Secondly, the effective signal of the spaceborne lidar was simulated according to the known atmospheric optical parameters and the parameters of the spaceborne lidar system. Finally, by changing the cumulative laser pulse number and atmospheric conditions, a simulation was carried out to further evaluate the detection performance of the spaceborne lidar, and some suggestions for the development of the system are given. The experimental results showed that the cloud layer and aerosol layer with an extinction coefficient above 0.3 km−1 could be easily obtained when the laser cumulative pulse number was 1000 and the vertical resolution was 15 m at night; the identification of moderate pollution aerosols and thick clouds could be easily identified in the daytime when the laser cumulative pulse number was 10,000 and the vertical resolution was 120 m.

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Diurnal variations of global clouds observed from the CATS spaceborne lidar and their links to large-scale meteorological factors

Cited by 14 publications

References 61 publications

Revealing Bias of Cloud Radiative Effect in WRF Simulation: Bias Quantification and Source Attribution

Revealing Bias of Cloud Radiative Effect in WRF Simulation: Bias Quantification and Source Attribution

Evaluation of Simulated Cloud Diurnal Variation in CMIP6 Climate Models

Simulation of Compact Spaceborne Lidar with High-Repetition-Rate Laser for Cloud and Aerosol Detection under Different Atmospheric Conditions

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