2022 Vol. 39, No. 12
Display Method:
2022, 39(12): 1-1.
Abstract:
2022, 39(12): 1986-1993.
doi: 10.1007/s00376-022-2034-1
Abstract:
An undersea volcano at Hunga Tonga-Hunga Ha'apai (HTHH) near the South Pacific island nation of Tonga, erupted violently on 15 January 2022. Potential climate impact of the HTHH volcanic eruption is of great concern to the public; here, we intend to size up the impact of the HTHH eruption from a historical perspective. The influence of historical volcanic eruptions on the global climate are firstly reviewed, which are thought to have contributed to decreased surface temperature, increased stratospheric temperature, suppressed global water cycle, weakened monsoon circulation and El Niño-like sea surface temperature. Our understanding of the impacts of past volcanic eruptions on global-scale climate provides potential implication to evaluate the impact of the HTHH eruption. Based on historical simulations, we estimate that the current HTHH eruption with an intensity of 0.4 Tg SO2 injection will decrease the global mean surface temperature by only 0.004°C in the first year after eruption, which is within the amplitude of internal variability at the interannual time scale and thus not strong enough to have significant impacts on the global climate.
An undersea volcano at Hunga Tonga-Hunga Ha'apai (HTHH) near the South Pacific island nation of Tonga, erupted violently on 15 January 2022. Potential climate impact of the HTHH volcanic eruption is of great concern to the public; here, we intend to size up the impact of the HTHH eruption from a historical perspective. The influence of historical volcanic eruptions on the global climate are firstly reviewed, which are thought to have contributed to decreased surface temperature, increased stratospheric temperature, suppressed global water cycle, weakened monsoon circulation and El Niño-like sea surface temperature. Our understanding of the impacts of past volcanic eruptions on global-scale climate provides potential implication to evaluate the impact of the HTHH eruption. Based on historical simulations, we estimate that the current HTHH eruption with an intensity of 0.4 Tg SO2 injection will decrease the global mean surface temperature by only 0.004°C in the first year after eruption, which is within the amplitude of internal variability at the interannual time scale and thus not strong enough to have significant impacts on the global climate.
2022, 39(12): 1994-2007.
doi: 10.1007/s00376-021-0366-x
Abstract:
Cloud Masking is one of the most essential products for satellite remote sensing and downstream applications. This study develops machine learning-based (ML-based) cloud detection algorithms using spectral observations for the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite. Collocated active observations from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used to provide reference labels for model development and validation. We introduce both daytime and nighttime algorithms that differ according to whether solar band observations are included, and the artificial neural network (ANN) and random forest (RF) techniques are adopted for comparison. To eliminate the influences of surface conditions on cloud detection, we introduce three models with different treatments of the surface. Instead of developing independent ML-based algorithms, we add surface variables in a binary way that enhances the ML-based algorithm accuracy by ~5%. Validated against CALIOP observations, we find that our daytime RF-based algorithm outperforms the AHI operational algorithm by improving the accuracy of cloudy pixel detection by ~5%, while at the same time, reducing misjudgment by ~3%. The nighttime model with only infrared observations is also slightly better than the AHI operational product but may tend to overestimate cloudy pixels. Overall, our ML-based algorithms can serve as a reliable method to provide cloud mask results for both daytime and nighttime AHI observations. We furthermore suggest treating the surface with a set of independent variables for future ML-based algorithm development.
Cloud Masking is one of the most essential products for satellite remote sensing and downstream applications. This study develops machine learning-based (ML-based) cloud detection algorithms using spectral observations for the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite. Collocated active observations from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used to provide reference labels for model development and validation. We introduce both daytime and nighttime algorithms that differ according to whether solar band observations are included, and the artificial neural network (ANN) and random forest (RF) techniques are adopted for comparison. To eliminate the influences of surface conditions on cloud detection, we introduce three models with different treatments of the surface. Instead of developing independent ML-based algorithms, we add surface variables in a binary way that enhances the ML-based algorithm accuracy by ~5%. Validated against CALIOP observations, we find that our daytime RF-based algorithm outperforms the AHI operational algorithm by improving the accuracy of cloudy pixel detection by ~5%, while at the same time, reducing misjudgment by ~3%. The nighttime model with only infrared observations is also slightly better than the AHI operational product but may tend to overestimate cloudy pixels. Overall, our ML-based algorithms can serve as a reliable method to provide cloud mask results for both daytime and nighttime AHI observations. We furthermore suggest treating the surface with a set of independent variables for future ML-based algorithm development.
2022, 39(12): 2008-2023.
doi: 10.1007/s00376-021-1148-1
Abstract:
Using the high spatiotemporal resolution (2 km-and-10 min) data from the Advanced Himawari Imager onboard the Himawari-8 satellite, this study documents the fine-scale characteristics of daytime cloud regimes (CRs) over coastal South China during the pre-summer rainy season (April–June). Six CRs (CR1–CR6) are identified based on the joint frequency distribution of cloud top brightness temperature and cloud optical thickness, namely, the optically thin-to-moderate cloud mixture, optically thin warm clouds with cirrus, optically thick warm clouds, weak convective cloud mixture, strong convective clouds, and extreme, deep convective clouds. The optically thick warm clouds are the major CR during April and May, with higher frequencies over land, especially along the urban agglomeration, rather than the offshore which may be an indicator of the higher aerosol concentrations being a contributing factor over the cities. The CRs with weak convective cloud mixtures and strong convective clouds appear more frequently over the land, while the two CRs with optically thinner clouds occur mainly offshore. Synoptic flow patterns (SPs) are objectively identified and examined focusing on those favoring the two major rain-producing CRs (CR5 and CR6) and the highly reflective CR with optically thick warm clouds (CR3). The two SPs favoring CR5 and CR6 are characterized by abundant moisture with low-level jets after monsoon onset, and a northwest high-southeast low pattern with strong dynamic convergence along the coastline, respectively. The non-convective CR3 with high reflectance is related to a SP that features the western North Pacific subtropical high extending more westward, leading to a moderate moisture supply and a wide range of convective available potential energy, but also, large convective inhibition.
Using the high spatiotemporal resolution (2 km-and-10 min) data from the Advanced Himawari Imager onboard the Himawari-8 satellite, this study documents the fine-scale characteristics of daytime cloud regimes (CRs) over coastal South China during the pre-summer rainy season (April–June). Six CRs (CR1–CR6) are identified based on the joint frequency distribution of cloud top brightness temperature and cloud optical thickness, namely, the optically thin-to-moderate cloud mixture, optically thin warm clouds with cirrus, optically thick warm clouds, weak convective cloud mixture, strong convective clouds, and extreme, deep convective clouds. The optically thick warm clouds are the major CR during April and May, with higher frequencies over land, especially along the urban agglomeration, rather than the offshore which may be an indicator of the higher aerosol concentrations being a contributing factor over the cities. The CRs with weak convective cloud mixtures and strong convective clouds appear more frequently over the land, while the two CRs with optically thinner clouds occur mainly offshore. Synoptic flow patterns (SPs) are objectively identified and examined focusing on those favoring the two major rain-producing CRs (CR5 and CR6) and the highly reflective CR with optically thick warm clouds (CR3). The two SPs favoring CR5 and CR6 are characterized by abundant moisture with low-level jets after monsoon onset, and a northwest high-southeast low pattern with strong dynamic convergence along the coastline, respectively. The non-convective CR3 with high reflectance is related to a SP that features the western North Pacific subtropical high extending more westward, leading to a moderate moisture supply and a wide range of convective available potential energy, but also, large convective inhibition.
2022, 39(12): 2024-2039.
doi: 10.1007/s00376-021-1375-5
Abstract:
Radiative transfer simulations and remote sensing studies fundamentally require accurate and efficient computation of the optical properties of non-spherical particles. This paper proposes a deep learning (DL) scheme in conjunction with an optical property database to achieve this goal. Deep neural network (DNN) architectures were obtained from a dataset of the optical properties of super-spheroids with extensive shape parameters, size parameters, and refractive indices. The dataset was computed through the invariant imbedding T-matrix method. Four separate DNN architectures were created to compute the extinction efficiency factor, single-scattering albedo, asymmetry factor, and phase matrix. The criterion for designing these neural networks was the achievement of the highest prediction accuracy with minimal DNN parameters. The numerical results demonstrate that the determination coefficients are greater than 0.999 between the prediction values from the neural networks and the truth values from the database, which indicates that the DNN can reproduce the optical properties in the dataset with high accuracy. In addition, the DNN model can robustly predict the optical properties of particles with high accuracy for shape parameters or refractive indices that are unavailable in the database. Importantly, the ratio of the database size (~127 GB) to that of the DNN parameters (~20 MB) is approximately 6810, implying that the DNN model can be treated as a highly compressed database that can be used as an alternative to the original database for real-time computing of the optical properties of non-spherical particles in radiative transfer and atmospheric models.
Radiative transfer simulations and remote sensing studies fundamentally require accurate and efficient computation of the optical properties of non-spherical particles. This paper proposes a deep learning (DL) scheme in conjunction with an optical property database to achieve this goal. Deep neural network (DNN) architectures were obtained from a dataset of the optical properties of super-spheroids with extensive shape parameters, size parameters, and refractive indices. The dataset was computed through the invariant imbedding T-matrix method. Four separate DNN architectures were created to compute the extinction efficiency factor, single-scattering albedo, asymmetry factor, and phase matrix. The criterion for designing these neural networks was the achievement of the highest prediction accuracy with minimal DNN parameters. The numerical results demonstrate that the determination coefficients are greater than 0.999 between the prediction values from the neural networks and the truth values from the database, which indicates that the DNN can reproduce the optical properties in the dataset with high accuracy. In addition, the DNN model can robustly predict the optical properties of particles with high accuracy for shape parameters or refractive indices that are unavailable in the database. Importantly, the ratio of the database size (~127 GB) to that of the DNN parameters (~20 MB) is approximately 6810, implying that the DNN model can be treated as a highly compressed database that can be used as an alternative to the original database for real-time computing of the optical properties of non-spherical particles in radiative transfer and atmospheric models.
2022, 39(12): 2040-2055.
doi: 10.1007/s00376-022-2003-8
Abstract:
This study investigates the cloud macro- and micro-physical characteristics in the convective and stratiform regions and their different responses to the seeding for mixed convective-stratiform clouds that occurred in Shandong province on 21 May 2018, based on the observations from the aircraft, the Suomi National Polar-Orbiting Partnership (NPP) satellite, and the high-resolution Himawari-8 (H8) satellite. The aircraft observations show that convection was deeper and radar echoes were significantly enhanced with higher tops in response to seeding in the convective region. This is linked with the conversion of supercooled liquid droplets to ice crystals with released latent heat, resulting in strengthened updrafts, enhanced radar echoes, higher cloud tops, and more and larger precipitation particles. In contrast, in the stratiform cloud region, after the Silver Iodide (AgI) seeding, the radar echoes become significantly weaker at heights close to the seeding layer, with the echo tops lowered by 1.4–1.7 km. In addition, a hollow structure appears at the height of 6.2–7.8 km with a depth of about 1.6 km and a diameter of about 5.5 km, and features such as icing seeding tracks appear. These suggest that the transformation between droplets and ice particles was accelerated by the seeding in the stratiform part. The NPP and H8 satellites also show that convective activity was stronger in the convective region after seeding; while in the stratiform region, a cloud seeding track with a width of 1–3 km appears 10 km downstream of the seeding layer 15 minutes after the AgI seeding, which moves along the wind direction as width increases.
This study investigates the cloud macro- and micro-physical characteristics in the convective and stratiform regions and their different responses to the seeding for mixed convective-stratiform clouds that occurred in Shandong province on 21 May 2018, based on the observations from the aircraft, the Suomi National Polar-Orbiting Partnership (NPP) satellite, and the high-resolution Himawari-8 (H8) satellite. The aircraft observations show that convection was deeper and radar echoes were significantly enhanced with higher tops in response to seeding in the convective region. This is linked with the conversion of supercooled liquid droplets to ice crystals with released latent heat, resulting in strengthened updrafts, enhanced radar echoes, higher cloud tops, and more and larger precipitation particles. In contrast, in the stratiform cloud region, after the Silver Iodide (AgI) seeding, the radar echoes become significantly weaker at heights close to the seeding layer, with the echo tops lowered by 1.4–1.7 km. In addition, a hollow structure appears at the height of 6.2–7.8 km with a depth of about 1.6 km and a diameter of about 5.5 km, and features such as icing seeding tracks appear. These suggest that the transformation between droplets and ice particles was accelerated by the seeding in the stratiform part. The NPP and H8 satellites also show that convective activity was stronger in the convective region after seeding; while in the stratiform region, a cloud seeding track with a width of 1–3 km appears 10 km downstream of the seeding layer 15 minutes after the AgI seeding, which moves along the wind direction as width increases.
2022, 39(12): 2056-2070.
doi: 10.1007/s00376-022-1274-4
Abstract:
The microphysical characteristics of wintertime cold clouds in North China were investigated from 22 aircraft observation flights from 2014 to 2017, 2020, and 2021. The clouds were generated by mesoscale weather systems with little orographic component. Over the mixed-phase temperature range (–40°C to 0°C), the average fraction of liquid, mixed-phase, and ice cloud was 4.9%, 23.3%, and 71.8%, respectively, and the probability distribution of ice mass fraction was a half-U-shape, suggesting that ice cloud was the primary cloud type. The wintertime mixed-phase clouds in North China were characterized by large cloud droplet number concentration, small liquid water content (LWC), and small effective diameter of cloud droplets. The main reason for larger cloud droplet number concentration and smaller effective diameter of cloud droplets was the heavy pollution in winter in North China, while for smaller LWC was the lower temperature during flights and the difference in air mass type. With the temperature increasing, cloud droplet number concentration, LWC, and the size of ice particles increased, but ice number concentration and effective diameter of cloud droplets decreased, similar to other mid-latitude regions, indicating the similarity in the temperature dependence of cloud properties of mixed-phase clouds. The variation of the cloud properties and ice habit at different temperatures indicated the operation of the aggregation and riming processes, which were commonly present in the wintertime mixed-phase clouds. This study fills a gap in the aircraft observation of wintertime cold clouds in North China.
The microphysical characteristics of wintertime cold clouds in North China were investigated from 22 aircraft observation flights from 2014 to 2017, 2020, and 2021. The clouds were generated by mesoscale weather systems with little orographic component. Over the mixed-phase temperature range (–40°C to 0°C), the average fraction of liquid, mixed-phase, and ice cloud was 4.9%, 23.3%, and 71.8%, respectively, and the probability distribution of ice mass fraction was a half-U-shape, suggesting that ice cloud was the primary cloud type. The wintertime mixed-phase clouds in North China were characterized by large cloud droplet number concentration, small liquid water content (LWC), and small effective diameter of cloud droplets. The main reason for larger cloud droplet number concentration and smaller effective diameter of cloud droplets was the heavy pollution in winter in North China, while for smaller LWC was the lower temperature during flights and the difference in air mass type. With the temperature increasing, cloud droplet number concentration, LWC, and the size of ice particles increased, but ice number concentration and effective diameter of cloud droplets decreased, similar to other mid-latitude regions, indicating the similarity in the temperature dependence of cloud properties of mixed-phase clouds. The variation of the cloud properties and ice habit at different temperatures indicated the operation of the aggregation and riming processes, which were commonly present in the wintertime mixed-phase clouds. This study fills a gap in the aircraft observation of wintertime cold clouds in North China.
2022, 39(12): 2071-2086.
doi: 10.1007/s00376-021-1192-x
Abstract:
Cirrus clouds related to transported dust layers were identified on 22 occasions with ground-based polarization lidar from December 2012 to February 2018 over Wuhan (30.5°N, 114.4°E), China. All the events occurred in spring and winter. Cirrus clouds were mostly located above 7.6 km on top of the aloft dust layers. In-cloud relative humidity with respect to ice (RHi) derived from water vapor Raman lidar as well as from ERA5 reanalysis data were used as criteria to determine the possible ice nucleation regimes. Corresponding to the two typical cases shown, the observed events can be classified into two categories: (1) category A (3 cases), in-cloud peak RHi ≥ 150%, indicating competition between heterogeneous nucleation and homogeneous nucleation; and (2) category B (19 cases), in-cloud peak RHi < 150%, revealing that only heterogeneous nucleation was involved. Heterogeneous nucleation generally took place during instances of cirrus cloud formation in the upper troposphere when advected dust particles were present. Although accompanying cloud-top temperatures ranged from –51.9°C to –30.4°C, dust-related heterogeneous nucleation contributed to primary ice nucleation in cirrus clouds by providing ice nucleating particle concentrations on the order of 10−3 L−1 to 102 L−1. Heterogeneous nucleation and subsequent crystal growth reduced the ambient RHi to be less than 150% by consuming water vapor and thus completely inhibited homogeneous nucleation.
Cirrus clouds related to transported dust layers were identified on 22 occasions with ground-based polarization lidar from December 2012 to February 2018 over Wuhan (30.5°N, 114.4°E), China. All the events occurred in spring and winter. Cirrus clouds were mostly located above 7.6 km on top of the aloft dust layers. In-cloud relative humidity with respect to ice (RHi) derived from water vapor Raman lidar as well as from ERA5 reanalysis data were used as criteria to determine the possible ice nucleation regimes. Corresponding to the two typical cases shown, the observed events can be classified into two categories: (1) category A (3 cases), in-cloud peak RHi ≥ 150%, indicating competition between heterogeneous nucleation and homogeneous nucleation; and (2) category B (19 cases), in-cloud peak RHi < 150%, revealing that only heterogeneous nucleation was involved. Heterogeneous nucleation generally took place during instances of cirrus cloud formation in the upper troposphere when advected dust particles were present. Although accompanying cloud-top temperatures ranged from –51.9°C to –30.4°C, dust-related heterogeneous nucleation contributed to primary ice nucleation in cirrus clouds by providing ice nucleating particle concentrations on the order of 10−3 L−1 to 102 L−1. Heterogeneous nucleation and subsequent crystal growth reduced the ambient RHi to be less than 150% by consuming water vapor and thus completely inhibited homogeneous nucleation.
2022, 39(12): 2087-2106.
doi: 10.1007/s00376-022-1419-5
Abstract:
Cloud microphysical properties are significantly affected by entrainment and mixing processes. However, it is unclear how the entrainment rate affects the relative dispersion of cloud droplet size distribution. Previously, the relationship between relative dispersion and entrainment rate was found to be positive or negative. To reconcile the contrasting relationships, the Explicit Mixing Parcel Model is used to determine the underlying mechanisms. When evaporation is dominated by small droplets, and the entrained environmental air is further saturated during mixing, the relationship is negative. However, when the evaporation of big droplets is dominant, the relationship is positive. Whether or not the cloud condensation nuclei are considered in the entrained environmental air is a key factor as condensation on the entrained condensation nuclei is the main source of small droplets. However, if cloud condensation nuclei are not entrained, the relationship is positive. If cloud condensation nuclei are entrained, the relationship is dependent on many other factors. High values of vertical velocity, relative humidity of environmental air, and liquid water content, and low values of droplet number concentration, are more likely to cause the negative relationship since new saturation is easier to achieve by evaporation of small droplets. Further, the signs of the relationship are not strongly affected by the turbulence dissipation rate, but the higher dissipation rate causes the positive relationship to be more significant for a larger entrainment rate. A conceptual model is proposed to reconcile the contrasting relationships. This work enhances the understanding of relative dispersion and lays a foundation for the quantification of entrainment-mixing mechanisms.
Cloud microphysical properties are significantly affected by entrainment and mixing processes. However, it is unclear how the entrainment rate affects the relative dispersion of cloud droplet size distribution. Previously, the relationship between relative dispersion and entrainment rate was found to be positive or negative. To reconcile the contrasting relationships, the Explicit Mixing Parcel Model is used to determine the underlying mechanisms. When evaporation is dominated by small droplets, and the entrained environmental air is further saturated during mixing, the relationship is negative. However, when the evaporation of big droplets is dominant, the relationship is positive. Whether or not the cloud condensation nuclei are considered in the entrained environmental air is a key factor as condensation on the entrained condensation nuclei is the main source of small droplets. However, if cloud condensation nuclei are not entrained, the relationship is positive. If cloud condensation nuclei are entrained, the relationship is dependent on many other factors. High values of vertical velocity, relative humidity of environmental air, and liquid water content, and low values of droplet number concentration, are more likely to cause the negative relationship since new saturation is easier to achieve by evaporation of small droplets. Further, the signs of the relationship are not strongly affected by the turbulence dissipation rate, but the higher dissipation rate causes the positive relationship to be more significant for a larger entrainment rate. A conceptual model is proposed to reconcile the contrasting relationships. This work enhances the understanding of relative dispersion and lays a foundation for the quantification of entrainment-mixing mechanisms.
Aerosol-Cloud-Precipitation Interactions in a Closed-cell and Non-homogenous MBL Stratocumulus Cloud
2022, 39(12): 2107-2123.
doi: 10.1007/s00376-022-2013-6
Abstract:
A closed-cell marine stratocumulus case during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) aircraft field campaign is selected to examine the heterogeneities of cloud and drizzle microphysical properties and the aerosol-cloud-precipitation interactions. The spatial and vertical variabilities of cloud and drizzle microphysics are found in two different sets of flight legs: Leg-1 and Leg-2, which are parallel and perpendicular to the cloud propagation, respectively. The cloud along Leg-2 was close to adiabatic, where cloud-droplet effective radius and liquid water content linearly increase from cloud base to cloud top with less drizzle. The cloud along Leg-1 was sub-adiabatic with lower cloud-droplet number concentration and larger cloud-droplet effective, but higher drizzle droplet number concentration, larger drizzle droplet median diameter and drizzle liquid water content. The heavier drizzle frequency and intensity on Leg-1 were enhanced by the collision-coalescence processes within cloud due to strong turbulence. The sub-cloud precipitation rate on Leg-1 was significantly higher than that along Leg-2. As a result, the sub-cloud accumulation mode aerosols and CCN on Leg-1 were depleted, but the coarse model aerosols increased. This further leads to a counter-intuitive phenomenon that the CCN is less than cloud-droplet number concentration for Leg-1. The average CCN loss rates are −3.89\begin{document}$\mathrm{c}{\mathrm{m}}^{-3}\;{\mathrm{h}}^{-1}$\end{document} ![]()
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A closed-cell marine stratocumulus case during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) aircraft field campaign is selected to examine the heterogeneities of cloud and drizzle microphysical properties and the aerosol-cloud-precipitation interactions. The spatial and vertical variabilities of cloud and drizzle microphysics are found in two different sets of flight legs: Leg-1 and Leg-2, which are parallel and perpendicular to the cloud propagation, respectively. The cloud along Leg-2 was close to adiabatic, where cloud-droplet effective radius and liquid water content linearly increase from cloud base to cloud top with less drizzle. The cloud along Leg-1 was sub-adiabatic with lower cloud-droplet number concentration and larger cloud-droplet effective, but higher drizzle droplet number concentration, larger drizzle droplet median diameter and drizzle liquid water content. The heavier drizzle frequency and intensity on Leg-1 were enhanced by the collision-coalescence processes within cloud due to strong turbulence. The sub-cloud precipitation rate on Leg-1 was significantly higher than that along Leg-2. As a result, the sub-cloud accumulation mode aerosols and CCN on Leg-1 were depleted, but the coarse model aerosols increased. This further leads to a counter-intuitive phenomenon that the CCN is less than cloud-droplet number concentration for Leg-1. The average CCN loss rates are −3.89
2022, 39(12): 2124-2136.
doi: 10.1007/s00376-021-1166-z
Abstract:
Cloud radiative kernels (CRK) built with radiative transfer models have been widely used to analyze the cloud radiative effect on top of atmosphere (TOA) fluxes, and it is expected that the CRKs would also be useful in the analyses of surface radiative fluxes, which determines the regional surface temperature change and variability. In this study, CRKs at the surface and TOA were built using the Rapid Radiative Transfer Model (RRTM). Longwave cloud radiative effect (CRE) at the surface is primarily driven by cloud base properties, while TOA CRE is primarily decided by cloud top properties. For this reason, the standard version of surface CRK is a function of latitude, longitude, month, cloud optical thickness (τ) and cloud base pressure (CBP), and the TOA CRK is a function of latitude, longitude, month, τ and cloud top pressure (CTP). Considering that the cloud property histograms provided by climate models are functions of CTP instead of CBP at present, the surface CRKs on CBP-τ histograms were converted to CTP-τ fields using the statistical relationship between CTP, CBP and τ obtained from collocated CloudSat and MODIS observations. For both climate model outputs and satellites observations, the climatology of surface CRE and cloud-induced surface radiative anomalies calculated with the surface CRKs and cloud property histograms are well correlated with those calculated from surface radiative fluxes. The cloud-induced surface radiative anomalies reproduced by surface CRKs and MODIS cloud property histograms are not affected by spurious trends that appear in Clouds and the Earth's Radiant Energy System (CERES) surface irradiances products.
Cloud radiative kernels (CRK) built with radiative transfer models have been widely used to analyze the cloud radiative effect on top of atmosphere (TOA) fluxes, and it is expected that the CRKs would also be useful in the analyses of surface radiative fluxes, which determines the regional surface temperature change and variability. In this study, CRKs at the surface and TOA were built using the Rapid Radiative Transfer Model (RRTM). Longwave cloud radiative effect (CRE) at the surface is primarily driven by cloud base properties, while TOA CRE is primarily decided by cloud top properties. For this reason, the standard version of surface CRK is a function of latitude, longitude, month, cloud optical thickness (τ) and cloud base pressure (CBP), and the TOA CRK is a function of latitude, longitude, month, τ and cloud top pressure (CTP). Considering that the cloud property histograms provided by climate models are functions of CTP instead of CBP at present, the surface CRKs on CBP-τ histograms were converted to CTP-τ fields using the statistical relationship between CTP, CBP and τ obtained from collocated CloudSat and MODIS observations. For both climate model outputs and satellites observations, the climatology of surface CRE and cloud-induced surface radiative anomalies calculated with the surface CRKs and cloud property histograms are well correlated with those calculated from surface radiative fluxes. The cloud-induced surface radiative anomalies reproduced by surface CRKs and MODIS cloud property histograms are not affected by spurious trends that appear in Clouds and the Earth's Radiant Energy System (CERES) surface irradiances products.
2022, 39(12): 2137-2155.
doi: 10.1007/s00376-022-1424-8
Abstract:
Current global climate models cannot resolve the complex topography over the Tibetan Plateau (TP) due to their coarse resolution. This study investigates the impacts of horizontal resolution on simulating aerosol and its direct radiative effect (DRE) over the TP by applying two horizontal resolutions of about 100 km and 25 km to the Chinese Academy of Sciences Flexible Global Ocean-Atmosphere Land System (CAS FGOALS-f3) over a 10-year period. Compared to the AErosol RObotic NETwork observations, a high-resolution model (HRM) can better reproduce the spatial distribution and seasonal cycles of aerosol optical depth (AOD) compared to a low-resolution model (LRM). The HRM bias and RMSE of AOD decreased by 0.08 and 0.12, and the correlation coefficient increased by 0.22 compared to the LRM. An LRM is not sufficient to reproduce the aerosol variations associated with fine-scale topographic forcing, such as in the eastern marginal region of the TP. The difference between hydrophilic aerosols in an HRM and LRM is caused by the divergence of the simulated relative humidity (RH). More reasonable distributions and variations of RH are conducive to simulating hydrophilic aerosols. An increase of the 10-m wind speed in winter by an HRM leads to increased dust emissions. The simulated aerosol DREs at the top of the atmosphere (TOA) and at the surface by the HRM are –0.76 W m–2 and –8.72 W m–2 over the TP, respectively. Both resolution models can capture the key feature that dust TOA DRE transitions from positive in spring to negative in the other seasons.
Current global climate models cannot resolve the complex topography over the Tibetan Plateau (TP) due to their coarse resolution. This study investigates the impacts of horizontal resolution on simulating aerosol and its direct radiative effect (DRE) over the TP by applying two horizontal resolutions of about 100 km and 25 km to the Chinese Academy of Sciences Flexible Global Ocean-Atmosphere Land System (CAS FGOALS-f3) over a 10-year period. Compared to the AErosol RObotic NETwork observations, a high-resolution model (HRM) can better reproduce the spatial distribution and seasonal cycles of aerosol optical depth (AOD) compared to a low-resolution model (LRM). The HRM bias and RMSE of AOD decreased by 0.08 and 0.12, and the correlation coefficient increased by 0.22 compared to the LRM. An LRM is not sufficient to reproduce the aerosol variations associated with fine-scale topographic forcing, such as in the eastern marginal region of the TP. The difference between hydrophilic aerosols in an HRM and LRM is caused by the divergence of the simulated relative humidity (RH). More reasonable distributions and variations of RH are conducive to simulating hydrophilic aerosols. An increase of the 10-m wind speed in winter by an HRM leads to increased dust emissions. The simulated aerosol DREs at the top of the atmosphere (TOA) and at the surface by the HRM are –0.76 W m–2 and –8.72 W m–2 over the TP, respectively. Both resolution models can capture the key feature that dust TOA DRE transitions from positive in spring to negative in the other seasons.
2022, 39(12): 2156-2171.
doi: 10.1007/s00376-022-2036-z
Abstract:
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.
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.
2022, 39(12): 2172-2187.
doi: 10.1007/s00376-021-0369-7
Abstract:
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 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.
2022, 39(12): 2188-2202.
doi: 10.1007/s00376-022-1441-7
Abstract:
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.
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.
Identification of Convective and Stratiform Clouds Based on the Improved DBSCAN Clustering Algorithm
2022, 39(12): 2203-2212.
doi: 10.1007/s00376-021-1223-7
Abstract:
A convective and stratiform cloud classification method for weather radar is proposed based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm. To identify convective and stratiform clouds in different developmental phases, two-dimensional (2D) and three-dimensional (3D) models are proposed by applying reflectivity factors at 0.5° and at 0.5°, 1.5°, and 2.4° elevation angles, respectively. According to the thresholds of the algorithm, which include echo intensity, the echo top height of 35 dBZ (ET), density threshold, and ε neighborhood, cloud clusters can be marked into four types: deep-convective cloud (DCC), shallow-convective cloud (SCC), hybrid convective-stratiform cloud (HCS), and stratiform cloud (SFC) types. Each cloud cluster type is further identified as a core area and boundary area, which can provide more abundant cloud structure information. The algorithm is verified using the volume scan data observed with new-generation S-band weather radars in Nanjing, Xuzhou, and Qingdao. The results show that cloud clusters can be intuitively identified as core and boundary points, which change in area continuously during the process of convective evolution, by the improved DBSCAN algorithm. Therefore, the occurrence and disappearance of convective weather can be estimated in advance by observing the changes of the classification. Because density thresholds are different and multiple elevations are utilized in the 3D model, the identified echo types and areas are dissimilar between the 2D and 3D models. The 3D model identifies larger convective and stratiform clouds than the 2D model. However, the developing convective clouds of small areas at lower heights cannot be identified with the 3D model because they are covered by thick stratiform clouds. In addition, the 3D model can avoid the influence of the melting layer and better suggest convective clouds in the developmental stage.
A convective and stratiform cloud classification method for weather radar is proposed based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm. To identify convective and stratiform clouds in different developmental phases, two-dimensional (2D) and three-dimensional (3D) models are proposed by applying reflectivity factors at 0.5° and at 0.5°, 1.5°, and 2.4° elevation angles, respectively. According to the thresholds of the algorithm, which include echo intensity, the echo top height of 35 dBZ (ET), density threshold, and ε neighborhood, cloud clusters can be marked into four types: deep-convective cloud (DCC), shallow-convective cloud (SCC), hybrid convective-stratiform cloud (HCS), and stratiform cloud (SFC) types. Each cloud cluster type is further identified as a core area and boundary area, which can provide more abundant cloud structure information. The algorithm is verified using the volume scan data observed with new-generation S-band weather radars in Nanjing, Xuzhou, and Qingdao. The results show that cloud clusters can be intuitively identified as core and boundary points, which change in area continuously during the process of convective evolution, by the improved DBSCAN algorithm. Therefore, the occurrence and disappearance of convective weather can be estimated in advance by observing the changes of the classification. Because density thresholds are different and multiple elevations are utilized in the 3D model, the identified echo types and areas are dissimilar between the 2D and 3D models. The 3D model identifies larger convective and stratiform clouds than the 2D model. However, the developing convective clouds of small areas at lower heights cannot be identified with the 3D model because they are covered by thick stratiform clouds. In addition, the 3D model can avoid the influence of the melting layer and better suggest convective clouds in the developmental stage.
2022, 39(12): 2213-2223.
doi: 10.1007/s00376-022-1414-x
Abstract:
Clouds are a dominant modulator of the energy budget. The cloud shortwave radiative effect at the surface (CRE) is closely related to the cloud macro- and micro-physical properties. Systematic observation of surface irradiance and cloud properties are needed to narrow uncertainties in CRE. In this study, 1-min irradiance and Total Sky Imager measurements from 2005 to 2009 at Xianghe in North China Plain are used to estimate cloud types, evaluate cloud fraction (CF), and quantify the sensitivities of surface irradiance with respect to changes in CF whether clouds obscure the sun or not. The annual mean CF is 0.50, further noting that CF exhibits a distinct seasonal variation, with a minimum in winter (0.37) and maximum in summer (0.68). Cumulus occurs more frequently in summer (32%), which is close to the sum of the occurrence of stratus and cirrus. The annual CRE is –54.4 W m–2, with seasonal values ranging from –29.5 W m–2 in winter and –78.2 W m–2 in summer. When clouds do not obscure the sun, CF is a dominant factor affecting diffuse irradiance, which in turn affects global irradiance. There is a positive linear relationship between CF and CRE under sun-unobscured conditions, the mean sensitivity of CRE for each CF 0.1 increase is about 1.2 W m–2 [79.5° < SZA (Solar Zenith Angle) < 80.5°] to 7.0 W m–2 (29.5° < SZA < 30.5°). When clouds obscure the sun, CF affects both direct and diffuse irradiance, resulting in a non-linear relationship between CF and CRE, and the slope decreases with increasing CF. It should be noted that, although only data at Xianghe is used in this study, our results are representative of neighboring areas, including most parts of the North China Plain.
Clouds are a dominant modulator of the energy budget. The cloud shortwave radiative effect at the surface (CRE) is closely related to the cloud macro- and micro-physical properties. Systematic observation of surface irradiance and cloud properties are needed to narrow uncertainties in CRE. In this study, 1-min irradiance and Total Sky Imager measurements from 2005 to 2009 at Xianghe in North China Plain are used to estimate cloud types, evaluate cloud fraction (CF), and quantify the sensitivities of surface irradiance with respect to changes in CF whether clouds obscure the sun or not. The annual mean CF is 0.50, further noting that CF exhibits a distinct seasonal variation, with a minimum in winter (0.37) and maximum in summer (0.68). Cumulus occurs more frequently in summer (32%), which is close to the sum of the occurrence of stratus and cirrus. The annual CRE is –54.4 W m–2, with seasonal values ranging from –29.5 W m–2 in winter and –78.2 W m–2 in summer. When clouds do not obscure the sun, CF is a dominant factor affecting diffuse irradiance, which in turn affects global irradiance. There is a positive linear relationship between CF and CRE under sun-unobscured conditions, the mean sensitivity of CRE for each CF 0.1 increase is about 1.2 W m–2 [79.5° < SZA (Solar Zenith Angle) < 80.5°] to 7.0 W m–2 (29.5° < SZA < 30.5°). When clouds obscure the sun, CF affects both direct and diffuse irradiance, resulting in a non-linear relationship between CF and CRE, and the slope decreases with increasing CF. It should be noted that, although only data at Xianghe is used in this study, our results are representative of neighboring areas, including most parts of the North China Plain.
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