In this study, a statistical cloud scheme is first introduced and coupledwith a first-order turbulence scheme with second-order turbulence moments parameterized by thetimescale of the turbulence dissipation and the ve...In this study, a statistical cloud scheme is first introduced and coupledwith a first-order turbulence scheme with second-order turbulence moments parameterized by thetimescale of the turbulence dissipation and the vertical turbulent diffusion coefficient. Then theability of the scheme to simulate cloud fraction at different relative humidity, verticaltemperature profile, and the timescale of the turbulent dissipation is examined by numericalsimulation. It is found that the simulated cloud fraction is sensitive to the parameter used in thestatistical cloud scheme and the timescale of the turbulent dissipation. Based on the analyses, theintroduced statistical cloud scheme is modified. By combining the modified statistical cloud schemewith a boundary layer cumulus scheme, a new statistically-based low-level cloud scheme is proposedand tentatively applied in NCAR (National Center for Atmospheric Research) CCM3 (Community ClimateModel version 3). It is found that the simulation of low-level cloud fraction is markedly improvedand the centers with maximum low-level cloud fractions are well simulated in the cold oceans off thewestern coasts with the statistically-based low-level cloud scheme applied in CCM3. It suggeststhat the new statistically-based low-level cloud scheme has a great potential in the generalcirculation model for improving the low-level cloud parameterization.展开更多
Three kinds of the widely-used cloudiness parameterizations are compared with data produced from the cloud-resolving model(CRM) simulations of the tropical cloud system. The investigated schemes include those based on...Three kinds of the widely-used cloudiness parameterizations are compared with data produced from the cloud-resolving model(CRM) simulations of the tropical cloud system. The investigated schemes include those based on relative humidity(RH), the semi-empirical scheme using cloud condensate as a predictor, and the statistical scheme based on probability distribution functions(PDFs). Results show that all three schemes are successful in reproducing the timing of cloud generation, except for the RH-based scheme, in which low-level clouds are artificially simulated during cloudless days. In contrast, the low-level clouds are well simulated in the semi-empirical and PDF-based statistical schemes, both of which are close to the CRM explicit simulations. In addition to the Gaussian PDF, two alternative PDFs are also explored to investigate the impact of different PDFs on cloud parameterizations. All the PDF-based parameterizations are found to be inaccurate for high cloud simulations, in either the magnitude or the structure. The primary reason is that the investigated PDFs are symmetrically assumed, yet the skewness factors in deep convective cloud regimes are highly significant, indicating the symmetrical assumption is not well satisfied in those regimes. Results imply the need to seek a skewed PDF in statistical schemes so that it can yield better performance in high cloud simulations.展开更多
基金This study is jointly supported by the Chinese Academy of Sciences "Innovation Program" under Grant ZKCX2-SW-210, theNational Natural Science Foundation of China under Grant Nos. 40233031, 40231004, and 40221503, and the National Key BasicResearch Projec
文摘In this study, a statistical cloud scheme is first introduced and coupledwith a first-order turbulence scheme with second-order turbulence moments parameterized by thetimescale of the turbulence dissipation and the vertical turbulent diffusion coefficient. Then theability of the scheme to simulate cloud fraction at different relative humidity, verticaltemperature profile, and the timescale of the turbulent dissipation is examined by numericalsimulation. It is found that the simulated cloud fraction is sensitive to the parameter used in thestatistical cloud scheme and the timescale of the turbulent dissipation. Based on the analyses, theintroduced statistical cloud scheme is modified. By combining the modified statistical cloud schemewith a boundary layer cumulus scheme, a new statistically-based low-level cloud scheme is proposedand tentatively applied in NCAR (National Center for Atmospheric Research) CCM3 (Community ClimateModel version 3). It is found that the simulation of low-level cloud fraction is markedly improvedand the centers with maximum low-level cloud fractions are well simulated in the cold oceans off thewestern coasts with the statistically-based low-level cloud scheme applied in CCM3. It suggeststhat the new statistically-based low-level cloud scheme has a great potential in the generalcirculation model for improving the low-level cloud parameterization.
基金supported by the National Basic Research Program of China(Grant Nos.2014CB441202,2013CB955803)the National Natural Science Foundation of China(Grant Nos.41305102,91337110)+1 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA11010402)the Joint Center for Global Change Studies(Grant No.105019)
文摘Three kinds of the widely-used cloudiness parameterizations are compared with data produced from the cloud-resolving model(CRM) simulations of the tropical cloud system. The investigated schemes include those based on relative humidity(RH), the semi-empirical scheme using cloud condensate as a predictor, and the statistical scheme based on probability distribution functions(PDFs). Results show that all three schemes are successful in reproducing the timing of cloud generation, except for the RH-based scheme, in which low-level clouds are artificially simulated during cloudless days. In contrast, the low-level clouds are well simulated in the semi-empirical and PDF-based statistical schemes, both of which are close to the CRM explicit simulations. In addition to the Gaussian PDF, two alternative PDFs are also explored to investigate the impact of different PDFs on cloud parameterizations. All the PDF-based parameterizations are found to be inaccurate for high cloud simulations, in either the magnitude or the structure. The primary reason is that the investigated PDFs are symmetrically assumed, yet the skewness factors in deep convective cloud regimes are highly significant, indicating the symmetrical assumption is not well satisfied in those regimes. Results imply the need to seek a skewed PDF in statistical schemes so that it can yield better performance in high cloud simulations.
