doi:10.1007/s00376-017-7083-5

Adler, R. F., Coauthors, 2003: The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present). Journal of Hydrometeorology, 4(6), 1147-1167, https://doi.org/10.1175/1525-7541(2003) 004<1147:TVGPCP>2.0.CO;2.

10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2

8df0378a8b3ea2ae84900f0170187135

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F224017907_Global_Precipitation_Climotology_Project_GPCP_monthly_precipitation_analysis_1979-present

http://journals.ametsoc.org/doi/abs/10.1175/1525-7541%282003%29004%3C1147%3ATVGPCP%3E2.0.CO%3B2The Global Precipitation Climatology Project (GPCP) Version-2 Monthly Precipitation Analysis is described. This globally complete, monthly analysis of surface precipitation at 2.5℃ latitude 2.5℃ longitude resolution is available from January 1979 to the present. It is a merged analysis that incorporates precipitation estimates from low-orbit satellite microwave data, geosynchronous-orbit satellite infrared data, and surface rain gauge observations. The merging approach utilizes the higher accuracy of the low-orbit microwave observations to calibrate, or adjust, the more frequent geosynchronous infrared observations. The dataset is extended back into the premicrowave era (before mid-1987) by using infrared-only observations calibrated to the microwave-based analysis of the later years. The combined satellite-based product is adjusted by the rain gauge analysis. The dataset archive also contains the individual input fields, a combined satellite estimate, and error estimates for each field. This monthly analysis is the foundation for the GPCP suite of products, including those at finer temporal resolution. The 23-yr GPCP climatology is characterized, along with time and space variations of precipitation.

Anderson T. L., R. J. Charlson, S. E. Schwartz, R. Knutti, O. Boucher, H. Rodhe, and J. Heintzenberg, 2003: Climate forcing by aerosols-A hazy picture.Science300,1103-1104,https://doi.org/10.1126/science.1084777.10.1126/science.1084777

12750507

85a0f790519bb1ccdeffc55fc6beee78

http%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpubmed%2F12750507

http://www.sciencemag.org/cgi/doi/10.1126/science.1084777Abstract Anthropogenic aerosol emissions are believed to have counteracted the global-warming effect of greenhouse gases over the past century. However, the magnitude of this cooling effect is highly uncertain. In their Perspective, Anderson et al. argue that the magnitude and uncertainty of aerosol forcing may be larger than is usually considered in models. This would have important implications for the total climate forcing by anthropogenic emissions, and hence for predicting future global warming.

Barkstrom B. R., J. B. Hall, 1982: Earth radiation budget experiment (ERBE): An overview.Journal of Energy6,141-146,https://doi.org/10.2514/3.62584.10.2514/3.62584

9df30244f9b39ad2c534f7a24439f7aa

http%3A%2F%2Farc.aiaa.org%2Fdoi%2Fabs%2F10.2514%2F3.62584

http://arc.aiaa.org/doi/10.2514/3.62584During the past 4 years, instruments of the Earth Radiation Budget Experiment (ERBE) have been collecting data on two satellites. The first of these is the Earth Radiation Budget Satellite (ERBS). The second is the operational NOAA-9 satellite. In addition, ERBE has instruments on the operational NOAA-10. The NOAA-10 instruments have been collecting data for the last 2 years. The ERBE Science Team has recently completed validation of an initial sampling of these data. As a result, the ERBE Project will be placing these data in the archive at the National Space Science Data Center (NSSDC). The validation activity has involved intensive examination of data in 4 months during 1985 and 1986: April, July, and October 1985, and January 1986. This paper reports on the data being placed in the archive to acquaint the scientific community with their availability.

Boucher O., H. Le Treut, and M. B. Baker, 1995: Precipitation and radiation modeling in a general circulation model: Introduction of cloud microphysical processes.J. Geophys. Res.,100,16 395-16 414,https://doi.org/10.1029/95JD01382.10.1029/95JD01382

2165bd258e9304eb5070a86953c97e74

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F95JD01382%2Fabstract

http://doi.wiley.com/10.1029/95JD01382Cloud microphysical processes are introduced in the precipitation parameterization of a general circulation model (GCM). Three microphysical processes are included in this representation of warm cloud precipitation: autoconversion of droplets, collection of droplets by falling raindrops, and evaporation of raindrops falling in clear sky. The mean droplet radius, r, is calculated from the cloud water mixing ratio, which is computed in the model, and the cloud droplet number concentration, N, which is prescribed. The autoconversion rate is set to zero for r < r0, a prescribed threshold mean droplet radius. We investigate the model sensitivity to r0 and to N, the cloud droplet concentration, which is linked to the concentration of cloud condensation nuclei and is likely to vary. We find that an increase in N leads to an increase in the amount of cloud water stored in the atmosphere. In further experiments the mean droplet radius used in the parameterization of cloud optical properties is calculated in the same way as in the precipitation parameterization in order to bring more consistency between the different schemes. We again investigate the model sensitivity to r0 and to N and we find that an increase in N significantly enhances cloud albedo.

Dai A. G., 2006: Precipitation characteristics in eighteen coupled climate models.J. Climate19,4605-4630,https://doi.org/10.1175/JCLI3884.1.10.1175/JCLI3884.1

53ca1a9b29045e436670dea48dc01572

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2006JCli...19.4605D

http://journals.ametsoc.org/doi/abs/10.1175/JCLI3884.1Monthly and 3-hourly precipitation data from twentieth-century climate simulations by the newest generation of 18 coupled climate system models are analyzed and compared with available observations. The characteristics examined include the mean spatial patterns, intraseasonal-to-interannual and ENSO-related variability, convective versus stratiform precipitation ratio, precipitation frequency and intensity for different precipitation categories, and diurnal cycle. Although most models reproduce the observed broad patterns of precipitation amount and year-to-year variability, models without flux corrections still show an unrealistic double-ITCZ pattern over the tropical Pacific, whereas the flux-corrected models, especially the Meteorological Research Institute (MRI) Coupled Global Climate Model (CGCM; version 2.3.2a), produce realistic rainfall patterns at low latitudes. As in previous generations of coupled models, the rainfall double ITCZs are related to westward expansion of the cold tongue of sea surface temperature (SST) that is observed only over the equatorial eastern Pacific but extends to the central Pacific in the models. The partitioning of the total variance of precipitation among intraseasonal, seasonal, and longer time scales is generally reproduced by the models, except over the western Pacific where the models fail to capture the large intraseasonal variations. Most models produce too much convective (over 95% of total precipitation) and too little stratiform precipitation over most of the low latitudes, in contrast to 45%09“65% in convective form in the Tropical Rainfall Measuring Mission (TRMM) satellite observations. The biases in the convective versus stratiform precipitation ratio are linked to the unrealistically strong coupling of tropical convection to local SST, which results in a positive correlation between the standard deviation of Ni01±o-3.4 SST and the local convective-to-total precipitation ratio among the models. The models reproduce the percentage of the contribution (to total precipitation) and frequency for moderate precipitation (1009“20 mm day-1), but underestimate the contribution and frequency for heavy (>20 mm day-1) and overestimate them for light (<10 mm day-1) precipitation. The newest generation of coupled models still rains too frequently, mostly within the 109“10 mm day-1 category. Precipitation intensity over the storm tracks around the eastern coasts of Asia and North America is comparable to that in the ITCZ (1009“12 mm day-1) in the TRMM data, but it is much weaker in the models. The diurnal analysis suggests that warm-season convection still starts too early in these new models and occurs too frequently at reduced intensity in some of the models. The results show that considerable improvements in precipitation simulations are still desirable for the latest generation of the world0964s coupled climate models.

Ghan S. J., X. Liu, R. C. Easter, R. Zaveri, P. J. Rasch, J.-H. Yoon, and B. Eaton, 2012: Toward a minimal representation of aerosols in climate models: Comparative decomposition of aerosol direct, semidirect, and indirect radiative forcing. J. Climate, 25, 6461-6476, https://doi.org/10.1175/JCLI-D-11-00650.1.

Han Q. Y., W. B. Rossow, J. Chou, and R. M. Welch, 1998: Global variation of column droplet concentration in low-level clouds.Geophys. Res. Lett.,25,1419-1422,https://doi.org/10.1029/98GL01095.10.1029/98GL01095

c6c70202ac3bf0585119d3420b17a02d

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F98GL01095%2Fpdf

http://doi.wiley.com/10.1029/98GL01095Cloud droplet concentration is a very important parameter in model studies. However, no global observation is available because it is hard to retrieve by current satellite remote sensing techniques. This study introduces another parameter, column droplet concentration, which can be retrieved by satellite data and used in models. The column droplet concentration (N) is the product of cloud geometrical thickness and droplet volume number concentration. This paper presents a method and the results of retrieving column droplet concentration for low-level clouds. The first near-global survey (50℃S to 50℃N) of Nreveals more clearly the effect of aerosol concentration variations on clouds. The survey shows the expected increase of column droplet concentrations between ocean and continental clouds and in tropical areas during dry seasons where biomass burning is prevalent. Therefore, column droplet concentration is demonstrated as a good indication of available CCN populations in certain areas.

Hurrell J. W., J. J. Hack, D. Shea, J. M. Caron, and J. Rosinski, 2008: A new sea surface temperature and sea ice boundary dataset for the Community Atmosphere Model.J. Climate21(19),5145-5153,https://doi.org/10.1175/2008JCLI 2292. 1.10.1175/2008JCLI2292.1

0ff603cbc3bfb3d5101e80534d7908a6

http%3A%2F%2Ficesjms.oxfordjournals.org%2Fexternal-ref%3Faccess_num%3D10.1175%2F2008JCLI2292.1%26amp%3Blink_type%3DDOI

http://journals.ametsoc.org/doi/abs/10.1175/2008JCLI2292.1

IPCC, 2007: Climate Change 2007: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change,S.Solomon et al.,Eds.,Cambridge University Press,Cambridge,UnitedKingdomandNewYork,NY,USA,996pp.

b8a2e070f63b71ac0ce4316f7fb3d5dd

http%3A%2F%2Fwww.science-open.com%2Freview%3Fvid%3D0ea9fbb5-bc2e-412c-9943-63de328e9899

http://www.science-open.com/review?vid=0ea9fbb5-bc2e-412c-9943-63de328e9899CiteSeerX - Scientific documents that cite the following paper: Zwiers Summary for Policymakers. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change

IPCC, 2013: Climate Change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,T.F. Stocker et al.,Eds.,Cambridge University Press,Cambridge,UnitedKingdomandNewYork,NY,USA,1535pp.

Khairoutdinov M., Y. Kogan, 2000: A new cloud physics parameterization in a large-eddy simulation model of marine stratocumulus. Mon. Wea. Rev., 128, 229-243, https://doi.org/10.1175/1520-0493(2000)128<0229:ANCPPI>2.0.CO;2.

10.1175/1520-0493(2000)1282.0.CO;2

db520ed59079d0d3bd81f87a0892cf23

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2000MWRv..128..229K

http://adsabs.harvard.edu/abs/2000MWRv..128..229KA new bulk microphysical parameterization for large-eddy simulation (LES) models of the stratocumulus-topped boundary layer has been developed using an explicit (drop spectrum resolving) microphysical model as a data source and benchmark for comparison. The liquid water is divided into two categories, nonprecipitable cloud water and drizzle, similar to traditional Kessler-type parameterizations. The cloud condensation nucleus (CCN) count, cloud/drizzle water mixing ratios, cloud/drizzle drop concentrations, and the cloud drop integral radius are predicted in the new scheme. The source/sink terms such as autoconversion/accretion of cloud water into/by drizzle are regressed using the cloud drop size spectra predicted by an explicit microphysical model. The results from the explicit and the new bulk microphysics schemes are compared for two cases: nondrizzling and heavily drizzling stratocumulus-topped boundary layers (STBLs). The evolution of the STBL (characterized by such parameters as turbulence intensity, drizzle rates, CCN depletion rates, fractional cloud cover, and drizzle effects on internal stratification) simulated by the bulk microphysical model was in good agreement with the explicit microphysical model.

Kinne, S., Coauthors, 2006: An AeroCom initial assessment-optical properties in aerosol component modules of global models.Atmos. Chem. Phys.6,1815-1834,https://doi.org/10.5194/acp-6-1815-2006.10.5194/acp-6-1815-2006

514557c0c8d0985cfd97e3f750c2dc8d

http%3A%2F%2Fonlinelibrary.wiley.com%2Fresolve%2Freference%2FXREF%3Fid%3D10.5194%2Facpd-5-8285-2005

http://www.atmos-chem-phys.net/6/1815/2006/The AeroCom exercise diagnoses multi-component aerosol modules in global modeling. In an initial assessment simulated global distributions for mass and mid-visible aerosol optical thickness (aot) were compared among 20 different modules. Model diversity was also explored in the context of previous comparisons. For the component combined aot general agreement has improved for the annual global mean. At 0.11 to 0.14, simulated aot values are at the lower end of global averages suggested by remote sensing from ground (AERONET ca. 0.135) and space (satellite composite ca. 0.15). More detailed comparisons, however, reveal that larger differences in regional distribution and significant differences in compositional mixture remain. Of particular concern are large model diversities for contributions by dust and carbonaceous aerosol, because they lead to significant uncertainty in aerosol absorption (aab). Since aot and aab, both, influence the aerosol impact on the radiative energy-balance, the aerosol (direct) forcing uncertainty in modeling is larger than differences in aot might suggest. New diagnostic approaches are proposed to trace model differences in terms of aerosol processing and transport: These include the prescription of common input (e.g. amount, size and injection of aerosol component emissions) and the use of observational capabilities from ground (e.g. measurements networks) or space (e.g. correlations between aerosol and clouds).

KovaČeviČ, N., M. Čurić, 2014: Sensitivity study of the influence of cloud droplet concentration on hail suppression effectiveness.Meteor. Atmos. Phys.,123,195-207,https://doi.org/10.1007/s00703-013-0296-y.10.1007/s00703-013-0296-y

bdac5e994f8c94b839894ccf7f928a03

http%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs00703-013-0296-y

http://link.springer.com/10.1007/s00703-013-0296-yA cloud-resolving mesoscale model with a two-moment microphysical scheme has been used. The software package for cloud seeding and two categories of precipitation elements (graupel and frozen raindrops) are incorporated in the model. The aim of this study was to investigate the impact of cloud droplet concentration on hail suppression effectiveness. The concentration level of cloud droplets is prescribed in the model. We performed sensitivity tests of precipitation amounts (rain and hail) on the cloud droplet concentration in unseeded and seeded cases. We demonstrated for the unseeded case that increasing the concentration of cloud droplets created a reduction in rain accumulation, while the amount of hail accumulation increased. It is necessary to understand whether natural diversity in the cloud droplet concentration can affect the effectiveness of hail suppression. For operational cloud seeding activities, it would be helpful to determine whether it is possible to suppress hail if we know the optimal level of concentration for cloud droplets. Our study showed that hail suppression effectiveness had the greatest influence on lowering cloud droplet concentration levels; suppression effectiveness decreased as the cloud droplet concentration increased.

Lamarque, J. F., Coauthors, 2010: Historical (1850-2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: Methodology and application.Atmos. Chem. Phys.10,7017-7039,https://doi.org/10.5194/acp-10-7017-2010.10.1530/acta.0.0440119

42b717d3f78659ab84c7f30113222c24

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2010EGUGA..1211523L

http://www.atmos-chem-phys.net/10/7017/2010/We present and discuss a new dataset of gridded emissions covering the historical period (18502000) in decadal increments at a horizontal resolution of 0.5℃ in latitude and longitude. The primary purpose of this inventory is to provide consistent gridded emissions of reactive gases and aerosols for use in chemistry model simulations needed by climate models for the Climate Model Intercomparison Program #5 (CMIP5) in support of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). Our best estimate for the year 2000 inventory represents a combination of existing regional and global inventories to capture the best information available at this point; 40 regions and 12 sectors are used to combine the various sources. The historical reconstruction of each emitted compound, for each region and sector, is then forced to agree with our 2000 estimate, ensuring continuity between past and 2000 emissions. Simulations from two chemistry-climate models is used to test the ability of the emission dataset described here to capture long-term changes in atmospheric ozone, carbon monoxide and aerosol distributions. The simulated long-term change in the Northern mid-latitudes surface and mid-troposphere ozone is not quite as rapid as observed. However, stations outside this latitude band show much better agreement in both present-day and long-term trend. The model simulations indicate that the concentration of carbon monoxide is underestimated at the Mace Head station; however, the long-term trend over the limited observational period seems to be reasonably well captured. The simulated sulfate and black carbon deposition over Greenland is in very good agreement with the ice-core observations spanning the simulation period. Finally, aerosol optical depth and additional aerosol diagnostics are shown to be in good agreement with previously published estimates and observations.

Lee H., J.-J. Baik, 2017: A physically based autoconversion parameterization.J. Atmos. Sci.,74,1599-1616,https://doi.org/10.1175/JAS-D-16-0207.1.10.1175/JAS-D-16-0207.1

53c3253343b553809c1141c94db5bd40

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F314023301_A_physically_based_autoconversion_parameterization

http://journals.ametsoc.org/doi/10.1175/JAS-D-16-0207.1Abstract A physically based parameterization for the autoconversion is derived by solving the stochastic collection equation (SCE) with an approximated collection kernel. The collection kernel is constructed using the terminal velocity of cloud droplets and the collision efficiency between cloud droplets that is obtained using a particle trajectory model. The new parameterization proposed in this study is validated through comparison with results obtained by a bin-based direct SCE solver and other autoconversion parameterizations using a box model. The autoconversion-related time scale and drop number concentration are employed for the validation. The results of the new parameterization are shown to most closely match those of the direct SCE solver. It is also shown that the dependency of the autoconversion rate on drop number concentration in the new parameterization is similar to that in the direct SCE solver, which is partially caused by the shape of drop size distribution. The new parameterization and other parameterizations are implemented into a cloud-resolving model, and idealized shallow warm clouds are simulated. The autoconversion parameterizations that yield the small (large) autoconversion rate tend to predict large (small) cloud optical thickness, small (large) cloud fraction, and small (large) surface precipitation amount. Cloud optical thickness and cloud fraction are changed by up to ~45% and ~20% by autoconversion parameterizations, respectively. The new parameterization tends to yield the moderate autoconversion rate among the autoconversion parameterizations. Moreover, it predicts cloud optical thickness, cloud fraction, and surface precipitation amount that are generally the closest to those of the bin microphysics scheme.

Lin Z.-H., Z. Yu, H. Zhang, and C.-L. Wu, 2016: Quantifying the attribution of model bias in simulating summer hot days in China with IAP AGCM 4.1. Atmos. Oceanic Sci. Lett.,9(6),436-442,https://doi.org/10.1080/16742834.2016. 1232585.10.1080/16742834.2016.1232585

8c9aa9337b2e31bf18a5098166439826

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https://www.tandfonline.com/doi/full/10.1080/16742834.2016.1232585

Liu, X., Coauthors, 2012: Toward a minimal representation of aerosols in climate models: Description and evaluation in the Community Atmosphere Model CAM5.Geoscientific Model Development5,709-739,https://doi.org/10.5194/gmd-5-709-2012.10.5194/gmd-5-709-2012

6ca8ebe374f2ee6396ff9e90077fdc04

http%3A%2F%2Fwww.oalib.com%2Fpaper%2F1377936

http://www.geosci-model-dev.net/5/709/2012/A modal aerosol module (MAM) has been developed for the Community Atmosphere Model version 5 (CAM5), the atmospheric component of the Community Earth System Model version 1 (CESM1). MAM is capable of simulating the aerosol size distribution and both internal and external mixing between aerosol components, treating numerous complicated aerosol processes and aerosol physical, chemical and optical properties in a physically-based manner. Two MAM versions were developed: a more complete version with seven lognormal modes (MAM7), and a version with three lognormal modes (MAM3) for the purpose of long-term (decades to centuries) simulations. In this paper a description and evaluation of the aerosol module and its two representations are provided. Sensitivity of the aerosol lifecycle to simplifications in the representation of aerosol is discussed. Simulated sulfate and secondary organic aerosol (SOA) mass concentrations are remarkably similar between MAM3 and MAM7. Differences in primary organic matter (POM) and black carbon (BC) concentrations between MAM3 and MAM7 are also small (mostly within 10%). The mineral dust global burden differs by 10% and sea salt burden by 30-40% between MAM3 and MAM7, mainly due to the different size ranges for dust and sea salt modes and different standard deviations of the log-normal size distribution for sea salt modes between MAM3 and MAM7. The model is able to qualitatively capture the observed geographical and temporal variations of aerosol mass and number concentrations, size distributions, and aerosol optical properties. However, there are noticeable biases; e.g., simulated BC concentrations are significantly lower than measurements in the Arctic. There is a low bias in modeled aerosol optical depth on the global scale, especially in the developing countries. These biases in aerosol simulations clearly indicate the need for improvements of aerosol processes (e.g., emission fluxes of anthropogenic aerosols and precursor gases in developing countries, boundary layer nucleation) and properties (e.g., primary aerosol emission size, POM hygroscopicity). In addition, the critical role of cloud properties (e.g., liquid water content, cloud fraction) responsible for the wet scavenging of aerosol is highlighted.

Liu Y. G., P. H. Daum, 2002: Anthropogenic aerosols: Indirect warming effect from dispersion forcing.Nature419,580-581,https://doi.org/10.1038/419580a.

10.1038/419580a

http://www.nature.com/articles/419580a

Liu Y. G., P. H. Daum, 2004: Parameterization of the autoconversion process. Part I: Analytical formulation of the Kessler-type parameterizations. J. Atmos. Sci., 61, 1539-1548, https://doi.org/10.1175/1520-0469(2004)061<1539:POTAPI>2.0.CO;2.10.1175/1520-0469(2004)0612.0.CO;2

63329be392ddc5cfd8d1d64ffdaba559

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2005JAtS...62.3003W

http://adsabs.harvard.edu/abs/2005JAtS...62.3003WABSTRACT Various commonly used Kessler-type parameterizations of the autoconversion of cloud droplets to embryonic raindrops are theoretically derived from the same formalism by applying the generalized mean value theorem for integrals to the general collection equation. The new formalism clearly reveals the approximations and assumptions that are implicitly embedded in these different parameterizations. A new Kessler-type parameterization is further derived by eliminating the incorrect and/or unnecessary assumptions inherent in the existing Kessler-type parameterizations. The new parameterization exhibits a different dependence on liquid water content and droplet concentration, and provides theoretical explanations for the multitude of values assigned to the tunable coefficients associated with the commonly used parameterizations. Relative dispersion of the cloud droplet size distribution (defined as the ratio of the standard deviation to the mean radius of the cloud droplet size distribution) is explicitly included in the new parameterization, allowing for investigation of the influences of the relative dispersion on the autoconversion rate and, hence, on the second indirect aerosol effect. The new analytical parameterization compares favorably with those parameterizations empirically obtained by curve-fitting results from simulations of detailed microphysical models.

Liu Y. G., P. H. Daum, R. McGraw, and M. Miller, 2006: Generalized threshold function accounting for effect of relative dispersion on threshold behavior of autoconversion process.Geophys. Res. Lett.,33,L11804,https://doi.org/10.1029/2005GL025500.10.1029/2005GL025500

54e2aa6fe365868f4048768c86395555

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2005GL025500%2Ffull

http://doi.wiley.com/10.1029/2005GL025500The recently derived theoretical threshold function associated with the autoconversion process is generalized to account for the effect of the relative dispersion of the cloud droplet size distribution. This generalized threshold function theoretically demonstrates that the relative dispersion, which has been largely neglected to date, essentially controls the cloud-to-rain transition if the liquid water content and the droplet concentration are fixed. Comparison of the generalized threshold function to existing ad hoc threshold functions further reveals that the essential role of the spectral shape of the cloud droplet size distribution in rain initiation has been unknowingly buried in the arbitrary use of ad hoc threshold functions in atmospheric models such as global climate models, and that commonly used ad hoc threshold functions are unable to fully describe the threshold behavior of the autoconversion process that likely occurs in ambient clouds.

Liu Y. G., P. H. Daum, R. L. McGraw, M. A. Miller, and S. J. Niu, 2007: Theoretical expression for the autoconversion rate of the cloud droplet number concentration.Geophys. Res. Lett.,34,L16821,https://doi.org/10.1029/2007GL030389.10.1029/2007GL030389

90213c1326054a2330ff7e1e9acc4972

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2007GL030389%2Fpdf

http://onlinelibrary.wiley.com/doi/10.1029/2007GL030389/pdfAccurate parameterization of the autoconversion rate of the cloud droplet concentration (number autoconversion rate in cms) is critical for evaluating aerosol indirect effects using climate models; however, existing parameterizations are empirical at best. A theoretical expression is presented in this contribution that analytically relates the number autoconversion rate to the liquid water content, droplet concentration and relative dispersion of the cloud droplet size distribution. The analytical expression is theoretically derived by generalizing the analytical formulation previously developed for the autoconversion rate of the cloud liquid water content (mass autoconversion rate in g cms). Further examination of the theoretical number and mass autoconversion rates reveals that the former is not linearly proportional to the latter as commonly assumed in existing parameterizations. The formulation forms a solid theoretical basis for developing multi-moment representation of the autoconversion process in atmospheric models in general.

Liu Y. G., P. H. Daum, H. Guo, and Y. R. Peng, 2008: Dispersion bias,dispersion effect, and the aerosol-cloud conundrum.Environ. Res. Lett.,3(4),045021,https://doi.org/10.1088/17489326/3/4/045021.

10.1088/1748-9326/3/4/045021

7816f42dfa19bcffc9d38a9c00b6b416

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2008AGUFM.A41E0151L

http://stacks.iop.org/1748-9326/3/i=4/a=045021?key=crossref.f226e956e6e4d721cc84794eed9d7453Recent studies have shown that relative dispersion (ratio of the standard deviation to the mean radius of the cloud droplet size distribution) significantly affects effective radius, and that anthropogenic aerosols increase not only cloud droplet number concentration but also relative dispersion, leading to a warming dispersion effect that acts to offset the cooling from the Twomey effect. This work extends the previous studies by further examining the effect of relative dispersion on cloud albedo and cloud radiative forcing, deriving an analytical formulation, and presenting a new approach for representing relative dispersion in climate models. Further analyses show that unrealistic representation of relative dispersion in parameterization of cloud radiative properties in general and evaluation of aerosol indirect effects in particular is at least in part responsible for several outstanding puzzles of the aerosol-cloud conundrum, e.g., overestimation of cloud radiative cooling by climate models compared to satellite observations, large uncertainty and discrepancy in estimates of the aerosol indirect effect, and the lack of difference in cloud albedo between the northern and southern hemispheres. Application of the new formulation to remote sensing spectral shape of the cloud droplet size distribution is also be explored.

Loeb, N. G., Coauthors, 2009: Toward optimal closure of the earth's top-of-atmosphere radiation budget.J. Climate22(3),748-766,https://doi.org/10.1175/2008JCLI2637.1.10.1175/2008JCLI2637.1

b1db4a34e04ae57cebe48689e297863e

http%3A%2F%2Fwww.cabdirect.org%2Fabstracts%2F20093117315.html

http://journals.ametsoc.org/doi/abs/10.1175/2008JCLI2637.1Despite recent improvements in satellite instrument calibration and the algorithms used to determine reflected solar (SW) and emitted thermal (LW) top-of-atmosphere (TOA) radiative fluxes, a sizeable imbalance persists in the average global net radiation at the TOA from satellite observations. This imbalance is problematic in applications that use earth radiation budget (ERB) data for climate model evaluation, estimate the earth's annual global mean energy budget, and in studies that infer meridional heat transports. This study provides a detailed error analysis of TOA fluxes based on the latest generation of Clouds and the Earth's Radiant Energy System (CERES) gridded monthly mean data products [the monthly TOA/surface averages geostationary (SRBAVG-GEO)] and uses an objective constrainment algorithm to adjust SW and LW TOA fluxes within their range of uncertainty to remove the inconsistency between average global net TOA flux and heat storage in the earthtmosphere system. The 5-yr global mean CERES net flux from the standard CERES product is 6.5 W m-2, much larger than the best estimate of 0.85 W m-2 based on observed ocean heat content data and model simulations. The major sources of uncertainty in the CERES estimate are from instrument calibration (4.2 W m-2) and the assumed value for total solar irradiance (1 W m-2). After adjustment, the global mean CERES SW TOA flux is 99.5 W m-2, corresponding to an albedo of 0.293, and the global mean LW TOA flux is 239.6 W m-2. These values differ markedly from previously published adjusted global means based on the ERB Experiment in which the global mean SW TOA flux is 107 W m-2 and the LW TOA flux is 234 W m-2.

Lohmann U., J. Feichter, 1997: Impact of sulfate aerosols on albedo and lifetime of clouds: A sensitivity study with the ECHAM4 GCM.J. Geophys. Res.,102,13 685-13 700,https://doi.org/10.1029/97JD00631.3.10.1029/97JD00631

0566b36205a34247cb9f24ace24b6ec1

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F97JD00631%2Fabstract

http://doi.wiley.com/10.1029/97JD00631A coupled sulfur chemistry-cloud microphysics scheme (COUPL) is used to study the impact of sulfate aerosols on cloud lifetime and albedo. The cloud microphysics scheme includes precipitation formation, which depends on the cloud droplet number concentration (CDNC) and on the liquid water content. On the basis of different observational data sets, CDNC is proportional to the sulfate aerosol mass, which is calculated by the model. Cloud cover is a function of relative humidity only. Additional sensitivity experiments with another cloud cover parameterization (COUPL-CC), which also depends on cloud water, and with a different autoconversion rate of cloud droplets (COUPL-CC-Aut) are conducted to investigate the range of the indirect effect due to uncertainties in cloud physics. For each experiment, two simulations, one using present-day and one using preindustrial sulfur emissions are carried out. The increase in liquid water path, cloud cover, and shortwave cloud forcing due to anthropogenic sulfur emissions depends crucially upon the parameterization of cloud cover and autoconversion of cloud droplets. In COUPL the liquid water path increases by 17% and cloud cover increases by 1% because of anthropogenic sulfur emissions, yielding an increase in shortwave cloud forcing of 611.4 W m 612 . In COUPL-CC the liquid water path increases by 32%, cloud cover increases by 3% and thus shortwave cloud forcing increases by 614.8 W m 612 . This large effect is caused by the strong dependence of cloud cover on cloud water and of the autoconversion rate on CDNC, cloud water, and cloud cover. Choosing a different autoconversion rate (COUPL-CC-Aut) with a reduced dependence on CDNC and cloud water results in an increase of liquid water path by only 11% and of cloud cover by 1%, and the increase in shortwave cloud forcing amounts to 612.2 W m 612 . These results clearly show that the uncertainties linked to the indirect aerosol effect are higher than was previously suggested.

Michibata T., T. Takemura, 2015: Evaluation of autoconversion schemes in a single model framework with satellite observations.J. Geophys. Res.,120,9570-9590,https://doi.org/10.1002/2015JD023818-T.10.1002/2015JD023818

d0cd98f4d4c55a7eec64af7e2264bf14

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2F2015JD023818%2Fpdf

http://onlinelibrary.wiley.com/doi/10.1002/2015JD023818/pdfAbstract We examined the performance of autoconversion (mass transfer from cloud water to rainwater by the coalescence of cloud droplets) schemes in warm rain, which are commonly used in general circulation models. To exclude biases in the different treatment of the aerosol-cloud-precipitation-radiation interaction other than that of the autoconversion process, sensitivity experiments were conducted within a single model framework using an aerosol-climate model, MIROC-SPRINTARS. The liquid water path (LWP) and cloud optical thickness have a particularly high sensitivity to the autoconversion schemes, and their sensitivity is of the same magnitude as model biases. In addition, the ratio of accretion to autoconversion (Acc/Aut ratio), a key parameter in the examination of the balance of microphysical conversion processes, also has a high sensitivity globally depending on the scheme used. Although the Acc/Aut ratio monotonically increases with increasing LWP, significantly lower ratio is observed in Kessler-type schemes. Compared to satellite observations, a poor representation of cloud macrophysical structure and optically thicker low cloud are found in simulations with any autoconversion scheme. As a result of the cloud-radiation interaction, the difference in the global mean net cloud radiative forcing (NetCRF) among the schemes reaches 10 Wm2. The discrepancy between the observed and simulated NetCRF is especially large with a high LWP. The potential uncertainty in the parameterization of the autoconversion process is nonnegligible, and no formulation significantly improves the bias in the cloud radiative effect yet. This means that more fundamental errors are still left in other processes of the model.

Morrison H., A. Gettelman, 2008: A new two-moment bulk stratiform cloud microphysics scheme in the Community Atmosphere Model,Version 3 (CAM3) Part I: Description and numerical tests. J. Climate,21,3642-3659,https://doi.org/10.1175/2008JCLI2105.1.10.1175/2008JCLI2105.1

2970c6765868c88201c6d425daeffd27

http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2008JCli...21.3642M%26amp%3Bdb_key%3DPHY%26amp%3Blink_type%3DABSTRACT

http://journals.ametsoc.org/doi/abs/10.1175/2008JCLI2105.1

Neale, R. B., Coauthors, 2010: Description of the NCAR Community Atmosphere Model (CAM5.0). NCAR Tech. Note NCAR/TN-486+STR,268 pp.

Peng Y. R., U. Lohmann, 2003: Sensitivity study of the spectral dispersion of the cloud droplet size distribution on the indirect aerosol effect.Geophys. Res. Lett.,30,1507,https://doi.org/10.1029/2003GL017192.10.1029/2003GL017192

d55729eefae889988a66a654e0d863ab

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2003GL017192%2Ffull

http://onlinelibrary.wiley.com/doi/10.1029/2003GL017192/fullTo study the influence of anthropogenic aerosols on the shape of the cloud droplet size spectra (dispersion effect), we analyze observed liquid water cloud data during two Canadian field studies. Scaled by the parameter , which is a function of the relative dispersion of cloud droplet spectra, the calculated cloud albedo shows better agreement with the independently measured cloud albedo than the cloud albedo calculated without scaling. The scaling factor is positively correlated with the cloud droplet number concentration. A linear relationship between and the cloud droplet number concentration obtained from different field studies is applied to the ECHAM4 general circulation model. The global mean indirect aerosol effect at the top of atmosphere including the dispersion effect is reduced by 0.2 W mas compared to the reference simulation. This accounts for about 1/3 of the reduction that needed to be imposed on the simulated anthropogenic indirect aerosol effect by Lohmann and Lesins [2002].

Planche C., J. H. Marsham, P. R. Field, K. S. Carslaw, A. A. Hill, G. W. Mann, and B. J. Shipway, 2015: Precipitation sensitivity to autoconversion rate in a numerical weather-prediction model.Quart. J. Roy. Meteor. Soc.,141,2032-2044,https://doi.org/10.1002/qj.2497.10.1002/qj.2497

c4987e93a5592caa7c403a557ae5012a

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fqj.2497%2Fpdf

http://doi.wiley.com/10.1002/qj.2497Aerosols are known to significantly affect cloud and precipitation patterns and intensity, but these interactions are ignored or very simplistically handled in climate and numerical weather-prediction (NWP) models. A suite of one-way nested Met Office Unified Model (UM) runs, with a single-moment bulk microphysics scheme was used to study two convective cases with contrasting characteristics observed in southern England. The autoconversion process that converts cloud water to rain is directly controlled by the assumed droplet number. The impact of changing cloud droplet number concentration (CDNC) on cloud and precipitation evolution can be inferred through changes to the autoconversion rate. This was done for a range of resolutions ranging from regional NWP (1 km) to high resolution (up to 100 m grid spacing) to evaluate the uncertainties due to changing CDNC as a function of horizontal grid resolution. <p>The first case is characterised by moderately intense convective showers forming below an upper-level potential vorticity anomaly, with a low freezing level. The second case, characterised by one persistent stronger storm, is warmer with a deeper boundary layer. The colder case is almost insensitive to even large changes in CDNC, while in the warmer case a change of a factor of 3 in assumed CDNC affects total surface rain rate by 17%. In both cases the sensitivity to CDNC is similar at all grid spacings

Platnick S., M. D. King, S. A. Ackerman, W. P. Menzel, B. A. Baum, J. C. Riedi, and R. A. Frey, 2003: The MODIS cloud products: Algorithms and examples from Terra. IEEE Transactions on Geoscience and Remote Sensing, 41, 459-473, https://doi.org/10.1109/TGRS.2002.808301.10.1109/TGRS.2002.808301

ca58c77901ee7f11bdb92bb064999fa4

http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Ficp.jsp%3Farnumber%3D1196061

http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=1196061The Moderate Resolution Imaging Spectroradiometer (MODIS) is one of five instruments aboard the Terra Earth Observing System (EOS) platform launched in December 1999. After achieving final orbit, MODIS began Earth observations in late February 2000 and has been acquiring data since that time. The instrument is also being flown on the Aqua spacecraft, launched in May 2002. A comprehensive set of remote sensing algorithms for cloud detection and the retrieval of cloud physical and optical properties have been developed by members of the MODIS atmosphere science team. The archived products from these algorithms have applications in climate change studies, climate modeling, numerical weather prediction, as well as fundamental atmospheric research. In addition to an extensive cloud mask, products include cloud-top properties (temperature, pressure, effective emissivity), cloud thermodynamic phase, cloud optical and microphysical parameters (optical thickness, effective particle radius, water path), as well as derived statistics. We will describe the various algorithms being used for the remote sensing of cloud properties from MODIS data with an emphasis on the pixel-level retrievals (referred to as Level-2 products), with 1-km or 5-km spatial resolution at nadir. An example of each Level-2 cloud product from a common data granule (5 min of data) off the coast of South America will be discussed. Future efforts will also be mentioned. Relevant points related to the global gridded statistics products (Level-3) are highlighted though additional details are given in an accompanying paper in this issue.

Quaas J., O. Boucher, and U. Lohmann, 2006: Constraining the total aerosol indirect effect in the LMDZ and ECHAM4 GCMs using MODIS satellite data.Atmos. Chem. Phys.6,947-955,https://doi.org/10.5194/acp-6-947-2006.10.5194/acp-6-947-2006

726bd75efd38510dcc8ebf29103319c2

http%3A%2F%2Fwww.oalib.com%2Fpaper%2F1367249

http://www.atmos-chem-phys.net/6/947/2006/Aerosol indirect effects are considered to be the most uncertain yet important anthropogenic forcing of climate change. The goal of the present study is to reduce this uncertainty by constraining two different general circulation models (LMDZ and ECHAM4) with satellite data. We build a statistical relationship between cloud droplet number concentration and the optical depth of the fine aerosol mode as a measure of the aerosol indirect effect using MODerate Resolution Imaging Spectroradiometer (MODIS) satellite data, and constrain the model parameterizations to match this relationship. We include here empirical formulations for the cloud albedo effect as well as parameterizations of the cloud lifetime effect. When fitting the model parameterizations to the satellite data, consistently in both models, the radiative forcing by the combined aerosol indirect effect is reduced considerably, down to minus;0.5 and minus;0.3 Wmsupminus;2/sup, for LMDZ and ECHAM4, respectively.

Rossow W. B., R. A. Schiffer, 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteor. Soc., 80, 2261-2287, https://doi.org/10.1175/1520-0477(1999)080 <2261:AIUCFI>2.0.CO;2.

10.1175/1520-0477(1999)0802.0.CO;2

15824ccc3c5a569b6ac70270d674cfee

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1999BAMS...80.2261R

http://adsabs.harvard.edu/abs/1999BAMS...80.2261RThis progress report on the International Satellite Cloud Climatology Project (ISCCP) describes changes made to produce new cloud data products (D data), examines the evidence that these changes are improvements over the previous version (C data), summarizes some results, and discusses plans for the ISCCP through 2005. By late 1999 all datasets will be available for the period from July 1983 through December 1997. The most significant changes in the new D-series cloud datasets are 1) revised radiance calibrations to remove spurious changes in the long-term record, 2) increased cirrus detection sensitivity over land, 3) increased low-level cloud detection sensitivity in polar regions, 4) reduced biases in cirrus cloud properties using an ice crystal microphysics model in place of a liquid droplet microphysics model, and 5) increased detail about the variations of cloud properties. The ISCCP calibrations are now the most complete and self-consistent set of calibrations available for all the weather satellite imaging radiometers: total relative uncertainties in the radiance calibrations are estimated to be 5% for visible and 2% for infrared; absolute uncertainties are < 10% and < 3%, respectively. Biases in (detectable) cloud amounts have been reduced to 0.05, except in the summertime polar regions where the bias may still be 0.10. Biases in cloud-top temperatures have been reduced to 2 K for lower-level clouds and 4 K for optically thin, upper-level clouds, except when they occur over lower-level clouds. Using liquid and ice microphysics models reduces the biases in cloud optical thicknesses to 10%, except in cases of mistaken phase identification; most of the remaining bias is caused by differences between actual and assumed cloud particle sizes and the small effects of cloud variations at scales < 5km. Global mean cloud properties averaged over the period July 1983-June 1994 are the following: cloud amount = 0.675 0.012; cloud-top temperature = 261.5 2.8 K; and cloud optical thickness = 3.7 0.3, where the plus-minus values are the rms deviations of global monthly mean values from their long-term average. Long-term, seasonal, synoptic, and diurnal cloud variations are illustrated. The ISCCP dataset quantifies the variations of cloud properties at mesoscale resolution (3 h, 30 km) covering the whole globe for more than a decade, making it possible to study cloud system evolution over whole life cycles, watching interactions with the atmospheric general circulation. Plans for the next decade of the World Climate Research Programme require continuing global observations of clouds and the most practical way to fulfill this requirement is to continue ISCCP until it can be replaced by a more capable system with similar time resolutions and global coverage.

Rotstayn L. D., Y. G. Liu, 2003: Sensitivity of the first indirect aerosol effect to an increase of cloud droplet spectral dispersion with droplet number concentration. J. Climate, 16, 3476-3481, https://doi.org/10.1175/1520-0442(2003)016 <3476:SOTFIA>2.0.CO;2.

a3e13c63d66830a45e7545487c8b7531

http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2003JCli...16.3476R%26amp%3Bdb_key%3DPHY%26amp%3Blink_type%3DABSTRACT

年度引用

Rotstayn L. D., Y. G. Liu, 2005: A smaller global estimate of the second indirect aerosol effect.Geophys. Res. Lett.,32,L05708,https://doi.org/10.1029/2004GL021922.10.1029/2004GL021922

7efbfef72364f6e8d9648f71b849a429

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2004GL021922%2Ffull

http://onlinelibrary.wiley.com/doi/10.1029/2004GL021922/fullGlobal estimates of the indirect aerosol effect much larger than 1 W min magnitude are difficult to reconcile with observations, yet climate models give estimates between -1 and -4.4 W m. We use a climate model with a new treatment of autoconversion to reevaluate the second indirect aerosol effect. We obtain a global-mean value of -0.28 W m, compared to -0.71 W mwith the autoconversion treatment most often used in climate models. The difference is due to (1) the new scheme's smaller autoconversion rate, and (2) an autoconversion threshold that increases more slowly with cloud droplet concentration. The impact of the smaller autoconversion rate shows the importance of accurately modeling this process. Our estimate of the total indirect aerosol effect on liquid-water clouds changes from -1.63 to -1.09 W m.

Sednev I., S. Menon, 2012: Analyzing numerics of bulk microphysics schemes in community models: Warm rain processes.Geoscientific Model Development5,975-987,https://doi.org/10.5194/gmd-5-975-2012.10.5194/gmdd-4-1403-2011

3a4fc4005b54a3d608547965721b1f3f

http%3A%2F%2Fwww.oalib.com%2Fpaper%2F1378523

http://www.geosci-model-dev.net/5/975/2012/In the last decade there has been only one study that discussed time integration scheme (TIS) applied to advance governing differential equations in bulk microphysics (BLK) schemes. Recently, Morrison and Gettelman (2008) examine numerical aspects of double-moment BLK scheme with diagnostic treatment of precipitating hydrometeors implemented into Community Atmosphere Model, version 3 (CAM) to find an acceptable level of accuracy and numerical stability. However, stability condition for their explicit non-positive definite TIS was not defined. lt;brgt;lt;brgt; It is conventionally thought that the Weather Research and Forecasting (WRF) model can be applied for a broad range of spatial scales from large eddy up to global scale simulations if time steps used for model integration satisfy to a certain limit imposed mainly by dynamics. However, numerics used in WRF BLK schemes has never been analyzed in detail. lt;brgt;lt;brgt; To improve creditability of BLK schemes we derive a general analytical stability and positive definiteness criteria for explicit Eulerian time integration scheme used to advanced finite-difference equations that govern warm rain formation processes in microphysics packages in Community models (CAM and WRF) and define well-behaved, conditionally well-behaved, and non-well-behaved Explicit Eulerian Bulk Microphysics Code (EEBMPC) classes. lt;brgt;lt;brgt; We highlight that source codes of BLK schemes, originally developed for use in cloud-resolving models, implemented in Community models belong to conditionally well-behaved EEBMPC class and exhibit better performance for finer spatial resolutions when time steps do not exceed seconds or tenths of seconds. For coarser spatial resolutions used in regional and global scale simulations time steps are usually increased from hundredths up to thousands of seconds. This might lead to a degradation of conditionally well-behaved EEBMPCs ability to calculate the amount of precipitation as well as its spatial and temporal distribution since both stability and positive definiteness conditions are not met in the TIS. The correction through the so called ass conservation technique commonly used in many models with bulk microphysics is a main characteristic of non-well-behaved EEBMPC, whose utilization leads to erroneous conclusions regarding relative importance of different microphysical processes. Moreover, surface boundary conditions for ocean, land, lake, and sea ice models are dependent on the precipitation and its spatial and temporal distribution. Uncertainties in calculations of temporal and spatial patterns of accumulated precipitation influence the global water cycle. In fact, numerics in non-well-behaved EEBMPCs, which are used in Community Earth System Model, act as a hidden climate forcing agent, if relatively long time steps are used for the host model integration. lt;brgt;lt;brgt; By analyzing numerics of warm rain processes in EEBMPCs implemented in Community models we provide general guidelines regarding appropriate choice of integration time steps for use in these models.

Sun H. C., G. Q. Zhou, and Q. C. Zeng, 2012: Assessments of the climate system model (CAS-ESM-C) Using IAP AGCM4 as its atmospheric component.Chinese Journal of Atmospheric Sciences36,215-233,https://doi.org/10.3878/j.issn.1006-9895.2011.11062. (in Chinese)10.1007/s11783-011-0280-z

bc78908b60f0f60b7698076adc7d4ba4

http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-DQXK201202003.htm

http://en.cnki.com.cn/Article_en/CJFDTOTAL-DQXK201202003.htmThis paper assesses the performance of a new climate system model, namely CAS-ESM-C (Chinese Academy of Sciences-Earth System Model-Climate system component), which employs the recently improved version of IAP AGCM, namely IAP AGCM4, as its atmospheric component. This paper first describes the development and framework of the model briefly, and then evaluates the performances of the model in simulating the climate mean states of the atmosphere, land surface, ocean, and sea ice. Some aspects of the seasonal cycle and interannual variability are also analyzed. The results indicate that the CAS-ESM-C succeeds in controlling the long-term climate drift and has acceptable performances in realistically reproducing the climate mean states of the atmosphere, ocean, land surface and sea ice. The CAS-ESM-C also successfully reproduces the seasonal cycle of SST over the tropical Pacific and the seasonal cycle of the sea ice cover in the Arctic. The seasonal migration of monsoon rain band is well reproduced in the model, indicating the acceptable performance of the East Asian monsoon simulation. Except for the slight underestimation of the ENSO period and overestimation of the average amplitude, other characteristics of interannual variability over the tropical Pacific are well reproduced in the CAS-ESM-C. It is particularly important that, benefiting from the realistic simulation of the seasonal cycle of SST over the tropical Pacific, a "phase-locked" phenomenon appears in the simulated ENSO, which is hardly reproduced in other coupled models. The main deficiency of the CAS-ESM-C is the tropic bias, which is common in other coupled models. Some analyses are made to reveal the possible reason behind these simulation biases especially the tropical bias. The results suggest that the biases in the atmosphere which are amplified by the ocean-atmosphere feedback are the key reasons of the tropic bias in the coupled system. According to the analyses of the biases, future improvements of the CAS-ESM-C should focus on the treatment of physical processes of cloud and precipitation in the AGCM. From this point, updating or improving the low-level cloud scheme and the convective parameterization of the atmosphere model may be the first step for the future development of the CAS-ESM-C.

Wang M., S. Ghan, M. Ovchinnikov, X. Liu, R. Easter, E. Kassianov, Y. Qian, and H. Morrison, 2011: Aerosol indirect effects in a multi-scale aerosol-climate model PNNL-MMF.Atmos. Chem. Phys.11,5431-5455,https://doi.org/10.5194/acp-11-5431-2011.10.5194/acpd-11-3399-2011

b051a7e0fe0f09ed35065c66875c1eb8

http%3A%2F%2Fwww.oalib.com%2Fpaper%2F1369275

http://www.atmos-chem-phys.net/11/5431/2011/Much of the large uncertainty in estimates of anthropogenic aerosol effects on climate arises from the multi-scale nature of the interactions between aerosols, clouds and large-scale dynamics, which are difficult to represent in conventional global climate models (GCMs). In this study, we use a multi-scale aerosol-climate model that treats aerosols and clouds across multiple scales to study aerosol indirect effects. This multi-scale aerosol-climate model is an extension of a multi-scale modeling framework (MMF) model that embeds a cloud-resolving model (CRM) within each grid cell of a GCM. The extension allows the explicit simulation of aerosol/cloud interactions in both stratiform and convective clouds on the global scale in a computationally feasible way. Simulated model fields, including liquid water path (LWP), ice water path, cloud fraction, shortwave and longwave cloud forcing, precipitation, water vapor, and cloud droplet number concentration are in agreement with observations. The new model performs quantitatively similar to the previous version of the MMF model in terms of simulated cloud fraction and precipitation. The simulated change in shortwave cloud forcing from anthropogenic aerosols is 610.77 W mlt;supgt;612lt;/supgt;, which is less than half of that in the host GCM (NCAR CAM5) (611.79 W mlt;supgt;612lt;/supgt;) and is also at the low end of the estimates of most other conventional global aerosol-climate models. The smaller forcing in the MMF model is attributed to its smaller increase in LWP from preindustrial conditions (PI) to present day (PD): 3.9% in the MMF, compared with 15.6% increase in LWP in large-scale clouds in CAM5. The much smaller increase in LWP in the MMF is caused by a much smaller response in LWP to a given perturbation in cloud condensation nuclei (CCN) concentrations from PI to PD in the MMF (about one-third of that in CAM5), and, to a lesser extent, by a smaller relative increase in CCN concentrations from PI to PD in the MMF (about 26% smaller than that in CAM5). The smaller relative increase in CCN concentrations in the MMF is caused in part by a smaller increase in aerosol lifetime from PI to PD in the MMF, a positive feedback in aerosol indirect effects induced by cloud lifetime effects. The smaller response in LWP to anthropogenic aerosols in the MMF model is consistent with observations and with high resolution model studies, which may indicate that aerosol indirect effects simulated in conventional global climate models are overestimated and point to the need to use global high resolution models, such as MMF models or global CRMs, to study aerosol indirect effects. The simulated total anthropogenic aerosol effect in the MMF is 611.05 W mlt;supgt;612lt;/supgt;, which is close to the Murphy et al. (2009) inverse estimate of 611.1 ± 0.4 W mlt;supgt;612lt;/supgt; (1σ) based on the examination of the Earths energy balance. Further improvements in the representation of ice nucleation and low clouds are needed.

Wylie D., D. L. Jackson, W. P. Menzel, and J. J. Bates, 2005: Trends in global cloud cover in two decades of HIRS observations.J. Climate18,3021-3031,https://doi.org/10.1175/JCLI3461.1

10.1175/JCLI3461.1

2a40cf27e2c2865c27b7668c35d057e4

http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2005JCli...18.3021W%26amp%3Bdb_key%3DPHY%26amp%3Blink_type%3DABSTRACT

http://journals.ametsoc.org/doi/abs/10.1175/JCLI3461.1

Xie P. P., P. A. Arkin, 1997: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, 2539-2558, https://doi.org/10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2.

Xie X. N., X. D. Liu, 2009: Analytical three-moment autoconversion parameterization based on generalized gamma distribution.J. Geophys. Res.,114,D17201,https://doi.org/10.1029/2008JD011633.10.1029/2008JD011633

f76908191bba0447b36cdd1f81c3cecb

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2008JD011633%2Freferences

http://doi.wiley.com/10.1029/2008JD011633ABSTRACT 1] Autoconversion of cloud droplets to embryonic raindrops is one of the most important microphysical processes in warm clouds. From first principles, a three-moment theoretical expression is analytically derived for the autoconversion rates of the number concentration, mass content, and radar reflectivity based on the generalized gamma distribution function for cloud droplet size distributions. Furthermore, the influence of the liquid water content L, droplet concentration N, shape parameter m, and tail parameter q on the autoconversion rate are investigated, respectively. It is found that the autoconversion rate increases significantly with decreasing value m, no matter how high or low the liquid water content is, but the parameter q only plays an important role at low liquid water content. These results may have many potential applications, especially to studies of the indirect aerosol effect and the influence of m and q on cloud and precipitation.

Xie X. N., X. D. Liu, 2011: Effects of spectral dispersion on clouds and precipitation in mesoscale convective systems.J. Geophys. Res.,116,D06202,https://doi.org/10.1029/2010JD014598.10.1029/2010JD014598

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http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2010JD014598%2Ffull

http://onlinelibrary.wiley.com/doi/10.1029/2010JD014598/full[1] The effects of spectral dispersion on clouds and precipitation in mesoscale convective systems have been studied by conducting 10 numerical simulations with different values of spectral dispersion ( = 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0) in the clean, semipolluted, and polluted backgrounds. The simulation results show that spectral dispersion affects cloud microphysical properties markedly in each aerosol regime. With an increase in spectral dispersion, both the raindrop concentration and the rainwater content increase, while the mean radius of the raindrops diminishes substantially. Moreover, it is found that the effects of spectral dispersion on simulated precipitation differ in these three aerosol backgrounds and relative humidity. In the clean background and at relatively lower humidity, the average accumulated precipitation is reduced significantly with an increase in spectral dispersion. Precipitation varies nonmonotonically in the semipolluted background, increasing with spectral dispersion at smaller values, while decreasing at larger values. In the mean time, precipitation is continuously enhanced with increasing spectral dispersion in the polluted background. Furthermore, sensitivity tests demonstrate that the possible impacts of spectral dispersion on precipitation varies depending on the relative humidity. For instance, at high relative humidity, an increase in spectral dispersion even in a clean atmosphere leads to more precipitation. Our results could shed light on understanding the influences of aerosols on clouds and precipitation, especially the second aerosol indirect effect.

Xie X. N., X. D. Liu, 2013: Analytical studies of the cloud droplet spectral dispersion influence on the first indirect aerosol effect.Adv. Atmos. Sci.,30(5),1313-1319,https://doi.org/10.1007/s00376-012-2141-5.10.1007/s00376-012-2141-5

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http%3A%2F%2Fkns.cnki.net%2FKCMS%2Fdetail%2Fdetail.aspx%3Ffilename%3Ddqjz201305008%26dbname%3DCJFD%26dbcode%3DCJFQ

http://link.springer.com/10.1007/s00376-012-2141-5

Xie X. N., X. D. Liu, 2015: Aerosol-cloud-precipitation interactions in WRF model: sensitivity to autoconversion parameterization.Journal of Meteorological Research29(2),72-81,https://doi.org/10.1007/s13351-014-4065-8.

10.1007/s13351-014-4065-8

ab34eb9203f5cc24275e2dcb50f47689

http%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs13351-014-4065-8

http://link.springer.com/10.1007/s13351-014-4065-8Cloud-to-rain autoconversion process is an important player in aerosol loading, cloud morphology, and precipitation variations because it can modulate cloud microphysical characteristics depending on the participation of aerosols, and affects the spatio-temporal distribution and total amount of precipitation. By applying the Kessler, the Khairoutdinov-Kogan (KK), and the Dispersion autoconversion parameterization schemes in a set of sensitivity experiments, the indirect effects of aerosols on clouds and precipitation are investigated for a deep convective cloud system in Beijing under various aerosol concentration backgrounds from 50 to 10000 cm 3 . Numerical experiments show that aerosol-induced precipitation change is strongly dependent on autoconversion parameterization schemes. For the Kessler scheme, the average cumulative precipitation is enhanced slightly with increasing aerosols, whereas surface precipitation is reduced significantly with increasing aerosols for the KK scheme. Moreover, precipitation varies non-monotonically for the Dispersion scheme, increasing with aerosols at lower concentrations and decreasing at higher concentrations. These different trends of aerosol-induced precipitation change are mainly ascribed to differences in rain water content under these three autoconversion parameterization schemes. Therefore, this study suggests that accurate parameterization of cloud microphysical processes, particularly the cloud-to-rain autoconversion process, is needed for improving the scientific understanding of aerosol-cloud-precipitation interactions.

Xie X. N., X. D. Liu, Y. R. Peng, Y. Wang, Z. G. Yue, and X. Z. Li, 2013: Numerical simulation of clouds and precipitation depending on different relationships between aerosol and cloud droplet spectral dispersion.Tellus B65,19054,https://doi.org/10.3402/tellusb.v65i0.19054.10.3402/tellusb.v65i0.19054

6a72bb608648c3da2bd267d802215bca

http%3A%2F%2Fwww.tandfonline.com%2Fdoi%2Ffull%2F10.3402%2Ftellusb.v65i0.19054

https://www.tandfonline.com/doi/full/10.3402/tellusb.v65i0.19054The aerosol effects on clouds and precipitation in deep convective cloud systems are investigated using the Weather Research and Forecast (WRF) model with the Morrison two-moment bulk microphysics scheme. Considering positive or negative relationships between the cloud droplet number concentration (Nc) and spectral dispersion (#x03B5;), a suite of sensitivity experiments are performed using an initial sounding data of the deep convective cloud system on 31 March 2005 in Beijing under either a maritime (‘clean’) or continental (‘polluted’) background. Numerical experiments in this study indicate that the sign of the surface precipitation response induced by aerosols is dependent on the ε−Nc relationships, which can influence the autoconversion processes from cloud droplets to rain drops. When the spectral dispersion ε is an increasing function of Nc, the domain-average cumulative precipitation increases with aerosol concentrations from maritime to continental background. That may be because the existence of large-sized rain drops can increase precipitation at high aerosol concentration. However, the surface precipitation is reduced with increasing concentrations of aerosol particles when ε is a decreasing function of Nc. For the ε−Nc negative relationships, smaller spectral dispersion suppresses the autoconversion processes, reduces the rain water content and eventually decreases the surface precipitation under polluted conditions. Although differences in the surface precipitation between polluted and clean backgrounds are small for all the ε−Nc relationships, additional simulations show that our findings are robust to small perturbations in the initial thermal conditions.

Xie X. N., H. Zhang, X. D. Liu, Y. R. Peng, and Y. G. Liu, 2017: Sensitivity study of cloud parameterizations with relative dispersion in CAM5.1: Impacts on aerosol indirect effects.Atmos. Chem. Phys.17,5877-5892,https://doi.org/10.5194/acp-17-5877-2017.10.5194/acp-17-5877-2017

f04a79f3c65d676b6dbb492cf8fbfa6b

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2017ACP....17.5877X

https://www.atmos-chem-phys.net/17/5877/2017/Abstract Aerosol-induced increase of relative dispersion of cloud droplet size distribution ε exerts a warming effect and partly offsets the cooling of aerosol indirect radiative forcing (AIF) associated with increased droplet concentration by increasing the cloud droplet effective radius (Re) and enhancing the cloud-to-rain autoconversion rate (Au) (labeled as dispersion effect), which can help reconcile global climate models (GCMs) with the satellite observations. However, the total dispersion effects on both Re and Au are not fully considered in most GCMs, especially in different versions of the Community Atmospheric Model (CAM). In order to accurately evaluate the dispersion effect on AIF, the new complete cloud parameterizations of Re and Au explicitly accounting for ε are implemented into the CAM version 5.1 (CAM5.1), and a suite of sensitivity experiments is conducted with different representations of ε reported in literature. It is shown that the shortwave cloud radiative forcing is much better simulated with the new cloud parameterizations as compared to the standard scheme in CAM5.1, whereas the influences on longwave cloud radiative forcing and surface precipitation are minimal. Additionally, consideration of dispersion effect can significantly reduce the changes induced by anthropogenic aerosols in the cloud top effective radius and the liquid water path, especially in Northern Hemisphere. The corresponding AIF with dispersion effect considered can also be reduced substantially, by a range of 0.10 to 0.21 W m612 at global scale, and by a much bigger margin of 0.25 to 0.39 W m612 for the Northern Hemisphere in comparison with that fixed relative dispersion, mainly dependent on the change of relative dispersion and droplet concentrations (Δε / ΔNc).

Yan Z.-B., Z.-H. Lin, and H. Zhang, 2014: The Relationship between the East Asian Subtropical Westerly Jet and Summer Precipitation over East Asia as Simulated by the IAP AGCM4.0.Atmospheric and Oceanic Science Letters7,487-492,https://doi.org/10.3878/AOSL20140048.

10.1080/16742834.2014.11447212

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http%3A%2F%2Fkns.cnki.net%2FKCMS%2Fdetail%2Fdetail.aspx%3Ffilename%3Daosl201406002%26dbname%3DCJFD%26dbcode%3DCJFQ

http://www.tandfonline.com/doi/full/10.1080/16742834.2014.11447212

Zhang H., Z. H. Lin, and Q. C. Zeng, 2009: The computational scheme and the test for dynamical framework of IAP AGCM-4.Chinese Journal of Atmospheric Sciences33,1267-1285,https://doi.org/10.3878/j.issn.1006-9895.2009.06. 13. (in Chinese)10.1016/S1003-6326(09)60084-4

e48d823d0599b7c54c758ab7fcdca2e4

http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTotal-DQXK200906014.htm

http://en.cnki.com.cn/Article_en/CJFDTotal-DQXK200906014.htmA flexible leaping-point scheme and a time-splitting method are introduced to the new generation of IAP (Institute of Atmospheric Physics, Chinese Academy of Sciences) atmospheric general circulation model (IAP AGCM-4), and the model's dynamical framework is tested by Rossby-Haurwitz (R-H) wave and by Held-Suarez proposal. The results show that the flexible leaping-point scheme can also conserve the available energy without computational chaos, and it can enlarge the time step especially in the model without filter. A time-splitting method is adopted to compute adjustment process and advection process respectively, and a nonlinear iterative time integration scheme with 3 times iteration is applied to both the processes. The time-splitting method can economize CPU time by 10.7% (N=5) and 19.9% (N=10), respectively. As R-H-type pattern of wave number 4 is taken as the initial condition, in the first 80 days of integration, the dynamical framework can preserve the wave pattern well, and the total available energy only reduce 0.1%. From the 80th day, the wave pattern of zonal wind becomes deformed and then breaks, while the corresponding kinetic energy and total available energy begin to decrease sharply. By the 365th day, the fields of zonal wind and geopotential height become parallel to the longitudinal direction, and the total available energy has decreased 8%. The analysis shows that it is due to the rotational adaption and the dissipation of advection term. The test by Held-Suarez proposal also shows the reliability of the dynamical framework of IAP AGCM-4.

Zhang H., M. H. Zhang, and Q.-C. Zeng, 2013: Sensitivity of simulated climate to two atmospheric models: Interpretation of differences between dry models and moist models.Mon. Wea. Rev.,141,1558-1576,https://doi.org/10.1175/MWR-D-11-00367.1.10.1175/MWR-D-11-00367.1

87168da529fac4dfda567e5e3e3ffc68

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2013MWRv..141.1558Z

http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-11-00367.1The dynamical core of the Institute of Atmospheric Physics of the Chinese Academy of Sciences Atmospheric General Circulation Model (IAP AGCM) and the Eulerian spectral transform dynamical core of the Community Atmosphere Model, version 3.1 (CAM3.1), developed at the National Center for Atmospheric Research (NCAR) are used to study the sensitivity of simulated climate. The authors report that when the dynamical cores are used with the same CAM3.1 physical parameterizations of comparable resolutions, the model with the TAP dynamical core simulated a colder troposphere than that from the CAM3.1 core, reducing the CAM3.1 warm bias in the tropical and midlatitude troposphere. However, when the two dynamical cores are used in the idealized Held-Suarez tests without moisture physics, the TAP AGCM core simulated a warmer troposphere than that in CAM3.1. The causes of the differences in the full models and in the dry models are then investigated.The authors show that the TAP dynamical core simulated weaker eddies in both the full physics and the dry models than those in the CAM due to different numerical approximations. In the dry TAP model, the weaker eddies cause smaller heat loss from poleward dynamical transport and thus warmer troposphere in the tropics and midlatitudes. When moist physics is included, however, weaker eddies also lead to weaker transport of water vapor and reduction of high clouds in the IAP model, which then causes a colder troposphere due to reduced greenhouse warming of these clouds. These results show how interactive physical processes can change the effect of a dynamical core on climate simulations between two models.