Application the Cloud Feedback Model Intercomparison Project (cfmip) to become a cmip6-Endorsed mip

Yüklə 160.65 Kb.
ölçüsü160.65 Kb.
1   2   3   4   5   6

2.6Change #6: Add MODIS cloud fractions (total, liquid, ice) to cfMonExtra (proposed by Robert Pincus)

The partitioning between liquid and ice phase has significant impacts on the energy and hydrologic impacts of clouds. As models move towards predicting more details of the aerosol distributions, including the ice nucleation ability, evaluation of the phase partitioning on the global scale will become more important. Evaluation to date has been based primarily on polarization measurements from active and passive sensors [e.g. Doutriaux-Boucher and Quaas, 2004; Komurcu et al., 2014] and height-resolved partitioning estimates from the CALIPSO sensor are requested below. Cloud phase estimates from the MODIS simulator were not available in CFMIP2 but may prove a useful complement by virtue of greater geographic sampling and longer time records.

2.7Change #7: MODIS COT-particle size histograms by phase in cfMonExtra, cfDayExtra, cf3hr (proposed by Robert Pincus)

The joint distribution of optical thickness and particle size provides a window on the microphysical processes within clouds [Nakajima et al., 1991] and is influenced by direct and some indirect effects of aerosols on cloud optical properties [Han et al. 2002]. As models move towards predicting more details of the aerosol properties and cloud-aerosol interactions the assessment of these processes becomes more pressing.

Estimate of particle size from MODIS have been difficult to use for model evaluation to date because of observational artefacts not treated by the MODIS simulator. These artefacts are reduced by the use of observations at wavelengths with greater absorption by condensed water (e.g. by exploiting reflectance at 3.7 µm instead of 2.1 µm). The MODIS simulator and accompanying data for CFMIP3 will use measurements at 3.7 µm to infer particle size. This will also act to make output from the MODIS simulator roughly consistent with the PATMOS-X observations in the same way that distributions of optical thickness from the MODIS, MISR, and ISCCP simulators are nearly equivalent.

2.8Change #8: add CALIPSO ice and liquid 3D cloud fractions to cfMonExtra (proposed by Hélène Chepfer)

Changes in cloud optical depth associated with cloud phase feedbacks can dominate the changes in high-latitude clouds in future climate projections [e.g. Senior and Mitchell, 1993]. Cloud phase identification capabilities have been recently added to the CALIPSO simulator in COSP, and a compatible observational dataset has been produced [Cesana and Chepfer, 2013]. We propose to include these in the AMIP DECK experiment to support the evaluation of the simulation of cloud phase.

2.9Change #9: CALIPSO total cloud fraction and PARASOL reflectance to cfDayExtra (proposed by Hélène Chepfer and Dimitra Konsta)

The multi-sensor A-train observations (CALIPSO-GOCCP and MODIS, PARASOL) allow to make the correlations between the different cloud variables at the instantaneous time scale, and at high resolution. The use of the high-frequency relationships between different variables allows for process-oriented model evaluation. These diagnostics will help test the realism of the co-variation of key cloud properties that control cloud feedbacks in models. Konsta at al. (2014) have used these diagnostics in a pilot analysis.

3References Using Satellite Simulators for the Evaluation of Clouds in Models (partial)

Bodas-Salcedo, A. et al., 2008: Evaluating cloud systems in the Met Office global forecast model using simulated CloudSat radar reflectivities, J. Geophys. Res., 113, D00A13. DOI: 10.1029/2007JD009620.

Bodas-Salcedo, A., et al., 2011: Satellite simulation software for model assessment. Bull. Am. Meteorol. Soc., 92. DOI: 10.1175/2011BAMS2856.1.

Bodas-Salcedo, A., et al., 2012: The surface downwelling solar radiation surplus over the Southern Ocean in the Met Office model: the role of midlatitude cyclone clouds, J. Climate, 25. DOI: 10.1175/JCLI-D-11-00702.1.

Bodas-Salcedo, A., et al., 2014: Origins of the Solar Radiation Biases over the Southern Ocean in CFMIP2 Models, J. Climate, 27. DOI: 10.1175/JCLI-D-13-00169.1.

Cesana, G., and Chepfer, H., 2012: How well do climate models simulate cloud vertical structure? A comparison between CALIPSO-GOCCP satellite observations and CMIP5 models, Geophys. Res. Let. DOI: 10.1029/2012GL053153.

Cesana, G., and Chepfer, H., 2013: Evaluation of the cloud thermodynamic phase in a climate model using CALIPSO-GOCCP, J. Geophys. Res., 118, 7922–7937. DOI: 10.1002/jgrd.50376.

Doutriaux-Boucher, M., and J. Quaas, 2004: Evaluation of cloud thermodynamic phase parametrizations in the LMDZ GCM by using POLDER satellite data, Geophys. Res. Lett., 31, L06126. DOI: 10.1029/2003GL019095.

Field, P. R., et al., 2011: Using model analysis and satellite data to assess cloud and precipitation in midlatitude cyclones, Q. J. R. Meteorol. Soc., 137, 1501-1515. DOI: 10.1002/qj.858.

Franklin, C. N., et al., 2013: Evaluation of clouds in ACCESS using the satellite simulator package COSP: Global, seasonal, and regional cloud properties, J. Geophys. Res.. DOI: 10.1029/2012JD018469.

Hillman, B., R. Marchand, T. P. Ackerman, A.Bodas-Salcedo, J. Cole, J.-C. Golaz, J. E. Kay, 2014: Comparing Cloud Biases in CMIP5: Insights Using MISR and ISCCP Observations and Satellite Simulators, in preparation.

Kay, J. E., et al., Exposing Global Cloud Biases in the Community Atmosphere Model (CAM) Using Satellite Observations and Their Corresponding Instrument Simulators, J. Climate, 25, 2012. DOI: 10.1175/JCLI-D-11-00469.1.

Klein, S. A. et al., 2013: Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator, J. Geophys. Res., 118. DOI: 10.1002/jgrd.50141.

Kodama, C., et al., 2012: An assessment of the cloud signals simulated by NICAM using ISCCP, CALIPSO, and CloudSat satellite simulators, J. Geophys. Res, 117. DOI: 10.1029/2011JD017317.

Komurcu, M., T. Storelvmo, I. Tan, U. Lohmann, Y. Yun, J. E. Penner, Y. Wang, X. Liu, and T. Takemura ,2014: Intercomparison of the cloud water phase among global climate models, J. Geophys. Res., 119, 3372–3400. DOI:10.1002/2013JD021119.

Konsta, D., J-L. Dufresne, H. Chepfer, A. Idelkadi and G. Cesana, 2014: Evaluation of clouds simulated by the LMDZ5 GCM using A-train satellite observations (CALIPSO, PARASOL, CERES). Climate Dynamics, under review.

Lacagnina, C., and Selten, F., 2014: Evaluation of clouds and radiative fluxes in the EC-Earth general circulation model, Clim. Dyn. DOI: 10.1007/s00382-014-2093-9.

Marchand, R., et al., 2009: A comparison of simulated cloud radar output from the multiscale modeling framework global climate model with CloudSat cloud radar observations, J. Geophys. Res., 114, D00A20. DOI: 10.1029/2008JD009790.

Marchand, R., T. Ackerman, M. Smyth, and W. B. Rossow ,2010: A review of cloud top height and optical depth histograms from MISR, ISCCP, and MODIS, J. Geophys. Res., 115, D16206. DOI:10.1029/2009JD013422.

Nam, C., S. Bony, J.-L. Dufresne, and H. Chepfer, 2012: The "too few, too bright" tropical low-cloud problem in CMIP5 models, Geophys. Res. Lett., 39. DOI:10.1029/2012GL053421.

Nam, C. C. W., and Quaas, J., 2012: Evaluation of Clouds and Precipitation in the ECHAM5 General Circulation Model Using CALIPSO and CloudSat Satellite Data. I, J. Climate, 25, 4975-4992. DOI:10.1175/JCLI-D-11-00347.1.

Nam, C. C. W. and Quaas, J., 2013: Geographically versus dynamically defined boundary layer cloud regimes and their use to evaluate general circulation model cloud parameterizations, Geophys. Res. Let. DOI: 10.1002/grl.50945.

Nam, C. W. W., et al., 2014: Evaluation of boundary layer cloud parametrizations in the ECHAM5 general circulation model using CALIPSO and CloudSat satellite data. JAMES. DOI: 10.1002/2013MS000277.

Tsushima, Y. et al., 2013: Quantitative evaluation of the seasonal variations in climate model cloud regimes, Clim. Dyn., 41. DOI: 10.1007/s00382-012-1609-4.

Williams, K. D., et al., 2013: The Transpose-AMIP II experiment and its application to the understanding of Southern Ocean cloud biases in climate models, J. Climate, 26, 3258-3274. DOI: 10.1175/JCLI-D-12-00429.1.

Zelinka, M. D., et al., 2012a: Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part I: Cloud Radiative Kernels. J. Climate. DOI: 10.1175/JCLI-D-11-00248.1.

Zelinka, M. D., et al., 2012b: Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part II: Attribution to Changes in Cloud Amount, Altitude, and Optical Depth. J. Climate. DOI: 10.1175/JCLI-D-11-00249.

Zelinka, M. D., et al., 2013: Contributions of Different Cloud Types to Feedbacks and Rapid Adjustments in CMIP5, J. Climate. DOI: 10.1175/JCLI-D-12-00555.1.

Zelinka, M. D., et al., 2014: Quantifying Components of Aerosol-Cloud-Radiation Interactions in Climate Models. J. Geophys. Res. DOI: 10.1002/2014JD021710.

1   2   3   4   5   6

Verilənlər bazası müəlliflik hüququ ilə müdafiə olunur © 2016
rəhbərliyinə müraciət

    Ana səhifə