The behavior of trade-wind cloudiness in observations and models: The major cloud components and their variability

Journal of Advances in Modeling Earth Systems

Volumn 7, Issue 2, 2015, Pages 600-616

  Nuijens, Louise ^a   Medeiros, Brian ^b   Sandu, Irina ^c   Ahlgrimm, Maike ^c  

^a MAX PLANCK INSTITUTE FOR METEOROLOGY   (Germany)

^b NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

^c EUROPEAN CENTRE FOR MEDIUM RANGE WEATHER FORECASTS   (United Kingdom)

Author keywords

climate; clouds

Indexed keywords

CLOUDS; COMMERCE; OPTICAL RADAR;

CLIMATE; CLIMATE PREDICTION; CLOUD COMPONENTS; CLOUD FEEDBACKS; FORECAST SYSTEMS; GROUND BASED RADAR; LIFTING CONDENSATION LEVELS; TOTAL CLOUD COVER;

CLIMATE MODELS;

CLIMATE MODELING; CLIMATE PREDICTION; CLOUD COVER; CONDENSATION; GROUND-BASED MEASUREMENT; INTEGRATED APPROACH; LIDAR; OBSERVATIONAL METHOD; OBSERVATORY; RADAR; STRATOCUMULUS; TRADE WIND; WEATHER FORECASTING;

BARBADOS;

EID: 85027925077     PISSN: None     EISSN: 19422466     Source Type: Journal    
DOI: 10.1002/2014MS000390     Document Type: Article

References (37)

1. Ahlgrimm, M., and, M. Köhler, (2010), Evaluation of trade cumulus in the ECMWF model with observations from CALIPSO, Mon. Weather Rev., 138 (8), 3071-3083, doi: 10.1175/2010MWR3320.1.
2. Bechtold, P., M. Köhler, T. Jung, F. Doblas-Reyes, M. Leutbecher, M. J. Rodwell, F. Vitart, and, G. Balsamo, (2008), Advances in simulating atmospheric variability with the ECMWF model: From synoptic to decadal time-scales, Q. J. R. Meteorol. Soc., 134 (634), 1337-1351, doi: 10.1002/qj.289.
3. Bellon, G., and, B. Stevens, (2013), Timescales of the trade-wind boundary layer adjustment, J. Atmos. Sci., 70, 1071-1083.
4. Bony, S., and, J.-L. Dufresne, (2005), Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models, Geophys. Res. Lett., 32, L20806, doi: 10.1029/2005GL023851.
5. Bony, S., and, K. A. Emanuel, (2001), A parameterization of the cloudiness associated with cumulus convection; evaluation using TOGA COARE data, J. Atmos. Sci., 58, 3158-3183.
6. Brueck, H. M., L. Nuijens, and, B. Stevens, (2015), On the seasonal and synoptic time-scale variability of the North Atlantic trade wind region and its low-level clouds, J. Atmos. Sci., 72, 1428-1446.
7. de Rooy, W. C., P. Bechtold, K. Fröhlich, C. Hohenegger, H. Jonker, D. Mironov, A. Pier Siebesma, J. Teixeira, and, J.-I. Yano, (2013), Entrainment and detrainment in cumulus convection: an overview, Q. J. R. Meteorol. Soc., 139 (670), 1-19, doi: 10.1002/qj.1959.
8. Emanuel, K., (1993), A cumulus representation based on the episodic mixing model: the importance of mixing and microphysics in predicting humidity, in The Representation of Cumulus Convection in Numerical Models, Meteorol. Monogr., edited by K. Emanuel, and D. J. Raymond, vol. 24, chap. 19, pp. 185-194, Am. Meteorol. Soc., Boston, Mass.
9. Gettelman, A., et al., (2010), Multi-model assessment of the upper troposphere and lower stratosphere: Tropics and global trends, J. Geophys. Res., 115, D00M09, doi: 10.1029/2009JD013638.
10. Gregory, D., and, P. R. Rowntree, (1990), A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure, Mon. Weather Rev., 118 (7), 1483-1506, doi: 10.1175/1520-0493(1990)118 <1483:AMFCSW>2.0.CO;2.
11. Lock, A. P., (2009), Factors influencing cloud area at the capping inversion for shallow cumulus clouds, Q. J. R. Meteorol. Soc., 952, 941-952, doi: 10.1002/qj.424.
12. Ma, H.-Y., et al., (2013), On the correspondence between mean forecast errors and climate errors in CMIP5 models, J. Clim., 27 (4), 1781-1798, doi: 10.1175/JCLI-D-13-00474.1.
13. Malkus, J. S., (1958), On the structure of the trade wind moist layer, Pap. Phys. Oceanogr. Meteorol., 13 (2), 1-47.
14. McFarlane, N., J. F. Scinocca, M. Lazare, R. Harvey, D. Verseghy, and, J. Li, (2005), The CCCma third generation atmospheric general circulation model (AGCM3), CCCma internal report, Can. Cent. for Clim. Modell. and Anal., 25 pp., Univ. of Victoria.
15. Medeiros, B., and, B. Stevens, (2011), Revealing differences in GCM representations of low clouds, Clim. Dyn., 36, 385-399.
16. 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, L21801, doi: 10.1029/2012GL053421.
17. Neggers, R., B. Stevens, and, J. D. Neelin, (2006), A simple equilibrium model for shallow-cumulus-topped mixed layers, Theor. Comput. Fluid Dyn., 20 (5-6), 305-322, doi: 10.1007/s00162-006-0030-1.
18. Nitta, T., and, S. Esbensen, (1974), Heat and moisture budget analyses using BOMEX data, Mon. Weather Rev., 102, 17-28.
19. Nuijens, L., and, B. Stevens, (2012), The influence of wind speed on shallow marine cumulus convection, J. Atmos. Sci., 69 (1), 168-184, doi: 10.1175/JAS-D-11-02.1.
20. Nuijens, L., I. Serikov, L. Hirsch, K. Lonitz, and, B. Stevens, (2014), The distribution and variability of low-level cloud in the North Atlantic trades, Q. J. R. Meteorol. Soc., 140, 2364-2374, doi: 10.1002/qj.2307.
21. Rasch, P. J., and, J. E. Kristjansson, (1998), A comparison of the CCM3 model climate using diagnosed and predicted condensate parameterizations, J. Clim., 11, 1587-1614.
22. Rauber, R. M., et al., (2007), In the driver's seat: Rico and education, Bull. Am. Meteorol. Soc., 88 (12), 1929-1937, doi: 10.1175/BAMS-88-12-1929.
23. Ricard, J. L., and, J. F. Royer, (1993), A statistical cloud scheme for use in an AGCM, Ann. Geophys., 11, 1095-1115.
24. Serikov, I., and, S. Bobrovnikov, (2010), Atmospheric temperature profiling with pure rotational Raman lidars, in Recent Advances in Atmospheric Lidars, edited by L. Fiorani and V. Mitev, Optoelectronic materials and devices, vol. 7, INOE, 149-216.
25. Sherwood, S. C., S. Bony, and, J.-L. Dufresne, (2014), Spread in model climate sensitivity traced to atmospheric convective mixing, Nature, 505 (7481), 37-42, doi: 10.1038/nature12829.
26. Siebesma, A. P., et al., (2003), A large eddy simulation intercomparison study of shallow cumulus convection, J. Atmos. Sci., 60 (10), 1201-1219, doi: 10.1175/1520-0469(2003)60 <1201:ALESIS>2.0.CO;2.
27. Slingo, J. M., (1987), The development and verification of a cloud prediction scheme for the ECMWF model, Q. J. R. Meteorol. Soc., 113, 899-927.
28. Stevens, B., (2006), Bulk boundary-layer concepts for simplified models of tropical dynamics, Theor. Comput. Fluid Dyn., 20 (5-6), 279-304, doi: 10.1007/s00162-006-0032-z.
29. Sundqvist, H., E. Berge, and, J. E. Kristjansson, (1989), Condensation and cloud parameterization studies with a mesoscale numerical weather prediction model, Mon. Weather Rev. 117, 1641-1657.
30. Tiedtke, M., (1989), A comprehensive mass flux scheme for cumulus parameterization in large-scale models, Mon. Weather Rev., 117, 1779-1800.
31. Tiedtke, M., (1993), Representation of clouds in large-scale models, Mon. Weather Rev., 121, 3040-3061.
32. VanZanten, M. C., et al., (2011), Controls on precipitation and cloudiness in simulations of trade-wind cumulus as observed during RICO, J. Adv. Model. Earth Syst., 3, M06001, doi: 10.1029/2011MS000056.
33. Vial, J., J. L. Dufresne, and, S. Bony, (2013), On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates, Clim. Dyn., 41, 3339-3362, doi: 10.1007/s00382-013-1725-9.
34. von Salzen, K., N. McFarlane, and, M. Lazare, (2005), The role of shallow convection in the water and energy cycles of the atmosphere, Clim. Dyn., 25 (7-8), 671-688, doi: 10.1007/s00382-005-0051-2.
35. Wu, T., R. Yu, F. Zhang, Z. Wang, M. Dong, L. Wang, X. Jin, D. Chen, and, L. Li, (2010), The Beijing Climate Center atmospheric general circulation model: Description and its performance for the present-day climate, Clim. Dyn., 34 (1), 123-147, doi: 10.1007/s00382-008-0487-2.
36. Yukimoto, S., et al., (2012), A new global climate model of the meteorological research institute: Mri-cgcm3 model description and basic performance, J. Meteorol. Soc. Jpn., 90A, 23-64, doi: 10.2151/jmsj.2012-A02.
37. Zhang, G. J., and, N. A. McFarlane, (1995), Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian climate centre general circulation model, Atmos. Ocean, 33 (3), 407-446, doi: 10.1080/07055900.1995.9649539.