Welcome to CGD

expand   collapse

 

Summary of achievements

description

Publications

Li, J.-L. F., D. Waliser, C. Woods, J. Teixeira, J. Bacmeister, J. Chern, B.-W. Shen, A. Tompkins, W.-K. Tao, and M. Köhler, 2008: Comparisons of satellites liquid water estimates to ECMWF and GMAO analyses, 20th century IPCC AR4 climate simulations, and GCM simulations Geophys. Res. Lett., 35, L19710, doi:10.1029/2008GL035427.

Figure 1: High resolution figure

Abstract: To assess the fidelity of general circulation models (GCMs) in simulating cloud liquid water, liquid water path (LWP) retrievals from several satellites with passive sensors and the vertically-resolved liquid water content (LWC) from the CloudSat are used. Comparisons are made with ECMWF and MERRA analyses, GCM simulations utilized in the IPCC 4th Assessment, and three GCM simulations. There is considerable disagreement amongst the LWP estimates and amongst the modeled values. The LWP from GCMs are much larger than the observed estimates and the two analyses. The largest values in the CloudSat LWP occur over the boundary-layer stratocumulus regions; this feature is not as evident in the analyses or models. Better agreement is found between the two analyses and CloudSat LWP when cases with surface precipitation are excluded. The upward vertical extent of LWC from the GCMs and analyses is greater than CloudSat estimates. The issues of representing LWC and precipitation consistently between satellite-derived and model values are discussed.

Figure caption: Multi-year mean zonal average values of cloud liquid water content (LWC; mg m-3) from (a) CloudSat (8/2006–7/2007) for total LWC, (b) LWC associated with precipitation at the surface, (c) non-precipitating LWC, (d) NASA GMAO/MERRA (01/1979–10/1979), (e) ECMWF R30 analysis (08/2005–07/2006), (f) GEOS5 AGCM (01/1999–12/2002), (g) NCAR CAM3 (1979–1999), and (h) fvMMF (01/2005–12/2006).

---

Waliser, D.E., J-L.F. Li, C.P. Woods, R.T. Austin, J. Bacmeister, J. Chern, A. Del Genio, J.H. Jiang, Z. Kuang, H. Meng, P. Minnis, S. Platnick, W.B. Rossow, G.L. Stephens, S. Sun-Mack, W-K. Tao, A.M. Tompins, D.G. Vane, C. Walker and D. Wu. 2009: Cloud ice: A climate model challenge with signs and expectations of progress. J. Geophys. Res., 114, D00A21, doi:10.1029/2008JD010015.

Figure 2: High resolution figure

Abstract: Present-day shortcomings in the representation of upper tropospheric ice clouds in general circulation models (GCMs) lead to errors in weather and climate forecasts as well as account for a source of uncertainty in climate change projections. An ongoing challenge in rectifying these shortcomings has been the availability of adequate, high-quality, global observations targeting ice clouds and related precipitating hydrometeors. In addition, the inadequacy of the modeled physics and the often disjointed nature between model representation and the characteristics of the retrieved/observed values have hampered GCM development and validation efforts from making effective use of the measurements that have been available. Thus, even though parameterizations in GCMs accounting for cloud ice processes have, in some cases, become more sophisticated in recent years, this development has largely occurred independently of the global-scale measurements. With the relatively recent addition of satellite-derived products from Aura/Microwave Limb Sounder (MLS) and CloudSat, there are now considerably more resources with new and unique capabilities to evaluate GCMs. In this article, we illustrate the shortcomings evident in model representations of cloud ice through a comparison of the simulations assessed in the Intergovernmental Panel on Climate Change Fourth Assessment Report, briefly discuss the range of global observational resources that are available, and describe the essential components of the model parameterizations that characterize their "cloud'' ice and related fields. Using this information as background, we (1) discuss some of the main considerations and cautions that must be taken into account in making model-data comparisons related to cloud ice, (2) illustrate present progress and uncertainties in applying satellite cloud ice (namely from MLS and CloudSat) to model diagnosis, (3) show some indications of model improvements, and finally (4) discuss a number of remaining questions and suggestions for pathways forward.

Figure caption: (left) Annual mean values of cloud ice water content (IWC; mg m-3) at 215 hPa and (right) zonal average. Values from (a, c) MLS are from 2007, and those from (b, d) CloudSat are from August 2006 to July 2007. For Figure 5c, MLS retrievals only extend down to 261 hPa (dotted line); inset shows same MLS data as larger panel but with different color scale.

---

B. Mapes, J. Bacmeister, M. Khairoutdinov, C. Hannay and M. Zhao. 2009: Virtual Field Campaigns on Deep Tropical Convection in Climate Models. J. Climate, 22, p. 244-257, doi:10.1175/2008JCLI2203.1.

Figure 2: High resolution figure

Abstract: High-resolution time-height data over warm tropical oceans are examined, from three global atmosphere models [GFDL's Atmosphere Model 2 (AM2), NCAR's Community Atmosphere Model, version 3 (CAM3), and a NASA Global Modeling and Assimilation Office (GMAO) model], field campaign observations, and observation-driven cloud model outputs. The character of rain events is shown in data samples and summarized in lagged regressions versus surface rain rate. The CAM3 humidity and cloud exhibit little vertical coherence among three distinct layers, and its rain events have a short characteristic time, reflecting the convection scheme's penetrative nature and its closure's concentrated sensitivity to a thin boundary layer source level. In contrast, AM2 rain variations have much longer time scales as convection scheme plumes whose entrainment gives them tops below 500 hPa interact with humidity variations in that layer. Plumes detraining at model levels above 500 hPa are restricted by cloud work function thresholds, and upper-tropospheric humidity and cloud layers fed by these are detached from the lower levels and are somewhat sporadic. With these discrete entrainment rates and instability thresholds, AM2 also produces some synthetic-looking noise (sharp features in height and time) on top of its slow rain variations. A distinctive feature of the NASA model is a separate anvil scheme, distinct from the main large-scale cloud scheme, fed by relaxed Arakawa-Schubert (RAS) plume ensemble convection (a different implementation than in AM2). Its variability is rich and vertically coherent, and involves a very strong vertical dipole component to its tropospheric heating variations, of both signs (limited-depth convective heating and top-heavy heating in strong deep events with significant nonconvective rain). Grid-scale saturation events occur in all three models, often without nonconvective surface rain, causing relatively rare episodes of large negative top-of-atmosphere cloud forcing. Overall, cloud forcing regressions show a mild net positive forcing by rain-correlated clouds in CAM3 and mild net cooling in the other models, as the residual of large canceling shortwave and longwave contributions.

Figure caption: (a), (b) KWAJEX observations and (c), (d) observation-driven model outputs from a 3D periodic cloud model forced by large-scale advection terms. Gray panels show relative humidity RH, cloud condensate [cyan, not available in (a), contour values are 2, 20, 40, 60, …, 200 mg kg-1], rainfall time series (green, axis at right), and T deviations from the initial state (red dotted contours 3, 4, and 5 K). Colored panels show budget-derived apparent heat source Q1 and cloud fraction if available [(black contours, not available in (b)]; 32 days in July–August 1999 are shown.

---

Ott, L.E. S. Pawson, M. Suarez, J.T. Bacmeister, K. Pickering, G. Stenchikov, H. Huntrieser, M. Loewenstein, J. Lopez and I. Xueref-Remy. 2009: Analysis of Convective Transport and Parameter Sensitivity in a Single Column Version of the Goddard Earth Observation System, Version 5, General Circulation Model. Journal of Atmospheric Research, 66, p. 627-646, doi:10.1175/2008JAS2694.1.

Figure 2: High resolution figure

Abstract: Convection strongly influences the distribution of atmospheric trace gases. General circulation models (GCMs) use convective mass fluxes calculated by parameterizations to transport gases, but the results are difficult to compare with trace gas observations because of differences in scale. The high resolution of cloud-resolving models (CRMs) facilitates direct comparison with aircraft observations. Averaged over a sufficient area, CRM results yield a validated product directly comparable to output from a single global model grid column. This study presents comparisons of vertical profiles of convective mass flux and trace gas mixing ratios derived from CRM and single column model (SCM) simulations of storms observed during three field campaigns. In all three cases, SCM simulations underpredicted convective mass flux relative to CRM simulations. As a result, the SCM simulations produced lower trace gas mixing ratios in the upper troposphere in two of the three storms than did the CRM simulations.

The impact of parameter sensitivity in the moist physics schemes employed in the SCM has also been examined. Statistical techniques identified the most significant parameters influencing convective transport. Convective mass fluxes are shown to be strongly dependent on chosen parameter values. Results show that altered parameter settings can substantially improve the comparison between SCM and CRM convective mass flux. Upper tropospheric trace gas mixing ratios were also improved in two storms. In the remaining storm, the SCM representation of CO2 was not improved because of differences in entrainment and detrainment levels in the CRM and SCM simulations.

Figure caption: Radar reflectivity at 0.5 km calculated from MM5 simulated hydrometeor fields at 2230 UTC during the 3 Jul CRYSTAL-FACE storm.