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.

The Madden–Julian oscillation (MJO) interacts with and influences a wide range of weather and climate phenomena (e.g., monsoons, ENSO, tropical storms, midlatitude weather), and represents an important, and as yet unexploited, source of predictability at the subseasonal time scale. Despite the important role of the MJO in climate and weather systems, current global circulation models (GCMs) exhibit considerable shortcomings in representing this phenomenon. These shortcomings have been documented in a number of multimodel comparison studies over the last decade. However, diagnosis of model performance has been challenging, and model progress has been difficult to track, because of the lack of a coherent and standardized set of MJO diagnostics. One of the chief objectives of the U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group is the development of observation-based diagnostics for objectively evaluating global model simulations of the MJO in a consistent framework. Motivation for this activity is reviewed, and the intent and justification for a set of diagnostics is provided, along with specification for their calculation, and illustrations of their application. The diagnostics range from relatively simple analyses of variance and correlation to more sophisticated space–time spectral and empirical orthogonal function analyses. These diagnostic techniques are used to detect MJO signals, to construct composite life cycles, to identify associations of MJO activity with the mean state, and to describe interannual variability of the MJO.

The representation of tropical convection remains a serious challenge to the skillfulness of our weather and climate prediction systems. To address this challenge, the World Climate Research Programme (WCRP) and The Observing System Research and Predictability Experiment (THORPEX) of the World Weather Research Programme (WWRP) are conducting a joint research activity consisting of a focus period approach along with an integrated research framework tailored to exploit the vast amounts of existing observations, expanding computational resources, and the development of new, high-resolution modeling frameworks. The objective of the Year of Tropical Convection (YOTC) is to use these constructs to advance the characterization, modeling, parameterization, and prediction of multiscale tropical convection, including relevant two-way interactions between tropical and extratropical systems. This article highlights the diverse array of scientifically interesting and socially important weather and climate events associated with the WCRP–WWRP/THORPEX YOTC period of interest: May 2008–April 2010. Notable during this 2-yr period was the change from cool to warm El Niño– Southern Oscillation (ENSO) states and the associated modulation of a wide range of smaller time- and space-scale tropical convection features. This period included a near-record-setting wet North American monsoon in 2008 and a very severe monsoon drought in India in 2009. There was also a plethora of tropical wave activity, including easterly waves, the Madden–Julian oscillation, and convectively coupled equatorial wave interactions. Numerous cases of high-impact rainfall events occurred along with notable features in the tropical cyclone record. The intent of this article is to highlight these features and phenomena, and in turn promote their interrogation via theory, observations, and models in concert with the YOTC program so that improved understanding and pre- dictions of tropical convection can be afforded.

[1] Climate models often ignore the radiative impact of precipitating hydrometeors. CloudSat retrievals provide the first means to distinguish between cloud versus precipitating ice mass and characterize its vertical structure. With this information, radiative transfer calculations are performed to examine the impact of excluding precipitating ice on atmospheric radiative fluxes and heating rates. The preliminary results show that such exclusion can result in underestimates of the reflective shortwave flux at the top of the atmosphere (TOA) and overestimates of the downwelling surface shortwave and emitted TOA longwave flux, with the differences being about 5–10 Wm⁻² in the most convective and rainfall intensive areas and greatest for the TOA longwave flux. There are also considerable differences (∼10–25%) in the vertical profiles of shortwave and longwave heating, resulting in an overestimation (∼up to 10%) of the integrated column cooling. The implications of these results are that models that exclude these ice components are achieving TOA radiation balance through compensating errors as well as possibly introducing biases in atmospheric circulations.

This study compares the tropical intraseasonal oscillation (TISO) variability in the Geophysical Fluid Dynamics Laboratory (GFDL) coupled general circulation model (CGCM) and the stand-alone atmospheric general circulation model (AGCM). For the AGCM simulation, the sea surface temperatures (SSTs) were specified using those from the CGCM simulation. This was done so that any differences in the TISO that emerged from the two simulations could be attributed to the coupling process and not to a difference in the mean background state. The comparison focused on analysis of the rainfall, 200-mb velocity potential, and 850-mb zonal wind data from the two simulations, for both summer and winter periods, and included comparisons to analogous diagnostics using NCEP–NCAR reanalysis and Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) rainfall data. The results of the analysis showed three principal differences in the TISO variability between the coupled and uncoupled simulations. The first was that the CGCM showed an improvement in the spatial variability associated with the TISO mode, particularly for boreal summer. Specifically, the AGCM exhibited almost no TISO variability in the Indian Ocean during boreal summer—a common shortcoming among AGCMs. The CGCM, on the other hand, did show a considerable enhancement in TISO variability in this region for this season. The second was that the wavenumber–frequency spectra of the AGCM exhibited an unrealistic peak in variability at low wavenumbers (1–3, depending on the variable) and about 3 cycles yr−1 (cpy). This unrealistic peak of variability was absent in the CGCM, which otherwise tended to show good agreement with the observations. The third difference was that the AGCM showed a less realistic phase lag between the TISO-related convection and SST anomalies. In particular, the CGCM exhibited a near-quadrature relation between precipitation and SST anomalies, which is consistent with observations, while the phase lag was reduced in the AGCM by about 1.5 pentads (∼1 week). The implications of the above results, including those for the notions of “perfect SST” and “two tier” experiments, are discussed, as are the caveats associated with the study's modeling framework and analysis.