temperature and help trigger large thunderstorms (weather effects). They also affect large-scale albedo and surface evaporation (climate effects), so they may play a role in the response of the climate system to changing greenhouse gas concentrations and aerosols on decadal and longer time scales. As a second example, it has become common practice within the past decade for weather prediction models used for forecasting tropical cyclones to employ a dynamical or mixed-layer model of the upper ocean (a traditional climate model component). Such examples have led to increasing recognition that because climate and weather share many of the same underlying physical processes, a more unified approach to model development and application could have many advantages.
For climate models, benefits of a more unified approach include the capability for more rigorous testing and improvement of parameterizations of “fast” physical processes that interact with weather. For example, biases in clouds and uncertainties in the response of clouds to changing greenhouse gases and aerosols are major challenges in projecting climate change over the next century. Biases in clouds appear very rapidly in climate simulations, often within the first days or weeks of a simulation. Therefore, it is appealing to test new parameterizations for clouds in a weather context, where relatively short simulations, initialized from the observed state of the climate system, can provide a rapid assessment of the strengths and weaknesses of model parameterizations.
To this end, one can test weather or climate simulation models in hindcast mode against the large data set of observed past weather variations. Such testing can be done using an initialization from another model. In this report, this testing is referred to as “seamless prediction” but not “unified modeling,” because it does not necessarily require a data assimilation capability that a unified weather-climate forecast model should have to make real-time forecasts. Over the past decade, this approach has started to gain popularity following the development of software infrastructure such as the Climate Change Science Program-Atmospheric Radiation Measurement Parameterization Testbed (Phillips et al., 2004) to support initialization of the atmospheric component of climate models from gridded reanalyses. For instance, Hannay et al. (2009) and Wyant et al. (2010) used global hindcasts by the National Center for Atmospheric Research (NCAR) and Geophysical Fluid Dynamics Laboratory (GFDL) climate models to evaluate their simulation of subtropical boundary layer clouds in specific regions against satellite and in situ observations. The Year of Tropical Convection Madden-Julian Oscillation (MJO) Task Force1 is coordinating multiweek global climate-