Global and regional climate sensitivity is defined primarily by feedbacks to the planets’ hydrological cycle that in turn alter the planet’s energy balance. These feedbacks involve processes that are ‘fast’ such as cloud and precipitation processes, evolving within the ‘weather envelope’, or slow such as changes related to land cover, carbon exchanges or ice sheet extent that evolve within the ‘climate envelope’. Research within CIRA and in conjunction with the ATS faculty has made important contributions to the observing, understanding and modeling of important processes key to improving numerical weather/climate prediction.

The severe decrease in Arctic sea ice in 2007 underscores the delicate connections between changing weather and climate trends (e.g. Kay et al., 2008). Shifts in weather processes, such as the drying of the atmosphere and the clearing of Arctic clouds (perhaps associated with decadal oscillations), accelerated the sea ice loss and amplified the slower climate forcing effects of sea ice. As another example, predictions of long-term trends in air quality must consider not only emissions projections but also the affect that climate change will have on regional to-global scale weather patterns (e.g., impacting transport, trapping inversions, etc.). In acknowledgement of the importance of the weather-climate connection, the Department of Energy’s Atmospheric Radiation Measurement and Climate Change Prediction Programs now run a climate model in NWP mode.

Research conducted under this theme employs a combination of numerical models and environmental data to understand processes that dictate changes on weather and climate timescales (minutes to months to years) and the two-way interactions between weather systems and regional and global climate. Research related to the hydrologic cycle, carbon exchanges and turbulent flux exchanges all have the ability to influence both weather and climate and thus form the basis of this research theme. Also included are those studies that look at weather systems from a process perspective and the development of data sets intended for climate trend analysis.