Sunday, February 19, 2017
Exhibit Hall (Hynes Convention Center)
Adria Schwarber, University of Maryland Department of Atmospheric and Oceanic Science, College Park, MD
The time scales of climate system responses to anthropogenic emissions vary depending on the chemical species emitted. Though carbon dioxide (CO2) emissions primarily drive anthropogenic climate change, emissions of various other radiative forcing agents, including short-lived climate forcers (SLCFs), also contribute significantly to Earth’s altered radiative budget. Much of the literature focuses on long-term climate responses emphasizing analysis with equilibrium climate sensitivity (ECS) or transient climate sensitivity (TCR). There is limited literature exploring short-term climate change responses occurring in a 20-30 year time horizon. To address this gap, we seek to clarify climate dynamics on decadal time scales with the ultimate goal of understanding the implications of near-term emission reductions on climate. Using coupled climate models from Phase 5 of the Coupled Model Intercomparison Project (CMIP5), we analyze the range of temperature response over time, with specific attention to the 20-30 year time horizon. Similarly, we also explore sub-global temperature responses at a hemispheric-scale. We find that the range of responses of land/ocean varied less than the range of hemispheric responses. We identified the time of emergence of the temperature signal, using the 5-95% range of spatial and temporal temperature response from the corresponding pre-industrial control runs. Our results are the first step of better quantifying the short-term climate responses to changes in SLCFs. However, studies on SLCFs within coupled models are difficult—perturbing this class of models requires large computational resources and generates noisy output. One goal of this work is to improve emulation in simple climate models (SCMs), so these models can better capture the full range of climate responses. A more accurate representation of climate responses within SCMs would improve analysis and decision support.