For specific cases, it appears that such predictions may be possible. Some El Niño events are forced from the extratropics. These events evolve over the course of a year, much more slowly that those that are not extra tropically forced. These more slowly evolving events are potentially predictable at 1-year lead times. In the figure, the left panels show the evolution of extratropically forced events and the right column shows the evolution of events that are not extratropically forced.

People: Pegion (GMU), Selman (GMU)

Publications: Pegion, K. and C. Selman 2016: Patterns of Climate Extremes: Trends and Mechanisms, Extratropical Precursors of the El Niño Southern Oscillation. S. -Y. Wang, R. Gillies, J -H Yoon, and C. Funk, Eds., AGU/Wiley-Blackwell, in press

Funding: This research is supported by NASA, NOAA, and NSF through a grant to the Center for Ocean-Land-Atmosphere Studies

Estimates of predictability together with calculations of current prediction skill are often used to define the gaps in our prediction capabilities to inform the scientific issues that must be addressed to build the next forecast system. However, different methods for estimating predictability can produce substantially different estimates of the upper limit of skill, leading to different conclusions regarding our prediction capabilities. How do we interpret our skill gaps given different estimates of predictability? Have we reached the upper limit of skill?

People: Pegion (GMU), Cicerone (GMU), DelSole (GMU), Becker (NOAA/CPC)

Publications: Pegion, K., T. Delsole, E. Becker, and T. Cicerone, 2016: Which Predictability Estimates are most Realistic?, to be submitted to Climate Dynamics NMME Special Issue

Funding: This research is supported by the NOAA/Climate Program Office/Modeling Analysis and Predictions Program

This project investigates if taking an introductory climate science course increased student's knowledge regarding weather and climate and how this knowledge impacted their beliefs about climate change related issues. The study digitized and analyzed six years of data from pre and post surveys given to students in George Mason University's CLIM 101: Weather, Climate, and Society course. The results from the surveys indicate that the majority of students who took this course understand that the Earth is warming. Although the results from the surveys show that students learned facts related to global warming and shifted their beliefs about climate change, many of the changes were small. This may be partially attributed to students already being aware of the issues of global warming as evidenced by the fact that most of the wrong answers on the factual questions tended to overestimate global warming and the U.S. CO2 emissions.

People: Staton (GMU), Pegion (GMU)

Funding: This research is supported by NASA, NOAA, and NSF through a grant to the Center for Ocean-Land-Atmosphere Studies

Arctic sea ice plays an important role in weather and climate. In addition to moderating the exchanges of energy and moisture at the surface, changes in sea ice have been shown to impact surface air temperature (SAT) and sea level pressure (SLP). Specifically, declines in fall sea ice have been linked to winter cold out breaks in the mid-latitudes and surface pressure patterns consistent with the negative phase of the Arctic Oscillation (AO). This study looks at how well models apart of the North American Multi-Model Ensemble (NMME) estimate fall sea ice extent between 1982 and 2009. Then, the models with the best skill are used to reanalyze the results of Liu, Curry, Wang, Song, and Horton (2012), which investigates the impacts of fall ice decline on winter weather and climate. Three of the 14 NMME models showed skill in ice estimation and when an ensemble average of those models is used to investigate the impact on SAT and SLP, it is found that cold outbreaks occur in the mid-latitudes consistent with previous studies while SLP shows negative AO like pressure patterns. Overall this analysis highlights the need for accurate model estimates of sea ice in order to ensure the models accurately capture the implications of changes in sea ice on climate local to and outside of the Arctic.

People: Elders (GMU), Pegion (GMU)

Publications: Elders, A. and K. Pegion, 2016: Diagnosing Sea Ice from the North American Multi Model Ensemble and Implications on Mid-latitude Winter Climate, to be submitted to Climate Dynamics NMME Special Issue

Funding: This research is supported by the GMU College of Science ad NASA, NOAA, and NSF through a grant to the Center for Ocean-Land-Atmosphere Studies

Recent advances in our understanding of sub-seasonal phenomena and improvements in our ability to simulate sub-seasonal variability along with the demand for sub-seasonal forecasts for decision support suggest that now is the time to develop an experimental multi-model sub-seasonal predictive capability. Indeed, preliminary multi-model sub-seasonal hindcasts conducted by the North American Multi-Model Ensemble (NMME) team indicate that a multi-model prediction system has more overall skill than any single system alone.

People: Kirtman (U. Miami), Pegion (GMU), DelSole (GMU), Robertson (Columbia University), Tippett (Columbia University), Burgman (Florida International), Lin (Environment Canada), Gottschalck (NCEP), Collins (NCEP), Cicerone (GMU)

Funding: NOAA/Climate Program Office/MAPP, Climate Testbed

SubX Project Website

El Niño and the Arctic Oscillation are not sufficient to explain the warm or wet extremes in Dec 2015. NOAA/CPC's Michelle L'Heureux d emonstrates this for temperature in her post on the NOAA ENSO Blog. We took her anlaysis a step further for precipitation and find only small contribution of increased precipitation associated with El Niño in the so utheastern US, while the contribution from the AO counteracts the impact from El Niño. We are currently investigating how well the North Americ an Multi-model Ensemble predicted each of these components.

People: Pegion (GMU), Cicerone (GMU), Wang (Utah State University), Fosu (Utah State University), Yoon (Korea)