Changes to the intrinsic predictability of weather under extreme climate change

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Wednesday, 7 January 2015
Justin G. McLay, NRL, Monterey, CA; and C. A. Reynolds and E. A. Satterfield

Differences between the intrinsic variability of the tropospheric flow on weather time scales in an extreme climate scenario and the intrinsic variability in a baseline, present-day scenario are explored using a novel ensemble methodology. Specifically, global numerical weather prediction (NWP) forecast ensembles are generated from pseudo-analyses taken from climate model data for the Representative Concentration Pathway (RCP) 8.5 scenario of the Fifth Climate Model Intercomparison Project (CMIP5). The ensembles are generated for both the summer and winter seasons over two multi-year periods, one in the early 21st century (the baseline scenario) and one in the late 21st century (the extreme scenario). The ensembles are constructed to have statistically identical initial condition (i.e. extrinsic) uncertainty, meaning that any general differences in forecast-ensemble variability between the extreme and baseline scenarios owe to differences in the intrinsic variability of the basic-state flow or to statistical sampling variability. The results are analyzed using standard metrics for the diagnosis of ensemble variability such as normalized perturbation energy. Notable, coherent differences in variability are seen in many of the metrics after accounting for statistical sampling variability. The geographical and vertical patterns of these differences are examined as a function of season and lead time. The differences are also explored in the context of synoptic features and sensible weather.