Relating CMIP5 Model Biases to Seasonal Forecast Skill in the Tropical Pacific

Abstract

We examine links between tropical Pacific mean state biases and El Niño/Southern Oscillation forecast skill, using model‐analog hindcasts of sea surface temperature (SST; 1961–2015) and precipitation (1979–2015) at leads of 0–12 months, generated by 28 different models from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Model‐analog forecast skill has been demonstrated to match or even exceed traditional assimilation‐initialized forecast skill in a given model. Models with the most realistic mean states and interannual variability for SST, precipitation, and 10‐m zonal winds in the equatorial Pacific also generate the most skillful precipitation forecasts in the central equatorial Pacific and the best SST forecasts at 6‐month or longer leads. These results show direct links between model climatological biases and seasonal forecast errors, demonstrating that model‐analog hindcast skill—that is, how well a model can capture the observed evolution of tropical Pacific anomalies—is an informative El Niño/Southern Oscillation metric for climate simulations.

Evaluating Climate Models with the CLIVAR 2020 ENSO Metrics Package

“…The remaining four background climatology metrics gauge the seasonal cycle amplitude of the above features. It is essential to evaluate these systematic model biases E201 before analyzing ENSO itself, because the mean state in the tropical Pacific Ocean strongly influences ENSO characteristics and its teleconnections (e.g., Wang and An 2002;Guilyardi 2006;Sun et al 2009;Yeh et al 2018;Bayr et al 2018Bayr et al , 2019bDing et al 2020). The mean state is also generally better constrained by the available observational records than ENSO itself.…”

“…Because the exploration of ENSO processes is still very much a research topic, this list was purposely kept short, unlike other comprehensive analysis frameworks (e.g., Jin et al 2006;Graham et al 2017;Chen et al 2017;Ray et al 2018a,b). To explain ENSO variability, more processes need to be considered, including nonlinear dynamical heating, tropical instability waves, equatorial Kelvin and Rossby waves, westerly wind events, involving numerous nonlinearities (An et al 2020). It is also common to consider separately the influence of the net heat flux components (latent heat flux, sensible heat flux, longwave radiation and shortwave radiation) on ENSO because they can have opposite effects.…”

Modulation of Cross-Isothermal Velocities with ENSO in the Tropical Pacific Cold Tongue

“…Understanding the mechanisms that control the strength and variability of the CT in the tropical Pacific is essential for reliable ENSO forecasts (Ji and Leetmaa 1997;Barnston et al 2012;Tang et al 2018;Ding et al 2020) and, given the importance of the eastern tropical Pacific in the global climate system, for well founded climate projections (Bayr et al 2019;Samanta et al 2019).…”

Projected Increase in Fast-Growing and Slow-Dissipating El Niño Events in the 21st Century

“…Simulations of ENSO have improved substantially over the past decade, but there remain common model biases that add caveats to the results presented here. These biases include ITCZs in wrong place, an overly-intense cold tongue, insufficient stirring by tropical instability waves, unresolved convective momentum transport in the atmospheric boundary layer, cloud-radiative feedbacks, difficulties representing near-surface ocean mixing, representing sub-grid scale processes, which may affect model projections of future ENSO behavior 27,31,57,58,59,60,61,62,63,64 .…”