On the seasonal prediction of the western United States El Niño precipitation pattern during the 2015/16 winter

Abstract

A "typical" El Niño leads to wet (dry) wintertime anomalies over the southern (northern) half of the Western United States (WUS). However, during the strong El Niño of 2015/16, the WUS winter precipitation pattern was roughly opposite to this canonical (average of the record) anomaly pattern. To understand why this happened, and whether it was predictable, we use a suite of high-resolution seasonal prediction experiments with coupled climate models. We find that the unusual 2015/16 precipitation pattern was predictable at zero-lead time horizon when the ocean/atmosphere/land components were initialized with observations. However, when the ocean alone is initialized the coupled model fails to predict the 2015/16 pattern, although ocean initial conditions alone can reproduce the observed WUS precipitation during the 1997/98 strong El Niño. Further observational analysis shows that the amplitudes of the El Niño induced tropical circulation anomalies during 2015/16 were weakened by about 50% relative to those of 1997/98. This was caused by relative cold (warm) anomalies in the eastern (western) tropical Pacific suppressing (enhancing) deep convection anomalies in the eastern (western) tropical Pacific during 2015/16. The reduced El Niño teleconnection led to a weakening of the subtropical westerly jet over the southeast North Pacific and southern WUS, resulting in the unusual 2015/16 winter precipitation pattern over the WUS. This study highlights the importance of initial conditions not only in the ocean, but in the land and atmosphere as well, for predicting the unusual El Niño teleconnection and its influence on the winter WUS precipitation anomalies during 2015/16.

Inferred inflow forecast horizons guiding reservoir release decisions across the United States

“…Despite these limitations, we find that the approach arrives at convincing evidence for regulated inflow forecast contribution as well as a range of other interesting operator behaviors. While the associated release policies are likely to be highly imperfect models of actual operations, they potentially offer a significant advance on general, theory-driven rules currently adopted in state-of-the-art large-scale, distributed hydrological models (see Yassin et al, 2019, for a state-of-theart review of existing approaches).…”

“…A conceptually similar piecewise model is presented in Yassin et al (2019) with the fundamental difference that the process omits forecasts and is therefore based on climatology and current storage and inflow conditions. Importantly, because our algorithm is also computed for individual weeks, it ensures that the effects of operational decisions driven by long-term average water availability conditions are intercepted and removed when estimating the horizon.…”

“…A few dams follow the same snowmelt-driven forecast behavior described above but also appear to use significant foresight during fall (i.e., at the start and end of the water year). This may indicate use of seasonal water outlooks informed by the El Niño-Southern Oscillation (ENSO), which improves the skill of winter precipitation forecasts in the region (Yang et al, 2018). Canyon Ferry, Montana (Fig.…”

“…To our knowledge, these data have yet to be collected through any qualitative or quantitative research study conducted at a national scale. Lacking accurate operational data and associated decision-making schemes, large-scale, distributed hydrology models (e.g., Van Vliet et al, 2016;Wada et al, 2016;Voisin et al, 2013bVoisin et al, , 2017Vernon et al, 2019; see Nazemi and Wheater, 2015 for a review) are liable to misrepresent the influence of human water management on river flows (Yassin et al, 2019), including during extreme flood and drought conditions. The applications of these models -increasingly, large-scale multisectoral planning studies aiming to predict stresses on water, energy, and food systems -may in turn suffer a mischaracterization of human systems' exposure to hydrological risks.…”

Seasonal Prediction Potential for Springtime Dustiness in the United States

“…FLOR is one of GFDL's present real-time seasonal prediction models and one of the North American Multimodel Ensemble [NMME; Kirtman et al, 2014] Delworth et al, 2006;Gnanadesikan et al, 2006;Wittenberg et al, 2006], with a horizontal resolution of about 100 km and 50 vertical layers. FLOR shows skill in seasonal prediction of different variables in the tropics and extratropics (Vecchi et al 2014;Jia et al, 2015;Kapnick et al, 2018;Yang et al, 2015;Yang et al, 2018).…”