Diagnosing Sources of U.S. Seasonal Forecast Skill

Abstract

In this study the authors diagnose the sources for the contiguous U.S. seasonal forecast skill that are related to sea surface temperature (SST) variations using a combination of dynamical and empirical methods. The dynamical methods include ensemble simulations with four atmospheric general circulation models (AGCMs) forced by observed monthly global SSTs from 1950 to 1999, and ensemble AGCM experiments forced by idealized SST anomalies. The empirical methods involve a suite of reductions of the AGCM simulations. These include uni- and multivariate regression models that encapsulate the simultaneous and one-season lag linear connections between seasonal mean tropical SST anomalies and U.S. precipitation and surface air temperature. Nearly all of the AGCM skill in U.S. precipitation and surface air temperature, arising from global SST influences, can be explained by a single degree of freedom in the tropical SST field—that associated with the linear atmospheric signal of El Niño–Southern Oscillation (ENSO). The results support previous findings regarding the preeminence of ENSO as a U.S. skill source. The diagnostic methods used here exposed another skill source that appeared to be of non-ENSO origins. In late autumn, when the AGCM simulation skill of U.S. temperatures peaked in absolute value and in spatial coverage, the majority of that originated from SST variability in the subtropical west Pacific Ocean and the South China Sea. Hindcast experiments were performed for 1950–99 that revealed most of the simulation skill of the U.S. seasonal climate to be recoverable at one-season lag. The skill attributable to the AGCMs was shown to achieve parity with that attributable to empirical models derived purely from observational data. The diagnostics promote the interpretation that only limited advances in U.S. seasonal prediction skill should be expected from methods seeking to capitalize on sea surface predictors alone, and that advances that may occur in future decades could be readily masked by inherent multidecadal fluctuations in skill of coupled ocean–atmosphere systems.

On the skill of seasonal sea surface temperature forecasts in the California Current System and its connection to ENSO variability

“…In other words, the dynamical forecast skill above persistence for 28-year hindcasts is largely captured by using dynamical forecasts for the 10 strongest ENSO events (1983,1987,1988,1989,1992,1998,1999,2000,2003,2008) and persistence forecasts for the other 18 years. This finding is consistent with previous studies that identify ENSO variability as the primary driver of seasonal predictability in air temperature and precipitation anomalies over the continental United States (Barnett and Preisendorfer 1987;Quan et al 2006). However, it should be noted that there is residual skill in the dynamical forecast beyond that generated during ENSO events, particularly for long lead forecasts (cf., left and middle panels of Fig.…”

“…The difficulty of accurately forecasting winds on seasonal timescales is not surprising given the chaotic nature and short memory of the atmosphere (Goddard et al 2001), though our findings do suggest promise at least when climate signals are large. Prior studies demonstrating ENSO variability as the primary driver of forecast skill for air temperatures and precipitation over the US continent (Barnett and Preisendorfer 1987;Quan et al 2006) have motivated consideration of conditional forecasts based on the ENSO state (Pegion and Kumar 2013), and a similar approach may be fruitful for SST forecasts off the US west coast.…”

Assessing the Skill of Precipitation and Temperature Seasonal Forecasts in Spain: Windows of Opportunity Related to ENSO Events

“…These hindcast experiments have been used to globally analyze the predictability at seasonal time scales, especially in areas influenced by ENSO activity (Palmer et al 2004;Gutiérrez et al 2005). Moreover, although the potential predictability is lower at extratropical latitudes, recent studies have shown certain skill in those regions (e.g., in North America) associated with ENSO teleconnections (see, e.g., Quan et al 2006), and also with other sources of seasonal predictability, such as the persistence of the North Pacific decadal oscillation (Gershunov and Cayan 2003) or the moisture content (Wang and Kumar 1998;Douville 2004). However, in most of the extratropics, the signals predicted by general circulation models are weak and do not add valuable information over a climatological forecast.…”

Ten Years of Stochastic Water Supply Modeling by Lower Colorado River Authority of Texas

“…ENSO has been shown to influence precipitation and streamflow in Central Texas (Gershunov 1998;Rajagopalan and Cooke 2000;Watkins and O'Connell 2005;Slade and Chow 2011;Vicente-Serrano et al 2011;Wei and Watkins 2011;Anderson and Rose 2012). The ability to predict ENSO has been improving with statistical and dynamic models (Quan et al 2006;Barnston et al 2012;. A more in-depth discussion of the model's use of ENSO ensemble forecasts can be found in Anderson et al (2015).…”