Quantifying stratospheric biases and identifying their potential sources in subseasonal forecast systems

Abstract

Abstract. The stratosphere can be a source of predictability for surface weather on timescales of several weeks to months. However, the potential predictive skill gained from stratospheric variability can be limited by biases in the representation of stratospheric processes and the coupling of the stratosphere with surface climate in forecast systems. This study provides a first systematic identification of model biases in the stratosphere across a wide range of subseasonal forecast systems. It is found that many of the forecast systems considered exhibit warm global mean temperature biases from the lower to middle stratosphere, too strong/cold wintertime polar vortices, and too cold extratropical upper troposphere/lower stratosphere regions. Furthermore, tropical stratospheric anomalies associated with the Quasi-Biennial Oscillation tend to decay toward each system's climatology with lead time. In the Northern Hemisphere (NH), most systems do not capture the seasonal cycle of extreme vortex event probabilities, with an underestimation of sudden stratospheric warming events and an overestimation of strong vortex events in January. In the Southern Hemisphere (SH), springtime interannual variability of the polar vortex is generally underestimated, but the timing of the final breakdown of the polar vortex often happens too early in many of the prediction systems. These stratospheric biases tend to be considerably worse in systems with lower model lid heights. In both hemispheres, most systems with low-top atmospheric models also consistently underestimate the upward wave driving that affects the strength of the stratospheric polar vortex. We expect that the biases identified here will help guide model development for sub-seasonal to seasonal forecast systems, and further our understanding of the role of the stratosphere for predictive skill in the troposphere.

Increased vertical resolution in the stratosphere reveals role of gravity waves after sudden stratospheric warmings

“…Over the polar cap, the ECMWF S2S hindcasts develop a mid-to-lower stratospheric cold bias of more than 2 K at 50hPa with increasing lead time (dashed grey line in Fig. 2a), which is in line with the findings of Lawrence et al (2022) for high-top models. Hindcasts with weak vortex initial conditions develop a stronger than average cold bias (dotted blue line in Fig.…”

“…Previous studies (e.g. Karpechko et al, 2018;Lawrence et al, 2022) and a composite analysis in Section 3 reveal that the magnitude of stratospheric polar-cap temperature anomalies during an SSW is commonly underestimated in sub-seasonal prediction models. In order to better understand this underestimation, this study investigates the means of improving the prediction by increasing vertical model resolution.…”

“…Specifically, it has been found that the magnitude of SSWs is generally underestimated (e.g. Karpechko et al, 2018;Lawrence et al, 2022). Another source of tropospheric errors for sub-seasonal forecasts can be a misrepresentation of stratosphere-troposphere coupling and the downward influence of SSWs (Kolstad et al, 2020).…”

Which Sudden Stratospheric Warming Events Are Most Predictable?

“…An important caveat of our study is that we are assuming that the currently realized predictability of SSWs from biased forecast systems (Lawrence et al, 2022;Schwartz & Garfinkel, 2020;Schwartz et al, 2022) is a proxy for the "true" potential predictability of these SSW events. While our computation of median predictability only considers high-top models which are known to be less biased (Lawrence et al, 2022;Schwartz & Garfinkel, 2020;Schwartz et al, 2022), future work should reconsider future generations of high-top models with smaller biases. This assumption is particularly suspect for the marginal SSW events, as for example, a relatively small too-strong vortex bias can lead to the forecast system missing the transition to easterlies (Figure 5).…”

“…Institute of Atmospheric Sciences and Climate (ISAC) and National Center for Environmental Prediction (NCEP) hindcasts are not archived on the subseasonal to seasonal servers for the 2013 event. Note that the low-top version of China Meteorological Administration (CMA) is used for SSWs in 2003 or earlier, as the hindcasts for the high-top CMA begin only in 2004.The median predictability excludes Bureau of Meteorology, and also CMA before 2004, as these models are known to suffer from large mean state biases(Lawrence et al, 2022;Schwartz & Garfinkel, 2020;Schwartz et al, 2022). Also indicated are the El Nino-Southern Oscillation (ENSO), Quasi-Biennial Oscillation (QBO), and Madden Julian Oscillation (MJO) conditions preceding the SSW, as well as the SSW morphology and peak easterly winds in the 2 weeks after the onset.…”