Assessing the robustness of multidecadal variability in Northern Hemisphere wintertime seasonal forecast skill

Abstract

Recent studies have found evidence of multidecadal variability in Northern Hemisphere wintertime seasonal forecast skill. Here we assess the robustness of this finding by extending the analysis to a diverse set of ensemble atmospheric model simulations. These simulations differ in either the numerical model or type of initialisation and include atmospheric model experiments initialised with reanalysis data and free-running atmospheric model ensembles. All ensembles are forced with observed sea-surface temperatures (SSTs) and sea-ice boundary conditions. Analysis of large-scale Northern Hemisphere circulation indices over the Northern Hemisphere (namely the North Atlantic Oscillation, the Pacific/North American pattern and the Arctic Oscillation) reveals that in all ensembles there is larger correlation skill in late-century periods than in mid-century periods. Similar multidecadal variability in skill is found in a measure of total skill integrated over the whole extratropical region. Most of the differences in large-scale circulation skill between the skilful late period (as well as the early period) and the less skilful mid-century period seem to be due to a reduction in skill over the North Pacific and a disappearance of skill over North America and the North Atlantic. The results are robust across different models and different types of initialisation, indicating that the multidecadal variability in Northern Hemisphere winter skill is a robust feature of twentieth-century climate variability. Multidecadal variability in skill therefore arises from the evolution of the observed SSTs, likely related to a weakened influence of the El Niño-Southern Oscillation on the predictable extratropical circulation signal during the middle of the twentieth century, and is evident in the signal-to-noise ratio of the different ensembles, particularly the larger ensembles.

Multidecadal Variation in the Seasonal Predictability of Winter PNA and Its Sources

“…The predicted seasonal mean PNA index is calculated based on the monthly mean 500 hPa geopotential height prediction in December, January, and February (DJF), that is, with a 2‐month lead time. To be consistent with previous studies (e.g., O’Reilly et al., 2017, 2020; Weisheimer et al., 2020), the PNA index is defined by using the pointwise method (Wallace & Gutzler, 1981), that is, a linear combination of the normalized 500 hPa geopotential height anomalies ( Z ) at the four active centers: PNA = 1/4[ Z (20°N, 160°W) − Z (45°N, 165°W) + Z (55°N, 115°W) − Z (30°N, 85°W)]. Consistent results are obtained if the PNA index is defined as the time series associated with the second leading rotated empirical orthogonal function pattern (Barnston & Livezey, 1987; Van Den Dool et al., 2000) of the 500 hPa geopotential height anomalies in the Northern Hemisphere.…”

“…The predicted seasonal mean PNA index is calculated based on the monthly mean 500 hPa geopotential height prediction in December, January, and February (DJF), that is, with a 2-month lead time. To be consistent with previous studies (e.g., O'Reilly et al, 2017O'Reilly et al, , 2020Weisheimer et al, 2020), the PNA index is defined by using the pointwise method (Wallace & Gutzler, 1981), that is, a linear combination of the normalized 500 hPa geopotential height anomalies (Z) at the four active centers:…”

The Forced Response and Decadal Predictability of the North Atlantic Oscillation: Nonstationary and Fragile Skills

“…natural forcings in the period 1920-1960 (Takemura et al 2006), where the weakest correlations are found in the historical ensemble, could also play a role. Also, it has been suggested that such non-stationarity can be due to a weakened influence of the El Niño-Southern Oscillation during the middle of the 20th century (O'Reilly et al 2020;Rieke et al 2021).…”

The Seasonal-to-Multiyear Large Ensemble (SMYLE) prediction system using the Community Earth System Model version 2

“…These pioneering results merit closer examination in experiments that explicitly target the multiyear timescale. Recent work has identified robust multidecadal modulations of seasonal prediction skill (Weisheimer et al, 2017;O'Reilly et al, 2020), which suggests that a focused multiyear prediction framework could shed further light on important interactions between seasonal, interannual, and decadal processes (e.g., the state dependence of multiyear predictability of seasonal climate) that would otherwise remain obscure. As a result of the signal-tonoise paradox (Scaife and Smith, 2018), a large ensemble size appears to be a prerequisite for skillful interannual predictions of some impactful atmospheric variations such as the NAO (Dunstone et al, 2016(Dunstone et al, , 2020.…”