Quantifying the Effect of Climate Change on Midlatitude Subseasonal Prediction Skill Provided by the Tropics

Abstract

Subseasonal timescales (∼2 weeks–2 months) are known for their lack of predictability, however, specific Earth system states known to have a strong influence on these timescales can be harnessed to improve prediction skill (known as “forecasts of opportunity”). As the climate continues warming, it is hypothesized these states may change and consequently, their importance for subseasonal prediction may also be impacted. Here, we examine changes to midlatitude subseasonal prediction skill provided by the tropics under anthropogenic warming using artificial neural networks to quantify skill. The network is tasked to predict the sign of the 500 hPa geopotential height for historical and future time periods in the Community Earth System Model Version 2 ‐ Large Ensemble across the Northern Hemisphere at a 3 week lead using tropical precipitation. We show prediction skill changes substantially in key midlatitude regions and these changes appear linked to changes in seasonal variability with the largest differences in accuracy occurring during forecasts of opportunity.

Exploring the Relative Importance of the MJO and ENSO to North Pacific Subseasonal Predictability

“…Recent research has shown that the MJO has become and will likely continue to become more predictable in a future climate (Du et al., 2023), which could subsequently improve midlatitude subseasonal skill provided by the MJO. On the other hand, previous research suggests ENSO may be a main tropical driver of future midlatitude subseasonal predictability changes (Mayer & Barnes, 2022). Therefore, future research should explore how our results may change in a future, warmer climate.…”

“…Wang & Robertson, 2019). Further, recent work suggests ENSO may play a main role in changes to midlatitude subseasonal predictability in a future, warmer climate (Mayer & Barnes, 2022). While ENSO is often used for seasonal prediction (e.g., Gibson et al, 2021;Winkler et al, 2001), there is also considerable literature that highlights ENSO teleconnections as a driver of midlatitude subseasonal variability, particularly in boreal winter by also modulating the Aleutian Low (e.g., Chapman et al, 2021;Kumar & Hoerling, 1998).…”

“…However, it can also influence the subseasonal timescale through its impact on the MJO (Hendon et al., 1999; Kessler, 2001; Pohl & Matthews, 2007) and the basic state in which MJO teleconnections propagate (Moon et al., 2011; Namias, 1986; Takahashi & Shirooka, 2014), ultimately impacting the MJO's influence in the midlatitudes (Arcodia et al., 2020; Henderson & Maloney, 2018; Stan et al., 2017; Tseng et al., 2020) and subsequent subseasonal prediction skill (Johnson et al, 2014; L. Wang & Robertson, 2019). Further, recent work suggests ENSO may play a main role in changes to midlatitude subseasonal predictability in a future, warmer climate (Mayer & Barnes, 2022). While ENSO is often used for seasonal prediction (e.g., Gibson et al., 2021; Winkler et al., 2001), there is also considerable literature that highlights ENSO teleconnections as a driver of midlatitude subseasonal variability, particularly in boreal winter by also modulating the Aleutian Low (e.g., Chapman et al., 2021; Kumar & Hoerling, 1998).…”

Assessing decadal variability of subseasonal forecasts of opportunity using explainable AI

“…The neural network approach allows us to make subseasonal predictions without the need for a forecast model. The fields of weather and climate science have benefitted from applications of neural networks to learn physical relationships within the Earth system via explainability techniques (Ham et al 2019, McGovern et al 2019, Toms et al 2020, Antonios et al 2021, Gordon et al 2021, Martin et al 2022, Mayer and Barnes 2022, Straaten et al 2023. Here, we use shallow artificial neural networks (ANNs) to quantify U.S. West Coast subseasonal precipitation skill and identify forecasts of opportunity provided by the tropics within CESM2.…”

Assessing Decadal Variability of Subseasonal Forecasts of Opportunity using Explainable AI

“…Specifically, we use the CESM2 historical simulation to train neural networks to quantify the subseasonal prediction skill within CESM2 without the need for a forecast model. The fields of weather and climate science have benefitted from applications of neural networks to learn physical relationships within the Earth system via explainability techniques (Gordon et al, 2021;Ham et al, 2019;Mamalakis et al, 2021;Martin et al, 2022;Mayer & Barnes, 2022;McGovern et al, 2019;Toms et al, 2020;van Straaten et al, 2023). Here, we use shallow artificial neural networks to quantify U.S. West Coast subseasonal precipitation skill and identify forecasts of opportunity provided by the tropics within CESM2.…”