Modulation of ENSO teleconnections over North America by the Pacific decadal oscillation

Maher, Nicola; Kay, J. E.; Capotondi, Antonietta

doi:10.1088/1748-9326/ac9327

Cited by 14 publications

(15 citation statements)

References 44 publications

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“…Relationships between the PDO and ENSO have also been shown to affect low-frequency variability of North American precipitation (Gershunov and Barnett 1998, Fuentes-Franco et al 2016, Nicola Kay Jennifer and Capotondi 2022. Additional work looking at the frequency of the PDO, ENSO, and PDO + ENSO in-phase periods with high or low accuracy was performed, but results were not significant to show that a PDO-ENSO phase relationship was fully responsible for variability in the subseasonal midlatitude prediction skill.…”

Section: Discussionmentioning

confidence: 99%

Assessing decadal variability of subseasonal forecasts of opportunity using explainable AI

Arcodia

Barnes

Mayer

et al. 2023

Environ. Res.: Climate

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Identifying predictable states of the climate system allows for enhanced prediction skill on the generally low-skill subseasonal timescale via forecasts with higher confidence and accuracy, known as forecasts of opportunity. This study takes a neural network approach to explore decadal variability of subseasonal predictability, particularly during forecasts of opportunity. Specifically, this work quantifies subseasonal prediction skill provided by the tropics within the CESM2 Large Ensemble and assesses how this skill evolves on decadal timescales. Utilizing the networks’ confidence and explainable artificial intelligence (XAI), physically meaningful sources of predictability associated with periods of enhanced skill are identified. Using these networks, we find that tropically-driven subseasonal predictability varies on decadal timescales during forecasts of opportunity. Analysis is extended to ECMWF Reanalysis v5 data, revealing that the relationships learned within the CESM2-LE holds in modern reanalysis data. These results indicate that the neural networks are capable of identifying predictable decadal states of the climate system within CESM2 that are useful for making confident, accurate subseasonal precipitation predictions in the real world.

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Section: Discussionmentioning

confidence: 99%

Assessing decadal variability of subseasonal forecasts of opportunity using explainable AI

Arcodia

Barnes

Mayer

et al. 2023

Environ. Res.: Climate

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“…By separating the anthropogenic forcing component of the ENSO‐monsoon relationship from the natural variability component, they concluded that the relationship between ENSO and the Asian monsoon is largely controlled by the natural decadal variability. Maher et al (2022) also noted that the ENSO teleconnection over North America is highly affected by the PDO and hence highly varies on the decadal timescale.…”

Section: Changes In Enso Teleconnections Based On Observationsmentioning

confidence: 99%

A review of ENSO teleconnections at present and under future global warming

Alizadeh

2023

WIREs Climate Change

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The El Niño‐Southern Oscillation (ENSO) is a major component of the Earth's climate that largely influences global climate variability through long‐distance teleconnections. Rossby wave trains emerging from the tropical convection and their propagation into extratropical regions are the key mechanism for tropical and extratropical teleconnections. Despite significant progress in the understanding of ENSO teleconnections over the recent past decades, several important issues have remained to be addressed. The global atmospheric teleconnections of ENSO vary substantially with the seasonal cycle, on the decadal timescale, and under the influence of global warming. It is essential to separate the internal decadal variability of ENSO teleconnections from changes caused by the external forcing of global warming. However, the post‐satellite observations are not long enough to compose a large number of ENSO events to distinguish the decadal variability of ENSO teleconnections from changes related to increasing greenhouse concentrations. The current climate models also suffer from common biases, such that they are unable to properly reproduce both the tropical mean state and some features of ENSO. Nevertheless, observational records can be extended back in time via reconstruction methods. Efforts have also already been made to remove some main common biases of climate models and to improve the representation of ENSO characteristics. The reliable reconstructed data along with a large number of ensemble members of the improved climate model simulations can be applied to advance our understanding of ENSO global teleconnections and their responses to internal decadal variability and externally forced global warming.This article is categorized under: Paleoclimates and Current Trends > Modern Climate Change Climate Models and Modeling > Earth System Models Assessing Impacts of Climate Change > Evaluating Future Impacts of Climate Change

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“…As ENSO and PDO drive inter-annual to inter-decadal variability across the region, assessing SPEAR's representation of these teleconnections is important for understanding how accurately the model may reproduce other extremes across the region, like SDs. Delworth et al (2020) shows SPEAR accurately captures the relationship between PDO and North American precipitation, while Maher et al (2022) finds that when PDO and ENSO are in phase, temperature and precipitation anomalies are amplified and vice versa. When comparing SPEAR's performance against other GCMs, Johnson et al (2022) reports that SPEAR improves on CMIP5-generation models with a better representation of global ENSO-related temperature and precipitation patterns, and Maher et al (2022) reports SPEAR has higher accuracy and resolution than five other large ensemble models after comparing correlations of ENSO and PDO with North American winter temperature and precipitation anomalies between observations and models.…”

Section: Observational Datamentioning

confidence: 99%

“…We chose to consider data at monthly resolution intervals for the following three reasons: (a) data availability, as SPEAR only recorded SWE at monthly intervals; (b) consistency with previous studies (Huning & AghaKouchak, 2020); and (c) because the monthly resolution is an appropriate timescale for monitoring snow drought (Hatchett et al, 2022). Delworth et al (2020) and Maher et al (2022) demonstrate that SPEAR accurately reproduces temperature and precipitation patterns across the US and Fasullo (2020) finds that CM4, a model that shares many of the components of SPEAR, outperforms many other CMIP6 state-of-the-art large ensemble climate models across multiple measures of skill, ranking 7th out of 37 CMIP6 models overall. Delworth et al (2020) finds that SPEAR has negligible temperature bias when compared to CRUTEM4 (Jones et al, 2012) and GISTEMP v4 (Lenssen et al, 2019), and a slight positive precipitation bias across the WUS, when compared with the precipitation data product detailed in Schneider et al (2017).…”

Section: Observational Datamentioning

confidence: 99%

“…The most substantial precipitation percentage biases are in regions of relatively low precipitation, while the largest absolute temperature biases are in mountainous regions, and partially attributable to the bilinear re-gridding. Delworth et al (2020), Johnson et al (2022), andMaher et al (2022) assess SPEAR's accuracy in representing teleconnections of the ENSO and the Pacific Decadal Oscillation (PDO) to North American climate. As ENSO and PDO drive inter-annual to inter-decadal variability across the region, assessing SPEAR's representation of these teleconnections is important for understanding how accurately the model may reproduce other extremes across the region, like SDs.…”

Section: Observational Datamentioning

confidence: 99%

See 1 more Smart Citation

Illuminating Snow Droughts: The Future of Western United States Snowpack in the SPEAR Large Ensemble

Schmitt,

Tseng,

Hughes

et al. 2024

JGR Atmospheres

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Seasonal snowpack in the Western United States (WUS) is vital for meeting summer hydrological demands, reducing the intensity and frequency of wildfires, and supporting snow‐tourism economies. While the frequency and severity of snow droughts (SD), that is, anomalously low snowpacks, are expected to increase under continued global warming, the uncertainty from internal climate variability remains challenging to quantify with observations alone. Using a 30‐member large ensemble from a state‐of‐the‐art global climate model, the Seamless System for Prediction and EArth System Research (SPEAR), and an observations‐based data set, we find WUS SD changes are already significant. By 2100, SPEAR projects SDs to be nearly 9 times more frequent under shared socioeconomic pathway 5‐8.5 (SSP5‐8.5) and 5 times more frequent under SSP2‐4.5, compared to a 1921–2011 average. By investigating the influence of the two primary drivers of SD, temperature and precipitation amount, we find the average WUS SD will become warmer and wetter. To assess how these changes affect future summer water availability, we track late winter and spring snowpack across WUS watersheds, finding differences in the onset time of a “no‐snow” threshold between regions and large internal variability within the ensemble that are both on the order of decades. We attribute the inter‐regional variability to differences in the regions' mean winter temperature and the intra‐regional variability to irreducible internal climate variability which is not well‐explained by temperature variations alone. Despite strong scenario forcing, internal climate variability will continue to drive variations in SD and no‐snow conditions through 2100.

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Modulation of ENSO teleconnections over North America by the Pacific decadal oscillation

Cited by 14 publications

References 44 publications

Assessing decadal variability of subseasonal forecasts of opportunity using explainable AI

Assessing decadal variability of subseasonal forecasts of opportunity using explainable AI

A review of ENSO teleconnections at present and under future global warming

Illuminating Snow Droughts: The Future of Western United States Snowpack in the SPEAR Large Ensemble

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