Human emissions drive recent trends in North Pacific climate variations

Area of Science:

• Climate Science
• Atmospheric Science
• Oceanography

Background:

• The Pacific decadal oscillation (PDO) is a major climate variability pattern influencing the North Pacific and surrounding continents.
• Previous understanding attributed PDO oscillations to internal climate system processes, involving ocean-atmosphere interactions.
• Recent climate events, like the 2015 El Niño, were expected to shift the PDO to a positive phase, but it has trended negatively for decades.

Purpose of the Study:

• To investigate the drivers of multidecadal variations in the Pacific decadal oscillation (PDO) index.
• To determine whether human emissions or internal processes are responsible for the observed PDO trends.
• To re-evaluate climate model performance in simulating forced climate variability.

Main Methods:

• Developed a new attribution technique to statistically correct for climate model underestimation of forced climate variability amplitude.
• Analyzed twentieth-century PDO index variations, focusing on the long-term negative trend.
• Applied the technique to attribute observed PDO impacts, such as western US drought, to anthropogenic forcing.

Main Results:

• Multidecadal variations in the PDO index, including the current negative trend, are primarily driven by human emissions of aerosols and greenhouse gases.
• Current climate models underestimate the amplitude of forced climate variability, obscuring the anthropogenic influence on the PDO.
• Observed PDO impacts, including the prolonged western US drought, are largely attributable to human-induced, externally forced changes in the PDO.

Conclusions:

• The Pacific decadal oscillation (PDO) is significantly influenced by anthropogenic factors, contrary to previous assumptions of internal generation.
• Rethinking the attribution and projection of multidecadal climate variability is necessary due to the dominant role of human emissions.
• This research highlights the need for improved climate models that accurately represent forced climate variability for reliable regional climate change assessments.