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CL4.13 | Disentangling internal variability and forced response: Changes, Methods, Mechanisms and Impacts
EDI
Disentangling internal variability and forced response: Changes, Methods, Mechanisms and Impacts
Convener: Raul R. Wood | Co-conveners: Laura Suarez-GutierrezECSECS, Nicola Maher, Robert Jnglin Wills, Andrea DittusECSECS
Internal variability of the climate system leads to spatiotemporal fluctuations in climate means and extremes on different time scales, often masking forced signals in the climate, complicating the detection and attribution of observed events and trends and the projection of future changes. Internal variability is driven by complex mechanisms, and can itself change under external forcing. Multiple datasets have been developed to separate and quantify internal variability and its changes, underlying drivers, and impacts, including single model initial-condition large ensembles (SMILEs), statistically derived synthetic observations, and machine learning approaches.

We invite studies that:
(1) Explore the drivers and impacts of internal climate variability on large-scale climate dynamics, hydrology, and biogeochemistry on different timescales and their response to climate change
(2) Develop new tools or datasets to separate and quantify the forced response and internal variability in single realizations or using large ensembles
(3) Investigate how the forced response and internal variability affect extreme and compound events
(4) Illustrate the skillfulness of different modes of internal variability for seasonal predictions