Characterizing climate predictability and model response variability from multiple initial condition and multi-model ensembles
Devashish Kumar, Auroop R. Ganguly

TL;DR
This study compares climate internal variability and model response variability using multi-model and multi-initial condition ensembles, revealing that internal variability often exceeds model response variability, especially for precipitation and at higher latitudes.
Contribution
It provides the first direct sensitivity analysis of CIV versus MRV across multiple regions, resolutions, and time horizons, informing climate predictability and model improvement.
Findings
CIV (MICE agreement) is lower than MRV (MME agreement) across all scales.
CIV dominates MRV at higher latitudes and for precipitation.
Precipitation shows larger uncertainties and greater CIV dominance than temperature.
Abstract
Climate models are thought to solve boundary value problems unlike numerical weather prediction, which is an initial value problem. However, climate internal variability (CIV) is thought to be relatively important at near-term (0-30 year) prediction horizons, especially at higher resolutions. The recent availability of significant numbers of multi-model (MME) and multi-initial condition (MICE) ensembles allows for the first time a direct sensitivity analysis of CIV versus model response variability (MRV). Understanding the relative agreement and variability of MME and MICE ensembles for multiple regions, resolutions, and projection horizons is critical for focusing model improvements, diagnostics, and prognosis, as well as impacts, adaptation, and vulnerability studies. Here we find that CIV (MICE agreement) is lower (higher) than MRV (MME agreement) across all spatial resolutions and…
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Taxonomy
TopicsClimate variability and models · Meteorological Phenomena and Simulations · Climate change impacts on agriculture
