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Improve Climate Predictions by Reducing Initial Prediction Errors: A Benefit Estimate Using Multi-model ENSO Predictions
  • Gan Zhang
Gan Zhang
University of Illinois at Urbana Champaign

Corresponding Author:gzhang13@illinois.edu

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Climate risk management relies on accurate predictions of key climate variations such as El Niño-Southern Oscillation (ENSO), but the skill of ENSO predictions has recently plateaued or even degraded. Here we analyze the North American Multi-Model Ensemble (NMME) and estimate how the seasonal prediction of ENSO may benefit from reducing initial prediction errors. An analysis of predictable signals and system noises identifies a high-predictability regime and a low-predictability regime. The latter corresponds to the spring predictability barrier and is related to a rapid drop in the signal-to-noise ratio, which is caused by the comparably strong dampening of predictable signals. Reducing first-month prediction errors (FPEs) will likely reduce root-mean-square errors of the ENSO prediction. As a conservative estimate, halving the FPEs may extend the NMME’s skill by one to two months. Importantly, this study identifies the regions where reducing FPE is the most effective. Unlike the predictions initialized after the boreal spring, the March-initialized predictions of the wintertime ENSO will likely benefit the most from FPE reductions in the tropical Northwest Pacific. An opportunistic thought experiment suggests the buoy observation changes during 1995–2020 may have contributed to FPEs associated with large cold biases (>1K) in some El Niño-year predictions. While data availability prevented in-depth analyses of physical processes, the findings suggest that prioritizing modeling and observation in certain regions can improve climate predictions cost-effectively. The analytical framework here is applicable to other climate processes, thus holding wide potential for benefiting climate predictions.
13 Sep 2023Submitted to ESS Open Archive
13 Sep 2023Published in ESS Open Archive