Preprints
https://doi.org/10.5194/egusphere-2022-98
https://doi.org/10.5194/egusphere-2022-98
 
11 Apr 2022
11 Apr 2022
Status: this preprint is open for discussion.

Constraining low-frequency variability in climate projections to predict climate on decadal to multi-decadal time scales – a ‘poor-man’ initialized prediction system

Rashed Mahmood1,2, Markus G. Donat1,3, Pablo Ortega1, Francisco J. Doblas-Reyes1,2, Carlos Delgado-Torres1, Margarida Samsó1, and Pierre-Antoine Bretonnière1 Rashed Mahmood et al.
  • 1Barcelona Supercomputing Center, Barcelona, Spain
  • 2University of Montreal, Montreal, Canada
  • 3Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain

Abstract. Near-term projections of climate change are subject to substantial uncertainty from internal climate variability. Here we present an approach to reduce this uncertainty by sub-selecting those ensemble members that more closely resemble observed patterns of ocean temperature variability immediately prior to a certain start date. This constraint aligns the observed and simulated variability phases and is conceptually similar to initialization in seasonal to decadal climate predictions. We apply this variability constraint to large multi-model projection ensembles from the Coupled Model Intercomparison Project phase 6 (CMIP6), consisting of more than 200 ensemble members, and evaluate the skill of the constrained ensemble in predicting the observed near-surface temperature, sea-level pressure and precipitation on decadal to multi-decadal time scales.

We find that the constrained projections show significant skill in predicting the climate of the following ten to twenty years, and added value over the ensemble of unconstrained projections. For the first decade after applying the constraint, the global patterns of skill are very similar and can even outperform those of the multi-model ensemble mean of initialized decadal hindcasts from the CMIP6 Decadal Climate Prediction Project (DCPP). In particular for temperature, larger areas show added skill in the constrained projections compared to DCPP, mainly in the Pacific and some neighboring land regions. Temperature and sea-level pressure in several regions are predictable multiple decades ahead, and show significant added value over the unconstrained projections for forecasting the first two decades and the 20-year averages. We further demonstrate the suitability of regional constraints to attribute predictability to certain ocean regions. On the example of global average temperature changes, we confirm the role of Pacific variability in modulating the reduced rate of global warming in the early 2000s, and demonstrate the predictability of reduced global warming rates over the following 15 years based on the climate conditions leading up to 1998. Our results illustrate that constraining internal variability can significantly improve the accuracy of near-term climate change estimates for the next few decades.

Rashed Mahmood et al.

Status: open (until 08 Jun 2022)

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  • RC1: 'Comment on egusphere-2022-98', Anonymous Referee #1, 11 May 2022 reply

Rashed Mahmood et al.

Rashed Mahmood et al.

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Short summary
Near-term climate change projections are strongly influenced by the uncertainty from internal climate variability. Here we present a novel approach to reduce such uncertainties by constraining decadal-scale variability in the projections using observations. The constrained ensembles show significant added value over the unconstrained ensemble in predicting the first two decades. We also show the applicability of regional constraints in attributing predictability to certain ocean regions.