Skillful decadal prediction of unforced southern European summer temperature variations

L F Borchert,G Sgubin,D Swingedouw,M B Menary,D J Befort,V Koul,J Mignot

doi:10.1088/1748-9326/ac20f5

Abstract

We assess the capability of decadal prediction simulations from the Coupled Model Intercomparison Project phase 6 (CMIP6) archive to predict European summer temperature during the period 1970–2014. Using a multi-model ensemble average, we show that Southern European (SEU) summer temperatures are highly predictable for up to ten years in CMIP6. Much of this predictive skill, is related to the externally forced response: historical simulations explain about 90% of observed SEU summer temperature variance. Prediction skill for the unforced signal of SEU summer temperature is low: initialized model simulations explain less than 10% of observed variance after removing the externally forced response. An observed link between unforced SEU summer temperature and preceding spring Eastern North Atlantic—Mediterranean sea surface temperature (SST) motivates the application of a dynamical-statistical model to overcome the low summer temperature skill over Europe. This dynamical-statistical model uses dynamical spring SST predictions to predict European summer temperature, and significantly increases decadal prediction skill of unforced European summer temperature variations, showing significant prediction skill for unforced Southern European summer temperature 2–9 years ahead. As a result, dynamical-statistical models can benefit the decadal prediction of variables with initially limited skill beyond the forcing, such as summer temperature over Europe.

Highlights

Introduction ce ptDecadal prediction of unforced southern European summer temperature Acmodels have been shown to be deficient in simulating observed atmospheric teleconnection mechanisms that transport that information from oceans to land (Qasmi et al 2017, Borchert et al 2018), which limit prediction skill over Europe
By comparing dyn-stat model and the decadal predictions, we examine the added value of a dyn-stat model for predicting unforced modulations of European summer temperature using Coupled Model Intercomparison Project phase 6 (CMIP6), which identifies the potential that exists for decadal temperature predictions over Europe in these simulations
sea surface temperature (SST) during spring and summer influences summer Southern European (SEU) surface air temperature (SAT) through a combination of advective transport of local heat anomalies and dynamic transport of heat from the

Summary

CMIP6 models

Our analysis is based on an ensemble of 8 initialized model systems, with 10 ensemble members each, from the DCPP (Table S1). We analyze a large multi-model ensemble (MME) of 192 historical members, subject to common forcing with observed atmospheric greenhouse gas concentrations, anthropogenic aerosols, solar variations and volcanic eruptions (Eyring et al 2016). These historical simulations are otherwise run freely to simulate diverse ce pte pt us cri internal climate variability around the common forced response. The 80 hindcast members have external forcing as well as the initialised observed climatic state at the start of their integration in common This improves the ability of these systems to predict decadal internal climate variability (Keenlyside et al 2008, Doblas-Reyes et al 2013, Marotzke et al 2016). We remap the information to a regular 1x1o grid, and analyze the period 1970-2015

Methods

Decadal predictions of European summer temperature in CMIP6 ce

Observed SST impact on European summer temperature

Findings

Dynamical-statistical predictions of European summer temperature