Leonard F. Borchert, Matthew B. Menary, Didier Swingedouw, Giovanni Sgubin, Leon Hermanson, Juliette Mignot
Geophysical Research Letters
Work Package 5
The sea surface temperatures around the North Atlantic Subpolar Gyre, a circulating ocean current system, are an important indicator of its impacts on weather in the area, including Europe. This study assesses the ability of our latest climate models to predict these temperatures, offering the possibility of predicting their weather impacts in advance. The new CMIP6 models are found to make markedly better predictions than their CMIP5 predecessors, explaining up to 84% of the observed temperature variance, largely down to accurately modelling the influence of natural climate forcing factors since 1980. Initialised projections, using accurate observational data about the current climate to help improve simulation accuracy, explain significantly more variance than uninitialised projections. These findings will help us better predict the influence of North Atlantic temperatures on European weather, including extreme weather events, up to 10 years in the future, allowing people and policymakers to put more effective plans in place to adapt to, and mitigate the impact of, future climate change.
The North Atlantic Subpolar Gyre is a large system of ocean currents circulating clockwise in the North Atlantic, connecting North America with Europe and Africa, and the equator with subpolar waters. This system of currents has a strong influence on the weather experienced around it, including in Europe. The temperature of the surface waters in the gyre is particularly known as a key indicator of potential impacts. This study is the first to assess the ability of the latest generation of climate models to simulate and predict the surface temperature in the North Atlantic gyre, as well as understand the physical processes that this prediction skill originates from. This will help tell us which models to trust in future predictions, improving our projections of future climate changes and weather events over Europe.
The team found that the latest CMIP6 climate models were markedly better at predicting North Atlantic subpolar gyre temperatures than their CMIP5 predecessors, explaining up to 47% of the observed temperature variance versus 37%. This is mainly due to CMIP6 models simulating post-1980 temperature variations particularly well, explaining up to 65% of variance against 50% in CMIP5, thanks to an accurate response to natural climate forcing factors in CMIP6 models. Initialising the models, that is supplying the model with highly accurate data about the present state of the climate system, brings significant improvements, with initialised CMIP6 model runs explaining 88% of the observed variance, while initialised CMIP5 models only explained 42% of the variance. Around 55% of the observed variance in CMIP6 models after 1980 can be attributed to natural forcings, which the CMIP6 models represent accurately. The significant additional predictive ability of initialised CMIP6 models beyond the natural forcing, proves them to be powerful predictive tools.
This study used a particularly large ensemble of 30 CMIP5 models and 28 newer CMIP6 models. Nine models from the Detection and Attribution Model Intercomparison Project (DAMIP) of CMIP6 were also used, which can represent the contribution of individual forcings to climate variations. Historical simulations using these models ran from 1850 to 2014. Initialised simulations were used to make predictions of the next ten years of North Atlantic gyre temperatures starting in 1960 and running until 2005 for the CMIP5 models, and 2014 for CMIP6 simulations. These simulations were then compared with observational data to assess the skill of the predictions. This is the first study of this kind to contrast CMIP5 and CMIP6 simulations, highlighting advancements made in the newer models, while combining different skill metrics to assess the contribution of different climate forcings to the prediction skill is also a new innovation.
The findings presented in this study improve our understanding of how we can predict North Atlantic temperatures up to 10 years into the future. This improves our ability to predict future weather patterns and how they may shift with climate change, including extreme weather events. This helps communities, businesses and policymakers make better-informed decisions around how best to adapt to these possible future events and mitigate their impact.
Due to its wide‐ranging impacts, predicting decadal variations of sea surface temperature (SST) in the subpolar North Atlantic remains a key goal of climate science. Here, we compare the representation of observed subpolar SST variations since 1960 in initialized and uninitialized historical simulations from the 5th and 6th phases of the Coupled Model Intercomparison Project (CMIP5/6). Initialized decadal hindcasts from CMIP6 explain 88% of observed SST variance post‐1980 in the subpolar gyre at lead years 5‐7 (77% in uninitialized simulations) compared to 42% (8%) in CMIP5, indicating a more prominent role for forcing in driving observed subpolar SST changes than previously thought. Analysis of single‐forcing experiments suggests much of this correlation is due to natural forcing, explaining ∼55% of the observed variance. The amplitude of observed subpolar SST variations is underestimated in historical simulations and improved by initialization in CMIP6, indicating continued value of initialization for predicting North Atlantic SST.