ALPACA - Advancing the Long-range Prediction, Attribution, and forecast Calibration of Amoc and its climate impacts
Lead Research Organisation:
NATIONAL OCEANOGRAPHY CENTRE
Department Name: Science and Technology
Abstract
The Atlantic Meridional Overturning Circulation (AMOC) is a crucial component of the climate system due to its role in heat and salt transports, as well as its role in transporting and storing carbon. Variability in the strength of AMOC has been linked to important climate impacts, for instance, the number of Atlantic Hurricanes, anomalous Sahel precipitation, and European weather. Therefore, improved predictions of the AMOC would have important societal benefits.
Despite its importance, the predictability of the AMOC remains relatively unexplored on timescales from one season to 10 years ahead, and many uncertainties persist in our understanding of AMOC variability. For example, we are unsure of the relative importance of different processes in driving AMOC variability on different timescales and latitudes, nor how predictable they are in state-of-the-art forecasting systems. Recent studies have provided considerable evidence that the atmospheric circulation in the North Atlantic is much more predictable than previously thought on these timescales. However, the predicted signals are far too small (the so-called signal-to-noise paradox) and predictions need to be calibrated to provide credible forecasts of society relevant variables, such as surface temperature. Given that atmospheric circulation is a key driver of AMOC, then it follows that AMOC predictions on these timescales may also suffer from similar signal-to-noise issues. Furthermore, predictions of AMOC, and its climate impact, could be improved by extending the published statistical calibrations to the ocean circulation.
ALPACA will utilise AMOC observations (RAPID and OSNAP) and observation-based AMOC reconstructions to assess the quality of current AMOC forecasts in state-of-the-art seasonal and decadal prediction systems. Furthermore, we will evaluate the processes that contribute to skill and assess their consistency across models. We will also use new simulations to better understand the relative roles of different processes in driving observed variability on different timescales, and we will leverage new large ensemble simulations to quantify the role of external forcing in driving AMOC variability and change. Finally, by exploiting this new understanding, we will determine whether seasonal-to-decadal predictions of AMOC and its climate impacts can be improved through physically-consistent statistical calibrations that reduce the signal-to-noise errors in predictions.
ALPACA is a collaboration between the National Centre for Atmospheric Science at the University of Reading, The National Oceanography Centre Southampton, The University of Exeter, and the Met Office Hadley Centre from the U.K., and The National Center for Atmospheric Research and the University of Miami, from the U.S, and the Barcelona Supercomputing Center from Spain.
Despite its importance, the predictability of the AMOC remains relatively unexplored on timescales from one season to 10 years ahead, and many uncertainties persist in our understanding of AMOC variability. For example, we are unsure of the relative importance of different processes in driving AMOC variability on different timescales and latitudes, nor how predictable they are in state-of-the-art forecasting systems. Recent studies have provided considerable evidence that the atmospheric circulation in the North Atlantic is much more predictable than previously thought on these timescales. However, the predicted signals are far too small (the so-called signal-to-noise paradox) and predictions need to be calibrated to provide credible forecasts of society relevant variables, such as surface temperature. Given that atmospheric circulation is a key driver of AMOC, then it follows that AMOC predictions on these timescales may also suffer from similar signal-to-noise issues. Furthermore, predictions of AMOC, and its climate impact, could be improved by extending the published statistical calibrations to the ocean circulation.
ALPACA will utilise AMOC observations (RAPID and OSNAP) and observation-based AMOC reconstructions to assess the quality of current AMOC forecasts in state-of-the-art seasonal and decadal prediction systems. Furthermore, we will evaluate the processes that contribute to skill and assess their consistency across models. We will also use new simulations to better understand the relative roles of different processes in driving observed variability on different timescales, and we will leverage new large ensemble simulations to quantify the role of external forcing in driving AMOC variability and change. Finally, by exploiting this new understanding, we will determine whether seasonal-to-decadal predictions of AMOC and its climate impacts can be improved through physically-consistent statistical calibrations that reduce the signal-to-noise errors in predictions.
ALPACA is a collaboration between the National Centre for Atmospheric Science at the University of Reading, The National Oceanography Centre Southampton, The University of Exeter, and the Met Office Hadley Centre from the U.K., and The National Center for Atmospheric Research and the University of Miami, from the U.S, and the Barcelona Supercomputing Center from Spain.
