A Novel Initialization Technique for Decadal Climate Predictions

Abstract

Model initialization is a matter of transferring the observed information available at the start of a forecast to the model. An optimal initialization is generally recognized to be able to improve climate predictions up to a few years ahead. However, systematic errors in models make the initialization process challenging. When the observed information is transferred to the model at the initialization time, the discrepancy between the observed and model mean climate causes the drift of the prediction toward the model-biased attractor. Although such drifts can be generally accounted for with a posteriori bias correction techniques, the bias evolving along the prediction might affect the variability that we aim at predicting, and disentangling the small magnitude of the climate signal from the initial drift to be removed represents a challenge. In this study, we present an innovative initialization technique that aims at reducing the initial drift by performing a quantile matching between the observed state at the initialization time and the model state distribution. The adjusted initial state belongs to the model attractor and the observed variability amplitude is scaled toward the model one. Multi-annual climate predictions integrated for 5 years and run with the EC-Earth3 Global Coupled Model have been initialized with this novel methodology, and their prediction skill has been compared with the non-initialized historical simulations from CMIP6 and with the same decadal prediction system but based on full-field initialization. We perform a skill assessment of the surface temperature, the heat content in the ocean upper layers, the sea level pressure, and the barotropic ocean circulation. The added value of the quantile matching initialization is shown in the North Atlantic subpolar region and over the North Pacific surface temperature as well as for the ocean heat content up to 5 years. Improvements are also found in the predictive skill of the Atlantic Meridional Overturning Circulation and the barotropic stream function in the Labrador Sea throughout the 5 forecast years when compared to the full field method.

Large spread in interannual Variance of atmospheric CO2 concentration across CMIP6 Earth System Models

“…Near-term carbon cycle forecasts attempt to predict the combined effect of human activity and natural climate variability on the global carbon cycle. These predictions are performed using Earth System Models (ESMs) that include detailed representations of the global carbon cycle, coupled to climate models, and build upon the progresses made by the near-term climate prediction community in the last years [6][7][8][9][10][11] .…”

Large spread in interannual variance of atmospheric CO2 concentration across CMIP6 Earth System Models

“…Near-term carbon cycle forecasts attempt to predict the combined effect of human activity and natural climate variability on the global carbon cycle. These predictions are performed using Earth System Models (ESMs) that include detailed representations of the global carbon cycle, coupled to climate models, and build upon the progress made by the near-term climate prediction community in the last years [6][7][8][9][10][11] .…”