Abstract
Abstract. In this paper, we present and evaluate the skill of an EC-Earth3.3 decadal prediction system contributing to the Decadal Climate Prediction Project – Component A (DCPP-A). This prediction system is capable of skilfully simulating past global mean surface temperature variations at interannual and decadal forecast times as well as the local surface temperature in regions such as the tropical Atlantic, the Indian Ocean and most of the continental areas, although most of the skill comes from the representation of the external radiative forcings. A benefit of initialization in the predictive skill is evident in some areas of the tropical Pacific and North Atlantic oceans in the first forecast years, an added value that is mostly confined to the south-east tropical Pacific and the eastern subpolar North Atlantic at the longest forecast times (6–10 years). The central subpolar North Atlantic shows poor predictive skill and a detrimental effect of initialization that leads to a quick collapse in Labrador Sea convection, followed by a weakening of the Atlantic Meridional Overturning Circulation (AMOC) and excessive local sea ice growth. The shutdown in Labrador Sea convection responds to a gradual increase in the local density stratification in the first years of the forecast, ultimately related to the different paces at which surface and subsurface temperature and salinity drift towards their preferred mean state. This transition happens rapidly at the surface and more slowly in the subsurface, where, by the 10th forecast year, the model is still far from the typical mean states in the corresponding ensemble of historical simulations with EC-Earth3. Thus, our study highlights the Labrador Sea as a region that can be sensitive to full-field initialization and hamper the final prediction skill, a problem that can be alleviated by improving the regional model biases through model development and by identifying more optimal initialization strategies.
Highlights
Interest in seasonal to decadal climate predictions has grown in recent years due to their potential to provide relevant climate information for decision-making in different socio-economic sectors (e.g. Suckling, 2018; SolarajuMurali et al, 2019; Merryfield et al, 2020)
We have presented and evaluated the predictive skill of a decadal forecast system with EC-Earth, based on full-field initialization, that contributes to the Decadal
– In agreement with other decadal forecast systems (e.g. Yeager et al, 2018; Robson et al, 2018), EC-Earth3 is able to skilfully simulate the global mean surface temperature at short and long forecast times, with a large part of the skill arising from changes in the external forcings
Summary
Interest in seasonal to decadal climate predictions has grown in recent years due to their potential to provide relevant climate information for decision-making in different socio-economic sectors (e.g. Suckling, 2018; SolarajuMurali et al, 2019; Merryfield et al, 2020). Climate predictions have provided novel ways of evaluating and comparing climate simulations with observations and improve our understanding of the intrinsic predictability of the climate system, including the key mechanisms operating at interannual to decadal timescales On these timescales a large part of the predictable signal of climate variations during the observational period is attributable to changes in external radiative forcings (i.e. changes in the climate system energy balance), which can be of natural (e.g. solar irradiance and volcanic aerosols) or anthropogenic (e.g. greenhouse gas concentrations, land use changes and anthropogenic aerosols) origin. Estimates of past changes in these radiative forcings are prescribed as boundary conditions to drive the so-called historical climate simulations, which investigate the influence of the forcings on the recent climate variations These experiments are continued into the future as climate projections with imposed anthropogenic radiative forcings that follow different theoretically derived socio-economic emission scenarios (O’Neill et al, 2016)
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