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The potential for multiyear prediction of impactful Earth system change remains relatively underexplored compared to shorter (subseasonal to seasonal) and longer (decadal) timescales. In this study, we introduce a new initialized prediction system using the Community Earth System Model version 2 (CESM2) that is specifically designed to probe potential and actual prediction skill at lead times ranging from 1 month out to 2 years. The Seasonal-to-Multiyear Large Ensemble (SMYLE) consists of a collection of 2-year-long hindcast simulations, with four initializations per year from 1970 to 2019 and an ensemble size of 20. A full suite of output is available for exploring near-term predictability of all Earth system components represented in CESM2. We show that SMYLE skill for El Niño–Southern Oscillation is competitive with other prominent seasonal prediction systems, with correlations exceeding 0.5 beyond a lead time of 12 months. A broad overview of prediction skill reveals varying degrees of potential for useful multiyear predictions of seasonal anomalies in the atmosphere, ocean, land, and sea ice. The SMYLE dataset, experimental design, model, initial conditions, and associated analysis tools are all publicly available, providing a foundation for research on multiyear prediction of environmental change by the wider community.

The prediction of atmospheric CO2 concentrations is limited by the high interannual variability (IAV) of terrestrial gross primary productivity (GPP). However, there are large uncertainties in the drivers of GPP IAV among Earth system models (ESMs). Here, we evaluate the impact of these uncertainties on the predictability of atmospheric CO2 in six ESMs. We use regression analysis to determine the role of environmental drivers on (i) the patterns of GPP IAV, and (ii) the predictability of GPP. There are large uncertainties in the spatial distribution of GPP IAV. Although all ESMs agree on the high IAV in the tropics, several ESMs have unique hotspots of GPP IAV. The main driver of GPP IAV is temperature in the ESMs using the Community Land Model, and soil moisture in IPSL-CM6A-LR and MPI-ESM-LR, revealing underlying differences in the source of GPP IAV among ESMs. Between 13 % and 24 % of the GPP IAV is predictable one year ahead, with four out of six ESMs between 19 % and 24 %. Up to 32 % of the GPP IAV induced by soil moisture is predictable, while only 7 % to 13 % of the GPP IAV induced by radiation. The results show that while ESMs are fairly similar in their ability to predict themselves, their predicted contribution to the atmospheric CO2 variability originates from different regions and is caused by different drivers. A higher coherence in atmospheric CO2 predictability could be achieved by reducing uncertainties of GPP sensitivity to soil moisture, and by accurate observational products for GPP IAV.

We map the weekly position of the Antarctic Polar Front (PF) in the Southern Ocean over a 12-year period (2002–2014) using satellite sea surface temperature (SST) estimated from cloud-penetrating microwave radiometers. Our study advances previous efforts to map the PF using hydrographic and satellite data and provides a unique realization of the PF at weekly resolution across all longitudes (doi:10.1594/PANGAEA.855640). The mean path of the PF is asymmetric; its latitudinal position spans from 44 to 64° S along its circumpolar path. SST at the PF ranges from 0.6 to 6.9 °C, reflecting the large spread in latitudinal position. The average intensity of the front is 1.7 °C per 100 km, with intensity ranging from 1.4 to 2.3 °C per 100 km. Front intensity is significantly correlated with the depth of bottom topography, suggesting that the front intensifies over shallow bathymetry. Realizations of the PF are consistent with the corresponding surface expressions of the PF estimated using expendable bathythermograph data in the Drake Passage and Australian and African sectors. The climatological mean position of the PF is similar, though not identical, to previously published estimates. As the PF is a key indicator of physical circulation, surface nutrient concentration, and biogeography in the Southern Ocean, future studies of physical and biogeochemical oceanography in this region will benefit from the provided data set.

We investigate variability in the surface ocean carbonate ion concentration ([CO32−]) on the basis of a long control simulation with a fully-coupled Earth System Model. The simulation is run with a prescribed, pre-industrial atmospheric CO2 concentration for 1000 years, permitting investigation of natural [CO32−] variability on interannual to multi-decadal timescales. We find high interannual variability in surface [CO32−] in the tropical Pacific and at the boundaries between the subtropical and subpolar gyres in the Northern Hemisphere, and relatively low interannual variability in the centers of the subtropical gyres and in the Southern Ocean. Statistical analysis of modeled [CO32−] variance and autocorrelation suggests that significant anthropogenic trends in the saturation state of aragonite (Ωaragonite) are already or nearly detectable at the sustained, open-ocean timeseries sites, whereas several decades of observations are required to detect anthropogenic trends in Ωaragonite in the tropical Pacific, North Pacific, and North Atlantic. The detection timescale for anthropogenic trends in pH is shorter than that for Ωaragonite, due to smaller noise-to-signal ratios and lower autocorrelation in pH. In the tropical Pacific, the leading mode of surface [CO32−] variability is primarily driven by variations in the vertical advection of dissolved inorganic carbon (DIC) in association with El Niño–Southern Oscillation. In the North Pacific, surface [CO32−] variability is caused by circulation-driven variations in surface DIC and strongly correlated with the Pacific Decadal Oscillation, with peak spectral power at 20–30 year periods. North Atlantic [CO32−] variability is also driven by variations in surface DIC, and exhibits weak correlations with both the North Atlantic Oscillation and the Atlantic Multidecadal Oscillation. As the scientific community seeks to detect the anthropogenic influence on ocean carbonate chemistry, these results will aid the interpretation of trends calculated from spatially- and temporally-sparse observations.