Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.

There is a high demand and expectation for sub-seasonal to seasonal (S2S) prediction which provides forecasts beyond 2 weeks, but less than 3 months ahead. To assess the potential benefit of artificial intelligence (AI) methods for S2S prediction through better postprocessing of ensemble prediction system outputs, the World Meteorological Organization (WMO) coordinated a prize challenge in 2021 to improve sub-seasonal prediction. The goal of this competition was to produce the most skillful forecasts of precipitation and two-meter temperature globally averaged over forecast weeks 3 and 4, and weeks 5 and 6 for the year 2020 using artificial intelligence techniques. The top three submissions, described in this article, succeeded in producing S2S forecasts significantly more skillful than the bias-corrected ECMWF operational reference forecasts, particularly for precipitation, through improved calibration of the ECMWF raw forecast outputs or multi-model combination. These forecast improvements should benefit the use of S2S forecasts in applications.

The growth rate of atmospheric CO2 on inter-annual time scales is largely controlled by the response of the land and ocean carbon sinks to climate variability. Therefore, the effect of CO2 emission reductions to achieve the Paris Agreement on atmospheric CO2 concentrations may be disguised by internal variability, and the attribution of a reduction in atmospheric CO2 growth rate to CO2 emission reductions induced by a policy change is unclear for the near term. We use 100 single-model simulations and interpret CO2 emission reductions starting in 2020 as a policy change from scenario Representative Concentration Pathway (RCP) 4.5 to 2.6 in a comprehensive causal theory framework. Five-year CO2 concentration trends grow stronger in 2021–2025 after CO2 emission reductions than over 2016–2020 in 30% of all realizations in RCP2.6 compared to 52% in RCP4.5 without CO2 emission reductions. This implies that CO2 emission reductions are sufficient by 42%, necessary by 31% and both necessary and sufficient by 22% to cause reduced atmospheric CO2 trends. In the near term, these probabilities are far from certain. Certainty implying sufficient or necessary causation is only reached after, respectively, ten and sixteen years. Assessments of the efficacy of CO2 emission reductions in the near term are incomplete without quantitatively considering internal variability.

On interannual timescales the growth rate of atmospheric CO 2 is largely controlled by the response of the land and ocean carbon sinks to climate variability. Yet, it is unknown to what extent this variability limits the predictability of atmospheric CO 2 variations. Using perfect-model Earth System Model simulations, we show that variations in atmospheric CO 2 are potentially predictable for 3 years. We find a 2-year predictability horizon for global oceanic CO 2 flux with longer regional predictability of up to 7 years. The 2-year predictability horizon of terrestrial CO 2 flux originates in the tropics and midlatitudes. With the predictability of the isolated effects of land and ocean carbon sink on atmospheric CO 2 of 5 and 12 years respectively, land dampens the overall predictability of atmospheric CO 2 variations. Our research shows the potential of Earth System Model-based predictions to forecast multiyear variations in atmospheric CO 2 . Plain Language SummaryThe amount of anthropogenic carbon emissions absorbed by land and ocean from the atmosphere varies annually due to their sensitivity to climate. Therefore, the atmospheric CO 2 growth rate does not strictly follow the emissions signal. Whether decadal prediction systems can also predict variations of atmospheric CO 2 has not been shown yet but is crucial to inform policy makers about the efficiency of the implementation of the Paris Agreement. Using numerical Earth System simulations in an idealized prediction framework, we show that global atmospheric CO 2 is predictable up to 3 years in advance. The global ocean and land CO 2 fluxes are predictable for 2 years. The isolated effects of the land and ocean carbon sink on atmospheric CO 2 are predictable for 5 and 12 years, respectively. Therefore, the land carbon cycle limits atmospheric CO 2 predictability. Our study demonstrates that simulation-based multiyear forecasts have the potential to predict natural atmospheric CO 2 variations.