skill of the global ocean carbon sink due to initialization is up to 6 years for some models, with longer regional predictability in single models • Predictive skill due to initialization for the land carbon sink of up to 2 years is primarily maintained in the tropics and extra-tropics • Anomalies of atmospheric CO 2 growth rate are predictable up to 2 years and are limited by the land carbon sink predictability horizon

Abstract. Major disruptions of the winter season, high-latitude stratospheric polar vortices can result in stratospheric anomalies that persist for months. These sudden stratospheric warming events are recognized as an important potential source of forecast skill for surface climate on subseasonal to seasonal timescales. Realizing this skill in operational subseasonal forecast models remains a challenge, as models must capture both the evolution of the stratospheric polar vortices in addition to their coupling to the troposphere. The processes involved in this coupling remain a topic of open research. We present here the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project. SNAPSI is a new model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortex disturbances for surface predictability in subseasonal to seasonal forecast models. Based on a set of controlled, subseasonal ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. First, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. Second, to attribute specific extreme events to stratospheric variability. Third, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models. Fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. Although not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. The output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of subseasonal forecasts.

Abstract. Major disruptions of the winter season, high-latitude, stratospheric polar vortices can result in stratospheric anomalies that persist for months. These sudden stratospheric warming events are recognized as an important potential source of forecast skill for surface climate on subseasonal to seasonal timescales. Realizing this skill in operational subseasonal forecast models remains a challenge, as models must capture both the evolution of the stratospheric polar vortices in addition to their coupling to the troposphere. The processes involved in this coupling remain a topic of open research. We present here the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project. SNAPSI is a new model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, subseasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. First, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. Second, to attribute specific extreme events to stratospheric variability. Third, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, and fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. Although not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. The output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of subseasonal forecasts.

Weather and climate extremes have enormous impacts on society, and are becoming more severe and frequent as the world warms. Most developing countries in the Asia-Pacific region are highly vulnerable to risks associated with heatwaves and cold spells, droughts and floods, tropical cyclones, wildfires, and other extremes. To support regional and international cooperation for research on weather and climate extremes in the Asia-Pacific region, the World Climate Research Programme (WCRP) hosted an online workshop on Extremes in Climate Prediction Ensembles (ExCPEns) from 25 to 28 October 2021 with the support of Asia-Pacific Network for Global Change Research (APN). The workshop aimed to advance the rapidly emerging science of exploiting subseasonal, seasonal, annual to decadal and long-term prediction ensembles to improve the prediction and understanding of weather and climate extreme events. An Early Career Scientist (ECS) event followed the ExCPEns workshop and consisted of a discussion and networking forum for ECS from APN member developing countries, along with a series of ECS training lectures and discussion sessions. Through the workshop and discussions among stakeholders, important scientific results on prediction and future changes in weather and climate extremes were communicated. Moreover, new research topics spanning these different time scales were identified and prioritized.

Multi-system seasonal hindcasts supporting operational seasonal forecasts of the Copernicus Climate Change Service (C3S) are examined to estimate probabilities that El Niño and La Niña episodes more extreme than any in the reliable observational record could occur in the current climate. With 184 total ensemble members initialized each month from 1993 to 2016, this dataset greatly multiplies the realizations of ENSO variability during this period beyond the single observed realization, potentially enabling a detailed assessment of the chances of extreme ENSO events. The validity of such an assessment is predicated on model fidelity, which is examined through two-sample Cramér–von Mises tests. These do not detect differences between observed and modeled distributions of the Niño 3.4 index once multiplicative adjustments are applied to the latter to match the observed variance, although differences too small to be detected cannot be excluded. Statistics of variance-adjusted hindcast Niño 3.4 values imply that El Niño and La Niña extremes exceeding any that have been instrumentally observed would be expected to occur with a > 3% chance per year on average across multiple realizations of the hindcast period. This estimation could also apply over the next several decades, provided ENSO variability remains statistically similar to the hindcast period.