Beschreibung: Antarctic sea ice prediction has garnered increasing attention in recent years, particularly in the context of the recent record lows of February 2022 and 2023. As Antarctica becomes a climate change hotspot, as polar tourism booms, and as scientific expeditions continue to explore this remote continent, the capacity to anticipate sea ice conditions weeks to months in advance is in increasing demand. Spurred by recent studies that uncovered physical mechanisms of Antarctic sea ice predictability and by the intriguing large variations of the observed sea ice extent in recent years, the Sea Ice Prediction Network South (SIPN South) project was initiated in 2017, building upon the Arctic Sea Ice Prediction Network. The SIPN South project annually coordinates spring-to-summer predictions of Antarctic sea ice conditions, to allow robust evaluation and intercomparison, and to guide future development in polar prediction systems. In this paper, we present and discuss the initial SIPN South results collected over six summer seasons (December-February 2017-2018 to 2022-2023). We use data from 22 unique contributors spanning five continents that have together delivered more than 3000 individual forecasts of sea ice area and concentration. The SIPN South median forecast of the circumpolar sea ice area captures the sign of the recent negative anomalies, and the verifying observations are systematically included in the 10-90% range of the forecast distribution. These statements also hold at the regional level except in the Ross Sea where the systematic biases and the ensemble spread are the largest. A notable finding is that the group forecast, constructed by aggregating the data provided by each contributor, outperforms most of the individual forecasts, both at the circumpolar and regional levels. This indicates the value of combining predictions to average out model-specific errors. Finally, we find that dynamical model predictions (i.e., based on process-based general circulation models) generally perform worse than statistical model predictions (i.e., data-driven empirical models including machine learning) in representing the regional variability of sea ice concentration in summer. SIPN South is a collaborative community project that is hosted on a shared public repository. The forecast and verification data used in SIPN South are publicly available in near-real time for further use by the polar research community, and eventually, policymakers.

Beschreibung: 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.

Beschreibung: This paper proposes a semiempirical method to reconstruct ultraviolet erythemal (UVER) irradiance in the past from total shortwave radiation (SW) and total ozone column (TOC) measurements and has been used to obtain a long-term reconstructed UVER series in central Spain. The method is based on radiative transfer modeling combined with empirical relationships, giving an equation that relates UVER and SW irradiance measurements, solar zenith angle, as well as UVER and SW irradiance values calculated under cloudless conditions. TOC measurements are needed as input for the cloudless modeling. Hourly UVER radiation values have been reconstructed and compared with ground-based measurements for seven Spanish locations. The reconstructed hourly UVER irradiance values are in good agreement with the measurements, showing a determination coefficient between 0.95 and 0.99, and the lowest root mean square errors (rmse) in summer taking values from 5% to 9% in the seven stations. Reconstructed daily UVER doses have been compared for eight stations, showing a better agreement than in the hourly case with rmse values from 3% to 8% in summer and from 4% to 9% when all seasons are taken into account. A reconstructed 10 min UVER irradiance data set from 1991 to 2010 has been calculated using the proposed method for the city of Valladolid. Statistically significant UVER trends appear in summer and autumn when UVER levels increased 3.5% and 4.1% per decade, respectively. Brightening was found for SW measurements in the same period, with a statistically significant trend of 4.4% and 5.8% per decade in summer and autumn.