부산대학교 도서관

Subseasonal forecasts lie between medium-range and seasonal time scales with an emerging attention due to the relevance in society and by the scientific challenges involved. This study aims to (i) evaluate the development of systematic errors with lead time in the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts of surface-related variables during late spring and summer, and (ii) investigate potential relationships between the systematic errors and predictive skill. The evaluation is performed over the Northern Hemisphere midlatitudes, focusing on several regions with different climate characteristics. The results indicate five key bias patterns: (i) cold bias of daily maximum temperature (mx2t) in the April–May forecasts at all lead times in most regions; (ii) central North America with a warm bias mostly in the daily minimum temperature (mn2t); (iii) east of the Caspian Sea region with a warm and dry bias; (iv) western and Mediterranean Europe with a cold bias in mn2t mainly in April–May forecasts; and (v) continental Europe with a cold bias in the mx2t and warm bias of mn2t in the June–July forecasts. We also found substantial deviations of soil moisture and terrestrial water storage variation in most regions compared to the fifth-generation ECMWF atmospheric reanalysis (ERA5). Despite the large differences in the systematic error characteristics among the different regions, there is little relation to the skill of the subseasonal forecasts. The systematic temperature biases require further attention from model developers as diurnal cycle improvements could enhance some of the potential predictability coming from the long-memory effect of soil moisture. SIGNIFICANCE STATEMENT: Weather forecasts up to 1 week in advance have increased quality substantially in the last decades. This drives an increased attention and demand of quality forecasts for longer lead times, with 2 to 3 weeks in advance. This "subseasonal" scale has many challenges due to the chaotic nature of the atmosphere, which limits predictability, but also due to limitations in the way the current models represent Earth's system. In this work, we assess one of the most significant modeling issues: systematic errors. We found systematic errors on the diurnal range of 2-m temperature that could hamper predictability from long-term memory soil moisture conditions, requiring further attention from model development. [ABSTRACT FROM AUTHOR]