First systematic verification of warm conveyor belts in weather prediction models
Warm conveyor belts (WCBs) are poleward moving air streams that ascend in the warm sector of low-pressure systems within two days from the lowest 2 km to the upper troposphere at a height of 8-12 km. During the ascent, condensation and the formation of cloud droplets is associated with the release of latent heat. These so-called diabatic processes are important for the properties of the rising air masses, the intensity of the air mass transport in the upper troposphere, and the impact on the atmospheric dynamics at an altitude of 8-12 km. The air masses can contribute significantly to the formation of high-pressure systems, their amplification, and their persistence over a certain region. Such persistent high pressure systems are often associated with heat waves in summer and cold air outbreaks in winter. Correspondingly, a reliable forecast is of enormous importance for socio-economic activities. Compared to other weather types, forecasting persistent high-pressure weather conditions over Europe is still a major challenge for current numerical weather prediction models [1].
The young investigator group "Large-scale Dynamics and Predictability" tackles these challenges within the project "SPREADOUT" (Sub-seasonal predictability: understanding the role of diabatic outflow) and examines the influence of diabatic processes in the WCB on the large-scale flow and its representation in numerical weather prediction models. For this purpose, we develop statistical diagnostics [2][3][4], which for the first time enable to compute the WCB activity in a large data set of weather forecasts spanning the last 20 years.
Our results show that the numerical weather prediction model of the European Center for Medium-Range Weather Forecasts (ECMWF) systematically underestimates the WCB activity in certain regions over the North Atlantic and North Pacific as early as three days after the forecast was initialized [5]. These errors in the model continue to grow in the following 10 forecast days and then remain constant. In particular, the relative underestimation of WCB occurrence by around 10% over the Atlantic is a first indication that there is a connection between the underestimation of WCB activity and the underestimation of persistent high-pressure systems over Europe found at a similar magnitude in several studies. Further, we find that the forecast of a WCB occurrence at a given location is possible up to around 8-10 days in the future which corresponds well to the forecast horizon of low-pressure systems in numerical models. The errors in the WCB activity can be attributed to errors in the moisture transport in the lower and middle layers of the troposphere (1-5 km altitude). This information is important to model developers as it indicates a potential pathway to improve weather prediction, especially for forecasts on sub-seasonal time scales beyond 10 days.
Fig. 2: Average forecast skill for the WCB ascent (3-6 km altitude) (winter, 1997-2017). A Fair Brier Skill Score of 1 implies a perfect forecast and a 0 means that the forecast is only as good as a simple reference forecast based on climatological statistics.
Overall, our study for the first time provides a systematic assessment of WCB activity in a numerical weather prediction model and suggests that a better representation of the processes in the WCB can lead to an improvement in the prediction of persistent high-pressure systems. Such a link between WCBs and various large-scale weather systems is part of our ongoing research.
Literature:
[1] Büeler, D., Ferranti, L., Magnusson, L., Quinting, J. F., Grams, C. M. (2021). Year-round sub-seasonal forecast skill for Atlantic-European weather regimes. Quarterly Journal of the Royal Atmospheric Society, https://doi.org/10.1002/qj.4178
[2] Quinting, J. F., & Grams, C. M. (2021). Toward a Systematic Evaluation of Warm Conveyor Belts in Numerical Weather Prediction and Climate Models. Part I: Predictor Selection and Logistic Regression Model. Journal of the Atmospheric Sciences, 78(5), 1465-1485, https://doi.org/10.1175/JAS-D-20-0139.1
[3] Quinting, J. F., & Grams, C. M. (2021). EuLerian Identification of ascending Air Streams (ELIAS 2.0) in Numerical Weather Prediction and Climate Models. Part I: Development of deep learning model. Geoscientific Model Development, in discussion. https://doi.org/10.5194/gmd-2021-276
[4] Quinting, J. F., C. M. Grams, A. Oertel, and M. Pickl, (2021): EuLerian Identification of Ascending air Streams (ELIAS 2.0) in Numerical Weather Prediction and Climate Models. Part II: Model application to different data sets. Geoscientific Model Development Discussions, 1–24, https://doi.org/10.5194/gmd-2021-278
[5] Wandel, J., Quinting, J. F., & Grams, C. M. (2021). Toward a Systematic Evaluation of Warm Conveyor Belts in Numerical Weather Prediction and Climate Models. Part II: Verification of operational reforecasts. Journal of the Atmospheric Sciences, https://doi.org/10.1175/JAS-D-20-0385.1
Jan Wandel, Junior Investigator Group: Large-scale Dynamics and Predictability
LINK: https://www.imk-tro.kit.edu/english/7425.php