Quantifying stratospheric biases and identifying their potential sources in subseasonal forecast systems
Zachary D. Lawrence,Marta Abalos,Blanca Ayarzagüena,David Barriopedro,Amy H. Butler,Natalia Calvo,Alvaro de la Cámara,Andrew Charlton-Perez,Daniela I. V. Domeisen,Etienne Dunn-Sigouin,Javier García-Serrano,Chaim I. Garfinkel,Neil P. Hindley,Liwei Jia,Martin Jucker,Alexey Y. Karpechko,Hera Kim,Andrea L. Lang,Simon H. Lee,Pu Lin,Marisol Osman,Froila M. Palmeiro,Judith Perlwitz,Inna Polichtchouk,Jadwiga H. Richter,Chen Schwartz,Seok-Woo Son,Irina Statnaia,Masakazu Taguchi,Nicholas L. Tyrrell,Corwin J. Wright,and Rachel W.-Y. Wu
Forecast models that are used to predict weather often struggle to represent the Earth’s stratosphere. This may impact their ability to predict surface weather weeks in advance, on subseasonal-to-seasonal (S2S) timescales. We use data from many S2S forecast systems to characterize and compare the stratospheric biases present in such forecast models. These models have many similar stratospheric biases, but they tend to be worse in systems with low model tops located within the stratosphere.
Forecast models that are used to predict weather often struggle to represent the Earth’s...