Group seminar on 3. May, 14:15 CET

Bias teleconnection and the associated atmospheric variability originated in erroneous sea surface temperature in equatorial Indian ocean

Yuan-Bing Zhao

New opportunities for understanding the atmospheric spatial and temporal variability are offered by coupled high-resolution climate models. However, the models still suffer from significant systematic errors (biases) calling for an approach that assesses circulation variability in relation to biases. Furthermore, biases in simulated variability are often of remote origin. In this talk I will present a novel framework and its application to the multivariate, multi-scale variability evaluation in relation to remote biases. Centennial simulations are first carried out using a general circulation model PLASIM and a perfect-model framework. Biases are set to originate from regional errors in the surface forcing by prescribed sea surface temperature (SST). A reference simulation is forced with the monthly SST from ERA-20C reanalyses from January 1900 to December 2010, and a sensitivity simulation is forced with the same SST with addition of regional perturbation in Indian ocean that mimic the errors in the surface forcing of the atmosphere and lead to systematic errors in the simulated circulation and variability. The biases are computed as the time-averaged difference between the reference and sensitivity simulations. Using the normal-mode function decomposition, the biases are then evaluated in two main dynamical regimes: quasi-geostrophic (balanced) regime and unbalanced regime. The results show that biases of balanced flow are mainly established in the zonal-mean (k=0) state and at planetary scales (k=1~4), which are manifested in baroclinic Rossby waves trapped in the tropics and barotropic Rossby wave train across the extra-tropics in winter Hemisphere with the latter being triggered by the former. For the unbalanced flow, the biases are projected predominantly on baroclinic Kelvin waves. The impacts of biases on spatio-temporal variability are further investigated in spectral space.