Potentially predictable patterns of extratropical tropospheric circulation in an ensemble of climate simulations with the COLA AGCM

Abstract

ABSTRACT:A variance decomposition approach to estimate the potentially predictable component of seasonal means has been further developed to separately diagnose the boundary-forced component and the slowly varying internal dynamics component. This decomposition of the potentially predictable component has been applied to an ensemble of ten 50-year simulations of the 500 hPa geopotential height field from the Center for Ocean-Land-Atmosphere Studies global atmospheric model forced by sea-surface temperatures. The model performance is analysed for the winter season and compared with the National Centers for Environmental Prediction reanalysis. The majority of the hemispheric potentially predictable patterns, identified as the empirical orthogonal functions of the potentially predictable component of the seasonal mean fields, are captured by the model. These include all the patterns forced by the El Niño-Southern Oscillation in the Southern Hemisphere, and the dominant potentially predictable patterns in the Northern Hemisphere, i.e. the Pacific-North American pattern. However, the boreal winter Western Pacific Oscillation and Tropical Northern Hemisphere pattern, which are well simulated by some other general circulation models, are not well captured.

Assessment of Modes of Interannual Variability of Southern Hemisphere Atmospheric Circulation in CMIP5 Models

“…Such methods are based on the premise that the seasonal mean can be considered as a random variable, with a seasonal ''population'' mean and departures from that mean (Zheng and Frederiksen 2004;Frederiksen and Zheng 2007b). Zheng and Frederiksen (2004) referred to these two components as the slow and intraseasonal components of the seasonal mean.…”

Projections of Southern Hemisphere atmospheric circulation interannual variability

“…Secondary intraseasonal modes of variability have mid-latitude wave-4 (DJF) or wave-3 (JJA) structures reflecting the impact on the seasonal mean of atmospheric blocking, the Madden-Julian oscillation, or the internal instability of the mid-latitude atmospheric flow. Subsequent studies (Zheng et al 2009;Grainger et al 2011b), found very similar modes of variability using ensembles of Atmosphere-only general circulation models (AGCMs). Grainger et al (2013) analysed the modes of variability in CGCMs from the coupled model intercomparison project phase 3 (CMIP3, Meehl et al 2007) dataset.…”

“…With an ensemble of general circulation model runs, it is possible to separate the internal variability in climate fields from influences associated with external forcing (Zheng et al 2009). For the CMIP5 CGCMs, the external forcing is associated with changes in anthropogenic greenhouse gases, aerosols, stratospheric ozone and land use; see Collins et al (2013) and references therein for forcing details.…”

“…Given this conceptual model, covariance matrices for the components of the seasonal mean climate field can be estimated using time series of monthly mean anomalies (Zheng et al 2009). In the following, i and j are any pair from the set of (1,…, N) geographical locations (e.g.…”

“…Covariance matrices for each component of the seasonal mean are found by estimating the interannual covariance of the components between all pairs of grid points using monthly anomalies Zheng et al 2009). As in other studies of atmospheric circulation variability (e.g.…”