A model intercomparison analysing the link between ozone and geopotential height anomalies in January
1NCAS-Climate, Department of Chemistry, University of Cambridge, Cambridge, UK
2Max-Plank-Institut für Chemie, Mainz, Germany
3DLR Oberpfaffenhofen, Institut für Physik der Atmosphäre, Wessling, Germany
4Max-Planck-Institut für Meteorologie, Hamburg, Germany
5University of L'Aquila, L'Aquila, Italy
6Istituto Nazionale di Geofisica e Vulcanologia e Centro Euro-Mediterraneo per i Cambiamenti Climatici, Bologna, Italy
Abstract. A statistical framework to evaluate the performance of chemistry-climate models with respect to the interaction between meteorology and ozone during northern hemisphere mid-winter, in particularly January, is used. Different statistical diagnostics from four chemistry-climate models (E39C, ME4C, UMUCAM, ULAQ) are compared with the ERA-40 re-analysis. First, we analyse vertical coherence in geopotential height anomalies as described by linear correlations between two different pressure levels (30 and 200 hPa) of the atmosphere. In addition, linear correlations between (partial) column ozone and geopotential height anomalies at 200 hPa are discussed to motivate a simple picture of the meteorological impacts on ozone on interannual timescales. Secondly, we discuss characteristic spatial structures in geopotential height and (partial) column ozone anomalies as given by their first two empirical orthogonal functions. Finally, we describe the covariance patterns between reconstructed anomalies of geopotential height and (partial) column ozone. In general we find good agreement between the models with higher horizontal resolution (E39C, ME4C, UMUCAM) and ERA-40. Some diagnostics seem to be capable of picking up model similarities (either that the models use the same dynamical core (E39C, ME4C), or that they have a high upper boundary (ME4C, UMUCAM)). The methodology allows to identify the leading modes of variability contributing to the overall ozone-geopotential height correlations and points to interesting differences between the chemistry-climate models and ERA-40. Those discrepancies have to be taken into account when providing confidence intervals for climate change integrations.