Abstract
Atmospheric blocking events are known to locally explain a large part of climate variability. However, despite their relevance, many current climate models still struggle to represent the observed blocking statistics. In this study, simulations of the global climate model EC-Earth are analysed with respect to atmospheric blocking. Seventeen simulations map the uncertainty space defined by the three-model characteristics: atmospheric resolution, physical parameterization and complexity of atmosphere–ocean interaction, namely an atmosphere coupled to an ocean model or forced by surface data. Representation of the real-world statistics is obtained from reanalyses ERA-20C, JRA-55 and ERA-Interim which agree on Northern Hemisphere blocking characteristics. Blocking events are detected on a central blocking latitude which is individually determined for each simulation. The frequency of blocking events tends to be underestimated relative to ERA-Interim over the Atlantic and western Eurasia in winter and overestimated during spring months. However, only few model setups show statistically significant differences compared to ERA-Interim which can be explained by the large inter-annual variability of blocking. Results indicate slightly larger biases relative to ERA-Interim in coupled than in atmosphere-only models but differences between the two are not statistically significant. Although some resolution dependence is present in spring, the signal is weak and only statistically significant if the physical parameterizations of the model are improved simultaneously. Winter blocking is relatively more sensitive to physical parameterizations, and this signal is robust in both atmosphere-only and coupled simulations, although stronger in the latter. Overall, the model can capture blocking frequency well despite biases in representing the mean state of geopotential height over this area. Blocking signatures of geopotential height are represented more similar to ERA-Interim and only weak sensitivities to model characteristics remain.
Highlights
Atmospheric blocking, both the phenomenon itself and its representation in numerical climate models, has been studied for almost seventy years (Elliott and Smith, 1949; Rex, 1950), especially in the Northern Hemisphere
ERA-20C differs the most from the other two reanalyses but the difference to ERA-I is not statistically significant. These small deviations of blocking frequency distribution in high-resolution reanalyses in winter on the Northern Hemisphere are in agreement with other blocking studies (e.g. Davini et al, 2012; Vial and Osborn, 2012) and considering results using extratropical cyclones (Hodges et al, 2011), which are dynamically linked to atmospheric blocking
This study examines a number of simulations with a suite of ECEarth model configurations and shows that blocking frequency, as determined by ERA-I, is statistically generally well reproduced by the model
Summary
Atmospheric blocking, both the phenomenon itself and its representation in numerical climate models, has been studied for almost seventy years (Elliott and Smith, 1949; Rex, 1950), especially in the Northern Hemisphere. Due to spatial and temporal persistence, blocking events explain a large part of the spatial and temporal climate variability (Dawson and Palmer, 2015). The phenomenon blocking is a useful tool for evaluating and improving models (D’Andrea et al, 1998; Hinton et al, 2009) and a necessity to represent for a chance at meaningful climate projections. Many climate models have a history of struggling to represent this mode of variability by underestimating the blocking occurrence (D’Andrea et al, 1998; Davini and D’Andrea, 2016) which continues in Coupled Model Intercomparison Project 3 (CMIP3), (Scaife et al, 2010) and CMIP5 generation models (Anstey et al, 2013; Dunn-Sigouin and Son, 2013; Masato et al, 2013). Many climate models have a history of struggling to represent this mode of variability by underestimating the blocking occurrence (D’Andrea et al, 1998; Davini and D’Andrea, 2016) which continues in Coupled Model Intercomparison Project 3 (CMIP3), (Scaife et al, 2010) and CMIP5 generation models (Anstey et al, 2013; Dunn-Sigouin and Son, 2013; Masato et al, 2013). Zappa et al (2014) discuss the close linkage of storm track and blocking biases during winter
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