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Abstract: The prevailed atmospheric blocking over Eastern Europe and Western Russia during July and August of 2010 led in the development of the devastating Russian heat wave. Therefore the question whether the event was predictable or not is highly important. The principal aim of this study is to examine the predictability of this high-impact atmospheric event on a seasonal time scale. To this end, a set of dynamical seasonal simulations have been carried out using an Atmospheric Global Circulation Model (AGCM). The impact of various model initializations on the predictability of this large scale event and its sensitivity to the initial conditions has been also investigated. The ensemble seasonal simulations are based on a modified version of the lagged average forecast method using different lead-time initializations of the model. The results indicated that only a few individual members reproduced the main features of the blocking system 3 months ahead. Most members missed the phase space and the propagation of the system setting limitations in the predictability of the event.

Discussion: The predictability of the Russian heat wave on a seasonal time scale has been investigated in this study. The dynamical seasonal simulations have been carried out using the state-of-the-art CAM3 AGCM. The impact of various model initializations on the predictability of the event has been also investigated because such comprehensive prognostic systems are sensitive to the initial conditions due to the chaotic nature of the atmosphere. According to the synoptic analysis, the Russian heat wave provoked by a strong omega blocking system persisted over Eastern Europe and driving warm air from Africa and Arabic peninsula to Western Russia. The vertical temperature profile over Moscow reveals an intense inversion layer coexisting with a dry air mass in the lower troposphere resulting to amplification of the anticyclone. During the blocking period the orientation of the anticyclone favored a cold northerly airflow towards the Indian Ocean which interacts with low-level warm and humid air and triggered heavy rainfall across Northern Pakistan.

Seasonal simulations of the event were based on a modified version of LAF method constructing 61 independent ensemble members initialized on January and April 2010. Each ensemble member has been integrated for 8 and 5 months ahead respectively and in this way, for the period of JJA were produced 31 members on a 5-8 months lead time and 30 members on a 2-5 months lead time.

As far as the predictability is concerned, only a few individual members in April reproduced the main features of the blocking system almost 3 months before the event. For both set of simulations the ensemble spread is relatively limited over Eastern Europe while the areas of high uncertainty are mainly located over central Russia. Most members displaced the basic characteristics of the phase space and the velocity of the system shifting the center eastward and predicting a short-lived blocking pattern. Despite the fact of the long lead period, both January and April members provided similar confidence of the forecast reliability. Thus, almost the entire members initialized on April 2010 and having 2-5 months lead time did not provide any further predictability improvement. Thus the predictability seems to be independent to the forecast horizon varying from seasonal to intra-annual time scales.

The results of this study underline the main difficulties and limitations in the seasonal simulation of such high-impact weather event. Many studies confirm that the seasonal scale predictability may be feasible but further work is required to properly assess these findings (Palmer and Anderson 1994; Hastenrath, 1995; Rowell, 1998; Lee et al., 2011). However, since the LAF method is operationally feasible, due to the fact that the LAF ensemble members can be produced during the normal operational cycle, it is of great importance to investigate furthermore the performance of such ensemble forecasting system. To this end, more high-impact weather events should be considered in order to evaluate the forecast skill and assess the effectiveness of the seasonal prediction.
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