Previous statistical detection methods based partially on climate model simulations indicate that, globally, the observed warming lies very probably outside the natural variations. We use a more simple approach to assess recent warming at different spatial scales without making explicit use of climate simulations. It considers the likelihood that the observed recent clustering of warm record-breaking mean temperatures at global, regional and local scales may occur by chance in a stationary climate. Under two statistical null-hypotheses, autoregressive and long-memory, this probability turns to be very low: for the global records lower than p = 0.001, and even lower for some regional records. The picture for the individual long station records is not as clear, as the number of recent record years is not as large as for the spatially averaged temperatures.
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