We develop a theoretical approach to quantify the effect of long-term trends on the expected number of extremes in generic time series, using analytical solutions and Monte Carlo simulations. We apply our method to study the effect of warming trends on heat records. We find that the number of record-breaking events increases approximately in proportion to the ratio of warming trend to short-term standard deviation. Short-term variability thus decreases the number of heat extremes, whereas a climatic warming increases it. For extremes exceeding a predefined threshold, the dependence on the warming trend is highly nonlinear. We further find that the sum of warm plus cold extremes increases with any climate change, whether warming or cooling. We estimate that climatic warming has increased the number of new global-mean temperature records expected in the last decade from 0.1 to 2.8. For July temperature in Moscow, we estimate that the local warming trend has increased the number of records expected in the past decade fivefold, which implies an approximate 80% probability that the 2010 July heat record would not have occurred without climate warming.I'm still mulling over how much I believe the causal claim here, but I really like the technique of modeling record-breaking as a function of the system's state over time. Some other potential applications immediately spring to mind...
Attributing climate records to climate change
RealClimate) and Dim Coumou just published a pretty interesting paper in this week's PNAS, in which they claim last summer's Moscow heat wave was 80% likely to have been due to the observed warming trend in the city. There's a great writeup of the paper (including how and why it contradicts an earlier finding claiming that the heat wave was not due to trends; urban heat island fans should check that one out), but what's particularly noteworthy is the methodology by which they claim attribution of the heat wave to the climate trend: