Detecting and attribution climate change - VVSOR - VVSOR

Detecting and attribution climate change

In 1896 de Swedish scientist Arrhenius published a first estimate how much the earth would warm for a doubling of the CO2 concentration. He thought that humanity would not burn enough coal to reach that for many thousands of years. He was wrong, with the addition of oil and gas CO2 concentrations rose rapidly. In 1981 Jim Hansen used better computations to predict that the global warming signal would be statistically significant by the end of the century. That turned out to be correct. A few years later we could show the warming in the Netherlands, and now we can even determine how much global warming has changed the probability of some types of extreme weather. The statistical analysis of global and large-scale trends in temperature and other variables is called "Detection and Attribution" (D&A). This technique has recently been extended to "Extreme Event Attribution". We will discuss both statistical techniques and give examples of recent applications to extreme weather, such as the rainfall of Hurricane Harvey, the heat wave "Lucifer" in the Mediterranean last summer and the recent cold wave in the US.

Dr. Geert Jan van Oldenborgh obtained a PhD in the phenomenology of elementary particles at the University of Amsterdam in 1990, supervised by J. Vermaseren. After three post-doc positions in this field he switched to climate research. His first research topics were El NiƱo and data assimilation. This naturally led to research into seasonal predictability and the KNMI Climate Explorer web-based climate analysis tool, now as senior researcher at KNMI. As the skill of seasonal forecasts in Europe is mainly determined by the trend he also started research into regional climate change projections and trend verification. This extended into verification of decadal forecasts. He was Lead Author of the IPCC WG1 AR5 report (Chapter 11, Near-term projections and predictability, and Annex I Atlas). He is now mainly active in extreme event attribution, which combines elements of seasonal forecasts, regional climate change and verification.