Short biography:
Juan José Gómez-Navarro obtained his Higher degree in Physics in 2005. He was granted with a FPU fellowship that allowed him to obtain a PhD degree in Science with the highest mark in 2012. After 4 years as postdoctoral researcher, he was awarded with a Juan de la Cierva-Incorporación contract to continue his research in the University of Murcia. His scientific interests include the statistical analysis of anthropogenic factors in the climate variability, the interactions between synoptic and mesoscalar phenomena, and the role of physical processes leading to extreme events. His activities concern the use of high-resolution regional climate simulations for Europe at various temporal scales, from the last two millennia to the end of the 21st century. His current focus is on utilising the WRF model in high-resolution simulations and the integration of Machine Learning in climate downscaling.
Juan José Gómez-Navarro has been researcher at the University of Giessen (Germany), the University St. Francis Xavier (Canada) and the Helmholtz-Zentrum Geesthacht (Germany). In 2012 he started a post-doctoral researcher position at the latter institution, where he initiated an international career by carrying out a number of long palaeoclimate simulations for Europe in the German Climate Computing Center (DKRZ). Between October 2013 and March 2016, he was responsible of the group of regional climate modelling at the Climate and Environmental Physics Department of the University of Bern, where he was co-applicant in numerous projects funded by the Swiss National Science Foundation. In March 2016 he returned to the University of Murcia funded by the program for the reincorporation of researchers. He maintains an international network of collaborations with various research centres worldwide, such as the Department of Geography of the University of Giessen and the Institute of Coastal Research in Geesthacht (Germany); the Universities of Murcia and Complutense of Madrid (Spain); the Centre for Research and Technology of Agro-Environmental and Biological Sciences (CITAB, in Portugal); or the ETH Zurich and the Departments of Hydrology and Geography of the University of Bern (Switzerland).
Abstract:
Earth’s climate history is often understood by breaking it into constituent climatic epochs. Over the Common Era (the past 2,000 years) these epochs, such as the “Little Ice Age”, have been characterized as temporally coherent across extensive spatial scales. While the rapid global warming seen in observations over the past 150 years shows nearly global coherence, the spatio-temporal coherence of climate epochs over the Common Era has yet to be robustly tested. Here we use an unprecedented variety of probabilistic global paleoclimate reconstructions to test the hypothesis that previously identified climate epochs over the past 2,000 years were indeed globally coherent. We find no evidence for pre-industrial globally-coherent cold and warm epochs. In particular, the coldest multi-decadal to centennial epoch of the last millennium, the putative Little Ice Age, had the highest probability of occurring during the 15th century over most of the central and eastern Pacific, during the 17th century in northwestern Europe and southeastern North America, and during the mid-19th century over most remaining regions. Furthermore, the spatial coherence that does exist over the pre-industrial Common Era is consistent with the spatial coherence of stochastic climatic variability. This lack of spatio-temporal coherence indicates that pre-industrial forcing was not sufficient to produce globally synchronous extreme temperatures at multi-decadal and centennial timescales. In contrast, we find that the warmest period of the last two millennia occurred simultaneously in the 20th century for over 98% of the globe. This provides strong evidence that anthropogenic global warming is not only unparalleled in terms of absolute temperatures, but also unprecedented in spatial consistency within the context of the last 2,000 years.
Arranged date for the seminar talk: Jun 07, 2019