Climate events linked to extremes of temperature (heatwaves/cold spells) have severe impacts on human health and natural ecosystems. Over the past few decades, the frequency and intensity of heatwaves have increased in Europe. Meanwhile, cold spells have become less frequent and weaker since 1950. Efforts to use climate models to estimate future climate extremes requires knowledge of how well such models reproduce temperature distributions, including extremes. Studies so far show substantial inconsistencies between observed and modeled temperature distributions. Most analyses focus on extremely low or high temperature values, and model these with Generalized Extreme Value (GEV) distributions. Studies focusing on climate variability have investigated the Fourier spectra of temperature from observations and climate model simulations.
In a new paper, LML External Fellow Davide Faranda and colleagues combine the paradigms of Fourier spectra and extreme events to assess how well different climate models simulate rare temperature events. In particular, they investigate the recurrence properties of temperature values in ensembles of climate model simulations, using a technique developed earlier by Faranda and Vaienti (Geophys.Res. Lett, 2013). This method quantifies the properties of rare values of the system by accounting for its chaotic nature. In the analysis, the authors use a multi-model ensemble of coupled ocean-atmosphere General Circulation Models (GCMs) provided by the Coupled Model Intercomparison Project Phase 5 (CMIP5). Their main aim is to evaluate how CMIP5 model simulations covering 1900–1999 represent the recurrences in extremes events and also to study the biases observed in different models and observations.
Their results reveal that differences in the European recurrence spectra between models can be much larger than the climate change signal in each model. Moreover, although temperature biases are generally centered around zero, their spatial variability suggests that these are caused not only by the differences in the average temperature of CMIP5 models but by differences in model dynamics and parametrization of subgrid physical phenomena. For cold temperatures, the authors find that biases depend on the chosen return period, whereas for the hot temperatures this dependence is observed only for return periods shorter than the seasonal cycle. An additional important difference is that, while for warm temperatures the biases are centered around zeros, for rare (m = 4y) cold temperatures the average biases are mostly positive, although bias model dependence is very large. The authors hope the results can be used to select models so as to avoid biased estimations of temperature extremes over extended spatial regions.
The paper is available at https://www.mdpi.com/2073-4433/10/4/166