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Geert Jan Van Oldenborgh, Rein Haarsma, Hylke De Vries, and Myles R. Allen

Abstract

The winter of 2013–14 had unusual weather in many parts of the world. Here we analyze the cold extremes that were widely reported in North America and the lack of cold extremes in western Europe. We perform a statistical analysis of cold extremes at two representative stations in these areas: Chicago, Illinois, and De Bilt, the Netherlands. This shows that the lowest minimum temperature of the winter was not very unusual in Chicago, even in the current warmer climate. Around 1950 it would have been completely normal. The same holds for multiday cold periods. Only the whole winter temperature was unusual, with a return time larger than 25 years. In the Netherlands, the opposite holds: the absence of any cold waves was highly unusual even now, and would have been extremely improbable halfway through the previous century. These results are representative of other stations in the regions. The difference is due to the skewness of the temperature distribution. In both locations, cold extremes are more likely than equally large warm extremes in winter. Severe cold outbreaks and cold winters, like the winter of 2013–14 in the Great Lakes area, are therefore not evidence against global warming: they will keep on occurring, even if they become less frequent. The absence of cold weather as observed in the Netherlands is a strong signal of a warming trend, as this would have been statistically extremely improbable in the 1950s.

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Anna-Lena Deppenmeier, Rein J. Haarsma, Chiel van Heerwaarden, and Wilco Hazeleger

Abstract

Warm sea surface temperature (SST) biases in the tropical Atlantic Ocean form a longstanding problem in coupled general circulation models (CGCMs). Considerable efforts to understand the origins of these biases and alleviate them have been undertaken, but state-of-the-art CGCMs still suffer from biases that are very similar to those of the generation of models before. In this study, we use a powerful combination of in situ moored buoy observations and a new coupled ocean–atmosphere single-column model (SCM) with parameterization that is identical to that of a three-dimensional CGCM to investigate the SST bias. We place the SCM at the location of a Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) mooring in the southeastern tropical Atlantic, where large SST biases occur in CGCMs. The SCM version of the EC-Earth state-of-the-art coupled GCM performs well for the first five days of the simulation. Then, it develops an SST bias that is very similar to that of its three-dimensional counterpart. Through a series of sensitivity experiments we demonstrate that the SST bias can be reduced by 70%. We achieve this result by enhancing the turbulent vertical ocean mixing efficiency in the ocean parameterization scheme. The underrepresentation of vertical mixing in three-dimensional CGCMs is a candidate for causing the warm SST bias. We further show that surface shortwave radiation does not cause the SST bias at the location of the PIRATA mooring. Rather, a warm atmospheric near-surface temperature bias and a wet moisture bias contribute to it. Strongly nudging the atmosphere to profiles from reanalysis data reduces the SST bias by 40%.

Open access
Eduardo S. P. R. Martins, Caio A. S. Coelho, Rein Haarsma, Friederike E. L. Otto, Andrew D. King, Geert Jan van Oldenborgh, Sarah Kew, Sjoukje Philip, Francisco C. Vasconcelos Júnior, and Heidi Cullen
Open access
Friederike E. L. Otto, Karsten Haustein, Peter Uhe, Caio A. S. Coelho, Jose Antonio Aravequia, Waldenio Almeida, Andrew King, Erin Coughlan de Perez, Yoshihide Wada, Geert Jan van Oldenborgh, Rein Haarsma, Maarten van Aalst, and Heidi Cullen
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Malcolm John Roberts, Joanne Camp, Jon Seddon, Pier Luigi Vidale, Kevin Hodges, Benoit Vanniere, Jenny Mecking, Rein Haarsma, Alessio Bellucci, Enrico Scoccimarro, Louis-Philippe Caron, Fabrice Chauvin, Laurent Terray, Sophie Valcke, Marie-Pierre Moine, Dian Putrasahan, Christopher Roberts, Retish Senan, Colin Zarzycki, and Paul Ullrich

Abstract

A multimodel, multiresolution set of simulations over the period 1950–2014 using a common forcing protocol from CMIP6 HighResMIP have been completed by six modeling groups. Analysis of tropical cyclone performance using two different tracking algorithms suggests that enhanced resolution toward 25 km typically leads to more frequent and stronger tropical cyclones, together with improvements in spatial distribution and storm structure. Both of these factors reduce typical GCM biases seen at lower resolution. Using single ensemble members of each model, there is little evidence of systematic improvement in interannual variability in either storm frequency or accumulated cyclone energy as compared with observations when resolution is increased. Changes in the relationships between large-scale drivers of climate variability and tropical cyclone variability in the Atlantic Ocean are also not robust to model resolution. However, using a larger ensemble of simulations (of up to 14 members) with one model at different resolutions does show evidence of increased skill at higher resolution. The ensemble mean correlation of Atlantic interannual tropical cyclone variability increases from ~0.5 to ~0.65 when resolution increases from 250 to 100 km. In the northwestern Pacific Ocean the skill keeps increasing with 50-km resolution to 0.7. These calculations also suggest that more than six members are required to adequately distinguish the impact of resolution within the forced signal from the weather noise.

Open access