Abstract. PRIMAVERA (process-based climate simulation: advances in high-resolution modelling and European climate risk assessments) was a European Union Horizon 2020 project whose primary aim was to generate advanced and well-evaluated high-resolution global climate model datasets for the benefit of governments, business and society in general. Following consultation with members of the insurance industry, we have used a PRIMAVERA multi-model ensemble to generate a European winter windstorm event set for use in insurance risk analysis, containing approximately 1300 years of windstorm data. The data are available at https://doi.org/10.5281/zenodo.6492182. To create the storm footprints for the event set, the storms in the PRIMAVERA models are identified through tracking. A method is developed to separate the winds from storms occurring in the domain at the same time. The wind footprints are bias corrected and converted to 3 s gusts onto a uniform grid using quantile mapping. The distribution of the number of model storms per season as a function of estimated loss is consistent with re-analysis, as are the total losses per season, and the additional event set data greatly reduce uncertainty on return period magnitudes. The event set also reproduces the temporally clustered nature of European windstorms. Since the event set is generated from global climate models, it can help to quantify the non-linear relationship between large-scale climate indices such as the North Atlantic Oscillation (NAO) and windstorm damage. Although we find only a moderate positive correlation between extended winter NAO and storm damage in northern European countries (consistent with re-analysis), there is a large change in risk of extreme seasons between negative and positive NAO states. The intensities of the most severe storms in the event set are, however, sensitive to the gust conversion and bias correction method used, so care should be taken when interpreting the expected damages for very long return periods.
PRIMAVERA was a European Union Horizon 2020 project whose primary aim was to generate advanced and well-evaluated high-resolution global climate model datasets, for the benefit of governments, business and society in general. Following consultation with members of the insurance industry, we have used a PRIMAVERA multi-model ensemble to generate a European winter windstorm event set for use in insurance risk analysis, containing approximately 1300 years of windstorm data. To create the storm footprints for the event set, the storms in the PRIMAVERA models are identified through tracking. A method is developed to separate the winds from storms occurring in the domain at the same time. The wind footprints are bias corrected and converted to 3-s gusts onto a uniform grid using quantile mapping. The distribution of the number of model storms per season as a function of estimated loss is consistent with re-analysis, as are the total losses per season, and the additional event set data greatly reduces uncertainty on return period magnitudes. The event set also reproduces the temporally clustered nature of European windstorms. Since the event set is generated from global climate models, it can help to quantify the non-linear relationship between large-scale climate indices such as the North Atlantic Oscillation (NAO) and windstorm damage. Although we find only a moderate positive correlation between extended winter NAO and storm damage in northern European countries (consistent with re-analysis), there is a large change in risk of extreme seasons between negative and positive NAO states. The intensities of the most severe storms in the event set are, however, sensitive to the gust conversion/bias correction method used, so care should be taken when interpreting the expected damages for very long return periods.
Global climate models (GCMs) are known to suffer from biases in the simulation of atmospheric blocking, and this study provides an assessment of how blocking is represented by the latest generation of GCMs. It is evaluated (i) how historical CMIP6 (Climate Model Intercomparison Project Phase 6) simulations perform compared to CMIP5 simulations and (ii) how horizontal model resolution affects the simulation of blocking in the CMIP6-HighResMIP (PRIMAVERA – PRocess-based climate sIMulation: AdVances in high-resolution modelling and European climate Risk Assessment) model ensemble, which is designed to address this type of question. Two blocking indices are used to evaluate the simulated mean blocking frequency and blocking persistence for the Euro-Atlantic and Pacific regions in winter and summer against the corresponding estimates from atmospheric reanalysis data. There is robust evidence that CMIP6 models simulate blocking frequency and persistence better than CMIP5 models in the Atlantic and Pacific and during winter and summer. This improvement is sizeable so that, for example, winter blocking frequency in the median CMIP5 model in a large Euro-Atlantic domain is underestimated by 33 % using the absolute geopotential height (AGP) blocking index, whereas the same number is 18 % for the median CMIP6 model. As for the sensitivity of simulated blocking to resolution, it is found that the resolution increase, from typically 100 to 20 km grid spacing, in most of the PRIMAVERA models, which are not re-tuned at the higher resolutions, benefits the mean blocking frequency in the Atlantic in winter and summer and in the Pacific in summer. Simulated blocking persistence, however, is not seen to improve with resolution. Our results are consistent with previous studies suggesting that resolution is one of a number of interacting factors necessary for an adequate simulation of blocking in GCMs. The improvements reported in this study hold promise for further reductions in blocking biases as model development continues. ; This research has been supported by the European Commission (PRIMAVERA (grant no. 641727)), the Spanish government (PALEOSTRAT (grant no. CGL2015-69699-R) and JEDiS (grant no. RTI2018-096402-B-I00)), and the Ministry of Science and Higher Education of Russia (grant no. 0149-2019-0015). ; Peer Reviewed ; Postprint (published version)
This study investigates how teleconnections linking tropical rainfall anomalies and wintertime circulation in the northern extra-tropics are represented in historical simulations for the period 1950–2010 run by partners of the EU-funded PRIMAVERA project, following the HighResMIP protocol of CMIP6. The analysis focusses on teleconnections from the western/central Indian Ocean in mid-winter and from the NINO4 region in both the early and the late part of winter; this choice is justified by a substantial change in the relationship between ENSO and the North Atlantic Oscillation (NAO) in the two parts of the season. Model results for both coupled integrations and runs with prescribed sea-surface temperature (SST) are validated against data from the latest ECMWF 20th-century re-analysis, CERA20C. Simulations from six modelling groups are considered, comparing the impact of increasing atmospheric resolution in runs with prescribed SST, and of moving from uncoupled to coupled simulations in the high-resolution version of each model. Single runs were available for each model configurations at the time of writing, with one centre (ECMWF) also providing a 6-member ensemble. Results from this ensemble are compared with those of a 6-member multi-model ensemble (MME) formed by including one simulation from each model. Using only a single historical simulation from each model configuration, it is difficult to detect a consistent change in the fidelity of model-generated teleconnections when either atmospheric resolution is increased or ocean coupling is introduced. However, when simulations from six different models are pooled together in the MME, some improvements in teleconnection patterns can be seen when moving from uncoupled to coupled simulations. For the ECMWF ensemble, improvements in the coupled simulations are only apparent for the late-winter NINO4 teleconnection. While the Indian Ocean teleconnection and the late-winter NINO4 teleconnection appear equally robust in the re-analysis record, the latter is well simulated in the majority of both uncoupled and coupled runs, while the former is reproduced with (generally) much larger errors, and a high degree of variability between individual models and ensemble members. Most of the simulations with prescribed SST fail to produce a realistic estimate of multi-decadal changes between the first and the second part of the 60-year record. This is (at least partially) due to their inability to simulate an Indian Ocean rainfall change which, in observations, has a zonal gradient out of phase with SST changes. In coupled runs, at least one model run with both realistic teleconnections and a good simulation of the inter-decadal pattern of Indian Ocean rainfall also shows a realistic NAO signal in extratropical multi-decadal variability. ; The simulations and diagnostics described in this paper have been funded by the European Union Horizon 2020 PRIMAVERA project, grant agreement no. 641727. Model output from the PRIMAVERA simulations can be accessed from the archive of the Centre for Environmental Data Analysis (CEDA). Re-analysis data from CERA20C is available from the European Centre for Medium-Range Weather Forecasts (ECMWF). ; Peer Reviewed ; Postprint (published version)
This study investigates tropical cyclone integrated kinetic energy, a measure which takes into account the intensity and the size of the storms and which is closely associated with their damage potential, in three different global climate models integrated following the HighResMIP protocol. In particular, the impact of horizontal resolution and of the ocean coupling are assessed. We find that, while the increase in resolution results in smaller and more intense storms, the integrated kinetic energy of individual cyclones remains relatively similar between the two configurations. On the other hand, atmosphere‐ocean coupling tends to reduce the size and the intensity of the storms, resulting in lower integrated kinetic energy in that configuration. Comparing cyclone integrated kinetic energy between a present and a future scenario did not reveal significant differences between the two periods. ; This research has been supported by the Horizon 2020 programme (PRIMAVERA, GA #641727). S. Wild received funding from the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska- Curie grant agreement 2020-MSCA- COFUND-2016-754433 and financial support from the Spanish Agencia Estatal de Investigación (FJC2019- 041186-I/AEI/10.13039/501100011033). M. J. Roberts acknowledges the support from the UK-China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. Finally, the authors are most grateful to three anonymous reviewers for their helpful comments in improving a previous version of this manuscript. ; Peer Reviewed ; Postprint (published version)
Copyright [15-02-2020] American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be "fair use" under Section 107 of the U.S. Copyright Act or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC §108) does not require the AMS's permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. All AMS journals and monograph publications are registered with the Copyright Clearance Center (http://www.copyright.com). Questions about permission to use materials for which AMS holds the copyright can also be directed to permissions@ametsoc.org. Additional details are provided in the AMS Copyright Policy statement, available on the AMS website (http://www.ametsoc.org/CopyrightInformation). ; A multimodel, multiresolution set of simulations over the period 1950–2014 using a common forcingprotocol from CMIP6 HighResMIP have been completed by six modeling groups. Analysis of tropicalcyclone performance using two different tracking algorithms suggests that enhanced resolution toward25 km typically leads to more frequent and stronger tropical cyclones, together with improvements inspatial distribution and storm structure. Both of these factors reduce typical GCM biases seen at lowerresolution. Using single ensemble members of each model, there is little evidence of systematic im-provement in interannual variability in either storm frequency or accumulated cyclone energy as comparedwith observations when resolution is increased. Changesin the relationships between large-scale drivers ofclimate variability and tropical cyclone variability in the Atlantic Ocean are also not robust to modelresolution. However, using a larger ensemble of simulations (of up to 14 members) with one model atdifferent resolutions does show evidence of increased skill at higher resolution. The ensemble mean cor-relation of Atlantic interannual tropical cyclone variability increases from;0.5 to;0.65 when resolutionincreases from 250 to 100 km. In the northwestern Pacific Ocean the skill keeps increasing with 50-kmresolution to 0.7. These calculations also suggest that more than six members are required to adequatelydistinguish the impact of resolution within the forced signal from the weather noise. ; Authors MR, JS, PLV, KH, BV, RH, AB, ES, LPC, LT, CR, RS, and DP acknowledge funding from the PRIMAVERA project, funded by the European Union's Horizon 2020 programme under Grant Agreement 641727. Author JM acknowledges funding from the Blue-Action project, funded by the European Union's Horizon 2020 programme under Grant Agreement 727852. Authors MR and JC acknowledge support from the U.K.–China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. Funding for authors PU and CZ to support use of the TempestExtremes suite was provided under NASA award NNX16AG62G and the U.S. Department of Energy Office of Science award DE-SC0016605. Many thanks are given to the editor and three anonymous reviewers for their comments, which have greatly strengthened this paper. ; Peer Reviewed ; Postprint (published version)
