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  • 1
    In: SSRN Electronic Journal, Elsevier BV
    Type of Medium: Online Resource
    ISSN: 1556-5068
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
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  • 2
    In: Cambridge Prisms: Water, Cambridge University Press (CUP)
    Type of Medium: Online Resource
    ISSN: 2755-1776
    Language: English
    Publisher: Cambridge University Press (CUP)
    Publication Date: 2023
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  • 3
    In: Earth's Future, American Geophysical Union (AGU), Vol. 12, No. 2 ( 2024-02)
    Abstract: We present a methodology for the construction of regional climate scenarios using a storyline approach to partition uncertainty Results from CMIP6 are reconstructed with a GCM‐RCM initial condition ensemble to produce high‐resolution scenario data for end‐users Six scenario variants cover emission uncertainty (high, moderate, low) and uncertainty in the regional response (dry‐trending, wet‐trending)
    Type of Medium: Online Resource
    ISSN: 2328-4277 , 2328-4277
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2024
    detail.hit.zdb_id: 2746403-9
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  • 4
    Online Resource
    Online Resource
    Copernicus GmbH ; 2020
    In:  Advances in Statistical Climatology, Meteorology and Oceanography Vol. 6, No. 2 ( 2020-11-10), p. 177-203
    In: Advances in Statistical Climatology, Meteorology and Oceanography, Copernicus GmbH, Vol. 6, No. 2 ( 2020-11-10), p. 177-203
    Abstract: Abstract. Over the last few years, methods have been developed to answer questions on the effect of global warming on recent extreme events. Many “event attribution” studies have now been performed, a sizeable fraction even within a few weeks of the event, to increase the usefulness of the results. In doing these analyses, it has become apparent that the attribution itself is only one step of an extended process that leads from the observation of an extreme event to a successfully communicated attribution statement. In this paper we detail the protocol that was developed by the World Weather Attribution group over the course of the last 4 years and about two dozen rapid and slow attribution studies covering warm, cold, wet, dry, and stormy extremes. It starts from the choice of which events to analyse and proceeds with the event definition, observational analysis, model evaluation, multi-model multi-method attribution, hazard synthesis, vulnerability and exposure analysis and ends with the communication procedures. This article documents this protocol. It is hoped that our protocol will be useful in designing future event attribution studies and as a starting point of a protocol for an operational attribution service.
    Type of Medium: Online Resource
    ISSN: 2364-3587
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2020
    detail.hit.zdb_id: 2840620-5
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  • 5
    In: Climatic Change, Springer Science and Business Media LLC, Vol. 166, No. 1-2 ( 2021-05)
    Abstract: The last few years have seen an explosion of interest in extreme event attribution, the science of estimating the influence of human activities or other factors on the probability and other characteristics of an observed extreme weather or climate event. This is driven by public interest, but also has practical applications in decision-making after the event and for raising awareness of current and future climate change impacts. The World Weather Attribution (WWA) collaboration has over the last 5 years developed a methodology to answer these questions in a scientifically rigorous way in the immediate wake of the event when the information is most in demand. This methodology has been developed in the practice of investigating the role of climate change in two dozen extreme events world-wide. In this paper, we highlight the lessons learned through this experience. The methodology itself is documented in a more extensive companion paper. It covers all steps in the attribution process: the event choice and definition, collecting and assessing observations and estimating probability and trends from these, climate model evaluation, estimating modelled hazard trends and their significance, synthesis of the attribution of the hazard, assessment of trends in vulnerability and exposure, and communication. Here, we discuss how each of these steps entails choices that may affect the results, the common problems that can occur and how robust conclusions can (or cannot) be derived from the analysis. Some of these developments also apply to other attribution methodologies and indeed to other problems in climate science.
    Type of Medium: Online Resource
    ISSN: 0165-0009 , 1573-1480
    RVK:
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 751086-X
    detail.hit.zdb_id: 1477652-2
    SSG: 14
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  • 6
    Online Resource
    Online Resource
    Copernicus GmbH ; 2021
    In:  Earth System Dynamics Vol. 12, No. 4 ( 2021-12-06), p. 1503-1527
    In: Earth System Dynamics, Copernicus GmbH, Vol. 12, No. 4 ( 2021-12-06), p. 1503-1527
    Abstract: Abstract. Unfavourable weather is a common cause for crop failures all over the world. Whilst extreme weather conditions may cause extreme impacts, crop failure commonly is induced by the occurrence of multiple and combined anomalous meteorological drivers. For these cases, the explanation of conditions leading to crop failure is complex, as the links connecting weather and crop yield can be multiple and non-linear. Furthermore, climate change is likely to perturb the meteorological conditions, possibly altering the occurrences of crop failures or leading to unprecedented drivers of extreme impacts. The goal of this study is to identify important meteorological drivers that cause crop failures and to explore changes in crop failures due to global warming. For that, we focus on a historical failure event, the extreme low soybean production during the 2012 season in the midwestern US. We first train a random forest model to identify the most relevant meteorological drivers of historical crop failures and to predict crop failure probabilities. Second, we explore the influence of global warming on crop failures and on the structure of compound drivers. We use large ensembles from the EC-Earth global climate model, corresponding to present-day, pre-industrial +2 and 3 ∘C warming, respectively, to isolate the global warming component. Finally, we explore the meteorological conditions inductive for the 2012 crop failure and construct analogues of these failure conditions in future climate settings. We find that crop failures in the midwestern US are linked to low precipitation levels, and high temperature and diurnal temperature range (DTR) levels during July and August. Results suggest soybean failures are likely to increase with climate change. With more frequent warm years due to global warming, the joint hot–dry conditions leading to crop failures become mostly dependent on precipitation levels, reducing the importance of the relative compound contribution. While event analogues of the 2012 season are rare and not expected to increase, impact analogues show a significant increase in occurrence frequency under global warming, but for different combinations of the meteorological drivers than experienced in 2012. This has implications for assessment of the drivers of extreme impact events.
    Type of Medium: Online Resource
    ISSN: 2190-4987
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2021
    detail.hit.zdb_id: 2578793-7
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  • 7
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2022
    In:  Climate Dynamics Vol. 59, No. 9-10 ( 2022-11), p. 2871-2886
    In: Climate Dynamics, Springer Science and Business Media LLC, Vol. 59, No. 9-10 ( 2022-11), p. 2871-2886
    Abstract: Regional climate projections indicate that European summer precipitation may change considerably in the future. Southern Europe can expect substantial drying while Northern Europe could actually become wetter. Model spread and internal variability in these projections are large, however, and unravelling the processes that underlie the changes is essential to get more confidence in these projections. Large-scale circulation change is one of the contributors to model spread. In this paper we quantify the role of future large-scale circulation changes to summer precipitation change, using a 16-member single-model ensemble obtained with the regional climate model RACMO2, forced by the global climate model EC-Earth2.3 and the RCP8.5 emission scenario. Using the method of circulation analogues three contributions to the future precipitation change are distinguished. The first is the precipitation change occurring without circulation change (referred to as the thermodynamic term). This contribution is characterised by a marked drying-to-wetting gradient as one moves north from the Mediterranean. The second contribution measures the effects of changes in the mean circulation. It has a very different spatial pattern and is closely related to the development of a region of high pressure (attaining its maximum west of Ireland) and the associated anti-cyclonic circulation response. For a large area east of Ireland including parts of western Europe, it is the major contributor to the overall drying signal, locally explaining more than 90% of the ensemble-mean change. In regions where the patterns overlap, the signal-to-noise ratio of the total change is either enhanced or reduced depending on their relative signs. Although the second term is expected to be particularly model dependent, the high-pressure region west of Ireland also appears in CMIP5 and CMIP6 ensemble-mean projections. The third contribution records the effects of changes in the circulation variability. This term has the smallest net contribution, but a relatively large uncertainty. The analogues are very good in partitioning the ensemble-mean precipitation change, but describe only up to 40% of the ensemble-spread. This demonstrates that other precipitation-drivers (SST, spring soil moisture etc.) will generally strongly influence trends in single climate realisations. This also re-emphasises the need for large ensembles or using alternative methods like the Pseudo Global Warming approach where signal to noise ratios are higher. Nevertheless, identifying the change mechanisms helps to understand the future uncertainties and differences between models.
    Type of Medium: Online Resource
    ISSN: 0930-7575 , 1432-0894
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 382992-3
    detail.hit.zdb_id: 1471747-5
    SSG: 16,13
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  • 8
    Online Resource
    Online Resource
    American Geophysical Union (AGU) ; 2023
    In:  Earth's Future Vol. 11, No. 4 ( 2023-04)
    In: Earth's Future, American Geophysical Union (AGU), Vol. 11, No. 4 ( 2023-04)
    Abstract: A hybrid crop model (i.e., physical crop model combined with machine learning) is presented, which outperforms the benchmark models Simultaneous soybean failures in the Americas under climate change are mostly driven by changes in mean climate Changes in climate variability increase country‐level soybean failures but such change is not found for simultaneous failures
    Type of Medium: Online Resource
    ISSN: 2328-4277 , 2328-4277
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2023
    detail.hit.zdb_id: 2746403-9
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  • 9
    In: Hydrology and Earth System Sciences, Copernicus GmbH, Vol. 21, No. 2 ( 2017-02-14), p. 897-921
    Abstract: Abstract. A stationary low pressure system and elevated levels of precipitable water provided a nearly continuous source of precipitation over Louisiana, United States (US), starting around 10 August 2016. Precipitation was heaviest in the region broadly encompassing the city of Baton Rouge, with a 3-day maximum found at a station in Livingston, LA (east of Baton Rouge), from 12 to 14 August 2016 (648.3 mm, 25.5 inches). The intense precipitation was followed by inland flash flooding and river flooding and in subsequent days produced additional backwater flooding. On 16 August, Louisiana officials reported that 30 000 people had been rescued, nearly 10 600 people had slept in shelters on the night of 14 August and at least 60 600 homes had been impacted to varying degrees. As of 17 August, the floods were reported to have killed at least 13 people. As the disaster was unfolding, the Red Cross called the flooding the worst natural disaster in the US since Super Storm Sandy made landfall in New Jersey on 24 October 2012. Before the floodwaters had receded, the media began questioning whether this extreme event was caused by anthropogenic climate change. To provide the necessary analysis to understand the potential role of anthropogenic climate change, a rapid attribution analysis was launched in real time using the best readily available observational data and high-resolution global climate model simulations. The objective of this study is to show the possibility of performing rapid attribution studies when both observational and model data and analysis methods are readily available upon the start. It is the authors' aspiration that the results be used to guide further studies of the devastating precipitation and flooding event. Here, we present a first estimate of how anthropogenic climate change has affected the likelihood of a comparable extreme precipitation event in the central US Gulf Coast. While the flooding event of interest triggering this study occurred in south Louisiana, for the purposes of our analysis, we have defined an extreme precipitation event by taking the spatial maximum of annual 3-day inland maximum precipitation over the region of 29–31° N, 85–95° W, which we refer to as the central US Gulf Coast. Using observational data, we find that the observed local return time of the 12–14 August precipitation event in 2016 is about 550 years (95 % confidence interval (CI): 450–1450). The probability for an event like this to happen anywhere in the region is presently 1 in 30 years (CI 11–110). We estimate that these probabilities and the intensity of extreme precipitation events of this return time have increased since 1900. A central US Gulf Coast extreme precipitation event has effectively become more likely in 2016 than it was in 1900. The global climate models tell a similar story; in the most accurate analyses, the regional probability of 3-day extreme precipitation increases by more than a factor of 1.4 due to anthropogenic climate change. The magnitude of the shift in probabilities is greater in the 25 km (higher-resolution) climate model than in the 50 km model. The evidence for a relation to El Niño half a year earlier is equivocal, with some analyses showing a positive connection and others none.
    Type of Medium: Online Resource
    ISSN: 1607-7938
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2017
    detail.hit.zdb_id: 2100610-6
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  • 10
    In: Environmental Research Letters, IOP Publishing, Vol. 13, No. 2 ( 2018-02-01), p. 024006-
    Type of Medium: Online Resource
    ISSN: 1748-9326
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2018
    detail.hit.zdb_id: 2255379-4
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