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Article ; Online: More accurate quantification of model-to-model agreement in externally forced climatic responses over the coming century.

Maher, Nicola / Power, Scott B / Marotzke, Jochem

2021 Volume 12, Issue 1, Page(s) 788

Abstract: Separating how model-to-model differences in the forced response ( ... ...

Abstract	Separating how model-to-model differences in the forced response (U
Language	English
Publishing date	2021-02-04
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-020-20635-w
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Assessment of the impact of the COVID-19 pandemic on Sexual Assault Treatment Unit activity.

Kane, Daniel / Maher, Nicola / Eogan, Maeve

BMJ sexual & reproductive health

2021 Volume 47, Issue 4, Page(s) 301–303

MeSH term(s)	COVID-19 ; Humans ; Pandemics ; SARS-CoV-2 ; Sex Offenses ; Sexual Behavior
Language	English
Publishing date	2021-05-20
Publishing country	England
Document type	Letter
ISSN	2515-2009
ISSN (online)	2515-2009
DOI	10.1136/bmjsrh-2021-201143
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Article ; Online: Collection and storage of forensic evidence to enable subsequent reporting of a sexual crime to the police "Option 3"-an Irish experience.

Kane, Daniel / Pucillo, Christine / Maher, Nicola / Eogan, Maeve

Irish journal of medical science

2021 Volume 190, Issue 4, Page(s) 1591–1596

Abstract: Background: Sexual Assault Treatment Units (SATUs) provide holistic care, frequently including forensic examination, for people who disclose sexual violence. Storage of forensic evidence without reporting to An Garda Síochána (AGS), the Irish national ... ...

Abstract	Background: Sexual Assault Treatment Units (SATUs) provide holistic care, frequently including forensic examination, for people who disclose sexual violence. Storage of forensic evidence without reporting to An Garda Síochána (AGS), the Irish national police service (Option 3), was introduced in August 2016. This allowed attendees time to decide whether they wanted to report to AGS, without the loss of all forensic evidence. Aims: This paper presents a retrospective analysis of all "Option 3" cases including their subsequent disclosures to AGS, at the Dublin SATU, between 1 Aug. 2016 and 30 Jul 2020. Methods: The contemporaneous medical charts of Option 3 cases were reviewed and anonymised data extracted from them. Results: During the study period, there were 1258 attendances to the Dublin SATU. Of these, 10% (n = 127/1258) were Option 3. Ninety-three percent (n = 118/127) were female and 7% (n = 9/127) were male. The mean age was 26. Seventy percent (89/127) indicated a sexual assault occurred and 30% (38/127) were unsure. Twenty percent (n = 25/127) subsequently reported the incident to AGS, 60% (n = 15/25) within 7 days, and 80% (n = 20/25) within 1 month. Eighty percent (n = 20/25) of these reported cases had their evidence retrieved by AGS for analysis. Three percent (n = 4/127) requested that their evidence kits be kept for an additional year. None of these patients reported over that following year, and their evidence was subsequently destroyed. Conclusion: In conclusion, the availability of Option 3 has afforded people the opportunity to access responsive SATU care including storage of forensic evidence which may have significant evidential value. This potentially provides further opportunities for comprehensive detection of a crime, even if reporting to AGS is delayed.
MeSH term(s)	Adult ; Crime Victims ; Female ; Forensic Medicine ; Humans ; Male ; Police ; Retrospective Studies ; Sex Offenses
Language	English
Publishing date	2021-01-13
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	390895-1
ISSN	1863-4362 ; 0021-1265
ISSN (online)	1863-4362
ISSN	0021-1265
DOI	10.1007/s11845-020-02491-1
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Article: Exploiting large ensembles for a better yet simpler climate model evaluation

Suarez-Gutierrez, Laura / Milinski, Sebastian / Maher, Nicola

Climate dynamics. 2021 Nov., v. 57, no. 9-10

2021

Abstract: We use a methodological framework exploiting the power of large ensembles to evaluate how well ten coupled climate models represent the internal variability and response to external forcings in observed historical surface temperatures. This evaluation ... ...

Abstract	We use a methodological framework exploiting the power of large ensembles to evaluate how well ten coupled climate models represent the internal variability and response to external forcings in observed historical surface temperatures. This evaluation framework allows us to directly attribute discrepancies between models and observations to biases in the simulated internal variability or forced response, without relying on assumptions to separate these signals in observations. The largest discrepancies result from the overestimated forced warming in some models during recent decades. In contrast, models do not systematically over- or underestimate internal variability in global mean temperature. On regional scales, all models misrepresent surface temperature variability over the Southern Ocean, while overestimating variability over land-surface areas, such as the Amazon and South Asia, and high-latitude oceans. Our evaluation shows that MPI-GE, followed by GFDL-ESM2M and CESM-LE offer the best global and regional representation of both the internal variability and forced response in observed historical temperatures.
Keywords	air temperature ; climate ; climate models ; dynamics ; latitude ; model validation ; surface temperature ; South Asia
Language	English
Dates of publication	2021-11
Size	p. 2557-2580.
Publishing place	Springer Berlin Heidelberg
Document type	Article
ZDB-ID	1471747-5
ISSN	1432-0894 ; 0930-7575
ISSN (online)	1432-0894
ISSN	0930-7575
DOI	10.1007/s00382-021-05821-w
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: Combining machine learning and SMILEs to classify, better understand, and project changes in ENSO events

Maher, Nicola / Tabarin, Thibault P. / Milinski, Sebastian

eISSN: 2190-4987

2022

Abstract: The El Niño–Southern Oscillation (ENSO) occurs in three phases: neutral, warm (El Niño), and cool (La Niña). While classifying El Niño and La Niña is relatively straightforward, El Niño events can be broadly classified into two types: central Pacific (CP) ...

Abstract	The El Niño–Southern Oscillation (ENSO) occurs in three phases: neutral, warm (El Niño), and cool (La Niña). While classifying El Niño and La Niña is relatively straightforward, El Niño events can be broadly classified into two types: central Pacific (CP) and eastern Pacific (EP). Differentiating between CP and EP events is currently dependent on both the method and observational dataset used. In this study, we create a new classification scheme using supervised machine learning trained on 18 observational and re-analysis products. This builds on previous work by identifying classes of events using the temporal evolution of sea surface temperature in multiple regions across the tropical Pacific. By applying this new classifier to seven single model initial-condition large ensembles (SMILEs) we investigate both the internal variability and forced changes in each type of ENSO event, where events identified behave similarly to those observed. It is currently debated whether the observed increase in the frequency of CP events after the late 1970s is due to climate change. We found it to be within the range of internal variability in the SMILEs for trends after 1950, but not for the full observed period (1896 onwards). When considering future changes, we do not project a change in CP frequency or amplitude under a strong warming scenario (RCP8.5/SSP370) and we find model differences in EP El Niño and La Niña frequency and amplitude projections. Finally, we find that models show differences in projected precipitation and sea surface temperature (SST) pattern changes for each event type that do not seem to be linked to the Pacific mean state SST change, although the SST and precipitation changes in individual SMILEs are linked. Our work demonstrates the value of combining machine learning with climate models, and highlights the need to use SMILEs when evaluating ENSO in climate models because of the large spread of results found within a single model due to internal variability alone.
Subject code	551
Language	English
Publishing date	2022-09-06
Publishing country	de
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: The sensitivity of the El Niño–Southern Oscillation to volcanic aerosol spatial distribution in the MPI Grand Ensemble

Ward, Benjamin / Pausata, Francesco S. R. / Maher, Nicola

eISSN: 2190-4987

2021

Abstract: Using the Max Planck Institute Grand Ensemble (MPI-GE) with 200 members for the historical simulation (1850–2005), we investigate the impact of the spatial distribution of volcanic aerosols on the El Niño–Southern Oscillation (ENSO) response. In ... ...

Abstract	Using the Max Planck Institute Grand Ensemble (MPI-GE) with 200 members for the historical simulation (1850–2005), we investigate the impact of the spatial distribution of volcanic aerosols on the El Niño–Southern Oscillation (ENSO) response. In particular, we select three eruptions (El Chichón, Agung and Pinatubo) in which the aerosol is respectively confined to the Northern Hemisphere, the Southern Hemisphere or equally distributed across the Equator. Our results show that relative ENSO anomalies start at the end of the year of the eruption and peak in the following one. We especially found that when the aerosol is located in the Northern Hemisphere or is symmetrically distributed, relative El Niño-like anomalies develop, while aerosol distribution confined to the Southern Hemisphere leads to a relative La Niña-like anomaly. Our results point to the volcanically induced displacement of the Intertropical Convergence Zone (ITCZ) as a key mechanism that drives the ENSO response, while suggesting that the other mechanisms (the ocean dynamical thermostat and the cooling of tropical northern Africa or the Maritime Continent) commonly invoked to explain the post-eruption ENSO response may be less important in our model.
Subject code	551
Language	English
Publishing date	2021-09-17
Publishing country	de
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Medication use in pregnancy and lactation: A gap to be filled in postgraduate medical education.

McCarthy, Claire M / O'Shaughnessy, Fergal / Maher, Nicola / Cleary, Brian J / Donnelly, Jennifer C

European journal of obstetrics, gynecology, and reproductive biology

2021 Volume 260, Page(s) 52–55

Abstract: Objective: Medication use is a common therapeutic intervention during pregnancy, in the postpartum period and during lactation. Women routinely consult a variety of medical practitioners to request advice and prescription of medication. However, it is ... ...

Abstract	Objective: Medication use is a common therapeutic intervention during pregnancy, in the postpartum period and during lactation. Women routinely consult a variety of medical practitioners to request advice and prescription of medication. However, it is noted internationally that healthcare providers have insufficient knowledge to support women through their therapeutic journeys, and continual education is not provided as routine during postgraduate training and practice. Study design: There are five colleges in Ireland responsible for postgraduate medical training in Ireland for medicine, surgery, general practice, anaesthesiology and psychiatry. These are responsible for the curriculum design and implementation of 45 training programs, with the Royal College of Physicians responsible for 26 training programs and the Royal College of Surgeons of Ireland responsible for 15 training programs. We reviewed the national postgraduate training curricula of all speciality in the Republic of Ireland, excluding care of the elderly and pathology (given these practitioners would not be actively prescribing and treating pregnant or lactating women). Results: We demonstrate that less than 50 % of the 43 post-graduate training programs mention medications in pregnancy and lactation. Pregnancy is not mentioned by 12 programs in any degree, and 18 programs do not mention lactation or breastfeeding in any form. Conclusion: It is imperative that consistent knowledge is provided and accessible to healthcare providers in order to support women and their families through healthy pregnancies, and support breastfeeding for as long as possible. Therefore, we call on postgraduate training bodies to include comprehensive education on medications in pregnancy and lactation in their syllabi going forward.
MeSH term(s)	Aged ; Breast Feeding ; Curriculum ; Education, Medical ; Education, Medical, Graduate ; Female ; Humans ; Ireland ; Lactation ; Pregnancy
Language	English
Publishing date	2021-03-11
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	190605-7
ISSN	1872-7654 ; 0301-2115 ; 0028-2243
ISSN (online)	1872-7654
ISSN	0301-2115 ; 0028-2243
DOI	10.1016/j.ejogrb.2021.03.010
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Book ; Online: How large does a large ensemble need to be?

Milinski, Sebastian / Maher, Nicola / Olonscheck, Dirk

eISSN: 2190-4987

2020

Abstract: Initial-condition large ensembles with ensemble sizes ranging from 30 to 100 members have become a commonly used tool for quantifying the forced response and internal variability in various components of the climate system. However, there is no consensus ...

Abstract	Initial-condition large ensembles with ensemble sizes ranging from 30 to 100 members have become a commonly used tool for quantifying the forced response and internal variability in various components of the climate system. However, there is no consensus on the ideal or even sufficient ensemble size for a large ensemble. Here, we introduce an objective method to estimate the required ensemble size that can be applied to any given application and demonstrate its use on the examples of global mean near-surface air temperature, local temperature and precipitation, and variability in the El Niño–Southern Oscillation (ENSO) region and central United States for the Max Planck Institute Grand Ensemble (MPI-GE). Estimating the required ensemble size is relevant not only for designing or choosing a large ensemble but also for designing targeted sensitivity experiments with a model. Where possible, we base our estimate of the required ensemble size on the pre-industrial control simulation, which is available for every model. We show that more ensemble members are needed to quantify variability than the forced response, with the largest ensemble sizes needed to detect changes in internal variability itself. Finally, we highlight that the required ensemble size depends on both the acceptable error to the user and the studied quantity.
Subject code	333
Language	English
Publishing date	2020-10-30
Publishing country	de
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Improving statistical projections of ocean dynamic sea-level change using pattern recognition techniques

Malagón-Santos, Víctor / Slangen, Aimée B. A. / Hermans, Tim H. J. / Dangendorf, Sönke / Marcos, Marta / Maher, Nicola

eISSN: 1812-0792

2023

Abstract: Regional emulation tools based on statistical relationships, such as pattern scaling, provide a computationally inexpensive way of projecting ocean dynamic sea-level change for a broad range of climate change scenarios. Such approaches usually require a ... ...

Abstract	Regional emulation tools based on statistical relationships, such as pattern scaling, provide a computationally inexpensive way of projecting ocean dynamic sea-level change for a broad range of climate change scenarios. Such approaches usually require a careful selection of one or more predictor variables of climate change so that the statistical model is properly optimized. Even when appropriate predictors have been selected, spatiotemporal oscillations driven by internal climate variability can be a large source of statistical model error. Using pattern recognition techniques that exploit spatial covariance information can effectively reduce internal variability in simulations of ocean dynamic sea level, significantly reducing random errors in regional emulation tools. Here, we test two pattern recognition methods based on empirical orthogonal functions (EOFs), namely signal-to-noise maximizing EOF pattern filtering and low-frequency component analysis, for their ability to reduce errors in pattern scaling of ocean dynamic sea-level change. We use the Max Planck Institute Grand Ensemble (MPI-GE) as a test bed for both methods, as it is a type of initial-condition large ensemble designed for an optimal characterization of the externally forced response. We show that the two methods tested here more efficiently reduce errors than conventional approaches such as a simple ensemble average. For instance, filtering only two realizations by characterizing their common response to external forcing reduces the random error by almost 60 %, a reduction that is only achieved by averaging at least 12 realizations. We further investigate the applicability of both methods to single-realization modeling experiments, including four CMIP5 simulations for comparison with previous regional emulation analyses. Pattern filtering leads to a varying degree of error reduction depending on the model and scenario, ranging from more than 20 % to about 70 % reduction in global-mean root mean squared error compared with unfiltered ...
Subject code	310 ; 551
Language	English
Publishing date	2023-04-21
Publishing country	de
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Improving Statistical Projections of Ocean Dynamic Sea-level Change Using Pattern Recognition Techniques

Malagón-Santos, Víctor / Slangen, Aimée B. A. / Hermans, Tim H. J. / Dangendorf, Sönke / Marcos, Marta / Maher, Nicola

eISSN:

2023

Abstract	Regional emulation tools based on statistical relationships, such as pattern scaling, provide a computationally inexpensive way of projecting ocean dynamic sea-level change for a broad range of climate change scenarios. Such approaches usually require a careful selection of one or more predictor variables of climate change so that the statistical model is properly optimized. Even when appropriate predictors have been selected, spatiotemporal oscillations driven by internal climate variability can be a large source of statistical model error. Using pattern recognition techniques that exploit spatial covariance information can effectively reduce internal variability in simulations of ocean dynamic sea level, significantly reducing random errors in regional emulation tools. Here, we test two pattern recognition methods based on empirical orthogonal functions (EOFs), namely signal-to-noise maximizing EOF pattern filtering and low-frequency component analysis, for their ability to reduce errors in pattern scaling of ocean dynamic sea-level change. We use the Max Planck Institute Grand Ensemble (MPI-GE) as a test bed for both methods, as it is a type of initial-condition large ensemble designed for an optimal characterization of the externally forced response. We show that the two methods tested here more efficiently reduce errors than conventional approaches such as a simple ensemble average. For instance, filtering only two realizations by characterizing their common response to external forcing reduces the random error by almost 60 %, a reduction that is only achieved by averaging at least 12 realizations. We further investigate the applicability of both methods to single-realization modeling experiments, including four CMIP5 simulations for comparison with previous regional emulation analyses. Pattern filtering leads to a varying degree of error reduction depending on the model and scenario, ranging from more than 20 % to about 70 % reduction in global-mean root mean squared error compared with unfiltered ...
Subject code	310 ; 551
Language	English
Publishing date	2023-04-21
Publishing country	de
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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