Generating samples of extreme winters to support climate adaptation

Recent extreme weather across the globe highlights the need to understand the potential for more extreme events in the present-day, and how such events may change with global warming. We present a methodology for more efficiently sampling extremes in future climate projections. As a proof-of-concept, we examine the UK’s most recent set of national Climate Projections (UKCP18). UKCP18 includes a 15-member perturbed parameter ensemble (PPE) of coupled global simulations, providing a range of climate projections incorporating uncertainty in both internal variability and forced response. However, this ensemble is too small to adequately sample extremes with very high return periods, which are of interest to policy-makers and adaptation planners. To better understand the statistics of these events, we use distributed computing to run three 1000-member initial-condition ensembles with the atmosphere-only HadAM4 model at 60km resolution on volunteers’ computers, taking boundary conditions from three distinct future extreme winters within the UKCP18 ensemble. We find that the magnitude of each winter extreme is captured within our ensembles, and that two of the three ensembles are conditioned towards producing extremes by the boundary conditions. Our ensembles contain several extremes that would only be expected to be sampled by a UKCP18 PPE of over 500 members, which would be prohibitively expensive with current supercomputing resource. The most extreme winters we simulate exceed those within UKCP18 by 0.85 K and 37% of the present-day average for UK winter means of daily maximum temperature and precipitation respectively. As such, our ensembles contain a rich set of multivariate, spatio-temporally and physically coherent samples of extreme winters with wide-ranging potential applications

Learning from the 2018 heatwave in the context of climate change: Are high-temperature extremes important for adaptation in Scotland?

To understand whether high temperatures and temperature extremes are important for climate change adaptation in Scotland, we place the 2018 heatwave in the context of past, present, and future climate, and provide a rapid but comprehensive impact analysis. The observed hottest day, 5-day, and 30-day period of 2018 and the 5-day period with the warmest nights had return periods of 5-15 years for 1950-2018. The warmest night and the maximum 30-day average nighttime temperature were more unusual with return periods of >30 years. Anthropogenic climate change since 1850 has made all these high-temperature extremes more likely. Higher risk ratios are found for experiments from the CMIP6-generation global climate model HadGEM3-GA6 compared to those from the very-large ensemble system weather@home. Between them, the best estimates of the risk ratios for daytime extremes range between 1.2-2.4, 1.2-2.3, and 1.4-4.0 for the 1-, 5-, and 30-day averages. For the corresponding nighttime extremes, the values are higher and the ranges wider (1.5->50, 1.5-5.5, and 1.6->50). The short-period nighttime extremes were more likely in 2018 than in 2017, suggesting a contribution from year-to-year climate variability to the risk enhancement of extreme temperatures due to anthropogenic effects. Climate projections suggest further substantial increases in the likelihood of 2018 temperatures between now and 2050, and that towards the end of the century every summer might be as hot as 2018. Major negative impacts occurred, especially on rural sectors, while transport and water infrastructure alleviated most impacts by implementing costly special measures. Overall, Scotland could cope with the impacts of the 2018 heatwave. However, given the likelihood increase of high-temperature extremes, uncertainty about consequences of even higher temperatures and/or repeated heatwaves, and substantial costs of preventing negative impacts, we conclude that despite its cool climate, high-temperature extremes are important to consider for climate change adaptation in Scotland

Attributing human influence on July 2017 Chinese heatwave: the influence of sea-surface temperatures

On 21st-25thJuly 2017 a record breaking heatwave occurred in Central Eastern China affecting nearly half of the national population and causing severe impacts on public health, agriculture and infrastructure. Here, we compare attribution results from two UK Met Office Hadley Centre models, HadGEM3-GA6 and weather@home (HadAM3P driving 50km HadRM3P). Within HadGEM3-GA6 July 2017-like heatwaves were unequaled in the ensemble representing the world without human influences. Such heatwaves became approximately a 1 in 50 year event and increased by a factor of 4.8 (5-95% range of 3.1 to 8.0) in weather@home as a result of human activity. Considering the risk ratio (RR) for the full range of return periods shows a discrepancy at all return times between the two model results. Within weather@home a range of different counterfactual Sea Surface Temperature (SST) patterns were used whereas HadGEM3-GA6 used a single estimate. The global mean difference in SST (between factual and counterfactual simulations) is shown to be related to the Generalised Extreme Value (GEV) location parameter and consequently the RR, especially for return periods less than 50 years. It is suggested that a suitable range of SST patterns are used for future attribution studies to ensure that this source of uncertainty is represented within the simulations and subsequent attribution results. It is shown that the risk change between factual and counterfactual simulations is not purely a simple shift in the distribution (i.e. change in GEV location parameter). For return periods greater than 50 years the GEV shape parameter is found to strongly influence the RR determined with the GEV scale parameter affecting only the most severe events

Larger spatial footprint of wintertime total precipitation extremes in a warmer climate

The simultaneous occurrence of extremely wet winters at multiple locations in the same region can contribute to widespread flooding and associated socio-economic losses. However, the spatial extent of precipitation extremes (i.e. the area in which nearby locations experience precipitation extremes simultaneously) and its future changes are largely overlooked in climate assessments. Employing new multi-thousand-year climate model simulations, we show that under both 2.0C and 1.5C warming scenarios, wintertime total precipitation extreme extents would increase over about 80-90% of the Northern Hemisphere extratropics (i.e. the latitude band 28-78N). Stabilising at 1.5C rather than 2.0C would reduce the average magnitude of the increase by 1.7-2 times. According to the climate model, the increased extents are caused by increases in precipitation intensity rather than changes in the spatial organisation of the events. Relatively small percentage increases in precipitation intensities (e.g., by 4%) can drive disproportionately larger, by 1-2 orders of magnitude, growth in the spatial extents (by 93%)

Event attribution of Parnaíba River floods in Northeastern Brazil

The climate modeling techniques of event attribution enable systematic assessments of the extent that anthropogenic climate change may be altering the probability or magnitude of extreme events. In the consecutive years of 2018, 2019, and 2020, rainfalls caused repeated flooding impacts in the lower Parnaíba River in Northeastern Brazil. We studied the effect that alterations in precipitation resulting from human influences on the climate had on the likelihood of flooding using two ensembles of the HadGEM3-GA6 atmospheric model: one driven by both natural and anthropogenic forcings; and the other driven only by natural atmospheric forcings, with anthropogenic changes removed from sea surface temperatures and sea ice patterns. We performed hydrological modeling to base our assessments on the peak annual streamflow. The change in the likelihood of flooding was expressed in terms of the ratio between probabilities of threshold exceedance estimated for each model ensemble. With uncertainty estimates at the 90% confidence level, the median (5% 95%) probability ratio at the threshold for flooding impacts in the historical period (1982–2013) was 1.12 (0.97 1.26), pointing to a marginal contribution of anthropogenic emissions by about 12%. For the 2018, 2019, and 2020 events, the median (5% 95%) probability ratios at the threshold for flooding impacts were higher at 1.25 (1.07 1.46), 1.27 (1.12 1.445), and 1.37 (1.19 1.59), respectively; indicating that precipitation change driven by anthropogenic emissions has contributed to the increase of likelihood of these events by about 30%. However, there are other intricate hydrometeorological and anthropogenic processes undergoing long-term changes that affect the flood hazard in the lower Parnaíba River. Trend and flood frequency analyses performed on observations showed a nonsignificant long-term reduction of annual peak flow, likely due to decreasing precipitation from natural climate variability and increasing evapotranspiration and flow regulation

Climate model forecast biases assessed with a perturbed physics ensemble

Perturbed physics ensembles have often been used to analyse long-timescale climate model behaviour, but have been used less often to study model processes on shorter timescales. We combine a transient perturbed physics ensemble with a set of initialised forecasts to deduce regional process errors present in the standard HadCM3 model, which cause the model to drift in the early stages of the forecast. First, it is shown that the transient drifts in the perturbed physics ensembles can be used to recover quantitatively the parameters that were perturbed. The parameters which exert most influence on the drifts vary regionally, but upper ocean mixing and atmospheric convective processes are particularly important on the 1-month timescale. Drifts in the initialised forecasts are then used to recover the ‘equivalent parameter perturbations’, which allow identification of the physical processes that may be at fault in the HadCM3 representation of the real world. Most parameters show positive and negative adjustments in different regions, indicating that standard HadCM3 values represent a global compromise. The method is verified by correcting an unusually widespread positive bias in the strength of wind-driven ocean mixing, with forecast drifts reduced in a large number of areas as a result. This method could therefore be used to improve the skill of initialised climate model forecasts by reducing model biases through regional adjustments to physical processes, either by tuning or targeted parametrisation refinement. Further, such regionally tuned models might also significantly outperform standard climate models, with global parameter configurations, in longer-term climate studies

Neutrophil-mediated IL-6 receptor trans-signaling and the risk of chronic obstructive pulmonary disease and asthma

The Asp358Ala variant in the interleukin-6 receptor (IL-6R) gene has been implicated in asthma, autoimmune and cardiovascular disorders, but its role in other respiratory conditions such as chronic obstructive pulmonary disease (COPD) has not been investigated. The aims of this study were to evaluate whether there is an association between Asp358Ala and COPD or asthma risk, and to explore the role of the Asp358Ala variant in sIL-6R shedding from neutrophils and its pro-inflammatory effects in the lung. We undertook logistic regression using data from the UK Biobank and the ECLIPSE COPD cohort. Results were meta-analyzed with summary data from a further three COPD cohorts (7,519 total cases and 35,653 total controls), showing no association between Asp358Ala and COPD (OR = 1.02 [95% CI: 0.96, 1.07]). Data from the UK Biobank showed a positive association between the Asp358Ala variant and atopic asthma (OR = 1.07 [1.01, 1.13]). In a series of in vitro studies using blood samples from 37 participants, we found that shedding of sIL-6R from neutrophils was greater in carriers of the Asp358Ala minor allele than in non-carriers. Human pulmonary artery endothelial cells cultured with serum from homozygous carriers showed an increase in MCP-1 release in carriers of the minor allele, with the difference eliminated upon addition of tocilizumab. In conclusion, there is evidence that neutrophils may be an important source of sIL-6R in the lungs, and the Asp358Ala variant may have pro-inflammatory effects in lung cells. However, we were unable to identify evidence for an association between Asp358Ala and COPD.This work was supported by the UK Medical Research Council [MR/L003120/1 and MR/J00345X/1]; the British Heart Foundation [RG/13/13/30194]; the UK National Institute for Health Research Cambridge Biomedical Research Centre; and the Cambridge NIHR BRC Cell Phenotyping Hub. The Cardiovascular Epidemiology Unit at the University of Cambridge is supported by the UK Medical Research Council [G0800270]; the British Heart Foundation [SP/09/002]; and the UK National Institute for Health Research Cambridge Biomedical Research Centre. The ECLIPSE study is supported by GlaxoSmithKline [SCO104960]. The COPDGene study was supported by National Institutes of Health [R01 HL089897 and R01 HL089856]. The Norway GenKOLS study is supported by GlaxoSmithKline [RES11080]. The VA Normative Aging Study is supported by the Cooperative Studies Program/Epidemiology Research and Information Center of the U.S. Department of Veterans Affairs and is a component of the Massachusetts Veterans Epidemiology Research and Information Center, Boston, MA. Funding to pay the Open Access publication charges for this article was provided by University of Cambridge block grants from the Research Councils UK and the Charity Open Access Fund

Parental phonological memory contributes to prediction of outcome of late talkers from 20 months to 4 years: a longitudinal study of precursors of specific language impairment

Background Many children who are late talkers go on to develop normal language, but others go on to have longer-term language difficulties. In this study, we considered which factors were predictive of persistent problems in late talkers. Methods Parental report of expressive vocabulary at 18 months of age was used to select 26 late talkers and 70 average talkers, who were assessed for language and cognitive ability at 20 months of age. Follow-up at 4 years of age was carried out for 24 late and 58 average talkers. A psychometric test battery was used to categorize children in terms of language status (unimpaired or impaired) and nonverbal ability (normal range or more than 1 SD below average). The vocabulary and non-word repetition skills of the accompanying parent were also assessed. Results Among the late talkers, seven (29%) met our criteria for specific language impairment (SLI) at 4 years of age, and a further two (8%) had low nonverbal ability. In the group of average talkers, eight (14%) met the criteria for SLI at 4 years, and five other children (8%) had low nonverbal ability. Family history of language problems was slightly better than late-talker status as a predictor of SLI.. The best predictors of SLI at 20 months of age were score on the receptive language scale of the Mullen Scales of Early Learning and the parent's performance on a non-word repetition task. Maternal education was not a significant predictor of outcome. Conclusions In this study, around three-quarters of late talkers did not have any language difficulties at 4 years of age, provided there was no family history of language impairment. A family history of language-literacy problems was found to be a significant predictor for persisting problems. Nevertheless, there are children with SLI for whom prediction is difficult because they did not have early language delay