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The Antarctic ice sheet (AIS) is the Earth’s largest store of frozen water; understanding how it changed in the past allows us to improve projections of how it, and sea levels, may change. Here, we use previous AIS reconstructions, water isotope ratios from ice cores, and simulator predictions of the relationship between the ice-sheet shape and isotope ratios to create a model of the AIS at the Last Glacial Maximum. We develop a prior distribution that captures expert opinion about the AIS, generate a designed ensemble of potential shapes, run these through the climate model HadCM3, and train a Gaussian process emulator of the link between ice-sheet shape and isotope ratios. To make the analysis computationally tractable, we develop a preferential principal component method that allows us to reduce the dimension of the problem in a way that accounts for the differing importance we place in reconstructions, allowing us to create a basis that reflects prior uncertainty. We use Markov chain Monte Carlo to sample from the posterior distribution, finding shapes for which HadCM3 predicts isotope ratios closely matching observations from ice cores. The posterior distribution allows us to quantify the uncertainty in the reconstructed shape, a feature missing in other analyses.

There is a growing need for past weather and climate data to support science and decision-making. This paper describes the compilation and construction of a global multivariable (air temperature, pressure, precipitation sum, number of precipitation days) monthly instrumental climate database that encompasses a substantial body of the known early instrumental time series. The dataset contains series compiled from existing databases that start before 1890 (though continuing to the present) as well as a large amount of newly rescued data. All series underwent a quality control procedure and subdaily series were processed to monthly mean values. An inventory was compiled, and the collection was deduplicated based on coordinates and mutual correlations. The data are provided in a common format accompanied by the inventory. The collection totals 12452 meteorological records in 118 countries. The data can be used for climate reconstructions and analyses. It is the most comprehensive global monthly climate dataset for the preindustrial period so far.

Quantitative estimates of future Antarctic climate change are derived from numerical global climate models. Evaluation of the reliability of climate model projections involves many lines of evidence on past performance combined with knowledge of the processes that need to be represented. Routine model evaluation is mainly based on the modern observational period, which started with the establishment of a network of Antarctic weather stations in 1957/58. This period is too short to evaluate many fundamental aspects of the Antarctic and Southern Ocean climate system, such as decadal-to-century time-scale climate variability and trends. To help address this gap, we present a new evaluation of potential ways in which long-term observational and paleo-proxy reconstructions may be used, with a particular focus on improving projections. A wide range of data sources and time periods is included, ranging from ship observations of the early 20th century to ice core records spanning hundreds to hundreds of thousands of years to sediment records dating back 34 million years. We conclude that paleo-proxy records and long-term observational datasets are an underused resource in terms of strategies for improving Antarctic climate projections for the 21st century and beyond. We identify priorities and suggest next steps to addressing this.

The meteorological conditions during the Amundsen and Scott South Pole expeditions in 1911/12 are examined using a combination of observations collected during the expeditions as well as modern reanalysis and reconstructed pressure datasets. It is found that over much of this austral summer, pressures were exceptionally high (more than two standard deviations above the climatological mean) at both main bases, as well as along the sledging journeys, especially in December 1911. In conjunction with the anomalously high pressures, Amundsen and his crew experienced temperatures that peaked above –16°C on the polar plateau on 6 December 1911, which is extremely warm for this region. While Scott also encountered unusually warm conditions at this time, the above-average temperatures were accompanied by a wet snowstorm that slowed his progress across the Ross Ice Shelf. Although January 1912 was marked with slightly below-average temperatures and pressure, high temperatures and good conditions were observed in early February 1912, when Scott and his companions were at the top of the Beardmore Glacier. When compared to the anomalously cold temperatures experienced by the Scott polar party in late February and March 1912, the temperature change is in the top 3% based on more than 35 years of reanalysis data. Scott and his companions therefore faced an exceptional decrease in temperature when transiting to the Ross Ice Shelf in February and March 1912, which likely made the persistent cold spell they experienced on the Ross Ice Shelf seem even more intense by comparison.

Understanding the causes of recent climatic trends and variability in the high-latitude Southern Hemisphere is hampered by a short instrumental record. Here, we analyse recent atmosphere, surface ocean and sea-ice observations in this region and assess their trends in the context of palaeoclimate records and climate model simulations. Over the 36-year satellite era, significant linear trends in annual mean sea-ice extent, surface temperature and sea-level pressure are superimposed on large interannual to decadal variability. Most observed trends, however, are not unusual when compared with Antarctic palaeoclimate records of the past two centuries. With the exception of the positive trend in the Southern Annular Mode, climate model simulations that include anthropogenic forcing are not compatible with the observed trends. This suggests that natural variability overwhelms the forced response in the observations, but the models may not fully represent this natural variability or may overestimate the magnitude of the forced response.

Mutations of the lipid phosphatase FIG4 that regulates PI(3,5)P2 are responsible for the recessive peripheral-nerve disorder CMT4J. We now describe nonsynonymous variants of FIG4 in 2% (9/473) of patients with amyotrophic lateral sclerosis (ALS) and primary lateral sclerosis (PLS). Heterozygosity for a deleterious allele of FIG4 appears to be a risk factor for ALS and PLS, extending the list of known ALS genes and increasing the clinical spectrum of FIG4-related diseases., Howard Hughes Medical Institute (Investigator), National Institutes of Health (U.S.) (grant GM24872), National Institutes of Health (U.S.) (grant NS050557), National Institutes of Health (U.S.) (grant NIH NS050557), National Institutes of Health (U.S.) (grant NIH NS050641), National Institutes of Health (U.S.) (grant NIH T32 GM007544), Pierre L. de Bourgknecht ALS Research Foundation, Project ALS Foundation, Angel Fund

Seasonal reconstructions of the Southern Hemisphere annular mode (SAM) index are derived to extend the record before the reanalysis period, using station sea level pressure (SLP) data as predictors. Two reconstructions using different predictands are obtained: one [Jones and Widmann (JW)] based on the first principal component (PC) of extratropical SLP and the other (Fogt) on the index of Marshall. A regional-based SAM index (Visbeck) is also considered. These predictands agree well post-1979; correlations decline in all seasons except austral summer for the full series starting in 1958. Predictand agreement is strongest in spring and summer; hence agreement between the reconstructions is highest in these seasons. The less zonally symmetric SAM structure in winter and spring influences the strength of the SAM signal over land areas, hence the number of stations included in the reconstructions. Reconstructions from 1865 were, therefore, derived in summer and autumn and from 1905 in winter and spring. This paper examines the skill of each reconstruction by comparison with observations and reanalysis data. Some of the individual peaks in the reconstructions, such as the most recent in austral summer, represent a full hemispheric SAM pattern, while others are caused by regional SLP anomalies over the locations of the predictors. The JW and Fogt reconstructions are of similar quality in summer and autumn, while in winter and spring the Marshall index is better reconstructed by Fogt than the PC index is by JW. In spring and autumn the SAM shows considerable variability prior to recent decades.

This second paper examines the Southern Hemisphere annular mode (SAM) variability from reconstructions, observed indices, and simulations from 17 Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) models from 1865 to 2005. Comparisons reveal the models do not fully simulate the duration of strong natural variability within the reconstructions during the 1930s and 1960s. Seasonal indices are examined to understand the relative roles of forced and natural fluctuations. The models capture the recent (1957-2005) positive SAM trends in austral summer, which reconstructions indicate is the strongest trend during the last 150 yr; ozone depletion is the dominant mechanism driving these trends. In autumn, negative trends after 1930 in the reconstructions are stronger than the recent positive trend. Furthermore, model trends in autumn during 1957-2005 are the most different from observations. Both of these conditions suggest the recent autumn trend is most likely natural climate variability, with external forcing playing a secondary role. Many models also produce significant spring trends during this period not seen in observations. Although insignificant, these differences arise because of vastly different spatial structures in the Southern Hemisphere pressure trends. As the trend differences between models and observations in austral spring have been increasing over the last 30 yr, care must be exercised when examining the future SAM projections and their impacts in this season.

The autosomal recessive mutation mnd2 results in early onset motor neuron disease with rapidly progressive paralysis, severe muscle wasting, regression of thymus and spleen, and death before 40 days of age. mnd2 has been mapped to mouse chromosome 6 with the gene order: centromere-Tcrb-Ly-2-Sftp-3-D6Mit4-mnd2-D6Mit6, D6Mit9-D6Rck132-Raf-1, D6Mit11-D6Mit12-D6Mit14. mnd2 is located within a conserved linkage group with homologs on human chromosome 2p12-p13. Spinal motor neurons of homozygous affected animals are swollen and stain weakly, and electromyography revealed spontaneous activity characteristic of muscle denervation. Myelin staining was normal throughout the neuraxis. The clinical observations are consistent with a primary abnormality of lower motor neuron function. This new animal model will be of value for identification of a genetic defect responsible for motor neuron disease and for evaluation of new therapies.

A DNA mapping panel derived from an interspecific backcross was used to position the mouse insulin-2 locus (Ins-2) on Chromosome 7, near H19 (0/114 recombinants) and Th (1/114 recombinants). Ins-2 is part of a human-mouse conserved linkage group that includes Th, H19, and Igf-2. Analysis of segregation in the F2 generation from the cross C57BL/6J-tub/tub x CAST/Ei demonstrated that Ins-2 and the obesity mutant tubby (tub) are distinct loci, thus eliminating Ins-2 as a candidate gene for tub. These results also refine the estimated genetic distance between tub and Hbb to 2.4 [plus-or-minus sign] 1.4 cM. The predicted location for a human homolog of tubby is HSA 11p15.

The population approach to pharmacokinetic analysis, and its application to the identification of patient characteristics that affect a drug's pharmacokinetic parameters, is achieving greater prominence in the drug development process. Specifically, population analyses are a way to gather information that might be difficult to capture in some subpopulations. In the fall of 1997, the Food and Drug Administration proposed new legislation, commonly known as the “Pediatric Rule”. This new legislation required pharmaceutical companies to collect pediatric data for drugs with indications applicable to children before the compound would be approved. Other than conducting traditional pharmacokinetic clinical trials, another way to collect this information would be to perform a population pharmacokinetic analysis. Two different examples of this approach are presented. The first study was conducted on traditional pharmacokinetic data (intense sampling) pooled from four pediatric trials. The second study is an example of the ability of the population approach to take advantage of sparse data obtained as a secondary objective of a clinical study. A population pharmacokinetic analysis was conducted for azithromycin on data from pediatric patients enrolled in four separate clinical trials. A two compartment model with parallel zero- and first-order absorption was found to best fit the data. Potential covariates were assessed for oral clearance (CL/F), oral volume of distribution in the peripheral compartment (V2/F), intercompartmental oral clearance (Q/F), and the first-order absorption rate constant (ka). Weight was found to be a significant covariate for both CL/F and V2/F. No covariates were found to be significant for Q/F or ka. A population pharmacokinetic analysis was conducted for prednisolone on data from thoracic organ transplant patients. A one compartment model with a fixed first order rate of absorption was found to best fit the data. Potential covariates were assesse

Atmospheric circulation in the oceans surrounding southern Africa plays an important role in determining its precipitation. This study uses wind information recorded in ships’ logbooks in order to statistically reconstruct summer and winter season precipitation at four southern African weather stations from 1796 to 1854. The reconstruction was obtained by first relating gridded 8° × 8° NCEP-DOE reanalysis seasonal mean wind vectors in the adjacent oceans to station precipitation. Over a 30-year calibration period (1979–2008), significant correlations between wind and precipitation at Cape Town, Mthatha and Royal National Park showed particular correspondence with those areas with the greatest concentration of logbook observations. Principal component regression was used to assess the potential of the dominant patterns of variability in the wind vectors as predictors to reconstruct precipitation. Cross-validation in the calibration period gave confidence that precipitation could be reconstructed at several stations across South Africa, meaning the regression relationships derived in the calibration period could be applied to the gridded seasonal mean logbook data to produce reconstructions of precipitation from 1796 to 1854. The reconstructions show a degree of correspondence with other regional data sets. For instance, the decade beginning in 1810 was the wettest of the period at Mthatha and Royal National Park, while the 1820s were the driest. At Cape Town, the 1820s were the wettest decade, with drier conditions observed in the 1830s. An index of west–east circulation in the summer season revealed correspondence with two documentary reconstructions of El Niño events and increased westerliness, although this did not always result in drier conditions. Attention is also drawn to the remaining 3000 yet to be digitised English East India Company logbooks which would provide a high-resolution picture of atmospheric circulation back to 1700 in the region under considerat

The role of climate variability in pre-colonial southern African history is highly disputed. We here provide a synthesis and critique of climate-society discourses relating to two regionally-defining periods of state formation and disaggregation. The first period involves the eleventh-thirteenth century development of socio-political complexity and the rise of southern Africa's first state, Mapungubwe, followed by its collapse and the shift in regional power to Great Zimbabwe. The later period encompasses the early-nineteenth century difaqane/mfecane mass migrations, violence and ensuing state-building activity. To further our assessment, we consider the wider contentious issues of climate causation and determinism in a regional context, but dispute suggestions of paradigm shift towards simplistic environmental collapse. Nevertheless, we specifically point to ambiguities in palaeoclimate records, a narrative tendency toward monocausal explanations and a lack of integration among the literature as reasons for a sustained divergence in interpretation regarding the significance of climate. We move on to discuss the potential of integrative approaches to illuminate understanding of the complex interactions between past climate variability and human activity. In order to do so, we highlight interlinked concepts such as vulnerability and resilience as key for bridging the gap between the natural and social sciences. To conclude, we point to future climate-society priorities and ways forward in the form of research areas, data prospects and questions.

Polar front jet stream variability is responsible for instances of extreme weather and is crucial for regional climate change. The North Atlantic Polar Front jet stream is of particular significance to the heavily populated areas of western Europe and eastern North America as storm track variability, atmospheric modes of variability such as the North Atlantic Oscillation (NAO), temperature and rainfall are all intimately linked with jet stream changes. Although seasonal and interannual variability are often attributed to internal variability, there are several possible drivers of polar front jet stream changes that are reviewed in this study. Cryospheric effects from sea-ice extent and snow cover, oceanic effects from North Atlantic sea-surface temperatures and tropical influences such as the El-Niño Southern Oscillation, and stratospheric effects due to stratospheric circulation variability, solar variability, volcanic eruptions and the Quasi-Biennial Oscillation are all identified in the literature as factors that impact on the Atlantic Polar Front jet stream. These drivers of jet stream variability can oppose or reinforce one another, and there are some indications of possible nonlinear interactions between them. We also review the modelling of jet stream variability. While a consensus has now been reached that some observed drivers can be reproduced in climate models, we conclude that improved understanding of more recently identified drivers of the Atlantic extratropical jet stream is crucial for making progress in regional climate predictions on all timescales from months to decades ahead. © 2015 Royal Meteorological Society.

The variability of the North Atlantic Oscillation is a key aspect of Northern Hemisphere atmospheric circulation and has a profound impact upon the weather of the surrounding land masses. Recent success with dynamical forecasts predicting the winter NAO at lead times of a few months has the potential to deliver great socio-economic impacts. Here we find that a linear regression model can provide skillful predictions of the winter NAO based on a limited number of statistical predictors. Identified predictors include El-Niño, Arctic sea ice, Atlantic SSTs and tropical rainfall. These statistical models can show significant skill when used to make out-of-sample forecasts and we extend the method to produce probabilistic predictions of the winter NAO. The statistical hindcasts can achieve similar levels of skill to state-of the art dynamical forecast models, although out-of-sample predictions are less skillful, albeit over a small period. Forecasts over a longer out-of-sample period suggest there is true skill in the statistical models, comparable with that of dynamical forecasting models. They can be used both to help evaluate, and to offer insight into sources of predictability and limitations of, dynamical models.

The variability of the North Atlantic polar front jet stream is crucial in determining summer weather around the North Atlantic basin. Recent extreme summers in western Europe and North America have highlighted the need for greater understanding of this variability, in order to aid seasonal forecasting and mitigate societal, environmental and economic impacts. Here we find that simple linear regression and composite models based on a few predictable factors are able to explain up to 35 of summertime jet stream speed and latitude variability from 1955 onwards. Sea surface temperature forcings impact predominantly on jet speed, whereas solar and cryospheric forcings appear to influence jet latitude. The cryospheric associations come from the previous autumn, suggesting the survival of an ice-induced signal through the winter season, whereas solar influences lead jet variability by a few years. Regression models covering the earlier part of the twentieth century are much less effective, presumably due to decreased availability of data, and increased uncertainty in observational reanalyses. Wavelet coherence analysis identifies that associations fluctuate over the study period but it is not clear whether this is just internal variability or genuine non-stationarity. Finally we identify areas for future research. © 2016 The Author(s)