Intra-annual link of spring and autumn precipitation over France

In a previous study, an intra-annual relationship of observed precipitation, manifested by negative correlations between domain-averaged spring and autumn precipitation of the same year, was found in two domains covering France and Central Europe for the period 1972-1990 (Hirschi etal., J Geophys Res 112(D22109), 2007). Here, this link and its temporal evolution over France during the twentieth century is further investigated and related to the atmospheric circulation and North Atlantic/Mediterranean sea surface temperature (SST) patterns. Observational datasets of precipitation, mean sea level pressure (MSLP), atmospheric teleconnection patterns, and SST, as well as various global and regional climate model simulations are analyzed. The investigation of observed precipitation by means of a running correlation with a 30-year time window for the period 1901-present reveals a decreasing trend in the spring-to-autumn correlations, which become significantly negative in the second half of the twentieth century. These negative correlations are connected with similar spring-to-autumn correlations in observed MSLP, and with negatively correlated spring East Atlantic (EA) and autumn Scandinavian (SCA) teleconnection pattern indices. Maximum covariance analyses of SST with these atmospheric variables indicate that at least part of the identified spring-to-autumn link is mediated through SST, as spring precipitation and MSLP are connected with the same autumn SST pattern as are autumn precipitation, MSLP and the SCA pattern index. Except for ERA-40 driven regional climate models from the EU-FP6 project ENSEMBLES, the analyzed regional and global climate models, including Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) simulations, do not capture this observed variability in precipitation. This is associated with the failure of most models in simulating the observed correlations between spring and autumn MSLP. While the causes for the identified relationship cannot be fully established its timing suggests a possible link with increased aerosol loading in the global dimming perio

Regional climate model projections underestimate future warming due to missing plant physiological CO 2 response

Many countries rely on regional climate model (RCM) projections to quantify the impacts of climate change and to design their adaptation plans accordingly. In several European regions, RCMs project a smaller temperature increase than global climate models (GCMs), which is hypothesised to be due to discrepant representations of topography, cloud processes, or aerosol forcing in RCMs and GCMs. Additionally, RCMs do generally not consider the vegetation response to elevated atmospheric CO2 concentrations; a process which is, however, included in most GCMs. Plants adapt to higher CO2 concentrations by closing their stomata, which can lead to reduced transpiration with concomitant surface warming, in particular, during temperature extremes. Here we show that embedding plant physiological responses to elevated CO2 concentrations in an RCM leads to significantly higher projected extreme temperatures in Europe. Annual maximum temperatures rise additionally by about 0.6 K (0.1 K in southern, 1.2 K in northern Europe) by 2070–2099, explaining about 67% of the stronger annual maximum temperature increase in GCMs compared to RCMs. Missing plant physiological CO2 responses thus strongly contribute to the underestimation of temperature trends in RCMs. The need for robust climate change assessments calls for a comprehensive implementation of this process in RCM land surface schemes

Effects of climate extremes on the terrestrial carbon cycle : concepts, processes and potential future impacts

This article is protected by copyright. All rights reserved. Acknowledgements This work emerged from the CARBO-Extreme project, funded by the European Community’s 7th framework programme under grant agreement (FP7-ENV-2008-1-226701). We are grateful to the Reviewers and the Subject Editor for helpful guidance. We thank to Silvana Schott for graphic support. Mirco Miglivacca provided helpful comments on the manuscript. Michael Bahn acknowledges support from the Austrian Science Fund (FWF; P22214-B17). Sara Vicca is a postdoctoral research associate of the Fund for Scientific Research – Flanders. Wolfgang Cramer contributes to the Labex OT-Med (n° ANR-11- LABX-0061) funded by the French government through the A*MIDEX project (n° ANR-11-IDEX-0001-02). Flurin Babst acknowledges support from the Swiss National Science Foundation (P300P2_154543).Peer reviewedPublisher PD

Adaptation of the Structured Clinical Interview for DSM-IV Disorders for assessing depression in women during pregnancy and post-partum across countries and cultures

BackgroundTo date, no study has used standardised diagnostic assessment procedures to determine whether rates of perinatal depression vary across cultures.AimsTo adapt the Structured Clinical Interview for DSM–IV Disorders (SCID) for assessing depression and other non-psychotic psychiatric illness perinatally and to pilot the instrument in different centres and cultures.MethodAssessments using the adapted SCID and the Edinburgh Postnatal Depression Scale were conducted during the third trimester of pregnancy and at 6 months postpartum with 296 women from ten sites in eight countries. Point prevalence rates during pregnancy and the postnatal period and adjusted 6-month period prevalence rates were computed for caseness, depression and major depression.ResultsThe third trimester and 6-month point prevalence rates for perinatal depression were 6.9% and 8.0%, respectively. Postnatal 6-month period prevalence rates for perinatal depression ranged from 2.1% to 31.6% across centres and there were significant differences in these rates between centres.ConclusionsStudy findings suggest that the SCID was successfully adapted for this context. Further research on determinants of differences inprevalence of depression across cultures isneeded

A few extreme events dominate global interannual variability in gross primary production

Understanding the impacts of climate extremes on the carbon cycle is important for quantifying the carbon-cycle climate feedback and highly relevant to climate change assessments. Climate extremes and fires can have severe regional effects, but a spatially explicit global impact assessment is still lacking. Here, we directly quantify spatiotemporal contiguous extreme anomalies in four global data sets of gross primary production (GPP) over the last 30 years. We find that positive and negative GPP extremes occurring on 7% of the spatiotemporal domain explain 78% of the global interannual variation in GPP and a significant fraction of variation in the net carbon flux. The largest thousand negative GPP extremes during 1982–2011 (4.3% of the data) account for a decrease in photosynthetic carbon uptake of about 3.5 Pg C yr−1, with most events being attributable to water scarcity. The results imply that it is essential to understand the nature and causes of extremes to understand current and future GPP variability

Instability in clinical risk stratification models using deep learning

While it has been well known in the ML community that deep learning models suffer from instability, the consequences for healthcare deployments are under characterised. We study the stability of different model architectures trained on electronic health records, using a set of outpatient prediction tasks as a case study. We show that repeated training runs of the same deep learning model on the same training data can result in significantly different outcomes at a patient level even though global performance metrics remain stable. We propose two stability metrics for measuring the effect of randomness of model training, as well as mitigation strategies for improving model stability.Comment: Accepted for publication in Machine Learning for Health (ML4H) 202

Nonlinear regional warming with increasing CO₂ concentration

When considering adaptation measures and global climate mitigation goals, stakeholders need regional-scale climate projections, including the range of plausible warming rates. To assist these stakeholders, it is important to understand whether some locations may see disproportionately high or low warming from additional forcing above targets such as 2 K (ref. 1). There is a need to narrow uncertainty2 in this nonlinear warming, which requires understanding how climate changes as forcings increase from medium to high levels. However, quantifying and understanding regional nonlinear processes is challenging. Here we show that regional-scale warming can be strongly superlinear to successive CO2 doublings, using five different climate models. Ensemble-mean warming is superlinear over most land locations. Further, the inter-model spread tends to be amplified at higher forcing levels, as nonlinearities grow—especially when considering changes per kelvin of global warming. Regional nonlinearities in surface warming arise from nonlinearities in global-mean radiative balance, the Atlantic meridional overturning circulation, surface snow/ice cover and evapotranspiration. For robust adaptation and mitigation advice, therefore, potentially avoidable climate change (the difference between business-as-usual and mitigation scenarios) and unavoidable climate change (change under strong mitigation scenarios) may need different analysis methods