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Abstract Studies have shown that prenatal maternal stress (PNMS) affects brain structure and function in childhood. However, less research has examined whether PNMS effects on brain structure and function extend to young adulthood. We recruited women who were pregnant during or within 3 months following the 1998 Quebec ice storm, assessed their PNMS, and prospectively followed‐up their children. T1‐weighted magnetic resonance imaging (MRI) and resting‐state functional MRI were obtained from 19‐year‐old young adults with ( n  = 39) and without ( n  = 65) prenatal exposure to the ice storm. We examined between‐group differences in gray matter volume (GMV), surface area (SA), and cortical thickness (CT). We used the brain regions showing between‐group GMV differences as seeds to compare between‐group functional connectivity. Within the Ice Storm group, we examined (1) associations between PNMS and the atypical GMV, SA, CT, and functional connectivity, and (2) moderation by timing of exposure. Primarily, we found that, compared to Controls, the Ice Storm youth had larger GMV and higher functional connectivity of the anterior cingulate cortex, the precuneus, the left occipital pole, and the right hippocampus; they also had larger CT, but not SA, of the left occipital pole. Within the Ice Storm group, maternal subjective distress during preconception and mid‐to‐late pregnancy was associated with atypical left occipital pole CT. These results suggest the long‐lasting impact of disaster‐related PNMS on child brain structure and functional connectivity. Our study also indicates timing‐specific effects of the subjective aspect of PNMS on occipital thickness.

Abstract Several studies have reported the factor structure of posttraumatic stress disorder (PTSD) using confirmatory factor analysis (CFA). The results show models with different number of factors, high correlations between factors, and symptoms that belong to different factors in different models without affecting the fit index. These elements could suppose the existence of considerable item cross-loading, the overlap of different factors or even the presence of a general factor that explains the items common source of variance. The aim is to provide new evidence regarding the factor structure of PTSD using CFA and exploratory structural equation modeling (ESEM). In a sample of 1,372 undergraduate students, we tested six different models using CFA and two models using ESEM and ESEM bifactor analysis. Trauma event and past-month PTSD symptoms were assessed with Life Events Checklist for DSM-5 (LEC–5) and PTSD Checklist for DSM-5 (PCL–5). All six tested CFA models showed good fit indexes (RMSEA = .051–.056, CFI = .969–.977, TLI = .965–.970), with high correlations between factors ( M = .77, SD = .09 to M = .80, SD = .09). The ESEM models showed good fit indexes (RMSEA = .027–.036, CFI = .991–.996, TLI = .985–.992). These models confirmed the presence of cross-loadings on several items as well as loads on a general factor that explained 76.3% of the common variance. The results showed that most of the items do not meet the assumption of dimensional exclusivity, showing the need to expand the analysis strategies to study the symptomatic organization of PTSD.

La thérapie cognitive-comportementale (TCC) pour le trouble de stress post-traumatique (TSPT) est validée empiriquement (Forman-Hoffman et al., 2018). Toutefois, à notre connaissance, aucune revue de la littérature ne s’intéresse précisément à l’efficacité à long terme de la TCC du TSPT. Il importe pourtant de s’assurer avec une vision d’ensemble de la durabilité des gains thérapeutiques afin de vérifier si la TCC du TSPT permet d’éviter un retour des symptômes après la thérapie. Des études ont observé que les gains thérapeutiques se maintiendraient entre 6 et 20 mois après la TCC (voir, p. ex., Hembree & Foa, 2000; Kline, Cooper, Rytwinksi, & Feeny, 2018) et qu’ils pourraient même s’améliorer (Hembree & Foa, 2000). La présente revue de littérature identifie des études de traitement, des revues de littérature et des méta-analyses abordant l’efficacité à long terme d’une TCC du TSPT. Ce projet répertorie également les facteurs influençant l’efficacité à long terme d’une TCC individuelle, de groupe et par vidéoconférence. Des articles publiés entre 2010 et 2018 ont été cherchés dans les bases de données MEDLINE et PsycINFO. Deux constats se dégagent de cette revue, soit que la TCC permettrait de traiter le TSPT de façon durable et que certaines variables comme la dépression ou l’anxiété comorbide, un âge avancé, des difficultés de sommeil persistantes et le fait de tarder à aller chercher de l’aide sont associées à une moins bonne efficacité à long terme de la TCC du TSPT. Il est possible que le développement d’habiletés d’adaptation en thérapie soit un facteur de maintien et même d’amélioration des gains après la TCC.

Epigenetic research in post-traumatic stress disorder (PTSD) is essential, given that environmental stressors and fear play such a crucial role in its development. As such, it may provide a framework for understanding individual differences in the prevalence of the disorder and in treatment response. This paper reviews the epigenetic markers associated with PTSD and its treatment, including candidate genes and epigenome-wide studies. Because the etiopathogenesis of PTSD rests heavily on learning and memory, we also draw upon animal neuroepigenetic research on the acquisition, update and erasure of fear memory, focusing on the mechanisms associated with memory reconsolidation. Reconsolidation blockade (or impairment) treatment in PTSD has been studied in clinical trials and, from a neurological perspective, may hold promise for identifying epigenetic markers of successful therapy. We conclude this paper by discussing several key considerations and challenges in epigenetic research on PTSD in humans.

Introduction Studies have shown that, following psychotherapy for posttraumatic stress disorder (PTSD), symptoms and quality of life (QoL) may improve in many patients, but not always to the same extent. Dysfunctional core beliefs, such as personality beliefs (PB), are associated to psychopathology, including PTSD, and could be associated with the types of coping strategies deployed by an individual. Beliefs and coping strategies were also linked to psychotherapeutic outcomes. Objectives (1) To examine the associations between baseline PB as well as pre- and post-treatment coping strategies; (2) To investigate the mediation effects between PB and the changes in QoL, through changes in coping strategies in a cognitive-behavioral psychotherapy (CBT). Method Seventy-one adults with PTSD participating in a correlational/observational CBT study were assessed for PB before a CBT, as well as for coping strategies and QoL, before and after a CBT. Results PB were generally associated with post-treatment distancing coping. Moreover, changes in distancing coping mediated the relationships between avoidant or dependent PB and psychological QoL improvements. Conclusion This is the first study to show the relationships between PB and coping strategies in PTSD patients, and that higher avoidant or dependent PB predicts a lower reduction in the use of distancing coping through psychotherapy, which is associated with less improvement in psychological QoL. Future studies are needed to further define the role of these variables and target more precisely factors that may hamper the treatment effects of CBT for PTSD.

Abstract. Climate change impact studies require a reference climatological dataset providing a baseline period to assess future changes and post-process climate model biases. High-resolution gridded precipitation and temperature datasets interpolated from weather stations are available in regions of high-density networks of weather stations, as is the case in most parts of Europe and the United States. In many of the world's regions, however, the low density of observational networks renders gauge-based datasets highly uncertain. Satellite, reanalysis and merged product datasets have been used to overcome this deficiency. However, it is not known how much uncertainty the choice of a reference dataset may bring to impact studies. To tackle this issue, this study compares nine precipitation and two temperature datasets over 1145 African catchments to evaluate the dataset uncertainty contribution to the results of climate change studies. These deterministic datasets all cover a common 30-year period needed to define the reference period climate. The precipitation datasets include two gauge-only products (GPCC and CPC Unified), two satellite products (CHIRPS and PERSIANN-CDR) corrected using ground-based observations, four reanalysis products (JRA55, NCEP-CFSR, ERA-I and ERA5) and one merged gauged, satellite and reanalysis product (MSWEP). The temperature datasets include one gauged-only (CPC Unified) product and one reanalysis (ERA5) product. All combinations of these precipitation and temperature datasets were used to assess changes in future streamflows. To assess dataset uncertainty against that of other sources of uncertainty, the climate change impact study used a top-down hydroclimatic modeling chain using 10 CMIP5 (fifth Coupled Model Intercomparison Project) general circulation models (GCMs) under RCP8.5 and two lumped hydrological models (HMETS and GR4J) to generate future streamflows over the 2071–2100 period. Variance decomposition was performed to compare how much the different uncertainty sources contribute to actual uncertainty. Results show that all precipitation and temperature datasets provide good streamflow simulations over the reference period, but four precipitation datasets outperformed the others for most catchments. They are, in order, MSWEP, CHIRPS, PERSIANN and ERA5. For the present study, the two-member ensemble of temperature datasets provided negligible levels of uncertainty. However, the ensemble of nine precipitation datasets provided uncertainty that was equal to or larger than that related to GCMs for most of the streamflow metrics and over most of the catchments. A selection of the four best-performing reference datasets (credibility ensemble) significantly reduced the uncertainty attributed to precipitation for most metrics but still remained the main source of uncertainty for some streamflow metrics. The choice of a reference dataset can therefore be critical to climate change impact studies as apparently small differences between datasets over a common reference period can propagate to generate large amounts of uncertainty in future climate streamflows.

Abstract Currently, there are a large number of diverse climate datasets in existence, which differ, sometimes greatly, in terms of their data sources, quality control schemes, estimation procedures, and spatial and temporal resolutions. Choosing an appropriate dataset for a given application is therefore not a simple task. This study compares nine global/near-global precipitation datasets and three global temperature datasets over 3138 North American catchments. The chosen datasets all meet the minimum requirement of having at least 30 years of available data, so they could all potentially be used as reference datasets for climate change impact studies. The precipitation datasets include two gauged-only products (GPCC and CPC-Unified), two satellite products corrected using ground-based observations (CHIRPS V2.0 and PERSIANN-CDR V1R1), four reanalysis products (NCEP CFSR, JRA55, ERA-Interim, and ERA5), and one merged product (MSWEP V1.2). The temperature datasets include one gauge-based (CPC-Unified) and two reanalysis (ERA-Interim and ERA5) products. High-resolution gauge-based gridded precipitation and temperature datasets were combined as the reference dataset for this intercomparison study. To assess dataset performance, all combinations were used as inputs to a lumped hydrological model. The results showed that all temperature datasets performed similarly, albeit with the CPC performance being systematically inferior to that of the other three. Significant differences in performance were, however, observed between the precipitation datasets. The MSWEP dataset performed best, followed by the gauge-based, reanalysis, and satellite datasets categories. Results also showed that gauge-based datasets should be preferred in regions with good weather network density, but CHIRPS and ERA5 would be good alternatives in data-sparse regions.

The following errata have been identified and approved in accordance with the IPCC protocol for addressing possible errors in IPCC assessment reports, synthesis reports and methodology reports as adopted by the Panel at the Thirty-Third Session (Abu Dhabi, 10-13 May 2011) and amended at the Thirty-Seventh Session (Batumi 14-18 October 2013). Errata identified following the approval and acceptance of the Special Report on Climate Change and Land (SRCCL) and prior to publication have been corrected in the final copyedited and laid out draft of the report. Note that page and line numbers for the SPM are based on the numbering used in the revised final draft as distributed Governments st 2019; and line numbers for the underlying chapters are based on the numbering used in the final draft as distributed to Governments on 24 th June 2019.

Adaptation to climate change is a challenge that is complex and involves increasing risk. Efforts to manage these risks involve many decision-makers, conflicting values, competing objectives and methodologies, multiple alternative options, uncertain outcomes, and debatable probabilities. Adaptation occurs at multiple levels in a complex decision environment and is generally evaluated as better–worse, not right–wrong, based on multiple criteria. Identifying the best adaptation response is difficult. Risk management techniques help to overcome these problems. Here, risk management is presented as a decision-making framework that assists in the selection of optimal strategies (according to various criteria) using a systems approach that has been well defined and generally accepted in public decision-making. In the context of adapting to climate change, the risk management process offers a framework for identifying, assessing, and prioritizing climate-related risks and developing appropriate adaptation responses. The theoretical discussion is illustrated with an example from Canada. It includes (a) the assessment of climate change-caused flood risk to the municipal infrastructure for the City of London, Ontario, Canada, and (b) analysis of adaptation options for management of the risk in one of the watersheds within the City of London – Dingman Creek.

Climate anomalies, such as floods and droughts, as well as gradual temperature changes have been shown to adversely affect economies and societies. Although studies find that climate change might increase global inequality by widening disparities across countries, its effects on within-country income distribution have been little investigated, as has the role of rainfall anomalies. Here, we show that extreme levels of precipitation exacerbate within-country income inequality. The strength and direction of the effect depends on the agricultural intensity of an economy. In high-agricultural-intensity countries, climate anomalies that negatively impact the agricultural sector lower incomes at the bottom end of the distribution and generate greater income inequality. Our results indicate that a 1.5-SD increase in precipitation from average values has a 35-times-stronger impact on the bottom income shares for countries with high employment in agriculture compared to countries with low employment in the agricultural sector. Projections with modeled future precipitation and temperature reveal highly heterogeneous patterns on a global scale, with income inequality worsening in high-agricultural-intensity economies, particularly in Africa. Our findings suggest that rainfall anomalies and the degree of dependence on agriculture are crucial factors in assessing the negative impacts of climate change on the bottom of the income distribution.

Adapting to some level of climate change has become unavoidable. However, there is surprisingly limited systematic knowledge about whether and how adaptation policies have diffused and could diffuse in the future. Most existing adaptation studies do not explicitly examine policy diffusion, which is a form of interdependent policy-making among jurisdictions at the same or across different levels of governance. To address this gap, we offer a new interpretation and assessment of the extensive adaptation policy literature through a policy diffusion perspective; we pay specific attention to diffusion drivers and barriers, motivations, mechanisms, outputs, and outcomes. We assess the extent to which four motivations and related mechanisms of policy diffusion—interests (linked with learning and competition), rights and duties (tied to coercion), ideology, and recognition (both connected with emulation)—are conceptually and empirically associated with adaptation. We also engage with adaptation policy characteristics, contextual conditions (e.g., problem severity) and different channels of adapation policy diffusion (e.g., transnational networks). We demonstrate that adaptation policy diffusion can be associated with different mechanisms, yet many of them remain remarkably understudied. So are the effects of adaptation policy diffusion in terms of changes in vulnerability and resilience. We thus identify manifold avenues for future research, and provide insights for practitioners who may hope to leverage diffusion mechanisms to enhance their adaptation efforts. This article is categorized under: Policy and Governance > Multilevel and Transnational Climate Change Governance Vulnerability and Adaptation to Climate Change > Institutions for Adaptation

Abstract Analyzing intra-annual stream flow can reveal the main causes for runoff changes and the contributions of climate variability and human activities. For this purpose, the Mann–Kendall and cumulative rank difference (CRD) tests, and the double mass curve method, were applied to a time series of hydro-meteorological variables from 1971 to 2010 in the Tajan River basin in Iran. Results indicated that runoff changes in the wet and dry seasons after 1999 had significant respective decreasing and increasing trends, at the 0.01 confidence level, due to dam construction. In the pre-dam period (1991–1998), the results of the double mass curve method showed that climate variability and human activities contributed 57.76% and 42.24%, respectively, to the runoff decrease during the wet season. For the post-dam period (1999–2010), climate variability and anthropogenic activities contributed 24.68% and 75.32%, respectively, to the wet season runoff decrease of 116.55 mm. On the other hand, in the same period during the dry season, climate variability contributed −30.68% and human activities contributed 130.68% to the runoff increase of 41.45 mm. It is evident that runoff changes in both wet and dry seasons were mainly due to human activities associated with dam construction to meet water supply demands for agriculture.