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Subseasonal-to-seasonal (S2S) prediction, especially the prediction of extreme hydroclimate events such as droughts and floods, is not only scientifically challenging, but also has substantial societal impacts. Motivated by preliminary studies, the Global Energy and Water Exchanges (GEWEX)/Global Atmospheric System Study (GASS) has launched a new initiative called "Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction"(LS4P) as the first international grass-roots effort to introduce spring land surface temperature (LST)/subsurface temperature (SUBT) anomalies over high mountain areas as a crucial factor that can lead to significant improvement in precipitation prediction through the remote effects of land-atmosphere interactions. LS4P focuses on process understanding and predictability, and hence it is different from, and complements, other international projects that focus on the operational S2S prediction. More than 40 groups worldwide have participated in this effort, including 21 Earth system models, 9 regional climate models, and 7 data groups. This paper provides an overview of the history and objectives of LS4P, provides the first-phase experimental protocol (LS4P-I) which focuses on the remote effect of the Tibetan Plateau, discusses the LST/SUBT initialization, and presents the preliminary results. Multi-model ensemble experiments and analyses of observational data have revealed that the hydroclimatic effect of the spring LST on the Tibetan Plateau is not limited to the Yangtze River basin but may have a significant large-scale impact on summer precipitation beyond East Asia and its S2S prediction. Preliminary studies and analysis have also shown that LS4P models are unable to preserve the initialized LST anomalies in producing the observed anomalies largely for two main reasons: (i) inadequacies in the land models arising from total soil depths which are too shallow and the use of simplified parameterizations, which both tend to limit the soil memory; (ii) reanalysis data, which are used for initial conditions, have large discrepancies from the observed mean state and anomalies of LST over the Tibetan Plateau. Innovative approaches have been developed to largely overcome these problems.

This paper surveys the current state of knowledge regarding large-scale meteorological patterns (LSMPs) associated with short-duration (less than 1 week) extreme precipitation events over North America. In contrast to teleconnections, which are typically defined based on the characteristic spatial variations of a meteorological field or on the remote circulation response to a known forcing, LSMPs are defined relative to the occurrence of a specific phenomenon—here, extreme precipitation—and with an emphasis on the synoptic scales that have a primary influence in individual events, have medium-range weather predictability, and are well-resolved in both weather and climate models. For the LSMP relationship with extreme precipitation, we consider the previous literature with respect to definitions and data, dynamical mechanisms, model representation, and climate change trends. There is considerable uncertainty in identifying extremes based on existing observational precipitation data and some limitations in analyzing the associated LSMPs in reanalysis data. Many different definitions of “extreme” are in use, making it difficult to directly compare different studies. Dynamically, several types of meteorological systems—extratropical cyclones, tropical cyclones, mesoscale convective systems, and mesohighs—and several mechanisms—fronts, atmospheric rivers, and orographic ascent—have been shown to be important aspects of extreme precipitation LSMPs. The extreme precipitation is often realized through mesoscale processes organized, enhanced, or triggered by the LSMP. Understanding of model representation, trends, and projections for LSMPs is at an early stage, although some promising analysis techniques have been identified and the LSMP perspective is useful for evaluating the model dynamics associated with extremes.

Numerical weather, climate, or Earth system models involve the coupling of components. At a broad level, these components can be classified as the resolved fluid dynamics, unresolved fluid dynamical aspects (i.e., those represented by physical parameterizations such as subgrid-scale mixing), and nonfluid dynamical aspects such as radiation and microphysical processes. Typically, each component is developed, at least initially, independently.Once development ismature, the components are coupled to deliver a model of the required complexity. The implementation of the coupling can have a significant impact on the model.As the error associated with each component decreases, the errors introduced by the coupling will eventually dominate. Hence, any improvement in one of the components is unlikely to improve the performance of the overall system. The challenges associated with combining the components to create a coherentmodel are here termed physics-dynamics coupling. The issue goes beyond the coupling between the parameterizations and the resolved fluid dynamics. This paper highlights recent progress and some of the current challenges. It focuses on three objectives: to illustrate the phenomenology of the coupling problemwith references to examples in the literature, to show howthe problem can be analyzed, and to create awareness of the issue across the disciplines and specializations. The topics addressed are different ways of advancing full models in time, approaches to understanding the role of the coupling and evaluation of approaches, coupling ocean and atmosphere models, thermodynamic compatibility between model components, and emerging issues such as those that arise as model resolutions increase and/ormodels use variable resolutions.

The objective of this paper is to review statistical methods, dynamics, modeling efforts, and trends related to temperature extremes, with a focus upon extreme events of short duration that affect parts of North America. These events are associated with large scale meteorological patterns (LSMPs). The statistics, dynamics, and modeling sections of this paper are written to be autonomous and so can be read separately. Methods to define extreme events statistics and to identify and connect LSMPs to extreme temperature events are presented. Recent advances in statistical techniques connect LSMPs to extreme temperatures through appropriately defined covariates that supplement more straightforward analyses. Various LSMPs, ranging from synoptic to planetary scale structures, are associated with extreme temperature events. Current knowledge about the synoptics and the dynamical mechanisms leading to the associated LSMPs is incomplete. Systematic studies of: the physics of LSMP life cycles, comprehensive model assessment of LSMP-extreme temperature event linkages, and LSMP properties are needed. Generally, climate models capture observed properties of heat waves and cold air outbreaks with some fidelity. However they overestimate warm wave frequency and underestimate cold air outbreak frequency, and underestimate the collective influence of low-frequency modes on temperature extremes. Modeling studies have identified the impact of large-scale circulation anomalies and land–atmosphere interactions on changes in extreme temperatures. However, few studies have examined changes in LSMPs to more specifically understand the role of LSMPs on past and future extreme temperature changes. Even though LSMPs are resolvable by global and regional climate models, they are not necessarily well simulated. The paper concludes with unresolved issues and research questions.

Diffusion tensor imaging studies show white matter (WM) abnormalities in children with autism spectrum disorder (ASD). However, investigations are often limited by small samples, particularly problematic given the heterogeneity of ASD. We explored WM using DTI in a large sample of 130 children and adolescents (7-15 years) with and without ASD, whether age-related changes differed between ASD and control groups, and the relation between DTI measures and ASD symptomatology. Reduced fractional anisotropy and axial diffusivity were observed in ASD in numerous WM tracts, including the corpus callosum and thalamocortical fibres--tracts crucial for interhemispheric connectivity and higher order information processing. Widespread WM compromise in ASD is consistent with the view that ASD is a disorder of generalized complex information processing.

Atrophins are evolutionarily conserved proteins that are thought to act as transcriptional co-repressors. Mammalian genomes contain two atrophin genes. Dominant polyglutamine-expanded alleles of atrophin 1 have been identified as the cause of dentatorubralpallidoluysian atrophy, an adult-onset human neurodegenerative disease with similarity to Huntington's. In a screen for recessive mutations that disrupt patterning of the early mouse embryo, we identified a line named openmind carrying a mutation in atrophin 2. openmind homozygous; embryos exhibit a variety of patterning defects that first appear at E8.0. Defects include a specific failure in ventralization of the anterior neural plate, loss of heart looping and irregular partitioning of somites. In mutant embryos, Shh expression fails to initiate along the anterior midline at E8.0, and Fgf8 is delocalized from the anterior neural ridge at E8.5, revealing a crucial role for atrophin 2 in the formation and function of these two signaling centers. Atrophin 2 is also required for normal organization of the apical ectodermal ridge, a signaling center that directs limb pattern. Elevated expression of atrophin 2 in neurons suggests it may interact with atrophin 1 in neuronal development or function. We further show that atrophin 2 associates with histone deacetylase 1 in mouse embryos, providing a biochemical link between Atr2 and a chromatin-modifying enzyme. Based on our results, and on those of others, we propose that atrophin proteins act as transcriptional co-repressors during embryonic development.

Diffusion tensor imaging studies show white matter (WM) abnormalities in children with autism spectrum disorder (ASD). However, investigations are often limited by small samples, particularly problematic given the heterogeneity of ASD. We explored WM using DTI in a large sample of 130 children and adolescents (7-15 years) with and without ASD, whether age-related changes differed between ASD and control groups, and the relation between DTI measures and ASD symptomatology. Reduced fractional anisotropy and axial diffusivity were observed in ASD in numerous WM tracts, including the corpus callosum and thalamocortical fibres-tracts crucial for interhemispheric connectivity and higher order information processing. Widespread WM compromise in ASD is consistent with the view that ASD is a disorder of generalized complex information processing., Author(s): V. M. Vogan[sup.1] [sup.2] , B. R. Morgan[sup.1] , R. C. Leung[sup.1] [sup.3] , E. Anagnostou[sup.4] , K. Doyle-Thomas[sup.4] , M. J. Taylor[sup.1] [sup.2] [sup.3] Author Affiliations: (1) Hospital [...]