Seasonal Arctic sea ice forecasting with probabilistic deep learning

Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss

The 5-HTTLPR polymorphism of the serotonin transporter gene and short term behavioral response to methylphenidate in children with ADHD

<p>Abstract</p> <p>Background</p> <p>Animal models of ADHD suggest that the paradoxical calming effect of methylphenidate on motor activity could be mediated through its action on serotonin transmission. In this study, we have investigated the relationship between the 5-HTTLPR polymorphism in the serotonin transporter gene (<it>SLC6A4</it>) and the response of ADHD relevant behaviors with methylphenidate treatment.</p> <p>Methods</p> <p>Patients between ages 6-12 (n = 157) were assessed with regard to their behavioral response to methylphenidate (0.5 mg/kg/day) using a 2-week prospective within-subject, placebo-controlled (crossover) trial. The children were then genotyped with regard to the triallelic 5-HTTLPR polymorphism in the <it>SLC6A4 </it>gene. Main outcome measure: Conners' Global Index for parents (CGI-Parents) and teachers (CGI-Teachers) at baseline and at the end of each week of treatment with placebo and methylphenidate. For both outcome measurements, we used a mixed model analysis of variance to determine gene, treatment and gene × treatment interaction effects.</p> <p>Results</p> <p>Mixed model analysis of variance revealed a gene × treatment interaction for CGI-Parents but not for CGI-Teachers. Children homozygous for the lower expressing alleles (<it>s+l_G= s'</it>) responded well to placebo and did not derive additional improvement with methylphenidate compared to children carrying a higher expressing allele (<it>l_A</it>). No genotype main effects on either CGI-Parents or CGI-teachers were observed.</p> <p>Conclusions</p> <p>A double blind placebo-controlled design was used to assess the behavioral effects of methylphenidate in relation to the triallelic 5-HTTLPR polymorphism of the <it>SLC6A4 </it>gene in children with ADHD. This polymorphism appears to modulate the behavioral response to methylphenidate in children with ADHD as assessed in the home environment by parents. Further investigation is needed to assess the clinical implications of this finding.</p> <p>Trial Registration</p> <p>ClinicalTrials.gov NCT00483106</p

Analysis of rainfall seasonality from observations and climate models

Two new indicators of rainfall seasonality based on information entropy, the relative entropy (RE) and the dimensionless seasonality index (DSI), together with the mean annual rainfall, are evaluated on a global scale for recently updated precipitation gridded datasets and for historical simulations from coupled atmosphere--ocean general circulation models. The RE provides a measure of the number of wet months and, for precipitation regimes featuring a distinct wet and dry season, it is directly related to the duration of the wet season. The DSI combines the rainfall intensity with its degree of seasonality and it is an indicator of the extent of the global monsoon region. We show that the RE and the DSI are fairly independent of the time resolution of the precipitation data, thereby allowing objective metrics for model intercomparison and ranking. Regions with different precipitation regimes are classified and characterized in terms of RE and DSI. Comparison of different land observational datasets reveals substantial difference in their local representation of seasonality. It is shown that two-dimensional maps of RE provide an easy way to compare rainfall seasonality from various datasets and to determine areas of interest. Models participating to the Coupled Model Intercomparison Project platform, Phase 5, consistently overestimate the RE over tropical Latin America and underestimate it in West Africa, western Mexico and East Asia. It is demonstrated that positive RE biases in a general circulation model are associated with excessively peaked monthly precipitation fractions, too large during the wet months and too small in the months preceding and following the wet season; negative biases are instead due, in most cases, to an excess of rainfall during the premonsoonal months