The last decade has seen the success of stochastic parameterizations in short-term, medium-range, and seasonal forecasts: operational weather centers now routinely use stochastic parameterization schemes to represent model inadequacy better and to improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations not only provides better estimates of uncertainty, but it is also extremely promising for reducing long-standing climate biases and is relevant for determining the climate response to external forcing. This article highlights recent developments from different research groups that show that the stochastic representation of unresolved processes in the atmosphere, oceans, land surface, and cryosphere of comprehensive weather and climate models 1) gives rise to more reliable probabilistic forecasts of weather and climate and 2) reduces systematic model bias. We make a case that the use of mathematically stringent methods for the derivation of stochastic dynamic equations will lead to substantial improvements in our ability to accurately simulate weather and climate at all scales. Recent work in mathematics, statistical mechanics, and turbulence is reviewed; its relevance for the climate problem is demonstrated; and future research directions are outlined.

Members in ensemble forecasts differ due to the representations of initial uncertainties and model uncertainties. The inclusion of stochastic schemes to represent model uncertainties has improved the probabilistic skill of the ECMWF ensemble by increasing reliability and reducing the error of the ensemble mean. Recent progress, challenges and future directions regarding stochastic representations of model uncertainties at ECMWF are described in this article. The coming years are likely to see a further increase in the use of ensemble methods in forecasts and assimilation. This will put increasing demands on the methods used to perturb the forecast model. An area that is receiving greater attention than 5–10 years ago is the physical consistency of the perturbations. Other areas where future efforts will be directed are the expansion of uncertainty representations to the dynamical core and other components of the Earth system, as well as the overall computational efficiency of representing model uncertainty.

Next-generation RNA sequence analysis of platelets from an individual with autosomal recessive gray platelet syndrome (GPS, MIM139090) detected abnormal transcript reads, including intron retention, mapping to NBEAL2 (encoding neurobeachin-like 2). Genomic DNA sequencing confirmed mutations in NBEAL2 as the genetic cause of GPS. NBEAL2 encodes a protein containing a BEACH domain that is predicted to be involved in vesicular trafficking and may be critical for the development of platelet α-granules.

Climate change challenges societal functioning, likely requiring considerable adaptation to cope with future altered weather patterns. Machine learning (ML) algorithms have advanced dramatically, triggering breakthroughs in other research sectors, and recently suggested as aiding climate analysis (Reichstein et al 2019 Nature 566 195-204, Schneider et al 2017 Geophys. Res. Lett. 44 12396-417).Although a considerable number of isolated Earth System features have been analysed with ML techniques, more generic application to understand better the full climate system has not occurred. For instance, ML may aid teleconnection identification, where complex feedbacks make characterisation difficult from direct equation analysis or visualisation of measurements and Earth System model (ESM) diagnostics. Artificial intelligence (AI) can then build on discovered climate connections to provide enhanced warnings of approaching weather features, including extreme events. While ESM development is of paramount importance, we suggest a parallel emphasis on utilising ML and AI to understand and capitalise far more on existing data and simulations.

Key Points• Nbeal2 2/2 mice are a model of human GPS, characterized by macrothrombocytopenia and a-granule-deficient platelets.• NBEAL2 is required for normal platelet function and megakaryocyte development.Gray platelet syndrome (GPS) is an inherited bleeding disorder associated with macrothrombocytopenia and a-granule-deficient platelets. GPS has been linked to loss of function mutations in NEABL2 (neurobeachin-like 2), and we describe here a murine GPS model, the Nbeal2 2/2 mouse. As in GPS, Nbeal2 2/2 mice exhibit splenomegaly, macrothrombocytopenia, and a deficiency of platelet a-granules and their cargo, including von Willebrand factor (VWF), thrombospondin-1, and platelet factor 4. The platelet a-granule membrane protein P-selectin is expressed at 48% of wild-type levels and externalized upon platelet activation. The presence of P-selectin and normal levels of VPS33B and VPS16B in Nbeal2 2/2 platelets suggests that NBEAL2 acts independently of VPS33B/VPS16B at a later stage of a-granule biogenesis. Impaired Nbeal2 2/2 platelet function was shown by flow cytometry, platelet aggregometry, bleeding assays, and intravital imaging of laser-induced arterial thrombus formation. Microscopic analysis detected marked abnormalities in Nbeal2 2/2 bone marrow megakaryocytes, which when cultured showed delayed maturation, decreased survival, decreased ploidy, and developmental abnormalities, including abnormal extracellular distribution of VWF. Our results confirm that a-granule secretion plays a significant role in platelet function, and they also indicate that abnormal a-granule formation in Nbeal2 2/2 mice has deleterious effects on megakaryocyte survival, development, and platelet production. (Blood. 2013;122(19):3349-3358)