Mutations in LRRK2 underlie an autosomal-dominant, inherited form of Parkinson's disease (PD) that mimics the clinical features of the common "sporadic" form of PD. The LRRK2 protein includes putative GTPase, protein kinase, WD40 repeat, and leucine-rich repeat (LRR) domains of unknown function. Here we show that PD-associated LRRK2 mutations display disinhibited kinase activity and induce a progressive reduction in neurite length and branching both in primary neuronal cultures and in the intact rodent CNS. In contrast, LRRK2 deficiency leads to increased neurite length and branching. Neurons that express PD-associated LRRK2 mutations additionally harbor prominent phospho-tau-positive inclusions with lysosomal characteristics and ultimately undergo apoptosis.

SUMMARY Recent genome-wide association studies have linked common variants in the human genome to Parkinson’s disease (PD) risk. Here we show that the consequences of variants at 2 such loci, PARK16 and LRRK2, are highly interrelated, both in terms of their broad impacts on human brain transcriptomes of unaffected carriers, and in terms of their associations with PD risk. Deficiency of the PARK16 locus gene RAB7L1 in primary rodent neurons, or of a RAB7L1 orthologue in Drosophila dopamine neurons, recapitulated degeneration observed with expression of a familial PD mutant form of LRRK2, whereas RAB7L1 overexpression rescued the LRRK2 mutant phenotypes. PD-associated defects in RAB7L1 or LRRK2 led to endolysosomal and Golgi apparatus sorting defects and deficiency of the VPS35 component of the retromer complex. Expression of wild-type VPS35, but not a familial PD-associated mutant form, rescued these defects. Taken together, these studies implicate retromer and lysosomal pathway alterations in PD risk.

While seasonal outlooks have been operational for many years, until recently the extended‐range timescale referred to as subseasonal‐to‐seasonal (S2S) has received little attention. S2S prediction fills the gap between short‐range weather prediction and long‐range seasonal outlooks. Decisions in a range of sectors are made in this extended‐range lead time; therefore, there is a strong demand for this new generation of forecasts. International efforts are under way to identify key sources of predictability, improve forecast skill and operationalize aspects of S2S forecasts; however, challenges remain in advancing this new frontier. If S2S predictions are to be used effectively, it is important that, along with science advances, an effort is made to develop, communicate and apply these forecasts appropriately. In this study, the emerging operational S2S forecasts are presented to the wider weather and climate applications community by undertaking the first comprehensive review of sectoral applications of S2S predictions, including public health, disaster preparedness, water management, energy and agriculture. The value of applications‐relevant S2S predictions is explored, and the opportunities and challenges facing their uptake are highlighted. It is shown how social sciences can be integrated with S2S development, from communication to decision‐making and valuation of forecasts, to enhance the benefits of ‘climate services’ approaches for extended‐range forecasting. While S2S forecasting is at a relatively early stage of development, it is concluded that it presents a significant new window of opportunity that can be explored for application‐ready capabilities that could allow many sectors the opportunity to systematically plan on a new time horizon.

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.

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