Impact of rheology on probabilistic forecasts of sea ice trajectories: application for search and rescue operations in the Arctic

We present a sensitivity analysis and discuss the probabilistic forecast capabilities of the novel sea ice model neXtSIM used in hindcast mode. The study pertains to the response of the model to the uncertainty on winds using probabilistic forecasts of ice trajectories. neXtSIM is a continuous Lagrangian numerical model that uses an elasto-brittle rheology to simulate the ice response to external forces. The sensitivity analysis is based on a Monte Carlo sampling of 12 members. The response of the model to the uncertainties is evaluated in terms of simulated ice drift distances from their initial positions, and from the mean position of the ensemble, over the mid-term forecast horizon of 10 days. The simulated ice drift is decomposed into advective and diffusive parts that are characterised separately both spatially and temporally and compared to what is obtained with a free-drift model, that is, when the ice rheology does not play any role in the modelled physics of the ice. The seasonal variability of the model sensitivity is presented and shows the role of the ice compactness and rheology in the ice drift response at both local and regional scales in the Arctic. Indeed, the ice drift simulated by neXtSIM in summer is close to the one obtained with the free-drift model, while the more compact and solid ice pack shows a significantly different mechanical and drift behaviour in winter. For the winter period analysed in this study, we also show that, in contrast to the free-drift model, neXtSIM reproduces the sea ice Lagrangian diffusion regimes as found from observed trajectories. The forecast capability of neXtSIM is also evaluated using a large set of real buoy's trajectories and compared to the capability of the free-drift model. We found that neXtSIM performs significantly better in simulating sea ice drift, both in terms of forecast error and as a tool to assist search and rescue operations, although the sources of uncertainties assumed for the present experiment are not sufficient for complete coverage of the observed IABP positions

Data assimilation using adaptive, non-conservative, moving mesh models

Numerical models solved on adaptive moving meshes have become increasingly prevalent in recent years. Motivating problems include the study of fluids in a Lagrangian frame and the presence of highly localized structures such as shock waves or interfaces. In the former case, Lagrangian solvers move the nodes of the mesh with the dynamical flow; in the latter, mesh resolution is increased in the proximity of the localized structure. Mesh adaptation can include remeshing, a procedure that adds or removes mesh nodes according to specific rules reflecting constraints in the numerical solver. In this case, the number of mesh nodes will change during the integration and, as a result, the dimension of the model's state vector will not be conserved. This work presents a novel approach to the formulation of ensemble data assimilation (DA) for models with this underlying computational structure. The challenge lies in the fact that remeshing entails a different state space dimension across members of the ensemble, thus impeding the usual computation of consistent ensemble-based statistics. Our methodology adds one forward and one backward mapping step before and after the ensemble Kalman filter (EnKF) analysis, respectively. This mapping takes all the ensemble members onto a fixed, uniform reference mesh where the EnKF analysis can be performed. We consider a high-resolution (HR) and a low-resolution (LR) fixed uniform reference mesh, whose resolutions are determined by the remeshing tolerances. This way the reference meshes embed the model numerical constraints and are also upper and lower uniform meshes bounding the resolutions of the individual ensemble meshes. Numerical experiments are carried out using 1-D prototypical models: Burgers and Kuramoto–Sivashinsky equations and both Eulerian and Lagrangian synthetic observations. While the HR strategy generally outperforms that of LR, their skill difference can be reduced substantially by an optimal tuning of the data assimilation parameters. The LR case is appealing in high dimensions because of its lower computational burden. Lagrangian observations are shown to be very effective in that fewer of them are able to keep the analysis error at a level comparable to the more numerous observers for the Eulerian case. This study is motivated by the development of suitable EnKF strategies for 2-D models of the sea ice that are numerically solved on a Lagrangian mesh with remeshing

Data assimilation using adaptive, non-conservative, moving mesh models

Numerical models solved on adaptive moving meshes have become increasingly prevalent in recent years. Motivating problems include the study of fluids in a Lagrangian frame and the presence of highly localized structures such as shock waves or interfaces. In the former case, Lagrangian solvers move the nodes of the mesh with the dynamical flow; in the latter, mesh resolution is increased in the proximity of the localized structure. Mesh adaptation can include remeshing, a procedure that adds or removes mesh nodes according to specific rules reflecting constraints in the numerical solver. In this case, the number of mesh nodes will change during the integration and, as a result, the dimension of the model's state vector will not be conserved. This work presents a novel approach to the formulation of ensemble data assimilation (DA) for models with this underlying computational structure. The challenge lies in the fact that remeshing entails a different state space dimension across members of the ensemble, thus impeding the usual computation of consistent ensemble-based statistics. Our methodology adds one forward and one backward mapping step before and after the ensemble Kalman filter (EnKF) analysis, respectively. This mapping takes all the ensemble members onto a fixed, uniform reference mesh where the EnKF analysis can be performed. We consider a high-resolution (HR) and a low-resolution (LR) fixed uniform reference mesh, whose resolutions are determined by the remeshing tolerances. This way the reference meshes embed the model numerical constraints and are also upper and lower uniform meshes bounding the resolutions of the individual ensemble meshes. Numerical experiments are carried out using 1-D prototypical models: Burgers and Kuramoto-Sivashinsky equations and both Eulerian and Lagrangian synthetic observations. While the HR strategy generally outperforms that of LR, their skill difference can be reduced substantially by an optimal tuning of the data assimilation parameters. The LR case is appealing in high dimensions because of its lower computational burden. Lagrangian observations are shown to be very effective in that fewer of them are able to keep the analysis error at a level comparable to the more numerous observers for the Eulerian case. This study is motivated by the development of suitable EnKF strategies for 2-D models of the sea ice that are numerically solved on a Lagrangian mesh with remeshing

Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020

Advanced data assimilation (DA) methods, widely used in geophysical and climate studies to merge observations with numerical models, can improve state estimates and consequent forecasts. We interface the deterministic ensemble Kalman filter (DEnKF) to the Lagrangian neXt generation Sea Ice Model, neXtSIM. The ensemble is generated by perturbing the atmospheric and oceanic forcing throughout the simulations and randomly initialized ice cohesion. Our ensemble–DA system assimilates sea ice concentration (SIC) from the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) and sea ice thickness (SIT) from the merged CryoSat-2 and SMOS datasets (CS2SMOS). Because neXtSIM is computationally solved on a time-dependent evolving mesh, it is a challenging application for ensemble–DA. As a solution, we perform the DEnKF analysis on a fixed and regular reference mesh, on which model variables are interpolated before the DA and then back to each member's mesh after the DA. We evaluate the impact of assimilating different types of sea ice observations on the model's forecast skills of the Arctic sea ice by comparing satellite observations and a free-run ensemble in an Arctic winter period, 2019–2020. Significant improvements in modeled SIT indicate the importance of assimilating weekly CS2SMOS SIT, while the improvements of SIC and ice extent are moderate but benefit from daily ingestion of the OSI-SAF SIC. For most of the winter, the correlation between SIT and SIC is weaker, which results in little cross-inference between the two variables in the assimilation step. Overall, the ensemble–DA system based on the stand-alone sea ice model demonstrates the feasibility of winter Arctic sea ice prediction with good computational efficiency. These results open the path toward operational implementation and the extension to multi-year assimilation.</p

CeLAND: search for a 4th light neutrino state with a 3 PBq 144Ce-144Pr electron antineutrino generator in KamLAND

The reactor neutrino and gallium anomalies can be tested with a 3-4 PBq (75-100 kCi scale) 144Ce-144Pr antineutrino beta-source deployed at the center or next to a large low-background liquid scintillator detector. The antineutrino generator will be produced by the Russian reprocessing plant PA Mayak as early as 2014, transported to Japan, and deployed in the Kamioka Liquid Scintillator Anti-Neutrino Detector (KamLAND) as early as 2015. KamLAND's 13 m diameter target volume provides a suitable environment to measure the energy and position dependence of the detected neutrino flux. A characteristic oscillation pattern would be visible for a baseline of about 10 m or less, providing a very clean signal of neutrino disappearance into a yet-unknown, sterile neutrino state. This will provide a comprehensive test of the electron dissaperance neutrino anomalies and could lead to the discovery of a 4th neutrino state for Delta_m^2 > 0.1 eV^2 and sin^2(2theta) > 0.05.Comment: 67 pages, 50 figures. Th. Lasserre thanks the European Research Council for support under the Starting Grant StG-30718

The 2017 reversal of the Beaufort Gyre: Can dynamic thickening of a seasonal ice cover during a reversal limit summer ice melt in the Beaufort Sea?

During winter 2017 the semi‐permanent Beaufort High collapsed and the anticyclonic Beaufort Gyre reversed. The reversal drove eastward ice motion through the Western Arctic, causing sea ice to converge against Banks Island, and halted the circulation of multiyear sea ice via the gyre, preventing its replenishment in the Beaufort Sea. Prior to the reversal, an anomalously thin seasonal ice cover had formed in the Beaufort following ice‐free conditions during September 2016. With the onset of the reversal in January 2017, convergence drove uncharacteristic dynamic thickening during winter. By the end of March, despite seasonal ice comprising 97% of the ice cover, the reversal created the thickest, roughest and most voluminous regional ice cover of the CryoSat‐2 record. Within the Beaufort Sea, previous work has shown that winter ice export can precondition the region for increased summer ice melt, but that a short reversal during April 2013 contributed to a reduction in summer ice loss. Hence the deformed ice cover at the end of winter 2017 could be expected to limit summer melt. In spite of this, the Beaufort ice cover fell to its fourth lowest September area as the gyre re‐established during April and divergent ice drift broke up the pack, negating the reversal's earlier preconditioning. Our work highlights that dynamic winter thickening of a regional sea ice cover, for instance during a gyre reversal, offers the potential to limit summer ice loss, but that dynamic forcing during spring dictates whether this conditioning carries through to the melt season

A novel signalling screen demonstrates that CALR mutations activate essential MAPK signalling and facilitate megakaryocyte differentiation.

Most MPN patients lacking JAK2 mutations harbour somatic CALR mutations that are thought to activate cytokine signalling although the mechanism is unclear. To identify kinases important for survival of CALR-mutant cells we developed a novel strategy (KISMET) which utilises the full range of kinase selectivity data available from each inhibitor and thus takes advantage of off-target noise that limits conventional siRNA or inhibitor screens. KISMET successfully identified known essential kinases in haematopoietic and non-haematopoietic cell lines and identified the MAPK pathway as required for growth of the CALR-mutated MARIMO cells. Expression of mutant CALR in murine or human haematopoietic cell lines was accompanied by MPL-dependent activation of MAPK signalling, and MPN patients with CALR mutations showed increased MAPK activity in CD34-cells, platelets and megakaryocytes. Although CALR mutations resulted in protein instability and proteosomal degradation, mutant CALR was able to enhance megakaryopoiesis and pro-platelet production from human CD34+ progenitors. These data link aberrant MAPK activation to the MPN phenotype and identify it as a potential therapeutic target in CALR-mutant positive MPNs.Leukemia accepted article preview online, 14 October 2016. doi:10.1038/leu.2016.280.Work in the Green lab is supported by Leukemia and Lymphoma Research, Cancer Research UK, the NIHR Cambridge Biomedical Research Centre, the Cambridge Experimental Cancer Medicine Centre and the Leukemia & Lymphoma Society of America. WW is supported by the Austrian Science Foundation (J 3578-B21). CGA is supported by Kay Kendall Leukaemia Fund clinical research fellowship. UM is supported by a Cancer Research UK Clinician Scientist Fellowship. Work in the Huntly lab is supported by the European Research Council, the MRC (UK), Bloodwise, the Cambridge NIHR funded BRC, KKLF and a WT/MRC Stem Cell centre grant. Work in the Green and Huntly Labs is supported by core support grants by the Wellcome Trust to the Cambridge Institute for Medical Research (100140/z/12/z) and Wellcome Trust-MRC Cambridge Stem Cell Institute (097922/Z/11/Z)

Ethnic differences in the association between blood pressure components and chronic kidney disease in middle aged and older Asian adults

10.1186/1471-2369-14-86BMC Nephrology141