Reduced-Complexity Maximum-Likelihood Detection in Downlink SDMA Systems

The literature of up-link SDMA systems is rich, but at the time of writing there is a paucity of information on the employment of SDMA techniques in the down-link. Hence, in this paper a Space Division Multiple Access (SDMA) down-link (DL) multi-user communication system invoking a novel low-complexity Maximum Likelihood (ML) space-time detection technique is proposed, which can be regarded as an advanced extension of the Complex Sphere Decoder (CSD). We demonstrate that as opposed to the previously published variants of the CSD, the proposed technique may be employed for obtaining a high effective throughput in the so-called “over-loaded” scenario, where the number of transmit antennas exceeds that of the receive antennas. The proposed method achieves the optimum performance of the ML detector even in heavily over-loaded scenarios, while the associated computational complexity is only moderately increased. As an illustrative example, the required Eb/N0 increased from 2 dB to 9 dB, when increasing the normalized system load from unity, representing the fully loaded system, to a normalized load of 1.556

Robust skill of decadal climate predictions

There is a growing need for skilful predictions of climate up to a decade ahead. Decadal climate predictions show high skill for surface temperature, but confidence in forecasts of precipitation and atmospheric circulation is much lower. Recent advances in seasonal and annual prediction show that the signal-to-noise ratio can be too small in climate models, requiring a very large ensemble to extract the predictable signal. Here, we reassess decadal prediction skill using a much larger ensemble than previously available, and reveal significant skill for precipitation over land and atmospheric circulation, in addition to surface temperature. We further propose a more powerful approach than used previously to evaluate the benefit of initialisation with observations, improving our understanding of the sources of skill. Our results show that decadal climate is more predictable than previously thought and will aid society to prepare for, and adapt to, ongoing climate variability and change.D.M.S., A.A.S., N.J.D., L.H. and R.E. were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra and by the European Commission Horizon 2020 EUCP project (GA 776613). L.P.C. was supported by the Spanish MINECO HIATUS (CGL2015-70353-R) project. F.J.D.R. was supported by the H2020 EUCP (GA 776613) and the Spanish MINECO CLINSA (CGL2017-85791-R) projects. W.A. M. and H.P. were supported by the German Ministry of Education and Research (BMBF) under the project MiKlip (grant 01LP1519A). The NCAR contribution was supported by the US National Oceanic and Atmospheric Administration (NOAA) Climate Program Office under Climate Variability and Predictability Program Grant NA13OAR4310138 and by the US National Science Foundation (NSF) Collaborative Research EaSM2 Grant OCE-1243015. The NCAR contribution is also based upon work supported by NCAR, which is a major facility sponsored by the US NSF under Cooperative Agreement No. 1852977. The Community Earth System Model Decadal Prediction Large Ensemble (CESM-DPLE) was generated using computational resources provided by the US National Energy Research Scientific Computing Center, which is supported by the Office of Science of the US Department of Energy under Contract DE-AC02-05CH11231, as well as by an Accelerated Scientific Discovery grant for Cheyenne (https://doi.org/10.5065/D6RX99HX) that was awarded by NCAR’s Computational and Information System Laboratory.Peer ReviewedPostprint (published version

The coronal structure of Speedy Mic - II:Prominence masses and off-disc emission

Observations of stellar prominences on young rapidly rotating stars provide unique probes of their magnetic fields out to many stellar radii. We compare two independently obtained data sets of the K3 dwarf Speedy Mic (BO Mic, HD 197890) using the Anglo-Australian Telescope (AAT) and the European Southern Observatory (ESO) Very Large Telescope (VLT). Taken more than a fortnight apart, they provide the first insight into the evolution of the prominence system on such a young rapidly rotating star. The largest prominences observed transiting the stellar disc are found at very similar rotational phases between the epochs. This suggests that the magnetic structures supporting the prominences retain their identity on a two to three week time-scale. By taking advantage of the high signal-to-noise ratio and large wavelength range of the VLT observations, we identify prominences as transient absorption features in all lines of the hydrogen Balmer series down to H-10. We use the ratios of the prominence equivalent widths (EWs) in these lines to determine their column densities in the first excited state of hydrogen. We determine the optical depths, finding prominences to be rather optically thick (tau approximate to 20) in the Ha line. The total hydrogen column density and thus the prominence masses are determined via observations of the Call H&amp;K lines. We find typical masses for four of the largest prominences to be in the range 0.5-2.3 x 10(14) kg, slightly larger than giant solar prominence masses. Rotationally modulated emission is seen outside of the H alpha line. These loops of emission are shown to be caused by prominences seen off the stellar disc. We find that all of the large emission loops can be associated with prominences we see transiting the stellar disc. This, combined with the fact that many prominences appear to eclipse the off-disc emission of others, strongly suggests that the prominence system is highly flattened and likely confined to low stellar latitudes.</p

The coronal structure of Speedy Mic - I: A densely packed prominence system beyond co-rotation

We present new observations of the prominence system on the K3 dwarf Speedy Mic (BO Mic, HD 197890). Using an improved technique to track the absorption features in H alpha we find a very active prominence system with approximately 10 prominences on the observable hemisphere per rotation. From a total of 25 prominences, we find an average axial distance of (2.85 +/- 0.54) R-* which is twice the corotation radius above the stellar surface. We discuss the consequences of these observations on the nature of the supporting magnetic structures. Two consecutive nights, with complete phase coverage, combined with a further night after a three-night gap allow us to study the evolution of the prominence system on two different time-scales. Several of the prominences have counterparts at similar phases on consecutive nights. During this interval, many prominences show evidence for evolution in their heights and phases of observation. Five nights (13 rotation cycles) later, we recover many prominences at approximately the same phases. Whilst individual prominences change axial distances or appear/reappear from night-to-night, the underlying prominence supporting structures appear to be stable over as many as 13 stellar rotations.</p

Stellar magnetic fields in swollen convection zones

Solar magnetic activity is generated through dynamo action operating at the base of the solar convection zone. However, for rapidly rotating solar-type stars this might not be the case with magnetic images showing regions of near-surface azimuthal field indicating that the operation of dynamo may in fact be distributed throughout the entire convection zone. Here we present the first magnetic images of a pre-main sequence star with both components having swollen outer convection zones. These results are part of an international study to understand how the generation of magnetic fields is affected by basic stellar parameters such as mass, rotation rate, the depth of the stellar convection zone, and binarity. The magnetic images were obtained by observing the star in circularly polarised light and using the technique of Zeeman Doppler imaging

Robust skill of decadal climate predictions

There is a growing need for skilful predictions of climate up to a decade ahead. Decadal climate predictions show high skill for surface temperature, but confidence in forecasts of precipitation and atmospheric circulation is much lower. Recent advances in seasonal and annual prediction show that the signal-to-noise ratio can be too small in climate models, requiring a very large ensemble to extract the predictable signal. Here, we reassess decadal prediction skill using a much larger ensemble than previously available, and reveal significant skill for precipitation over land and atmospheric circulation, in addition to surface temperature. We further propose a more powerful approach than used previously to evaluate the benefit of initialisation with observations, improving our understanding of the sources of skill. Our results show that decadal climate is more predictable than previously thought and will aid society to prepare for, and adapt to, ongoing climate variability and change.D.M.S., A.A.S., N.J.D., L.H. and R.E. were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra and by the European Commission Horizon 2020 EUCP project (GA 776613). L.P.C. was supported by the Spanish MINECO HIATUS (CGL2015-70353-R) project. F.J.D.R. was supported by the H2020 EUCP (GA 776613) and the Spanish MINECO CLINSA (CGL2017-85791-R) projects. W.A. M. and H.P. were supported by the German Ministry of Education and Research (BMBF) under the project MiKlip (grant 01LP1519A). The NCAR contribution was supported by the US National Oceanic and Atmospheric Administration (NOAA) Climate Program Office under Climate Variability and Predictability Program Grant NA13OAR4310138 and by the US National Science Foundation (NSF) Collaborative Research EaSM2 Grant OCE-1243015. The NCAR contribution is also based upon work supported by NCAR, which is a major facility sponsored by the US NSF under Cooperative Agreement No. 1852977. The Community Earth System Model Decadal Prediction Large Ensemble (CESM-DPLE) was generated using computational resources provided by the US National Energy Research Scientific Computing Center, which is supported by the Office of Science of the US Department of Energy under Contract DE-AC02-05CH11231, as well as by an Accelerated Scientific Discovery grant for Cheyenne (https://doi.org/10.5065/D6RX99HX) that was awarded by NCAR’s Computational and Information System Laboratory.Peer Reviewe

Predicted Chance That Global Warming Will Temporarily Exceed 1.5 °C

The Paris Agreement calls for efforts to limit anthropogenic global warming to less than 1.5 °C above preindustrial levels. However, natural internal variability may exacerbate anthropogenic warming to produce temporary excursions above 1.5 °C. Such excursions would not necessarily exceed the Paris Agreement, but would provide a warning that the threshold is being approached. Here we develop a new capability to predict the probability that global temperature will exceed 1.5 °C above preindustrial levels in the coming 5 years. For the period 2017 to 2021 we predict a 38% and 10% chance, respectively, of monthly or yearly temperatures exceeding 1.5 °C, with virtually no chance of the 5‐year mean being above the threshold. Our forecasts will be updated annually to provide policy makers with advanced warning of the evolving probability and duration of future warming events.D.M.S., A.A.S., N.J.D., L.H., and R.E. were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra and by the European Commission Horizon 2020 EUCP project (GA 776613). R.B., L.P.C., F.J.D.R., and M. M. were supported by the H2020 EUCP (GA 776613) and the Spanish MINECO CLINSA (CGL2017-85791-R) and HIATUS (CGL2015-70353-R) projects. L.P.C.’s contract is cofinanced by the MINECO under Juan de la Cierva Incorporación postdoctoral fellowship number IJCI-2015-23367. W.A.M. and H.P. acknowledge funding from the German Federal Ministry for Education and Research (BMBF) project MiKlip (FKZ 01LP1519A). The NCAR contribution was supported by the US National Oceanic and Atmospheric Administration (NOAA) Climate Program Office under Climate Variability and Predictability Program grant NA13OAR4310138, by the US National Science Foundation (NSF) Collaborative Research EaSM2 grant OCE-1243015, by the Regional and Global Climate Modeling Program (RGCM) of the US Department of Energy’s, Office of Science (BER), Cooperative Agreement DE-FC02 97ER62402, and by the NSF through its sponsorship of NCAR. The NCAR simulations were generated using computational resources provided by the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract DE-AC02-05CH11231, as well as by an Accelerated Scientific Discovery grant for Cheyenne that was awarded by NCAR’s Computational and Information Systems Laboratory. The EC-EARTH simulations by SMHI were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at NSC. Data used to create the figures are available at 10.5281/zenodo.1434700.Peer Reviewe