Skilful seasonal prediction of winter gas demand

In Britain, residential properties are predominantly heated using gas central heating systems. Ensuring a reliable supply of gas is therefore vital in protecting vulnerable sections of society from the adverse effects of cold weather. Ahead of the winter, the grid operator makes a prediction of gas demand to better anticipate possible conditions. Seasonal weather forecasts are not currently used to inform this demand prediction. Here we assess whether seasonal weather forecasts can skilfully predict the weather-driven component of both winter mean gas demand and the number of extreme gas demand days over the winter period. We find that both the mean and the number of extreme days are predicted with some skill from early November using seasonal forecasts of the large-scale atmospheric circulation (r > 0.5). Although temperature is most strongly correlated with gas demand, the more skilful prediction of the atmospheric circulation means it is a better predictor of demand. If seasonal weather forecasts are incorporated into pre-winter gas demand planning, they could help improve the security of gas supplies and reduce the impacts associated with extreme demand events

SAGA1 and SAGA2 promote starch formation around proto-pyrenoids in Arabidopsis chloroplasts

The pyrenoid is a chloroplastic microcompartment in which most algae and some terrestrial plants condense the primary carboxylase, Rubisco (ribulose-1,5-bisphosphate carboxylase/oxygenase) as part of a CO2-concentrating mechanism that improves the efficiency of CO2 capture. Engineering a pyrenoid-based CO2-concentrating mechanism (pCCM) into C3 crop plants is a promising strategy to enhance yield capacities and resilience to the changing climate. Many pyrenoids are characterized by a sheath of starch plates that is proposed to act as a barrier to limit CO2 diffusion. Recently, we have reconstituted a phase-separated “proto-pyrenoid” Rubisco matrix in the model C3 plant Arabidopsis thaliana using proteins from the alga with the most well-studied pyrenoid, Chlamydomonas reinhardtii [N. Atkinson, Y. Mao, K. X. Chan, A. J. McCormick, Nat. Commun.11, 6303 (2020)]. Here, we describe the impact of introducing the Chlamydomonas proteins StArch Granules Abnormal 1 (SAGA1) and SAGA2, which are associated with the regulation of pyrenoid starch biogenesis and morphology. We show that SAGA1 localizes to the proto-pyrenoid in engineered Arabidopsis plants, which results in the formation of atypical spherical starch granules enclosed within the proto-pyrenoid condensate and adjacent plate-like granules that partially cover the condensate, but without modifying the total amount of chloroplastic starch accrued. Additional expression of SAGA2 further increases the proportion of starch synthesized as adjacent plate-like granules that fully encircle the proto-pyrenoid. Our findings pave the way to assembling a diffusion barrier as part of a functional pCCM in vascular plants, while also advancing our understanding of the roles of SAGA1 and SAGA2 in starch sheath formation and broadening the avenues for engineering starch morphology

Improved seasonal hydrological forecasting for Great Britain

Great Britain’s variable maritime climate has until relatively recently limited the utility of seasonal hydrological forecasts. The latest generations of seasonal atmospheric forecasting systems have created new opportunities to improve flow forecasting across Great Britain, such as for the UK Hydrological Outlook. Here, newly-developed high-resolution rainfall forecasts derived from historical weather analogues (HWA) conditioned on large-scale circulation patterns are used to drive a monthly-resolution national-scale hydrological model. We use rainfall hindcasts from 1993–2016 to evaluate the performance of these flow forecasts and demonstrate their skill, particularly for the UK winter. We show that the high performance of the rainfall forecasts is spatially complementary to the skill provided by hydrological memory in groundwater-dominated catchments. Our analyses pinpoint the regions which would benefit most from future improvements in the rainfall forecasting or hydrological modelling systems. The introduction of these rainfall forecasts now enables hydrological forecasting at unprecedented levels of detail across Great Britain and is a model that may be similarly beneficial elsewhere in the world

UK Hydrological Outlook using historic weather analogues

Skilful seasonal hydrological forecasts are beneficial for water resources planning and disaster risk reduction. The UK Hydrological Outlook (UKHO) provides river flow and groundwater level forecasts at the national scale. Alongside the standard Ensemble Streamflow Prediction (ESP) method, a new Historic Weather Analogues (HWA) method has recently been implemented. The HWA method samples within high resolution historical observations for analogue months that matches the atmospheric circulation patterns forecasted by a dynamical weather forecasting model. In this study, we conduct a hindcast experiment using the GR6J hydrological model to assess where and when the HWA method is skilful across a set of 314 UK catchments for different seasons. We benchmark the skill against the standard ESP and climatology forecasts to understand to what extent the HWA method represents an improvement to existing forecasting methods. Results show the HWA method yields skilful winter river flow forecasts across the UK compared to the standard ESP method where skilful forecasts were only possible in southeast England. Winter river flow forecasts using the HWA method were also more skilful in discriminating high and low flows across all regions. Catchments with the greatest improvement tended to be upland, fast responding catchments with limited catchment storage and where river flow variability is strongly tied with climate variability. Skilful winter river flow predictability was possible due to relatively high forecast skill of atmospheric circulation patterns (e.g. winter NAO) and the ability of the HWA method to derive high resolution meteorological inputs suitable for hydrological modelling. However, skill was not uniform across different seasons. Improvement in river flow forecast skill for other seasons was modest, such as moderate improvements in northern England and northeast Scotland during spring and little change in autumn. Skilful summer flow predictability remains possible only for southeast England and skill scores were mostly reduced compared to the ESP method elsewhere. This study demonstrates that the HWA method can leverage both climate information from dynamical weather forecasting models and the influence of initial hydrological conditions. An incorporation of climate information improved winter river flow predictability nationally, with the advantage of exploring historically unseen weather sequences. The strong influence of initial hydrological conditions contributed to retaining year-round forecast skill of river flows in southeast England. Overall, this study provides justification for when and where the HWA method is more skilful than existing forecasting approaches and confirms the standard ESP method as a “tough to beat” forecasting system that future improvements should be tested against

Duration of androgen deprivation therapy with postoperative radiotherapy for prostate cancer: a comparison of long-course versus short-course androgen deprivation therapy in the RADICALS-HD randomised trial

Background Previous evidence supports androgen deprivation therapy (ADT) with primary radiotherapy as initial treatment for intermediate-risk and high-risk localised prostate cancer. However, the use and optimal duration of ADT with postoperative radiotherapy after radical prostatectomy remains uncertain. Methods RADICALS-HD was a randomised controlled trial of ADT duration within the RADICALS protocol. Here, we report on the comparison of short-course versus long-course ADT. Key eligibility criteria were indication for radiotherapy after previous radical prostatectomy for prostate cancer, prostate-specific antigen less than 5 ng/mL, absence of metastatic disease, and written consent. Participants were randomly assigned (1:1) to add 6 months of ADT (short-course ADT) or 24 months of ADT (long-course ADT) to radiotherapy, using subcutaneous gonadotrophin-releasing hormone analogue (monthly in the short-course ADT group and 3-monthly in the long-course ADT group), daily oral bicalutamide monotherapy 150 mg, or monthly subcutaneous degarelix. Randomisation was done centrally through minimisation with a random element, stratified by Gleason score, positive margins, radiotherapy timing, planned radiotherapy schedule, and planned type of ADT, in a computerised system. The allocated treatment was not masked. The primary outcome measure was metastasis-free survival, defined as metastasis arising from prostate cancer or death from any cause. The comparison had more than 80% power with two-sided α of 5% to detect an absolute increase in 10-year metastasis-free survival from 75% to 81% (hazard ratio [HR] 0·72). Standard time-to-event analyses were used. Analyses followed intention-to-treat principle. The trial is registered with the ISRCTN registry, ISRCTN40814031, and ClinicalTrials.gov , NCT00541047 . Findings Between Jan 30, 2008, and July 7, 2015, 1523 patients (median age 65 years, IQR 60–69) were randomly assigned to receive short-course ADT (n=761) or long-course ADT (n=762) in addition to postoperative radiotherapy at 138 centres in Canada, Denmark, Ireland, and the UK. With a median follow-up of 8·9 years (7·0–10·0), 313 metastasis-free survival events were reported overall (174 in the short-course ADT group and 139 in the long-course ADT group; HR 0·773 [95% CI 0·612–0·975]; p=0·029). 10-year metastasis-free survival was 71·9% (95% CI 67·6–75·7) in the short-course ADT group and 78·1% (74·2–81·5) in the long-course ADT group. Toxicity of grade 3 or higher was reported for 105 (14%) of 753 participants in the short-course ADT group and 142 (19%) of 757 participants in the long-course ADT group (p=0·025), with no treatment-related deaths. Interpretation Compared with adding 6 months of ADT, adding 24 months of ADT improved metastasis-free survival in people receiving postoperative radiotherapy. For individuals who can accept the additional duration of adverse effects, long-course ADT should be offered with postoperative radiotherapy. Funding Cancer Research UK, UK Research and Innovation (formerly Medical Research Council), and Canadian Cancer Society

Improving Meteorological Seasonal Forecasts for Hydrological Modeling in European Winter

AbstractRecent advances in the skill of seasonal forecasts in the extratropics during winter mean they could offer improvements to seasonal hydrological forecasts. However, the signal-to-noise paradox, whereby the variability in the ensemble mean signal is lower than would be expected given its correlation skill, prevents their use to force hydrological models directly. We describe a postprocessing method to adjust for this problem, increasing the size of the predicted signal in the large-scale circulation. This reduces the ratio of predictable components in the North Atlantic Oscillation (NAO) from 3 to 1. We then derive a large ensemble of daily sequences of spatially gridded rainfall that are consistent with the seasonal mean NAO prediction by selecting historical observations conditioned on the adjusted NAO forecasts. Over northern and southwestern Europe, where the NAO is strongly correlated with winter mean rainfall, the variability of the predicted signal in the adjusted rainfall forecasts is consistent with the correlation skill (they have a ratio of predictable components of ~1) and are as skillful as the unadjusted forecasts. The adjusted forecasts show larger predicted deviations from climatology and can be used to better assess the risk of extreme seasonal mean precipitation as well as to force hydrological models.</jats:p

Predictability of European Winter 2020/21

&amp;lt;p&amp;gt;Winter (DJF) 2020/21 in the North Atlantic/European sector was characterised by the negative phase of the North Atlantic Oscillation (NAO). However, this was not well forecast by the leading seasonal prediction systems. We focus on forecasts from GloSea5, which was the Met Office operational seasonal prediction system at the time. Forecasts initialised in November 2020, at the 1-month lead time, indicated that a positive NAO was likely, although a few ensemble members did agree with the eventual outcome. Analysis suggests that the sudden stratospheric warming (SSW) that occurred in early January 2021 and an active MJO in late January/early February 2021 probably contributed to the observed negative NAO. In particular, GloSea5 indicated a rather low probability for SSW activity, which may well have been exacerbated by the forecast of a stronger than observed La Ni&amp;amp;#241;a by this system.&amp;lt;/p&amp;gt;</jats:p

Seasonal prediction of UK mean and extreme winds

&amp;lt;p&amp;gt;For several years the Met Office has produced a seasonal outlook for the UK every month, which is issued to the UK Government and contingency planners.&amp;amp;#160; The outlook gives predictions of the probability of having average, low, or high seasonal mean UK temperature and precipitation for the coming three-months.&amp;amp;#160; In recent years, there has been increasing demand from sectors such as energy and insurance to include similar probabilistic predictions of UK wind speed: both for the seasonal mean and for measures of extreme winds such as storm numbers.&amp;amp;#160; In this presentation we show the skill of the Met Office&amp;amp;#8217;s GloSea system in predicting seasonal (three-month average) UK mean wind and a measure of UK storminess throughout the year, and discuss the drivers of predictability.&amp;amp;#160; Skill in predicting the UK mean wind speed and storminess peaks in winter (December&amp;amp;#8211;February), owing to predictability of the North Atlantic oscillation.&amp;amp;#160; In summer (June&amp;amp;#8211;August), there is evidence that a significant proportion of variability in UK winds is driven by a Rossby wave train which the model has little skill in predicting. Nevertheless there are signs that the wave is potentially predictable and skill may be improved by reducing model errors.&amp;lt;/p&amp;gt;</jats:p