Parameter uncertainty in forecast recalibration

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.Ensemble forecasts of weather and climate are subject to systematic biases in the ensemble mean and variance, leading to inaccurate estimates of the forecast mean and variance. To address these biases, ensemble forecasts are post-processed using statistical recalibration frameworks. These frameworks often specify parametric probability distributions for the verifying observations. A common choice is the Normal distribution with mean and variance specified by linear functions of the ensemble mean and variance. The parameters of the recalibration framework are estimated from historical archives of forecasts and verifying observations. Often there are relatively few forecasts and observations available for parameter estimation, and so the fitted parameters are also subject to uncertainty. This artefact is usually ignored. This study reviews analytic results that account for parameter uncertainty in the widely used Model Output Statistics recalibration framework. The predictive bootstrap is used to approximate the parameter uncertainty by resampling in more general frameworks such as Non-homogeneous Gaussian Regression. Forecasts on daily, seasonal and annual time scales are used to demonstrate that accounting for parameter uncertainty in the recalibrated predictive distributions leads to probability forecasts that are more skilful and reliable than those in which parameter uncertainty is ignored. The improvements are attributed to more reliable tail probabilities of the recalibrated forecast distributions.Stefan Siegert was supported by the European Union Programme FP7/2007–2013 under grant agreement 3038378 (SPECS). Philip Sansom was supported by a grant from the National Oceanic and Atmospheric Administration (NOAA) NA12OAR4310086

State space models for non‐stationary intermittently coupled systems: an application to the North Atlantic oscillation

This is the final version. Available on open access from Wiley via the DOI in this recordData availability: The data that are analysed in the paper and the programs that were used to analyse them can be obtained from https://rss.onlinelibrary.wiley.com/hub/journal/14679876/seriescdatasetsWe develop Bayesian state space methods for modelling changes to the mean level or temporal correlation structure of an observed time series due to intermittent coupling with an unobserved process. Novel intervention methods are proposed to model the effect of repeated coupling as a single dynamic process. Latent time varying auto‐regressive components are developed to model changes in the temporal correlation structure. Efficient filtering and smoothing methods are derived for the resulting class of models. We propose methods for quantifying the component of variance attributable to an unobserved process, the effect during individual coupling events and the potential for skilful forecasts. The methodology proposed is applied to the study of winter time variability in the dominant pattern of climate variation in the northern hemisphere: the North Atlantic oscillation. Around 70% of the interannual variance in the winter (December–January–February) mean level is attributable to an unobserved process. Skilful forecasts for the winter (December–January–February) mean are possible from the beginning of December.Natural Environment Research Council (NERC

Best practices for post-processing ensemble climate forecasts, part I: selecting appropriate recalibration methods

ArticleThis is the final version of the article. Available from the publisher via the DOI in this record.This study describes a systematic approach to selecting optimal statistical recalibration methods and hindcast designs for producing reliable probability forecasts on seasonal-to-decadal time scales. A new recalibration method is introduced that includes adjustments for both unconditional and conditional biases in the mean and variance of the forecast distribution, and linear time-dependent bias in the mean. The complexity of the recalibration can be systematically varied by restricting the parameters. Simple recalibration methods may outperform more complex ones given limited training data. A new cross-validation methodology is proposed that allows the comparison of multiple recalibration methods and varying training periods using limited data. Part I considers the effect on forecast skill of varying the recalibration complexity and training period length. The interaction between these factors is analysed for grid box forecasts of annual mean near-surface temperature from the CanCM4 model. Recalibration methods that include conditional adjustment of the ensemble mean outperform simple bias correction by issuing climatological forecasts where the model has limited skill. Trend-adjusted forecasts outperform forecasts without trend adjustment at almost 75% of grid boxes. The optimal training period is around 30 years for trend-adjusted forecasts, and around 15 years otherwise. The optimal training period is strongly related to the length of the optimal climatology. Longer training periods may increase overall performance, but at the expense of very poor forecasts where skill is limited

Prevalence of antibody seroconversion to Toxoplasma gondii in uveitis and non-uveitis dogs

This is the final version. Available on open access from BMJ Publishing Group via the DOI in this recordData sharing statement: No additional data are available.Objectives To evaluate the prevalence of seroconversion to Toxoplasma gondii in dogs with uveitis and dogs without uveitis. Methods In total, 135 dogs were evaluated: 51 dogs were diagnosed with uveitis, and 84 dogs were without uveitis. Latex agglutination tests were performed on all sera, and the results were evaluated. Results Overall, 7.8 and 6.0 per cent of sera were positive for the presence of anti-T gondii antibodies (dilution ≥1:64) in the groups with uveitis and non-uveitis dogs, respectively. The frequency distribution of variables (positive and negative results in the uveitis and the non-uveitis group of dogs) was tested with Fisher’s exact test. There was no statistically significant difference between groups (P=0.73). Clinical significance These findings suggest that evidence of exposure to T gondii was not significantly different between uveitis and non-uveitis groups of dogs and that the possible association between exposure to T gondii and canine uveitis requires further investigation. This study is the first to report the seroprevalence of anti-T gondii antibodies in dogs in the UK population and the first to report the seroprevalence of anti-T gondii antibodies in dogs with uveitis

Compound wind and rainfall extremes: Drivers and future changes over the UK and Ireland

\ua9 2024The co-occurrence of wind and rainfall extremes can yield larger impacts than when either hazard occurs in isolation. This study assesses compound extremes produced by Extra-tropical cyclones (ETCs) during winter from two perspectives. Firstly, we assess ETCs with extreme footprints of wind and rainfall; footprint severity is measured using the wind severity index (WSI) and rain severity index (RSI) which account for the intensity, duration, and area of either hazard. Secondly, we assess local co-occurrences of 6-hourly wind and rainfall extremes within ETCs. We quantify the likelihood of compound extremes in these two perspectives and characterise a number of their drivers (jet stream, cyclone tracks, and fronts) in control (1981–2000) and future (2060–2081, RCP8.5) climate simulations from a 12-member ensemble of local convection-permitting 2.2 km climate projections over the UK and Ireland. Simulations indicate an increased probability of ETCs producing extremely severe WSI and RSI in the same storm in the future, occurring 3.6 times more frequently (every 5 years compared to every 18 years in the control). This frequency increase is mainly driven by increased rainfall intensities, pointing to a predominantly thermodynamic driver. However, future winds also increase alongside a strengthened jet stream, while a southward displaced jet and cyclone track in these events leads to a dynamically-enhanced increase in temperature. This intensifies rainfall in line with Clausius-Clapeyron, and potentially wind speeds due to additional latent heat energy. Future simulations also indicate an increase in the land area experiencing locally co-occurring wind and rainfall extremes; largely explained by increased rainfall within warm and cold fronts, although the relative increase is highest near cold fronts suggesting increased convective activity. These locally co-occurring extremes are more likely in storms with severe WSI and RSI, but not exclusively so as local co-occurrence requires the coincidence of separate drivers within ETCs. Overall, our results reveal many contributing factors to compound wind and rainfall extremes and their future changes. Further work is needed to understand the uncertainty in the future response by sampling additional climate models

Sources of uncertainty in future projections of the carbon cycle

This is the final version of the article. Available from the publisher via the DOI in this record.The inclusion of carbon cycle processes within CMIP5 Earth System Models provides the opportunity to explore the relative importance of differences in scenario and climate model representation to future land and ocean carbon fluxes. A two-way ANOVA approach was used to quantify the variability owing to differences between scenarios and between climate models at different lead times. For global ocean carbon fluxes, the variance attributed to differences between Representative Concentration Pathway scenarios exceeds the variance attributed to differences between climate models by around 2025, completely dominating by 2100. This contrasts with global land carbon fluxes, where the variance attributed to differences between climate models continues to dominate beyond 2100. This suggests that modelled processes that determine ocean fluxes are currently better constrained than those of land fluxes, thus we can be more confident in linking different future socio-economic pathways to consequences of ocean carbon uptake than for land carbon uptake. The apparent agreement in atmosphere-ocean carbon fluxes, globally, masks strong climate model differences at a regional level. The North Atlantic and Southern Ocean are key regions, where differences in modelled processes represent an important source of variability in projected regional fluxesMOHC authors were supported by the Joint DECC / Defra Met Office Hadley Centre Cli- mate Programme (GA01101). SY was supported by the Hong Kong Polytechnic University grant “Bayesian Modelling for Quantifying Uncertainty in Climate Predictions” (1-ZV9Z). We acknowl- edge use of R software package (R Core Team 2013). We acknowledge the World Climate Re- search Programme’s Working Group on Coupled Modelling, which is responsible for CMIP and we thank the climate modelling groups for providing their GCM output (listed in Table 1). Support of this dataset was provided by the Office of Science, U.S. Department of Energy

Compound wind and rainfall extremes: Drivers and future changes over the UK and Ireland

This is the author accepted manuscript. The final version is available on open access from Elsevier via the DOI in this recordData availability: Wind and rainfall data is freely available. Other data can be made available upon reasonable request.The co-occurrence of wind and rainfall extremes can yield larger impacts than when either hazard occurs in isolation. This study assesses compound extremes produced by Extra-tropical cyclones (ETCs) during winter from two perspectives. Firstly, we assess ETCs with extreme footprints of wind and rainfall; footprint severity is measured using the wind severity index (WSI) and rain severity index (RSI) which account for the intensity, duration, and area of either hazard. Secondly, we assess local co-occurrences of 6-hourly wind and rainfall extremes within ETCs. We quantify the likelihood of compound extremes in these two perspectives and characterise a number of their drivers (jet stream, cyclone tracks, and fronts) in control (1981-2000) and future (2060-2081, RCP8.5) climate simulations from a 12-member ensemble of local convection-permitting 2.2 km climate projections over the UK and Ireland. Simulations indicate an increased probability of ETCs producing extremely severe WSI and RSI in the same storm in the future, occurring 3.6 times more frequently (every 5 years compared to every 18 years in the control). This frequency increase is mainly driven by increased rainfall intensities, pointing to a predominantly thermodynamic driver. However, future winds also increase alongside a strengthened jet stream, while a southward displaced jet and cyclone track in these events leads to a dynamically-enhanced increase in temperature. This intensifies rainfall in line with Clausius-Clapeyron, and potentially wind speeds due to additional latent heat energy. Future simulations also indicate an increase in the land area experiencing locally co-occurring wind and rainfall extremes; largely explained by increased rainfall within warm and cold fronts, although the relative increase is highest near cold fronts suggesting increased convective activity. These locally co-occurring extremes are more likely in storms with severe WSI and RSI, but not exclusively so as local co-occurrence requires the coincidence of separate drivers within ETCs. Overall, our results reveal many contributing factors to compound wind and rainfall extremes and their future changes. Further work is needed to understand the uncertainty in the future response by sampling additional climate models.Natural Environment Research Council (NERC)Joint UK BEIS/Defra Hadley Centre Climate ProgrammeEuropean Union Horizon 202

MIR21-induced loss of junctional adhesion molecule A promotes activation of oncogenic pathways, progression and metastasis in colorectal cancer.

Junctional adhesion molecules (JAMs) play a critical role in cell permeability, polarity and migration. JAM-A, a key protein of the JAM family, is altered in a number of conditions including cancer; however, consequences of JAM-A dysregulation on carcinogenesis appear to be tissue dependent and organ dependent with significant implications for the use of JAM-A as a biomarker or therapeutic target. Here, we test the expression and prognostic role of JAM-A downregulation in primary and metastatic colorectal cancer (CRC) (n = 947). We show that JAM-A downregulation is observed in ~60% of CRC and correlates with poor outcome in four cohorts of stages II and III CRC (n = 1098). Using JAM-A knockdown, re-expression and rescue experiments in cell line monolayers, 3D spheroids, patient-derived organoids and xenotransplants, we demonstrate that JAM-A silencing promotes proliferation and migration in 2D and 3D cell models and increases tumour volume and metastases in vivo. Using gene-expression and proteomic analyses, we show that JAM-A downregulation results in the activation of ERK, AKT and ROCK pathways and leads to decreased bone morphogenetic protein 7 expression. We identify MIR21 upregulation as the cause of JAM-A downregulation and show that JAM-A rescue mitigates the effects of MIR21 overexpression on cancer phenotype. Our results identify a novel molecular loop involving MIR21 dysregulation, JAM-A silencing and activation of multiple oncogenic pathways in promoting invasiveness and metastasis in CRC

Allocating HIV Prevention Funds in the United States: Recommendations from an Optimization Model

The Centers for Disease Control and Prevention (CDC) had an annual budget of approximately $327 million to fund health departments and community-based organizations for core HIV testing and prevention programs domestically between 2001 and 2006. Annual HIV incidence has been relatively stable since the year 2000 [1] and was estimated at 48,600 cases in 2006 and 48,100 in 2009 [2]. Using estimates on HIV incidence, prevalence, prevention program costs and benefits, and current spending, we created an HIV resource allocation model that can generate a mathematically optimal allocation of the Division of HIV/AIDS Prevention’s extramural budget for HIV testing, and counseling and education programs. The model’s data inputs and methods were reviewed by subject matter experts internal and external to the CDC via an extensive validation process. The model projects the HIV epidemic for the United States under different allocation strategies under a fixed budget. Our objective is to support national HIV prevention planning efforts and inform the decision-making process for HIV resource allocation. Model results can be summarized into three main recommendations. First, more funds should be allocated to testing and these should further target men who have sex with men and injecting drug users. Second, counseling and education interventions ought to provide a greater focus on HIV positive persons who are aware of their status. And lastly, interventions should target those at high risk for transmitting or acquiring HIV, rather than lower-risk members of the general population. The main conclusions of the HIV resource allocation model have played a role in the introduction of new programs and provide valuable guidance to target resources and improve the impact of HIV prevention efforts in the United States

Bias adjustment and ensemble recalibration methods for seasonal forecasting: a comprehensive intercomparison using the C3S dataset

This work presents a comprehensive intercomparison of diferent alternatives for the calibration of seasonal forecasts, ranging from simple bias adjustment (BA)-e.g. quantile mapping-to more sophisticated ensemble recalibration (RC) methods- e.g. non-homogeneous Gaussian regression, which build on the temporal correspondence between the climate model and the corresponding observations to generate reliable predictions. To be as critical as possible, we validate the raw model and the calibrated forecasts in terms of a number of metrics which take into account diferent aspects of forecast quality (association, accuracy, discrimination and reliability). We focus on one-month lead forecasts of precipitation and temperature from four state-of-the-art seasonal forecasting systems, three of them included in the Copernicus Climate Change Service dataset (ECMWF-SEAS5, UK Met Ofce-GloSea5 and Météo France-System5) for boreal winter and summer over two illustrative regions with diferent skill characteristics (Europe and Southeast Asia). Our results indicate that both BA and RC methods efectively correct the large raw model biases, which is of paramount importance for users, particularly when directly using the climate model outputs to run impact models, or when computing climate indices depending on absolute values/thresholds. However, except for particular regions and/or seasons (typically with high skill), there is only marginal added value-with respect to the raw model outputs-beyond this bias removal. For those cases, RC methods can outperform BA ones, mostly due to an improvement in reliability. Finally, we also show that whereas an increase in the number of members only modestly afects the results obtained from calibration, longer hindcast periods lead to improved forecast quality, particularly for RC methods.This work has been funded by the C3S activity on Evaluation and Quality Control for seasonal forecasts. JMG was partially supported by the project MULTI-SDM (CGL2015-66583-R, MINECO/FEDER). FJDR was partially funded by the H2020 EUCP project (GA 776613)