Heatwave attribution based on reliable operational weather forecasts.

The 2021 Pacific Northwest heatwave was so extreme as to challenge conventional statistical and climate-model-based approaches to extreme weather attribution. However, state-of-the-art operational weather prediction systems are demonstrably able to simulate the detailed physics of the heatwave. Here, we leverage these systems to show that human influence on the climate made this event at least 8 [2-50] times more likely. At the current rate of global warming, the likelihood of such an event is doubling every 20 [10-50] years. Given the multi-decade lower-bound return-time implied by the length of the historical record, this rate of change in likelihood is highly relevant for decision makers. Further, forecast-based attribution can synthesise the conditional event-specific storyline and unconditional event-class probabilistic approaches to attribution. If developed as a routine service in forecasting centres, it could provide reliable estimates of human influence on extreme weather risk, which is critical to supporting effective adaptation planning.

Invasive cervical cancer incidence following bivalent human papillomavirus vaccination: a population-based observational study of age at immunization, dose, and deprivation.

BACKGROUND: High-risk human papillomavirus causes cervical cancer. Vaccines have been developed that significantly reduce the incidence of preinvasive and invasive disease. This population-based observational study used linked screening, immunization, and cancer registry data from Scotland to assess the influence of age, number of doses, and deprivation on the incidence of invasive disease following administration of the bivalent vaccine. METHODS: Data for women born between January 1, 1988, and June 5, 1996, were extracted from the Scottish cervical cancer screening system in July 2020 and linked to cancer registry, immunization, and deprivation data. Incidence of invasive cervical cancer per 100 000 person-years and vaccine effectiveness were correlated with vaccination status, age at vaccination, and deprivation; Kaplan Meier curves were calculated. RESULTS: No cases of invasive cancer were recorded in women immunized at 12 or 13 years of age irrespective of the number of doses. Women vaccinated at 14 to 22 years of age and given 3 doses of the bivalent vaccine showed a significant reduction in incidence compared with all unvaccinated women (3.2/100 000 [95% confidence interval (CI) = 2.1 to 4.6] vs 8.4 [95% CI = 7.2 to 9.6]). Unadjusted incidence was significantly higher in women from most deprived (Scottish Index of Multiple Deprivation 1) than least deprived (Scottish Index of Multiple Deprivation 5) areas (10.1/100 000 [95% CI = 7.8 to 12.8] vs 3.9 [95% CI = 2.6 to 5.7]). Women from the most deprived areas showed a significant reduction in incidence following 3 doses of vaccine (13.1/100 000 [95% CI = 9.95 to 16.9] vs 2.29 [95% CI = 0.62 to 5.86]). CONCLUSION: Our findings confirm that the bivalent vaccine prevents the development of invasive cervical cancer and that even 1 or 2 doses 1 month apart confer benefit if given at 12-13 years of age. At older ages, 3 doses are required for statistically significant vaccine effectiveness. Women from more deprived areas benefit more from vaccination than those from less deprived areas.

The changing nature of HPV associated with high grade cervical lesions in vaccinated populations, a retrospective study of over 1700 cases in Scotland.

BACKGROUND: Understanding the pattern and dominance of HPV types in high grade cervical disease within increasingly vaccinated populations will help inform the development of appropriate screening and management protocols. METHODS: Over 1700 cases of cervical intraepithelial neoplasia (CIN) diagnosed between 2011 and 2017 in women younger than 25 were genotyped for HPV. Logistic regression was used to assess the association between HPV 16/18 positivity with biopsy-collection year, birth year, deprivation and vaccination status. Regression analysis was repeated for cross-protective types (31, 33 and 45). Type specific detail of non-vaccine types by vaccination status was presented descriptively. RESULTS: Detection of HPV 16/18 or 16/18/31/33 and 45 was lower in CIN2 associated with full vaccination vs no vaccination (OR 0.3; 95% CI 0.2-0.5 & 0.4; 95% CI 0.3-0.6 respectively) Similar observations were made for CIN3. The relative contribution of non-established high-risk types including those considered low risk was greater among vaccinated women with CIN2+ vs unvaccinated women with CIN2+. CONCLUSIONS: The change in HPV distribution in CIN2+ in vaccinated populations is a further marker of vaccine impact. Additionally, the progression rate of CIN2+ in vaccinated populations may be lower given the shift in type distribution. The definition of high grade disease in vaccinated populations may warrant reassessment.

Periodic orbits in chaotic systems simulated at low precision.

Non-periodic solutions are an essential property of chaotic dynamical systems. Simulations with deterministic finite-precision numbers, however, always yield orbits that are eventually periodic. With 64-bit double-precision floating-point numbers such periodic orbits are typically negligible due to very long periods. The emerging trend to accelerate simulations with low-precision numbers, such as 16-bit half-precision floats, raises questions on the fidelity of such simulations of chaotic systems. Here, we revisit the 1-variable logistic map and the generalised Bernoulli map with various number formats and precisions: floats, posits and logarithmic fixed-point. Simulations are improved with higher precision but stochastic rounding prevents periodic orbits even at low precision. For larger systems the performance gain from low-precision simulations is often reinvested in higher resolution or complexity, increasing the number of variables. In the Lorenz 1996 system, the period lengths of orbits increase exponentially with the number of variables. Moreover, invariant measures are better approximated with an increased number of variables than with increased precision. Extrapolating to large simulations of natural systems, such as million-variable climate models, periodic orbit lengths are far beyond reach of present-day computers. Such orbits are therefore not expected to be problematic compared to high-precision simulations but the deviation of both from the continuum solution remains unclear.

Mixed-Precision for Linear Solvers in Global Geophysical Flows.

Semi-implicit (SI) time-stepping schemes for atmosphere and ocean models require elliptic solvers that work efficiently on modern supercomputers. This paper reports our study of the potential computational savings when using mixed precision arithmetic in the elliptic solvers. Precision levels as low as half (16 bits) are used and a detailed evaluation of the impact of reduced precision on the solver convergence and the solution quality is performed. This study is conducted in the context of a novel SI shallow-water model on the sphere, purposely designed to mimic numerical intricacies of modern all-scale weather and climate (W&C) models. The governing algorithm of the shallow-water model is based on the non-oscillatory MPDATA methods for geophysical flows, whereas the resulting elliptic problem employs a strongly preconditioned non-symmetric Krylov-subspace Generalized Conjugated-Residual (GCR) solver, proven in advanced atmospheric applications. The classical longitude/latitude grid is deliberately chosen to retain the stiffness of global W&C models. The analysis of the precision reduction is done on a software level, using an emulator, whereas the performance is measured on actual reduced precision hardware. The reduced-precision experiments are conducted for established dynamical-core test-cases, like the Rossby-Haurwitz wavenumber 4 and a zonal orographic flow. The study shows that selected key components of the elliptic solver, most prominently the preconditioning and the application of the linear operator, can be performed at the level of half precision. For these components, the use of half precision is found to yield a speed-up of a factor 4 compared to double precision for a wide range of problem sizes.

Fluid Simulations Accelerated With 16 Bits: Approaching 4x Speedup on A64FX by Squeezing ShallowWaters.jl Into Float16.

Most Earth-system simulations run on conventional central processing units in 64-bit double precision floating-point numbers Float64, although the need for high-precision calculations in the presence of large uncertainties has been questioned. Fugaku, currently the world's fastest supercomputer, is based on A64FX microprocessors, which also support the 16-bit low-precision format Float16. We investigate the Float16 performance on A64FX with ShallowWaters.jl, the first fluid circulation model that runs entirely with 16-bit arithmetic. The model implements techniques that address precision and dynamic range issues in 16 bits. The precision-critical time integration is augmented to include compensated summation to minimize rounding errors. Such a compensated time integration is as precise but faster than mixed precision with 16 and 32-bit floats. As subnormals are inefficiently supported on A64FX the very limited range available in Float16 is 6 × 10-5 to 65,504. We develop the analysis-number format Sherlogs.jl to log the arithmetic results during the simulation. The equations in ShallowWaters.jl are then systematically rescaled to fit into Float16, using 97% of the available representable numbers. Consequently, we benchmark speedups of up to 3.8x on A64FX with Float16. Adding a compensated time integration, speedups reach up to 3.6x. Although ShallowWaters.jl is simplified compared to large Earth-system models, it shares essential algorithms and therefore shows that 16-bit calculations are indeed a competitive way to accelerate Earth-system simulations on available hardware.

A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts.

Despite continuous improvements, precipitation forecasts are still not as accurate and reliable as those of other meteorological variables. A major contributing factor to this is that several key processes affecting precipitation distribution and intensity occur below the resolved scale of global weather models. Generative adversarial networks (GANs) have been demonstrated by the computer vision community to be successful at super-resolution problems, that is, learning to add fine-scale structure to coarse images. Leinonen et al. (2020, https://doi.org/10.1109/TGRS.2020.3032790) previously applied a GAN to produce ensembles of reconstructed high-resolution atmospheric fields, given coarsened input data. In this paper, we demonstrate this approach can be extended to the more challenging problem of increasing the accuracy and resolution of comparatively low-resolution input from a weather forecasting model, using high-resolution radar measurements as a "ground truth." The neural network must learn to add resolution and structure whilst accounting for non-negligible forecast error. We show that GANs and VAE-GANs can match the statistical properties of state-of-the-art pointwise post-processing methods whilst creating high-resolution, spatially coherent precipitation maps. Our model compares favorably to the best existing downscaling methods in both pixel-wise and pooled CRPS scores, power spectrum information and rank histograms (used to assess calibration). We test our models and show that they perform in a range of scenarios, including heavy rainfall.

Forecast-based attribution of a winter heatwave within the limit of predictability.

Attribution of extreme weather events has expanded rapidly as a field over the past decade. However, deficiencies in climate model representation of key dynamical drivers of extreme events have led to some concerns over the robustness of climate model-based attribution studies. It has also been suggested that the unconditioned risk-based approach to event attribution may result in false negative results due to dynamical noise overwhelming any climate change signal. The "storyline" attribution framework, in which the impact of climate change on individual drivers of an extreme event is examined, aims to mitigate these concerns. Here we propose a methodology for attribution of extreme weather events using the operational European Centre for Medium-Range Weather Forecasts (ECMWF) medium-range forecast model that successfully predicted the event. The use of a successful forecast ensures not only that the model is able to accurately represent the event in question, but also that the analysis is unequivocally an attribution of this specific event, rather than a mixture of multiple different events that share some characteristic. Since this attribution methodology is conditioned on the component of the event that was predictable at forecast initialization, we show how adjusting the lead time of the forecast can flexibly set the level of conditioning desired. This flexible adjustment of the conditioning allows us to synthesize between a storyline (highly conditioned) and a risk-based (relatively unconditioned) approach. We demonstrate this forecast-based methodology through a partial attribution of the direct radiative effect of increased CO2 concentrations on the exceptional European winter heatwave of February 2019.

Machine Learning Emulation of Gravity Wave Drag in Numerical Weather Forecasting.

We assess the value of machine learning as an accelerator for the parameterization schemes of operational weather forecasting systems, specifically the parameterization of nonorographic gravity wave drag. Emulators of this scheme can be trained to produce stable and accurate results up to seasonal forecasting timescales. Generally, networks that are more complex produce emulators that are more accurate. By training on an increased complexity version of the existing parameterization scheme, we build emulators that produce more accurate forecasts. For medium range forecasting, we have found evidence that our emulators are more accurate than the version of the parametrization scheme that is used for operational predictions. Using the current operational CPU hardware, our emulators have a similar computational cost to the existing scheme, but are heavily limited by data movement. On GPU hardware, our emulators perform 10 times faster than the existing scheme on a CPU.

Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI.

In September 2019, a workshop was held to highlight the growing area of applying machine learning techniques to improve weather and climate prediction. In this introductory piece, we outline the motivations, opportunities and challenges ahead in this exciting avenue of research. This article is part of the theme issue 'Machine learning for weather and climate modelling'.

Compressing atmospheric data into its real information content.

Hundreds of petabytes are produced annually at weather and climate forecast centers worldwide. Compression is essential to reduce storage and to facilitate data sharing. Current techniques do not distinguish the real from the false information in data, leaving the level of meaningful precision unassessed. Here we define the bitwise real information content from information theory for the Copernicus Atmospheric Monitoring Service (CAMS). Most variables contain fewer than 7 bits of real information per value and are highly compressible due to spatio-temporal correlation. Rounding bits without real information to zero facilitates lossless compression algorithms and encodes the uncertainty within the data itself. All CAMS data are 17× compressed relative to 64-bit floats, while preserving 99% of real information. Combined with four-dimensional compression, factors beyond 60× are achieved. A data compression Turing test is proposed to optimize compressibility while minimizing information loss for the end use of weather and climate forecast data.

Human Creativity and Consciousness: Unintended Consequences of the Brain's Extraordinary Energy Efficiency?

It is proposed that both human creativity and human consciousness are (unintended) consequences of the human brain's extraordinary energy efficiency. The topics of creativity and consciousness are treated separately, though have a common sub-structure. It is argued that creativity arises from a synergy between two cognitive modes of the human brain (which broadly coincide with Kahneman's Systems 1 and 2). In the first, available energy is spread across a relatively large network of neurons, many of which are small enough to be susceptible to thermal (ultimately quantum decoherent) noise. In the second, available energy is focussed on a smaller subset of larger neurons whose action is deterministic. Possible implications for creative computing in silicon are discussed. Starting with a discussion of the concept of free will, the notion of consciousness is defined in terms of an awareness of what are perceived to be nearby counterfactual worlds in state space. It is argued that such awareness arises from an interplay between memories on the one hand, and quantum physical mechanisms (where, unlike in classical physics, nearby counterfactual worlds play an indispensable dynamical role) in the ion channels of neural networks, on the other. As with the brain's susceptibility to noise, it is argued that in situations where quantum physics plays a role in the brain, it does so for reasons of energy efficiency. As an illustration of this definition of consciousness, a novel proposal is outlined as to why quantum entanglement appears to be so counter-intuitive.

The scientific challenge of understanding and estimating climate change.

Given the slow unfolding of what may become catastrophic changes to Earth's climate, many are understandably distraught by failures of public policy to rise to the magnitude of the challenge. Few in the science community would think to question the scientific response to the unfolding changes. However, is the science community continuing to do its part to the best of its ability? In the domains where we can have the greatest influence, is the scientific community articulating a vision commensurate with the challenges posed by climate change? We think not.

Prevalence of cervical disease at age 20 after immunisation with bivalent HPV vaccine at age 12-13 in Scotland: retrospective population study.

OBJECTIVE: To quantify the effect on cervical disease at age 20 years of immunisation with bivalent human papillomavirus (HPV) vaccine at age 12-13 years. DESIGN: Retrospective population study, 1988-96. SETTING: National vaccination and cervical screening programmes in Scotland. PARTICIPANTS: 138 692 women born between 1 January 1988 and 5 June 1996 and who had a smear test result recorded at age 20. MAIN OUTCOME MEASURES: Effect of vaccination on cytology results and associated histological diagnoses from first year of screening (while aged 20), calculated using logistic regression. RESULTS: 138 692 records were retrieved. Compared with unvaccinated women born in 1988, vaccinated women born in 1995 and 1996 showed an 89% reduction (95% confidence interval 81% to 94%) in prevalent cervical intraepithelial neoplasia (CIN) grade 3 or worse (from 0.59% (0.48% to 0.71%) to 0.06% (0.04% to 0.11%)), an 88% reduction (83% to 92%) in CIN grade 2 or worse (from 1.44% (1.28% to 1.63%) to 0.17% (0.12% to 0.24%)), and a 79% reduction (69% to 86%) in CIN grade 1 (from 0.69% (0.58% to 0.63%) to 0.15% (0.10% to 0.21%)). Younger age at immunisation was associated with increasing vaccine effectiveness: 86% (75% to 92%) for CIN grade 3 or worse for women vaccinated at age 12-13 compared with 51% (28% to 66%) for women vaccinated at age 17. Evidence of herd protection against high grade cervical disease was found in unvaccinated girls in the 1995 and 1996 cohorts. CONCLUSIONS: Routine vaccination of girls aged 12-13 years with the bivalent HPV vaccine in Scotland has led to a dramatic reduction in preinvasive cervical disease. Evidence of clinically relevant herd protection is apparent in unvaccinated women. These data are consistent with the reduced prevalence of high risk HPV in Scotland. The bivalent vaccine is confirmed as being highly effective vaccine and should greatly reduce the incidence of cervical cancer. The findings will need to be considered by cervical cancer prevention programmes worldwide.

Increased risk of HPV-associated genital cancers in men and women as a consequence of pre-invasive disease.

To assess the excess risk of HPV-associated cancer (HPVaC) in two at-risk groups-women with a previous diagnosis of high grade cervical intraepithelial neoplasia (CIN3) and both men and women treated for non-cervical pre-invasive anogenital disease. All CIN3 cases diagnosed in 1989-2015 in Scotland were extracted from the Scottish cancer registry (SMR06). All cases of pre-invasive penile, anal, vulval, and vaginal disease diagnosed in 1990-2015 were identified within the NHS pathology databases in the two largest NHS health boards in Scotland. Both were linked to SMR06 to extract subsequent incidence of HPVaC following the diagnosis of CIN3 or pre-invasive disease. Standardised incidence ratios were calculated for the risk of acquiring HPVaC for the two at-risk groups compared to the general Scottish population. Among 69,714 females in Scotland diagnosed with CIN3 (890,360.9 person-years), 179 developed non-cervical HPVaC. CIN3 cases were at 3.2-fold (95% CI: 2.7 to 3.7) increased risk of developing non-cervical HPVaC, compared to the general female population. Among 1,235 patients diagnosed with non-cervical pre-invasive disease (9,667.4 person-years), 47 developed HPVaC. Individuals with non-cervical pre-invasive disease had a substantially increased risk of developing HPVaC - 15.5-fold (95% CI: 11.1 to 21.1) increased risk for females and 28-fold (11.3 to 57.7) increased risk for males. We report a significant additional risk of HPV-associated cancer in those have been diagnosed with pre-invasive HPV-associated lesions including but not confined to the cervix. Uncovering the natural history of pre-invasive disease has potential for determining screening, prevention and treatment.

Seasonal to annual ocean forecasting skill and the role of model and observational uncertainty.

Accurate forecasts of the ocean state and the estimation of forecast uncertainties are crucial when it comes to providing skilful seasonal predictions. In this study we analyse the predictive skill and reliability of the ocean component in a seasonal forecasting system. Furthermore, we assess the effects of accounting for model and observational uncertainties. Ensemble forcasts are carried out with an updated version of the ECMWF seasonal forecasting model System 4, with a forecast length of ten months, initialized every May between 1981 and 2010. We find that, for essential quantities such as sea surface temperature and upper ocean 300 m heat content, the ocean forecasts are generally underdispersive and skilful beyond the first month mainly in the Tropics and parts of the North Atlantic. The reference reanalysis used for the forecast evaluation considerably affects diagnostics of forecast skill and reliability, throughout the entire ten-month forecasts but mostly during the first three months. Accounting for parametrization uncertainty by implementing stochastic parametrization perturbations has a positive impact on both reliability (from month 3 onwards) as well as forecast skill (from month 8 onwards). Skill improvements extend also to atmospheric variables such as 2 m temperature, mostly in the extratropical Pacific but also over the midlatitudes of the Americas. Hence, while model uncertainty impacts the skill of seasonal forecasts, observational uncertainty impacts our assessment of that skill. Future ocean model development should therefore aim not only to reduce model errors but to simultaneously assess and estimate uncertainties.

Automated quantification of steatosis: agreement with stereological point counting.

BACKGROUND: Steatosis is routinely assessed histologically in clinical practice and research. Automated image analysis can reduce the effort of quantifying steatosis. Since reproducibility is essential for practical use, we have evaluated different analysis methods in terms of their agreement with stereological point counting (SPC) performed by a hepatologist. METHODS: The evaluation was based on a large and representative data set of 970 histological images from human patients with different liver diseases. Three of the evaluated methods were built on previously published approaches. One method incorporated a new approach to improve the robustness to image variability. RESULTS: The new method showed the strongest agreement with the expert. At 20× resolution, it reproduced steatosis area fractions with a mean absolute error of 0.011 for absent or mild steatosis and 0.036 for moderate or severe steatosis. At 10× resolution, it was more accurate than and twice as fast as all other methods at 20× resolution. When compared with SPC performed by two additional human observers, its error was substantially lower than one and only slightly above the other observer. CONCLUSIONS: The results suggest that the new method can be a suitable automated replacement for SPC. Before further improvements can be verified, it is necessary to thoroughly assess the variability of SPC between human observers.

Bitwise efficiency in chaotic models.

Motivated by the increasing energy consumption of supercomputing for weather and climate simulations, we introduce a framework for investigating the bit-level information efficiency of chaotic models. In comparison with previous explorations of inexactness in climate modelling, the proposed and tested information metric has three specific advantages: (i) it requires only a single high-precision time series; (ii) information does not grow indefinitely for decreasing time step; and (iii) information is more sensitive to the dynamics and uncertainties of the model rather than to the implementation details. We demonstrate the notion of bit-level information efficiency in two of Edward Lorenz's prototypical chaotic models: Lorenz 1963 (L63) and Lorenz 1996 (L96). Although L63 is typically integrated in 64-bit 'double' floating point precision, we show that only 16 bits have significant information content, given an initial condition uncertainty of approximately 1% of the size of the attractor. This result is sensitive to the size of the uncertainty but not to the time step of the model. We then apply the metric to the L96 model and find that a 16-bit scaled integer model would suffice given the uncertainty of the unresolved sub-grid-scale dynamics. We then show that, by dedicating computational resources to spatial resolution rather than numeric precision in a field programmable gate array (FPGA), we see up to 28.6% improvement in forecast accuracy, an approximately fivefold reduction in the number of logical computing elements required and an approximately 10-fold reduction in energy consumed by the FPGA, for the L96 model.

Changes in the prevalence of human papillomavirus following a national bivalent human papillomavirus vaccination programme in Scotland: a 7-year cross-sectional study.

BACKGROUND: On Sept 1, 2008, Scotland launched routine vaccination for human papillomavirus (HPV) types 16 and 18, targeted at 12-13-year-old girls, of whom 92·4% were fully vaccinated in 2008-09. In this study, we report on vaccine effectiveness of the bivalent vaccine in these vaccinated women who attended for routine cervical screening at age 20-21 years. METHODS: In this 7-year cross-sectional study (covering birth cohorts 1988-1995), we sampled approximately 1000 samples per year from those attending cervical screening at age 20-21 years and tested each for HPV. By linkage to vaccination records we ascertained prevalence by birth cohort and vaccination status. Estimates of vaccine effectiveness for HPV types 16 and 18, HPV types 31, 33, and 45, other high-risk types, and any HPV were calculated using logistic regression. FINDINGS: In total, 8584 samples were HPV genotyped. Prevalence of HPV types 16 and 18 reduced substantially from 30·0% (95% CI 26·9-33·1) in the 1988 cohort to 4·5% (3·5-5·7) in the 1995 cohort, giving a vaccine effectiveness of 89·1% (85·1-92·3) for those vaccinated at age 12-13 years. All cross-protective types showed significant vaccine effectiveness (HPV type 31, 93·8% [95% CI 83·8-98·5]; HPV type 33, 79·1% [64·2-89·0]; HPV type 45, 82·6% [61·5-93·9]). Unvaccinated individuals born in 1995 had a reduced odds of HPV types 16 and 18 infection compared with those born in 1988 (adjusted odds ratio 0·13 [95% CI 0·06-0·28]) and reduced odds of HPV types 31, 33, and 45 (odds ratio 0·45 [0·23-0·89]). INTERPRETATION: Bivalent vaccination has led to a startling reduction in vaccine and cross-protective HPV types 7 years after vaccination. There is also evidence of herd protection against the vaccine-specific and cross-protective types in unvaccinated individuals born in 1995. These findings should be considered in cost-effectiveness models informing vaccine choice and models to shape the future of cervical screening programmes. FUNDING: Scottish Government and Chief Scientists Office.