# Publications

## The response of the Pacific storm track and atmospheric circulation to Kuroshio Extension variability

Quarterly Journal of the Royal Meteorological Society **141** (2015) 52-66

## Early warning products for severe weather events derived from operational medium-range ensemble forecasts

Meteorological Applications **22** (2015) 213-222

## Optimisation of an idealised ocean model, stochastic parameterisation of sub-grid eddies

OCEAN MODELLING **88** (2015) 38-53

## Variations of temperature, wind speed and humidity within Birmingham New Street Station during hot weather

Weather **70** (2015) 129-134

## Evaluation of ensemble forecast uncertainty using a new proper score: Application to medium-range and seasonal forecasts

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY **141** (2015) 538-549

## Invariant set theory: Violating measurement independence without fine tuning, conspiracy, constraints on free will or retrocausality

Electronic Proceedings in Theoretical Computer Science, EPTCS **195** (2015) 285-294

© 2015 T. N. Palmer. Invariant Set (IS) theory is a locally causal ontic theory of physics based on the Cosmological Invariant Set postulate that the universe U can be considered a deterministic dynamical system evolving precisely on a (suitably constructed) fractal dynamically invariant set in U's state space. IS theory violates the Bell inequalities by violating Measurement Independence. Despite this, IS theory is not fine tuned, is not conspiratorial, does not constrain experimenter free will and does not invoke retrocausality. The reasons behind these claims are discussed in this paper. These arise fromproperties not found in conventional ontic models: the invariant set has zero measure in its Euclidean embedding space, has Cantor Set structure homeomorphic to the p-adic integers (p⋙0) and is non-computable. In particular, it is shown that the p-adic metric encapulates the physics of the Cosmological Invariant Set postulate, and provides the technical means to demonstrate no fine tuning or conspiracy. Quantum theory can be viewed as the singular limit of IS theory when when p is set equal to infinity. Since it is based around a top-down constraint from cosmology, IS theory suggests that gravitational and quantum physics will be unified by a gravitational theory of the quantum, rather than a quantum theory of gravity. Some implications arising from such a perspective are discussed.

## Demonstration of successful malaria forecasts for Botswana using an operational seasonal climate model

ENVIRONMENTAL RESEARCH LETTERS **10** (2015) ARTN 044005

## On the use of programmable hardware and reduced numerical precision in earth-system modeling.

Journal of advances in modeling earth systems **7** (2015) 1393-1408

Programmable hardware, in particular Field Programmable Gate Arrays (FPGAs), promises a significant increase in computational performance for simulations in geophysical fluid dynamics compared with CPUs of similar power consumption. FPGAs allow adjusting the representation of floating-point numbers to specific application needs. We analyze the performance-precision trade-off on FPGA hardware for the two-scale Lorenz '95 model. We scale the size of this toy model to that of a high-performance computing application in order to make meaningful performance tests. We identify the minimal level of precision at which changes in model results are not significant compared with a maximal precision version of the model and find that this level is very similar for cases where the model is integrated for very short or long intervals. It is therefore a useful approach to investigate model errors due to rounding errors for very short simulations (e.g., 50 time steps) to obtain a range for the level of precision that can be used in expensive long-term simulations. We also show that an approach to reduce precision with increasing forecast time, when model errors are already accumulated, is very promising. We show that a speed-up of 1.9 times is possible in comparison to FPGA simulations in single precision if precision is reduced with no strong change in model error. The single-precision FPGA setup shows a speed-up of 2.8 times in comparison to our model implementation on two 6-core CPUs for large model setups.

## Impact of hindcast length on estimates of seasonal climate predictability

Geophysical Research Letters **42** (2015) 1554-1559

© 2015. The Authors. It has recently been argued that single-model seasonal forecast ensembles are overdispersive, implying that the real world is more predictable than indicated by estimates of so-called perfect model predictability, particularly over the North Atlantic. However, such estimates are based on relatively short forecast data sets comprising just 20 years of seasonal predictions. Here we study longer 40 year seasonal forecast data sets from multimodel seasonal forecast ensemble projects and show that sampling uncertainty due to the length of the hindcast periods is large. The skill of forecasting the North Atlantic Oscillation during winter varies within the 40 year data sets with high levels of skill found for some subperiods. It is demonstrated that while 20 year estimates of seasonal reliability can show evidence of overdispersive behavior, the 40 year estimates are more stable and show no evidence of overdispersion. Instead, the predominant feature on these longer time scales is underdispersion, particularly in the tropics.

## Simulating weather regimes: impact of model resolution and stochastic parameterization

Climate Dynamics **44** (2015) 2177-2193

© 2014, Springer-Verlag Berlin Heidelberg. The simulation of quasi-persistent regime structures in an atmospheric model with horizontal resolution typical of the Intergovernmental Panel on Climate Change fifth assessment report simulations, is shown to be unrealistic. A higher resolution configuration of the same model, with horizontal resolution typical of that used in operational numerical weather prediction, is able to simulate these regime structures realistically. The spatial patterns of the simulated regimes are remarkably accurate at high resolution. A model configuration at intermediate resolution shows a marked improvement over the low-resolution configuration, particularly in terms of the temporal characteristics of the regimes, but does not produce a simulation as accurate as the very-high-resolution configuration. It is demonstrated that the simulation of regimes can be significantly improved, even at low resolution, by the introduction of a stochastic physics scheme. At low resolution the stochastic physics scheme drastically improves both the spatial and temporal aspects of the regimes simulation. These results highlight the importance of small-scale processes on large-scale climate variability, and indicate that although simulating variability at small scales is a necessity, it may not be necessary to represent the small-scales accurately, or even explicitly, in order to improve the simulation of large-scale climate. It is argued that these results could have important implications for improving both global climate simulations, and the ability of high-resolution limited-area models, forced by low-resolution global models, to reliably simulate regional climate change signals.

## Does the ECMWF IFS Convection Parameterization with Stochastic Physics Correctly Reproduce Relationships between Convection and the Large-Scale State?

JOURNAL OF THE ATMOSPHERIC SCIENCES **72** (2015) 236-242

## Architectures and precision analysis for modelling atmospheric variables with chaotic behaviour

2015 IEEE 23RD ANNUAL INTERNATIONAL SYMPOSIUM ON FIELD-PROGRAMMABLE CUSTOM COMPUTING MACHINES (FCCM) (2015) 171-178

## Impact of hindcast length on estimates of seasonal climate predictability.

Geophysical research letters **42** (2015) 1554-1559

It has recently been argued that single-model seasonal forecast ensembles are overdispersive, implying that the real world is more predictable than indicated by estimates of so-called perfect model predictability, particularly over the North Atlantic. However, such estimates are based on relatively short forecast data sets comprising just 20 years of seasonal predictions. Here we study longer 40 year seasonal forecast data sets from multimodel seasonal forecast ensemble projects and show that sampling uncertainty due to the length of the hindcast periods is large. The skill of forecasting the North Atlantic Oscillation during winter varies within the 40 year data sets with high levels of skill found for some subperiods. It is demonstrated that while 20 year estimates of seasonal reliability can show evidence of overdispersive behavior, the 40 year estimates are more stable and show no evidence of overdispersion. Instead, the predominant feature on these longer time scales is underdispersion, particularly in the tropics.Predictions can appear overdispersive due to hindcast length sampling errorLonger hindcasts are more robust and underdispersive, especially in the tropicsTwenty hindcasts are an inadequate sample size to assess seasonal forecast skill.

## Localization in a spanwise-extended model of plane Couette flow.

Physical review. E, Statistical, nonlinear, and soft matter physics **91** (2015) 043005-

We consider a nine-partial-differential-equation (1-space and 1-time) model of plane Couette flow in which the degrees of freedom are severely restricted in the streamwise and cross-stream directions to study spanwise localization in detail. Of the many steady Eckhaus (spanwise modulational) instabilities identified of global steady states, none lead to a localized state. Spatially localized, time-periodic solutions were found instead, which arise in saddle node bifurcations in the Reynolds number. These solutions appear global (domain filling) in narrow (small spanwise) domains yet can be smoothly continued out to fully spanwise-localized states in very wide domains. This smooth localization behavior, which has also been seen in fully resolved duct flow (S. Okino, Ph.D. thesis, Kyoto University, Kyoto, 2011), indicates that an apparently global flow structure does not have to suffer a modulational instability to localize in wide domains.

## Opportunities for Energy Efficient Computing: A Study of Inexact General Purpose Processors for High-Performance and Big-data Applications

2015 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE) (2015) 764-769

## Simulating weather regimes: impact of model resolution and stochastic parameterization

CLIMATE DYNAMICS **44** (2015) 2177-2193

## Solving difficult problems creatively: a role for energy optimised deterministic/stochastic hybrid computing.

Frontiers in computational neuroscience **9** (2015) 124-

How is the brain configured for creativity? What is the computational substrate for 'eureka' moments of insight? Here we argue that creative thinking arises ultimately from a synergy between low-energy stochastic and energy-intensive deterministic processing, and is a by-product of a nervous system whose signal-processing capability per unit of available energy has become highly energy optimised. We suggest that the stochastic component has its origin in thermal (ultimately quantum decoherent) noise affecting the activity of neurons. Without this component, deterministic computational models of the brain are incomplete.

## The character of polar tidal signatures in the extended Canadian Middle Atmosphere Model

Journal of Geophysical Research: Atmospheres **119** (2014) 5928-5948

## Atmospheric science. Record-breaking winters and global climate change.

Science (New York, N.Y.) **344** (2014) 803-804