Publications


Simulating weather regimes: impact of stochastic and perturbed parameter schemes in a simple atmospheric model

CLIMATE DYNAMICS 44 (2015) 2195-2214

HM Christensen, IM Moroz, TN Palmer


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

CH O'Reilly, A Czaja


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

Meteorological Applications 22 (2015) 213-222

M Matsueda, T Nakazawa


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

OCEAN MODELLING 88 (2015) 38-53

FC Cooper, L Zanna


Stochastic and Perturbed Parameter Representations of Model Uncertainty in Convection Parameterization*

JOURNAL OF THE ATMOSPHERIC SCIENCES 72 (2015) 2525-2544

HM Christensen, IM Moroz, TN Palmer


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

PD Düben, FP Russell, X Niu, W Luk, TN Palmer

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.


Bell's conspiracy, Schrödinger's black cat and global invariant sets.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences 373 (2015)

TN Palmer

A locally causal hidden-variable theory of quantum physics need not be constrained by the Bell inequalities if this theory also partially violates the measurement independence condition. However, such violation can appear unphysical, implying implausible conspiratorial correlations between the hidden variables of particles being measured and earlier determinants of instrumental settings. A novel physically plausible explanation for such correlations is proposed, based on the hypothesis that states of physical reality lie precisely on a non-computational measure-zero dynamically invariant set in the state space of the universe: the Cosmological Invariant Set Postulate. To illustrate the relevance of the concept of a global invariant set, a simple analogy is considered where a massive object is propelled into a black hole depending on the decay of a radioactive atom. It is claimed that a locally causal hidden-variable theory constrained by the Cosmological Invariant Set Postulate can violate the Clauser-Horne-Shimony-Holt inequality without being conspiratorial, superdeterministic, fine-tuned or retrocausal, and the theory readily accommodates the classical compatibilist notion of (experimenter) free will.


New geometric concepts in the foundations of physics.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences 373 (2015)

A Döring, T Palmer


Impact of hindcast length on estimates of seasonal climate predictability

Geophysical Research Letters 42 (2015) 1554-1559

W Shi, N Schaller, D Macleod, TN Palmer, A Weisheimer

© 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

A Dawson, TN Palmer

© 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.


Impact of Initial Conditions versus External Forcing in Decadal Climate Predictions: A Sensitivity Experiment*

JOURNAL OF CLIMATE 28 (2015) 4454-4470

S Corti, T Palmer, M Balmaseda, A Weisheimer, S Drijfhout, N Dunstone, W Hazeleger, J Kroeger, H Pohlmann, D Smith, J-S von Storch, B Wouters


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

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

M Chantry, RR Kerswell

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

P Duben, J Schlachter, Parishkrati, S Yenugula, J Augustine, C Enz, K Palem, TN Palmer, IEEE


Simulating weather regimes: impact of model resolution and stochastic parameterization

CLIMATE DYNAMICS 44 (2015) 2177-2193

A Dawson, TN Palmer


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

Frontiers in computational neuroscience 9 (2015) 124-

TN Palmer, M O'Shea

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.


Simulating weather regimes: impact of stochastic and perturbed parameter schemes in a simple atmospheric model

Climate Dynamics 44 (2015) 2195-2214

HM Christensen, IM Moroz, TN Palmer

© 2014, Springer-Verlag Berlin Heidelberg. Representing model uncertainty is important for both numerical weather and climate prediction. Stochastic parametrisation schemes are commonly used for this purpose in weather prediction, while perturbed parameter approaches are widely used in the climate community. The performance of these two representations of model uncertainty is considered in the context of the idealised Lorenz ’96 system, in terms of their ability to capture the observed regime behaviour of the system. These results are applicable to the atmosphere, where evidence points to the existence of persistent weather regimes, and where it is desirable that climate models capture this regime behaviour. The stochastic parametrisation schemes considerably improve the representation of regimes when compared to a deterministic model: both the structure and persistence of the regimes are found to improve. The stochastic parametrisation scheme represents the small scale variability present in the full system, which enables the system to explore a larger portion of the system’s attractor, improving the simulated regime behaviour. It is important that temporally correlated noise is used in the stochastic parametrisation—white noise schemes performed similarly to the deterministic model. In contrast, the perturbed parameter ensemble was unable to capture the regime structure of the attractor, with many individual members exploring only one regime. This poor performance was not evident in other climate diagnostics. Finally, a ‘climate change’ experiment was performed, where a change in external forcing resulted in changes to the regime structure of the attractor. The temporally correlated stochastic schemes captured these changes well.


It's all just physics

PHYSICS WORLD 27 (2014) 18-19

T Palmer


Visualizing the uncertainty in the relationship between seasonal average climate and malaria risk.

Scientific reports 4 (2014) 7264-

DA MacLeod, AP Morse

Around $1.6 billion per year is spent financing anti-malaria initiatives, and though malaria morbidity is falling, the impact of annual epidemics remains significant. Whilst malaria risk may increase with climate change, projections are highly uncertain and to sidestep this intractable uncertainty, adaptation efforts should improve societal ability to anticipate and mitigate individual events. Anticipation of climate-related events is made possible by seasonal climate forecasting, from which warnings of anomalous seasonal average temperature and rainfall, months in advance are possible. Seasonal climate hindcasts have been used to drive climate-based models for malaria, showing significant skill for observed malaria incidence. However, the relationship between seasonal average climate and malaria risk remains unquantified. Here we explore this relationship, using a dynamic weather-driven malaria model. We also quantify key uncertainty in the malaria model, by introducing variability in one of the first order uncertainties in model formulation. Results are visualized as location-specific impact surfaces: easily integrated with ensemble seasonal climate forecasts, and intuitively communicating quantified uncertainty. Methods are demonstrated for two epidemic regions, and are not limited to malaria modeling; the visualization method could be applied to any climate impact.


Climate forecasting: build high-resolution global climate models.

Nature 515 (2014) 338-339

T Palmer


On the reliability of seasonal climate forecasts.

Journal of the Royal Society, Interface 11 (2014) 20131162-

A Weisheimer, TN Palmer

Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: how good are seasonal forecasts on a scale of 1-5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal forecasts are made from ensembles of integrations of numerical models of climate. We argue that 'goodness' should be assessed first and foremost in terms of the probabilistic reliability of these ensemble-based forecasts; reliable inputs are essential for any forecast-based decision-making. We propose that a '5' should be reserved for systems that are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as one of the world-leading operational institutes producing seasonal climate forecasts. A wide range of 'goodness' rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching '5' across all regions and variables in 30 years time.

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