Improved seasonal prediction of the hot summer of 2003 over Europe through better representation of uncertainty in the land surface

Quarterly Journal of the Royal Meteorological Society John Wiley and Sons Ltd (2015) n/a-n/a

D MacLeod, HL Cloke, F Pappenberger, A Weisheimer

<p>Methods to represent uncertainties in weather and climate models explicitly have been developed and refined over the past decade and have reduced biases and improved forecast skill when implemented in the atmospheric component of models. These methods have not yet been applied to the land-surface component of models. Since the land surface is strongly coupled to the atmospheric state at certain times and in certain places (such as the European summer of 2003), improvements in the representation of land-surface uncertainty may potentially lead to improvements in atmospheric forecasts for such events.</p> <p>Here we analyze seasonal retrospective forecasts for 1981–2012 performed with the European Centre for Medium-Range Weather Forecasts (ECMWF) coupled ensemble forecast model. We consider two methods of incorporating uncertainty into the land-surface model (H-TESSEL): stochastic perturbation of tendencies and static perturbation of key soil parameters.</p> <p>We find that the perturbed parameter approach improves the forecast of extreme air temperature for summer 2003 considerably, through better representation of negative soil-moisture anomalies and upward sensible heat flux. Averaged across all the reforecasts, the perturbed parameter experiment shows relatively little impact on the mean bias, suggesting perturbations of at least this magnitude can be applied to the land surface without any degradation of model climate. There is also little impact on skill averaged across all reforecasts and some evidence of overdispersion for soil moisture.</p> <p>The stochastic tendency experiments show a large overdispersion for the soil temperature fields, indicating that the perturbation here is too strong. There is also some indication that the forecast of the 2003 warm event is improved for the stochastic experiments; however, the improvement is not as large as observed for the perturbed parameter experiment.</p>

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.

Architectures and precision analysis for modelling atmospheric variables with chaotic behaviour


FP Russell, PD Duben, X Niu, W Luk, TN Palmer, IEEE

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


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

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.

The use of imprecise processing to improve accuracy in weather &amp; climate prediction

Journal of Computational Physics 271 (2014) 2-18

PD Düben, H McNamara, TN Palmer

The use of stochastic processing hardware and low precision arithmetic in atmospheric models is investigated. Stochastic processors allow hardware-induced faults in calculations, sacrificing bit-reproducibility and precision in exchange for improvements in performance and potentially accuracy of forecasts, due to a reduction in power consumption that could allow higher resolution. A similar trade-off is achieved using low precision arithmetic, with improvements in computation and communication speed and savings in storage and memory requirements. As high-performance computing becomes more massively parallel and power intensive, these two approaches may be important stepping stones in the pursuit of global cloud-resolving atmospheric modelling. The impact of both hardware induced faults and low precision arithmetic is tested using the Lorenz '96 model and the dynamical core of a global atmosphere model. In the Lorenz '96 model there is a natural scale separation; the spectral discretisation used in the dynamical core also allows large and small scale dynamics to be treated separately within the code. Such scale separation allows the impact of lower-accuracy arithmetic to be restricted to components close to the truncation scales and hence close to the necessarily inexact parametrised representations of unresolved processes. By contrast, the larger scales are calculated using high precision deterministic arithmetic. Hardware faults from stochastic processors are emulated using a bit-flip model with different fault rates. Our simulations show that both approaches to inexact calculations do not substantially affect the large scale behaviour, provided they are restricted to act only on smaller scales. By contrast, results from the Lorenz '96 simulations are superior when small scales are calculated on an emulated stochastic processor than when those small scales are parametrised. This suggests that inexact calculations at the small scale could reduce computation and power costs without adversely affecting the quality of the simulations. This would allow higher resolution models to be run at the same computational cost. © 2013 The Authors.

The real butterfly effect

NONLINEARITY 27 (2014) R123-R141

TN Palmer, A Doering, G Seregin

Energy- and enstrophy-conserving schemes for the shallow-water equations, based on mimetic finite elements


ATT McRae, CJ Cotter

Build high-resolution global climate models

NATURE 515 (2014) 338-339

T Palmer

Stochastic modelling and energy-efficient computing for weather and climate prediction.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences 372 (2014) 20140118-

T Palmer, P Düben, H McNamara

On the use of inexact, pruned hardware in atmospheric modelling.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences 372 (2014) 20130276-

PD Düben, J Joven, A Lingamneni, H McNamara, G De Micheli, KV Palem, TN Palmer

Inexact hardware design, which advocates trading the accuracy of computations in exchange for significant savings in area, power and/or performance of computing hardware, has received increasing prominence in several error-tolerant application domains, particularly those involving perceptual or statistical end-users. In this paper, we evaluate inexact hardware for its applicability in weather and climate modelling. We expand previous studies on inexact techniques, in particular probabilistic pruning, to floating point arithmetic units and derive several simulated set-ups of pruned hardware with reasonable levels of error for applications in atmospheric modelling. The set-up is tested on the Lorenz '96 model, a toy model for atmospheric dynamics, using software emulation for the proposed hardware. The results show that large parts of the computation tolerate the use of pruned hardware blocks without major changes in the quality of short- and long-time diagnostics, such as forecast errors and probability density functions. This could open the door to significant savings in computational cost and to higher resolution simulations with weather and climate models.

Benchmark Tests for Numerical Weather Forecasts on Inexact Hardware

MONTHLY WEATHER REVIEW 142 (2014) 3809-3829

PD Dueben, TN Palmer

More reliable forecasts with less precise computations: a fast-track route to cloud-resolved weather and climate simulators?

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences 372 (2014) 20130391-

TN Palmer

This paper sets out a new methodological approach to solving the equations for simulating and predicting weather and climate. In this approach, the conventionally hard boundary between the dynamical core and the sub-grid parametrizations is blurred. This approach is motivated by the relatively shallow power-law spectrum for atmospheric energy on scales of hundreds of kilometres and less. It is first argued that, because of this, the closure schemes for weather and climate simulators should be based on stochastic-dynamic systems rather than deterministic formulae. Second, as high-wavenumber elements of the dynamical core will necessarily inherit this stochasticity during time integration, it is argued that the dynamical core will be significantly over-engineered if all computations, regardless of scale, are performed completely deterministically and if all variables are represented with maximum numerical precision (in practice using double-precision floating-point numbers). As the era of exascale computing is approached, an energy- and computationally efficient approach to cloud-resolved weather and climate simulation is described where determinism and numerical precision are focused on the largest scales only.

Atlantic meridional overturning circulation and the prediction of North Atlantic sea surface temperature


M Kloewer, M Latif, H Ding, RJ Greatbatch, W Park

Addressing model error through atmospheric stochastic physical parametrizations: impact on the coupled ECMWF seasonal forecasting system.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences 372 (2014) 20130290-

A Weisheimer, S Corti, T Palmer, F Vitart

The finite resolution of general circulation models of the coupled atmosphere-ocean system and the effects of sub-grid-scale variability present a major source of uncertainty in model simulations on all time scales. The European Centre for Medium-Range Weather Forecasts has been at the forefront of developing new approaches to account for these uncertainties. In particular, the stochastically perturbed physical tendency scheme and the stochastically perturbed backscatter algorithm for the atmosphere are now used routinely for global numerical weather prediction. The European Centre also performs long-range predictions of the coupled atmosphere-ocean climate system in operational forecast mode, and the latest seasonal forecasting system--System 4--has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather forecasts. Here, we present results of the impact of these schemes in System 4 by contrasting the operational performance on seasonal time scales during the retrospective forecast period 1981-2010 with comparable simulations that do not account for the representation of model uncertainty. We find that the stochastic tendency perturbation schemes helped to reduce excessively strong convective activity especially over the Maritime Continent and the tropical Western Pacific, leading to reduced biases of the outgoing longwave radiation (OLR), cloud cover, precipitation and near-surface winds. Positive impact was also found for the statistics of the Madden-Julian oscillation (MJO), showing an increase in the frequencies and amplitudes of MJO events. Further, the errors of El Niño southern oscillation forecasts become smaller, whereas increases in ensemble spread lead to a better calibrated system if the stochastic tendency is activated. The backscatter scheme has overall neutral impact. Finally, evidence for noise-activated regime transitions has been found in a cluster analysis of mid-latitude circulation regimes over the Pacific-North America region.

Lorenz, Godel and Penrose: new perspectives on determinism and causality in fundamental physics

CONTEMPORARY PHYSICS 55 (2014) 157-178

TN Palmer

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.

Regional moisture change over India during the past Millennium: A comparison of multi-proxy reconstructions and climate model simulations

Global and Planetary Change Elsevier BV 122 (2014) 176-185

S Polanski, B Fallah, DJ Befort, S Prasad, U Cubasch