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

Journal of Computational Physics (2013)

PD Düben, TN Palmer, H McNamara

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.

Predicting multiyear North Atlantic Ocean variability

Journal of Geophysical Research: Oceans 118 (2013) 1087-1098

W Hazeleger, B Wouters, GJ Van Oldenborgh, S Corti, T Palmer, D Smith, N Dunstone, J Kröger, H Pohlmann, JS Von Storch

We assess the skill of retrospective multiyear forecasts of North Atlantic ocean characteristics obtained with ocean-atmosphere-sea ice models that are initialized with estimates from the observed ocean state. We show that these multimodel forecasts can skilfully predict surface and subsurface ocean variability with lead times of 2 to 9 years. We focus on assessment of forecasts of major well-observed oceanic phenomena that are thought to be related to the Atlantic meridional overturning circulation (AMOC). Variability in the North Atlantic subpolar gyre, in particular that associated with the Atlantic Multidecadal Oscillation, is skilfully predicted 2-9 years ahead. The fresh water content and heat content in major convection areas such as the Labrador Sea are predictable as well, although individual events are not captured. The skill of these predictions is higher than that of uninitialized coupled model simulations and damped persistence. However, except for heat content in the subpolar gyre, differences between damped persistence and the initialized predictions are not significant. Since atmospheric variability is not predictable on multiyear time scales, initialization of the ocean and oceanic processes likely provide skill. Assessment of relationships of patterns of variability and ocean heat content and fresh water content shows differences among models indicating that model improvement can lead to further improvements of the predictions. The results imply there is scope for skilful predictions of the AMOC. © 2013. American Geophysical Union. All Rights Reserved.

Singular vectors, predictability and ensemble forecasting for weather and climate

Journal of Physics A: Math. Theor. 46 (2013) 254018

TN Palmer, L Zanna

Should weather and climate prediction models be deterministic or stochastic?

Weather Wiley 68 (2013) 264-264

HM Arnold

The Effect of Climate Change on the Variability of the Northern Hemisphere Stratospheric Polar Vortex


Mitchell, DM, SM Osprey, Gray, LJ, Butchart, N, Hardiman, SC, Charlton-Perez, A, P Watson

Towards the probabilistic Earth-system simulator: A vision for the future of climate and weather prediction

Quarterly Journal of the Royal Meteorological Society (2012)

TN Palmer

High-Resolution Global Climate Simulations with the ECMWF Model in Project Athena: Experimental Design, Model Climate, and Seasonal Forecast Skill

JOURNAL OF CLIMATE 25 (2012) 3155-3172

T Jung, MJ Miller, TN Palmer, P Towers, N Wedi, D Achuthavarier, JM Adams, EL Altshuler, BA Cash, KJL III, L Marx, C Stan, KI Hodges

Statistical analysis of global variations of atmospheric relative humidity as observed by AIRS

Journal of Geophysical Research: Atmospheres 117 (2012) n/a-n/a

J Du, F Cooper, S Fueglistaler

Quantifying uncertainty in future Southern Hemisphere circulation trends

Geophysical Research Letters 39 (2012)

PAG Watson, DJ Karoly, MR Allen, N Faull, DS Lee

The Antarctic polar night jet has intensified during spring in recent decades due to stratospheric ozone depletion and rising greenhouse gas (GHG) concentrations and this has had substantial effects on the region's climate. GHG concentrations will rise over the 21st century whereas stratospheric ozone is expected to recover and there is uncertainty in future southern hemisphere (SH) circulation trends. We examine sensitivity to the physics parameterisation of the 21st century SH circulation projection of a coupled atmosphere-ocean General Circulation Model and the sensitivity of the contribution from stratospheric ozone recovery. Different model parameterizations give a greater range of future trends in the position of the tropospheric jet than has been found in previous multi-model comparisons. Ozone recovery causes a weakening and northward shift of the DJF tropospheric jet. Varying the physics parameterization affects the zonal wind response to ozone recovery of the SON stratosphere by ∼10% and that of the DJF troposphere by ∼25%. The projected future SAM index changes with and without ozone recovery and the SAM index response to ozone recovery alone are found to be strongly positively correlated with projected 21st century global warming. © 2012. American Geophysical Union. All Rights Reserved.

Systematic Model Error: The Impact of Increased Horizontal Resolution versus Improved Stochastic and Deterministic Parameterizations

JOURNAL OF CLIMATE 25 (2012) 4946-4962

J Berner, T Jung, TN Palmer

Simulating regime structures in weather and climate prediction models

Geophysical Research Letters 39 (2012) L21805

A Dawson, TN Palmer, S Corti

The emergence of zonal ocean jets under large-scale stochastic wind forcing

Geophysical Research Letters 39 (2012) n/a-n/a

CH O'Reilly, A Czaja, JH LaCasce

Climate Simulations Using MRI-AGCM3.2 with 20-km Grid

Journal of the Meteorological Society of Japan 90A (2012) 233-258


Reliability of decadal predictions

Geophysical Research Letters 39 (2012)

S Corti, A Weisheimer, TN Palmer, FJ Doblas-Reyes, L Magnusson

The reliability of multi-year predictions of climate is assessed using probabilistic Attributes Diagrams for near-surface air temperature and sea surface temperature, based on 54 member ensembles of initialised decadal hindcasts using the ECMWF coupled model. It is shown that the reliability from the ensemble system is good over global land areas, Europe and Africa and for the North Atlantic, Indian Ocean and, to a lesser extent, North Pacific basins for lead times up to 6-9years. North Atlantic SSTs are reliably predicted even when the climate trend is removed, consistent with the known predictability for this region. By contrast, reliability in the Indian Ocean, where external forcing accounts for most of the variability, deteriorates severely after detrending. More conventional measures of forecast quality, such as the anomaly correlation coefficient (ACC) of the ensemble mean, are also considered, showing that the ensemble has significant skill in predicting multi-annual temperature averages. © 2012. American Geophysical Union. All Rights Reserved.

Useful decadal climate prediction at regional scales? A look at the ENSEMBLES stream 2 decadal hindcasts

Environmental Research Letters 7 (2012)

DA MacLeod, C Caminade, AP Morse

Decadal climate prediction is a branch of climate modelling with the theoretical potential to anticipate climate impacts years in advance. Here we present analysis of the ENSEMBLES decadal simulations, the first multi-model decadal hindcasts, focusing on the skill in prediction of temperature and precipitation - important for impact prediction. Whilst previous work on this dataset has focused on the skill in multi-year averages, we focus here on the skill in prediction at smaller timescales. Considering annual and seasonal averages, we look at correlations, potential predictability and multi-year trend correlations. The results suggest that the prediction skill for temperature comes from the long-term trend, and that precipitation predictions are not skilful. The potential predictability of the models is higher for annual than for seasonal means and is largest over the tropics, though it is low everywhere else and is much lower for precipitation than for temperature. The globally averaged temperature trend correlation is significant at the 99% level for all models and is higher for annual than for seasonal averages; however, for smaller spatial regions the skill is lower. For precipitation trends, the correlations are not skilful on either annual or seasonal scales. Whilst climate models run in decadal prediction mode may be useful by other means, the hindcasts studied here have limited predictive power on the scales at which climate impacts and the results presented suggest that they do not yet have sufficient skill to drive impact models on decadal timescales. © 2012 IOP Publishing Ltd.

The Intra-Seasonal Oscillation and its control of tropical cyclones simulated by high-resolution global atmospheric models

CLIMATE DYNAMICS 39 (2012) 2185-2206

M Satoh, K Oouchi, T Nasuno, H Taniguchi, Y Yamada, H Tomita, C Kodama, J Kinter, D Achuthavarier, J Manganello, B Cash, T Jung, T Palmer, N Wedi

Comparing TIGGE multimodel forecasts with reforecast-calibrated ECMWF ensemble forecasts

Quarterly Journal of the Royal Meteorological Society (2012)

R Hagedorn, R Buizza, TM Hamill, M Leutbecher, TN Palmer

Towards the probabilistic Earth-system simulator: A vision for the future of climate and weather prediction

Quarterly Journal of the Royal Meteorological Society 138 (2012) 841-861

TN Palmer

There is no more challenging problem in computational science than that of estimating, as accurately as science and technology allows, the future evolution of Earth's climate; nor indeed is there a problem whose solution has such importance and urgency. Historically, the simulation tools needed to predict climate have been developed, somewhat independently, at a number of weather and climate institutes around the world. While these simulators are individually deterministic, it is often assumed that the resulting diversity provides a useful quantification of uncertainty in global or regional predictions. However, this notion is not well founded theoretically and corresponding 'multi-simulator' estimates of uncertainty can be prone to systemic failure. Separate to this, individual institutes are now facing considerable challenges in finding the human and computational resources needed to develop more accurate weather and climate simulators with higher resolution and full Earth-system complexity. A new approach, originally designed to improve reliability in ensemble-based numerical weather prediction, is introduced to help solve these two rather different problems. Using stochastic mathematics, this approach recognizes uncertainty explicitly in the parametrized representation of unresolved climatic processes. Stochastic parametrization is shown to be more consistent with the underlying equations of motion and, moreover, provides more skilful estimates of uncertainty when compared with estimates from traditional multi-simulator ensembles, on time-scales where verification data exist. Stochastic parametrization can also help reduce long-term biases which have bedevilled numerical simulations of climate from the earliest days to the present. As a result, it is suggested that the need to maintain a large 'gene pool' of quasi-independent deterministic simulators may be obviated by the development of probabilistic Earth-system simulators. Consistent with the conclusions of the World Summit on Climate Modelling, this in turn implies that individual institutes will be able to pool human and computational resources in developing future-generation simulators, thus benefitting from economies of scale; the establishment of the Airbus consortium provides a useful analogy here. As a further stimulus for such evolution, discussion is given to a potential new synergy between the development of dynamical cores, and stochastic processing hardware. However, it is concluded that the traditional challenge in numerical weather prediction, of reducing deterministic measures of forecast error, may increasingly become an obstacle to the seamless development of probabilistic weather and climate simulators, paradoxical as that may appear at first sight. Indeed, going further, it is argued that it may be time to consider focusing operational weather forecast development entirely on high-resolution ensemble prediction systems. Finally, by considering the exceptionally challenging problem of quantifying cloud feedback in climate change, it is argued that the development of the probabilistic Earth-system simulator may actually provide a route to reducing uncertainty in climate prediction. © 2012 Royal Meteorological Society.

Evaluation of probabilistic quality and value of the ENSEMBLES multimodel seasonal forecasts: Comparison with DEMETER

Monthly Weather Review 139 (2011) 581-607

A Alessandri, A Borrelli, A Navarra, A Arribas, M Déqué, P Rogel, A Weisheimer

The performance of the new multimodel seasonal prediction system developed in the framework of the European Commission FP7 project called ENSEMBLE-based predictions of climate changes and their impacts (ENSEMBLES) is compared with the results from the previous project [i.e., Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER)]. The comparison is carried out over the five seasonal prediction systems (SPSs) that participated in both projects. Since DEMETER, the contributing SPSs have improved in all aspects with the main advancements including the increase in resolution, the better representation of subgrid physical processes, land, sea ice, and greenhouse gas boundary forcing, and the more widespread use of assimilation for ocean initialization. The ENSEMBLES results show an overall enhancement for the prediction of anomalous surface temperature conditions. However, the improvement is quite small and with considerable space-time variations. In the tropics, ENSEMBLES systematically improves the sharpness and the discrimination attributes of the forecasts. Enhancements of the ENSEMBLES resolution attribute are also reported in the tropics for the forecasts started 1 February, 1 May, and 1 November. Our results indicate that, in ENSEMBLES, an increased portion of prediction signal from the single-models effectively contributes to amplify the multimodel forecasts skill. On the other hand, a worsening is shown for the multimodel calibration over the tropics compared to DEMETER. Significant changes are also shown in northern midlatitudes, where the ENSEMBLES multimodel discrimination, resolution, and reliability improve for February, May, and November starting dates. However, the ENSEMBLES multimodel decreases the capability to amplify the performance with respect to the contributing single models for the forecasts started in February, May, and August. This is at least partly due to the reduced overconfidence of the ENSEMBLES single models with respect to the DEMETER counterparts. Provided that they are suitably calibrated beforehand, it is shown that the ENSEMBLES multimodel forecasts represent a step forward for the potential economical value they can supply. A warning for all potential users concerns the need for calibration due to the degraded tropical reliability compared to DEMETER. In addition, the superiority of recalibrating the ENSEMBLES predictions through the discrimination information is shown. Concerning the forecasts started inAugust, ENSEMBLES exhibitsmixed results over both tropics and northernmidlatitudes. In this case, the increased potential predictability compared to DEMETER appears to be balanced by the reduction in the independence of the SPSs contributing to ENSEMBLES. Consequently, for the August start dates no clear advantage of using one multimodel system instead of the other can be evidenced. © 2011 American Meteorological Society.

Future Change in Extratropical Cyclones Associated with Change in the Upper Troposphere

JOURNAL OF CLIMATE 24 (2011) 6456-6470

R Mizuta, M Matsueda, H Endo, S Yukimoto