Publications


The real butterfly effect

NONLINEARITY 27 (2014) R123-R141

TN Palmer, A Doering, G Seregin


Benchmark Tests for Numerical Weather Forecasts on Inexact Hardware

MONTHLY WEATHER REVIEW 142 (2014) 3809-3829

PD Dueben, TN Palmer


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.


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

EARTH AND PLANETARY SCIENCE LETTERS 406 (2014) 1-6

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


Studying edge geometry in transiently turbulent shear flows

JOURNAL OF FLUID MECHANICS 747 (2014) 506-517

M Chantry, TM Schneider


Automating the solution of PDEs on the sphere and other manifolds in FEniCS 1.2

GEOSCIENTIFIC MODEL DEVELOPMENT 6 (2013) 2099-2119

ME Rognes, DA Ham, CJ Cotter, ATT McRae


Should weather and climate prediction models be deterministic or stochastic?

Weather Wiley 68 (2013) 264-264

HM Arnold


Singular vectors, predictability and ensemble forecasting for weather and climate

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

TN Palmer, L Zanna


Impact of snow initialization on sub-seasonal forecasts

Climate Dynamics 41 (2013) 1969-1982

YJ Orsolini, R Senan, G Balsamo, FJ Doblas-Reyes, F Vitart, A Weisheimer, A Carrasco, RE Benestad

The influence of the snowpack on wintertime atmospheric teleconnections has received renewed attention in recent years, partially for its potential impact on seasonal predictability. Many observational and model studies have indicated that the autumn Eurasian snow cover in particular, influences circulation patterns over the North Pacific and North Atlantic. We have performed a suite of coupled atmosphere-ocean simulations with the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecast system to investigate the impact of accurate snow initialisation. Pairs of 2-month ensemble forecasts were started every 15 days from the 15th of October through the 1st of December in the years 2004-2009, with either realistic initialization of snow variables based on re-analyses, or else with "scrambled" snow initial conditions from an alternate autumn date and year. Initially, in the first 15 days, the presence of a thicker snowpack cools surface temperature over the continental land masses of Eurasia and North America. At a longer lead of 30-day, it causes a warming over the Arctic and the high latitudes of Eurasia due to an intensification and westward expansion of the Siberian High. It also causes a cooling over the mid-latitudes of Eurasia, and lowers sea level pressures over the Arctic. This "warm Arctic-cold continent" difference means that the forecasts of near-surface temperature with the more realistic snow initialization are in closer agreement with re-analyses, reducing a cold model bias over the Arctic and a warm model bias over mid-latitudes. The impact of realistic snow initialization upon the forecast skill in snow depth and near-surface temperature is estimated for various lead times. Following a modest skill improvement in the first 15 days over snow-covered land, we also find a forecast skill improvement up to the 30-day lead time over parts of the Arctic and the Northern Pacific, which can be attributed to the realistic snow initialization over the land masses. © 2013 Springer-Verlag Berlin Heidelberg.


Stochastic Parameterisations and Model Uncertainty in the Lorenz '96 system

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Royal Society 371 (2013) 20110479

HM Arnold, IM Moroz, TN Palmer


REVOLUTIONIZING CLIMATE MODELING WITH PROJECT ATHENA A Multi-Institutional, International Collaboration

BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY 94 (2013) 231-245

KJL III, B Cash, D Achuthavarier, J Adams, E Altshuler, P Dirmeyer, B Doty, B Huang, EK Jin, L Marx, J Manganello, C Stan, T Wakefield, T Palmer, M Hamrud, T Jung, M Miller, P Towers, N Wedi, M Satoh, H Tomita, C Kodama, T Nasuno, K Oouchi, Y Yamada, H Taniguchi, P Andrews, T Baer, M Ezell, C Halloy, D John, B Loftis, R Mohr, K Wong


Near-term climate change: Projections and predictability

in Climate Change 2013 the Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 9781107057999 (2013) 953-1028

NL Bindoff, PJ Durack, A Slater, P Cameron-Smith, Y Chikamoto, O Clifton, P Ginoux, M Holland, C Holmes, J Infanti, D Jacob, J John, T Knutson, D Lawrence, J Lu, D Murphy, V Naik, A Robock, S Vavrus, M Ishii, S Corti, T Fichefet, J García-Serrano, V Guemas, L Rodrigues, L Gray, E Hawkins, D Smith, DS Stevenson, A Voulgarakis, A Weisheimer, O Wild, T Woollings, P Young, G Krinner, Z Klimont, J Sedláček, B van den Hurk, T van Noije

© Intergovernmental Panel on Climate Change 2014. Executive Summary. This chapter assesses the scientific literature describing expectations for near-term climate (present through mid-century). Unless otherwise stated, ‘near-term’ change and the projected changes below are for the period 2016-2035 relative to the reference period 1986-2005. Atmospheric composition (apart from CO2; see Chapter 12) and air quality projections through to 2100 are also assessed. Decadal Prediction. The nonlinear and chaotic nature of the climate system imposes natual limits on the extent to which skilful predictions of climate statistics may be made. M.del-based ‘predictability’ studies, which probe these limits and investigate the physical mechanisms involved, support the potential for the skilful prediction of annual to decadal average temperature and, to a lesser extent precipitation. Predictions for averages of temperature, over large regions of the planet and for the global mean, exhibit positive skill when verified against observations for forecast periods up to ten years (high confidence). Predictions of precipitation over some land areas also exhibit positive skill. Decadal prediction is a new endeavour in climate science. The level of quality for climate predictions of annual to decadal average quantities is assessed from the past performance of initialized predictions and non-initialized simulations. {11.2.3, Figures 11.3 and 11.4}. In current results, observation-based initialization is the dominant contributor to the skill of predictions of annual mean temperature for the first few years and to the skill of predictions of the global mean surface temperature and the temperature over the North Atlantic, regions of the South Pacific and the tropical Indian Ocean for longer periods (high confidence).


Aspects of weather parameters at Neumayer station, Antarctica, and their representation in reanalysis and climate model data

METEOROLOGISCHE ZEITSCHRIFT 22 (2013) 699-709

M Kloewer, T Jung, G Koenig-Langlo, T Semmler


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.


Climate extremes and the role of dynamics.

Proc Natl Acad Sci U S A 110 (2013) 5281-5282

TN Palmer


Impact of snow initialization on sub-seasonal forecasts

Climate Dynamics (2013) 1-14

YJ Orsolini, R Senan, G Balsamo, F Vitart, A Weisheimer, FJ Doblas-Reyes, A Carrasco, RE Benestad

The influence of the snowpack on wintertime atmospheric teleconnections has received renewed attention in recent years, partially for its potential impact on seasonal predictability. Many observational and model studies have indicated that the autumn Eurasian snow cover in particular, influences circulation patterns over the North Pacific and North Atlantic. We have performed a suite of coupled atmosphere-ocean simulations with the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecast system to investigate the impact of accurate snow initialisation. Pairs of 2-month ensemble forecasts were started every 15 days from the 15th of October through the 1st of December in the years 2004-2009, with either realistic initialization of snow variables based on re-analyses, or else with "scrambled" snow initial conditions from an alternate autumn date and year. Initially, in the first 15 days, the presence of a thicker snowpack cools surface temperature over the continental land masses of Eurasia and North America. At a longer lead of 30-day, it causes a warming over the Arctic and the high latitudes of Eurasia due to an intensification and westward expansion of the Siberian High. It also causes a cooling over the mid-latitudes of Eurasia, and lowers sea level pressures over the Arctic. This "warm Arctic-cold continent" difference means that the forecasts of near-surface temperature with the more realistic snow initialization are in closer agreement with re-analyses, reducing a cold model bias over the Arctic and a warm model bias over mid-latitudes. The impact of realistic snow initialization upon the forecast skill in snow depth and near-surface temperature is estimated for various lead times. Following a modest skill improvement in the first 15 days over snow-covered land, we also find a forecast skill improvement up to the 30-day lead time over parts of the Arctic and the Northern Pacific, which can be attributed to the realistic snow initialization over the land masses. © 2013 Springer-Verlag Berlin Heidelberg.


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

Geophysical Research Letters American Geophysical Union (AGU) 39 (2012) n/a-n/a

CH O'Reilly, A Czaja, JH LaCasce


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


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.

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