Publications


Rift valley fever outbreaks in Mauritania and related environmental conditions

International Journal of Environmental Research and Public Health 11 (2014) 903-918

C Caminade, JA Ndione, M Diallo, DA Macleod, O Faye, Y Ba, I Dia, AP Morse

Four large outbreaks of Rift Valley Fever (RVF) occurred in Mauritania in 1998, 2003, 2010 and 2012 which caused lots of animal and several human deaths. We investigated rainfall and vegetation conditions that might have impacted on RVF transmission over the affected regions. Our results corroborate that RVF transmission generally occurs during the months of September and October in Mauritania, similarly to Senegal. The four outbreaks were preceded by a rainless period lasting at least a week followed by heavy precipitation that took place during the second half of the rainy season. First human infections were generally reported three to five weeks later. By bridging the gap between meteorological forecasting centers and veterinary services, an early warning system might be developed in Senegal and Mauritania to warn decision makers and health services about the upcoming RVF risk. © 2014 by the authors; licensee MDPI, Basel, Switzerland.


Benchmark Tests for Numerical Weather Forecasts on Inexact Hardware

MONTHLY WEATHER REVIEW 142 (2014) 3809-3829

PD Dueben, TN Palmer


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

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

J Du, WE Ward, FC Cooper


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.


The real butterfly effect

NONLINEARITY 27 (2014) R123-R141

TN Palmer, A Doering, G Seregin


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.


Potential sea ice predictability and the role of stochastic sea ice strength perturbations

Geophysical Research Letters 41 (2014) 8396-8403

S Juricke, HF Goessling, T Jung


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.


Towards seasonal forecasting of malaria in India.

Malaria journal 13 (2014) 310-

JM Lauderdale, C Caminade, AE Heath, AE Jones, DA MacLeod, KC Gouda, US Murty, P Goswami, SR Mutheneni, AP Morse

Malaria presents public health challenge despite extensive intervention campaigns. A 30-year hindcast of the climatic suitability for malaria transmission in India is presented, using meteorological variables from a state of the art seasonal forecast model to drive a process-based, dynamic disease model.The spatial distribution and seasonal cycles of temperature and precipitation from the forecast model are compared to three observationally-based meteorological datasets. These time series are then used to drive the disease model, producing a simulated forecast of malaria and three synthetic malaria time series that are qualitatively compared to contemporary and pre-intervention malaria estimates. The area under the Relative Operator Characteristic (ROC) curve is calculated as a quantitative metric of forecast skill, comparing the forecast to the meteorologically-driven synthetic malaria time series.The forecast shows probabilistic skill in predicting the spatial distribution of Plasmodium falciparum incidence when compared to the simulated meteorologically-driven malaria time series, particularly where modelled incidence shows high seasonal and interannual variability such as in Orissa, West Bengal, and Jharkhand (North-east India), and Gujarat, Rajastan, Madhya Pradesh and Maharashtra (North-west India). Focusing on these two regions, the malaria forecast is able to distinguish between years of "high", "above average" and "low" malaria incidence in the peak malaria transmission seasons, with more than 70% sensitivity and a statistically significant area under the ROC curve. These results are encouraging given that the three month forecast lead time used is well in excess of the target for early warning systems adopted by the World Health Organization. This approach could form the basis of an operational system to identify the probability of regional malaria epidemics, allowing advanced and targeted allocation of resources for combatting malaria in India.


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.


It's all just physics

PHYSICS WORLD 27 (2014) 18-19

T Palmer


Singular vectors, predictability and ensemble forecasting for weather and climate

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

TN Palmer, L Zanna


Future projections of heat waves around Japan simulated by CMIP3 and high-resolution Meteorological Research Institute atmospheric climate models

Journal of Geophysical Research: Atmospheres 118 (2013) 3097-3109

M Nakano, M Matsueda, M Sugi


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.


Effects of Stochastic Ice Strength Perturbation on Arctic Finite Element Sea Ice Modeling

Journal of Climate 26 (2013) 3785-3802

S Juricke, P Lemke, R Timmermann, T Rackow


The influence of tropical cyclones on heat waves in Southeastern Australia

Geophysical Research Letters 40 (2013) 6264-6270

TJ Parker, GJ Berry, MJ Reeder


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.


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.


Trapped mountain waves during a light aircraft accident

Australian Meteorological and Oceanographic Journal Australian Bureau of Meteorology 63 (2013) 377-389

TJ Parker, TP Lane

On 31 July 2007 a fatal light aircraft crash occurred near Clonbinane, Victoria, Australia and the official investigation concluded that mountain wave turbulence was the likely cause. This study uses three-dimensional numerical modelling and linear wave theory to examine the dynamics of mountain waves during this turbulence event and their role in generating turbulence. Analysis of the observed environment and three-dimensional idealised simulations elucidate the occurrence of trapped mountain waves and their role in creating regions of enhanced turbulence in the vicinity of the aircraft accident. Specifically, these waves perturb layers of low dynamic stability in the upstream flow, promoting turbulence in those layers. A simple ensemble of these three-dimensional simulations is also used to assess the robustness of the model solutions and demonstrate the utility of high-resolution ensembles for explicit mountain wave turbulence prediction.


Importance of oceanic resolution and mean state on the extra-tropical response to El Niño in a matrix of coupled models

Climate Dynamics 41 (2013) 1439-1452

A Dawson, AJ Matthews, DP Stevens, MJ Roberts, PL Vidale

The extra-tropical response to El Niño in configurations of a coupled model with increased horizontal resolution in the oceanic component is shown to be more realistic than in configurations with a low resolution oceanic component. This general conclusion is independent of the atmospheric resolution. Resolving small-scale processes in the ocean produces a more realistic oceanic mean state, with a reduced cold tongue bias, which in turn allows the atmospheric model component to be forced more realistically. A realistic atmospheric basic state is critical in order to represent Rossby wave propagation in response to El Niño, and hence the extra-tropical response to El Niño. Through the use of high and low resolution configurations of the forced atmospheric-only model component we show that, in isolation, atmospheric resolution does not significantly affect the simulation of the extra-tropical response to El Niño. It is demonstrated, through perturbations to the SST forcing of the atmospheric model component, that biases in the climatological SST field typical of coupled model configurations with low oceanic resolution can account for the erroneous atmospheric basic state seen in these coupled model configurations. These results highlight the importance of resolving small-scale oceanic processes in producing a realistic large-scale mean climate in coupled models, and suggest that it might may be possible to "squeeze out" valuable extra performance from coupled models through increases to oceanic resolution alone. © 2012 Springer-Verlag.