Publications


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.


Climate forecasting: build high-resolution global climate models.

Nature 515 (2014) 338-339

T Palmer


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

© Author(s) 2013. CC Attribution 3.0 License. Differential equations posed over immersed manifolds are of particular importance in studying geophysical flows; for instance, ocean and atmosphere simulations crucially rely on the capability to solve equations over the sphere. This paper presents the extension of the FEniCS software components to the automated solution of finite element formulations of differential equations defined over general, immersed manifolds. We describe the implementation and, in particular detail, how the required extensions essentially reduce to the extension of the FEniCS form compiler to cover this case. The resulting implementation has all the properties of the FEniCS pipeline and we demonstrate its flexibility by an extensive range of numerical examples covering a number of geophysical benchmark examples and test cases. The results are all in agreement with the expected values. The description here relates to DOLFIN/FEniCS 1.2.


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


Estimation of the local response to a forcing in a high dimensional system using the fluctuation-dissipation theorem

Nonlinear Processes in Geophysics 20 (2013) 239-248

FC Cooper, JG Esler, PH Haynes

The fluctuation-dissipation theorem (FDT) has been proposed as a method of calculating the response of the earth's atmosphere to a forcing. For this problem the high dimensionality of the relevant data sets makes truncation necessary. Here we propose a method of truncation based upon the assumption that the response to a localised forcing is spatially localised, as an alternative to the standard method of choosing a number of the leading empirical orthogonal functions. For systems where this assumption holds, the response to any sufficiently small non-localised forcing may be estimated using a set of truncations that are chosen algorithmically. We test our algorithm using 36 and 72 variable versions of a stochastic Lorenz 95 system of ordinary differential equations. We find that, for long integrations, the bias in the response estimated by the FDT is reduced from ∼75% of the true response to ∼30%. © 2013 Author(s).


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.


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.


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


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


THE BUTTERFLY AND THE PHOTON: NEW PERSPECTIVES ON UNPREDICTABILITY, AND THE NOTION OF CASUAL REALITY, IN QUANTUM PHYSICS

SCIENCE: IMAGE IN ACTION (2012) 129-139

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 (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

Pages