Publications by Tim Palmer


Experimental Non-Violation of the Bell Inequality

ENTROPY 20 (2019) ARTN 356

TN Palmer


How confident are predictability estimates of the winter North Atlantic Oscillation?

Quarterly Journal of the Royal Meteorological Society (2019)

A Weisheimer, D Decremer, D MacLeod, C O'Reilly, TN Stockdale, S Johnson, TN Palmer

© 2018 The Authors. Quarterly Journal of the Royal Meteorological Society published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. Atmospheric seasonal predictability in winter over the Euro-Atlantic region is studied with an emphasis on the signal-to-noise paradox of the North Atlantic Oscillation. Seasonal hindcasts of the ECMWF model for the recent period 1981–2009 show, in agreement with other studies, that correlation skill over Greenland and parts of the Arctic is higher than the signal-to-noise ratio implies. This leads to the paradoxical situation where the real world appears more predictable than the models suggest, with the forecast ensembles being overly dispersive (or underconfident). However, it is demonstrated that these conclusions are not supported by the diagnosed relationship between ensemble mean root-mean-square error (RMSE) and ensemble spread which indicates a slight under-dispersion (overconfidence). Furthermore, long atmospheric seasonal hindcasts suggest that over the 110-year period from 1900 to 2009 the ensemble system is well calibrated (neither over- nor under-dispersive). The observed skill changed drastically in the middle of the twentieth century and paradoxical regions during more recent hindcast periods were strongly under-dispersive during mid-century decades. Due to non-stationarities of the climate system in the form of decadal variability, relatively short hindcasts are not sufficiently representative of longer-term behaviour. In addition, small hindcast sample size can lead to skill estimates, in particular of correlation measures, that are not robust. It is shown that the relative uncertainty due to small hindcast sample size is often larger for correlation-based than for RMSE-based diagnostics. Correlation-based measures like the RPC are shown to be highly sensitive to the strength of the predictable signal, implying that disentangling of physical deficiencies in the models on the one hand, and the effects of sampling uncertainty on the other hand, is difficult. Given the current lack of a causal physical mechanism to unravel the puzzle, our hypotheses of non-stationarity and sampling uncertainty provide simple yet plausible explanations for the paradox.


Scale-Selective Precision for Weather and Climate Forecasting

MONTHLY WEATHER REVIEW 147 (2019) 645-655

M Chantry, T Thornes, T Palmer, P Duben


Signal and noise in regime systems: A hypothesis on the predictability of the North Atlantic Oscillation

Quarterly Journal of the Royal Meteorological Society (2019)

K Strommen, TN Palmer

© 2018 Royal Meteorological Society Studies conducted by the UK Met Office reported significant skill in predicting the winter North Atlantic Oscillation (NAO) index with their seasonal prediction system. At the same time, a very low signal-to-noise ratio was observed, as measured using the “ratio of predictable components” (RPC) metric. We analyse both the skill and signal-to-noise ratio using a new statistical toy model, which assumes NAO predictability is driven by regime dynamics. It is shown that if the system is approximately bimodal in nature, with the model consistently underestimating the level of regime persistence each season, then both the high skill and high RPC value of the Met Office hindcasts can easily be reproduced. Underestimation of regime persistence could be attributable to any number of sources of model error, including imperfect regime structure or errors in the propagation of teleconnections. In particular, a high RPC value for a seasonal mean prediction may be expected even if the model's internal level of noise is realistic.


Estimates of flow-dependent predictability of wintertime Euro-Atlantic weather regimes in medium-range forecasts

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 144 (2018) 1012-1027

M Matsueda, TN Palmer


Choosing the Optimal Numerical Precision for Data Assimilation in the Presence of Model Error

JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS 10 (2018) 2177-2191

S Hatfield, P Dueben, M Chantry, K Kondo, T Miyoshi, T Palmer


The ECMWF ensemble prediction system: Looking back (more than) 25 years and projecting forward 25 years

Quarterly Journal of the Royal Meteorological Society (2018)

T Palmer

© 2018 The Authors. Quarterly Journal of the Royal Meteorological Society published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. This paper has been written to mark 25 years of operational medium-range ensemble forecasting. The origins of the ECMWF Ensemble Prediction System are outlined, including the development of the precursor real-time Met Office monthly ensemble forecast system. In particular, the reasons for the development of singular vectors and stochastic physics – particular features of the ECMWF Ensemble Prediction System - are discussed. The author speculates about the development and use of ensemble prediction in the next 25 years.


A power law for reduced precision at small spatial scales: Experiments with an SQG model

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 144 (2018) 1179-1188

T Thornes, P Duben, T Palmer


Improving Weather Forecast Skill through Reduced-Precision Data Assimilation

MONTHLY WEATHER REVIEW 146 (2018) 49-62

S Hatfield, A Subramanian, T Palmer, P Duben


The impact of stochastic parametrisations on the representation of the Asian summer monsoon

CLIMATE DYNAMICS 50 (2018) 2269-2282

K Strommen, HM Christensen, J Berner, TN Palmer


Seasonal to annual ocean forecasting skill and the role of model and observational uncertainty.

Quarterly journal of the Royal Meteorological Society. Royal Meteorological Society (Great Britain) 144 (2018) 1947-1964

S Juricke, D MacLeod, A Weisheimer, L Zanna, TN Palmer

Accurate forecasts of the ocean state and the estimation of forecast uncertainties are crucial when it comes to providing skilful seasonal predictions. In this study we analyse the predictive skill and reliability of the ocean component in a seasonal forecasting system. Furthermore, we assess the effects of accounting for model and observational uncertainties. Ensemble forcasts are carried out with an updated version of the ECMWF seasonal forecasting model System 4, with a forecast length of ten months, initialized every May between 1981 and 2010. We find that, for essential quantities such as sea surface temperature and upper ocean 300 m heat content, the ocean forecasts are generally underdispersive and skilful beyond the first month mainly in the Tropics and parts of the North Atlantic. The reference reanalysis used for the forecast evaluation considerably affects diagnostics of forecast skill and reliability, throughout the entire ten-month forecasts but mostly during the first three months. Accounting for parametrization uncertainty by implementing stochastic parametrization perturbations has a positive impact on both reliability (from month 3 onwards) as well as forecast skill (from month 8 onwards). Skill improvements extend also to atmospheric variables such as 2 m temperature, mostly in the extratropical Pacific but also over the midlatitudes of the Americas. Hence, while model uncertainty impacts the skill of seasonal forecasts, observational uncertainty impacts our assessment of that skill. Future ocean model development should therefore aim not only to reduce model errors but to simultaneously assess and estimate uncertainties.


A SIMPLE PEDAGOGICAL MODEL LINKING INITIAL-VALUE RELIABILITY WITH TRUSTWORTHINESS IN THE FORCED CLIMATE RESPONSE

BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY 99 (2018) 605-614

TN Palmer, A Weisheimer


Flow dependent ensemble spread in seasonal forecasts of the boreal winter extratropics

ATMOSPHERIC SCIENCE LETTERS 19 (2018) UNSP e815

D MacLeod, C O'Reilly, T Palmer, A Weisheimer


Seasonal and decadal forecasts of Atlantic Sea surface temperatures using a linear inverse model

CLIMATE DYNAMICS 49 (2017) 1833-1845

B Huddart, A Subramanian, L Zanna, T Palmer


Stochastic Parameterization and El Nino-Southern Oscillation

JOURNAL OF CLIMATE 30 (2017) 17-38

HM Christensen, J Berner, DRB Coleman, TN Palmer


Exploiting the chaotic behaviour of atmospheric models with reconfigurable architectures

COMPUTER PHYSICS COMMUNICATIONS 221 (2017) 160-173

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


The primacy of doubt: Evolution of numerical weather prediction from determinism to probability

JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS 9 (2017) 730-734

T Palmer


Ensemble superparameterization versus stochastic parameterization: A comparison of model uncertainty representation in tropical weather prediction

JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS 9 (2017) 1231-1250

AC Subramanian, TN Palmer


Bitwise efficiency in chaotic models.

Proceedings. Mathematical, physical, and engineering sciences 473 (2017) 20170144-

S Jeffress, P Düben, T Palmer

Motivated by the increasing energy consumption of supercomputing for weather and climate simulations, we introduce a framework for investigating the bit-level information efficiency of chaotic models. In comparison with previous explorations of inexactness in climate modelling, the proposed and tested information metric has three specific advantages: (i) it requires only a single high-precision time series; (ii) information does not grow indefinitely for decreasing time step; and (iii) information is more sensitive to the dynamics and uncertainties of the model rather than to the implementation details. We demonstrate the notion of bit-level information efficiency in two of Edward Lorenz's prototypical chaotic models: Lorenz 1963 (L63) and Lorenz 1996 (L96). Although L63 is typically integrated in 64-bit 'double' floating point precision, we show that only 16 bits have significant information content, given an initial condition uncertainty of approximately 1% of the size of the attractor. This result is sensitive to the size of the uncertainty but not to the time step of the model. We then apply the metric to the L96 model and find that a 16-bit scaled integer model would suffice given the uncertainty of the unresolved sub-grid-scale dynamics. We then show that, by dedicating computational resources to spatial resolution rather than numeric precision in a field programmable gate array (FPGA), we see up to 28.6% improvement in forecast accuracy, an approximately fivefold reduction in the number of logical computing elements required and an approximately 10-fold reduction in energy consumed by the FPGA, for the L96 model.


STOCHASTIC PARAMETERIZATION Toward a New View of Weather and Climate Models

BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY 98 (2017) 565-587

J Berner, U Achatz, L Batte, L Bengtsson, A de la Camara, HM Christensen, M Colangeli, DRB Coleman, D Crommelin, SI Dolaptchiev, CLE Franzke, P Friederichs, P Imkeller, H Jarvinen, S Juricke, V Kitsios, F Lott, V Lucarini, S Mahajan, TN Palmer, C Penland, M Sakradzija, J-S von Storch, A Weisheimer, M Weniger, PD Williams, J-I Yano

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