Energy budget-based backscatter in a shallow water model of a double gyre basin

OCEAN MODELLING 132 (2018) 1-11

M Kloewer, MF Jansen, M Claus, RJ Greatbatch, S Thomsen

Predicting El Niño in 2014 and 2015.

Scientific reports 8 (2018) 10733-

S Ineson, MA Balmaseda, MK Davey, D Decremer, NJ Dunstone, M Gordon, H-L Ren, AA Scaife, A Weisheimer

Early in 2014 several forecast systems were suggesting a strong 1997/98-like El Niño event for the following northern hemisphere winter 2014/15. However the eventual outcome was a modest warming. In contrast, winter 2015/16 saw one of the strongest El Niño events on record. Here we assess the ability of two operational seasonal prediction systems to forecast these events, using the forecast ensembles to try to understand the reasons underlying the very different development and outcomes for these two years. We test three hypotheses. First we find that the continuation of neutral ENSO conditions in 2014 is associated with the maintenance of the observed cold southeast Pacific sea surface temperature anomaly; secondly that, in our forecasts at least, warm west equatorial Pacific sea surface temperature anomalies do not appear to hinder El Niño development; and finally that stronger westerly wind burst activity in 2015 compared to 2014 is a key difference between the two years. Interestingly, in these years at least, this interannual variability in wind burst activity is predictable. ECMWF System 4 tends to produce more westerly wind bursts than Met Office GloSea5 and this likely contributes to the larger SST anomalies predicted in this model in both years.

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


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

Systematic Errors in Weather and Climate Models: Nature, Origins, and Way Forward

Bulletin of the American Meteorological Society (2017)

A Zadra, K Williams, A Frassoni, M Rixen, Á Adames, J Berner, F Bouyssel, B Casati, HM Christensen, MB Ek, G Flato, Y Huang, F Judt, H Lin, E Maloney, W Merryfield, A van Niekerk, T Rackow, K Saito, N Wedi, P Yadav

Extreme Rainfall and Flooding over Central Kenya Including Nairobi City during the Long-Rains Season 2018: Causes, Predictability, and Potential for Early Warning and Actions

ATMOSPHERE 9 (2018) ARTN 472

M Kilavi, D MacLeod, M Ambani, J Robbins, R Dankers, R Graham, H Titley, AAM Salih, MC Todd

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


T Thornes, P Duben, T Palmer

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 predictability of onset and cessation of the east African rains

Weather and Climate Extremes 21 (2018) 27-35

D MacLeod

© 2018 The Author Advanced warning of delayed onset or early cessation of the rainy seasons would be extremely valuable information for farmers in east Africa and is a common request from regional stakeholders. Such warnings are beginning to be provided, however forecast skill for these metrics has not been demonstrated. Here the forecast skill of the ECMWF seasonal hindcasts is evaluated for onset and cessation forecasts over east Africa. Correlation of forecast with observed long rains anomalies only above a 95% statistical significance level for a small part of the domain, whilst short rains are significance a large part of the region. The added value of updating the forecast outlook with the extended range 46 day forecast is assessed and this gives a small improvement. For the short rains detection of early onset is better near the coast, and late onset detection is better over northwestern Kenya. During exceptionally dry years the method to detect onset and cessation fails. Using this as a definition of a failed season, the model shows significant skill at anticipating long rains season failure in the northwest of Kenya, and short rains failure in Somalia and northeast Kenya. In addition the strength of the correlation between long rains cessation and seasonal total is shown to be particularly weak in observations but too strong in the hindcasts. Predictability of onset and cessation for both seasons appears to arise primarily from the link with seasonal total and it is unclear that the model represents variability in onset and cessation beyond this. This has important implications for operational forecasting: any forecast of season timing which is ‘inconsistent’ with seasonal total (e.g. an early onset but low total rainfall) must be treated with caution. Finally links with zonal winds are investigated. Late onset is correlated with easterly (westerly) anomalies during the long (short) rains, though the strength and spatial pattern of the relationship is not well represented in the model. Early cessation is correlated with easterly anomalies in both seasons for most of the region in both observations and hindcasts. However for the long rains the sign of the correlation is reversed along the coast in observations but not in the hindcasts. These dynamical inconsistencies may have a negative impact on forecast skill and have the potential to inform process-based development of climate modelling in the region.

Grand European and Asian-Pacific multi-model seasonal forecasts: maximization of skill and of potential economical value to end-users

Climate Dynamics (2017) 1-20

A Alessandri, MD Felice, F Catalano, JY Lee, B Wang, DY Lee, JH Yoo, A Weisheimer

© 2017 Springer-Verlag GmbH Germany Multi-model ensembles (MMEs) are powerful tools in dynamical climate prediction as they account for the overconfidence and the uncertainties related to single-model ensembles. Previous works suggested that the potential benefit that can be expected by using a MME amplifies with the increase of the independence of the contributing Seasonal Prediction Systems. In this work we combine the two MME Seasonal Prediction Systems (SPSs) independently developed by the European (ENSEMBLES) and by the Asian-Pacific (APCC/CliPAS) communities. To this aim, all the possible multi-model combinations obtained by putting together the 5 models from ENSEMBLES and the 11 models from APCC/CliPAS have been evaluated. The grand ENSEMBLES-APCC/CliPAS MME enhances significantly the skill in predicting 2m temperature and precipitation compared to previous estimates from the contributing MMEs. Our results show that, in general, the better combinations of SPSs are obtained by mixing ENSEMBLES and APCC/CliPAS models and that only a limited number of SPSs is required to obtain the maximum performance. The number and selection of models that perform better is usually different depending on the region/phenomenon under consideration so that all models are useful in some cases. It is shown that the incremental performance contribution tends to be higher when adding one model from ENSEMBLES to APCC/CliPAS MMEs and vice versa, confirming that the benefit of using MMEs amplifies with the increase of the independence the contributing models. To verify the above results for a real world application, the Grand ENSEMBLES-APCC/CliPAS MME is used to predict retrospective energy demand over Italy as provided by TERNA (Italian Transmission System Operator) for the period 1990–2007. The results demonstrate the useful application of MME seasonal predictions for energy demand forecasting over Italy. It is shown a significant enhancement of the potential economic value of forecasting energy demand when using the better combinations from the Grand MME by comparison to the maximum value obtained from the better combinations of each of the two contributing MMEs. The above results demonstrate for the first time the potential of the Grand MME to significantly contribute in obtaining useful predictions at the seasonal time-scale.

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.

Ensemble sensitivity analysis of Greenland blocking in medium-range forecasts


T Parker, T Woollings, A Weisheimer

Changes in European wind energy generation potential within a 1.5 degrees C warmer world


JS Hosking, D MacLeod, T Phillips, CR Holmes, P Watson, EF Shuckburgh, D Mitchell

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


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

Stochastic representations of model uncertainties at ECMWF: state of the art and future vision


M Leutbecher, S-J Lock, P Ollinaho, STK Lang, G Balsamo, P Bechtold, M Bonavita, HM Christensen, M Diamantakis, E Dutra, S English, M Fisher, RM Forbes, J Goddard, T Haiden, RJ Hogan, S Juricke, H Lawrence, D MacLeod, L Magnusson, S Malardel, S Massart, I Sandu, PK Smolarkiewicz, A Subramanian, F Vitart, N Wedi, 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

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


AC Subramanian, TN Palmer

The Dynamical Influence of the Atlantic Multidecadal Oscillation on Continental Climate

JOURNAL OF CLIMATE 30 (2017) 7213-7230

CH O'Reilly, T Woollings, L Zanna

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


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