Publications


Can bias correction and statistical downscaling methods improve the skill of seasonal precipitation forecasts?

Climate Dynamics Springer Verlag 50 (2017) 1161-1176

R Manzanas, A Lucero, A Weisheimer, JM Gutiérrez

Statistical downscaling methods are popular post-processing tools which are widely used in many sectors to adapt the coarse-resolution biased outputs from global climate simulations to the regional-to-local scale typically required by users. They range from simple and pragmatic Bias Correction (BC) methods, which directly adjust the model outputs of interest (e.g. precipitation) according to the available local observations, to more complex Perfect Prognosis (PP) ones, which indirectly derive local predictions (e.g. precipitation) from appropriate upper-air large-scale model variables (predictors). Statistical downscaling methods have been extensively used and critically assessed in climate change applications; however, their advantages and limitations in seasonal forecasting are not well understood yet. In particular, a key problem in this context is whether they serve to improve the forecast quality/skill of raw model outputs beyond the adjustment of their systematic biases. In this paper we analyze this issue by applying two state-of-the-art BC and two PP methods to downscale precipitation from a multimodel seasonal hindcast in a challenging tropical region, the Philippines. To properly assess the potential added value beyond the reduction of model biases, we consider two validation scores which are not sensitive to changes in the mean (correlation and reliability categories). Our results show that, whereas BC methods maintain or worsen the skill of the raw model forecasts, PP methods can yield significant skill improvement (worsening) in cases for which the large-scale predictor variables considered are better (worse) predicted by the model than precipitation. For instance, PP methods are found to increase (decrease) model reliability in nearly 40% of the stations considered in boreal summer (autumn). Therefore, the choice of a convenient downscaling approach (either BC or PP) depends on the region and the season.


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


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.


Transforming climate model output to forecasts of wind power production: how much resolution is enough?

Meteorological Applications Wiley 25 (2017) 1–10-

D Macleod, V Torralba, F Doblas-Reyes, M Davis

Wind power forecasts are useful tools for power load balancing, energy trading and wind farm operations. Long-range monthly-to-seasonal forecasting allows prediction of departures from average weather conditions beyond traditional weather forecast timescales, months in advance. However it has not yet been demonstrated how these forecasts can be optimally transformed to wind power. The predictable part of a seasonal forecast is for longer monthly averages, not daily averages, but to use monthly averages misses information on variability. To investigate, here we build a model relating average weather conditions to average wind power output, based on the relationship between instantaneous wind speed and power production and incorporating fluctuations in air density due to temperature and wind speed variability. Observed monthly average power output from UK stations is used to validate the model and to investigate the optimal temporal resolution for the data used to drive the model. Multiple simulations of wind power are performed based on reanalysis data, making separate simulations based on monthly, daily and sub-daily averages, using a distribution defined by the mean across the period to incorporate information on variability. Basing the simulation on monthly averages alone is sub-optimal: using daily average winds gives the highest correlation against observations. No improvement over this is gained by using sub-daily averages or including temperature variability. This signifies that to transform seasonal forecasts to wind power a compromise must be made between using the daily averages with debatable skill and the more predictable monthly averages, losing information on day-to-day variability.


The impact of stochastic physics on tropical rainfall variability in global climate models on daily to weekly time scales

Journal of Geophysical Research: Atmospheres American Geophysical Union 122 (2017) 5738–5762-

P Watson, J Berner, S Corti, P Davini, J von Hardenberg, C Sanchez, A Weisheimer, TN Palmer

Many global atmospheric models have too little precipitation variability in the tropics on daily to weekly time scales, and also poor representation of tropical precipitation extremes associated with intense convection. Stochastic parameterisations have the potential to mitigate this problem by representing unpredictable subgrid variability that is left out of deterministic models. We evaluate the impact on the statistics of tropical rainfall of two stochastic schemes, the stochastically perturbed parameterization tendency scheme (SPPT) and stochastic kinetic energy backscatter scheme (SKEBS), in three climate models: EC-Earth, the Met Office Unified Model and the Community Atmosphere Model, version 4 (CAM4). The schemes generally improve the statistics of simulated tropical rainfall variability, particularly by increasing the frequency of heavy rainfall events, reducing its persistence and increasing the high-frequency component of its variability. There is a large range in the size of the impact between models, with EC-Earth showing the largest improvements. The improvements are greater than those obtained by increasing horizontal resolution to ∼20km. Stochastic physics also strongly affects projections of future changes in the frequency of extreme tropical rainfall in EC-Earth. This indicates that small-scale variability that is unresolved and unpredictable in these models has an important role in determining tropical climate variability statistics. Using these schemes, and improved schemes currently under development, is therefore likely to be important for producing good simulations of tropical variability and extremes in the present day and future.


Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in climate simulations

Geoscientific Model Development European Geosciences Union (0)

P Davini, J von Hardenberg, S Corti, HM Christensen, S Juricke, A Subramanian, PAG Watson, A Weisheimer, TN Palmer

<jats:p>&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Abstract.&amp;lt;/strong&amp;gt; The Climate SPHINX (Stochastic Physics HIgh resolutioN eXperiments) project is a comprehensive set of ensemble simulations aimed at evaluating the sensitivity of present and future climate to model resolution and stochastic parameterisation. The EC-Earth Earth-System Model is used to explore the impact of stochastic physics in a large ensemble of 30-year climate integrations at five different atmospheric horizontal resolutions (from 125&amp;amp;#8201;km up to 16&amp;amp;#8201;km). The project includes more than 120 simulations in both a historical scenario (1979&amp;amp;#8211;2008) and a climate change projection (2039&amp;amp;#8211;2068), together with coupled transient runs (1850&amp;amp;#8211;2100). A total of 20.4&amp;amp;#8201;million core hours have been used, made available from a single year grant from PRACE (the Partnership for Advanced Computing in Europe), and close to 1.5&amp;amp;#8201;PBytes of output data have been produced on SuperMUC IBM Petascale System at the Leibniz Supercomputing Center (LRZ) in Garching, Germany. About 140&amp;amp;#8201;TBytes of post-processed data are stored on the CINECA supercomputing center archives and are freely accessible to the community thanks to an EUDAT Data Pilot project. This paper presents the technical and scientific setup of the experiments, including the details on the forcing used for the simulations performed, defining the SPHINX v1.0 protocol. In addition, an overview of preliminary results is given: an improvement in the simulation of Euro-Atlantic atmospheric blocking following resolution increases is observed. It is also shown that including stochastic parameterisation in the low resolution runs helps to improve some aspects of the tropical climate &amp;amp;#8211; specifically the Madden-Julian Oscillation and the tropical rainfall variability. These findings show the importance of representing the impact of small scale processes on the large scale climate variability either explicitly (with high resolution simulations) or stochastically (in low resolution simulations).&amp;lt;/p&amp;gt; </jats:p>


Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in the EC-Earth global climate model

Geoscientific Model Development Copernicus Publications 10 (2017) 1383-1402

P Davini, J von Hardenburg, S Corti, HM Christensen, S Juricke, A Subramanian, PAG Watson, A Weisheimer, TN Palmer

The Climate SPHINX (Stochastic Physics HIgh resolutioN eXperiments) project is a comprehensive set of ensemble simulations aimed at evaluating the sensitivity of present and future climate to model resolution and stochastic parameterisation. The EC-Earth Earth system model is used to explore the impact of stochastic physics in a large ensemble of 30-year climate integrations at five different atmospheric horizontal resolutions (from 125 up to 16 km). The project includes more than 120 simulations in both a historical scenario (1979–2008) and a climate change projection (2039–2068), together with coupled transient runs (1850–2100). A total of 20.4 million core hours have been used, made available from a single year grant from PRACE (the Partnership for Advanced Computing in Europe), and close to 1.5 PB of output data have been produced on Super- MUC IBM Petascale System at the Leibniz Supercomputing Centre (LRZ) in Garching, Germany. About 140 TB of postprocessed data are stored on the CINECA supercomputing centre archives and are freely accessible to the community thanks to an EUDAT data pilot project. This paper presents the technical and scientific set-up of the experiments, including the details on the forcing used for the simulations performed, defining the SPHINX v1.0 protocol. In addition, an overview of preliminary results is given. An improvement in the simulation of Euro-Atlantic atmospheric blocking following resolution increase is observed. It is also shown that including stochastic parameterisation in the low-resolution runs helps to improve some aspects of the tropical climate – specifically the Madden–Julian Oscillation and the tropical rainfall variability. These findings show the importance of representing the impact of small-scale processes on the large-scale climate variability either explicitly (with highresolution simulations) or stochastically (in low-resolution simulations).


Introducing independent patterns into the Stochastically Perturbed Parametrisation Tendencies (SPPT) scheme

Quarterly Journal of the Royal Meteorological Society Wiley 143 (2017) 2168-2181

HM Christensen, S-J Lock, I Moroz, TN Palmer

The Stochastically Perturbed Parametrisation Tendencies (SPPT) scheme is used at weather and climate forecasting centres worldwide to represent model uncertainty that arises from simplifications involved in the parametrisation process. It uses spatio-temporally correlated multiplicative noise to perturb the sum of the parametrised tendencies. However, SPPT does not distinguish between different parametrisation schemes, which do not necessarily have the same error characteristics. A generalisation to SPPT is proposed, whereby the tendency from each parametrisation scheme can be perturbed using an independent stochastic pattern. This acknowledges that the forecast errors arising from different parametrisations are not perfectly correlated. Two variations of this ‘independent SPPT’ (iSPPT) approach are tested in the Integrated Forecasting System (IFS). The first perturbs all parametrised tendencies independently while the second groups tendencies before perturbation. The iSPPT schemes lead to statistically significant improvements in forecast reliability in the tropics in medium range weather forecasts. This improvement can be attributed to a large, beneficial increase in ensemble spread in regions with significant convective activity. The iSPPT schemes also lead to improved forecast skill in the extra tropics for a set of cases in which the synoptic initial conditions were more likely to result in European ‘forecast busts’. Longer 13-month simulations are also considered to indicate the effect of iSPPT on the mean climate of the IFS.


Variability in seasonal forecast skill of Northern Hemisphere winters over the 20th century

Geophysical Research Letters American Geophysical Union 44 (2017) 5729-5738

C O'Reilly, J Heatley, D MacLeod, A Weisheimer, T Palmer, N Schaller, T Woollings

Seasonal hindcast experiments, using prescribed SSTs, are analysed for Northern Hemisphere winters from 1900-2010. Ensemble mean Pacific/North American index (PNA) skill varies dramatically, dropping towards zero during the mid-twentieth century, with similar variability in North Atlantic Oscillation (NAO) hindcast skill. The PNA skill closely follows the correlation between the observed PNA index and tropical Pacific SST anomalies. During the mid-century period the PNA and NAO hindcast errors are closely related. The drop in PNA predictability is due to mid-century negative PNA events, which were not forced in a predictable manner by tropical Pacific SST anomalies. Overall, negative PNA events are less predictable and seem likely to arise more from internal atmospheric variability than positive PNA events. Our results suggest that seasonal forecasting systems assessed over the recent 30-year period may be less skillful in periods, such as the mid-twentieth century, with relatively weak forcing from tropical Pacific SST anomalies.


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


Potential applications of subseasonal-to-seasonal (S2S) predictions

METEOROLOGICAL APPLICATIONS 24 (2017) 315-325

CJ White, H Carlsen, AW Robertson, RJT Klein, JK Lazo, A Kumar, F Vitart, EC de Perez, AJ Ray, V Murray, S Bharwani, D MacLeod, R James, L Fleming, AP Morse, B Eggen, R Graham, E Kjellstrom, E Becker, KV Pegion, NJ Holbrook, D McEvoy, M Depledge, S Perkins-Kirkpatrick, TJ Brown, R Street, L Jones, TA Remenyi, I Hodgson-Johnston, C Buontempo, R Lamb, H Meinke, B Arheimer, SE Zebiak


The dynamical influence of the Atlantic Multidecadal Oscillation on continental climate

Journal of Climate American Meteorological Society 30 (2017) 7213-7230

CH O’Reilly, T Woollings, L Zanna

The Atlantic multidecadal oscillation (AMO) in sea surface temperature (SST) has been shown to influence the climate of the surrounding continents. However, it is unclear to what extent the observed impact of the AMO is related to the thermodynamical influence of the SST variability or the changes in large-scale atmospheric circulation. Here, an analog method is used to decompose the observed impact of the AMO into dynamical and residual components of surface air temperature (SAT) and precipitation over the adjacent continents. Over Europe the influence of the AMO is clearest during the summer, when the warm SAT anomalies are interpreted to be primarily thermodynamically driven by warm upstream SST anomalies but also amplified by the anomalous atmospheric circulation. The overall precipitation response to the AMO in summer is generally less significant than the SAT but is mostly dynamically driven. The decomposition is also applied to the North American summer and the Sahel rainy season. Both dynamical and residual influences on the anomalous precipitation over the Sahel are substantial, with the former dominating over the western Sahel region and the latter being largest over the eastern Sahel region. The results have potential implications for understanding the spread in AMO variability in coupled climate models and decadal prediction systems.


Bitwise efficiency in chaotic models

Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences Royal Society 473 (2017) 20170144

T Palmer, P Düben, S Jeffress

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: Towards a new view of weather and climate models

Bulletin of the American Meteorological Society American Meteorological Society 98 (2017) 565–588-

J Berner, U Achatz, L Batté, M Colangeli, D Crommelin, L Bengtsson, A De la Cámara, HM Christensen, DRB Coleman, SI Dolaptchiev, C Penland, M Sakradzija, J-S Von Storch, A Weisheimer, PD Williams, CLE Franzke, P Friederichs, P Imkeller, H Järvinen, S Juricke, V Kitsios, F Lott, V Lucarini, S Mahajan, TN Palmer

The last decade has seen the success of stochastic parameterizations in short-term, medium-range, and seasonal forecasts: operational weather centers now routinely use stochastic parameterization schemes to represent model inadequacy better and to improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations not only provides better estimates of uncertainty, but it is also extremely promising for reducing long-standing climate biases and is relevant for determining the climate response to external forcing. This article highlights recent developments from different research groups that show that the stochastic representation of unresolved processes in the atmosphere, oceans, land surface, and cryosphere of comprehensive weather and climate models 1) gives rise to more reliable probabilistic forecasts of weather and climate and 2) reduces systematic model bias. We make a case that the use of mathematically stringent methods for the derivation of stochastic dynamic equations will lead to substantial improvements in our ability to accurately simulate weather and climate at all scales. Recent work in mathematics, statistical mechanics, and turbulence is reviewed; its relevance for the climate problem is demonstrated; and future research directions are outlined.


Universal continuous transition to turbulence in a planar shear flow

Journal of Fluid Mechanics Cambridge University Press 824 (2017)

M Chantry, LS Tuckerman, D Barkley

We examine the onset of turbulence in Waleffe flow - the planar shear flow between stress-free boundaries driven by a sinusoidal body force. By truncating the wall-normal representation to four modes, we are able to simulate system sizes an order of magnitude larger than any previously simulated, and thereby to attack the question of universality for a planar shear flow. We demonstrate that the equilibrium turbulence fraction increases continuously from zero above a critical Reynolds number and that statistics of the turbulent structures exhibit the power-law scalings of the (2 + 1)-D directed-percolation universality class.


A study of reduced numerical precision to make superparameterization more competitive using a hardware emulator in the OpenIFS model

Journal of Advances in Modeling Earth Systems Wiley (2017)

PD Düben, A Subramanian, A Dawson, TN Palmer

<p>The use of reduced numerical precision to reduce computing costs for the cloud resolving model of superparameterised simulations of the atmosphere is investigated. An approach to identify the optimal level of precision for many different model components is presented and a detailed analysis of precision is performed. This is non-trivial for a complex model that shows chaotic behaviour such as the cloud resolving model in this paper.</p> <br/> <p>results of the reduced precision analysis provide valuable information for the quantification of model uncertainty for individual model components. The precision analysis is also used to identify model parts that are of less importance thus enabling a reduction of model complexity. It is shown that the precision analysis can be used to improve model efficiency for both simulations in double precision and in reduced precision. Model simulations are performed with a superparametrised single-column model version of the OpenIFS model that is forced by observational datasets. A software emulator was used to mimic the use of reduced precision floating point arithmetic in simulations.</p>


Seasonal Climate Prediction: A New Source of Information for the Management of Wind Energy Resources

JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY 56 (2017) 1231-1247

V Torralba, FJ Doblas-Reyes, D MacLeod, I Christel, M Davis


Stochastic subgrid-scale ocean mixing: Impacts on low-frequency variability

Journal of Climate American Meteorological Society 30 (2017) 4997-5019

S Juricke, TN Palmer, L Zanna

In global ocean models, the representation of small-scale, high-frequency processes considerably influences the large-scale oceanic circulation and its low-frequency variability. This study investigates the impact of stochastic perturbation schemes based on three different subgrid-scale parameterizations in multidecadal ocean-only simulations with the ocean model NEMO at 1° resolution. The three parameterizations are an enhanced vertical diffusion scheme for unstable stratification, the Gent-McWilliams (GM) scheme, and a turbulent kinetic energy mixing scheme, all commonly used in state-of-the-art ocean models. The focus here is on changes in interannual variability caused by the comparatively high-frequency stochastic perturbations with subseasonal decorrelation time scales. These perturbations lead to significant improvements in the representation of low-frequency variability in the ocean, with the stochastic GM scheme showing the strongest impact. Interannual variability of the Southern Ocean eddy and Eulerian streamfunctions is increased by an order of magnitude and by 20%, respectively. Interannual sea surface height variability is increased by about 20%-25% as well, especially in the Southern Ocean and in the Kuroshio region, consistent with a strong underestimation of interannual variability in the model when compared to reanalysis and altimetry observations. These results suggest that enhancing subgrid-scale variability in ocean models can improve model variability and potentially its response to forcing on much longer time scales, while also providing an estimate of model uncertainty.

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