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Advanced Review Seasonal climate predictability and forecasting: status and prospects Francisco J. Doblas-Reyes, 1,2Javier García-Serrano, 2 Fabian Lienert, 2 Aida Pint ´ o Biescas 2 and Luis R. L. Rodrigues 2 Seasonal climate forecasts occupy an intermediate zone between weather forecasting and climate projections. They share with the numerical weather prediction the difficulty of initializing the simulations with a realistic state of the atmosphere and the need to periodically verify different aspects of their quality, while additionally are burdened by uncertainties in feedback processes that also play a central role in constraining climate projections. Seasonal predictions have to deal also with the challenge of initializing all the components of the climate system (ocean, sea ice, and land surface). The value of skilful seasonal forecasts is obvious for many societal sectors and is currently being included in the framework of developing climate services. Seasonal forecasts will in addition be valuable by increasing the acceptance of climate projections among the general public. This advanced-review article presents an overview of the state-of-the-art in global seasonal predictability and forecasting for climate researchers and discusses fundamental advances to increase forecast quality in the near future. The article concludes with a list of challenges where seasonal forecasting is expected to focus on in the near future. © 2013 John Wiley & Sons, Ltd. How to cite this article: WIREs Clim Change 2013, 4:245–268. doi: 10.1002/wcc.217 INTRODUCTION T he seasonal time scale deals with forecasts for future times ranging between more than two 2 weeks and slightly longer than 1 year. 1 Shorter time scales are dealt with by weather and sub-seasonal forecasting, while climate predictions for future times beyond the first forecast year and up to 30 years are covered by decadal prediction. For longer time scales, climate projections aim at estimating the possible evolutions of climate over several decades based on the past and future forcing scenarios. The boundaries between these climate scales are loose. As this article will illustrate, substantial overlap exists between them because, among several other reasons, many of the processes involved are common. Correspondence to: [email protected] 1 Instituci ` o Catalana de Recerca i Estudis Avanc ¸ats (ICREA), Barcelona, Spain 2 Institut Catal ` a de Ci` encies del Clima (IC3), Barcelona, Spain Conflict of interest: The authors have declared no conflicts of interest for this article. The feasibility of seasonal prediction largely rests on the existence of slow, and predictable, variations in the soil moisture, snow cover, sea-ice, and ocean surface temperature, 2 and how the atmosphere inter- acts and is affected by these boundary conditions. At seasonal time scales, the storage of heat and moisture by the ocean and the land and the presence or absence of snow and sea ice become important factors. The El Ni˜ no-Southern Oscillation (ENSO 3 ) is the main process that contributes to the forecast quality on sea- sonal time scales. 4–7 A warm sea surface temperature anomaly (SSTA) in the tropical Pacific Ocean leads to increased heat flux from the ocean to the atmosphere which, if sufficiently large can alter the atmospheric boundary layer and ultimately change the structure of the rainfall and the release of latent heat in the tropo- sphere. The extra latent heat release will impact the atmospheric circulation leading to climatic anomalies in remote regions of the globe. As climate is driven by a coupled system, the atmospheric response itself interacts with and changes the sea surface temperature (SST). The direct coupling of the different components of the climate system is in fact slightly more complex, Volume 4, July/August 2013 © 2013 John Wiley & Sons, Ltd. 245

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Advanced Review

Seasonal climate predictability andforecasting: status and prospectsFrancisco J. Doblas-Reyes,1,2∗ Javier García-Serrano,2 Fabian Lienert,2

Aida Pinto Biescas2 and Luis R. L. Rodrigues2

Seasonal climate forecasts occupy an intermediate zone between weatherforecasting and climate projections. They share with the numerical weatherprediction the difficulty of initializing the simulations with a realistic state ofthe atmosphere and the need to periodically verify different aspects of theirquality, while additionally are burdened by uncertainties in feedback processesthat also play a central role in constraining climate projections. Seasonal predictionshave to deal also with the challenge of initializing all the components of theclimate system (ocean, sea ice, and land surface). The value of skilful seasonalforecasts is obvious for many societal sectors and is currently being included inthe framework of developing climate services. Seasonal forecasts will in additionbe valuable by increasing the acceptance of climate projections among the generalpublic. This advanced-review article presents an overview of the state-of-the-art inglobal seasonal predictability and forecasting for climate researchers and discussesfundamental advances to increase forecast quality in the near future. The articleconcludes with a list of challenges where seasonal forecasting is expected to focuson in the near future. © 2013 John Wiley & Sons, Ltd.

How to cite this article:WIREs Clim Change 2013, 4:245–268. doi: 10.1002/wcc.217

INTRODUCTION

The seasonal time scale deals with forecasts forfuture times ranging between more than two

2 weeks and slightly longer than 1 year.1 Shorter timescales are dealt with by weather and sub-seasonalforecasting, while climate predictions for future timesbeyond the first forecast year and up to 30 years arecovered by decadal prediction. For longer time scales,climate projections aim at estimating the possibleevolutions of climate over several decades based onthe past and future forcing scenarios. The boundariesbetween these climate scales are loose. As this articlewill illustrate, substantial overlap exists between thembecause, among several other reasons, many of theprocesses involved are common.

∗Correspondence to: [email protected] Catalana de Recerca i Estudis Avancats (ICREA),Barcelona, Spain2Institut Catala de Ciencies del Clima (IC3), Barcelona, Spain

Conflict of interest: The authors have declared no conflicts ofinterest for this article.

The feasibility of seasonal prediction largely restson the existence of slow, and predictable, variationsin the soil moisture, snow cover, sea-ice, and oceansurface temperature,2 and how the atmosphere inter-acts and is affected by these boundary conditions. Atseasonal time scales, the storage of heat and moistureby the ocean and the land and the presence or absenceof snow and sea ice become important factors. TheEl Nino-Southern Oscillation (ENSO3) is the mainprocess that contributes to the forecast quality on sea-sonal time scales.4–7 A warm sea surface temperatureanomaly (SSTA) in the tropical Pacific Ocean leads toincreased heat flux from the ocean to the atmospherewhich, if sufficiently large can alter the atmosphericboundary layer and ultimately change the structure ofthe rainfall and the release of latent heat in the tropo-sphere. The extra latent heat release will impact theatmospheric circulation leading to climatic anomaliesin remote regions of the globe. As climate is drivenby a coupled system, the atmospheric response itselfinteracts with and changes the sea surface temperature(SST). The direct coupling of the different componentsof the climate system is in fact slightly more complex,

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as it will be explained in the following. In addition, theobserved evolution of temperature and other climatevariables at the seasonal time scale can also be consid-ered as externally forced low-frequency variability dueto human-induced changes in greenhouse gas (GHG)and aerosol concentrations, land-use changes as wellas natural variations in solar activity and volcanicaerosol, superimposed on the natural variability ofthe system. Hence, seasonal prediction is not just aninitial-condition problem of the internal variability ofthe whole climate system, but also a boundary prob-lem similar to what is currently dealt with in decadalprediction8 and anthropogenic climate change.9

Both statistical10,11 and dynamical12,13 meth-ods are employed to formulate seasonal predictions,although mixed methodologies are also employedbecause statistical postprocessing of the dynamicalpredictions is required by the users.14,15 In the caseof dynamical prediction, the initial conditions areusually obtained through data assimilation, a wayof optimally combining short-range predictions withobservations to obtain an optimal estimate of the stateof the climate system. In this way, the ocean anomaliesassociated with ENSO events and other ocean vari-ability, soil moisture, snow, and ice cover can be takeninto account when initializing the predictions.5,16

Unfortunately, less information is available aboutthe state of the ocean, the sea-ice, snow, and landthan about the atmosphere,17,18 and often the pre-dictions are penalized by a lack of understanding ofthe relevant physical processes and the interactionsamong the subsystems.19 Owing to many differentreasons, among them the initial-condition uncertainty,model inadequacy, and lack of appropriate computa-tional resources,20,21 the ability to make predictionson time scales longer than 2 weeks is still limited.22

Current climate prediction systems can provide accu-rate information of the SSTAs associated with ENSOand other tropical phenomena with lead times up to9 months,23–25 although the spread among the modelsis substantial, sometimes even differing in the sign ofthe tropical Pacific SSTA.6,26,27

SEASONAL PREDICTABILITY

Climate predictability is the extent to which aninformative prediction is possible if an optimumprocedure is used. Seasonal predictability estimateswere originally based on signal-to-noise ratioestimates that assumed a perfect knowledge ofocean and/or land conditions, where the varianceof climate variables related to the lower boundaryforcing is the signal, the portion of the climatevariance related to atmospheric internal dynamics is

the noise, and the ratio of the two represents onepossible measure of predictability.28–30 Such studiesusually lead to estimates of predictability that farexceed the actual forecast quality achieved with bothprevious and current systems because they assumethat the boundary conditions are predicted perfectlyand assume that feedbacks between the atmosphereand the other subsystems do not contribute to theforecast quality, while the interaction between theatmosphere, sea-ice, land, and ocean has been shownto be important.19,31,32

The predictability of the surface air temperatureand precipitation patterns, which are the variablesthat play the most important role on humanactivities, is linked to our ability to predict theboundary conditions of the climate system suchas the SST, especially in the tropics.33,34 ENSOis the most important source of predictability atseasonal time scales. The large spatial shifts intropical Pacific rainfall associated with ENSO leadto large-scale changes in global circulation andprecipitation,35 which make ENSO a primary sourcefor predictability in remote regions. Figure 1 showsthat the linear association of an ENSO index withthe local precipitation is high in many regions, evenin the extratropics, although the relationship hasa strong seasonality. ENSO impacts the circulationand precipitation patterns in the Pacific Oceanitself and in several other regions around theglobe.36 Besides, observational and modeling studies37

demonstrated that there are relationships betweenseasonal variability observed in the tropical oceansand the variability of the extratropical atmosphere.Therefore, the assessment of the skill of ENSO SSTpredictions is a fundamental requirement for anyseasonal prediction system.25

ENSO events last approximately 1 year and thetime between events varies between 2 and 7 years.The predictability of ENSO is largely determined bythe life cycle of individual events, which dependson the memory or inertia associated with upperocean heat content and coupled ocean–atmosphereinteractions.40 The equatorial Pacific wind stress andheat content anomalies are not in equilibrium.41

This makes their relationship the primary mechanismdriving the ENSO predictability, where the quasi-stochastic atmospheric forcing plays a key role.42,43

Other tropical ocean basins such as the tropicalSST over the Atlantic and Indian Oceans also havea major impact on the climate variability of thesurrounding regions.33 For instance, the SSTA over thetropical Atlantic region directly influences the positionof the Intertropical Convergence Zone (ITCZ), whichplays a role on the precipitation patterns over

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WIREs Climate Change Seasonal climate predictability and forecasting

(a) (b)

(c) (d)

FIGURE 1 | Correlation between the ERSST38 SST Nino 3.4 index (average temperature over 5◦N–5◦S, 170◦–120◦W) and the GPCCv539 griddedprecipitation over the period 1960–2009. (a) March to May, (b) June to August, (c) September to November, and (d) December to February.

northeastern Brazil and western Africa, while theIndian Ocean SSTA have impacts over eastern parts ofthe African continent. Another interesting feature isthat the SST variability of the Atlantic and Indianbasins is somehow linked to that of the tropicalPacific.33,44 As in the tropical Pacific, modes ofvariability exist in the tropical Atlantic and Indianbasins. For instance, the Atlantic zonal mode orAtlantic Nino3 seems to be strongly linked to ENSOand, hence, its predictability might be determinedby the Pacific variability and, at the same time,affect the occurrence of ENSO events,45 while theIndian Ocean Dipole46 can develop some variabilityindependent from ENSO and could be predictable upto 6 months into the future.47 Ocean climate indicesare an important tool used in operational seasonalprediction as they can be linked to major patterns ofclimate variability.

Beyond the tropical oceans, there are othersources of seasonal predictability. Relatively long-lived (up to 2 months) atmospheric anomalies canarise from stratospheric disturbances. In areas such

as Europe in winter, experiments suggest that theinfluence of stratospheric variability on surfaceair temperatures can be important.48 Seasonalpredictability is known to arise from interactionsbetween the troposphere and the stratosphereassociated with the quasi-biennial oscillation (QBO49)and sudden stratospheric warmings (SSW50,51).

The realistic initialization of the soil componentin models can increase the accuracy of precipitationand temperature predictions at intraseasonal andseasonal time scales. Early studies recognized theinfluence of the land component of the climatesystem, which includes soil moisture and snowcover.52 The preconditioning of extreme summertemperatures by the preceding precipitation has beenrecently documented,53,54 which suggests that initial-condition soil moisture information is paramountfor correctly predicting summer temperatures overland. The question of whether the realisticinitialization of soil moisture could quantitativelyimprove dynamical forecasts of surface temperatureand precipitation on the sub-seasonal to seasonal

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time scales was investigated in a number ofrecent studies.55–59 In particular, the Global Land-Atmosphere Coupling Experiment (GLACE) wasa coordinated, international numerical modelingexperiment designed to isolate the coupling ofsoil moisture with the atmosphere. The first phase(GLACE-1) focused on predictability and determinedthe geographical regions where soil moisture exertsa significant influence on surface air temperatureor precipitation (hot spots) of land-atmospherecoupling.60 The second phase (GLACE-2) focusedon forecast quality, and assessed the impact ofaccurate soil moisture initialization on actual skillusing a multimodel approach. The multimodel meanin GLACE-2 indicates a significant soil moisturecontribution to surface temperature forecast skillin summer, even at a lead time of 2 monthsover North America, equatorial Africa, and SouthAmerica.56,57,59 Snow covered land is another slowlyvarying component of the climate system. Its relevancecomes from the remarkable insulating and reflectingproperties of snow61,62 and from the role of the snowpack in the hydrological cycle. Local effects of snowon surface air temperatures have been documentedin the spring.63,64 In addition to its effect on localmeteorological conditions, many observational andmodel studies in recent years have indicated thatthe snow pack can impact large-scale circulationpatterns such as the North Atlantic Oscillation(NAO65,66) and North Pacific variability,67 althoughthis relationship has proven nonstationary.68 Whenan extensive snow pack prevails over Eurasia, thecolder surface influences the propagation of planetary-scale wave trains downstream of the Eurasianlandmass,69 and their upward propagation in thetroposphere and even the stratosphere.70 To maximizethe impact of land feedbacks on prediction quality,the mechanisms underlying the land–atmospherecoupling (e.g., evaporation, boundary layer dynamics,and convection) need to be understood and wellrepresented in forecast systems.

Utilizing the increasing knowledge of sea-iceconditions is an untapped and unknown reservoir ofseasonal predictability. Current seasonal predictionsystems consider sea-ice in various ways, rangingfrom using prescribed sea-ice climatologies25 tothermodynamic interactive sea-ice models initializedfrom an observed state.71 While some analyses72

suggested a longer predictability of the sea-ice areawith an above-average initial sea ice thicknessthan with an initial below-average one in acoupled climate model, others73 estimated that thepredictability of Arctic sea-ice beyond 3 years isdominated by the external forcing rather than the

initial conditions. There have been suggestions thatwinter preconditioning and anomalous spring sea-ice extent could influence Northern Hemispherecirculation.74,75 Effects of sea-ice on the locationof the summertime North Atlantic storm track76

and the winter circulation77 have been suggested.However, it is not yet known if these effects arerobust and in particular whether the initializationof sea-ice can in practice improve predictions ofseasonal climate variability. To gain understandingof the potential impact of sea-ice initialization onthe atmosphere, it is necessary to perform modelcomparisons with and without sea-ice initialization,in a similar way as the soil-moisture experimentsmentioned above. Figure 2 illustrates an exampleof the impact on the surface air temperature inboreal autumn of initializing the sea ice with realisticconditions when compared to a similar experimentinitialized with climatological initial conditions. Theinitialization shows a local impact in the Arctic.However, the differences between the two experimentsare small, while no substantial differences in skillcan be seen in the atmospheric circulation. Morerelevant results should be available soon as part ofthe multimodel experiment coordinated by the sea-Ice Historical Forecast Project (IceHFP), which ispart of the Climate-system Historical Forecast Project(CHFP).

Predictability can also be originated by long-term changes in atmospheric composition suchas increases in GHG and aerosol concentrationsand land cover change, which are an importantsource of nonstationarity of the climate system.Besides, unusual, but high-impact events such asvolcanic eruptions can cause a sudden change tothe atmospheric aerosol composition and, hence, theradiative balance,80 whereas solar variability modifiesultraviolet irradiance, which might affect atmosphericcirculation.81

SEASONAL FORECASTING METHODS

Two types of prediction methods, those based oneither statistical–empirical approaches or on process-based dynamical models, are described in thisarticle. Both methods are complementary becauseadvances in statistical prediction are often associatedwith enhanced understanding, which usually leadsto improved dynamical prediction, and vice versa.Both techniques rely heavily on the availability ofobservations, although with different constraints. Itis important to consider that although systematiccomparisons between the two are useful, it hasbeen shown82 that the best forecasts from the user

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(a)

(b)

FIGURE 2 | Correlation of the ensemble mean for boreal autumn (September to November) surface air temperature of 3-month lead hindcasts(start date first of May) performed with the EC-Earth2 prediction system78 over the period 1991–2005 where the sea-ice initial conditions are takenfrom the restart files of a sea-ice simulation forced by a specified forcing from reanalyses (a) with interannual variability and (b) taking theclimatological mean. The reference data are taken from ERAInterim.79 Dots are used for the 95% confidence significant correlations, where atwo-sided test is applied using a bootstrap method with a sample of 1000.

perspective can ultimately be obtained by optimallycombining all predictions available to provide betterguidance for decision support.

Owing to the chaotic nature of the climatesystem and the inadequacy of current forecast systems,quantifying forecast uncertainty plays an importantrole in climate forecasting.83 The unavoidableuncertain character of climate prediction forcesclimate forecasts to be formulated in a probabilisticway.84,85 Dealing with uncertainty helps decisionmakers with better decisions on whether or not to takeany action given the probability forecast of an event.However, the probabilistic formulation requires an

appropriate assessment of, besides the different aspectsassociated with forecast accuracy, how reliable (i.e.,whether the forecast uncertainty estimate is accurate)the forecasts are.86

All seasonal forecasts, as any other forecast,have to be systematically compared to a reference,preferably observations, in a multifaceted processknown as forecast quality assessment.87 Most effortsto estimate seasonal forecast quality are relativelyrecent and involve the analysis of predictionsfrom diverse forecast systems for a similar timeperiod.6,26,27,82,88,89 The multifaceted nature offorecast quality dictates that no single metric is

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sufficiently comprehensive to single-out the bestforecast system.90 Forecast quality is fundamental tothe prediction problem because a prediction has novalue without an estimate of its quality based on pastperformance.

Empirical PredictionA simple seasonal prediction could be obtained byjust persisting the initial observed SSTA. This alreadygives a fair degree of skill relative to climatology forthe first few forecast months, so that practical workcould be done even when dynamical prediction ofSST was still in its infancy. A simple example of thisapproach is illustrated in Figure 3(a), where a linearregression-based model is used to predict the averageSST in the western tropical Indian Ocean. A number ofmeasures of forecast quality are also displayed. Moresophisticated statistical methods are still widely usedto perform skillful seasonal predictions and, in somecases, tend to have a forecast quality comparableto those of dynamical models.91,92 Some methodsuse subsurface ocean information,93 which containspredictive signals that surface months later.

Statistical forecasts also have an advantageover other methods in that continuously acquiredknowledge obtained from data analysis of climatevariability can be easily applied. However, applicationof the statistical approach to long-lead predictionrequires careful use of conventional statisticalmodeling techniques, in particular, due to therelatively short history of the observed database andthe existence of long-term changes in a nonstationaryclimate system. One notable area of success wasthe relatively strong relationship between tropicalAtlantic SSTs and March-to-May rainfall in theNordeste region of Brazil97,98 where both empiricaland dynamical predictions are competitive in terms ofskill.

Statistical approaches have also been used topredict variables over extra-tropical regions.99–101

In higher latitudes, the autumn Eurasian snowcover extent or its tendency has been used asa predictor in statistical prediction models.102,103

Spectacularly skilful seasonal predictions have beenmade for precipitation over the Iberian Peninsula andScandinavia, two regions where dynamical methodsshow very low skill. At even higher latitudes, sea-iceprediction becomes the main target at seasonal timescales. The first attempts at producing sea-ice climatepredictions relied on statistical methods that used thepersistence of seasonal anomalies.104,105 However,with rapid changes occurring in the Arctic climate,the relations between the sea-ice variables and their

predictors do not necessarily hold,106 hence the needfor robust dynamical prediction systems.

Dynamical Seasonal PredictionFollowing observational and theoretical results, theuse of dynamical models of the climate systemwas explored beginning in the 1980s to predictENSO events. The relationships between the tropicaloceans and the extratropical atmosphere supportedthe idea that predictions of atmospheric variablescould be made using coupled ocean–atmospheresystems. On the basis of this effort, the intermediatecomplexity Cane-Zebiak model was the first onethat predicted the onset of an ENSO event.107

General circulation models (GCMs), which representour knowledge about the dynamics of the climatesystem in the most complete form possible, wereused only later because computer resources limitedthe resolution (i.e., the size of the processes thatcould be resolved explicitly in the simulation) ofthe models. At the same time, the knowledgeand experience to parameterize the sub-grid scaleprocesses that could not be represented explicitlywas less developed than today. Initial predictionattempts with GCMs regionally coupled in the tropicalPacific108 were followed by the use of globally-coupledGCMs.109–111 Simultaneously, operational forecastingcenters started issuing experimental seasonal forecastsbased on coupled GCMs.13,112

The technical complexity of dynamical seasonalprediction motivated the existence of two typesof dynamical seasonal forecast systems. In tier-twosystems, seasonal predictions are performed using onlyan atmospheric model with prescribed SST boundaryconditions, which are previously predicted with acoupled dynamical or a statistical model. A tier-one system uses a coupled global model, where allsubsystems interact simultaneously. Tier-one systemshave a relatively higher seasonal predictive skillthan tier-two systems113 and also tend to performbetter than simple statistical models over the tropicaloceans.4,91,94

Both past and current prediction systems haverelatively large errors in their representation ofthe mean climate, the climate variability, and theirinteraction. These systematic errors are indicativeof problems in the model formulations. It hasbeen suggested that systems with larger systematicerrors tend to have lower forecast quality.114 Someclassic examples of these errors include (1) the so-called double ITCZ problem, (2) the excessivelystrong equatorial cold tongue, (3) weak or incoherentintraseasonal variability, (4) failure to represent the

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(a) (b)

(c) (d)

FIGURE 3 | Monthly predicted anomalies of the average western tropical Indian Ocean SST for (a) a statistical model based on linearregression,94 (b) an 11-member ensemble from ECMWF System 3,13 (c) a 24-member ensemble from NCEP CFSv2,18 and (d) the forecast assimilationcombination of the dynamical systems using the statistical system as a prior.94 The predictions are for the target month of October with a lead time of5 months. The plots show the reference values (HadSST1.1,95 black solid line), the mean predicted values (red solid line), the 95% predicted interval(gray area) and the climatological value of November (black dashed line). The correlation with the reference of the mean prediction, the Brier skillscore and its reliability and resolution components for dichotomous events of SST anomalies exceeding the climatological median and the upperquartile are shown in the upper left corner. The statistical model was trained in forecast mode, where the first set of parameters were estimated forthe period 1951 to 1981 (the first training period), and the predictions were performed for the target years 1982–2010, extending the training periodby 1 year at a time mimicking an operational context.96

multiscale organization of tropical convection, and(5) poorly represented cloud processes, particularlylow-level stratus. Systematic errors, which in mostcases are common to those identified in models usedin climate-change experiments,115 have both regionaland global impacts.

Figure 4 shows an example of the systematicerror of the tropical Atlantic SST and the WestAfrican summer precipitation as a function of thestart date, which includes predictions starting onthe first of May, February, and November of theprevious year. The seasonal forecast system used inthe example tends to underestimate the precipitationin the Sahel and overestimate it in the Guinean region.The systematic error is different depending on the timeof the year the simulations are initialized in, beinglarger when the forecast time (i.e., the time betweenthe start date and the beginning of the monsoonseason) increases. The increase of the systematic errorwith the forecast time illustrates the dynamical modeldrift, i.e., the tendency of the predictions to evolvefrom the observed climate corresponding to the initial

conditions to the dynamical model climate. The natureof the drift is more obvious in the equatorial AtlanticSSTs, which are directly associated with the behaviorof the Guinean coast precipitation. The equatorialboreal summer cold tongue is adequately representedwhen the predictions are started the first of May, buta warm systematic error appears when they start inFebruary and it is even more obvious when they startin November the previous year.

The origin of the systematic error should befound in the simplification of the fluid dynamicequations required to solve them numerically, thelimited spatial and temporal resolution of the models,which implies that some of the important climatevariables are solved through parameterization, and thelack of perfect knowledge of all aspects of the climatesystem physics,83 such as for instance the behaviorof the ocean mixed layer,118 among other reasons.Process-based evaluation and metrics that allowidentifying the main physical processes responsiblefor the systematic error in seasonal prediction have

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FIGURE 4 | Hovmoller latitude-time diagrams of WestAfrican precipitation climatology (mm/day) for GPCP116

(top left), where the precipitation has been longitudinallyaveraged over 10◦W–10◦E. The rest of the left columnshows the ECMWF-System4117 for three different startdates: May (zero lead time), February (3-month lead time),and November of the previous year (6-month lead time).The corresponding systematic error, estimated as themodel minus GPCP climatology, appears immediately inthe right column. The annual cycle of the equatorialAtlantic SST averaged over the region 4◦S–4◦N/15◦W–10◦E is shown (top right) for ERSST38 (gray bars)and System4 for the three start dates: May (solid black),February (dashed black), and November (dotted black). Theperiod of study is 1982–2008.

10N

15N

20N

M J J A S O N

M J J A S O N M J J A S O N

M J J A S O N M J J A S O N

M J J A S O N M J J A S O N

5N

10N

15N

5N

10N

15N

5N

10N

15N

5N

10N

15N

5N

10N

15N

5N

10N

15N

5N

0 5 10−5−10

GPCP

SYST4 (May)

SYST4 (Feb)

SYST4 (Nov)

SYST4 (May) - GPCP

SYST4 (Feb) - GPCP

SYST4 (Nov) - GPCP

°C

N D J F M A M J J A S O N24

25

26

27

28

29

30ERSST verses SYST4

MayFebNov

recently started by exploring the sensitivity of themodel adjustment to the initial conditions or drift.119

InitializationOnce a GCM is available, the initialization is thefirst critical stage to be addressed in dynamicalprediction. The information about the state of theocean surface, sea-ice cover, snow, soil moisture,and so on is useful for making climate predictions.Each component of a forecast system needs tobe appropriately initialized with the best availableobservations. Observational information has to bemade available to the model on the GCM grid,which is very different from the locations wherethe observations are usually taken. This is carried

out in a process known as analysis which, usingprocedures of various degrees of complexity, attemptsto obtain a physically balanced, optimal (in thesense of an objective metric) extrapolation of theobservations onto the grid where the model equationsare resolved. Great experience exists in initializingthe atmosphere and ocean components of dynamicalprediction systems.5 However, aspects such as theinitialization of the land surface56 or the sea-ice73

have been to a certain degree neglected, for goodreasons, up to now. For instance, the skill associatedto the persistent behavior of the sea-ice encourages itscorrect initialization.120 In fact, preliminary results121

showed that accurate predictions of sea-ice propertiescould be obtained several months ahead using a

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dynamical prediction system with initialized sea-ice.The relevance of the initialization has been laterconfirmed73 and important efforts are currently beingundertaken to improve sea-ice initialization.122

Seasonal forecast systems are typically initializedwith observational information, which is usually notanalyzed in a consistent way between the differentmodel components (e.g., ocean and atmosphereanalyses are carried out independently), althoughdifferent strategies have been tested.123 The factthat GCMs have systematic errors is a problemadded to the lack of initial physical balance betweenthe model components that generate a continuousevolution of the climatological characteristics of theforecasts from the observational climatology in whathas been defined above as model drift. The drift mightbe complicated by initial-condition inhomogeneities,such as sudden changes in the analyses from whichthe initial conditions for the re-forecasts are taken.124

The estimation of the drift requires a sufficientlylarge sample of re-forecasts for a past period withhigh-quality observations, which makes dynamicalprediction a particularly expensive computationalexercise. These re-forecasts are used to estimate theforecast quality, which also requires a large sampleof predictions that can be compared with appropriateobservations. In all cases, the drift should be removed,typically using simple statistical methods, before thepredictions are distributed for general use.13

Sources of Uncertainty and Model InadequacyTwo of the main sources of uncertainty in dynamicalclimate prediction are the lack of perfect knowledgeof the initial conditions of the climate system andthe inability to perfectly model this system.86 Thefirst source of uncertainty is usually addressed bygenerating a set of several independent forecasts withslightly different initial conditions using dynamicalmodels, the so called ensemble technique.83,125

However, the estimation of the optimal set of initialconditions to generate the appropriate ensemble ofa complex model where different components aretightly coupled given a certain amount of computingresources is far from trivial.

The ensemble technique does not take intoaccount the model imperfections that are at theorigin of the model-specific systematic errors, bothin the mean state and in the internal variability,mentioned above. For this reason, ensemble forecastsare usually over-confident86 (i.e., under-dispersive).This source of uncertainty is addressed by usingtechniques that deal with model inadequacy. Someof these techniques are the multimodel ensemble,the perturbation of model parameters126 and the

stochastic parameterizations.127 The multimodelapproach, which is the most widely explored upto now, pulls together the predictions produced bydifferent forecast systems, independently designedfrom one another, and is hence an ad-hoc approach.Several studies128–130 have shown that multimodelensembles can on average outperform the best singleforecast system if the single forecast systems areoverconfident, as it is usually the case in climateforecasting. Simple postprocessing methods cannotyield the same level of improvement,131,132 somethingthat has implications for climate simulations at longertime scales.133 The perturbed-parameter approachcreates ensembles by perturbing uncertain parametersin the physical parameterizations of a single forecastsystem, while stochastic parameterizations treat sub-grid scale physical processes in a probabilistic wayadding extra terms to the model equations usingsimplified linear and nonlinear stochastic models.In all these experiments, precipitation probabilitypredictions suffer particularly from overconfidence.134

Forecast Quality of Current Dynamical SystemsIn the last two decades, a considerable effort hasbeen made to use the improved understanding ofthe physical phenomena responsible for the observedseasonal variability and trends, and to transferthe advances in forecast initialization, ensemblegeneration, and modeling of the climate system tooperational dynamical prediction systems.18,23,25 Thistransfer requires an appropriate assessment of theforecast quality achieved in different regions fordifferent variables and times of the year. Figure 5shows the skill of the ENSEMBLES multimodelforecast system6 for temperature and precipitation inboreal summer and winter over the period 1980–2005,which illustrates the skill of typical seasonal forecastsystems. The skill is high over the tropics, particularlyfor temperature, while over the extratropics it isvery limited.4,135 The ENSEMBLES multimodel alsoshowed that probabilistic forecast quality over landareas has about half of the skill over the ocean,while the land-averaged potential predictability issimilar to the ocean counterpart.27 Although thepredictability is lower at extratropical latitudes thanover the tropics, some positive skill has been foundin those regions (e.g., North America) associatedwith ENSO teleconnections,136 and also with othersources of seasonal-to-interannual predictability, suchas soil moisture56,137 and snow.63 In contrast, seasonaldynamical predictions have currently particularlylimited forecast quality over Europe.134 In manyinstances, a warming trend is the main source of skill intemperature forecasts.10,138 The low forecast quality

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(a) (b)

(c) (d)

FIGURE 5 | Correlation of the ensemble mean of one-month lead surface air temperature (top row) and precipitation (bottom row) from theENSEMBLES multimodel seasonal predictions in boreal summer (June to August, left column) and winter (December to February, right column). Thepredictions have been performed over the period 1980–2005 with five different forecast systems, each one running nine-member ensembles. Thereference data are taken from ERAInterim79 for temperature and GPCP116 for precipitation.

of seasonal-to-interannual predictions in extratropicalregions has motivated some analyses that take intoaccount conditional skill, using a concept also knownas ‘windows of opportunity’.139,140 Other systematicforecast quality assessments have also been performedusing comprehensive re-forecast data sets such asEUROSIP,a CHFPb and its associated projects likeGLACE2,c and APCC/CliPAS.d

Forecasts of variables other than near-surfacetemperature and precipitation have also proven to beskilful. This is the case of the Northern extratropicalmodes of variability88,141 and of tropical cyclonecounts.142,143 In particular, there exists a variety ofmethods to forecast seasonal North Atlantic hurricaneactivity, with most agreeing that useful forecasts fromboreal spring and early summer are feasible withcorrelations with observational records within therange 0.6–0.8,142,144–146 while simplified techniquesobtain correlations of 0.5–0.6 in predictions initializedby the preceding boreal fall.147 The prediction ofseasonal high-impact extremes has recently beenfound skilful.148–150 Figure 6 shows an example fromthe DePreSys perturbed-parameter forecast system,143

where predictions for the total precipitation, the 90thpercentile of the daily precipitation and the numberof days where the precipitation is larger than the 90thpercentile of the climatological distribution for themonth of August depict a similar level of positiveskill over Europe for predictions of the first forecast

month. The figure also illustrates the effect of thetime distance to the initial conditions on the forecastquality, where the skill increases substantially as theforecast time decreases.

The interannual or decadal modulation ofthe variability by processes like changes in theradiative forcing, addressing for instance the role ofweak volcanic eruptions,152 the GHG anthropogenicforcing153 or the internal variability have beenconsidered relevant at longer time scales, but havenot been widely dealt with climate prediction. Untilrecently most seasonal forecasts did not explicitlyinclude the effects of anthropogenic GHG forcing, butassumed that the effect is small compared to that ofthe natural variability. The global warming signal wassupposed to be largely incorporated into the forecastin the observation-based initial conditions. It wasargued that the forecasts would lack radiative supportfor the warmer temperatures over land that constitutethe trends there and that this adds an avoidable errorto the forecasts. Pioneering work138,154 investigatedthe differences between seasonal forecasts with andwithout GHG forcing in. They showed that thetemperature forecast skill increased due to a betterrepresentation of the regional temperature trendpatterns. The effect was not just constrained to theland areas, but improvements in skill also appearedover the extratropical oceans and for tropical cyclonecounts.155 The effect could be appreciated as quickly

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FIGURE 6 | Correlation of the ensemble mean of the predictions of total monthly precipitation, the 90th percentile of the daily precipitation andthe number of days in a month where the precipitation is larger than the 90th percentile of the climatological distribution of the month of August forthe 3-month (May start date, forecast time 4 months, left column) and zero-month (August start date, forecast time 1 month, right column) lead timeseasonal predictions of the perturbed-parameter DePreSys system. The predictions were performed over the period 1960–2005 and verified againstthe E-Obs data set151 interpolated bilinearly on the DePreSys grid.

as a few weeks into the forecast. Other authors156

suggested that those systems without representationof the anthropogenic forcing could instead usean a posteriori correction to increase the skill.As a consequence, the most recent versions ofoperational seasonal forecast systems1,157 includerealistic estimates of the anthropogenic forcing in thesimulations.

Improved SystemsA reduction of the drift and systematic error, andan increase of both accuracy and reliability by betterunderstanding and representing the physical processesat the origin of the seasonal predictability overland areas have been a priority for many years.Solutions to rapidly alleviate the systematic errorproblem have been elusive, progress up to now havingbeen incremental.117 New candidates to reduce thesystematic error are continuously explored as partof research and operational initiatives. Systematicerror reductions require the improvement of thespatial structure of both low-frequency tropical SSTvariability and the associated teleconnections, aswell as of deep convection,158 air-sea fluxes andstratospheric processes.159 A better treatment of theland-surface processes148 or vegetation160 has not

been considered yet widely in climate forecasting.This has been also the case of the impact ofa better description of radiative changes of bothnatural and anthropogenic origin. Besides, newapproaches to address model inadequacy134 haverecently emerged. Above all, it seems important thatthe climate prediction community takes advantage ofthe substantial efforts that take place in the weatherand climate-change communities to improve currentEarth system models, in addition to making progress inthe aspects specific to the climate prediction problem,such as the initialization and the ensemble generation.

Dynamical Prediction for Other Time ScalesAt time scales shorter than a season, efforts to formu-late forecasts at the sub-seasonal time scale have onlyrecently begun with the development of a Madden-Julian Oscillation (MJO) prediction metric and a com-mon approach to its application amongst a numberof international forecast centres161 and operationaldynamical forecast systems that target predictions ofboth intraseasonal tropical variability,162,163 tropi-cal cyclones164 and extratropical weather types.165

The recently created Subseasonal-to-Seasonal Predic-tion Research Projecte aims at moving forward these

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initiatives by creating a multimodel operational systemfor climate prediction at those time scales.

At the other end of the seasonal spectrum, longertime scales beyond the first forecast year have beenconsidered in dynamical climate prediction.126,166

These efforts bridge the time scales between sea-sonal and decadal forecasting problems, and showthat ENSO has skill with respect to simple bench-marks beyond the first forecast year and illustrate thatuser needs cover a wide range of time scales withpermeable separations between them.

As seasonal variability can be observed at arelatively high frequency (typically multiple times peryear) when compared to longer-term phenomena (e.g.,decadal or multidecadal oscillations), users are pro-vided with a relatively larger number of realizations toexploit in a comparison with the observational recordthan in the decadal prediction143,167 or long-term cli-mate change cases. The larger sample of forecastsavailable in the seasonal prediction experiments hasrecently encouraged a number of studies that use theseamless approach,168–173 which aims at using infor-mation from the predictions (e.g., reliability, drift orthe representation of tropical-extratropical telecon-nections) to infer properties of the forecast systemthat are relevant to the trustworthiness of simulationsperformed at longer time scales.174

Seasonal Predictions for the Local ScaleMany stakeholders require climate information atregional and/or local spatial scales. This is some timesreadily available with empirical prediction systems.However, global forecast systems used to generatedynamical climate predictions are typically unableto provide information at the spatial scale required.This is the case in spite of the planned increasesin horizontal resolution and, hence, regionalizationor downscaling methods are necessary. Althoughthere are both empirical and dynamical approachesto downscaling, local-scale seasonal predictions usu-ally have explored the empirical or statistical methodsdue both to the enormous amount of re-forecasts to bedownscaled to estimate the model systematic error andthe necessary forecast quality, and to the large compu-tational demands of dynamical downscaling.140,175,176

The merits of empirical or statistical downscaling con-sist mainly in providing seasonal climate informationfor specific locations and with much reduced system-atic error,140,177 but with a marginal increase of theskill178 and some times a degradation.140

In contrast with long-term climate change exper-iments, in seasonal prediction many re-forecasts andlarge ensembles have to be downscaled. However,

dynamical downscaling of climate predictions can bejustified because local feedbacks between processes,such as soil moisture, clouds, and precipitation arelocally important, in particular in summer. Untilnow, no clear advantages in terms of forecast qual-ity of dynamically downscaled predictions has beenfound,176,179–181 although some improvements couldbe noticed when the predictions are compared againsthigh-resolution rain gauge data sets.

Calibration and CombinationApart from the regionalization to increase the spatialresolution of the predictions, a postprocessing methodknown as calibration is needed to prevent climateinformation to suffer from systematic errors. In addi-tion, as different sources of information are available,such as different dynamical forecast systems (as in themultimodel approach), several complementary statis-tical predictions,182 and climatology estimates, robustcombination methods are needed to prepare a single,reliable, and probabilistic message for the stakehold-ers. Note that this approach avoids the temptingdiscussion of whether statistical or dynamical pre-diction methods are superior100,183 because the finalaim is to provide the user with the best and mostreliable climate information possible by combiningthe strengths of both approaches.99 Both calibrationand combination have been explored in research andoperational seasonal prediction.

When applying the multimodel approach, aquestion that immediately arises is to find the bestway to combine the predictions made with the dif-ferent forecast systems. It has been demonstrated thatcombining several dynamical forecast systems withequal weights (or simple multimodel) has, on aver-age, improved deterministic and probabilistic forecastquality with respect to the single models.128,131 Someinitiatives131 explored several combination methodsto combine different dynamical models using differ-ent flavors of multiple linear regression and settingdifferent weights to each one based on their pastperformance. However, the small sample size typi-cally available in climate prediction produces resultswith unequally weighted combination methods thatare not conclusive or robust,132,133 making the differ-ent definitions of a simple multimodel a particularlysuccessful benchmark.85,131,184,185 In a more general-ized framework,14,186 Bayesian methods to combinethe predictions of dynamical systems with simplestatistical models showed that in certain situationscombinations with unequal weights could providemore skilful and reliable results. This general frame-work takes into account the colinearity of the forecast

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(a) (b)

(c) (d)

(e) (f)

(g) (h)

FIGURE 7 | Surface downward solar radiation and 10-m wind seasonal 1-month lead boreal summer (June to August) 15-member ensemblepredictions from ECMWF’s System 4.117 Panels (a) and (b) show the correlation between the ensemble-mean prediction and ERAInterim79 over1981–2010. Panels (c) and (d) display probabilistic forecasts for the most likely tercile event (where below normal, normal and above normal areconsidered) in summer 2011, where blue (yellow-red) colors correspond to probabilities for the event below (above) the normal summer values.Panels (e) and (f) show the ERAInterim mean values for summer 2011. Panels (g) and (h) depict examples of the ensemble predictions of anomalies(with respect to the period 1981–2010) for specific sites for every summer, where the central red box corresponds to the interquartile range of theensemble, the thick horizontal bar to the median, the whiskers the 5th and 95th percentiles and the small dots some outliers, while the blue line is forthe ERAInterim value. These panels show the correlation of the ensemble-mean prediction with ERAInterim in the top left corner.

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errors of the individual systems, which showed advan-tages. This is illustrated in Figure 3, where an exampleof combined prediction using the forecast assimilationmethod is provided. However, as in the case of theBayesian model averaging,99 the results suggest that aminimum number of skilful dynamical and statisticalforecast systems is desirable as each of them providesskill in different locations and seasons to the combinedpredictions.

SEASONAL FORECASTING ANDCLIMATE SERVICES

Knowledge of the climate system variability at sea-sonal time scales has proven to be a useful inputto making resource management and planning deci-sions. The variability of climate at seasonal time scalesis one of the factors that determine the evolution ofmany climate-sensitive sectors. Management decisionswith climate information on seasonal-to-interannualtime scales have been illustrated for fields such asagriculture,187 health,188 water management,189–191

and energy.192

In addition, many sectors adapt to climatechange on an interannual basis instead of on thebasis of long-term climate change projections, whichadds long-term value and an additional dimension toseasonal forecasts.193 The need for the use of seasonalforecasting within agriculture has been noted earlyon.194 However, the potential economic benefits ofclimate prediction are far from being fully realized.This is often linked to the aversion of a range of usersto integrate climate predictions into their existingdecision support systems without a clear assessmentof how they might affect their vulnerability, amongother reasons.195 The socioeconomic applicationof the climate information, including its use asan adaptation tool, requires to either develop orimprove the visualization of the predictions, thepublic dissemination of the data generated and toelaborate introductory, general-public informationon the prediction generation methodologies and theregions, variables and times of the year where positiveskill can be expected.

To structure these initiatives, the WorldMeteorological Organization (WMO) sponsoredWorld Climate Conference-3 (WCC3) and theresulting Global Framework for Climate Services(GFCS196) have expressed in clear terms the increasingneed for robust climate information covering futureperiods ranging from several months up to centuriesfor economic, industrial, and political planning.Climate services aim to identify the main problemsthat limit the production of climate information197

and develop a battery of solutions from a seamlessperspective, both in terms of time scale169,171 andbetween information producers and users.193 Asseasonal forecasts have somehow been neglectedduring the development of impact assessment andadaptation strategies based on weather forecastsand long-term climate change projection, GFCS isparticularly interesting for the seasonal time scale.198

The GFCS has created a conceptual structure toclose the gap in the provision of climate informationand services between the information producers andits users, the latter ranging from policy makers tothe industry. The WMO-designated Global ProducingCentres (GPCsf ) for long-range predictions are a keypiece in this structure, where the GPCs feed theRegional Climate Centres (RCCs) and the NationalHydro-Meteorological Services (NHMSs) with thebest climate information possible. Climate predictionsproduced by all designated GPCs are collectedoperationally by WMO’s Lead Centre for Long-Range Forecasts (LC-LRFMMEg). This structurebrings together seasonal prediction generators andlocal to regional focal points like the RegionalClimate Outlook Fora (RCOFh), NHMSs and privatepartners via the LC-LRFMME to facilitate thedevelopment of consensus-based seasonal predictionswith socio-economic potential. These forecasts aresupplemented by additional forecasts performed byother institutions,i but which are among the mostpopular sources of information.

Climate services have focused mainly on helpingthe socioeconomic sectors in developing countries.199

However, the methodology can be extended to manyother sectors that show vulnerability to climatevariability on seasonal time scales. Renewable energy(RE) yields from solar and wind projects are directlylinked to an array of climate parameters (wind speedand direction, surface downward solar radiation,etc.). As a result, strategic decisions related toRE investments, the management of the powerplants and the integration of wind and/or solarenergy into the energy grid system is intricatelytied to climate variability.200 For instance, some REprojects are financed with up to 80% debt, andinterest on the debt remains payable every year,whether energy is generated or not.201 There istherefore the possibility of considerable risk thatnot enough energy will be generated to repay thedebt, with investors particularly vulnerable in thefirst year of investment. Climate predictions canthen prove useful to reduce the uncertainty in thoseproblems.202 Figure 7 shows an example of the sortof information that is being prepared to contributeto this aim. It illustrates probability forecasts of

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the relevant variables, along with estimates ofthe accuracy and performance over past cases,both at regional and local scales as requested bythe potential users. This information will then betranslated by ‘midstream’ RE stakeholders (i.e., REconsultants) into useful and usable information for‘downstream’ RE investor stakeholders, which includeboth investors in and managers of RE projects andthe energy grid system, to adapt or reinforce theirdecision-making processes in light of newly identifiedclimate risks. This information can also stimulate newbusiness markets, including innovative RE insurancecovers that manage periods of high risk, such asperiods of lower than expected climate resourcesleading to lower than expected energy yields, andcapitalize on periods of higher than expected climateresources leading to higher than expected energyyields.

CONCLUSION

Seasonal prediction is expected to address a long listof challenges to produce climate information thatresponds to the expectations of both existing andfuture climate services. Among others, we want toemphasize the following:

• Achieve an objective exhaustive evaluation ofcurrent forecast quality, including the differentaspects of the systematic error, from dynamical,statistical, and consolidated systems to identifythe factors limiting predictive capability, whilethe interaction between the research andoperational communities is enhanced via thecommunication of these results.

• Examine the role of both tropical andextratropical atmosphere–ocean coupling, toinvestigate the need to more realistically repre-sent such coupling over a wide range of spatialscales (including down to the scales of the sharpSST gradients associated with ocean fronts), andto better observe and more realistically representair–sea fluxes in models.

• Test specific hypotheses for the improvementof seasonal predictions using a process-basedverification approach, and propose solutions thatinclude modeling the mechanisms responsible forhigh-impact events.

• Implement the battery of new methods availablefor a comprehensive forecast quality assessment.

• Facilitate the integration of multidimensionalobservational data of the atmosphere–o-cean–cryosphere–land system as sources of initial

conditions, which implies an upgrade of dataassimilation systems, to obtain better estimatesof the initial-condition uncertainties, and to val-idate and calibrate climate predictions.

• Achieve an improved forecast quality at regionalscales by an increase in the spatial resolution ofthe global forecast systems and the introductionof new process descriptions of the most relevantprocesses; activities should aim at includingmore climate processes in the dynamical systemsand computing capabilities should be improvedto permit the explicit simulation of subgrid-scale processes, removing as much reliance onparameterization as possible.

• Assess the best alternatives to characterize anddeal with the uncertainties in climate predictionfrom both dynamical and statistical perspectivesfor the increase of forecast reliability.

• Achieve reliable and accurate local-to-regionalpredictions via the combination and calibrationof the information from different sourcesand the implementation of state-of-the-artregionalization tools.

• Illustrate the usefulness of the improvements forspecific applications and develop methodologiesto better communicate the climate informationto policy-makers, stakeholders, and the public,aiming to support the provision of climateservices according to WMO protocols; this goalimplies the development of a set of publictools, including prediction datasets, outlooks,coordinated forecast quality assessments, andappropriate documentation.

Apart from the set of international coordinatedinitiatives and the operational activities mentionedin the previous paragraphs, the EU-funded projectsSPECSj and EUPORIASk will offer unique opportuni-ties to address these issues.

NOTESa European multimodel operational seasonal predic-tion system hosted by ECMWF.bhttp://www.clivar.org/organization/wgsip/chfp.chttp://gmao.gsfc.nasa.gov/research/GLACE-2/.dAPCC/CliPAS is the Asia-Pacific multimodel ensem-ble, which provides a precious independent set ofseasonal re-forecasts that can be used for the compar-ison with and improvement of the European strategics2d prediction capability.ehttp://www.wmo.int/pages/prog/arep/wwrp/new/thorpex_new.html.

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f http://www.wmo.int/pages/prog/wcp/wcasp/clips/producers_forecasts.html.ghttp://www.wmolc.org.hhttp://www.wmo.int/pages/prog/wcp/wcasp/RCOF_Concept.html.

ihttp://portal.iri.columbia.edu/portal/server.pt?open=512&objID=945&PageID=0&cached=true&mode=2#plm.jhttp://www.specs-fp7.eu.khttp://www.euporias.eu/.

ACKNOWLEDGMENTS

This work was supported by the EU-funded QWeCI (FP7-ENV-2009-1-243964), CLIM-RUN (FP7-ENV-2010-1-265192) and SPECS (FP7-ENV-2012-2-308378), the MINECO-funded RUCSS (CGL2010-20657) projectsand the Catalan Government. The authors want to acknowledge the fruitful discussions with M. Davis aboutthe development of climate services. The KNMI Climate Explorer has been employed to produce some ofthe figures included in this paper. Computing resources provided by the European Centre for Medium-RangeWeather Forecasts through its special project programme and the Barcelona Supercomputing Centre via theRed Espanola de Supercomputacion are greatly appreciated.

REFERENCES1. Luo J-J, Behera SK, Masumoto Y, Yamagata T.

Impact of global ocean surface warming on seasonal-to-interannual climate prediction. J Clim 2011,24:1626–1646. doi:10.1175/2010JCLI3645.1.

2. Shukla J, Kinter JL. Predictability of seasonal climatevariations a pedagogical review. In: Palmer T, Hage-dorn R, eds. Predictability of Weather and Climate.Cambridge, UK: Cambridge University Press; 2006.

3. Chang P, Yamagata T, Schopf P, Behera SK, Carton J,Kessler WS, Meyers G, Qu T, Schott F, Shetye S, et al.Climate fluctuations of tropical coupled systems—therole of ocean dynamics. J Clim 2006, 19:5122–5174.doi:10.1175/JCLI3903.1.

4. van Oldenborgh GJ, Balmaseda MA, Ferranti L,Stockdale TN, Anderson DLT. Evaluation of atmo-spheric fields from the ECMWF seasonal forecastsover a 15-year period. J Clim 2005, 18:3250–3269.doi:10.1175/JCLI3421.1.

5. Balmaseda M, Anderson DLT. Impact of initializa-tion strategies and observations on seasonal fore-cast skill. Geophys Res Lett 2009, 36:L01701.doi:10.1029/2008GL035561.

6. Weisheimer A, Doblas-Reyes FJ, Palmer TN, Alessan-dri A, Arribas A, Deque M, Keenlyside N, MacVeanM, Navarra A, Rogel P. ENSEMBLES—a new multi-model ensemble for seasonal-to-annual predictions:skill and progress beyond DEMETER in forecast-ing tropical Pacific SSTs. Geophys Res Lett 2009,36:L21711. doi:10.1029/2009GL040896.

7. Wu R, Kirtman BP, van den Dool H. An anal-ysis of ENSO prediction skill in the CFS ret-rospective forecasts. J Clim 2009, 22:1801–1818.doi:10.1175/2008JCLI2565.1.

8. Smith DM, Cusack S, Colman AW, Folland CK, Har-ris GR, Murphy JM. Improved surface temperature

prediction for the coming decade from a global climatemodel. Science 2007, 317:796–799.

9. Solomon S, Qin D, Manning M, Chen Z, Marquis M,Averyt KB, Tignor M, Miller HL. The physical sci-ence basis. Contribution of Working Group I to theFourth Assessment Report of the Intergovernmen-tal Panel on Climate Change; 2007. Available at:http://www.ipcc.ch/publications_and_data/ar4/wg1/en/contents.html. (Accessed December 1, 2009)

10. van den Dool HM. Empirical Methods in Short-TermClimate Prediction. Oxford, USA: Oxford UniversityPress; 2007.

11. Fan K. A prediction model for Atlantic named stormfrequency using a year-by-year increment approach.Weather Forecast 2010, 25:1842–1851.

12. Bengtsson L, Schlese U, Roeckner E, Latif M, Bar-nett T, Graham N. A two-tiered approach tolong-range climate forecasting. Science 1993,261:1026–1029.

13. Stockdale TN, Anderson DLT, Alves JOS, Bal-maseda MA. Global seasonal rainfall forecasts usinga coupled ocean-atmosphere model. Nature 1998,392:370–373.

14. Stephenson DB, Coelho CADS, Doblas-Reyes FJ, Bal-maseda MA. Forecast assimilation: a unified frame-work for the combination of multimodel weather andclimate predictions. Tellus A 2005, 57:253–264.

15. Lang XM, Wang HJ. Improving extra-seasonal sum-mer rainfall prediction by merging information fromGCM and observation. Weather Forecast 2010,25:1263–1274. doi:10.1175/2010WAF2222342.1.

16. Balmaseda MA, Vidard A, Anderson D. The ECMWFocean analysis system: ORAS3. Mon Weather Rev2008, 136:3018–3034.

260 © 2013 John Wiley & Sons, Ltd. Volume 4, Ju ly/August 2013

Page 17: Seasonal climate predictability and forecasting: status and …climateknowledge.org/figures/Rood_Climate_Change_AOSS480... · 2014-05-29 · Advanced Review Seasonal climate predictability

WIREs Climate Change Seasonal climate predictability and forecasting

17. Balmaseda MA, Anderson DLT, Vidard A. Impact ofArgo on analyses of the global ocean. Geophys ResLett 2007, 34:L16605. doi:10.1029/2007GL030452.

18. Saha S., and coauthors. The NCEP climate fore-cast system reanalysis. Bull Am Meteorol Soc 2010,91:1015–1057. doi:10.1175/2010BAMS3001.1.

19. Pegion K, Kirtman BP. The impact of air-sea inter-actions on the simulation of tropical intraseasonalvariability. J Clim 2008, 21:616–6635.

20. Palmer TN, Shutts GJ, Hagedorn R, Doblas-Reyes FJ,Jung T, Leutbecher M. Representing model uncer-tainty in weather and climate prediction. Ann RevEarth Planet Sci 2005, 33:163–193.

21. Palmer TN, Doblas-Reyes FJ, Hagedorn R,Weisheimer A. Probabilistic prediction of climateusing multi-model ensembles: from basics to applica-tions. Phil Trans Roy Soc B2005, 360:1991–1998.doi:10.1098/rstb.2005.1750.

22. Lee S-S, Lee J-Y, Ha K-J, Wang B, Schemm JKE. Defi-ciencies and possibilities for long-lead coupled climateprediction of the Western North Pacific-East Asiansummer monsoon. Clim Dyn 2011, 36:1173–1188.doi:10.1007/s00382-010-0832-0.

23. Saha S, Nadiga S, Thiaw C, Wang J, Wang W,Zhang Q, van den Dool HM, Pan H-L, Moorthi S,Behringer D, et al. The NCEP climate forecast system.J Clim 2006, 19:3483–3517.

24. Wang B, Lee J-Y, Kang I-S, Shukla J, Park C-K,Kumar A, Schemm J, Cocke S, Kug J-S, Luo J-J et al.Advance and prospectus of seasonal prediction: assess-ment of the APCC/CliPAS 14-model ensemble retro-spective seasonal prediction (1980–2004). Clim Dyn2009, 33:93–117. doi:10.1007/s00382-008-0460-0.

25. Stockdale TN, Anderson DLT, Balmaseda MA,Doblas-Reyes FJ, Ferranti L, Mogensen K, PalmerTN, Molteni F, Vitart F. ECMWF seasonal forecastSystem 3 and its prediction of sea surface temperature.Clim Dyn 2011, 37:455–471.doi:10.1007/s00382-010-0947-3.

26. Kirtman B, Pirani A. The state of the art of seasonalprediction: Outcomes and recommendations from theFirst World Climate Research Program Workshopon Seasonal Prediction. Bull Am Meteorol Soc 2009,90:455–458.

27. Alessandri A, Borrelli A, Navarra A, Arribas A, DequeM, Rogel P, Weisheimer A. Evaluation of prob-abilistic quality and value of the ENSEM-BLES multi-model seasonal forecasts: compari-son with DEMETER. Mon Weather Rev 2011,139:581–607.doi:10.1175/2010MWR3417.1.

28. Rowell DP. Assessing potential seasonal predictabilitywith an ensemble of multidecadal GCM simulations.J Clim 1998, 11:109–120.

29. Zwiers FW, Kharin VV. Intercomparison of interan-nual variability and potential predictability: an AMIPdiagnostic subproject. Clim Dyn 1998, 14:517–528.

30. Kharin VV, Zwiers FW. Improved seasonal probabil-ity forecasts. J Clim 2003, 16:1684–1701.

31. Wang B, Ding Q, Fu X, Kang I-S, Jin K, Shukla J,Doblas-Reyes FJ. Fundamental challenges in sim-ulation and prediction of summer monsoonrainfall. Geophys Res Lett 2005, 32:L15711.doi:10.1029/2005GL02273412.

32. Fu X, Wang B, Waliser DE, Li T. Impact ofatmosphere-ocean coupling on the predictability ofmonsoon intraseasonal oscillations (MISO). J AtmosSci 2006, 64:157–174.

33. Goddard L, Mason SJ, Zebiak SE, Ropelewski CF,Basher R, Cane MA. Current approaches to seasonalto interannual climate predictions. Int J Climatol2001, 21:1111–1152.

34. Shukla J. Predictability in the midst of chaos: A sci-entific basis for climate forecasting. Science 1998,282:728–731.

35. Alexander MA, Blade I, Newman M, LanzanteJR, Lau N-C, Scott JD. The atmospheric bridge:the influence of ENSO teleconnections on air-sea interaction over the global oceans. J Clim2002, 15:2205–2231. doi:10.1175/1520-0442.015<2205:TABTIO>2.0.CO;2.

36. Ropelewski CF, Halpert M. Global and regionalscale precipitation patterns associated with the ElNino/Southern Oscillation. Mon Weather Rev 1987,115:1606–1626.

37. Sarachik ES, Cane MA. The El Nino-Southern Oscil-lation Phenomenon. New York: Cambridge UniversityPress; 2010.

38. Smith TM, Reynolds RW, Peterson TC, Lawrimore J.Improvements to NOAA’s historical merged land-ocean surface temperature analysis (1880–2006).J Clim 2008, 21:2283–2296.

39. Rudolf B, Becker A, Schneider U, Meyer-Christoffer A, Ziese M. The new ‘‘GPCC Full DataReanalysis Version 5’’ providing high-quality griddedmonthly precipitation data for the global land-surfaceis public available since December . 2010. GPCCTechnical Report; 2010 Available at: gpcc.dwd.de.(Accessed 15, January 2011)

40. Latif M, Anderson DLT, Barnett T, Cane M, Klee-man R, Leetmaa A, O’Brien J, Rosati A, Schnei-der E. A review of the predictability and pre-diction of ENSO. J Geophys Res 1998, 103:C7.doi:10.1029/97JC03413.

41. Meinen CS, McPhaden MJ. Observations of warmwater volume changes in the equatorial Pacific andtheir relationship to El Nino and La Nina. J Clim2000, 13:3551–3559.

42. Zavala-Garay J, Zhang C, Moore AM, Kleeman R.The linear response of ENSO to the Madden-Julianoscillation. J Clim 2005, 18:2441–2459.

43. Gebbie G, Eisenman I, Wittenberg A, Tziper-man E. Modulation of westerly wind bursts by

Volume 4, Ju ly/August 2013 © 2013 John Wiley & Sons, Ltd. 261

Page 18: Seasonal climate predictability and forecasting: status and …climateknowledge.org/figures/Rood_Climate_Change_AOSS480... · 2014-05-29 · Advanced Review Seasonal climate predictability

Advanced Review wires.wiley.com/climatechange

sea surface temperature: a semi-stochastic feed-back for ENSO. J Atmos Sci 2007, 64:3281–3295.doi:10.1175/JAS4029.1.

44. Losada T, Rodríguez-Fonseca B, Mohino E, BaderJ, Janicot S, Mechoso CR. Tropical SST and Sahelraifall: a non-stationary relationship. Geophys ResLett 2012, 39:L12705. doi:10.1029/2012GL052423.

45. Rodríguez-Fonseca B, Polo I, García-SerranoJ, Losada T, Mohino E, Mechoso CR, Kucharski F.Are Atlantic Ninos enhancing Pacific ENSO events inrecent decades? Geophys Res Lett 2009, 36:L20705.doi:10.1029/2009GL040048.

46. Saji NH, Goswami BN, Vinayachandran PN, Yama-gata T. A dipole mode in the tropical Indian Ocean.Nature 1999, 401:360–363.

47. Zhao M, Hendon HH. Representation and predictionof the Indian Ocean dipole in the POAMA sea-sonal forecast model. Q J Roy Meteorol Soc 2009,135:337–352.

48. Ineson S, Scaife AA. The role of the stratosphere inthe European climate response to El Nino. Nat Geosci2009, 2:32–36.

49. Marshall AG, Scaife AA. Impact of the QBOon surface winter climate. J Geophys Res 2009,114:D18110. doi:10.1029/2009JD011737.

50. Marshall AG, Scaife AA. Improved predictability ofstratospheric sudden warming events in an atmo-spheric general circulation model with enhancedstratospheric resolution. J Geophys Res 2010,115:D16114. doi:10.1029/2009JD012643.

51. Orsolini YJ, Kindem IT, Kvamstø NG. On thepotential impact of the stratosphere upon seasonaldynamical hindcasts of the North Atlantic Oscilla-tion: a pilot study. Clim Dyn 2011, 36:579–588.doi:10.1007/s00382-009-0705-6.

52. Walsh JE, Ross B. Sensitivity of 30-day dynamicalforecasts to continental snow cover. J Clim 1988,1:739–754.

53. Quesada B, Vautard R, Yiou P, Hirschi M, Senevi-ratne SI. Asymmetric European summer heat pre-dictability from wet and dry southern winters andsprings. Nature Clim Change 2012, 2:736–741.doi:10.1038/nclimate1536.

54. Muller B, Seneviratne SI. Hot days induced by pre-cipitation deficits at the global scale. PNAS 2012,109:12398–12403.

55. Guo Z, Dirmeyer PA, Koster RD, Sud YC, Bonan G,Oleson KW, Chan E, Verseghy D, Cox P, Gordon CT,et al. GLACE: the global land-atmosphere couplingexperiment. Part II: analysis. J Hydrometeorol 2006,7:611–625.

56. Koster RD, Mahanama SPP, Yamada TJ, Bal-samo G, Vitart F, Berg AA, Drewitt G, Boisserie M,Dirmeyer PA, Guo Z, et al. The contribution of landsurface initialization to subseasonal forecast skill: first

results from multi-model experiment. Geophys ResLett 2010, 37:L02402. doi:10.1029/2009GL041677.

57. Koster RD, et al. GLACE2: The second phaseof the Global land–atmosphere coupling experi-ment: soil moisture contributrion to subseasonalforecast skill. J Hydrometeorol 2011, 12:805–822.doi:10.1175/2011JHM1365.1.

58. Douville H. Relative contributions of soil andsnow hydrology to seasonal climate predictabil-ity: a pilot study. Clim Dyn 2009, 34:797–818.doi:10.1007/s00382-008-0508-1.

59. van den Hurk B, Doblas-Reyes FJ, Balsamo G,Koster RD, Seneviratne SI, Camargo H. Soil moistureeffects on seasonal temperature and precipitation fore-cast scores in Europe. Clim Dyn 2012, 38:349–362.doi:10.1007/s00382-010-0956-2.

60. Koster RD, Dirmeyer PA, Guo Z, Bonan G,Chan E, Cox P, Gordon CT, Kanae S, Kowalczyk E,Lawrence D, et al. Regions of strong couplingbetween soil moisture and precipitation. Science 2004,305:1138–1140. doi:10.1126/science.1100217.

61. Dutra E, Schar C, Viterbo P, Miranda PMA.Land–atmosphere coupling associated with snowcover. Geophys Res Lett 2011, 38:L15707.doi:1.1029/2011GL048435.

62. Dutra E, Viterbo P, Miranda PMA, Balsamo. G.Complexity of snow schemes in a climatemodel and its impact on surface energy andhydrology. J Hydrometeorol 2011, 13:521–538.doi:10.1175/JHM-D-11-072.1.

63. Shongwe ME, Ferro CAT, Coelho CAS, van Olden-borgh GJ. Predictability of cold spring seasons inEurope. Mon Weather Rev 2007, 135:4185–4201.

64. Xu L, Dirmeyer P. Snow-atmosphere couplingstrength in a global circulation model. Geophys ResLett 2011, 38:L13401. doi:10.1029/2011GL048049.

65. Gong G, Entekhabi D, Cohen J. A large-ensemblemodel study of the wintertime AO-NAO and therole of interannual snow perturbations. J Clim 2002,16:3917–3931.

66. Cohen J, Barlow M, Kushner PJ, Saito K.Stratosphere-troposphere coupling and link withEurasian land-surface variability. J Clim 2007,20:5335–5343.

67. Peings Y, Saint-Martin D, Douville H. A numericalsensitivity study of the influence of Siberian snow onthe northern annular mode. J Clim 2012, 25:592–607.doi:10.1175/JCLI-D-11-00038.1.

68. Peings Y, Brun E, Mauvais V, Douville H. Howstationary is the relationship between Siberiansnow and Arctic Oscillation over the 20th cen-tury? Geophys Res Lett 2013, 40:183–188.doi:10.1029/2012GL054083.

262 © 2013 John Wiley & Sons, Ltd. Volume 4, Ju ly/August 2013

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WIREs Climate Change Seasonal climate predictability and forecasting

69. Orsolini YJ, Kvamstø N. The role of the Eurasian snowcover upon the wintertime circulation: decadal simu-lations forced with satellite observations. J GeophysRes 2009, 114:D19108. doi:10.1029/2009JD012253.

70. Cohen J, Foster J, Barlow M, Saito K, Jones J. Winter2009/10: a case study of an extreme Arctic Oscil-lation event. Geophys Res Lett 2010, 37:L17707.doi:1.1029/2010GL044256.

71. Arribas A, Cusack S, Glover M, Maidens A, Peter-son K, Gordon M, Maclachlan C, Graham R, Fere-day D, Camp J, et al. The GloSea4 ensemble predic-tion system for seasonal forecasting. Mon WeatherRev 2011, 139:1891–1910.

72. Holland MM, Serreze MC, Stroeve J. The sea ice massbudget of the Arctic and its future change as sim-ulated by coupled climate models. Clim Dyn 2010,34:185–200. doi:10.1007/s00382-008-0493-4.

73. Blanchard-Wrigglesworth E, Blitz CM, Holland MM.Influence of initial conditions and climate forcing onpredicting Arctic sea ice. Geophys Res Lett 2011,38:L18503. doi:10.1029/2011GL048807.

74. Chevallier M, Salas-Melia D. The role of seaice thickness distribution in the Arctic sea icepotential predictability: a diagnostic approach witha coupled GCM. J Clim 2012, 25:3025–3038.doi:10.1175/JCLI-D-11-00209.1.

75. Orsolini YJ, Senan R, Benestad R, Melsom A. Autumnatmospheric response to the 2007 low Arctic sea iceextent in coupled ocean-atmosphere hindcasts. ClimDyn 2012, 38:2437–2448. doi:10.1007/s00382-011-1169-z.

76. Balmaseda MA, Ferranti L, Molteni F, Palmer TN.Impact of 2007 and 2008 Arctic ice anomalies on theatmospheric circulation: implications for long-rangepredictions. Q J Meteorol Soc 2010, 136:1655–1664.doi:10.1002/qj.661.

77. Francis JA, Chan W, Leathers DJ, Miller JR,Veron DE. Winter Northern Hemisphere weatherpatterns remember summer Arctic sea-ice extent.Geophys Res Lett 2009, 36:L07503.

78. Du H, Doblas-Reyes FJ, García-Serrano J, GuemasV, Soufflet Y, Wouters B. Sensitivity of decadal pre-dictions to the initial atmospheric and oceanicperturbations. Clim Dyn 2012. 39:2013–2023.doi:10.1007/s00382-011-1285-9.

79. Dee D, Uppala S. Variational bias correction inERA-Interim. ECMWF Technical Memorandum;2008, 575. Available at: http://www.ecmwf.int/publications/library/do/references/show?id=88715.(Accessed November 20, 2011)

80. Robock A. Stratospheric forcing needed for dynami-cal seasonal prediction. Bull Am Meteorol Soc 2001,82:2189–2192.

81. Ineson S, Scaife AA, Knight JR, Manners JC, Dun-stone NJ, Gray LJ, Haigh JD. Solar forcing of winter

climate variability in the Northern Hemisphere. NatGeosci 2011, 4:753–757. doi:10.1038/ngeo1282.

82. Coelho CAS, Stephenson DB, Balmaseda M, Doblas-Reyes FJ, van Oldenborgh GJ. Towards an integratedseasonal forecasting system for South America. J Clim2006, 19:3704–3721.

83. Palmer TN. Predicting uncertainty in forecasts ofweather and climate. Rep Prog Phys 2000, 63:71–116.

84. Murphy AH, Winkler RL. Probability forecasting inmeteorology. J Am Stat Assoc 1984, 79:489–500.

85. Tippett MK, Barnston AG. Skill of multimodel ENSOprobability forecasts. Mon Weather Rev 2008,136:3933–3946. doi:10.1175/2008MWR2431.1.

86. Slingo J, Palmer TN. Uncertainty in weather andclimate prediction. Phil Trans Roy Soc A 2011,369:4751–4767.

87. Mason SJ. Understanding forecast verification statis-tics. Meteorol Appl 2008, 15:31–40.

88. Palmer TN, Alessandri A, Andersen U, Cantelaube P,Davey M, Delecluse P, Deque M, Díez E, Doblas-Reyes FJ, Feddersen H, et al. Development of a Euro-pean multi-model ensemble system for seasonal tointer-annual prediction (DEMETER). Bull Am Mete-orol Soc 2004, 85:853–872.

89. Kirtman BP, Min D. Multimodel ensemble ENSO pre-diction with CCSM and CFS. Mon Weather Rev2009,137:2908–2930.

90. Jolliffe IT, Stephenson DB, eds. Forecast Verification:A Practitioner’s Guide in Atmospheric Science. Chich-ester: John Wiley & Sons; 2011, 274 pp.

91. van Oldenborgh GJ, Balmaseda MA, Ferranti L,Stockdale TN, Anderson DLT. Did the ECMWF sea-sonal forecast model outperform statistical ENSOforecast models over the last 15 years? J Clim 2005,18:3240–3249. doi:10.1175/JCLI3420.1.

92. Tippett MK, Barnston AG, Li S. Performance of recentmultimodel ENSO forecasts. J Appl Meteorol Climatol2012, 51:637–654. doi:10.1175/JAMC-D-11-093.1.

93. Lima CHR, Lall U, Jebara T, Barnston AG. Statisticalprediction of ENSO from subsurface sea temperatureusing a nonlinear dimensionality reduction. J Clim2009, 22:4501–4519. doi:10.1175/2009JCLI2524.1.

94. Rodrigues LRL, Doblas-Reyes FJ, Coelho CAS. Multi-model calibration and combination of tropical sea-sonal sea surface temperature forecasts. Clim Dyn.Submitted.

95. Rayner NA, Parker DE, Horton EB, Folland CK,Alexander LV, Rowell DP, Kent EC, Kaplan A.Global analyses of sea surface temperature, sea ice, andnight marine air temperature since the late nineteenthcentury. J Geophys Res 2003, 108:4407–4444.

96. Mason SJ, Mimmack GM. Comparison of some sta-tistical methods of probabilistic forecasting of ENSO.J Clim 2002, 15:8–29.

Volume 4, Ju ly/August 2013 © 2013 John Wiley & Sons, Ltd. 263

Page 20: Seasonal climate predictability and forecasting: status and …climateknowledge.org/figures/Rood_Climate_Change_AOSS480... · 2014-05-29 · Advanced Review Seasonal climate predictability

Advanced Review wires.wiley.com/climatechange

97. Hastenrath S, Greischar L. Further work on the pre-diction of Northeast Brazil rainfall anomalies. J Clim1993, 6:743–758.

98. Moura AD, Hastenrath S. Climate prediction forBrazil’s Nordeste: performance of empiricaland numerical modeling methods. J Clim 2004,17:2667–2672. doi:10.1175/1520-0442.017<2667:CPFBNP>2.0.CO;2.

99. Schepen A, Wang QJ, Robertson DE. Combining thestrengths of statistical and dynamical modelingapproaches for forecasting Australian seasonal rain-fall. J Geophys Res 2012, 117:D20107. doi:10.1029/2012JD018011.

100. Folland CK, Scaife AA, Lindesay J, Stephenson DB.How predictable is European winter climate aseason ahead? Int J Climatol2012, 32:801–818.doi:10.1002/joc.2314.

101. Sohn S-J, Min Y-M, Lee J-Y, Tam C-Y, Kang I-S,Wang B, Ahn J-B, Yamagata T. Assessment of thelong-lead probabilistic prediction for the Asian sum-mer monsoon precipitation (1983–2011) based onthe APCC multimodel system and a statistical model.J Geophys Res 2012, 117:D04102. doi:10.1029/2011JD016308.

102. Cohen J, Jones J. A new index for more accurate win-ter predictions. Geophys Res Lett 2012, 38:L21701.doi:1.1029/2011GL049626.

103. Brands S, Manzanas R, Gutierrez JM, Cohen J. Sea-sonal predictability of wintertime precipitation inEurope using the snow advance index. J Clim 2012,25:4023–4028. doi:10.1175/JCLI-D-12-00083.1.

104. Drobbot SD, Maslanik JA, Fowler C. A long-rangeforecast of Arctic summer sea-ice minimum extent.Geophys Res Lett 2006, 33:L10501. doi:10.1029/2006GL026216.

105. Lindsay RW, Zhang J, Schweiger AJ, Steele MA. Sea-sonal predictions of ice extent in the Arctic Ocean.J Geophys Res 2008, 113:C02023. doi:10.1029/2007JC004259.

106. Holland MM, Stroeve J. Changing seasonal sea icepredictor relationships in a changing Arctic climate.Geophys Res Lett 2011, 38:L18501. doi:10.1029/2011GL049303.

107. Cane MA, Zebiak SE, Dolan SC. Experimental fore-casts of El Nino. Nature 1986, 321:827–832.

108. Delecluse P, Davey MK, Kitamura Y, Philander SGH,Suarez M, Bengtsson L. Coupled general circulationmodeling of the tropical Pacific. J Geophys Res 1998,C103:14357–14373.

109. Latif M, Sterl A, Maierreimer E, Junge MM. Structureand predictability of the El Nino Southern Oscillationphenomenon in a coupled ocean atmosphere generalcirculation model. J Clim 1993, 6:700–708.

110. Kirtman BP, Shukla J, Huang BH, Zhu ZX, Schnei-der EK. Multiseasonal predictions with a coupled trop-ical ocean-global atmosphere system. Mon WeatherRev 1997, 125:789–808.

111. Rosati A, Miyakoda K, Gudgel R. The impact ofocean initial conditions on ENSO forecastingwith a coupled model. Mon Weather Rev 1997,125:754–772.

112. Ji M, Kumar A, Leetmaa A. A multiseason climateforecast system at the National Meteorological Center.Bull Am Meteorol Soc 1994, 75:569–577.

113. Kug JS, Kang IS, Choi DH. Seasonal climate pre-dictability with tier-one and tier-two prediction sys-tems. Clim Dyn 2008, 31:403–416. doi:10.1007/s00382-007-0264-7.

114. DelSole T, Shukla J. Model fidelity versus skill in sea-sonal forecasting. J Clim 2010, 23:4794–4806.

115. Xavier PK, Duvel J-Ph, Braconnot P, Doblas-Reyes FJ.An evaluation metric for intraseasonal variabil-ity and its application to CMIP3 20th cen-tury simulations. J Clim 2010, 23:3497–3508. doi:10.1175/2010JCLI3260.1.

116. Adler RF, Huffman GJ, Chang A, Ferraro R, Xie P,Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D,et al. The Version 2 Global Precipitation ClimatologyProject (GPCP) monthly precipitation analysis (1979-present). J Hydrometeorol 2003, 4:1147–1167.

117. Molteni F, Stockdale T, Balmaseda M, Balsamo G,Buizza R, Ferranti L, Magnusson L, Mogensen K,Palmer TN, Vitart F. The new ECMWF seasonalforecast system (System 4). ECMWF Technical Mem-orandum; 2011, 656. Available at: http://www.ecmwf.int/publications/library/do/references/show?id=90277.(Accessed January 2, 2012)

118. Lienert F, Fyfe JC, Merryfield WJ. Do climate mod-els capture the tropical influences on North Pacificsea surface temperature variability? J Clim 2011,24:6203–6209. doi:10.1175/JCLI-D-11-00205.1.

119. Vanniere B, Guilyardi E, Madec G, Doblas-ReyesFJ, Woolnough S. Using seasonal hindcasts to under-stand the origin of the equatorial cold tongue bias inCGCMs and its impact on ENSO. Clim Dyn2012,40:963–981. doi:10.1007/s00382-012-1429-6.

120. Sigmond M, Fyfe JC, Flato GM, Kharin VV, Merry-field WJ. Seasonal forecast skill of Arctic sea ice areain a dynamical forecast system. Geophys Res Lett2013. doi:10.1002/grl.50129.

121. Zhang J, Steele M, Lindsay R, Schweiger A, Mori-son J. Ensemble 1-year predictions of Arctic sea icefor the spring and summer of 2008. Geophys Res Lett2008, 35:L08502. doi:10.1029/2008GL033244.

122. Lindsay R, Haas C, Hendricks S, Hunkeler P,Kurtz N, Paden J, Panzer B, Sonntag J, Yungel J,Zhang J. Seasonal forecasts of Arctic sea ice initial-ized with observations of ice thickness. Geophys ResLett 2012, 39:L21502. doi: 10.1029/2012GL053576.

264 © 2013 John Wiley & Sons, Ltd. Volume 4, Ju ly/August 2013

Page 21: Seasonal climate predictability and forecasting: status and …climateknowledge.org/figures/Rood_Climate_Change_AOSS480... · 2014-05-29 · Advanced Review Seasonal climate predictability

WIREs Climate Change Seasonal climate predictability and forecasting

123. Magnusson L, Alonso-Balmaseda M, Corti S,Molteni F, Stockdale T. Evaluation of forecast strate-gies for seasonal and decadal forecasts in pres-ence of systematic model errors. Clim Dyn 2012,doi:10.1007/s00382-012-1599-2.

124. Kumar A, Chen M, Zhang L, Wang W, Xue Y,Wen C, Marx L, Huang B. An analysis of the non-stationarity in the bias of sea surface temperatureforecasts for the NCEP Climate Forecast System (CFS)version 2. Mon Weather Rev 2012, 140:3003–3016.

125. Gneiting T, Raftery AE. Weather forecasting withensemble methods. Science 2005, 310:248–249.

126. Doblas-Reyes FJ, Weisheimer A, Deque M, KeenlysideN, McVean M, Murphy JM, Rogel P, SmithD, Palmer TN. Addressing model uncertainty in sea-sonal and annual dynamical seasonal forecasts. QJ Roy Meteorol Soc 2009, 135:1538–1559. doi:10.1002/qj.464.

127. Berner J, Doblas-Reyes FJ, Palmer TN, Shutts G,Weisheimer A. Impact of a cellular automatonbackscatter scheme on the systematic error and sea-sonal prediction skill of a global climate model. PhilTrans Roy Soc A2008, 366:2561–2579. doi:10.1098/rsta.2008.0033.

128. Hagedorn R, Doblas-Reyes FJ, Palmer TN. The ratio-nale behind the success of multi-model ensembles inseasonal forecasting. Part I: basic concept. Tellus A2005, 57:219–233.

129. Weigel A, Liniger MA, Appenzeller C. Can multi-model combination really enhance the prediction skillof probabilistic ensemble forecasts? Q J Roy MeteorolSoc2008, 134:241–260.

130. Batte L, Deque M. Seasonal predictions of precipi-tation over Africa using coupled ocean-atmospheregeneral circulation models: skill of the ENSEMBLESproject multimodel ensemble forecasts. Tellus A 201163:283–299. doi:10.1111/j1600-0870.2010.00493.x.

131. Doblas-Reyes FJ, Hagedorn R, Palmer TN. The ratio-nale behind the success of multi-model ensembles inseasonal forecasting. Part II: calibration and combina-tion. Tellus A 2005, 57:234–252.

132. Weigel AP, Liniger MA, Appenzeller C. Seasonalensemble forecasts: are recalibrated single modelsbetter than multimodels? Mon Weather Rev 2009,137:1460–1479.

133. Weigel AP, Knutti R, Liniger MA, Appenzeller C.Risks of model-weighting in multimodel climate pro-jections. J Clim 2010, 23:4175–4191.

134. Weisheimer A, Palmer TN, Doblas-Reyes FJ. Assess-ment of representations of model uncertainty inmonthly and seasonal forecast ensembles. Geo-phys Res Lett 2011, 38:L16703. doi:10.1029/2011GL048123.

135. Rodwell M, Doblas-Reyes FJ. Predictability and pre-diction of European monthly to seasonal climateanomalies. J Clim 2006, 19:6025–6046.

136. Quan XW, Hoerling MP, Whitaker JS, Bates GT,Xu TY. Diagnosing sources of U.S. seasonal forecastskill. J Clim 2006, 19:3279–3293.

137. Douville H. Relevance of soil moisture for seasonalatmospheric predictions: is it an initial value problem?Clim Dyn 2004, 22:429–446.

138. Doblas-Reyes FJ, Hagedorn R, Palmer TN, Mor-crette J-J. Impact of increasing greenhouse gas concen-trations in seasonal ensemble forecasts. Geophys ResLett 2006, 33:L07708. doi:10.1029/2005GL025061.

139. Sordo CM, Frías MD, Herrera S, Cofino AS, GutierrezJM. Interval-based statistical validation of operationalseasonal forecasts in Spain conditioned to El Nino-Southern Oscillation events. J Geophys Res 2008,113:D17121. doi:10.1029/2007JD009536.

140. Frías MD, Herrera S, Cofino AS, Gutierrez JM.Assessing the skill of precipitation and temperatureseasonal forecasts in Spain: Windows of opportunityrelated to ENSO events. J Clim 2010, 23:209–220.doi:10.1175/2009JCLI2824.1.

141. Johansson A. Prediction skill of the NAO and PNAfrom daily to seasonal time scales. J Clim 2007,20:1957–1975. doi:10.1175/JCLI4072.1.

142. Vitart F, Huddleston M, Deque M, Peake D, PalmerTN, Stockdale T, Davey M, Ineson S, Weisheimer A.Dynamically-based seasonal forecast of Atlantic trop-ical storm activity issued in June by EUROSIP.Geophys Res Lett 2007, 34:L16815. doi:10.1029/2007GL030740.

143. Smith DM, Eade R, Dunstone NJ, Fereday D, Mur-phy JM, Pohlmann H, Scaife AA. Skilful multi-yearpredictions of Atlantic hurricane frequency. NatGeosci 2010, 3:846–849. doi:10.1038/ngeo1004.

144. Wang H, Schemm J-KE, Kumar A, Wang W, Long L,Chelliah M, Bell GD, Peng P. A statistical forecastmodel for Atlantic seasonal hurricane activity based onthe NCEP dynamical seasonal forecast. J Clim 2009,22:4481–4500.

145. Zhao M, Held IM, Vecchi GA. Retrospective forecastsof the hurricane season using a global atmosphericmodel assuming persistence of SST anomalies. MonWeather Rev 2010, 138:3858–3868.

146. LaRow T, Stefanova L, Shin D-W, Cocke S. Sea-sonal Atlantic tropical cyclone hindcasting/forecastingusing two sea surface temperature datasets. GeophysRes Lett 2010, 37:L02804. doi:10.1029/2009GL041459.

147. Vecchi GA, Zhao M, Wang H, Villarini G, Rosati A,Kumar A, Held IM, Gudgel R. Statistical-dynamicalpredictions of seasonal North Atlantic hurricaneactivity. Mon Weather Rev 2011, 139:1070–1082.doi:10.1175/2010MWR3499.1.

148. Weisheimer A, Doblas-Reyes FJ, Jung T, Palmer TN.On the predictability of the extreme summer 2003over Europe. Geophys Res Lett 2011, 38:L05704.doi:10.1029/2010GL046455.

Volume 4, Ju ly/August 2013 © 2013 John Wiley & Sons, Ltd. 265

Page 22: Seasonal climate predictability and forecasting: status and …climateknowledge.org/figures/Rood_Climate_Change_AOSS480... · 2014-05-29 · Advanced Review Seasonal climate predictability

Advanced Review wires.wiley.com/climatechange

149. Barnston AG, Mason SJ. Evaluation of IRI’s seasonalclimate forecasts for the extreme 15% tails. WeatherForecast 2011, 26:545–554. doi:10.1175/WAF-D-10-05009.1.

150. Hamilton E, Eade R, Graham RJ, Scaife AA,Smith DM, Maidens A, MacLachlan C. Forecastingthe number of extreme daily events on seasonaltimescales. J Geophys Res 2012, 117:D03114. doi:10.1029/2011JD016541.

151. Haylock MR, Hofstra N, Klein Tank AMG, Klok EJ,Jones PD, New M, European A. daily high-resolutiongridded dataset of surface temperature andprecipitation. J Geophys Res 2008, 113:D20119. doi:10.1029/2008JD10201.

152. Solomon S, Daniel JS, Neely III RR, Vernier J-P,Dutton EG, Thomason LW. The persistently vari-able ‘‘background’’ stratospheric aerosol layer andglobal climate change. Science 2011, 333:866–870.doi:10.1126/science.1206027.

153. Kaufmann RK, Kauppi H, Mann ML, Stock JH.Reconciling anthropogenic climate change withobserved temperature 1998–2008. PNAS 2011.doi:10.1073/pnas.1102467108.

154. Liniger MA, Mathis H, Appenzeller C, Doblas-Reyes FJ. Realistic greenhouse gas forcing and sea-sonal forecasts. Geophys Res Lett 2007, 34:L04705.doi:10.1029/2006GL028335.

155. Vitart F, Doblas-Reyes FJ. Impact of an increase ofgreenhouse gas concentrations during the past 50 yearson tropical storms in a coupled GCM. Tellus A 2007,59:417–427.

156. Boer GJ. Climate trends in a seasonal forecasting sys-tem. Atmos Ocean 2009, 47:123–138. doi:10.3137/AO1002.2009.

157. Wang W, Chen M, Kumar A. An assessmentof the CFS real-time seasonal forecasts.Weather Forecast 2010, 25:950–969. doi:10.1175/2010WAF2222345.1.

158. Shin SI, Sardeshmukh PD. Critical influence of thepattern of tropical ocean warming on remoteclimate trends. Clim Dyn 2011, 36:1577–1591.doi:10.1007/s00382-009-0732-3.

159. Maycock AC, Keeley SPE, Charlton-Perez AJ, Doblas-Reyes FJ. Stratospheric circulation in seasonal fore-casting models: implications for seasonal prediction.Clim Dyn 2011, 36:309–321.

160. Teuling AJ, Seneviratne SI, Stockli R, ReichsteinM, Moors E, Ciais P, Luyssaert S, van den HurkB, Ammann C, Bernhofer C, et al. Contrastingresponse of European forest and grassland energyexchange to heatwaves. Nat Geosci 2010, 3:722–727.doi:10.1038/ngeo950.

161. Gottschalck J, Wheeler M, Weickmann K, Vitart F,Savage N, Lin H, Hendon H, Waliser D, Sperber K,Nakagawa M, et al. A framework for assessingoperational model MJO forecasts: a project of

the CLIVAR Madden-Julian Oscillation WorkingGroup. Bull Am Meteorol Soc 2010, 91:1247–1258.doi:10.1175/2010BAMS2816.1.

162. Vitart F, Molteni F. Dynamical extended-range pre-diction of early monsoon rainfall over India. MonWeather Rev 2009, 137:1480–1492.

163. Rashid HA, Hendon HH, Wheeler MC, Alves O. Pre-diction of the Madden-Julian oscillation with thePOAMA dynamical prediction system. Clim Dyn2010, 36:649–661.

164. Elsberry RL, Hordan MS, Vitart F. Predictability oftropical cyclone events on intraseasonal timescalewith the ECMWF monthly forecast model. Asia-PacJ Atmos Sci 2009, 46:135–153.

165. Vitart F, Molteni F. Simulation of the MJO and its tele-connections in the ECMWF forecast system. Q J RoyMeteorol Soc 2010, 136:842–855.

166. Hermanson L, Sutton RT. Climate predictability inthe second year. Phil Trans Roy Soc A 2009, 367:913–916. doi:10.1098/rsta.2008.0181.

167. Doblas-Reyes FJ, Balmaseda MA, Weisheimer A,Palmer TN. Decadal climate prediction with theECMWF coupled forecast system: Impact of oceanobservations. J Geophys Res 2011, 116:D19111.doi:10.1029/2010JD015394.

168. Bechtold P, Koehler M, Jung T, Doblas-Reyes FJ,Leutbecher M, Rodwell MJ, Vitart F, Balsamo G.Advances in simulating atmospheric variability withthe ECMWF model: From synoptic to decadal time-scales. Q J Royal Meteorol Soc 2008, 134:1337–1351.

169. Palmer TN, Doblas-Reyes FJ, Weisheimer A, Rod-well M. Towards ‘‘seamless’’ prediction: Calibrationof climate-change projections using seasonal forecasts.Bull Am Meteorol Soc 2008, 89:459–470.

170. Vitart F, Buizza R, Alonso Balmaseda M, Bal-samo G, Bidlot J-R, Bonet A, Fuentes M, Hofs-tadler A, Molteni F, Palmer TN. The new VAREPS-monthly forecasting system: a first step towards seam-less prediction. Q J Roy Meteorol Soc 2008, 134:1789–1799.

171. Palmer TN, Doblas-Reyes FJ, Weisheimer A, Rod-well MJ. Reply to comments to ‘‘Toward seamlessprediction: calibration of climate change projectionsusing seasonal forecasts. Bull Am Meteorol Soc 2009,90:1551–1554. doi:10.1175/2009BAMS2916.1.

172. Scaife AA, Buontempo C, Ringer M, Sanderson M,Gordon C, Mitchell JFB. Toward seamless predic-tion: Calibration of climate change projections usingseasonal forecasts. Bull Am Meteorol Soc 2000990:1549–1551. doi:10.1175/2009BAMS2753.1.

173. Martin GM, Milton SF, Senior CA, Brooks ME, Ine-son S, Reichler T, Kim J. Analysis and reduction of sys-tematic errors through a seamless approach to model-ing weather and climate. J Clim 2010, 23:5933–5957.doi:10.1175/2010JCLI3541.1.

266 © 2013 John Wiley & Sons, Ltd. Volume 4, Ju ly/August 2013

Page 23: Seasonal climate predictability and forecasting: status and …climateknowledge.org/figures/Rood_Climate_Change_AOSS480... · 2014-05-29 · Advanced Review Seasonal climate predictability

WIREs Climate Change Seasonal climate predictability and forecasting

174. Brunet G, Shapiro M, Hoskins B, Moncrieff M,Dole R, Kiladis GN, Kirtman B, Lorenc A, Mills B,Morss R, et al. Collaboration of the weather andclimate communities to advance subseasonal-to-seasonal prediction. Bull Am Meteorol Soc 2010,91:1397–1406. doi:10.1175/2010BAMS3013.1.

175. Díez E, Primo C, García-Moya JA, GutierrezJM, Orfila B. Statistical and dynamical downscal-ing of precipitation over Spain from DEMETERseasonal forecasts. Tellus A 2005, 57:409–423.doi:10.1111/j1600-0870.2005.00130.x.

176. Díez E, Orfila B, Frías MD, Fernandez J, CofinoAS, Gutierrez JM. Downscaling ECMWF seasonalprecipitation forecasts in Europe using the RCA model.Tellus A 2011, 63:757–762. doi:10.1111/j1600-0870.2011.00523.x.

177. Charles A, Timbal B, Fernandez E, Hendon H. Ana-logue downscaling of seasonal rainfall forecasts inthe Murray Darling basin. Mon Weather Rev 2013,141:1099–1117. doi:10.1175/MWR-D-12-00098.1.

178. Chen H, Sun J, Wang H. A statistical downscalingmodel for forecasting summer rainfall in China fromDEMETER hindcast datasets. Weather Forecast 2012,27:608–628. doi:10.1175/WAF-D-11-00079.1.

179. Nobre P, Moura AD, Sun L. Dynamical downscalingof seasonal climate prediction over Nordeste Brazilwith ECHAM3 and NCEP’s regional spectral modelsat IRI. Bull Am Meteorol Soc 2001, 82:2787–2796.

180. Sun L, Li H, Zebiak SE, Moncunill DF, Filho FDADS,Moura AD. An operational dynamical downscal-ing prediction system for Nordeste Brazil and the2002–04 real-time forecast evaluation. J Clim 2006,19:1990–2007. doi:10.1175/JCLI3715.1.

181. Diro GT, Tompkins AM, Bi X. Dynamical downscal-ing of ECMWF Ensemble seasonal forecasts overEast Africa with RegCM3. J Geophys Res 2012,117:D16103.

182. Hawkins E, Robson J, Sutton R, Smith D, Keenly-side N. Evaluating the potential for statistical decadalpredictions of sea surface temperatures with a per-fect model approach. Clim Dyn 2011, 37:2495–2509.doi:10.1007/s00382-011-1023-3.

183. Anderson J, van den Dool H, Barnston A, Chen W,Stern W, Ploshay J. Present-day capabilities of numeri-cal and statistical models for atmospheric extratropicalseasonal simulation and prediction. Bull Am MeteorolSoc 1999, 80:1349–1361.

184. Kulkarni MA, Acharya N, Kar SC, Mohanty UC,Tippett MK, Robertson AW, Luo J-J, Yamagata T.Probabilistic prediction of Indian summer monsoonrainfall using global climate models. Theor ApplClimatol 2011, 107:441–450. doi:10.1007/s00704-011-0493-x.

185. DelSole T, Yang X, Tippett MK. Is unequal weight-ing significantly better than equal weighting formulti-model forecasting? Q J Roy Meteor Soc 2013,139:176–183. doi:10.1002/qj.1961.

186. Coelho CAS, Pezzulli S, Balmaseda MA, Doblas-Reyes FJ, Stephenson DB. Forecast calibration andcombination: a simple Bayesian approach for ENSO.J Clim 2004, 17:1504–1516.

187. Challinor AJ, Slingo JM, Wheeler TR, Doblas-Reyes FJ. Probabilistic simulations of crop yield overwestern India using the DEMETER seasonal hindcastensembles. Tellus 2005, 57A:498–512.

188. Thompson MC, Doblas-Reyes FJ, Mason SJ, Hage-dorn R, Connor SJ, Phindela T, Morse AP, PalmerTN. Malaria early warnings based on seasonal climateforecasts from multi-model ensembles. Nature 2006,439:576–579.

189. Gamiz-Fortis SR, Pozo-Vazquez D, Trigo R, Castro-Díez Y. Quantifying the predictability of winter riverflow in Iberia. Part II: seasonal predictability. J Clim2008, 21:2503–2518.

190. Gamiz-Fortis SR, Pozo-Vazquez D, Trigo R, Castro-Díez Y. Quantifying the predictability of winter riverflow in Iberia. Part I: interannual predictability. J Clim2008, 21:2484–2502.

191. Yuan X, Wood EF, Luo L, Pan M. A first look atClimate Forecast System version 2 (CFSv2) for hydro-logical seasonal prediction. Geophys Res Lett 2011,38:L13402. doi:10.1029/2011GL047792.

192. García-Morales M, Dubus L. Forecasting precipitationfor hydroelectric power management: how to exploitGCM’s seasonal ensemble forecasts. Int J Climatol2007, 27:1691–1705.

193. Challinor AJ, Osborne T, Shaffrey L, Weller H,Morse A, Wheeler T, Vidale PL. Methods andresources for climate impacts research. BullAm Meteor Soc 2009, 90:836–848. doi:10.1175/2008BAMS2403.1.

194. Sivakumar MVK, Gommes R, Baier W. Agromete-orology and sustainable agriculture. Agric For-est Meteorol 2000, 103:11–26. doi:10.1016/S0168-1923(00)00115-5.

195. Jarvis AJ, Leedal DT, Hewitt CN. Climate-societyfeedbacks and the avoidance of dangerous cli-mate change. Nat Clim Change 2012, 2:668–671.doi:10.1038/nclimate1586.

196. Hewitt C, Mason S, Walland D. The global frame-work for climate services. Nat Clim Change 2012,2:831–832. doi:10.1038/nclimate1745.

197. Asrar G, Hurrell J, Busalacchi A. A need for ‘‘action-able’’ climate science and information: summary ofWCRP Open Science Conference. Bull Am MeteorolSoc 2013, 94:ES8–ES12. doi:10.1175/BAMS-D-12-00011.1.

198. Graham RJ, Yun W-T, Kim J, Kumar A, Jones D, Bet-tio L, Gagnon N, Kolli RK, Smith D. Long-range fore-casting and the global framework for climate services.Clim Res 2011, 47:47–55. doi:10.3354/cr00963.

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199. Asrar G, Ryabinin V, Detemmerman V. Climate sci-ence and services: providing climate information foradaptation, sustainable development and risk manage-ment. Curr Opin Environ Sustain 2012, 4:88–100.doi:10.1016/j.cosust.2012.01.003.

200. IEA. Risk Quantification and Risk Management inRenewable Energy Projects. International EnergyAgency report. 2011. Available at: http://en.openei.org/wiki/IEA-Risk_Quantification_and_Risk_Management_in_Renewable_Energy_Projects.(Accessed March 4, 2012)

201. The Economist. Managing the risk in renew-able energy. Economist Intelligence Unit report onbehalf of SwissRe. 2011. Available at: http://www.managementthinking.eiu.com/managing-renewables-risks.html. (Accessed March 4, 2012)

202. Troccoli A, Boulahya MS, Dutton JA, Furlow J, Gur-ney RJ, Harrison M. Weather and climate risk man-agement in the energy sector. Bull Am Meteorol Soc2010, 91:785–788.

FURTHER READING/RESOURCES

A very useful and comprehensive resource to learn about seasonal forecasting is NRC, 2010. Assessment ofintraseasonal to interannual climate prediction and predictability. The National Academies Press. Available at:http://www.nap.edu/catalog.php?record_id=12878.The Working Group on Seasonal-to-Interannual Prediction (WGSIP, http://www.wcrp-climate.org/wgsip/)coordinates research activities among the seasonal forecasting community and sponsors the Climate-systemHistorical Forecast Project (CHFP), which collects and publicly disseminates global dynamical seasonalpredictions.The World Weather Research Programme/Working Group on Numerical Experimentation (WWRP/WGNE)Joint Working Group on Forecast Verification Research makes available continuously-updated descriptions ofdifferent forecast verification tools and links to well-tested functions (http://www.cawcr.gov.au/projects/verifica-tion/). Besides, arguably, the most comprehensive forecast verification reference available is Jolliffe IT,Stephenson DB, eds. Forecast Verification: A Practitioner’s Guide in Atmospheric Science, Wiley; 2011,274 pp. (http://empslocal.ex.ac.uk/people/staff/dbs202/fvb2010/).Full information about the Global Framework on Climate Services (GFCS) can be found inhttp://www.wmo.int/pages/gfcs/gfcs_en.html. Other related initiatives such as the Climate Services Partnership(CSP; http://climate-services.org/node and http://csp.iri.columbia.edu/) and the Advancing Renewable Energywith Climate Services (ARECS; http://www.ic3.cat/openbox.php?menu=195) have a wealth of information onthis issue.

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