ECMWF Seasonal Forecasting System Research Articles

Assessment of seasonal forecasting errors of the ECMWF system in the eastern Indian Ocean

The interannual variability of the Equatorial Eastern Indian Ocean (EEIO) is highly relevant for the climate anomalies on adjacent continents and affects global teleconnection patterns. Yet, this is an area where seasonal forecasting systems exhibit large errors. Here we investigate the reasons for these errors in the ECMWF seasonal forecasting system SEAS5 using tailored diagnostics and a series of numerical experiments.Results indicate that there are two fundamental and independent sources of forecast errors in the EEIO. The first one is of atmospheric nature and is largely related with too strong and stable easterly atmospheric circulation present in the equatorial Indian Ocean. This induces an easterly bias which leaves the coupled model predominantly in a state with a shallow thermocline and cold SSTs in the EEIO. The second error is of oceanic origin, associated with a too shallow thermocline, which enhances the SST errors arising from errors in the wind. Ocean initial conditions, which depend on both the quality of the assimilation and the ocean model, play an important role in this context. Nevertheless, it is found that the version of the ocean model used for the forecast can also play a non-negligible role at the seasonal time scales, by amplifying or damping the subsurface errors in the initial conditions.Errors in the EEIO are regime-dependent, having different causes in the warm (deep thermocline) regime with strong atmospheric convection and in the cold (shallow thermocline) regime. Errors also exhibit decadal variations, which challenges the calibration methods used in seasonal forecasts.

Atmospheric Periodic Oscillations

The North Atlantic Oscillation, Quasi Biennial Oscillation, their negative and positive phases are discussed in presented article An El Niño event is a prolonged period of abnormally high sea-surface temperatures (SST) in the tropical Pacific Ocean. It goes hand in hand with changes in atmospheric conditions and can have strong repercussions on global weather patterns. El Niño can also significantly affect the global average temperature The ECMWF seasonal forecast system has been operational for more than five years and will soon be replaced by an upgraded system SEAS5.

Seasonal streamflow forecasting in South America’s largest rivers

Study regionSouth America large basins (>5000 km²) Study focusThis work represents a first assessment of seasonal streamflow forecasts in South America based on a continental-scale application of a large scale hydrologic-hydrodynamic model and ECMWF's seasonal forecasting system precipitation forecasts (SEAS5-SSF) with bias correction. Seasonal streamflow forecasts were evaluated against a reference model run. Forecast skill was estimated relative to the Ensemble Streamflow Prediction (ESP) method. New hydrological insightsWe observed that bias correction was essential to obtain positive skill of SEAS5-SSF over ESP, which remained a hard to beat benchmark, especially in regions with high seasonality, and highly dependent on initial conditions. SEAS5-SSF skill was found to be dependent on initialization month, basin and lead time. Rivers where the skill is higher were Amazon, Araguaia, Tocantins and Paraná.

Seasonal fire danger forecasts for supporting fire prevention management in an eastern Mediterranean environment: the case of Attica, Greece

Abstract. Forest fires constitute a major environmental and socioeconomic hazard in the Mediterranean. Weather and climate are among the main factors influencing forest fire potential. As fire danger is expected to increase under changing climate, seasonal forecasting of meteorological conditions conductive to fires is of paramount importance for implementing effective fire prevention policies. The aim of the current study is to provide high-resolution (∼9 km) probabilistic seasonal fire danger forecasts, utilizing the Canadian Forest Fire Weather Index (FWI) for the Attica region, one of the most fire-prone regions in Greece and the Mediterranean, employing the fifth-generation ECMWF seasonal forecasting system (SEAS5). Results indicate that, depending on the lead time of the forecast, both the FWI and ISI (Initial Spread Index) present statistically significant high discrimination scores and can be considered reliable in predicting above-normal fire danger conditions. When comparing the year-by-year fire danger predictions with the historical fire occurrence recorded by the Hellenic Fire Service database, both seasonal FWI and ISI forecasts are skilful in identifying years with a high number of fire occurrences. Overall, fire danger and its subcomponents can potentially be exploited by regional authorities in fire prevention management regarding preparedness and resources allocation.

The suitability of a seasonal ensemble hybrid framework including data-driven approaches for hydrological forecasting

Abstract. Hydrological forecasts are important for operational water management and near-future planning, even more so in light of the increased occurrences of extreme events such as floods and droughts. Having a forecasting framework, which is flexible in terms of input forcings and forecasting locations (local, regional, or national) that can deliver this information in fast and computational efficient manner, is critical. In this study, the suitability of a hybrid forecasting framework, combining data-driven approaches and seasonal (re)forecasting information from dynamical models, to predict hydrological variables was explored. Target variables include discharge and surface water levels for various stations at a national scale, with the Netherlands as the focus. Five different machine learning (ML) models, ranging from simple to more complex and trained on historical observations of discharge, precipitation, evaporation, and seawater levels, were run with seasonal (re)forecast data, including the European Flood Awareness System (EFAS) and ECMWF seasonal forecast system (SEAS5), of these driver variables in a hindcast setting. The results were evaluated using the evaluation metrics, i.e. anomaly correlation coefficient (ACC), continuous ranked probability (skill) score (CRPS and CRPSS), and Brier skill score (BSS), in comparison to a climatological reference hindcast. Aggregating the results of all stations and ML models revealed that the hindcasting framework outperformed the climatological reference forecasts by roughly 60 % for discharge predictions (80 % for surface water level predictions). Skilful prediction for the first lead month, independently of the initialization month, can be made for discharge. The skill extends up to 2–3 months for spring months due to snowmelt dynamic captured in the training phase of the model. Surface water level hindcasts showed similar skill and skilful lead times. While the different ML models showed differences in performance during a testing and training phase using historical observations, running the ML framework in a hindcast setting showed only minor differences between the models, which is attributed to the uncertainty in seasonal forecasts. However, despite being trained on historical observations, the hybrid framework used in this study shows similar skilful predictions to previous large-scale forecasting systems. With our study, we show that a hybrid framework is able to bring location-specific skilful seasonal forecast information with global seasonal forecast inputs. At the same time, our hybrid approach is flexible and fast, and as such, a hybrid framework could be adapted to make it even more interesting to water managers and their needs, for instance, as part of a fast model-predictive control framework.

Calibration of ECMWF Seasonal Ensemble Precipitation Reforecasts in Java (Indonesia) Using Bias-Corrected Precipitation and Climate Indices

AbstractThe seasonal precipitation forecast is one of the essential inputs for economic and agricultural activities and has significant impact on decision-making. Large-scale modes of climate variability have strong relationships with seasonal rainfall in Java and are natural candidates for use as potential predictors in a statistical postprocessing application. We explore whether using climate indices as additional predictors in the statistical postprocessing of ECMWF Seasonal Forecast System 5 (SEAS5) precipitation can improve skill. We use parametric statistical postprocessing by applying a logistic distribution-based ensemble model output statistics (EMOS) technique. We add a variety of potential predictors in the analysis, namely SEAS5 raw and empirical quantile mapping (EQM) bias-corrected precipitation, Niño-3.4 index, dipole mode index (DMI), Madden–Julian oscillation (MJO) indices, sea surface temperature (SST) around Java, and several other predictors. We analyze the period of 1981–2010, focusing on July, August, September, and October. We use the continuous ranked probability skill score (CRPSS) and Brier skill score (BSS) in a comparative verification of raw, EQM, and EMOS seasonal precipitation forecasts. We have found that it is essential to use EQM-corrected precipitation as a predictor instead of raw precipitation in the latter. Besides, Niño-3.4 and DMI forecasts are not needed as extra predictors to improve monthly precipitation forecasts for the first lead month, except for September. However, for somewhat longer lead months, in September and October when there is more skill than climatology, the model that includes only Niño-3.4 and DMI forecasts as potential predictors performs about the same compared to the model that uses only EQM-corrected precipitation as a predictor.

When and where do ECMWF seasonal forecast systems exhibit anomalously low signal‐to‐noise ratio?

Seasonal predictions of wintertime climate in the Northern Hemisphere midlatitudes, while showing clear correlation skill, suffer from anomalously low signal‐to‐noise ratio. The low signal‐to‐noise ratio means that forecasts need to be made with large ensemble sizes and require significant post‐processing to correct the forecast distribution. In this study, a recently introduced statistical model of seasonal climate predictability is adapted so that it can be used to examine the signal‐to‐noise ratio in two versions of the ECMWF seasonal forecast system. Three novel features of the low signal‐to‐noise ratio are revealed. The low signal‐to‐noise ratio is present only for forecasts initialized on 1 November and not for forecasts initialized on 1 December. The low signal‐to‐noise ratio is predominantly a feature of the lower and middle troposphere and is not present in the stratosphere. The low signal‐to‐noise ratio is linked to low signal amplitude of the forecast systems in early winter. Future studies attempting to examine the signal‐to‐noise ratio should focus on the extent to which this early winter variability is predictable.

A Comparative Verification of Raw and Bias-Corrected ECMWF Seasonal Ensemble Precipitation Reforecasts in Java (Indonesia)

AbstractDynamical seasonal forecasts are afflicted with biases, including seasonal ensemble precipitation forecasts from the new ECMWF seasonal forecast system 5 (SEAS5). In this study, biases have been corrected using empirical quantile mapping (EQM) bias correction (BC). We bias correct SEAS5 24-h rainfall accumulations at seven monthly lead times over the period 1981–2010 in Java, Indonesia. For the observations, we have used a new high-resolution (0.25°) land-only gridded rainfall dataset [Southeast Asia observations (SA-OBS)]. A comparative verification of both raw and bias-corrected reforecasts is performed using several verification metrics. In this verification, the daily rainfall data were aggregated to monthly accumulated rainfall. We focus on July, August, and September because these are agriculturally important months; if the rainfall accumulation exceeds 100 mm, farmers may decide to grow a third rice crop. For these months, the first 2-month lead times show improved and mostly positive continuous ranked probability skill scores after BC. According to the Brier skill score (BSS), the BC reforecasts improve upon the raw reforecasts for the lower precipitation thresholds at the 1-month lead time. Reliability diagrams show that the BC reforecasts have good reliability for events exceeding the agriculturally relevant 100-mm threshold. A cost/loss analysis, comparing the potential economic value of the raw and BC reforecasts for this same threshold, shows that the value of the BC reforecasts is larger than that of the raw ones, and that the BC reforecasts have value for a wider range of users at 1- to 7-month lead times.

Contributions of SST Anomalies in the Indo-Pacific Ocean to the Interannual Variability of Tropical Cyclone Genesis Frequency over the Western North Pacific

AbstractThis study attempts to evaluate quantitatively the contributions of sea surface temperature (SST) anomalies in the Indo-Pacific Ocean to the interannual variability of tropical cyclone (TC) genesis frequency (TCGF) over the western North Pacific (WNP). Three SST factors in the Indo-Pacific Ocean are found to play key roles in modulating the interannual variability of WNP TCGF. They are summer SST anomaly in the east Indian Ocean (EIO), the summer El Niño–Southern Oscillation Modoki index (EMI), and the spring SST gradient (SSTG) between the southwestern Pacific and the western Pacific warm pool. Results show that the three factors together can explain 72% of the total variance of WNP TCGF in the typhoon season for the period 1980–2015. Among them, the spring SSTG and the summer EIO contribute predominantly to the interannual variability of TCGF, followed by the summer EMI, with respective contributions being 39%, 38%, and 23%. Further analysis shows that the summer EMI was affected significantly by the spring SSTG and thus had a relatively lower contribution to the TCGF than the spring SSTG. In addition, a statistical model is constructed to predict the WNP TCGF in the typhoon season by a combination of the May EIO and the spring SSTG. The new model can reproduce well the observed WNP TCGF and shows an overall better skill than the ECMWF Seasonal Forecasting System 5 (SEAS5) hindcasts. This statistical model provides a good tool for seasonal prediction of WNP TCGF.

SEAS5: the new ECMWF seasonal forecast system

Abstract. In this paper we describe SEAS5, ECMWF's fifth generation seasonal forecast system, which became operational in November 2017. Compared to its predecessor, System 4, SEAS5 is a substantially changed forecast system. It includes upgraded versions of the atmosphere and ocean models at higher resolutions, and adds a prognostic sea-ice model. Here, we describe the configuration of SEAS5 and summarise the most noticeable results from a set of diagnostics including biases, variability, teleconnections and forecast skill. An important improvement in SEAS5 is the reduction of the equatorial Pacific cold tongue bias, which is accompanied by a more realistic El Niño amplitude and an improvement in El Niño prediction skill over the central-west Pacific. Improvements in 2 m temperature skill are also clear over the tropical Pacific. Sea-surface temperature (SST) biases in the northern extratropics change due to increased ocean resolution, especially in regions associated with western boundary currents. The increased ocean resolution exposes a new problem in the northwest Atlantic, where SEAS5 fails to capture decadal variability of the North Atlantic subpolar gyre, resulting in a degradation of DJF 2 m temperature prediction skill in this region. The prognostic sea-ice model improves seasonal predictions of sea-ice cover, although some regions and seasons suffer from biases introduced by employing a fully dynamical model rather than the simple, empirical scheme used in System 4. There are also improvements in 2 m temperature skill in the vicinity of the Arctic sea-ice edge. Cold temperature biases in the troposphere improve, but increase at the tropopause. Biases in the extratropical jets are larger than in System 4: extratropical jets are too strong, and displaced northwards in JJA. In summary, development and added complexity since System 4 has ensured that SEAS5 is a state-of-the-art seasonal forecast system which continues to display a particular strength in the El Niño Southern Oscillation (ENSO) prediction.

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

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

Characterization of the near surface wind speed distribution at global scale: ERA-Interim reanalysis and ECMWF seasonal forecasting system 4

The present developments in 10 m wind seasonal forecast products have lead to a growth in the number of studies analysing different aspects of both its predictability and applicability. However, there is still a lack of global studies analysing the statistical properties of the probability distribution of 10 m wind speed comparing the seasonal forecast systems with the widely used reanalysis products. To fill this gap we have studied the properties of the probability distributions of 10 m wind speed from the ERA-Interim reanalysis and the European Centre for Medium-Range Weather Forecasts System 4 seasonal forecast system. We have focused on two seasons, JJA and DJF, considering both their interannual and intraseasonal variability. The 10 m wind speed distribution has been characterized in terms of the four main moments of the probability distribution (mean, standard deviation, skewness and kurtosis). We have also computed the coefficient of variation to identify the regions with the higher wind variability and the Shapiro–Wilks goodness of fit test to assess their normality. This set of parameters is important to provide useful climate information in wind energy decision-making processes that use simple assumptions of the wind speed frequency distribution to properly estimate the wind energy potential. Besides, this study also illustrates where the discrepancies of the distributions of the seasonal predictions and the reference dataset are higher and, thus, which might need special attention from a bias adjustment perspective.

Seasonal streamflow forecasts for Europe – Part I: Hindcast verification with pseudo- and real observations

Abstract. Seasonal predictions of river flow can be exploited among others to optimise hydropower energy generation, navigability of rivers and irrigation management to decrease crop yield losses. This paper is the first of two papers dealing with a physical model-based system built to produce probabilistic seasonal hydrological forecasts, applied here to Europe. This paper presents the development of the system and the evaluation of its skill. The variable infiltration capacity (VIC) hydrological model is forced with bias-corrected output of ECMWF's seasonal forecast system 4. For the assessment of skill, we analysed hindcasts (1981–2010) against a reference run, in which VIC was forced by gridded meteorological observations. The reference run was also used to generate initial hydrological conditions for the hindcasts. The skill in run-off and discharge hindcasts is analysed with monthly temporal resolution, up to 7 months of lead time, for the entire annual cycle. Using the reference run output as pseudo-observations and taking the correlation coefficient as metric, hot spots of significant theoretical skill in discharge and run-off were identified in Fennoscandia (from January to October), the southern part of the Mediterranean (from June to August), Poland, northern Germany, Romania and Bulgaria (mainly from November to January), western France (from December to May) and the eastern side of Great Britain (January to April). Generally, the skill decreases with increasing lead time, except in spring in regions with snow-rich winters. In some areas some skill persists even at the longest lead times (7 months). Theoretical skill was compared to actual skill as determined with real discharge observations from 747 stations. Actual skill is generally substantially less than theoretical skill. This effect is stronger for small basins than for large basins. Qualitatively, the use of different skill metrics (correlation coefficient; relative operating characteristics, ROC, area; and ranked probability skill score, RPSS) leads to broadly similar spatio-temporal patterns of skill, but the level of skill decreases, and the area of skill shrinks, in the following order: correlation coefficient; ROC area below-normal (BN) tercile; ROC area above-normal (AN) tercile; ranked probability skill score; and, finally, ROC near-normal (NN) tercile.

Seasonal Drought Forecasting for Latin America Using the ECMWF S4 Forecast System

Meaningful seasonal prediction of drought conditions is key information for end-users and water managers, particularly in Latin America where crop and livestock production are key for many regional economies. However, there are still not many studies of the feasibility of such a forecasts at continental level in the region. In this study, precipitation predictions from the European Centre for Medium Range Weather (ECMWF) seasonal forecast system S4 are combined with observed precipitation data to generate forecasts of the standardized precipitation index (SPI) for Latin America, and their skill is evaluated over the hindcast period 1981–2010. The value-added utility in using the ensemble S4 forecast to predict the SPI is identified by comparing the skill of its forecasts with a baseline skill based solely on their climatological characteristics. As expected, skill of the S4-generated SPI forecasts depends on the season, location, and the specific aggregation period considered (the 3- and 6-month SPI were evaluated). Added skill from the S4 for lead times equaling the SPI accumulation periods is primarily present in regions with high intra-annual precipitation variability, and is found mostly for the months at the end of the dry seasons for 3-month SPI, and half-yearly periods for 6-month SPI. The ECMWF forecast system behaves better than the climatology for clustered grid points in the North of South America, the Northeast of Argentina, Uruguay, southern Brazil and Mexico. The skillful regions are similar for the SPI3 and -6, but become reduced in extent for the severest SPI categories. Forecasting different magnitudes of meteorological drought intensity on a seasonal time scale still remains a challenge. However, the ECMWF S4 forecasting system does capture the occurrence of drought events for the aforementioned regions and seasons reasonably well. In the near term, the largest advances in the prediction of meteorological drought for Latin America are obtainable from improvements in near-real-time precipitation observations for the region. In the longer term, improvements in precipitation forecast skill from dynamical models, like the fifth generation of the ECMWF seasonal forecasting system, will be essential in this effort.

Hydrological drought forecasting and skill assessment for the Limpopo River basin, southern Africa

Abstract. Ensemble hydrological predictions are normally obtained by forcing hydrological models with ensembles of atmospheric forecasts produced by numerical weather prediction models. To be of practical value to water users, such forecasts should not only be sufficiently skilful, they should also provide information that is relevant to the decisions end users make. The semi-arid Limpopo Basin in southern Africa has experienced severe droughts in the past, resulting in crop failure, economic losses and the need for humanitarian aid. In this paper we address the seasonal prediction of hydrological drought in the Limpopo River basin by testing three proposed forecasting systems (FS) that can provide operational guidance to reservoir operators and water managers at the seasonal timescale. All three FS include a distributed hydrological model of the basin, which is forced with either (i) a global atmospheric model forecast (ECMWF seasonal forecast system – S4), (ii) the commonly applied ensemble streamflow prediction approach (ESP) using resampled historical data, or (iii) a conditional ESP approach (ESPcond) that is conditional on the ENSO (El Niño–Southern Oscillation) signal. We determine the skill of the three systems in predicting streamflow and commonly used drought indices. We also assess the skill in predicting indicators that are meaningful to local end users in the basin. FS_S4 shows moderate skill for all lead times (3, 4, and 5 months) and aggregation periods. FS_ESP also performs better than climatology for the shorter lead times, but with lower skill than FS_S4. FS_ESPcond shows intermediate skill compared to the other two FS, though its skill is shown to be more robust. The skill of FS_ESP and FS_ESPcond is found to decrease rapidly with increasing lead time when compared to FS_S4. The results show that both FS_S4 and FS_ESPcond have good potential for seasonal hydrological drought forecasting in the Limpopo River basin, which is encouraging in the context of providing better operational guidance to water users.

Understanding and modelling extra-tropical teleconnections with the Indo-Pacific region during the northern winter

Teleconnections originated by anomalous heat sources over the tropical Indo-Pacific oceans are investigated in this paper comparing observational results with numerical simulations run as part of the re-forecast set for the latest ECMWF seasonal forecasting system (System 4). We show that the traditional methodology of linearly relating circulation anomalies to SST anomalies, while appropriate for signals originated in central and east Pacific, fails to adequately identify the response to anomalous heating over the west Pacific and most of the Indian Ocean, because of the relatively weak (or even negative) correlation between SST and rainfall anomalies in these regions. Instead, if teleconnections are computed from covariances with rainfall anomalies, a stronger consistency is found between observed and modelled patterns, as well as between diagnostics derived from seasonal and intra-seasonal time scales. The main mode of inter-annual variability in Indo-Pacific rainfall associated with planetary-scale teleconnections is a tri-polar structure with two positively correlated centres in the western Indian Ocean and the central Pacific, and a third centre around the maritime continents which is anti-correlated with the other two. In the extratropical response, positive rainfall anomalies over the western and central Indian Ocean (WCIO) are connected with a negative height anomaly centred over Alaska and a positive North Atlantic oscillation (NAO) signal, in a way reminiscent of the Cold-Ocean–Warm-Land pattern. This teleconnection cannot be explained by the ENSO-forced component of Indian Ocean rainfall, and is in phase with signals associated with the Madden–Julian oscillation. Results from the System-4 re-forecasts show that the ECMWF coupled model reproduces the broad features of tropical and extratropical teleconnections with a good degree of fidelity. However, the model significantly over-estimates the correlation between rainfall anomalies in the WCIO with those over the western and central Pacific. The impact of this deficiency on the extratropical flow is to weaken the relationship between the NAO and Indian Ocean rainfall on the seasonal scale, and to affect the projection of the ENSO response on the NAO. Finally, we argue that reproducing the correct relationship between SST and rainfall anomalies in different part of the Indo-Pacific basin is also crucial for the correct simulation of inter-decadal variability. Particular care should be taken in interpreting results of AGCM simulations with prescribed SST, where the absence of feedbacks between convection and SST over the warm pool region affects the simulation of rainfall anomalies.

Addressing model error through atmospheric stochastic physical parametrizations: impact on the coupled ECMWF seasonal forecasting system.

The finite resolution of general circulation models of the coupled atmosphere–ocean system and the effects of sub-grid-scale variability present a major source of uncertainty in model simulations on all time scales. The European Centre for Medium-Range Weather Forecasts has been at the forefront of developing new approaches to account for these uncertainties. In particular, the stochastically perturbed physical tendency scheme and the stochastically perturbed backscatter algorithm for the atmosphere are now used routinely for global numerical weather prediction. The European Centre also performs long-range predictions of the coupled atmosphere–ocean climate system in operational forecast mode, and the latest seasonal forecasting system—System 4—has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather forecasts. Here, we present results of the impact of these schemes in System 4 by contrasting the operational performance on seasonal time scales during the retrospective forecast period 1981–2010 with comparable simulations that do not account for the representation of model uncertainty. We find that the stochastic tendency perturbation schemes helped to reduce excessively strong convective activity especially over the Maritime Continent and the tropical Western Pacific, leading to reduced biases of the outgoing longwave radiation (OLR), cloud cover, precipitation and near-surface winds. Positive impact was also found for the statistics of the Madden–Julian oscillation (MJO), showing an increase in the frequencies and amplitudes of MJO events. Further, the errors of El Niño southern oscillation forecasts become smaller, whereas increases in ensemble spread lead to a better calibrated system if the stochastic tendency is activated. The backscatter scheme has overall neutral impact. Finally, evidence for noise-activated regime transitions has been found in a cluster analysis of mid-latitude circulation regimes over the Pacific–North America region.

A Physical Basis for the Probabilistic Prediction of the Accumulated Tropical Cyclone Kinetic Energy in the Western North Pacific

Abstract The relationship between El Niño–Southern Oscillation (ENSO) and tropical storm (TS) activity over the western North Pacific Ocean is examined for the period from 1981 to 2010. In El Niño years, TS genesis locations are generally shifted to the southeast relative to normal years and the passages of TSs tend to recurve to the northeast. TSs of greater duration and more intensity during an El Niño summer induce an increase of the accumulated tropical cyclone kinetic energy (ACE). Based on the strong relationship between the TS properties and ENSO, a probabilistic prediction for seasonal ACE is investigated using a hybrid dynamical–statistical model. A statistical relationship is developed between the observed ACE and large-scale variables taken from the ECMWF seasonal forecast system 4 hindcasts. The ACE correlates positively with the SST anomaly over the central to eastern Pacific and negatively with the vertical wind shear near the date line. The vertical wind shear anomalies over the central and western Pacific are selected as predictors based on sensitivity tests of ACE predictive skill. The hybrid model performs quite well in forecasting seasonal ACE with a correlation coefficient between the observed and predicted ACE at 0.80 over the 30-yr period. A relative operating characteristic analysis also indicates that the ensembles have significant probabilistic skill for both the above-normal and below-normal categories. By comparing the ACE prediction over the period from 2003 to 2011, the hybrid model appears more skillful than the forecast from the Tropical Storm Risk consortium.

Seasonal forecasts of droughts in African basins using the Standardized Precipitation Index

Abstract. Vast parts of Africa rely on the rainy season for livestock and agriculture. Droughts can have a severe impact in these areas, which often have a very low resilience and limited capabilities to mitigate drought impacts. This paper assesses the predictive capabilities of an integrated drought monitoring and seasonal forecasting system (up to 5 months lead time) based on the Standardized Precipitation Index (SPI). The system is constructed by extending near-real-time monthly precipitation fields (ECMWF ERA-Interim reanalysis and the Climate Anomaly Monitoring System–Outgoing Longwave Radiation Precipitation Index, CAMS-OPI) with monthly forecasted fields as provided by the ECMWF seasonal forecasting system. The forecasts were then evaluated over four basins in Africa: the Blue Nile, Limpopo, Upper Niger, and Upper Zambezi. There are significant differences in the quality of the precipitation between the datasets depending on the catchments, and a general statement regarding the best product is difficult to make. The generally low number of rain gauges and their decrease in the recent years limits the verification and monitoring of droughts in the different basins, reinforcing the need for a strong investment on climate monitoring. All the datasets show similar spatial and temporal patterns in southern and north-western Africa, while there is a low correlation in the equatorial area, which makes it difficult to define ground truth and choose an adequate product for monitoring. The seasonal forecasts have a higher reliability and skill in the Blue Nile, Limpopo and Upper Niger in comparison with the Zambezi. This skill and reliability depend strongly on the SPI timescale, and longer timescales have more skill. The ECMWF seasonal forecasts have predictive skill which is higher than using climatology for most regions. In regions where no reliable near-real-time data is available, the seasonal forecast can be used for monitoring (first month of forecast). Furthermore, poor-quality precipitation monitoring products can reduce the potential skill of SPI seasonal forecasts in 2 to 4 months lead time.

How Predictable is the Indian Ocean Dipole?

Abstract In light of the growing recognition of the role of surface temperature variations in the Indian Ocean for driving global climate variability, the predictive skill of the sea surface temperature (SST) anomalies associated with the Indian Ocean dipole (IOD) is assessed using ensemble seasonal forecasts from a selection of contemporary coupled climate models that are routinely used to make seasonal climate predictions. The authors assess predictions from successive versions of the Australian Bureau of Meteorology Predictive Ocean–Atmosphere Model for Australia (POAMA 15b and 24), successive versions of the NCEP Climate Forecast System (CFSv1 and CFSv2), the ECMWF seasonal forecast System 3 (ECSys3), and the Frontier Research Centre for Global Change system (SINTEX-F) using seasonal hindcasts initialized each month from January 1982 to December 2006. The lead time for skillful prediction of SST in the western Indian Ocean is found to be about 5–6 months while in the eastern Indian Ocean it is only 3–4 months when all start months are considered. For the IOD events, which have maximum amplitude in the September–November (SON) season, skillful prediction is also limited to a lead time of about one season, although skillful prediction of large IOD events can be longer than this, perhaps up to about two seasons. However, the tendency for the models to overpredict the occurrence of large events limits the confidence of the predictions of these large events. Some common model errors, including a poor representation of the relationship between El Niño and the IOD, are identified indicating that the upper limit of predictive skill of the IOD has not been achieved.