Seasonal Forecast System Research Articles

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.

Impact of convection scheme on ENSO prediction of SINTEX-F2

A spectral cumulus parameterization (spectral scheme) is implemented in Scale Interaction Experiment Frontier version 2 (SINTEX-F2) seasonal prediction system, and the impact on the El Niño Southern Oscillation (ENSO) prediction is examined. By conducting hindcast experiments using the original convection scheme (Tiedtke scheme) and the spectral scheme, and comparing the ENSO prediction skill, the impact of the spectral scheme is analyzed in detail. It was found that prediction skill in terms of ENSO phase and the sea surface temperature (SST) persistence were improved by using the spectral scheme, but the root-mean-square error (RMSE) increased. The ENSO feedback was also changed by changing the convection scheme. The original scheme failed to predict the zonal wind stress anomaly toward the Niño 3.4 region, whereas the spectral scheme simulated it over the equatorial eastern Pacific with narrowing the meridional width, indicating that the spectral scheme strengthened the ENSO feedback. The spectral scheme also improved zonal-vertical atmospheric response to the Niño 3.4 index due to its advantageous features. Analysis of the ENSO feedback terms revealed that strengthened forcing in the eastern Pacific improved the thermocline feedback of ENSO, as its reversed timing of positive and negative tendencies for the mixed layer temperature matched that estimated from the reanalysis data. In conclusion, the spectral scheme can improve ENSO prediction through the atmospheric forcing and mean state in the eastern Pacific which impacted the ocean properties. It improved the phase error by improving thermocline feedback, but did not improve the RMSE. Tuning of the original scheme to obtain additional improvements to ENSO prediction would be difficult, since it requires modification of detailed convective cloud properties to correct the phase error. The spectral scheme tends to overestimate the ENSO amplitude, i.e., large RMSE, but this drawback can be mitigated by tuning the convection scheme so that it suppresses the warm SST climate drift, and this is considered the more promising method to further improve ENSO prediction.

Seasonal Streamflow Forecasting for Fresh Water Reservoir Management in the Netherlands: An Assessment of Multiple Prediction Systems

Abstract For efficient management of the Dutch surface water reservoir Lake IJssel, (sub)seasonal forecasts of the water volumes going in and out of the reservoir are potentially of great interest. Here, streamflow forecasts were analyzed for the river Rhine at Lobith, which is partly routed through the river IJssel, the main influx into the reservoir. We analyzed seasonal forecast datasets derived from the European Flood Awareness System (EFAS), the Swedish Meteorological and Hydrological Institute (SMHI) European Hydrological Predictions for the Environment (E-HYPE), and Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL), which differ in their underlying hydrological formulation, but are all forced by meteorological forecasts from ECMWF’s fifth generation seasonal forecast system (SEAS5). We postprocessed the streamflow forecasts using quantile mapping (QM) and analyzed several forecast quality metrics. Forecast performance was assessed based on the available reforecast period, as well as on individual summer seasons. QM increased forecast skill for nearly all metrics evaluated. Averaged over the reforecast period, forecasts were skillful for up to 4 months in spring and early summer. Later in summer the skillful period deteriorated to 1–2 months. When investigating specific years with either low- or high-flow conditions, forecast skill increased with the extremity of the event. Although raw forecasts for both E-HYPE and EFAS were more skillful than HTESSEL, bias correction based on QM can significantly reduce the difference. In operational mode, the three forecast systems show comparable skill. In general, dry conditions can be forecasted with high success rates up to 3 months ahead, which is very promising for successful use of Rhine streamflow forecasts in downstream reservoir management. Significance Statement Lake IJssel is the Netherlands’ largest freshwater reservoir, with its main water source coming from a branch of the river Rhine. We investigate whether seasonal forecasts of river discharge can help in managing the lake level to create extra buffer capacity for dry periods. We compare three seasonal forecast systems and assess their quality. We find that statistical corrections are needed for all systems to be used. In spring discharge can be predicted up to 4 months ahead due to snow processes. In summer this time is shorter, but it increases with event extremity: severe low-flow events can be predicted longer ahead. This offers potential for water managers to base their lake management on other similar reservoirs.

Signal‐to‐noise and predictable modes of variability in winter seasonal forecasts

AbstractRecent studies suggest seasonal forecasts for European winters are now skilful, but they also identify a “signal‐to‐noise paradox”, wherein models predict the real world more skilfully (higher correlation) than the evolution of their ensemble members. Here, we analyse seasonal hindcasts from the Met Office GloSea5 seasonal forecast system to identify sources of predictability and seek insight into the signal‐to‐noise problem. For the first time, we use an optimal detection method to identify predictable signals over the North Atlantic region within the forecast system on subseasonal time‐scales. We find two primary predictable modes: a Pacific North America (PNA)‐like mode and a North Atlantic oscillation (NAO)‐like mode. The latter is the leading predictable mode in December–January, and its spatial pattern closely resembles the NAO. The PNA‐like mode dominates in January–February. Whereas the PNA‐like mode is driven by Pacific Ocean sea‐surface temperatures, the NAO‐like mode is driven at least partly by Indian Ocean sea‐surface temperatures, not solely due to the common trend. We develop a novel method of comparing the magnitude of these modes in the forecast system and observations that complements previous approaches. This suggests that the signal‐to‐noise problem in GloSea5 is primarily a feature of the December–January NAO‐like mode, with the observed mode being three times larger than in the model. The magnitude of the PNA‐like mode is better captured by the forecasts, although there is still evidence of a weaker signal‐to‐noise problem. This suggests particular mechanisms may lead to the lower signal to noise seen in NAO hindcasts, rather than a global weakness of the forecast system in responding to initialization and external forcing. Our results, though specific to GloSea5, provide insights into the causes of the signal‐to‐noise problem in seasonal forecasts of European winters. They also imply there is significant potential for improving such forecasts and suggest how such improvements may be achieved.

Seasonal forecasting of subsurface marine heatwaves

Marine heatwaves damage marine ecosystems and services, with effects identified mostly below the ocean surface. To create a truly user-relevant detection system, it is necessary to provide subsurface forecasts. Here, we demonstrate the feasibility of seasonal forecasting of subsurface marine heatwaves by using upper ocean heat content. We validate surface and subsurface events forecast by an operational dynamical seasonal forecasting system against satellite observations and an ocean reanalysis, respectively. We show that indicators of summer events (number of days, strongest intensity, and number of events) are predicted with greater skill than surface equivalents across much of the global ocean. We identify regions which do not display significant surface skill but could still benefit from accurate subsurface early warning tools (e.g., the mid-latitudes). The dynamical system used here outperforms a persistence model and is not widely influenced by warming trends, demonstrating the ability of the system to capture relevant subseasonal variability.

KEMAMPUAN RAMAL SISTEM PERKIRAAN IKLIM MUSIMAN MENGGUNAKAN FENOMENA ENSO DI KABUPATEN LOMBOK TENGAH, NUSA TENGGARA BARAT

Lombok Island, particularly the southern central part of Lombok, often experiences droughts that result in failed cultivation or harvests due to the El Nino and Southern Oscillation (ENSO) phenomena. Therefore, the objective of this research is to determine the forecasting skill or predictive ability of a seasonal climate prediction system using the ENSO indicator in the Central Lombok regency, West Nusa Tenggara Province. This study employs a descriptive method by comparing the average rainfall in El Nino years with non-El Nino years. The research is conducted in the Central Lombok regency, which is divided into the northern and southern parts. The data used includes monthly precipitation recorded by BMKG for approximately 50 years (1970-2019) in all districts of Central Lombok. The SOI data is accessed at http://www.bom.gov.au/climate/current/soi2.shtml. The results of this study indicate that the use of the ENSO indicator to forecast seasonal rainfall patterns in the Central Lombok regency shows high consistency and significant predictive ability. There is no significant difference in the predictive ability and consistency of the forecast results between the northern and southern parts of Central Lombok. In general, the predictive ability during the dry season exhibits higher consistency (25%) compared to the rainy season (14%).

Impact of Indian Ocean Dipole on Atlantic Niño predictive skill

Recent studies suggest that tropical Indian Ocean sea surface temperature (SST) anomalies, especially those associated with the Indian Ocean Dipole (IOD), may trigger Atlantic Niño through atmospheric teleconnection. However, it remains unclear whether the former has an impact on the prediction skill of the latter. This work applies hindcasts from 21 operational seasonal forecast systems based on dynamical climate models to verify the impact of the IOD in boreal autumn on the predictive skill of the Atlantic Niño in the following winter. The results indicate that the prediction skills of both the IOD and Atlantic Niño show pronounced seasonality, with a peak in the target season of boreal autumn for the former and in the following winter for the latter. The models with a stronger connection between the boreal autumn IOD and the following winter Atlantic Niño appear to have a higher skill in predicting the latter when compared to the weaker connection models. This leads to a significant in-phase relationship between the prediction skills of the boreal winter Atlantic Niño and the strength of the IOD–Atlantic Niño connection among the models. When compared to the weaker connection models, the stronger connection models tend to simulate a warmer climatological mean of SST in the tropical Indian Ocean during boreal autumn and winter. The warmer climatological mean of SST benefits a stronger atmospheric circulation response to the underlying SST forcing associated with the IOD. This suggests that mean state bias in the tropical Indian Ocean plays a role in modulating the strength of the simulated IOD–Atlantic Niño connection and thus prediction skill of the Atlantic Niño in boreal winter.

Improved Predictability of Summertime Rossby Wave Breaking Frequency near Japan in JMA/MRI-CPS3 Seasonal Forecasts

Abstract The seasonal predictability of the Rossby wave breaking (RWB) frequency near Japan in July–August (JA) is examined using daily JMA/MRI-CPS3 (CPS3) hindcast data, which is an operational seasonal prediction system of the Japan Meteorological Agency. Although the RWB frequency near Japan during JA in CPS3 is underestimated in comparison with the reanalysis, interannual variabilities of the frequency are generally predicted with moderate or high skill for hindcasts, initiating from February to June. The RWB frequency forecast skill in CPS3 is much higher than that in the previous version of the seasonal prediction system due to the improvement in the model bias of the Asian jet stream meridional position. A regression analysis for the RWB frequency near Japan utilizing all ensemble members is conducted to evaluate the reproducibility of the increased (decreased) RWB frequency associated with La Niña (El Niño) conditions, as indicated by previous studies. The regressed anomalies demonstrate an anomalous sea surface temperature (SST) pattern similar to that of La Niña and a negative phase of the Indian Ocean dipole mode with the associated anomalous convection in the tropics. For the La Niña condition, the regressed geopotential height in the upper troposphere demonstrates negative anomalies over the tropical Pacific and positive anomalies in the extratropical Northern Hemisphere, corresponding to the enhanced mid-Pacific trough and northward-shifted subtropical jet. The regressed meridional wind anomalies demonstrate a wavy pattern along the Asian jet over Eurasia, consistent with the relationship between the Silk Road pattern and the RWB near the Asian jet exit region.

Seasonal Streamflow Forecast in the Tocantins River Basin, Brazil: An Evaluation of ECMWF-SEAS5 with Multiple Conceptual Hydrological Models

The assessment of seasonal streamflow forecasting is essential for appropriate water resource management. A suitable seasonal forecasting system requires the evaluation of both numerical weather prediction (NWP) and hydrological models to represent the atmospheric and hydrological processes and conditions in a specific region. In this paper, we evaluated the ECMWF-SEAS5 precipitation product with four hydrological models to represent seasonal streamflow forecasts performed at hydropower plants in the Legal Amazon region. The adopted models included GR4J, HYMOD, HBV, and SMAP, which were calibrated on a daily scale for the period from 2014 to 2019 and validated for the period from 2005 to 2013. The seasonal streamflow forecasts were obtained for the period from 2017 to 2019 by considering a daily scale streamflow simulation comprising an ensemble with 51 members of forecasts, starting on the first day of every month up to 7 months ahead. For each forecast, the corresponding monthly streamflow time series was estimated. A post-processing procedure based on the adjustment of an autoregressive model for the residuals was applied to correct the bias of seasonal streamflow forecasts. Hence, for the calibration and validation period, the results show that the HBV model provides better results to represent the hydrological conditions at each hydropower plant, presenting NSE and NSElog values greater than 0.8 and 0.9, respectively, during the calibration stage. However, the SMAP model achieves a better performance with NSE values of up to 0.5 for the raw forecasts. In addition, the bias correction displayed a significant improvement in the forecasts for all hydrological models, specifically for the representation of streamflow during dry periods, significantly reducing the variability of the residuals.

Improved forecast of 2015/16 El Niño event in an experimental coupled seasonal ensemble forecasting system

To improve NOAA’s seasonal forecasting capabilities, a new coupled system within the Unified Forecast System (UFS) framework is being developed through a community-wide effort led by NOAA’s Environmental Modeling Center targeting the configuration of a future operational Seasonal Forecast System (SFS v1). An experimental version of this ensemble seasonal forecasting system is tested on forecasting the strong El Niño of 2015/16. The then-operational systems and NCEP real-time seasonal forecasts (CFSv2) underestimated its strength towards the end of 2015 and beginning of 2016. In addition to perturbing the atmospheric initial conditions, run-time stochastic physics-based perturbations are applied in both atmosphere and ocean components of this new coupled system to represent the model uncertainties. The UFS ensembles are initialized on June 1st, 2015 and run through a 9-month period. Compared to CFSv2, the forecast of Niño 3.4 SST and intra-seasonal zonal windstress for the 2015/16 El Niño in the UFS system are improved, as is the ensemble spread. A cold SST forecast error develops in the central equatorial Pacific, likely from excess evaporative cooling, shallower thermocline, and an excessively strong vertical current shear driven cooling. Near the eastern equatorial Pacific coast, on the other hand, warm surface and cool subsurface errors persist from initialization until the end of the forecast. The results suggest that further improvement in the seasonal forecast may be achieved by a combination of factors, including, but not limited to, improving the coupled system initialization, along with the atmospheric physics.

Seasonal and interannual variation of equatorial waves in ERA5 and GloSea5

AbstractThis study presents an analysis of the equatorial waves in the ERA5 reanalysis and forecasts of the Met Office Global Seasonal Forecast System version 5 (GloSea5), by projecting dynamical fields onto theoretical equatorial wave modes. The seasonal and interannual variation of equatorial wave activity in GloSea5 is evaluated against ERA5 waves. We find that in ERA5 the seasonal and spatial variations of eastward‐moving Kelvin wave activity are mainly determined by the ambient zonal flow, with wave activity being stronger in easterlies than westerlies. For the westward‐moving mixed Rossby–gravity waves and equatorial Rossby waves, the seasonal and spatial variations of the upper‐tropospheric wave activity are determined by both the ambient flow and extratropical forcing with stronger wave activity generally in the westerlies, whilst the lower‐tropospheric wave activity is related to the ambient flow and tropical convection. In GloSea5, the Kelvin wave activity is not well simulated in the upper troposphere, and in general the wave activity and wave‐related convective signal are too weak. In contrast, GloSea5 performs better for the westward‐moving equatorial waves, including their Doppler‐shifted eastward‐moving components, despite a significant overestimation in the Atlantic and African region. The phase of ENSO has a substantial impact on equatorial waves in both ERA5 and GloSea5. The mechanism of the impact is through changes in the ENSO‐related ambient flow, upper‐tropospheric extratropical forcing, and tropical convection. GloSea5 can capture the low‐level Kelvin wave–ENSO relationship reasonably well, with stronger wave activity over the eastern Pacific in El Niño, although the ENSO‐driven variation is too weak. The wave–ENSO relationship for westward‐moving waves is well represented in GloSea5, especially in the upper‐level eastern‐Pacific westerly duct region in the extended boreal winter and the low‐level Indo‐Pacific in all seasons. We conclude that in GloSea5 the representation of the wave–ENSO teleconnection is the key driver for the performance of interannual variability in the wave activity.

The intraseasonal surface air temperature reversal and its predictability analysis in 2021/2022 winter over China

AbstractIn 2021/2022 winter, an intraseasonal surface air temperature (SAT) reversal occurred in China, shifting from regional warm to cold phase in late January 2022. The intraseasonal variations of winter SAT in China are tightly connected with 500 hPa potential height. In particular, the Arctic Oscillation (AO) positive phase is diagnosed to be contributing to the SAT reversal. Under its influence, the intraseasonal build‐up and collapse of blockings over Urals Mountain led to the cold surges in February 2022. Seasonal prediction systems (SPSs) such as NCEP_CFSv2, EC_SYSTEM5 and BCC_CSM1.1 are assessed to have poor predictability in this SAT reversal and similar historical events, owing to not only the poor skills in winter SAT predictions but also the integral inertia in SPSs. Prediction skill only increases as the forecasting time approaches, owing to initial values. Besides, the SPSs can simulate the physical process of the influence on SAT from circulation. The poor predictability is attributed to the poor prediction skills in mid–high latitude circulation pattern and potential height over China.

Sources of skill in lake temperature, discharge and ice-off seasonal forecasting tools

Abstract. Despite high potential benefits, the development of seasonal forecasting tools in the water sector has been slower than in other sectors. Here we assess the skill of seasonal forecasting tools for lakes and reservoirs set up at four sites in Australia and Europe. These tools consist of coupled hydrological catchment and lake models forced with seasonal meteorological forecast ensembles to provide probabilistic predictions of seasonal anomalies in water discharge, temperature and ice-off. Successful implementation requires a rigorous assessment of the tools' predictive skill and an apportionment of the predictability between legacy effects and input forcing data. To this end, models were forced with two meteorological datasets from the European Centre for Medium-Range Weather Forecasts (ECMWF), the seasonal forecasting system, SEAS5, with 3-month lead times and the ERA5 reanalysis. Historical skill was assessed by comparing both model outputs, i.e. seasonal lake hindcasts (forced with SEAS5), and pseudo-observations (forced with ERA5). The skill of the seasonal lake hindcasts was generally low although higher than the reference hindcasts, i.e. pseudo-observations, at some sites for certain combinations of season and variable. The SEAS5 meteorological predictions showed less skill than the lake hindcasts. In fact, skilful lake hindcasts identified for selected seasons and variables were not always synchronous with skilful SEAS5 meteorological hindcasts, raising questions on the source of the predictability. A set of sensitivity analyses showed that most of the forecasting skill originates from legacy effects, although during winter and spring in Norway some skill was coming from SEAS5 over the 3-month target season. When SEAS5 hindcasts were skilful, additional predictive skill originates from the interaction between legacy and SEAS5 skill. We conclude that lake forecasts forced with an ensemble of boundary conditions resampled from historical meteorology are currently likely to yield higher-quality forecasts in most cases.

Current status and progress in the seasonal prediction of the Asian summer monsoon

The Asian summer monsoon (ASM) has a considerable impact on human lives in the most populated region in the world. Thus, its seasonal prediction is a high-profile application in Earth Science. However, the prediction skill of the regional ASM variability has long been limited due to a formidable difficulty in accurately simulating the complex interactions of the atmosphere-ocean variability and its remote influence on regional climate in numerical models. This study updates the current status and assesses progress in the ASM seasonal prediction performance. This study evaluated the seasonal prediction skill of two generations of models in hindcast data archived by the WCRP Climate-system Historical Forecast Project (CHFP) and Copernicus Climate Change Service (C3S). A special focus was put on the representation of the predominant teleconnections associated with the ENSO and Indian Ocean variability. It was found that the latest seasonal prediction systems (C3S) generally outperform previous-generation systems (CHFP) in terms of the reproducibility of the observed precipitation climatology and the prediction skill of the interannual variability of seasonal precipitation over the ASM region. Furthermore, the results suggested that the improvement of the prediction skill of the ASM likely stems from the improved representation of the monsoon climatology and teleconnections in the models. These analyses highlight the steady progress of the atmosphere-ocean coupled modelling and promise future improvements in the seasonal ASM prediction.

High resolution monitoring and probabilistic prediction of meteorological drought in a Mediterranean environment

Drought remains a costly natural disaster, with strong socio-economic and environmental impacts. Skilful seasonal drought forecasts can help to make early decisions. Here we assess the quality of a prototype seasonal forecasting system for a Mediterranean region (peninsular Spain + Balearic Islands) to predict meteorological drought as measured by the standardised precipitation index (SPI). We first show that there is a high agreement between the official data provided by the Spanish Meteorological Agency and the state-of-the art ERA5 reanalysis, building confidence in using these datasets. Thus, since the ERA5 data are provided in near-real time as it is updated on a monthly basis, it can be used to monitor drought evolution. Then, we demonstrate that it is possible to obtain skilful and reliable seasonal drought predictions several months in advance by applying an ensemble-based streamflow prediction system (ESP, an ensemble based on the rearrangement of historical data) using ERA5 data as initial conditions. The results indicate that a statistical persistence-based model could lead to an actionable seasonal drought forecasting skill thus providing the basis for a cheap and fast prototype for drought early warning.

A statistical perspective on the signal‐to‐noise paradox

AbstractAn anomalous signal‐to‐noise ratio (also called the signal‐to‐noise paradox) present in climate models has been widely reported, affecting predictions and projections from seasonal to centennial timescales and encompassing prediction skill from internal processes and external climate forcing. An anomalous signal‐to‐noise ratio describes a situation where the mean of a forecast ensemble correlates better with the corresponding verification than with its individual ensemble members. This situation has severe implications for climate science, meaning that large ensembles might be required to extract prediction signals. Although a number of possible physical mechanisms for this paradox have been proposed, none has been universally accepted. From a statistical point of view, an anomalous signal‐to‐noise ratio indicates that forecast ensemble members are not statistically interchangeable with the verification, and an apparent paradox arises only if such an interchangeability is assumed. It will be demonstrated in this study that an anomalous signal‐to‐noise ratio is a consequence of the relative magnitudes of the variance of the observations, the ensemble mean, and the error of the ensemble mean. By analysing the geometric triangle formed by these three quantities, and given that for typical seasonal forecasting systems both the correlation and the forecast signal are relatively small, it is concluded that an anomalous signal‐to‐noise ratio should, in fact, be expected in such circumstances.

Climate stressors modulate interannual olive yield at province level in Italy: A composite index approach to support crop management

AbstractAlthough a large part of Italy is characterized by a Mediterranean climate intrinsically highly suitable for olive cultivation, farmers may experience variable interannual yields with associated agronomic and management costs. A detailed overview of major climate stressors and their ongoing impacts on olive yield variability at a broad spatial–temporal scale across Italy could improve the understanding of how interannual olive yields are modulated by seasonal local climate and would enhance the development of actionable services to alert stakeholders of potential climate risks. We analysed aggregated olive yield data from the Italian National Statistics Institute (ISTAT) at the provincial level during 2006–2020, and several climatic variables from the Reanalysis v5 (ERA5) of the European Centre for Medium‐Range Weather Forecasts (ECMWF) as a basis to (i) explore olive yield trends and inter‐annual variations over the whole country; (ii) identify major seasonal climate stressors likely responsible for the largest variations in yield and (iii) develop a composite index that summarizes the risk of having exceptionally low yields due to the co‐occurrence of multiple stressors. To this end, we defined two extreme classes of yield, namely, exceptionally low and high yields (LY and HY, respectively) and explored the climatic variables aggregated on a bimonthly time scale that influenced yield outcomes. Our analysis showed that exceptional low yields have been erratically increasing since 2014 with temperature‐related variables having the highest impact, especially a relatively warm winter and cool early autumn. These period‐specific variables were major components of the resulting composite index predicting the likelihood of LY ranging from 28% to 49% due to increasing stress effects. Possible explanations of our findings are discussed, including the proliferation of the olive fly. We suggest the composite risk approach could lay the groundwork for an integrated meteorological seasonal forecasting system that provides timely insights on factors affecting within‐season olive yield development.

Underpredicted ENSO Teleconnections in Seasonal Forecasts

AbstractThe El Niño‐Southern Oscillation (ENSO) influences climate variability across the globe. ENSO is highly predictable on seasonal timescales and therefore its teleconnections are a source of extratropical forecast skill. To fully harness this predictability, teleconnections must be represented accurately in seasonal forecasts. We find that a multimodel ensemble from five seasonal forecast systems can successfully capture the spatial structure of the late winter (JFM) El Niño teleconnection to the North Atlantic via North America, but the simulated amplitude is half of that observed. We find that weak amplitude teleconnections exist in all five models throughout the troposphere, and that the La Niña teleconnection is also weak. We find evidence that tropical forcing of the El Niño teleconnection is not underestimated and instead, deficiencies are likely to emerge in the extratropics. We investigate the impact of underestimated teleconnection strength on North Atlantic winter predictability, including its relevance to the signal‐to‐noise paradox.

Enhancing Forecast Skill of Winter Temperature of East Asia Using Teleconnection Patterns Simulated by GloSea5 Seasonal Forecast Model

GloSea5, a seasonal forecast system of the UK Met Office, shows reasonable skill among state-of-the-art operational seasonal forecast systems. However, the average surface temperature (T2m) in winter (December–February) of GloSea5 is particularly low in East Asia. To improve the seasonal forecast skill over East Asia, we focused on the high skill score of global teleconnection patterns simulated by GloSea5. Among the well-predicted teleconnection patterns, we selected those highly correlated with the East Asian T2m: East Atlantic (EA), Polar/Eurasia (PE), East Atlantic/Western Russia (EAWR), and West Pacific (WP) patterns. A multiple linear regression model was constructed using the selected teleconnection indices as predictors. These results are promising. The statistical skill-score evaluation of the constructed linear regression model using the anomaly correlation coefficient (ACC), root mean squared error (RMSE), and mean-squared skill score (MSSS) showed an improvement in the predicted T2m of East Asia, where the values of ACC and MSSS increased by 0.25 and 0.37, respectively, and the RMSE decreased by 0.63 compared to the dynamic forecast model results. These results suggest that a well-designed combined statistical and dynamical approach for seasonal prediction can be beneficial for some regions where the predictability of the dynamic model exhibits a low value.