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In this study, we developed a flow‐dependent oceanic initialization system for initializing the oceanic temperature and salinity in the Global Seasonal forecast system version 5 (GloSea5). Our algorithm overcomes the limitation of stationary perturbations for Ensemble Optimal Interpolation (EnOI) by spreading observed information along isopycnal lines to create three‐dimensional snapshot density states. The proposed algorithm, which we call state‐dependent ensemble‐based EnOI (SD‐EnOI), takes into account changes in the background error covariance over time without relying on ensemble model simulations. To evaluate the quality of the oceanic initial conditions (ICs) produced by SD‐EnOI, we compared them with those generated by the Global Ocean Data Assimilation and Prediction System version 1 (GODAPS1) operated by the Korea Meteorological Agency (KMA) throughout January 2017 to December 2017. Our findings show that the thermal construction of the SD‐EnOI ICs is more realistic than that of GODAPS1, particularly in the tropical Pacific region. The strong warm bias in sea surface temperature (SST) and the shallow mixed‐layer depth bias observed in the GODAPS1 ICs are not shown in SD‐EnOI. Due to the more realistic oceanic thermal structure present in the SD‐EnOI ICs, their use in retrospective forecast experiments resulted in a systematic reduction in climatological SST drift in the central‐eastern Pacific for forecasts up to four lead months compared to using GODAPS1 ICs. This demonstrates the significant impact of the initialization process on the quality of dynamical seasonal forecasts. [ABSTRACT FROM AUTHOR]

We investigate why the North American Multi‐Model Ensemble (NMME) upper‐level height forecast for December–February (DJF) 2023/24 differs from the expected El Niño response. These atypical height anomalies emerged despite the fact a strong El Niño was forecast. The analysis focuses on diagnosing the NMME forecasts of DJF 2023/24 for SSTs and 200‐hPa heights initialized at the beginning of November 2023 relative to other ensemble mean NMME DJF forecasts dating back to 1982. The results demonstrate that forecasts of the 200‐hPa height anomalies had a large contribution from warming trends in global SSTs. It is the combination of trends and the expected El Niño teleconnection that results in the forecast height anomalies. Increasingly, for forecasts of geopotential height anomalies during the recent El Niño winters, the amplitude of trends is nearly equal to the signal from El Niño and has implications for the climatological base period selection for seasonal forecasts. Plain Language Summary: Seasonal forecasts are cast as anomalies as users want to know what can be expected beyond the typical seasonal swings of the climate. This necessitates a choice for the climatological base period relative to which forecast anomalies are computed. It, however, poses a challenge under rapid climate change. In this scenario, climate trends become part of the real‐time forecast anomalies, and if the climatological base period is sufficiently different, may even start to dominate. This was the case for the NMME DJF 2023/24 forecast of 200‐hPa heights which was forecast to be a strong El Niño, and yet, forecast for 200‐hPa heights differed from typical El Niño signal. The analysis implies that seasonal forecasts for some variables, consideration of trends is important and reliance on expected signal from El Niño—Southern Oscillation alone may not be sufficient. Key Points: The North American Multi‐Model Ensemble (NMME) seasonal forecasts for December–February (DJF) 2023/24 upper‐level height differ from the expected El Niño signalIt is the combination of trends in heights and the expected El Niño signal that results in the forecast NMME ensemble mean heights anomaliesThe forecast of trends is increasingly important to account for NMME forecast anomaly and their amplitude in recent years can be of same magnitude as the signal from El Niño [ABSTRACT FROM AUTHOR]

Understanding the impacts of high‐resolution ocean model provides valuable insights for future research. However, the outcomes of sea surface state changes in both the tropics and mid‐latitudes remain unclear, and initialized seasonal forecasts have not been studied extensively. This study investigates the impact of ocean model resolution with the first long‐term hindcast experiment of an eddy‐resolving (0.1°) ocean model used for global seasonal forecasting. We show that using the high‐resolution ocean model significantly changes boreal winter jet streams in the atmosphere, based on the comparison of 30‐year hindcasts with ocean resolutions ranging from 1° to 0.1° for the Japan Meteorological Agency/Meteorological Research Institute Coupled Prediction System version 3. In boreal winters, the cold sea surface bias in the equatorial Pacific is significantly reduced, leading to an equatorward shift in the intertropical convergence zone (ITCZ) and enhanced convective activity in the western equatorial Pacific. The subtropical jet shifts equatorward due to the ITCZ shift and the weakening of equatorward propagation of mid‐latitude atmospheric eddies. The enhanced convective activity in the tropics has a remote influence in the mid‐latitudes, significantly reducing the upward eddy propagation of zonal wavenumber 1. Sea surface warm‐up in the mid‐latitudes partially cancels the reduction impact by enhancing the zonal wavenumber 2. Overall, the polar night jet accelerates due to the reduced supply of eddy forcing. Plain Language Summary: Atmospheric and oceanic eddies play an important role in shaping the climate. In numerical climate simulations, the Earth is divided into a grid of discrete boxes, and the exchange of energy and mass between the boxes is computed step‐by‐step. Coarse grids are often used to reduce the computational cost, but recent studies have suggested that resolving fine ocean phenomena can make a significant difference. In this study, numerical seasonal forecasting experiments with ocean resolutions of 100 km, 25 km, and 10 km are compared to investigate the high‐resolution effects on the atmospheric flow for boreal winter. This is the first 30‐year‐long, retrospective seasonal forecast experiment for the past winters using a global eddy‐resolving (10 km) ocean model. The high‐resolution ocean model greatly warms the surface seawater in the tropical Pacific and in the mid‐latitude oceans. These changes in the sea surface influence atmospheric convection and eddies, with far‐reaching effects on distant regions and altitudes. Such effects on the westerly jet streams, which flow several kilometers to tens of kilometers above the sea surface, are observed in the southward shift of the subtropical jet and the strengthening of the polar night jet. Key Points: Altering the ocean model resolution from 1° to 0.1° in a seasonal forecast model shifts and enhances the subtropical and polar night jetsAtmospheric responses to both tropical and mid‐latitude oceans are observedWave‐mean flow diagnostics indicates that the jet stream responses are more strongly influenced by the tropics [ABSTRACT FROM AUTHOR]

Accurate regional seasonal forecasts of the rainy season are essential for the implementation of effective socioeconomic activities and policy. However, current characteristics of the period of occurrence of the rainy season in the Eastern Northeast Brazil (ENEB) region demonstrated that maximum precipitation varies substantially depending on the period analyzed. From 1972 to 2002, the rainy season occurred during the June–July–August (JJA) quarter, while from 1981 to 2011, it occurred in the April–May–June (AMJ) quarter. To access how these differences may be due to different patterns of sea surface temperature (SST), using observed precipitation and SST data from NOAA for the period from 1982 to 2018, this study identified the spatial patterns of inter-annual changes in Pacific and Atlantic SST related to the occurrence of the ENEB rainy seasons. We focus on the statistical method of symmetric mean absolute percentage error (sMAPE) for forecasting these periods based on SST information. Our results revealed five different quarterly periods (FMA, MAM, AMJ, MJJ, JJA) to the rainy season, in which MJJ is more prevalent. The sMAPE values of the SST patterns are inversely proportional to precipitation in the ENEB. Hence, it may be concluded that our climate analysis demonstrates that seasonal SST patterns can be used for forecasting the period of the rainy season. [ABSTRACT FROM AUTHOR]

For the territory of Northern Eurasia, a scheme for the statistical correction of surface air temperature forecasts has been developed for periods of 1–4 months on the basis of the SL-AV model using the MOS concept. For statistical correction of operational temperature forecasts, the regression parameters and EOF expansion coefficients obtained by cross-validation on historical forecasts were used. Due to the internal relationships of the model output data, the proposed scheme allows improving the skill of surface characteristic forecasts. A significant improvement in the skill of deterministic air temperature forecasts by using statistical correction is manifested in transition seasons. The scheme of statistical correction is constantly evolving. Further development of the statistical correction technology involves the use of neural networks and forecast indices of atmospheric circulation. [ABSTRACT FROM AUTHOR]

Seasonal precipitation forecasting is vital for weather-sensitive sectors. Global Circulation Models (GCM) routinely produce ensemble Seasonal Climate Forecasts (SCFs) but suffer from issues like low forecast resolution and skills. To address these issues in this study, we introduce a post-processing method, Quantile Ensemble Bayesian Model Averaging (QEBMA). It utilises quantiles from a GCM ensemble forecast to create a pseudo-ensemble forecast. Through their reasonable linear relationships with observations, each pseudo-member connects a hurdle distribution with a point mass at zero for dry months and a gamma distribution for wet months. These distributions are mixed to construct a forecast probability distribution with their weights, proportional to the quantiles' historical forecast performance. QEBMA is applied to three GCMs, including GloSea5 from the United Kingdom, ECMWF from Europe and ACCESS-S1 from Australia, for monthly precipitation forecasts in 32 locations across four climate zones in Australia. Leave-one-month-out cross-validation results illustrate that QEBMA enhances forecast skills compared to raw GCMs and other post-processing techniques, including quantile mapping and Extended Copula Post-Processing (ECPP), for forecast lead time of 0 to 2 months, based on five metrics. The skill improvements achieved by QEBMA are often statistically significant, particularly when compared to raw GCM forecasts across the 32 study locations. Among these post-processing models, only QEBMA consistently outperforms the SCF benchmark climatology, offering a promising alternative for improving seasonal precipitation forecasts. [ABSTRACT FROM AUTHOR]

In the boreal summer and autumn of 2023, the globe experienced an extremely hot period across both oceans and continents. The consecutive record-breaking mean surface temperature has caused many to speculate upon how the global temperature will evolve in the coming 2023/24 boreal winter. In this report, as shown in the multi-model ensemble mean (MME) prediction released by the Institute of Atmospheric Physics at the Chinese Academy of Sciences, a medium-to-strong eastern Pacific El Niño event will reach its mature phase in the following 2–3 months, which tends to excite an anomalous anticyclone over the western North Pacific and the Pacific-North American teleconnection, thus serving to modulate the winter climate in East Asia and North America. Despite some uncertainty due to unpredictable internal atmospheric variability, the global mean surface temperature (GMST) in the 2023/24 winter will likely be the warmest in recorded history as a consequence of both the El Niño event and the long-term global warming trend. Specifically, the middle and low latitudes of Eurasia are expected to experience an anomalously warm winter, and the surface air temperature anomaly in China will likely exceed 2.4 standard deviations above climatology and subsequently be recorded as the warmest winter since 1991. Moreover, the necessary early warnings are still reliable in the timely updated medium-term numerical weather forecasts and sub-seasonal-to-seasonal prediction. [ABSTRACT FROM AUTHOR]

Water stress and conflict can emerge in regions with high water demand, making effective water resource management crucial for regional water security. This case study presents a comprehensive analysis of the hydroclimate behavior in Western Bahia, Brazil, and describes the development, evaluation, and operationalization of a hydroclimatic forecast system that forecasts minimum annual discharges, water stress levels, and rainy season onset, aiming to support decision-making, improve the regional governance of water resources, and improve water security. Western Bahia has experienced water stress in recent years, leading to a reduction in the amount of water in highly stressed regions like the Grande basin. The water scarcity issue may extend to other basins, as evidenced by lower streamflow values observed in the Corrente basin. To address this challenge, hydroclimatic forecasts were developed. The minimum discharge forecasts delivered a relative mean absolute error of 16.5%–27.8% on average for the study region. When combined with water stress forecasts, both provided valuable information for decision-making. Our rainy season onset forecasts had a mean absolute error of 15–20 days for two months' lead time and captured the region's west–east gradient of rainy season behavior. These combined hydroclimatic forecasts have the potential to enhance water security and support decision-making in Western Bahia. [ABSTRACT FROM AUTHOR]

Abstract We investigate why the North American Multi‐Model Ensemble (NMME) upper‐level height forecast for December–February (DJF) 2023/24 differs from the expected El Niño response. These atypical height anomalies emerged despite the fact a strong El Niño was forecast. The analysis focuses on diagnosing the NMME forecasts of DJF 2023/24 for SSTs and 200‐hPa heights initialized at the beginning of November 2023 relative to other ensemble mean NMME DJF forecasts dating back to 1982. The results demonstrate that forecasts of the 200‐hPa height anomalies had a large contribution from warming trends in global SSTs. It is the combination of trends and the expected El Niño teleconnection that results in the forecast height anomalies. Increasingly, for forecasts of geopotential height anomalies during the recent El Niño winters, the amplitude of trends is nearly equal to the signal from El Niño and has implications for the climatological base period selection for seasonal forecasts.

Predictions of tropical cyclone (TC) activity have been a topic of recurrent interest and research in the past. Here we utilize reanalysis datasets of sea surface temperature (SST) and mixed layer depth (MLD) to build a statistical seasonal forecasting model that produces outlooks of expected TC counts in the region of the Southwest Pacific (SWP). Nevertheless, the model applicability can be extended to other regions and basins. A novel TC predictor index is developed at the daily scale and used to obtain an objective classification of synoptic weather patterns. This classification has been performed by clustering the daily index predictor fields, previously transformed into principal components, using a K‐mean algorithm. As a result, 49 daily weather types (DWTs) are presented which inform about the mean representative features and spatial patterns of both predictor and predictand variables. Thus, statistical relationships between TC activity and nonlinear combinations of predictor variables are found to assign daily rates of expected TCs. The cluster‐based model is calibrated from 1982 to 2019 and validated by recent TC season observations, demonstrating the operational application using ensembles of long‐term predictions in the Southwest Pacific. Results have shown which synoptic types of SST and MLD are favourable to cyclogenesis and activity, with additional information related to concurrent sea level pressure and precipitation synoptic patterns, as well as seasonal and interannual climate variability. [ABSTRACT FROM AUTHOR]

Extreme states of the stratospheric polar vortex (SPV) affect the average position of the main propagation trajectories of synoptic vortices in the Northern Hemisphere over a time period from 2 weeks to 2 months. This time scale is considered one of the most difficult periods in forecasting. Based on the analysis of data from idealized numerical experiments on the Isca platform, we have studied the processes of formation of anomalous positions of storm tracks in the Atlantic–European region as a response to sudden stratospheric warmings (SSWs) and events of extremely strong SPV during various phases of the El Niño Southern Oscillation (ENSO). It is shown that in winter it is impossible to unambiguously talk about the southward displacement of the Atlantic storm track during El Niño events without taking into account the intensity of SPV. The intensity of SPV, expressed as the zonal component of wind speed, averaged along 60° N at the level of 10 hPa, has its maximum predictive potential during El Niño. [ABSTRACT FROM AUTHOR]

East Africa is highly vulnerable to extreme weather events, such as droughts and floods. Skillful seasonal forecasts exist for the October–November–December short rains, enabling informed decisions, whereas seasonal forecasts for the March–April–May (MAM) long rains have historically had low skill, limiting preparation capacity. Therefore, improved long rains prediction is a high priority and would contribute to climate change resilience in the region. Recent work has highlighted how lower‐troposphere Congo zonal winds in MAM strongly impact regional moisture fluxes and the long rains total precipitation. We therefore approach long rains predictability through the predictability of the Congo winds. We analyze a set of hindcasts from a dynamical prediction system that is able to reproduce the long rains—Congo winds relationship in its individual ensemble members. Encouragingly, in observations, the strength of MAM Congo zonal winds and East Africa rainfall show substantial correlation with the MAM Atlantic (including North Atlantic Oscillation, NAO) and Indo‐Pacific variability, suggestive of ocean influence and potential predictability. However, these features are replaced by different teleconnections in the hindcast ensemble mean fields. This is also true for NAO linkage to Congo winds, despite correct representation in individual members, and good skill in hindcasting the NAO itself. The net effect is strongly negative skill for the Congo winds. We explore statistical correction methods, including using the Congo zonal wind as an anchor index in a signal‐to‐noise calibration for the long rains. This is considered a demonstration of concept, for subsequent implementation using models with better Congo zonal wind skill. Indeed, the clear signals found in the Atlantic (including Mediterranean) and Indo‐Pacific, studied here both in observations and a dynamical prediction system, motivate evaluation of these features across other prediction systems, and offer the prospect of improved physically‐informed long rains dynamical predictions. [ABSTRACT FROM AUTHOR]

Viticulturists developing adaptation strategies to mitigate the impact of climate change, which affects a grapevine’s physiology and wine typicity, can benefit from climate services. Climate services translate physically based variables, such as temperature and precipitation, into actionable, decision relevant bioclimatic indicators, such as Spring Rain, Heat Stress Days, and Warm Spell Duration. These bioclimatic indicators enable the mitigation of fungal diseases, specifically downy and powdery mildew, as well as sunburn. Accurate seasonal forecasts of these bioclimatic indicators can help farmers with viticulture, labor, and stock management, as well as improve the yield and value of wine-quality grapes. Seasonal forecasts of these indicators are available on the MED-GOLD project’s dashboard. This study determines an annual service fee to access these forecasts on the dashboard. The annual fee accounts for the seasonal forecast accuracy over part of the Douro wine region of Portugal, as well as the potential savings and losses of micro (⩽1 ha) holding grape growers. The revenue generated from this climate service fee exceeds the cost of dashboard maintenance by nearly 10 times, even with a fee which is less than half of the potential savings of the micro holding farmer.

Accurate regional seasonal forecasts of the rainy season are essential for the implementation of effective socioeconomic activities and policy. However, current characteristics of the period of occurrence of the rainy season in the Eastern Northeast Brazil (ENEB) region demonstrated that maximum precipitation varies substantially depending on the period analyzed. From 1972 to 2002, the rainy season occurred during the June–July–August (JJA) quarter, while from 1981 to 2011, it occurred in the April–May–June (AMJ) quarter. To access how these differences may be due to different patterns of sea surface temperature (SST), using observed precipitation and SST data from NOAA for the period from 1982 to 2018, this study identified the spatial patterns of inter-annual changes in Pacific and Atlantic SST related to the occurrence of the ENEB rainy seasons. We focus on the statistical method of symmetric mean absolute percentage error (sMAPE) for forecasting these periods based on SST information. Our results revealed five different quarterly periods (FMA, MAM, AMJ, MJJ, JJA) to the rainy season, in which MJJ is more prevalent. The sMAPE values of the SST patterns are inversely proportional to precipitation in the ENEB. Hence, it may be concluded that our climate analysis demonstrates that seasonal SST patterns can be used for forecasting the period of the rainy season.

Abstract East Africa is highly vulnerable to extreme weather events, such as droughts and floods. Skillful seasonal forecasts exist for the October–November–December short rains, enabling informed decisions, whereas seasonal forecasts for the March–April–May (MAM) long rains have historically had low skill, limiting preparation capacity. Therefore, improved long rains prediction is a high priority and would contribute to climate change resilience in the region. Recent work has highlighted how lower‐troposphere Congo zonal winds in MAM strongly impact regional moisture fluxes and the long rains total precipitation. We therefore approach long rains predictability through the predictability of the Congo winds. We analyze a set of hindcasts from a dynamical prediction system that is able to reproduce the long rains—Congo winds relationship in its individual ensemble members. Encouragingly, in observations, the strength of MAM Congo zonal winds and East Africa rainfall show substantial correlation with the MAM Atlantic (including North Atlantic Oscillation, NAO) and Indo‐Pacific variability, suggestive of ocean influence and potential predictability. However, these features are replaced by different teleconnections in the hindcast ensemble mean fields. This is also true for NAO linkage to Congo winds, despite correct representation in individual members, and good skill in hindcasting the NAO itself. The net effect is strongly negative skill for the Congo winds. We explore statistical correction methods, including using the Congo zonal wind as an anchor index in a signal‐to‐noise calibration for the long rains. This is considered a demonstration of concept, for subsequent implementation using models with better Congo zonal wind skill. Indeed, the clear signals found in the Atlantic (including Mediterranean) and Indo‐Pacific, studied here both in observations and a dynamical prediction system, motivate evaluation of these features across other prediction systems, and offer the prospect of improved physically‐informed long rains dynamical predictions.

The increased seasonal demand for water puts pressure on Mediterranean water resources, which are often exploited in a non-renewable way. Besides, climate change can alter hydroclimatic patterns and exaggerate freshwater stress. Flexible operation of existing water reservoirs is one of the most cost-effective ways to mitigate water-related stress by storing water when it is abundant and releasing it when droughts persist. In this context, hydroclimatic forecasts can be central in properly informing reservoir operation. Nevertheless, the link between forecast skill and forecast value is neither easily predictable nor necessarily positive. Each system requires specific forecasts according to its characteristics, and the skill of existing forecast systems does not necessarily translate into a significant gain in system performance. In this work, we develop downscaled seasonal forecasts of reservoir inflow for the Faneromeni irrigation dam on Crete island. We then quantify the value of these seasonal forecasts in informing the reservoir operations. While the current operation of this reservoir is based on the available storage at the beginning of the irrigation season, we investigate alternatives by using the Evolutionary Multi Objectives Direct Policy Search method, which allows the design of flexible rules to cope with the variability of the hydrologic conditions as well as to include forecast information for conditioning operational decisions. Under historical climate conditions, results demonstrate a notable enhancement in performance solely by implementing more flexible operating policies. Incorporating perfect forecasts results in an additional improvement of 4% on average throughout the period from 1993 to 2019. However, when using actual forecasts, this gain diminishes to 1%. These outcomes support the exploration of potential trade-off solutions that effectively balance the competing demands within the region.

Recent studies have shown that seasonal forecasting systems have significant skill in predicting northern European winds and storms in winter, but other seasons have not been so extensively analysed. Given this fact, and coupled with requests from users of the Met Office 3-month outlook for the United Kingdom (UK), we have investigated the skill in predicting seasonal (3-month) mean UK wind speed and storms (extreme winds) with a one-month lead-time, throughout the year, using a large ensemble from the Met Office's seasonal prediction system, GloSea. We find that seasonal UK storms and mean wind speeds are well correlated, and therefore a single prediction of UK mean wind speed will give an indication of predicted storm counts. Skill for these predictions is highest in winter (December-February), related to predictability of the North Atlantic Oscillation. In contrast, summer (June-August) UK wind skill is not significant and furthermore appears to be negative. We find evidence, in both observations and model members, for a Rossby wave from the tropics influencing UK summer winds and forming a significant predictable component in the model ensemble mean. However, the model predictable signal appears to be out of phase with that observed leading to the negative correlation. Further investigation into summer Rossby wave generation and propagation is necessary to understand whether summer predictions could be improved. [ABSTRACT FROM AUTHOR]

Predicting Indian Summer Monsoon (ISM) is a challenging task due to its complexity and nonlinear interactions. Any improvement in the seasonal prediction skill of models would highly benefit a large population and the economy. In this context, three sets of hindcast experiments are carried out for 9 months each, for the period 1993–2019, using the National Centers for Environmental Prediction‐Climate Forecast System version 2 (NCEP‐CFSv2). The experiments differ from each other in the way they are initialized: one is initialized in February (FebIC), and the second in May (MayIC), whereas in the third one (Exp), ocean is initialized in February and allowed to evolve but the atmosphere reinitialized every month up to May. So, hindcasts of Exp are from the pre‐evolved ocean with realistic atmospheric initial conditions of May. The representation of mean tropical Indo‐Pacific Sea surface temperature (SST), Walker circulation, mean monsoon circulation and moisture transport to Indian landmass are better represented in Exp. The ISM rainfall prediction (interannual) skill improved in Exp as compared to FebIC and MayIC over central India, Indian land mass and extended monsoon region. The initialization strategy adopted in Exp reduces the model initial shocks especially in the upper ocean heat content and SST over the Indo‐Pacific region, thereby offering a cost‐effective alternative approach for reduction of the initial shock. The well‐known cold tongue SST bias over the equatorial Pacific in MayIC is reduced significantly in Exp with improved monsoon teleconnections and the overdependence of ISM rainfall on El Niño Southern Oscillation in MayIC is also reduced in Exp. [ABSTRACT FROM AUTHOR]

Experimental seasonal forecasts using the INM-CM5 climate model have been applied as the input data for the temperature–time phenological model of birch pollination. A test method has been developed within the framework of the joint model for the seasonal forecasting of the time of the beginning of birch pollen dispersion in the European territory of Russia. Verification of this method on seasonal retrospective forecasts of the INM-CM5 model (in 1991–2019) has shown an adequate reproduction of the days of the beginning of birch pollen season simulated for the same period based on ERA5 reanalysis. The mean systematic errors are ±2 days, and the spatial correlation coefficients exceed +0.84. The forecasts of the date of pollen dispersion beginning in 2022 simulated from the experimental operational seasonal forecasts using the INM-CM5 model with a monthly lead time and with a zero lead time are also evaluated. It is shown that the errors in forecasting the onset of pollen dispersion are ±5–10 days, with smaller errors of forecasts with a 1-month lead time. The results allow us to conclude that the seasonal forecast of the surface temperature based on the INM-CM5 model can be used as input information for the temperature–time phenological model for the operational forecast of the start time of birch pollen dispersion in the European territory of Russia. [ABSTRACT FROM AUTHOR]

Although 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. [ABSTRACT FROM AUTHOR]

Ocean and climate drivers affect the distribution and abundance of marine life on a global scale. Marine ecological forecasting seeks to predict how living marine resources respond to physical variability and change, enabling proactive decision‐making to support climate adaptation. However, the skill of ecological forecasts is constrained by the skill of underlying models of both ocean state and species‐environment relationships. As a test of the skill of data‐driven forecasts for fisheries, we developed predictive models of catch‐per‐unit‐effort (CPUE) of tuna and billfish across the south‐west Pacific Ocean, using a 12‐year time series of catch data and a large ensemble climate reanalysis. Descriptors of water column structure, particularly temperature at depth and upper ocean heat content, emerged as useful predictors of CPUE across species. Enhancing forecast skill over sub‐seasonal to multi‐year timescales in any system is likely to require the inclusion of sub‐surface ocean data and explicit consideration of regional physical dynamics. [ABSTRACT FROM AUTHOR]

In the first half of winter 2020/2021, several unprecedented cold events occurred in most parts of China and caused record-breaking low temperatures in many cities. However, seasonal predictions related to the onset and evolution of extreme cold events are still challenging. In this study, we first evaluated the short-term climate prediction skills in winter of the CAS-ESM-c global coupled model and revealed the key dynamic processes of the onset and development of 2020/2021 extreme cold events based on the ensemble forecasts starting on October 1st, 2020. Under the background provided by the synergistic effect of the warm Arctic and the cold tropical Pacific (La Niña), the model captured the abnormal meridional atmospheric pattern well, including the intensification of the Ural Blocking High and the negative phase of the Arctic Oscillation, forecasted the negative geopotential height anomalies over the Eastern Asian region, and finally, successfully predicted the outbreak of cold waves in December 2020 two months in advance. Moreover, the dominant differences in the initial ocean fields between the best and the worst forecast members were further compared to isolate the triggering factor for predicting cold events by CAS-ESM-c. The initial warm sea surface temperature over the Barents Sea can gradually form a meridional pattern between the warm Arctic and the cold Asian continent, which could lead to a reduction in the large-scale meridional temperature gradient at mid-high latitudes and weaken the atmospheric baroclinicity with more conducive to the southward outbreak of cold waves, highlighting that realistic Arctic ocean conditions in autumn can be reasonably assimilated into coupled models to stimulate the successful prediction of extreme cold events in the 2020/2021 winter. [ABSTRACT FROM AUTHOR]

Seasonal forecasts coupled with crop models can potentially enhance decision-making in smallholder farming in Africa. The study sought to inform future research through identifying and critiquing crop and climate models, and techniques for integrating seasonal forecast information and crop models. Peer-reviewed articles related to crop modelling and seasonal forecasting were sourced from Google Scholar, Web of Science, AGRIS, and JSTOR. Nineteen articles were selected from a search outcome of 530. About 74% of the studies used mechanistic models, which are favored for climate risk management research as they account for crop management practices. European Centre for Medium-Range Weather Forecasts and European Centre for Medium-Range Weather Forecasts, Hamburg, are the predominant global climate models (GCMs) used across Africa. A range of approaches have been assessed to improve the effectiveness of the connection between seasonal forecast information and mechanistic crop models, which include GCMs, analogue, stochastic disaggregation, and statistical prediction through converting seasonal weather summaries into the daily weather. GCM outputs are produced in a format compatible with mechanistic crop models. Such outputs are critical for researchers to have information on the merits and demerits of tools and approaches on integrating seasonal forecast and crop models. There is however need to widen such research to other regions in Africa, crop, farming systems, and policy.

The land surface is an important component of numerical weather and climate forecast models due to their effect on energy–water balances and fluxes, and it is essential for forecasts on a seasonal scale. The present study aimed to understand the effects of land surface processes on initialization of seasonal forecasts in the austral spring, in particular soil moisture. We built forecasts with the Brazilian global Atmospheric Model hindcast from 2000 to 2010, with a configuration similar to those used in the operational environment. To improve it, we developed a new initial condition of the land surface using the Land Information System over South America and the Global Land Data Assimilation System for the rest of the globe and used it as the input in the forecast model. The results demonstrated that the model is sensitive to changes in soil moisture and that the new high–resolution soil moisture dataset can be used in model initialization, which resulted in an increase in the correlation of precipitation over part of South America. We also noticed an improvement in the representation of surface fluxes and an increase in soil moisture content and specific humidity at medium and low levels of the atmosphere. The analysis of the coupling between the land surface and the atmosphere showed that, for Central Brazil, the states of the continental surface define the surface fluxes. For the Amazon and La Plata Basins, the model did not correctly represent the coupling because it underestimated the soil moisture content. [ABSTRACT FROM AUTHOR]

The Northern Hemisphere (NH) often experiences frequent cold air outbreaks and heavy snowfalls during La Niña winters. In 2022, a third-year La Niña event has exceeded both the oceanic and atmospheric thresholds since spring and is predicted to reach its mature phase in December 2022. Under such a significant global climate signal, whether the Eurasian Continent will experience a tough cold winter should not be assumed, despite the direct influence of mid- to high-latitude, large-scale atmospheric circulations upon frequent Eurasian cold extremes, whose teleconnection physically operates by favoring Arctic air invasions into Eurasia as a consequence of the reduction of the meridional background temperature gradient in the NH. In the 2022/23 winter, as indicated by the seasonal predictions from various climate models and statistical approaches developed at the Institute of Atmospheric Physics, abnormal warming will very likely cover most parts of Europe under the control of the North Atlantic Oscillation and the anomalous anticyclone near the Ural Mountains, despite the cooling effects of La Niña. At the same time, the possibility of frequent cold conditions in mid-latitude Asia is also recognized for this upcoming winter, in accordance with the tendency for cold air invasions to be triggered by the synergistic effect of a warm Arctic and a cold tropical Pacific on the hemispheric scale. However, how the future climate will evolve in the 2022/23 winter is still subject to some uncertainty, mostly in terms of unpredictable internal atmospheric variability. Consequently, the status of the mid- to high-latitude atmospheric circulation should be timely updated by medium-term numerical weather forecasts and sub-seasonal-to-seasonal prediction for the necessary date information and early warnings. [ABSTRACT FROM AUTHOR]

The polar vortex is a strong jet of westerly wind which forms each winter around the polar stratosphere. Sometimes, roughly every other winter, the polar vortex in the Northern Hemisphere experiences a dramatic breakdown and associated warming of the polar stratosphere. Such events are called sudden stratospheric warmings (SSWs) and they are known to have a significant influence on ground weather in Northern Eurasia and large parts of North America. Typically, these events are thought to occur due to planetary waves propagating to the stratosphere where they may disrupt the vortex. Here, we show that the SSW probability depends significantly on a favorable combination of geomagnetic and solar activity and the phase of the Quasi‐Biennial Oscillation (QBO). Using logistic regression models, we find that more SSWs occur when early‐winter geomagnetic activity (aa index) is low and QBO winds are easterly and when solar activity (F10.7 index) is high and QBO winds are westerly. We then examine the possibility of using these results to predict the occurrence probability of SSWs with several months lead time and evaluate the optimal lead times for all variables using cross‐validation methods. As a result, we find that the SSW probability can be predicted rather well and we can issue a probabilistic SSW prediction for the coming winter season with a success ratio of about 86% already in the preceding August. The results presented here are an important step toward improving the seasonal predictability of wintertime weather using information about solar and geomagnetic activity. Plain Language Summary: In the stratosphere, the atmospheric layer from 10 to 50 km, strong westerly wind, the polar vortex forms around the pole during each winter. The vortex is quite variable and sometimes it may weaken or even break completely allowing intrusions of cold Arctic air into middle latitudes in the troposphere. These events are called sudden stratospheric warmings (SSW) due to the corresponding abrupt increase of polar stratospheric temperature. The SSWs have been traditionally thought to result from enhanced planetary wave activity that disrupts the vortex. However, recent findings suggest that the occurrence of SSWs is influenced also by the joint effect of equatorial stratospheric winds (exhibiting Quasi‐Biennial Oscillation, QBO) and energetic particle precipitation from space into the atmosphere as well as solar UV radiation. Based on these recent findings, we develop here a probabilistic seasonal forecast model for SSW occurrence based on the joint influence of QBO, geomagnetic activity (indicator for particle precipitation), and solar F10.7 index (indicator for UV radiation), which can be predicted with long lead times. We show that the inclusion of these factors significantly improves the long‐term predictions of SSW occurrence. Key Points: We develop models, predicting the wintertime sudden stratospheric warming (SSW) probability in preceding August using Quasi‐Biennial Oscillation, geomagnetic aa index and solar F10.7 indexThe success ratio of the predictions is about 86%The study is an important step toward improved long‐term forecasting of SSWs [ABSTRACT FROM AUTHOR]

Seasonal forecast is an early warning system that contributes to anticipatory management by providing spatial and temporal information of the near future. This study first examined the skill of ECMWF system 5 (SEAS5) sub–seasonal–to–seasonal (S2S) forecasts over Mainland Southeast Asia (MSEA). We evaluated the SEAS5 skill of temperature and precipitation for 30 years (1985–2014) against two reference model datasets, WFDE5 and APHRODITE, using probabilistic forecast verification skill metrics at grid cells for each month. Then, the SEAS5 data was used to force the Variable Infiltration Capacity (VIC) hydrological model to predict runoff and streamflow. These hydrological results were compared against the WFDE5-driven streamflow reanalysis and observed station data, using the same probabilistic skill statistics. The results show a prediction potential for temperature beyond two months in advance. The skill of precipitation and streamflow forecasting is limited to the first month. Strong seasonal and regional dependence occurs. The model shows high forecast skills during the pre-monsoon (April–May) and post-monsoon (October–November), arguably the period when its usefulness is potentially highest. Conversely, poor skill is observed during the rainy monsoon season (June–August). In eastern and southern MSEA, i.e. in eastern Thailand, Cambodia, Vietnam and Malaysia, considerable skill levels are found. Year–to–year precipitation tercile plots highlight skill in predicting the anomalous seasonal conditions caused by the ENSO. Overall, SEAS5 and derived hydrological forecasts show useful skill that can potentially be used for hydrological and agricultural anticipatory management in this region. [ABSTRACT FROM AUTHOR]

A new operational seasonal forecast system, Japan Meteorological Agency (JMA)/Meteorological Research Institute (MRI) Coupled Prediction System (CPS) version 3 (JMA/MRI-CPS3), has been developed. This system represents a major upgrade from the former system, CPS2. CPS3 comprises atmosphere, land, ocean, and sea ice forecast models and the necessary initialization systems for these models. For historical reforecasts, the atmospheric reanalysis dataset JRA-3Q provides initial conditions for the atmosphere and external forcings for land, ocean, and sea ice analysis. In the operational forecast, to initialize the system in near-real time, JMA's operational atmospheric analysis is used in conjunction with JRA-3Q. Next, the land surface model is initialized using an uncoupled free simulation, forced by the atmospheric analysis. The ocean and sea ice models are initialized with the global ocean data assimilation system MOVE-G3, which incorporates a newly developed four-dimensional variational method for temperature, salinity, and sea surface height and a three-dimensional method for sea ice concentration. Compared with the previous system, the CPS3 forecast model components exhibit ~2-4 times higher resolution: the atmosphere and land models are configured with ~ 55 km horizontal resolution, with 100 vertical atmosphere layers; and the ocean and sea ice models exhibit a resolution of 0.25° x 0.25°, with 60 vertical ocean layers. The physical processes of the atmosphere are greatly refined in CPS3 relative to CPS2, resulting in improved representation of subseasonal to seasonal scale variability, including the eastward propagation of the Madden-Julian Oscillation, winter blocking highs in the North Atlantic, and coupled atmosphere-ocean variability during El Nino-Southern Oscillation events. Our historical reforecast experiment for 1991-2020 suggests that CPS3 demonstrates greater forecast skills than CPS2. The usability of the model output has been improved in CPS3 by reorganizing the operation schedule to provide daily updates of five-member ensemble forecasts. [ABSTRACT FROM AUTHOR]

Although the seasonal prediction skill of climate models has improved significantly in recent decades, the prediction skill of the Arctic Oscillation (AO), the dominant climate mode over the Northern Hemisphere, remains poor. Additionally, the local representation of AO impacts has diverged from observations, which limits seasonal prediction skill of climate models. In this study, we attempted to improve prediction skill of surface air temperature (SAT) with two post-processing on dynamical model’s seasonal forecast: (1) correction of the AO impact on SAT pattern, and (2) correction of AO index (AOI). The first correction involved replacing the inaccurately simulated impact of AO on SAT with that observed. For the second correction, we employed a empirical prediction model of AOI based on multiple linear regression model based on three precursors: summer sea surface temperature, autumn sea-ice concentration, and autumn snow cover extent. The application of the first correction led to a decrease in prediction skills. However, a significant improvement in SAT prediction skills is achieved when both corrections are applied. The average correlation coefficients for the North America and Eurasian regions increased from 0.23 and 0.06 to 0.28 and 0.30, respectively.

Increasing coastal inundation risk in a warming climate will require accurate and reliable seasonal forecasts of sea level anomalies at fine spatial scales. In this study, we explore statistical downscaling of monthly hindcasts from six current seasonal prediction systems to provide a high‐resolution prediction of sea level anomalies along the North American coast, including at several tide gauge stations. This involves applying a seasonally invariant downscaling operator, constructing by linearly regressing high‐resolution (1/12°) ocean reanalysis data against its coarse‐grained (1°) counterpart, to each hindcast ensemble member for the period 1982–2011. The resulting high‐resolution coastal hindcasts have significantly more deterministic skill than the original hindcasts interpolated onto the high‐resolution grid. Most of this improvement occurs during summer and fall, without impacting the seasonality of skill noted in previous studies. Analysis of the downscaling operator reveals that it boosts skill by amplifying the most predictable patterns while damping the less predictable patterns. Plain Language Summary: Currently, the large computer models that form the basis of seasonal climate prediction systems produce coastal sea level forecasts spaced about 100 km apart. This is too coarse to meet the needs of U.S. coastal ocean management and services, which are becoming increasingly important as sea levels rise in a warming climate. In this study, we explored a method to provide such information on much smaller spatial scales, which better correspond to local coastal sea level measurements by tide gauges. We developed an efficient way to generate monthly sea level predictions on distances as small as 10 km apart, by applying the observed statistical relationship between sea level variations on scales of 100–1,000 km and finer‐scale coastal ocean observations to the original coarser model predictions. By testing our approach on past forecasts ("hindcasts") from existing climate forecast systems, we found that we could improve forecasts for different local regions along both the U.S. West and East Coasts. Key Points: Sea level prediction from relatively coarse operational forecasts can be enhanced to finer coastal scales using statistical downscalingDownscaling can be determined by multivariate linear regression trained from high‐resolution reanalysis and its coarse‐grained counterpartThis downscaling method significantly improves skill compared to bilinearly interpolated hindcasts at several U.S. tide gauge locations [ABSTRACT FROM AUTHOR]

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Understanding seasonal climatic conditions is critical for better management of resources such as water, energy, and agriculture. Recently, there has been a great interest in utilizing the power of Artificial Intelligence (AI) methods in climate studies. This paper presents cutting-edge deep-learning models (UNet++, ResNet, PSPNet, and DeepLabv3) trained by state-of-the-art global CMIP6 models to forecast global temperatures a month ahead using the ERA5 reanalysis dataset. ERA5 dataset was also used for fine-tuning as well performance analysis in the validation dataset. Ten different setups (with CMIP6 and CMIP6 + ERA5 fine-tuning) including six meteorological parameters (i.e., 2 m temperature, 10 m eastward component of wind, 10 m northward component of wind, geopotential height at 500 hPa, mean sea-level pressure, and precipitation flux) and elevation were used with both four different algorithms. For each model 14 different sequential and nonsequential temporal settings were used. The mean absolute error (MAE) analysis revealed that UNet++ with CMIP6 with 2 m temperature + elevation and ERA5 fine-tuning model with “Year 3 Month 2” temporal case provided the best outcome with an MAE of 0.7. Regression analysis over the validation dataset between the ERA5 data values and the corresponding AI model predictions revealed slope and R² values close to 1 suggesting a very good agreement. The AI model predicts significantly better than the mean CMIP6 ensemble between 2016 and 2021. Both models predict the summer months more accurately than the winter months. [ABSTRACT FROM AUTHOR]

When one is using climate simulation outputs, one critical issue to consider is the systematic bias affecting the modelled data. The bias correction of modelled data is often used when one is using impact models to assess the effect of climate events on human activities. However, the efficacy of most of the currently available methods is reduced in the case of extreme events because of the limited number of data for these low probability and high impact events. In this study, a novel bias correction methodology is proposed, which corrects the bias of extreme events. To do so, we extended one of the most popular bias correction techniques, i.e., quantile mapping (QM), by improving the description of extremes through a generalised extreme value distribution (GEV) fitting. The technique was applied to the daily mean temperature and total precipitation data from three seasonal forecasting systems: SEAS5, System7 and GCFS2.1. The bias correction efficiency was tested over the Southern African Development Community (SADC) region, which includes 15 Southern African countries. The performance was verified by comparing each of the three models with a reference dataset, the ECMWF reanalysis ERA5. The results reveal that this novel technique significantly reduces the systematic biases in the forecasting models, yielding further improvements over the classic QM. For both the mean temperature and total precipitation, the bias correction produces a decrease in the Root Mean Squared Error (RMSE) and in the bias between the simulated and the reference data. After bias correcting the data, the ensemble forecasts members that correctly predict the temperature extreme increases. On the other hand, the number of members identifying precipitation extremes decreases after the bias correction. [ABSTRACT FROM AUTHOR]

The variability of the North Atlantic Subtropical High and its influence on the behavior of tropical cyclogenesis are characterized and analyzed. The database of the Center for Environmental Prediction and the Center for Atmospheric Research was consulted for the months of May to October between 1950 and 2019. The variables used were the central pressure to determine the position of the North Atlantic Subtropical High on the surface and the geopotential to obtain the position of the anticyclonic center at 850 hPa, as well as the geopotential at 500 and 200 hPa over the region of the anticyclone on the surface. This system weakens at the surface level and intensifies at other tropospheric levels. The relationship with tropical cyclone activity in the Atlantic basin was assured and updated. Low levels play an important role in tropical cyclogenesis; the position and extension of the anticyclonic ridge at this level are the parameters with the highest coincidence, and in the present century, the anticyclone parameters in the months of June and July have increased their significance.

Abstract Increasing coastal inundation risk in a warming climate will require accurate and reliable seasonal forecasts of sea level anomalies at fine spatial scales. In this study, we explore statistical downscaling of monthly hindcasts from six current seasonal prediction systems to provide a high‐resolution prediction of sea level anomalies along the North American coast, including at several tide gauge stations. This involves applying a seasonally invariant downscaling operator, constructing by linearly regressing high‐resolution (1/12°) ocean reanalysis data against its coarse‐grained (1°) counterpart, to each hindcast ensemble member for the period 1982–2011. The resulting high‐resolution coastal hindcasts have significantly more deterministic skill than the original hindcasts interpolated onto the high‐resolution grid. Most of this improvement occurs during summer and fall, without impacting the seasonality of skill noted in previous studies. Analysis of the downscaling operator reveals that it boosts skill by amplifying the most predictable patterns while damping the less predictable patterns.

Understanding seasonal climatic conditions is critical for better management of resources such as water, energy, and agriculture. Recently, there has been a great interest in utilizing the power of Artificial Intelligence (AI) methods in climate studies. This paper presents cutting-edge deep-learning models (UNet++, ResNet, PSPNet, and DeepLabv3) trained by state-of-the-art global CMIP6 models to forecast global temperatures a month ahead using the ERA5 reanalysis dataset. ERA5 dataset was also used for fine-tuning as well performance analysis in the validation dataset. Ten different setups (with CMIP6 and CMIP6 + ERA5 fine-tuning) including six meteorological parameters (i.e., 2 m temperature, 10 m eastward component of wind, 10 m northward component of wind, geopotential height at 500 hPa, mean sea-level pressure, and precipitation flux) and elevation were used with both four different algorithms. For each model 14 different sequential and nonsequential temporal settings were used. The mean absolute error (MAE) analysis revealed that UNet++ with CMIP6 with 2 m temperature + elevation and ERA5 fine-tuning model with “Year 3 Month 2” temporal case provided the best outcome with an MAE of 0.7. Regression analysis over the validation dataset between the ERA5 data values and the corresponding AI model predictions revealed slope and $ {R}^2 $ values close to 1 suggesting a very good agreement. The AI model predicts significantly better than the mean CMIP6 ensemble between 2016 and 2021. Both models predict the summer months more accurately than the winter months.

Among many other options, seasonal weather forecasts and the use of cultivars that are better adapted to local climate and climate variability have been discussed as two potential supporting measures to assist farmer adaptation to climatic variability and change. In this article, we evaluate the potential benefit of combining these two measures, i.e., choosing specific crop varieties based on seasonal forecasts – focusing on the Central Rift Valley in Ethiopia. We base our value of information analysis on the available records of field trial data for publicly released crop varieties. We find that experimental evidence must be extended and improved in order to provide reliable evidence of yield performance differences between crop varieties, which is an essential prerequisite for exploiting forecast information. Classification of cumulative seasonal rainfall based on the modified Rainfall Anomaly Index provides a sharper distinction than using the standard tercile-based approach employed in the Ethiopian seasonal forecast communication. Even with a fairly optimistic interpretation of the evidence with respect to exploitable yield differences, we find only modest benefits of seasonal forecasts at realistic forecast accuracy for the region. Given the empirical limitations when assembling long-run yield data, the presented results have to be understood as a first approximation. Apart from sufficiently accurate forecasts, success of forecast-based cultivar choice will depend on (i) more reliable evidence on performance differences between crop varieties under different weather conditions, and (ii) changes in the current seed breeding and distribution system in developing countries, because the full potential of high-accuracy seasonal forecast information could only be tapped, if forecast-matching cultivars are being made available to farmers in time.

Japan Meteorological Agency (JMA) started to use a new global ocean data assimilation system for the operational seasonal predictions in February 2022. The system is composed of two subsystems with non-eddy-permitting (lower) and eddy-permitting (higher) resolutions. The lower-resolution subsystem adopts a four-dimensional variational (4DVAR) method to optimize the temperature and salinity fields, and the data-assimilated fields are downscaled into the higher-resolution subsystem using incremental analysis updates. The impact of introducing the 4DVAR method in the new ocean data assimilation system is investigated through the comparison of a regular reanalysis run of the system using the 4DVAR method with another run using a three-dimensional variational (3DVAR) method. A comparison of the temperature fields before the downscaling between the two reanalysis runs indicates that the 4DVAR method can more effectively reduce the misfits between the model field and assimilated observation data. However, the increase of the temperature root mean square difference (RMSD) relative to independent Argo float data, along with the larger variance, for the run with the 4DVAR method reveals that the 4DVAR method adjusts the temperature field more significantly but the adjustments are inconsistent with the independent data due to insufficient model physics and resolution. The increase of the RMSD is mitigated after the assimilated fields are downscaled into the higher-resolution subsystem. The 4DVAR method reduces the bias and RMSD of temperature relative to the independent data along the thermocline, as well as near the surface, in the equatorial vertical section, which is expected to affect the prediction of El Niño-Southern Oscillation (ENSO).

Crop models fed by seasonal forecasts to deliver yield forecasts are becoming a valuable tool to tackle food production shocks and food price spikes triggered by climate change and extreme events. However, to what extent seasonal climate prediction can be coupled to crop models in an effective climate service to provide durum wheat yield forecasting at local, regional and national scales remains unknown. Within the H2020 MED-GOLD project, based on participatory action research and through a co-designing approach, a complete assessment of raw and bias-adjusted ECMWF-System5 seasonal forecasts data in feeding the Delphi model was performed and explicitly compared with a previous version of the Delphi model fed by a historical scenario, both benchmarked with yield observation data. The 5 and 3-months lead prediction accuracies of year-to-year variations in durum wheat yield in Italy for 31 crop years were explored and evaluated for which techniques to remove biases from input data to adopt and for which areas they performed better. The raw seasonal forecast (BC0) showed better yield forecasting correlation than the historical scenarios (0.42 vs 0.17) in southern Italy, but not better accuracy (53 % for both), while a first bias-adjusted seasonal forecast (BC1) had again a positive effect in terms of correlation (r = 0.43) especially in southern Italy and generally in terms of accuracy (59 %). Finally, by the implementation of a second bias-adjusted seasonal forecast (BC2) the best yield prediction system was reached, especially for the areas of central and southern Italy with a total accuracy of 62 % and r = 0.52.

Climate variability affects crop production in multiple and often complex ways. The development and use hybrid crops with greater productivity and tolerance to climate shocks is one of the approaches to climate adaptation and agricultural intensification. Since hybrid crops are more expensive for the producer, risk management is of paramount importance. Here, we pose that there is high potential for the Colombian maize sector to use crop-specific climate services for risk reduction. We used the CERES-Maize crop model connected to seasonal climate forecasts developed via Canonical Correlation Analysis (CCA) across key maize growing areas in Colombia to assess the performance of a maize-specific agroclimatic forecast to inform two key decisions, namely, the choice of sowing dates and genotypes. We find that the agroclimatic models perform well at discriminating yield categories (above, below, and normal) with discrimination capacity of up to 70–80 % for the ‘below normal’ and ‘above + below normal’ categories. Consistent with this, agroclimatic forecasts typically predict the optimal planting date with an error of 3 pentads or less. They also predict the optimal choice of genotype correctly around 50–70 % of the time depending on the site or season of interest. Notably, we identify specific cases in which the agroclimatic forecast is misleading but argue that the overall value of the forecasts outweighs these cases. Future work should focus on expanding the scope of the agroclimatic prediction to include other relevant farming decisions that are influenced by climate, and on the improvement of climate forecast performance.

The predictability of the wintertime Western Pacific (WP) pattern is evaluated based on seasonal predictions from five models participating in the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) multi-model ensemble (MME) for the winters from 1982/1983 to 2021/2022. The temporal correlation coefficient (TCC) between the observed and MME-predicted WP indices was 0.61 (0.37–0.54 for individual models) for the entire series. However, when only three Super El Niño (SEN) years (Niño3.4 ≥ 2.0) out of the 40-year series were excluded, the TCC dropped down to 0.54 (0.27–0.42). During the SEN years, the WP was strongly affected by the SEN-excited anomalies via the PNA. In observations from non-SEN years, the WP pattern was strongly related to the dipole pattern in Northwestern Pacific SST (TCC = 0.8), for the description of which we suggested a Northwestern Pacific (NWP) index, and it was significantly weakly related to the ENSO and IOD, whereas in the model simulations, the main role was played by the ENSO (TCC = 0.6). The NWP index was well predictable in MME (TCC = 0.73) and individual models (0.56–0.71). We showed that the prediction of the WP index polarity is reliable when both predicted WP and NWP anomalies are significant and indicate the same WP sign that has implications for the seasonal forecasting. [ABSTRACT FROM AUTHOR]

Accurate prediction of the summer precipitation over the middle and lower reaches of the Yangtze River (MLYR) is of urgent demand for the local economic and societal development. This study assesses the seasonal forecast skill in predicting summer precipitation over the MLYR region based on the global Climate Forecast System of Nanjing University of Information Science and Technology (NUIST-CFS1.0, previously SINTEX-F). The results show that the model can provide moderate skill in predicting the interannual variations of the MLYR rainbands, initialized from 1 March. In addition, the nine-member ensemble mean can realistically reproduce the links between the MLYR precipitation and tropical sea surface temperature (SST) anomalies, but the individual members show great discrepancies, indicating large uncertainty in the forecasts. Furthermore, the NUIST-CFS1.0 can predict five of the seven extreme summer precipitation anomalies over the MLYR during 1982–2020, albeit with underestimated magnitudes. The Weather Forecast and Research (WRF) downscaling hindcast experiments with a finer resolution of 30 km, which are forced by the large-scale information of the NUIST-CFS1.0 predictions with a spectral nudging method, display improved predictions of the extreme summer precipitation anomalies to some extent. However, the performance of the downscaling predictions is highly dependent on the global model forecast skill, suggesting that further improvements on both the global and regional climate models are needed. [ABSTRACT FROM AUTHOR]

The Meteo-France seasonal forecasting system 7 provides a 7-month forecast range with 25 ensembles. The seasonal precipitation re-forecast (from May to November 1993–2015) was evaluated by the Brier score in terms of accuracy and reliability based on tercile probabilities. Multiple analyses were performed to assess the robustness of the score. These results show that the spatial distribution of the Brier score depends significantly on tercile thresholds, reference data, sampling methods, and ensemble types. Large probabilistic errors over the dry regions on land and the Nino regions in the Pacific can be reduced by adjusting the tercile thresholds. The forecast errors were identified when they were insensitive to different analysis methods. All the analyses detected that the errors increase/decrease with the lead time over the tropical Indian/Pacific Ocean. The intra-seasonal analysis reveals that some of these errors are inherited from monthly forecasts, which may be related to large-scale, short-term variability modes. A new confidence interval calculation was formulated for the "uncertain" case in the reference data. The confidence interval at a 95% level for the mean Brier score over the entire tropical region was quantified. The best estimations are ~6% the mean Brier score for both the above and below-normal terciles. [ABSTRACT FROM AUTHOR]

A weather forecast is a result of applying science and technology to predict the conditions of the atmosphere in a selected location and time in the future. The main input is a collection of data (atmospheric, land, and ocean), and the resulting output is meteorology (how atmospheric conditions will change). People have been trying to predict the weather by observing nature for thousands of years, but in a scientific way only since the 19th century. First manually and based mostly on changes in barometric pressure, and later in modern times with the contribution of computer-based models (numerical weather prediction). Despite the high inaccuracy of numerical weather prediction beyond 10 days, the interest in long-term weather forecasts is very high due to social reasons—energy sector, civil protection, etc.—and the scientific effort is constant. Here, we propose a statistical weather model for long-term weather forecasts based on weather/climate data time series. We will analyze atmospheric data in 850 hPa for a period of 35 years, resulting in temperature ensembles and temperature deviations for specific periods. Finally, we will contrast the results of the statistical weather model (prediction) with the real data to check the accuracy of the model.