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The characteristics of tropical cyclones (TCs) in sub-seasonal forecasting with the Global Seasonal Forecast System 5 (GloSea5) of the Korea Meteorological Administration (KMA) were assessed for June–September (JJAS) from 1991 to 2010 over the western North Pacific (WNP). The performance of GloSea5 was examined for its ability to reproduce observed TC climatology as well as changes in TC genesis with the El Niño-Southern Oscillation (ENSO) and a 1998/1999 climate regime shift (e.g., frequency, genesis spatial distribution). GloSea5 showed skillful performance in predicting the frequency and genesis spatial distribution of TCs in climatology and both ENSO phases; this performance was best during periods of La Niña. Environmental fields related to TC genesis (e.g., sea surface temperature [SST], vertical wind shear [VWS], 850-hPa wind and relative vorticity) were also reasonably captured, despite some systematic biases in SST, low-level circulation, relative vorticity, and VWS. GloSea5 performed well in terms of characteristic of changes in TC genesis before and after the regime shift. However, there were biases in TC frequency before the regime shift and changes in TC-related environmental fields. Our results imply that GloSea5 with a high predictive skill for TC genesis over the WNP can be used as an operational model for sub-seasonal TC forecasting, although it requires continuous improvements to reduce systematic errors.

The dynamical prediction of the Asian-Australian monsoon (AAM) has been an important and long-standing issue in climate science. In this study, the predictability of the first two leading modes of the AAM is studied using retrospective prediction datasets from the seasonal forecasting models in four operational centers worldwide. Results show that the model predictability of the leading AAM modes is sensitive to how they are defined in different seasonal sequences, especially for the second mode. The first AAM mode, from various seasonal sequences, coincides with the El Niño phase transition in the eastern-central Pacific. The second mode, initialized from boreal summer and autumn, leads El Niño by about one year but can exist during the decay phase of El Niño when initialized from boreal winter and spring. Our findings hint that ENSO, as an early signal, is conducive to better performance of model predictions in capturing the spatiotemporal variations of the leading AAM modes. Still, the persistence barrier of ENSO in spring leads to poor forecasting skills of spatial features. The multimodel ensemble (MME) mean shows some advantage in capturing the spatiotemporal variations of the AAM modes but does not provide a significant improvement in predicting its temporal features compared to the best individual models in predicting its temporal features. The BCC_CSM1.1M shows promising skill in predicting the two AAM indices associated with two leading AAM modes. The predictability demonstrated in this study is potentially useful for AAM prediction in operational and climate services.

Hydrological forecasting becomes an important tool in water resources management in forecasting the future state of the water resources in a catchment. The need for a reliable seasonal hydrologic forecast is significant and is becoming even more urgent under future climate conditions, as the assimilation of seasonal forecast information in decision making becomes part of the short and long-term climate change adaptation strategies in a range of contexts, such as energy supply, water supply and management, ruralurban, agriculture, infrastructure and disaster preparedness and relief. This work deals with the framework for implementation of the Pitman-WR2012 model in a hydrological forecasting mode. The Pitman-WR2012 model was forced with 10-member ensemble seasonal climate forecast from Climate Forecast Systems v.2 (CFSv2), which is downscaled using the principal components regression (PCR) approach. The generated seasonal hydrological forecast focused on the summer season, in particular on the Dec-Jan-Feb (DJF) period, which is the rainy season in the studied catchment (Kraai River catchment in the Eastern Cape Province of South Africa). The hydrological forecast issued at the end of November showed skill in December and February (assessed through Receiver Operating Characteristic (ROC) and Ranked Probability Skill Score (RPSS)), with poorer skill in January. Importantly, the skill of streamflow forecast was better than that of rainfall forecast, which likely results from the influence of the initial conditions of the hydrological model. In conclusion Pitman-WR2012 model performed realistically when implemented in seasonal hydrological forecasts mode, and it is important that in that mode the model is run with near-real-time rainfall data in order to maximize forecast skill arising from initial conditions.

This study assesses the suitability of convolutional neural networks (CNNs) for downscaling precipitation over East Africa in the context of seasonal forecasting. To achieve this, we design a set of experiments that compare different CNN configurations and deployed the best-performing architecture to downscale one-month lead seasonal forecasts of June–July–August–September (JJAS) precipitation from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0) for 1982–2020. We also perform hyper-parameter optimization and introduce predictors over a larger area to include information about the main large-scale circulations that drive precipitation over the East Africa region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results show that the CNN-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme precipitation spatial patterns. Besides, CNN-based downscaling yields a much more accurate forecast of extreme and spell indicators and reduces the significant relative biases exhibited by the raw model predictions. Moreover, our results show that CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of East Africa. The results demonstrate the potential usefulness of CNN in downscaling seasonal precipitation predictions over East Africa, particularly in providing improved forecast products which are essential for end users.

Land surface initial conditions have been recognized as a potential source of predictability in sub-seasonal to seasonal forecast systems, at least for near-surface air temperature prediction over the mid-latitude continents. Yet, few studies have systematically explored such an influence over a sufficient hindcast period and in a multi-model framework to produce a robust quantitative assessment. Here, a dedicated set of twin experiments has been carried out with boreal summer retrospective forecasts over the 1992–2010 period performed by five different global coupled ocean–atmosphere models. The impact of a realistic versus climatological soil moisture initialization is assessed in two regions with high potential previously identified as hotspots of land–atmosphere coupling, namely the North American Great Plains and South-Eastern Europe. Over the latter region, temperature predictions show a significant improvement, especially over the Balkans. Forecast systems better simulate the warmest summers if they follow pronounced dry initial anomalies. It is hypothesized that models manage to capture a positive feedback between high temperature and low soil moisture content prone to dominate over other processes during the warmest summers in this region. Over the Great Plains, however, improving the soil moisture initialization does not lead to any robust gain of forecast quality for near-surface temperature. It is suggested that models biases prevent the forecast systems from making the most of the improved initial conditions.

Seasonal rainfall forecasting is important for water resources management, agriculture, and disaster prevention. Our study aims to provide an automated deep learning method for the seasonal prediction of monthly rainfall at stations in seven climatic sub-regions in Vietnam with lead times of up to 6 months. An appropriate set of predictors was selected based on numerous climate indices and neighbor station data for the period 1980–2020. We developed an adapted deep learning pipeline for both short- and long-term analysis. The predicted rainfall was verified against the observed data using mean absolute error (MAE), root mean squared error (RMSE), and Pearson correlation coefficients. The results showed that our model generally captured well observed data reflected by low error (MAE and RMSE < 0.2) and high correlation (at 0.8–0.9) for all climatic sub-regions. For the leadtimes of 1–3 months, the rainfall predictionsmade using climate indices as predictors were outperformed by those using neighbor stations data; while for longer leadtimes (4–6 months), the climate indices themselve were able to improve the performance. The rainfall predictions of our methods on all three lead times climatological predictions by factoring additional values. However, there is room for improvement in predicting extreme and abrupt shifts in time series patterns.

California experienced a historic run of nine consecutive landfalling atmospheric rivers (ARs) in three weeks’ time during winter 2022/23. Following three years of drought from 2020 to 2022, intense landfalling ARs across California in December 2022–January 2023 were responsible for bringing reservoirs back to historical averages and producing damaging floods and debris flows. In recent years, the Center for Western Weather and Water Extremes and collaborating institutions have developed and routinely provided to end users peer-reviewed experimental seasonal (1–6 month lead time) and subseasonal (2–6 week lead time) prediction tools for western U.S. ARs, circulation regimes, and precipitation. Here, we evaluate the performance of experimental seasonal precipitation forecasts for winter 2022/23, along with experimental subseasonal AR activity and circulation forecasts during the December 2022 regime shift from dry conditions to persistent troughing and record AR-driven wetness over the western United States. Experimental seasonal precipitation forecasts were too dry across Southern California (likely due to their overreliance on La Niña), and the observed above-normal precipitation across Northern and Central California was underpredicted. However, experimental subseasonal forecasts skillfully captured the regime shift from dry to wet conditions in late December 2022 at 2–3 week lead time. During this time, an active MJO shift from phases 4 and 5 to 6 and 7 occurred, which historically tilts the odds toward increased AR activity over California. New experimental seasonal and subseasonal synthesis forecast products, designed to aggregate information across institutions and methods, are introduced in the context of this historic winter to provide situational awareness guidance to western U.S. water managers.

Ten years, 16 fully coupled global models, and hundreds of research papers later, the North American Multimodel Ensemble (NMME) monthly-to-seasonal prediction system is looking ahead to its second decade. The NMME comprises both real-time, initialized predictions and a substantial research database; both retrospective and real-time forecasts are archived and freely available for research and development. Many U.S.-based and international entities, both private and public, use NMME data for regional or otherwise tailored forecasts. The system’s built-in evolution, with new models gradually replacing older ones, has been demonstrated to gradually improve the skill of 2-m temperature and sea surface temperature, although precipitation prediction remains a difficult problem. This paper reviews some of the NMME-based contributions to seasonal climate prediction research and applications, progress on scientific understanding of seasonal prediction and multimodel ensembles, and new techniques. Several prediction-oriented aspects are explored, including model representation of observed trends and the underprediction of below-average temperature. We discuss potential new directions, such as higher-resolution models, hybrid statistical–dynamical techniques, or prediction of environmental hazards such as coastal flooding and the risk of mosquito-borne diseases.

Marine heatwaves (MHWs)—periods of exceptionally warm ocean temperature lasting weeks to years—are now widely recognized for their capacity to disrupt marine ecosystems 1 – 3 . The substantial ecological and socioeconomic impacts of these extreme events present significant challenges to marine resource managers 4 – 7 , who would benefit from forewarning of MHWs to facilitate proactive decision-making 8 – 11 . However, despite extensive research into the physical drivers of MHWs 11 , 12 , there has been no comprehensive global assessment of our ability to predict these events. Here we use a large multimodel ensemble of global climate forecasts 13 , 14 to develop and assess MHW forecasts that cover the world’s oceans with lead times of up to a year. Using 30 years of retrospective forecasts, we show that the onset, intensity and duration of MHWs are often predictable, with skilful forecasts possible from 1 to 12 months in advance depending on region, season and the state of large-scale climate modes, such as the El Niño/Southern Oscillation. We discuss considerations for setting decision thresholds based on the probability that a MHW will occur, empowering stakeholders to take appropriate actions based on their risk profile. These results highlight the potential for operational MHW forecasts, analogous to forecasts of extreme weather phenomena, to promote climate resilience in global marine ecosystems. Climate forecast systems are used to develop and evaluate global predictions of marine heatwaves (MHWs), highlighting the feasibility of predicting MHWs and providing a foundation for operational MHW forecasts to support climate adaptation and resilience.

GCMs are used by many national weather services to produce seasonal outlooks of atmospheric and oceanic conditions and fluxes. Postprocessing is often a necessary step before GCM forecasts can be applied in practice. Quantile mapping (QM) is rapidly becoming the method of choice by operational agencies to postprocess raw GCM outputs. The authors investigate whether QM is appropriate for this task. Ensemble forecast postprocessing methods should aim to 1) correct bias, 2) ensure forecasts are reliable in ensemble spread, and 3) guarantee forecasts are at least as skillful as climatology, a property called ‘‘coherence.’’ This study evaluates the effectiveness of QM in achieving these aims by applying it to precipitation forecasts from the POAMA model. It is shown that while QM is highly effective in correcting bias, it cannot ensure reliability in forecast ensemble spread or guarantee coherence. This is because QM ignores the correlation between raw ensemble forecasts and observations. When raw forecasts are not significantly positively correlated with observations, QM tends to produce negatively skillful forecasts. Even when there is significant positive correlation, QM cannot ensure reliability and coherence for postprocessed forecasts. Therefore, QM is not a fully satisfactory method for post- processing forecasts where the issues of bias, reliability, and coherence pre-exist. Alternative post- processing methods based on ensemble model output statistics (EMOS) are available that achieve not only unbiased but also reliable and coherent forecasts. This is shown with one such alternative, the Bayesian joint probability modeling approach