Seasonal Forecast System Research Articles

Regional-scale seasonal forecast of surface water availability in a semi-arid environment: The case of Ceará State in Northeast of Brazil

Study region:Ceará (Brazil). Study focus:Considerable intra- and inter-annual variability of rainfall in this semi-arid region lead to strong temporal variations in water availability. To store and supply water in times of water scarcity, tens of thousands of freshwater reservoirs have been built over time, most of which are unmonitored. Here, we develop a hydrological forecasting system for the entire state of Ceará which integrates satellite-based monitoring of reservoir water storage, bias-corrected seasonal weather forecasts and hydrological modeling of freshwater availability. We test and demonstrate the applicability of this system by conducting experiments with historic data, hindcasts and forecasts. New hydrological insights for the region:The assimilation of in-situ and Sentinel-1 based observations of reservoir fillings into the hydrological model WASA-SED proved to be feasible and an important step in the modeling of available water resources dynamics. Hydrological simulations for January to June from 1990–2019 based on meteorological observations resulted in a median average NRMSE between observed and modeled reservoir fillings of strategic reservoirs of 29.51%. The comparison of observed and predicted precipitation from two different seasonal forecasting systems were in the same order of magnitude (i.e. 19.51% and 24.52%). Hindcast experiments suggested the superposition of uncertainties of different model components. Efforts are currently being made to further test and improve the developed integrated framework as part of the operational service.

How do model biases affect large-scale teleconnections that control Southwest US precipitation? Part II: seasonal models

Abstract We explore the skill in predicting Southwest United States (SWUS) October to March precipitation and associated large-scale teleconnections in an ensemble of hindcasts from seasonal prediction systems. We identify key model biases that degrade the models’ capability to predict SWUS precipitation. The subtropical jet in the Pacific sector is generally too zonal and elongated. This is reflected in the models’ North Pacific ENSO teleconnections that are generally too weak with exaggerated northwest-southeast tilt, compared to observations. Also, the models are too dependent on tropical, El Niño-like, wave train anomalies for producing high seasonal SWUS precipitation, when in observations there is a larger influence of zonal Rossby wave trains such as the one observed in 2016/17. Overall, this is consistent with biases in the basic flow inducing errors in the propagation of zonal wave trains in the North Pacific, which affects SWUS precipitation downstream. Although higher skill may be gained from reducing mean flow biases in the models, a case study of the 2016/17 winter illustrates the great challenge behind skillful seasonal prediction of SWUS precipitation. Unsurprisingly, the almost record-breaking precipitation observed that year in the absence of ENSO is not predicted in the hindcasts, and model perturbation experiments suggest that even a perfect prediction of tropical sea surface temperature and tropical atmospheric variability would not have sufficed to produce a reasonable seasonal precipitation prediction. On a more positive note, our perturbation experiments suggest a potential role for Arctic variability that supports findings from prior studies and suggests re-examining high-latitude drivers of SWUS precipitation.

Predictability of the 2020 Strong Vortex in the Antarctic Stratosphere and the Role of Ozone

AbstractThe Antarctic vortex of October–December 2020 was the strongest on record in the satellite era for the season in the mid‐ to lower stratosphere. However, it was poorly predicted by the Australian Bureau of Meteorology's operational seasonal climate forecast system of that time, ACCESS‐S1, even at a short lead time of a month. Using the current operational forecast system, ACCESS‐S2, we have, therefore, tried to find a primary cause of the limited predictability of this event and conducted forecast sensitivity experiments to understand the potential role of ozone in the event and its associated anomalies of the Southern Annular Mode (SAM) and rainfall over south–eastern Australia and western Patagonia. Here, we show that the 2020 strong vortex event did not follow the canonical dynamical evolution seen in previous strong vortex events in spring but suddenly appeared as a result of the record‐low upward propagating wave activity in September 2020. The ACCESS‐S2 forecasts significantly underestimated the negative wave forcing in September even at zero lead time, irrespective of the ozone configuration, therefore falling short in predicting the record strength of the polar vortex in late spring 2020. Nevertheless, ACCESS‐S2 with prescribed realistic ozone that had large anomalies in the Antarctic stratosphere significantly better predicted the strong vortex and the subsequent positive SAM and related rainfall anomalies over south–eastern Australia and western Patagonia in the austral summer of 2020–21. This highlights the potentially important role of ozone variations for seasonal climate forecasting as a source of long‐lead predictability.

Predictability of European winter 2022/23

AbstractThe boreal winter of 2022/23 was notable as a third consecutive winter in which La Niña had an influence on the European weather. The GloSea6 seasonal forecast system predicted a blocked circulation pattern in the North Atlantic in early winter (December), and then a transition through mid‐winter (January) into a more zonal pattern in late winter (February), consistent with the canonical La Niña teleconnection pattern seen previously. The seasonal forecast for the UK was an increased likelihood of near average temperatures, and drier‐ and calmer‐than‐average conditions. Both the predicted broad‐scale circulation patterns and UK winter mean weather conditions verified well against observations, and we show that seasonal forecasts of the North Atlantic Oscillation (NAO) over the last 10 winters show similar skill to previously reported hindcasts. Throughout the winter, the Madden–Julian Oscillation (MJO) was particularly active. On three occasions, it exhibited strong phases 6 and 7. There was also a sudden stratospheric warming (SSW) that occurred on 16th February. This was followed by colder conditions and associated impacts similar to the canonical negative NAO response over the UK, although the main impact fell in March and so did not affect the winter (December–January–February) mean conditions.

Economic Benefits and Uses of Indigenous Seasonal Weather Forecasts in Zimbabwe

This study presents a seminal contribution regarding the economic value of indigenous seasonal weather forecasts in Zimbabwe. Many farmers (58%) use indigenous seasonal weather forecasts to make maize farming decisions such as selecting suitable varieties. The main indicators used for indigenous seasonal weather forecasts are flowering and fruition of specific trees. Based on travel cost analysis, which incorporates a multi-purpose visit scenario, the study establishes the economic importance of indigenous seasonal weather forecasts with a consumer surplus of US$1,044 per year among the 290 farmers using the forecasts. There is therefore a need to integrate indigenous weather forecasts into national seasonal weather forecasting and disaster risk reduction systems to complement modern seasonal weather forecasts. Co-production of seasonal weather forecasts with farmers is proposed in this regard. This further calls for the need to digitally document, visualize, and disseminate indigenous seasonal weather forecast indicators to a wider audience to increase their use.

A hybrid approach for skillful multiseasonal prediction of winter North Pacific blocking

Wintertime atmospheric blocking often brings adverse environmental and socioeconomic impacts through its accompanying temperature and precipitation extremes. However, due to the chaotic nature of the extratropical atmospheric circulation and the challenges in simulating blocking, the skillful seasonal prediction of blocking remains elusive. In this study, we leverage both observational data and seasonal hindcasts from a state-of-the-art seasonal prediction system to investigate the prediction skill of North Pacific wintertime blocking frequency and its linkage to downstream cold extremes. The observational results show that North Pacific blocking has a local maximum over the central North Pacific Ocean and that the occurrence of North Pacific blocking drives significant cold anomalies over northwestern North America within a week, which are both well reproduced by the model. The model skillfully predicts the western North Pacific blocking frequency near the subtropical jet exit region at the shortest forecast lead, but skill drops off rapidly with lead time partly due to model drift in the background flow. To overcome this rapid drop in skill, we develop a linear hybrid dynamical-statistical model that uses the forecasted Niño 3.4 index and upstream precipitation as predictors and that maintains significant forecast skill of high-latitude North Pacific blocking up to 7 lead months in advance. Our results indicate that an improvement in the seasonal prediction skill of winter North Pacific blocking frequency may be achieved by the enhanced representation of the links among sea surface temperature anomalies, tropical convection, and the ensuing tropical-extratropical interaction that initiates North Pacific blocking.

The Relative Role of Indian and Pacific Tropical Heating as Seasonal Predictability Drivers for the North Atlantic Oscillation

AbstractUnderstanding the predictability drivers for the North Atlantic Oscillation (NAO) during boreal winter at seasonal time scales remains challenging. This study uses large ensembles with the ECMWF seasonal forecasting system to investigate the relative impact of tropical Indian and Pacific heating on NAO predictability by examining the tropical forcing, teleconnection pathways, and sources of uncertainty. We select three case studies ‐ 1997/98, 2015/16 and 2019/20 ‐ with strong Indian Ocean heating anomalies, but with different El Niño conditions. We show that in 2019/20, with neutral ENSO conditions, Indian Ocean SSTs favor a positive NAO response via stratospheric and tropospheric pathways. In the cases with strong El Niño, we find contrasting results: in 1997/98, the Pacific forcing dominates, producing a negative NAO. In 2015/16, despite the strong El Niño, the Indian Ocean forcing dominates, leading to a positive NAO via intensification of the stratospheric polar vortex (SPV). While the stratospheric pathway exhibits varying responses to Indian Ocean forcing ‐ being weaker in 1997/98 and strongest in 2015/16, the Indian Ocean‐related tropospheric pathway remains robust along the Pacific subtropical jet across years. However, there is destructive interference between teleconnections from Indian and Pacific SST anomalies in both the tropospheric and stratospheric pathways. The competing effects of tropical heating in both basins, uncertainties in the Rossby wave response to tropical heating and SPV variability contribute to uncertainty in seasonal NAO predictions. The flow‐dependent nature of the stratospheric pathway underscores the complexity of seasonal forecast predictability, and the existence of windows of opportunity.

Skillful Long-Lead Seasonal Predictions in the Summertime Northern Hemisphere Midlatitudes

Abstract In contrast to boreal winter when extratropical seasonal predictions benefit greatly from ENSO-related teleconnections, our understanding of forecast skill and sources of predictability in summer is limited. Based on 40 years of hindcasts of the Canadian Seasonal to Interannual Prediction System, version 3 (CanSIPSv3), this study shows that predictions for the Northern Hemisphere summer surface air temperature are skillful more than 6 months in advance in several midlatitude regions, including eastern Europe–Middle East, central Siberia–Mongolia–North China, and the western United States. These midlatitude regions of statistically significant predictive skill appear to be connected to each other through an upper-tropospheric circumglobal wave train. Although a large part of the forecast skill for the surface air temperature and 500-hPa geopotential height is attributable to the linear trend associated with global warming, there is significant long-lead seasonal forecast skill related to interannual variability. Two additional idealized hindcast experiments are performed to help shed light on sources of the long-lead forecast skill using one of the CanSIPSv3 models and its uncoupled version. It is found that tropical ENSO-related sea surface temperature (SST) anomalies contribute to the forecast skill in the western United States, while land surface conditions in winter, including snow cover and soil moisture, in the Siberian and western U.S. regions have a delayed or long-lasting impact on the atmosphere, which leads to summer forecast skill in these regions. This implies that improving land surface initial conditions and model representation of land surface processes is crucial for the further development of a seasonal forecasting system. Significance Statement Useful seasonal predictions in the boreal summer midlatitude regions are of great value. In this study, we show that predictions for the boreal summer season are skillful more than 6 months in advance in several midlatitude regions, including eastern Europe–Middle East, central Siberia–Mongolia–North China, and the western United States. The forecast skill in these regions is associated with a circumglobal teleconnection atmospheric circulation pattern. Sources of the long-lead forecast skill include the global warming–related trend and anomalies in the ocean and land surface initial conditions. It is found that the wintertime snow cover and soil moisture in the Siberian and western U.S. regions have a delayed or long-lasting impact on the atmosphere, which leads to summer forecast skill.

Skillful prediction of the maximum air temperature over India using a seasonal prediction system

Skillful prediction of the maximum air temperature over India using a seasonal prediction system

Impact of the ocean in-situ observations on the ECMWF seasonal forecasting system

Impact of the ocean in-situ observations on the ECMWF seasonal forecasting system

Predictability and prediction skill of summertime East/Japan Sea surface temperature events

The East/Japan Sea (EJS), a marginal sea of the Northwestern Pacific, is one of the ocean regions showing the most rapid warming and greatest increases in ocean heatwaves over the last several decades. Predictability and skillful prediction of the summer season EJS variability are crucial, given the increasing severity of ocean temperature events impacting fisheries and reinforcing climate conditions like the East Asian rainy season, which in turn affects adjacent high-population density areas over East Asia. We use observations and the Geophysical Fluid Dynamics Laboratory (GFDL) Seamless System for Prediction and Earth System Research (SPEAR) seasonal forecast system to investigate the summertime EJS Sea Surface Temperature (SST) predictability and prediction skill. The observations and seasonal prediction system show that the summer season EJS SST can be closely linked to the previous winter air-sea coupling and predictable 8–9 months in advance. The SPEAR seasonal prediction system demonstrates skillful forecast of EJS SST events from summer to late fall, with added skill for long-lead forecasts initialized in winter. We find that winter large-scale atmospheric circulations linked to Barents Sea variability can induce persistent surface wind anomalies and corresponding northward Ekman heat transport over the East China Sea. The ocean advection anomalies that enter the EJS in prior seasons appear to play a role in developing anomalous SST during summer, along with instantaneous atmospheric forcing, as the source of long-lead predictability. Our findings provide potential applications of large-scale ocean-atmosphere interactions in understanding and predicting seasonal variability of East Asian marginal seas.

Seasonal climate forecasts show skill in predicting winter chill for specialty crops in California

Many fruits and nuts crops in California require sufficient winter chill to break dormancy, and insufficient chill can harm fruit quantity and quality. Early information on winter chill forecast can help growers prepare for a low chill year. Here we evaluate use of dynamic climate models for chill accumulation forecast in California. Using temperature forecasts from seasonal prediction systems, we found that the multimodel forecasts can predict chill. This is evident from the anomaly correlation coefficients exceeding 0.5 between the model-predicted and reference chill values for most California regions. The forecasts correctly identified chill categories in over 50% instances in more than 40% of the Central Valley and southern parts of California. The forecasts also demonstrated skill in capturing the interannual variability of chill, especially during years with substantial decrease in chill. Additionally, the seasonal forecast can provide potentially useful crop specific chill sufficiency prediction. However, forecasts beyond a one-month lead time showed reduced forecast skills.

ACCESS-S2 seasonal forecasts of rainfall and the SAM–rainfall relationship during the grain growing season in south-west Western Australia

South-west Western Australia (SWWA) is home to a world class grains industry that is significantly affected by periods of drought. Previous research has shown a link between the Southern Annular Mode (SAM) and rainfall in SWWA, especially during winter months. Hence, the predictability of the SAM and its relationship to SWWA rainfall can potentially improve forecasts of SWWA drought, which would provide valuable information for farmers. In this paper, focusing on the 0-month lead time forecast, we assess the bias and skill of ACCESS-S2, the Australian Bureau of Meteorology’s current operational sub-seasonal to seasonal forecasting system, in simulating seasonal rainfall for SWWA during the growing season (May–October). We then analyse the relationship between the SAM and SWWA precipitation and how well this is captured in ACCESS-S2 as well as how well ACCESS-S2 forecasts the monthly SAM index. Finally, ACCESS-S2 rainfall forecasts and the simulation of SAM are assessed for a case study of extreme drought in 2010. Our results show that forecasts tend to have greater skill in the earlier part of the season (May–July). ACCESS-S2 captures the significant inverse SAM–rainfall relationship but underestimates its strength. The model also shows overall skill in forecasting the monthly SAM index and simulating the MSLP and 850-hPa wind anomaly patterns associated with positive and negative SAM phases. However, for the 2010 drought case study, ACCESS-S2 does not indicate strong likelihoods of the upcoming dry conditions, particularly for later in the growing season, despite predicting a positive (although weaker than observed) SAM index. Although ACCESS-S2 is shown to skillfully depict the SAM–SWWA rainfall relationship and generally forecast the SAM index well, the seasonal rainfall forecasts still show limited skill. Hence it is likely that model errors unrelated to the SAM are contributing to limited skill in seasonal rainfall forecasts for SWWA, as well as the generally low seasonal-timescale predictability for the region.

Tropical Cyclones in the GEOS-S2S-2 Subseasonal Forecasts

Abstract This paper analyzes the climatology, prediction skill, and predictability of tropical cyclones (TCs) in NASA’s Goddard Earth Observing System Subseasonal to Seasonal (GEOS-S2S) forecast system version 2. GEOS reasonably simulates the number and spatial distribution of TCs compared to observations except in the Atlantic where the model simulates too few TCs due to low genesis rates in the Caribbean Sea and Gulf of Mexico. The environmental conditions, diagnosed through a genesis potential index, do not clearly explain model biases in the genesis rates, especially in the Atlantic. At the storm scale, GEOS reforecasts replicate several key aspects of the thermodynamic and dynamic structures of observed TCs, such as a warm core and the secondary circulation. The model, however, fails to simulate an off-center eyewall when evaluating vertical velocity, precipitation, and moisture. The analysis of the prediction skill of TC genesis and occurrence shows that GEOS has comparable skill to other global models in WMO S2S archive and that its skill could be further improved by increasing the ensemble size. After calibration, GEOS forecasts are skillful in the western North Pacific and southern Indian Ocean up to 20 days in advance. A model-based predictability analysis demonstrates the importance of the Madden–Julian oscillation (MJO) as a source of predictability of TC occurrence beyond the 14-day lead time. Forecasts initialized under strong MJO conditions show evidence of predictability beyond week 3. However, due to model biases in the forecast distribution, there are notable gaps between the MJO-related prediction skill and predictability, which require further study.

Prediction Performance of Ocean Temperature and Salinity in Global Seasonal Forecast System Version 5 (GloSea5) on ARGO Float Data

Prediction Performance of Ocean Temperature and Salinity in Global Seasonal Forecast System Version 5 (GloSea5) on ARGO Float Data

Ensemble seasonal forecasting of typhoon frequency over the western North Pacific using multiple machine learning algorithms

Abstract This study introduces an ensemble prediction methodology employing multiple machine learning algorithms for forecasting the frequency of typhoons (TYFs) over the western North Pacific (WNP) during June‒November. Potential predictors were initially identified based on the relationships between the year-by-year variation (DY) of the TYFs and preseason (March–May) environmental factors. These predictors were subsequently further refined, resulting in the selection of eight key predictors. Prediction models were constructed using twenty machine learning algorithms, utilizing data from 1965 to 2010. These trained models were then applied to perform hindcasts of TYFs from 2011 to 2023. The forecasted DY was added to the observed TYF of the preceding year to obtain the current year’s TYF. The results indicate that the TYFs predicted by the multi-model ensemble (MME) closely align with the observation during the hindcast period. Compared to individual models, the MME improves the prediction skill for the DY by at least 5.56% and up to 56.92%. Furthermore, the mean bias of the MME for TYF is notably smaller than that of the ECMWF’s most recent seasonal forecasting system (SEAS5) in the years of 2017‒2023. The superior performance of the ensemble prediction approach was also validated through leave-one-out cross-validation. This research underscores the potential of ensemble prediction approach utilizing multiple machine learning algorithms to improve the forecasting skill of TYF over the WNP.

Beyond the regional average: Drivers of geographical rainfall variability during East Africa's short rains

AbstractThe East African “short rains” from October–December (OND) are crucial for the region's cultural and agricultural landscape. Traditional climate studies have often treated these rains as a single mode, representing the average rainfall across the region. This approach, however, fails to capture the complex geographical variations in seasonal rainfall. In our study, we analyse 4200 reforecasts from a seasonal prediction system spanning 1981–2022, identifying distinct clusters that represent different geographical patterns of the short rains. We explore the influence of tropical sea‐surface temperature patterns, upper‐level tropospheric flow, and low‐level moisture fluxes on these clusters. A key revelation of our research is the limited predictability of certain geographical rainfall structures based on large‐scale climatic drivers. This finding highlights a gap in current forecasting methodologies, emphasising the necessity for further research to understand and predict these intricate patterns. Our study illuminates the complexities of regional rainfall variability in East Africa, underlining the importance of continued investigation to improve climate resilience strategies in the region.

Predictability of the early summer surface air temperature over Western South Asia

Variability of the Surface Air Temperature (SAT) over the Western South Asia (WSA) region leads to frequent heatwaves during the early summer (May-June) season. The present study uses the European Centre for Medium-Range Weather Forecast’s fifth-generation seasonal prediction system, SEAS5, from 1981 to 2022 based on April initial conditions (1-month lead) to assess the SAT predictability during early summer season. The goal is to evaluate the SEAS5’s ability to predict the El Niño-Southern Oscillation (ENSO) related interannual variability and predictability of the SAT over WSA, which is mediated through upper-level (200-hPa) geopotential height anomalies. This teleconnection leads to anomalously warm surface conditions over the region during the negative ENSO phase, as observed in the reanalysis and SEAS5. We evaluate SEAS5 prediction skill against two observations and three reanalyses datasets. The SEAS5 SAT prediction skill is higher with high spatial resolution observations and reanalysis datasets compared to the ones with low-resolution. Overall, SEAS5 shows reasonable skill in predicting SAT and its variability over the WSA region. Moreover, the predictability of SAT during La Niña is comparable to El Niño years over the WSA region.

Effects of extreme stratospheric polar vortex events on near-surface wind gusts across Europe

Abstract Extreme stratospheric polar vortex (SPV) events can influence winter tropospheric circulation for up to 60 d. Their impacts on air temperature have been extensively studied recently. However, there is a research gap in their effects on wind speeds and its extremes. This study aims to evaluate, for the first time, the impacts of such extreme SPV events on observed and modelled near-surface wind gusts across Europe. We have analysed wind gust data coming from: station-based observations (for the Iberian Peninsula and Scandinavia), the ERA5 reanalysis and the SEAS5 and GloSea6 seasonal forecasting systems. We assess their similarities in reproducing 4 parameters of their corresponding distributions: median, standard deviation, skewness and kurtosis. For all these datasets, the results indicate that extreme positive SPV events are followed by negative wind gust anomalies in Southern Europe and positive in Northern Europe. Whereas, negative SPV events (such as Sudden Stratospheric Warmings) have positive gust anomalies in Southern Europe and negative in the north. A central region shows negligible anomalies in both cases. This highlights the ability of SPV as a predictor for short-medium-term forecasting of extreme wind events, which would have direct applications to many socioeconomic and environmental issues such as the estimation of wind-power generation.

Generation of state‐dependent ensemble perturbations based on time‐varying seawater density for GloSea5 initialization

AbstractIn 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.