Subseasonal Prediction Research Articles

Boreal Summer Extratropical Intraseasonal Oscillation Prediction in Current Subseasonal‐To‐Seasonal Operational Models Over Eurasia

AbstractExtratropical intraseasonal oscillations (EISO), which is akin to the tropical ISO, significantly influence mid‐high‐latitude intraseasonal variability. Previous studies have shown that biases in the dynamical prediction of EISOs reduce the accuracy of subseasonal forecasts for extreme events, particularly in extratropical regions. However, the current dynamical prediction skills of EISO and associated dependence remain unclarified. Based on the recently proposed EISO metrics, which represent three dominant zonally propagating EISO modes traveling along the westerly jets, this study conducts a comprehensive evaluation of the EISOs' prediction skill in 13 state‐of‐the‐art subseasonal‐to‐seasonal (S2S) prediction project models. Firstly, the S2S models display skill to predict the EISOs out to a range of 9–20 days, which demonstrates higher predictability compared to other conventional circulation modes in the extratropics. In the subseasonal prediction, EISOs' amplitudes are underestimated, and their propagating speeds feature large random errors in all S2S models. Secondly, the EISOs' prediction skill depends on their initial/target amplitude, particularly for strong target amplitudes, which showcase higher skill than weak‐amplitude EISOs. Thirdly, although the EISOs' prediction skill is sensitive to the EISOs' phase, it is not locked to any fixed phase, indicating that EISOs do not have an inherent prediction barrier like the tropical ISO. This study also discusses how the humidity initialization and ensemble sizes influence the EISOs' prediction skill, suggesting that increasing the ensemble members in the saturation range is an effective way of improving the prediction for extratropical subseasonal variation and related extreme events.

Tropospheric links to uncertainty in stratospheric subseasonal predictions

Abstract. Variability in the stratosphere, especially extreme events such as sudden stratospheric warmings (SSWs), can impact surface weather. Understanding stratospheric prediction uncertainty is therefore crucial for skillful surface weather forecasts on weekly to monthly timescales. Using ECMWF subseasonal hindcasts, this study finds that stratospheric uncertainty is most strongly linked to tropospheric uncertainty over the North Pacific and Northern Europe, regions that can modulate but also respond to stratospheric variability, suggesting a two-way propagation of uncertainty. A case study of the 2018 SSW event shows an initial poleward and upward propagation of uncertainty from tropical convection, followed by a downward propagation where ensemble members that accurately predict the SSW are also better at predicting its downward impacts. These findings highlight the locations in the troposphere that are linked to stratospheric uncertainty and suggest that improved model representation of tropospheric mechanisms linked to polar vortex variability could enhance both stratospheric and extratropical surface prediction.

Improving subseasonal forecast skill in the Norwegian Climate Prediction Model using soil moisture data assimilation

This study shows the importance of soil moisture (SM) in subseasonal-to-seasonal (S2S) predictions at mid-latitudes. We do this through introducing the Norwegian Climate Prediction Model Land (NorCPM-Land), a land reanalysis framework tailored for integration with the Norwegian Climate Prediction Model (NorCPM). NorCPM-Land assimilates blended SM data from the European Space Agency’s Climate Change Initiative into a 30-member offline simulation of the Community Land Model with fluxes from the coupled model. The assimilation of SM data reduces error in SM by 10.5 % when validated against independent SM observations. It also improves latent heat flux estimates, illustrating that the adjustment of underlying SM significantly augments the capacity to model land surface dynamics. We evaluate the added value of land initialisation for subseasonal predictions, by comparing the performance of hindcasts (retrospective prediction) using the standard NorCPM with a version where the land initial condition is taken from NorCPM-Land reanalysis. The hindcast covers the period 2000 to 2019 with four start dates per year. Land initialisation enhances SM predictions, reducing error by up to 2.5-month lead time. Likewise, the error for precipitation and temperature shows improvement up to a lead time of 1.5-month. The largest improvements are observed in regions with significant land-atmospheric coupling, such as the Central United States, the Sahel, and Central India. This method further enhances the prediction of extreme temperature variations, both high and low, with the most notable improvements seen in regions at mid and high latitudes, including parts of Europe, the United States, and Asia. Overall, our study provides further evidence for the significant role of SM content in enhancing the accuracy of subseasonal predictions. This study introduces a technique for improved land initialisation, utilising the same model employed in climate predictions.

Opportunities and Barriers for Skillful Subseasonal Prediction of East Asian Summer Precipitation

Abstract Accurate subseasonal (2–8 weeks) prediction of monsoon precipitation is crucial for mitigating flood and heatwave disasters caused by intraseasonal variability (ISV). However, current state-of-the-art subseasonal-to-seasonal (S2S) models have limited prediction skills beyond 1 week when predicting weekly precipitation. Our findings suggest that predictability primarily arises from strong ISV events, and the prediction skills for ISV events depend on the propagation stability of preceding signals, regardless of models. This allows us to identify opportunities and barriers (OBs) within S2S models, clarifying what the models can and cannot achieve in ISV event prediction. Focusing on the complex East Asian summer monsoon (EASM), we discover that stable propagation of Eurasian and tropical atmospheric wave trains toward East Asia serves as an opportunity. This opportunity offers a 1-week leading prediction skill of up to 0.85 and skillful prediction up to 13 days ahead for 43% of all ISV events. However, the Tibetan Plateau barrier highlights the limitation of EASM predictability. Identifying these OBs will help us gain confidence in making more accurate subseasonal prediction.

How Do Forecast Model Biases Affect Large-Scale Teleconnections That Control Southwest U.S. Precipitation? Part I: S2S Models

Abstract We analyze biases in subseasonal forecast models and their effect on Southwest United States (SWUS) precipitation prediction (2–6-week time scale). Cluster analyses identify three primary wave trains associated with SWUS precipitation: a meridional El Niño–Southern Oscillation (ENSO)–type wave train, an arching Pacific–North American (PNA)–type wave train, and a circumglobal zonal wave train. Compared to reanalysis, the models overrepresent the arching pattern, underrepresent the zonal pattern, and produce mixed results for the meridional pattern. The arching pattern overrepresentation is linked to model mean flow biases in the midlatitude–subpolar North Pacific, which cause a westward retraction of the region of forbidden linear Rossby wave propagation. The zonal pattern underrepresentation is linked to westerly biases in the subtropical jet, which cause a westward retraction of the waveguide in the midlatitude eastern North Pacific and divert wave trains southward. These results are confirmed using linear, barotropic ray-tracing analysis. In addition to mean state biases, the models also contain errors in their representation of the Madden–Julian oscillation (MJO). Tropical convection anomalies associated with the MJO are too weak and incoherent at lead times greater than 2 weeks when compared to reanalysis. Additionally, there is a strong SWUS precipitation signal as far out as 5 weeks after a strong MJO in reanalysis, associated with its persistent eastward propagation, but this signal is absent in the models. Our results indicate that there is still significant room for improvement in subseasonal predictions if we can reduce model biases in the background flow and improve the representation of the MJO.

An Assessment of Subseasonal Prediction Skill of the Antarctic Sea Ice Edge.

In this study, the subseasonal Antarctic sea ice edge prediction skill of the Copernicus Climate Change Service (C3S) and Subseasonal to Seasonal (S2S) projects was evaluated by a probabilistic metric, the spatial probability score (SPS). Both projects provide subseasonal to seasonal scale forecasts of multiple coupled dynamical systems. We found that predictions by individual dynamical systems remain skillful for up to 38days (i.e., the ECMWF system). Regionally, dynamical systems are better at predicting the sea ice edge in the West Antarctic than in the East Antarctic. However, the seasonal variations of the prediction skill are partly system-dependent as some systems have a freezing-season bias, some had a melting-season bias, and some had a season-independent bias. Further analysis reveals that the model initialization is the crucial prerequisite for skillful subseasonal sea ice prediction. For those systems with the most realistic initialization, the model physics dictates the propagation of initialization errors and, consequently, the temporal length of predictive skill. Additionally, we found that the SPS-characterized prediction skill could be improved by increasing the ensemble size to gain a more realistic ensemble spread. Based on the C3S systems, we constructed a multi-model forecast from the above principles. This forecast consistently demonstrated a superior prediction skill compared to individual dynamical systems or statistical observation-based benchmarks. In summary, our results elucidate the most important factors (i.e., the model initialization and the model physics) affecting the currently available subseasonal Antarctic sea ice prediction systems and highlighting the opportunities to improve them significantly.

Tropical intraseasonal oscillations as key driver and source of predictability for the 2022 Pakistan record-breaking rainfall event

In August 2022, Pakistan experienced unprecedented monsoon rains, leading to devastating floods and landslides affecting millions. While previous research has mainly focused on the contributions of seasonal and synoptic anomalies, this study elucidates the dominant influences of tropical and extratropical intraseasonal oscillations on both the occurrence and subseasonal prediction of this extreme rainfall event. Our scale-decomposed moisture budget analysis revealed that intense rainfall in Pakistan was triggered and sustained by enhanced vertical moisture transport anomalies, primarily driven by interactions between intraseasonal circulation anomalies and the prevailing background moisture field when tropical and mid-latitude systems coincided over Pakistan. Evaluation of subseasonal-to-seasonal prediction models further highlighted the critical role of tropical intraseasonal modes in causing this extreme rainfall event in Pakistan. Models that accurately predicted northward-propagating intraseasonal convection with a forecast lead time of 8–22 days demonstrated good skill in predicting the extreme event over Pakistan.

Advancing Subseasonal Surface Air Temperature and Heat Wave Prediction Skill in China by Incorporating Scale Interaction in a Deep Learning Model

AbstractAccurate subseasonal predictions of high surface air temperature (SAT) and heat wave events 10–30 days in advance are crucial for mitigating the risks of extreme weather; however, they pose a challenge for current operational models. In this study, we trained a convolutional neural network (CNN)‐based deep learning model to exploit the modulations in China's SAT by precursor signals across different timescales to improve predictions of future SAT and heat wave events. This CNN model demonstrated superior capability in capturing the evolution of SAT anomalies and the occurrence of heat wave events with forecast lead times beyond 20 days, compared with that of the operational models of the China Meteorological Administration and European Centre for Medium‐Range Weather Forecasts. Explainability analysis highlighted that subseasonal SAT predictability in China is driven primarily by large‐scale intraseasonal perturbations from both lower‐ and higher‐latitude regions of Eurasia and interannual variability.

How Does the Tibetan Plateau Land Thermal Initial Condition Influence the Subseasonal Prediction of 2020 Record‐Breaking Mei‐Yu Rainfall

AbstractAccurate subseasonal prediction of heavy rainfall is helpful for disaster mitigation but challenging. The land thermal condition of Tibetan Plateau (TP), usually with climate memory ranging from weeks to seasons, has been seen as a potential predictability source for subseasonal prediction. Aiming at 2020 record‐breaking Mei‐yu rainfall, this study attempts to investigate whether and how the influence of initial TP surface thermal condition near late June influences the July rainfall prediction over the Middle and Lower Yangtze River Region (MLYR), based on two contrasting prediction experiments using a global climate ensemble prediction system. The results show that the most distinguishable change in the downstream prediction in July is the anomalous low‐tropospheric cyclone and the associated increased rainfall over MLYR corresponding to the warmer initial condition of surface TP. Influenced by the invasion of the positive potential vorticity (PV) center that generated over TP and propagated eastward, this low‐level cyclone anomaly over MLYR is formed within the first week of prediction, and persists for the next 3 weeks maintained by the positive feedback between the low‐level cyclone and middle‐tropospheric latent heating over MLYR in the prediction. This study confirmed the significant effect of TP initial thermal condition on downstream prediction ahead of 3 weeks during the Mei‐yu season (peak summer) with strong land–atmosphere coupling over TP.

Indonesian Throughflow promoted eastward propagation of the Madden-Julian Oscillation

Understanding the impacts of the Indonesian Throughflow (ITF) on the eastward propagation of the Madden-Julian Oscillation (MJO) is crucial for accurately simulating the MJO and achieving high-skill sub-seasonal predictions. Our analyses demonstrate a significant enhancement of MJO eastward propagation due to the strong ITF. Blocking the ITF decreases the eastward sea surface temperature (SST) gradient over the tropical Indian Ocean, hindering MJO propagation across the Maritime Continent (MC). Removing the MJO circulation-induced intraseasonal variability of the ITF transport also weakens the eastward propagation of the MJO, as the MJO easterly winds enhance the ITF transport and warm the eastern tropical Indian Ocean. These experiments reveal that mean and intraseasonal variability of the ITF transport contribute to 73% and 42% of the eastward propagation of the MJO over the MC, respectively. The findings presented in this study highlight the significant role of the ITF in shaping the propagation of the MJO.

Modulation of Tropical Cyclogenesis by Convectively Coupled Kelvin Waves

Abstract Tropical cyclone numbers can vary from week to week within a hurricane season. Recent studies suggest that convectively coupled Kelvin waves can be partly responsible for such variability. However, the precise physical mechanisms responsible for that modulation remain uncertain partly due to the inability of previous studies to isolate the effects of Kelvin waves from other factors. This study uses an idealized modeling framework—called an aquaplanet—to uniquely isolate the effects of Kelvin waves on tropical cyclogenesis. The framework also captures the convective-scale dynamics of both tropical cyclones and Kelvin waves. Our results confirm an uptick in tropical cyclogenesis after the passage of a Kelvin wave—twice as many tropical cyclones form 2 days after a Kelvin wave peak than at any other time lag from the peak. A detailed composite analysis shows anomalously weak ventilation during and after (or to the west of) the Kelvin wave peak. The weak ventilation stems primarily from anomalously moist conditions, with weaker vertical wind shear playing a secondary role. In contrast to previous studies, our results demonstrate that Kelvin waves modulate both kinematic and thermodynamic synoptic-scale conditions that are necessary for tropical cyclone formation. These results suggest that numerical models must capture the three-dimensional structure of Kelvin waves to produce accurate subseasonal predictions of tropical cyclone activity. Significance Statement Anticipating active tropical cyclone periods several weeks in advance could help mitigate the loss of lives and property due to these phenomena. Recent studies suggest that a type of tropical cloud cluster—known as convectively coupled Kelvin waves—can promote tropical cyclone formation. Kelvin waves travel around the world and can be detected days to weeks in advance. We use a simplified numerical model to isolate the effects of Kelvin waves on tropical cyclone formation. Our unique approach confirms that tropical cyclones are more likely to form 2 days after a Kelvin wave than before the wave. We also demonstrate that—contrary to previous perception—the enhancement of tropical cyclogenesis is due to both more moisture and weaker wind currents following the waves.

Roles of the Tropical Intra-Seasonal Oscillations in the Sub-Seasonal Prediction of the Long-Lasting Compound Heatwave over Southern China in 2010

Roles of the Tropical Intra-Seasonal Oscillations in the Sub-Seasonal Prediction of the Long-Lasting Compound Heatwave over Southern China in 2010

RUFCO: A Deep Learning Framework to Postprocess Subseasonal Precipitation Accumulation Forecasts

Abstract Postprocessing is a critical step in attaining calibrated and reliable probabilistic forecast output from numerical weather prediction models. A novel deep learning framework is proposed to postprocess 20 years of 7- and 14-day precipitation accumulation reforecasts from the Global Ensemble Forecast System at subseasonal time scales (week 1, week 2, and combined weeks 3–4 forecasts) over the contiguous United States. The network builds upon previous studies and is a combination of three parallel-trained components suitable for subseasonal prediction. The first is a ResUnet architecture which learns nonlinear relationships between binned observed precipitation and input images of weather and geographical variables. The second conditions the network to the month-of-year via a feature-wise linear modulation (FiLM) layer. The third helps the network learn when to revert the forecast to that of climatology. The RUFCO (named for its components ResUnet, FiLM, and Climatological-Offramp) forecasts are compared against raw and climatological forecasts as well as those from a state-of-the-art distributional regression postprocessing model, “censored, shifted gamma distribution (CSGD),” and a simple bias-corrected model. At week 1, every method exhibited a competitive advantage over climatological forecasts. At week 2, RUFCO generated forecasts with statistically significant improvement over climatology at 82%–94% of the domain, beating CSGD’s coverage of 76%–90% of the grid points. At week 3, RUFCO’s skillful coverage was 65%–85%, while CSGDs dropped to only 12%–37%. At the longer lead times, RUFCO achieved the highest domain-averaged skill scores across seasons. However, the network tends to “smooth” forecast skill, making it less competitive with CSGD in limited areas with strongly spatially varying biases. Significance Statement Precipitation accumulation forecasts 1, 2, and 3–4 weeks in advance are increasingly in-demand for a variety of decision-making applications around hydrologic forecasting, flood and drought awareness, and wildfire preparedness. However, raw forecasts from numerical weather prediction systems have errors that hinder skill. Postprocessing methods remove those errors and provide more reliable and skillful forecasts. We show that a new neural network technique is an effective and competitive postprocessing tool compared to more traditional techniques.

The influence of latent heating on the sub-seasonal prediction of winter blocking over Northern Hemisphere

The influence of latent heating on the sub-seasonal prediction of winter blocking over Northern Hemisphere

Weather to subseasonal prediction from the UFS coupled Global Ensemble Forecast System

Weather to subseasonal prediction from the UFS coupled Global Ensemble Forecast System

A statistic of the subseasonal forecast skill windows of 2-meter air temperature

The forecast skill on subseasonal time scale is currently limited, inhibiting its effective application broadly. Under some special conditions, the subseasonal-seasonal (S2S) forecast skill would be increased intermittently, namely, the forecast opportunity window. The identification of such forecast windows can increase the credibility of subseasonal prediction results, providing final users the opportunity to make the appropriate decision. To identify the subseasonal forecast window of 2-m air temperature (T2m), based on the S2S multi-model prediction results, this study evaluated the pattern correlation coefficient between the third-week T2m forecasted by S2S models and the observation. When the forecast skills of the majority of models reach the prescribed threshold, this period is defined as a forecast skill window. In this way, the subseasonal forecast skill windows of T2m over different regions of the global lands are identified. From the perspective of seasonal distribution, the forecast skill windows over almost all the continents appear more in boreal winter, while the forecast skill windows of the Australian continent mainly appear in boreal summer. Significant differences can be found in the occurrence frequency of forecast windows during different phases of ENSO. During El Niño events, forecast windows appear more frequently over North America, Asia, and South America, especially during winter-spring from January to April. From the T2m spatial pattern during the window periods, the forecast skill windows have some relevance among several continents, and the windows over the whole global land mainly correspond to those over Asia, Europe, and North America. Deep investigation of the physical mechanism behind the forecast skill windows helps understand the sources of predictability and improves the skill of subseasonal prediction.

Exploring machine learning in the context of environmental usage prediction

The use of environmental prediction refers to predicting the impact that human activity will have on ecosystems, natural resources and other environmental factors in the future. This strategy looks at historical patterns, present situations and future predictions to hypothesize about the ecological effects of human activities, climate change and other factors. This research suggests machine learning(ML) techniques to predict environmental uses. Prediction accuracy declinesover time and models face challenges due to the need for observable data integration in sectors like agriculture, energy and waterfor successful sub-seasonal predictions. To tackle these issues, we proposed a Next Generation Bumble Bee Mating Optimized Naïve Bayes Algorithm (NGBBMO-NBA) method that is used to enhance weather prediction. The research gathers the SSF dataset to make predictions on the usage of the environment. We use a min-max normalization approach for data preprocessing. The principalcomponent analysis (PCA) method extracts features from the SSF data. Environmental uncertainty inhibits sub-seasonal projections. Our suggested method, NGBBMO-NBA, surpasses the current techniques for ecological prediction in terms of energy consumption (96.5%), F1-Score (96%), Mean Absolute Error (MAE) (97) and Root Mean Square Error (RMSE) (98.5).

Synchronous Eurasian heat extremes tied to boreal summer combined extratropical intraseasonal waves

Heat extremes frequently hit different regions synchronously during boreal summer over the Eurasian continent. A remarkable coupling is first revealed between Eurasian heat extreme occurrence and individual extratropical intraseasonal oscillation (EISO). Further, the combined EISOs facilitate and largely increase the occurrence probabilities of synchronous Eurasian heat extremes. These dominant combined EISOs together contribute 20–45% to the total heat extreme days over the five Eurasian regions where the climatological heat extremes occur most frequently. A multi-model hindcast further shows that the subseasonal prediction exhibits higher skills for synchronous heat extremes over the combined-EISO hotspot regions when the associated combined EISOs are active, supporting the notion that the monitoring and prediction of EISOs are crucial for heat extremes’ early warning. Skillful prediction of EISOs opens a pathway for heat extremes’ prediction by extending it from the weather to the subseasonal timescales.

Sub-seasonal prediction skill: is the mean state a good model evaluation metric?

Evaluating forecast models encompasses assessing their ability to accurately depict observed climate states and predict future climate variables. Various evaluation methods, from computationally efficient measures like the anomaly correlation coefficient to more intricate approaches, have been formulated. While simpler methods provide limited information, climatology, due to its simplicity and immediate linkage to model performance, is a commonly utilized primary evaluation metric. In this study focusing on temperature and precipitation, we propose a novel metric based on the model’s mean state, integrating both climatology and the seasonal cycle for a more accurate assessment of the relationship between mean state performance and prediction skill on weather and sub-seasonal time scales compared to relying solely on climatology. This integrated metric reveals a robust correlation between temperature and precipitation across diverse geographical locations, with a more pronounced effect in tropical areas when considering the seasonal cycle. Additionally, we find that temperature exhibits higher prediction skill compared to precipitation. The discovered relationship serves as a potential early indicator for predicting the efficacy of Seasonal to Sub-seasonal (S2S) models and offers valuable insights for model development, emphasizing the significance of this integrated metric in enhancing S2S model performance and advancing climate prediction capabilities.

Sub-Seasonal Predictability of the Northeast China Cold Vortex in BCC and ECMWF S2S Model Forecasts for 2006–2021

Sub-Seasonal Predictability of the Northeast China Cold Vortex in BCC and ECMWF S2S Model Forecasts for 2006–2021