Subseasonal Prediction Research Articles

Linkages between Potential Vorticity Streamer Activity and Tropical Cyclone Predictability on Subseasonal Time Scales

Abstract Potential vorticity streamers (PVSs) are elongated filaments of high-PV air near the tropopause. In the warm season, anticyclonic Rossby wave breaking (AWB) produces enhanced PVS activity, which in turn modifies the equatorward tropical environment by enhancing vertical wind shear (VWS). This enhanced VWS can play an important role in suppressing nearby tropical cyclone (TC) activity. Given the important role that PVSs play in modifying their local environment, forecasts of PVS activity on subseasonal time scales may also influence forecasts of TC activity. This study uses Navy Earth System Prediction Capability (Navy ESPC) 45-day forecasts initialized during boreal summer 2009–15 to investigate subseasonal predictability of PVSs and TCs in the North Atlantic. PVSs are identified on the 350-K isentropic surface bounded by the 2 PV unit (PVU; 1 PVU = 10−6 K kg−1 m2 s−1) contour and defined as the high-PV trough axis downstream of the AWB axis. TCs are identified in the forecasts using the TempestExtremes detection algorithm that tracks warm-core lows. PVS and TC activity metrics that sum the number and intensity of events for a given time period are also computed. We first use skill scores and mean-state biases to determine the typical predictability of PVS activity, and then subselect high- and low-PVS-activity forecasts to determine how PVS forecast errors impact TC activity forecast errors. Results show that PVS activity can modulate TC activity at subseasonal time scales, with over-forecasted PVS activity corresponding to underestimated forecasts of TC activity and vice versa. This inverse correlation is consistent with enhanced VWS occurring equatorward of PVS troughs in the high-PVS forecasts.

Impact of Ural Blocking on Early Winter Climate Variability Under Different Barents‐Kara Sea Ice Conditions

AbstractUral blocking (UB) is a prominent mode of variability of the Northern Hemisphere atmospheric circulation, particularly in fall. It can persist for several days and exert a lagged influence on the wintertime NH circulation, providing predictability at the subseasonal time scale. Using two atmospheric models, we explore how the early winter atmospheric circulation responds to a 2‐week persistent UB anomaly imposed in early November. Experiments are carried out with two different configurations of Barents‐Kara (BK) sea‐ice concentration to examine whether it plays a role in how UB impacts atmospheric variability. In both models, the UB anomaly is followed by a weakening of the stratospheric polar vortex and a negative phase of the North Atlantic Oscillation (NAO), which lasts up to 2 months after the forcing is released. Interestingly, the response is more persistent under low BK sea‐ice conditions, that is, BK sea ice modulates the atmospheric response to UB. Additional experiments with prescribed sea ice concentration anomalies alone suggest that BK sea ice exerts a limited influence on early winter NH atmospheric variability. The response to UB involves a weakening of the polar vortex that persists longer under low BK sea ice, which explains the more persistent response in that configuration. Our study highlights that UB variability in November is a robust precursor for early winter NAO/polar stratosphere anomalies, and this may be more relevant in the context of declining Arctic sea‐ice extent. Provided that climate models accurately capture this teleconnection, it has the potential to improve subseasonal predictions of the NH wintertime climate.

Improved MJO Forecasts Using the Experimental Global‐Nested GFDL SHiELD Model

AbstractSitting at the crossroads of weather and climate, the Madden‐Julian Oscillation (MJO) is considered a primary source of subseasonal predictability. Despite its importance, numerical models struggle with MJO prediction as its convection moves through the complex Maritime Continent (MC) environment. Motivated by the ongoing effort to improve MJO prediction, we use the System for High‐resolution prediction on Earth‐to‐Local Domains (SHiELD) model to run two sets of forecasts, one with and one without a nested grid over the MC. By efficiently leveraging high‐resolution grid spacing, the nested grid reduces amplitude and phase errors and extends the model's predictive skill by about 10 days. These enhancements are tied to improvements in predicted zonal wind from the Indian Ocean to the Pacific, facilitated by westerly wind bias reduction in the nested grid. Results from this study suggest that minimizing circulation biases over the MC can lead to substantial advancements in skillful MJO prediction.

Subseasonal Prediction of Early-summer Northeast Asian Cut-off Lows by BCC-CSM2-HR and GloSea5

Northeast Asian cut-off lows are crucial cyclonic systems that can bring temperature and precipitation extremes over large areas. Skillful subseasonal forecasting of Northeast Asian cut-off lows is of great importance. Using two dynamical forecasting systems, one from the Beijing Climate Center (BCC-CSM2-HR) and the other from the Met Office (GloSea5), this study assesses simulation ability and subseasonal prediction skill for early-summer Northeast Asian cut-off lows. Both models are shown to have good ability in representing the spatial structure of cut-off lows, but they underestimate the intensity. The skillful prediction time scales for cut-off low intensity are about 10.2 days for BCC-CSM2-HR and 11.4 days for GloSea5 in advance. Further examination shows that both models can essentially capture the initial Rossby wave train, rapid growth and decay processes responsible for the evolution of cut-off lows, but the models show weaker amplitudes for the three-stage processes. The underestimated simulated strength of both the Eurasian midlatitude and East Asian subtropical jets may lead to the weaker local eddy-mean flow interaction responsible for the cut-off low evolution.

Subseasonal Great Plains Rainfall via Remote Extratropical Teleconnections: Regional Application of Theory‐Guided Causal Networks

AbstractLong‐range U.S. summer rainfall prediction skill is low. Monsoon variability, especially over the West North Pacific Monsoon (WNPM) and/or East Asian Monsoon (EAM) region, can influence U.S. Great Plains hydroclimate variability via a forced Rossby wave response. Here, we explored subseasonal monsoon variability as a source of predictability for Great Plains rainfall. The boreal summer intraseasonal oscillation (BSISO) is related to Great Plains convection and Great Plains low‐level jet (LLJ) anomalies as well as a cross‐Pacific wave train. Using a causal effect network, we found that the time between BSISO‐related geopotential height anomalies and Great Plains rainfall anomalies is about 2 weeks; therefore, BSISO convection may be a valuable forecast of opportunity for subseasonal prediction of Great Plains convection anomalies. More specifically, causal link patterns/maps revealed that the above‐normal weekly EAM rainfall, rather than WNPM rainfall or general geopotential height activity over the East Asia, was causally linked to Great Plains LLJ strengthening and active Great Plains convection the following week.

Subseasonal Predictability of the July 2021 Extreme Rainfall Event Over Henan China in S2S Operational Models

AbstractA record‐breaking flooding event occurred in Zhengzhou, Henan Province of China during 17–23 July 2021, causing hundreds of deaths and vast economic losses. Here, we evaluated the predictability of this extreme rainfall event and the impacts of tropical cyclones (TCs) using subseasonal‐to‐seasonal (S2S) operational models. On the monthly timescale, most models initialized in late June reasonably predicted the wet‐in‐north and dry‐in‐south patterns of anomalous rainfall over China in July, accompanied by the well‐predicted westward extension of the western North Pacific subtropical high (WNPSH) and eastward stretching of the South Asian High. On the weekly timescale, only four models captured the location, probability, and sudden intensification of the rainfall extremes in advance of 1 week, largely due to their reasonable prediction of WNPSH variability in mid‐latitudes. However, the S2S models still underestimated the super extremeness of this event. The prediction discrepancies came from the poor predictability of Typhoon IN‐FA and its impact on the daily evolution of the extreme rainfall event, even within a few days forecast lead. Compared with the observation, the prediction bias of tropical disturbance changed the environmental monsoon airflow to induce the earlier warning of rainfall extremes prior to the formation of IN‐FA. After the formation of IN‐FA, the prediction bias of the typhoon's moving speed distorted the typhoon location, which incorrectly predicted the moisture convergence center and underestimated their remote impacts on this heavy rainfall event.

Subseasonal predictions for climate services, a recipe for operational implementation

The implementation of operational climate service prototypes, which encompasses the co-design and delivery of real-time actionable products with/to stakeholders, contributes to efficiently leveraging operational climate predictions into actionable climate information by providing practical insight on the actual use of climate predictions. This work showcases a general guideline for implementing an operational climate service based on subseasonal predictions. At this timescale, many strategic decisions can benefit from timely predictions of climate variables. Still, the use of subseasonal predictions is not fully exploited. Here, we describe the key aspects considered to set up an operational climate service from the conception to the production phase. These include the choice of the subseasonal systems, the data sources and the methodology employed for post-processing the predictions. To illustrate the process with a real case, we present the detailed workflow design of the implementation of a climate service based on subseasonal predictions and describe the bias adjustment and verification methodologies implemented. This work was developed in the H2020 S2S4E project, where industrial and research partners co-developed a fully-operational Decision Support Tool (DST) providing 18 months of real-time subseasonal and seasonal forecasts tailored to the specific needs of the renewable energy sector. The operational workflow can be adapted to serve forecast products to other sectors, as has been proved in the H2020 vitiGEOSS project, where the workflow was modified to provide downscaled subseasonal predictions to specific locations. We consider this a valuable contribution to future developments of similar service implementations and the producers of the climate data.

Increased vertical resolution in the stratosphere reveals role of gravity waves after sudden stratospheric warmings

Abstract. Sudden stratospheric warmings (SSWs) have a long-lasting effect within the stratosphere as well as impacts on the underlying troposphere. However, sub-seasonal forecasts of the winter polar stratosphere fail to use their full potential for predictability as they tend to underestimate the magnitude and persistence of these events already within the stratosphere. The origin of this underestimation is unknown. Here, we demonstrate that the associated polar stratospheric cold bias following SSW events in sub-seasonal hindcasts can be halved by increasing vertical model resolution, suggesting a potential sensitivity to gravity wave forcing. While the predictability of the planetary Rossby wave flux into the stratosphere at lead times longer than a week is limited, the existence of a critical layer for gravity waves with a low zonal phase speed caused by the disturbed polar vortex provides predictability to the upper stratosphere. Gravity wave breaking near that critical layer can, therefore, decelerate the zonal flow consistently with anomalous subsidence over the polar cap leading to warmer temperatures in the middle polar stratosphere. Since the spectrum of gravity waves involves vertical wavelengths of less than 4000 m, as estimated by wavelet analysis, a high vertical model resolution is needed to resolve the positive feedback between gravity wave forcing and the state of the polar vortex. Specifically, we find that at a spectral resolution of TCo639 (approximate horizontal grid spacing of 18 km) at least 198 levels are needed to correctly resolve the spectrum of gravity waves in the ECMWF Integrated Forecasting System. Increasing vertical resolution in operational forecasts will help to mitigate stratospheric temperature biases and improve sub-seasonal predictions of the stratospheric polar vortex.

Quantify the Coupled GEFS Forecast Uncertainty for the Weather and Subseasonal Prediction

AbstractThe Global Ensemble Forecast System version 12 (GEFSv12) has been implemented into National Centers For Environmental Prediction operations since September 2020, which was uncoupled, but increased the horizontal resolution from 34 to 25 km, increased ensemble members from 21 to 31, and extended forecasts from 16 to 35 days. It significantly improved probabilistic forecast skills in many categories, such as precipitation, tropical storms, Madden‐Julian Oscillation (MJO), etc. The improvements resulted from many aspects, including model resolution increase, dynamical core upgrade, advances in hybrid data assimilation and physical parameterizations, and more importantly, from using new stochastic schemes to improve forecast uncertainty. To further improve GEFS's sub‐seasonal forecast skill, a coupled GEFS was built up on the Unified Forecast System prototype version 5 that fully couples an atmospheric model with a land surface model, ocean model, ice model, and wave model. A set of coupled GEFS experiments were conducted to test different horizontal resolutions at approximately 50 and 25 km while adjusting the stochastic parameterization schemes for the atmosphere to better represent forecast uncertainties. The experiments were run for a 2‐year period from October 2017 to September 2019, with one initialization per week at Wednesday 00 UTC, 11 ensemble members, and were forecasted out to 35 days. The coupled GEFS significantly improves 500 hPa height anomaly correlation in week‐1, week‐2, and MJO skills compared to the current operational forecast. The forecast spread of tropical wind is greatly reduced by improved stochastic schemes and matches well with the forecast root mean square errors. The correlation of forecast error variance and ensemble variance is improved for the coupled GEFSs. Meanwhile, the spread of MJO has been greatly reduced for the coupled GEFSs to improve the MJO forecast uncertainty.

Evaluation and comparison of the subseasonal prediction skill of Indian summer monsoon in IITM CFSv2 and UKMO GloSea5

Evaluation and comparison of the subseasonal prediction skill of Indian summer monsoon in IITM CFSv2 and UKMO GloSea5

Subseasonal swing of cold and warm extremes between Eurasia and North America in winter of 2020/21: initiation and physical process

Eurasia and North America experienced a robust subseasonal swing of surface air temperature (SAT) extremes in 2020/21 winter, featuring severe cold (warm) extremes over Eurasia before (after) 15 January and conversely over North America. This sharp subseasonal swing of intercontinental cold and warm extremes exerted considerable severe impacts on human activities and the global economy. Here we examined the initiation and physical process based on data analyses. Our results show annual cycle (AC) anomalies of SAT caused this subseasonal alternating of temperature extremes in two regions. The AC anomalies of SAT are regulated by the phase transition of the North-Pacific-Oscillation-like (NPO-like) circumglobal Rossby wave (CRW) train. Unprecedented warming sea surface temperature over midlatitude Northwest Atlantic in early winter initiated a positive phase of the NPO-like CRW train, via eddy-mediated physical processes and the resultant feedback of sea ice loss over the Barents-Kara Seas. While, the subsequent downward feedback of stratospheric processes resulted in the negative phase of the NPO-like CRW pattern in late winter. This work advances the understanding of the subseasonal predictability of SAT extremes from impacts of AC anomalies and intercontinental seesawing.

Statistical Downscaling to Improve the Subseasonal Predictions of Energy-Relevant Surface Variables

Abstract Owing to the increasing share of variable renewable energies in the electricity mix, the European energy sector is becoming more weather sensitive. In this regard, skillful subseasonal predictions of essential climate variables can provide considerable socioeconomic benefits to the energy sector. The aim of this study is therefore to improve the European subseasonal predictions of 100-m wind speed and 2-m temperature, which we achieve through statistical downscaling. We employ redundancy analysis (RDA) to estimate spatial patterns of variability from large-scale fields that allow for the best prediction of surface fields. We compare explanatory powers between the patterns obtained using RDA against those derived using principal component analysis (PCA), when used as predictors in multilinear regression models to predict surface fields, and show that the explanatory power of the former is superior to that of the latter. Subsequently, we employ the estimated relationship between RDA patterns and surface fields to produce statistical probabilistic predictions of gridded surface fields using dynamical ensemble predictions of RDA patterns. We finally demonstrate how a simple combination of dynamical and statistical predictions of surface fields significantly improves the accuracy of subseasonal predictions of both variables over a large part of Europe. We attribute the improved accuracy of these combined predictions to improvements in reliability and resolution.

Seasonality of MJO Impacts on Precipitation Extremes over the Western United States

Abstract The Madden–Julian oscillation (MJO) is a unique type of organized tropical convection varying primarily on subseasonal time scales and is recognized as an important source of subseasonal predictability for midlatitude weather phenomena. This study provides observational evidence of MJO impacts on precipitation extreme intensity, frequency, and duration over the western United States. The results suggest a robust increase in precipitation extremes, especially in frequency, relative to climatological conditions over most of the western United States when the MJO is in its western Pacific phases during the extended boreal winter (October–March). Opposite changes are observed when the MJO is located over the Indian Ocean and Maritime Continent. The above MJO influence is characterized by strong seasonality, with the increase in extreme frequency mainly found in late autumn/early winter (OND) over California and a weaker or opposite response found in late winter (JFM). Also, MJO impacts have stronger regional consistency and persist for a longer time in OND compared to JFM. The seasonality of MJO impacts largely originates from the different amplitudes and patterns of both the MJO and basic states that are weaker and located/retreated more northwestward in OND than in JFM. This leads to different responses in MJO teleconnections including moisture transport and AR activity that contribute to the different precipitation extreme changes. The strong seasonality of the relationship between the MJO and western U.S. extreme precipitation shown in this study has implications to the source of subseasonal-to-seasonal predictions, which has a potential value to stakeholders including water resource managers.

Impact of the SST-Front on Subseasonal Predictions of North Atlantic Winter Circulation Using the JMA Operational Seasonal Prediction System

Impact of the SST-Front on Subseasonal Predictions of North Atlantic Winter Circulation Using the JMA Operational Seasonal Prediction System

Stratospheric impacts on dust transport and air pollution in West Africa and the Eastern Mediterranean

Saharan dust intrusions strongly impact Atlantic and Mediterranean coastal regions. Today, most operational dust forecasts extend only 2–5 days. Here we show that on timescales of weeks to months, North African dust emission and transport are impacted by sudden stratospheric warmings (SSWs), which establish a negative North Atlantic Oscillation-like surface signal. Chemical transport models show a large-scale dipolar dust response to SSWs, with the burden in the Eastern Mediterranean enhanced up to 30% and a corresponding reduction in West Africa. Observations of inhalable particulate (PM10) concentrations and aerosol optical depth confirm this dipole. On average, a single SSW causes 680–2460 additional premature deaths in the Eastern Mediterranean and prevents 1180–2040 premature deaths in West Africa from exposure to dust-source fine particulate (PM2.5). Currently, SSWs are predictable 1–2 weeks in advance. Altogether, the stratosphere represents an important source of subseasonal predictability for air quality over West Africa and the Eastern Mediterranean.

Weather patterns in Southeast Asia: Enhancing high‐impact weather subseasonal forecast skill

AbstractWhile skilful forecasts of heavy rainfall are highly desirable for weather warnings and mitigating impacts, forecasting such events is notoriously difficult, even with the most advanced numerical weather prediction models, due to the strong dependence on convective‐scale processes. The large‐scale circulation, on the other hand, is typically more predictable. Weather patterns (WPs) are a set of circulation types obtained statistically that can be used to characterize regional weather and harness the predictability of the large‐scale circulation. In this work we produce pattern‐conditioned probabilistic rainfall forecasts by projecting the horizontal winds from the Met Office GloSea5 prediction system on to WPs and then using the observed relationship between each WP and rainfall estimated by satellite. The WPs are derived following a novel two‐tier clustering technique: the WPs in the first tier represent planetary‐scale variability, such as El Niño–Southern Oscillation (ENSO), while the WPs in the second tier capture synoptic‐scale variability. We investigate WP predictability as well as the improvement in skill of subseasonal rainfall forecasts gained by this technique. GloSea5 predicts the WP occurrence with skill extending beyond lead day 10. The pattern‐conditioned rainfall forecasts were evaluated against climatological forecasts and model‐simulated rainfall hindcasts. We show that the pattern‐conditioned forecasts are skilful and outperform the model‐simulated rainfall hindcasts for lead times extending to days 10–20, depending on the specific exceedance criteria and region. Spatial aggregation leads to increased levels of skill, but not to a significant extension of the skilful prediction horizon. These results constitute a fundamental step for the development of subseasonal prediction systems for Southeast Asia.

Subseasonal predictability of the transition of the stratospheric polar vortex: A case study in winter 1987/88

The stratospheric polar vortex (SPV), which is an important factor in subseasonal-to-seasonal climate variability and climateprediction, exhibited a remarkable transition from weak in early winter to strong in late winter in 1987/88 (most significant on the interannual timescale during 1979–2019). Therefore, in this study, the subseasonal predictability of this transition SPV case in 1987/88 was investigated using the hindcasts from a selected model (that of the Japan Meteorological Agency) in the Subseasonal-to-Seasonal Prediction project database. Results indicated that the predictability of both weak and strong SPV stages in winter 1987/88, especially near their peak dates, exhibited large sensitivity to the initial condition, which derived mainly from the sensitivity in capturing the 100-hPa eddy heat flux anomalies. Meanwhile, the key tropospheric precursory systems with respect to the occurrence and predictability of this transition SPV case were investigated. The Eurasian teleconnection wave trains might have been a key precursor for the weak SPV stage, while significant tropospheric precursors for the strong SPV stage were not found in this study. In addition, positive correlation (r = 0.41) existed between the forecast biases of the SPV and the NAO in winter 1987/88, which indicates that reducing the forecast biases of the SPV might help to improve the forecasting of the NAO and tropospheric weather.摘要平流层极涡作为冬季次季节尺度上一个重要的可预测性来源, 其强度在1987/88年冬季表现为1979–2019年最显著的转折, 即在前 (后) 冬极端偏弱 (强). 因此在本文中选取这一个例研究了该年冬季平流层极涡在次季节尺度上的可预测性. 结果表明弱极涡和强极涡事件的预测与模式能否准确预测上传行星波的强度紧密相关. 同时, 发现前期对流层欧亚遥相关波列可能是弱极涡事件发生的关键预兆信号. 此外, 模式对平流层极涡强度和北大西洋涛动预测误差之间存在显著正相关关系, 表明模式减少平流层极涡的预测误差可能可以提高北大西洋涛动及相关对流层气候预测.

Subseasonal predictability of onset, duration, and intensity of European heat extremes

AbstractSuccessful weather forecasts on subseasonal time‐scales can support societal preparedness and mitigate the impacts of extreme events. Heatwaves in particular can, in certain cases, be predicted on time‐scales of several weeks in advance. Heatwave predictability is commonly assessed in terms of heatwave intensity. In addition to heatwave intensity, we assess the predictability of heatwave onset and duration, which are crucial components of early‐warning systems and emergency preparedness plans. The forecast skill of heatwaves is investigated over the European region in the subseasonal forecasting system of the European Centre for Medium‐Range Weather Forecasts (ECMWF). The heatwaves are first detected in ERA‐Interim reanalysis data over the period 1998–2017 and then allocated into six clusters in the following regions: Black Sea (BSea), Russia (Ru), Western Europe (WEu), North Sea (NSea), Scandinavia (Sc), and Eastern Europe (EEu). The European regions with the highest predictability in heatwave onset and duration are the clusters Ru, Sc, and NSea. The WEu cluster has the lowest bias in heatwave intensity and is found to be the most predictable region in terms of the number of heatwave events with predictable intensity at lead week 2. Heatwave intensity is generally found to be the most predictable characteristic of European heatwaves, being predictable by the model ensemble mean up to lead times of 3 weeks. Furthermore, this analysis identifies the most predictable heatwaves, allowing for a further investigation of the physical mechanisms and heatwave characteristics leading to enhanced heatwave forecast skill over different European regions.

Sub-Seasonal Experiment (SubX) Model-based Assessment of the Prediction Skill of Recent Multi-Year South Korea Droughts

Reliable sub-seasonal forecast of precipitation is essential to manage the risk of multi-year droughts in a timely manner. However, comprehensive assessments of sub-seasonal prediction skill of precipitation remain limited, particularly during multi-year droughts. This study used various verification metrics to assess the sub-seasonal prediction skill of hindcasts of five Sub-seasonal Experiment (SubX) models for precipitation during two recent multi-year South Korea droughts (2007 − 10 and 2013 − 16). Results show that the sub-seasonal prediction skill of the SubX models were stage-, event-, and model-dependent over the recent multi-year droughts. According to the Brier skill scores, SubX models show a more skillful in one to four lead weeks during the drought onset and persistence stages, than the recovery stage. While the prediction skill of the SubX models in the first two initial weeks show more skillful prediction during the 2007–10 drought, the impact of the forecast initial time on the prediction skill is relatively weak during the 2013–16 drought. Overall, the EMC-GEFSv12 model with the 11 ensemble members (the largest among the five SubX models) show the most skillful forecasting skill. According to the sensitivity test to the ensemble member size, the EMC-GEFSv12 model had no gain for biweekly precipitation forecast with the nine ensemble members or more. This study highlights the importance of a robust evaluation of the predictive performance of sub-seasonal climate forecasts via multiple verification metrics.

Outcomes of the WMO Prize Challenge to Improve Subseasonal to Seasonal Predictions Using Artificial Intelligence

Abstract There is a high demand and expectation for subseasonal to seasonal (S2S) prediction, which provides forecasts beyond 2 weeks, but less than 3 months ahead. To assess the potential benefit of artificial intelligence (AI) methods for S2S prediction through better postprocessing of ensemble prediction system outputs, the World Meteorological Organization (WMO) coordinated a prize challenge in 2021 to improve subseasonal prediction. The goal of this competition was to produce the most skillful forecasts of precipitation and 2-m temperature globally averaged over forecast weeks 3 and 4 and over weeks 5 and 6 for the year 2020 using artificial intelligence techniques. The top three submissions, described in this article, succeeded in producing S2S forecasts significantly more skillful than the bias-corrected ECMWF operational reference forecasts, particularly for precipitation, through improved calibration of the ECMWF raw forecast outputs or multimodel combination. These forecast improvements should benefit the use of S2S forecasts in applications.