S2S Prediction Research Articles

How to choose credible ensemble members for the sub-seasonal to seasonal prediction of precipitation?

The sub-seasonal to seasonal (S2S) prediction of precipitation is not only a hot topic but also a challenge. The traditional ensemble mean and ensemble probabilistic forecast methods cannot avoid the uncertainty of the initial value in the S2S prediction. Is there a more suitable ensemble postprocessing method for the S2S prediction? In this study, the hindcast data during the 1999–2010 summers from nine operational models in the international S2S prediction project has been evaluated. Based on the quantitative objective precipitation evaluation methods, such as the Equitable Threat Score and frequency bias methods, the climatological spatio-temporal distribution of the optimal probabilistic threshold on the S2S scale is proven to exist, and it can be used as the standard to judge how many ensemble members are credible. Then, different ensemble forecast strategies are adopted in different regions to construct a Deterministic Ensemble Forecast using an Optimal Probabilistic Threshold (DEFOPT) method for precipitation prediction. The hindcast data of eight S2S models outside the period 1999–2010 are used to verify the applicability of the DEFOPT method by using the historical optimal probabilistic threshold during 1999–2010. The results show that the DEFOPT outperforms the deterministic forecast from one initial value, the ensemble mean, and the deterministic ensemble forecast using a probabilistic threshold for the occurrence days of rainfall at the 1 mm and 5 mm thresholds (≥ 1 mm and ≥ 5 mm) over China during each pentad in most S2S models.

A Case Study of Bias Correction and the Dynamical Downscaling of CFSv2 S2S Forecasts Using a WRF Model: Heatwave in 2018 over South Korea

Extreme weather events caused by climate change affect the growth of crops, requiring reliable weather forecasts. In order to provide day-to-season seamless forecasting data for the agricultural sector, improving the forecasting performance of the S2S period is necessary. A number of studies have been conducted to improve prediction performance based on the bias correction of systematic errors in GCM or by producing high-resolution data via dynamic detailing. In this study, a daily simple mean bias correction technique is applied on CFSv2 (∼100 km) data. We then use case studies to evaluate how beneficial the precision of the high-resolution RCM simulation is in improving S2S prediction performance using the bias-corrected lateral boundary. Based on our examination of 45-day sequences of WRF simulations with 27–9–3 km resolution, it can be concluded that a higher resolution is correlated with better prediction in the case of the extreme heatwave in Korea in 2018. However, the effect of bias correction in improving predictive performances is not significant, suggesting that further studies on more cases are necessary to obtain more solid conclusions in the future.

Spring Land Temperature in Tibetan Plateau and Global-Scale Summer Precipitation: Initialization and Improved Prediction

Abstract Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface ­temperatures show a lag correlation with summer precipitation in several remote regions, but current global land–atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the “Impact of Initialized Land Temperature and Snowpack on Subseasonal-to-Seasonal Prediction” (LS4P) initiative as a community effort to test the impact of land temperature in high-mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-m temperature over the TP in the LS4P experiment, results from a multimodel ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high-mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global “hotspot” regions identified here; the ensemble means in some “hotspots” produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.

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.

Water Savings with Irrigation Water Management at Multi-week Lead Time Using Extended Range Predictions

The Extended Range Predictions (ERP) provide Sub-seasonal to Seasonal (S2S) prediction of meteorological variables at a lead time of multiple weeks. Despite the considerable improvements in the ERP system, their applications in real-time irrigation water management are limited. Such limited use of ERP is due to uncertainty in predictions and a spatial mismatch in scales between the model grids and farms. Here, we develop a chance-constrained optimization framework for farm-scale irrigation water management planning for multiple weeks in advance. The degrading model performance with time and the related uncertainty are addressed by generating large ensembles using weather generators. Working with grape farmers in India has revealed that such an advance planning could save water by 3–25 % at a farm-scale without losing yield, compared with the existing approach requiring near real-time decisions. Moreover, the co-development of such irrigation tools with farmers increases the use and usability of S2S predictions.

Advances in the Prediction of MJO Teleconnections in the S2S Forecast Systems

Abstract This study evaluates the ability of state-of-the-art subseasonal-to-seasonal (S2S) forecasting systems to represent and predict the teleconnections of the Madden–Julian oscillation and their effects on weather in terms of midlatitude weather patterns and North Atlantic tropical cyclones. This evaluation of forecast systems applies novel diagnostics developed to track teleconnections along their preferred pathways in the troposphere and stratosphere, and to measure the global and regional responses induced by teleconnections across both the Northern and Southern Hemispheres. Results of this study will help the modeling community understand to what extent the potential to predict the weather on S2S time scales is achieved by the current generation of forecasting systems, while informing where to focus further development efforts. The findings of this study will also provide impact modelers and decision-makers with a better understanding of the potential of S2S predictions related to MJO teleconnections.

Advances in the Subseasonal Prediction of Extreme Events: Relevant Case Studies across the Globe

Abstract Extreme weather events have devastating impacts on human health, economic activities, ecosystems, and infrastructure. It is therefore crucial to anticipate extremes and their impacts to allow for preparedness and emergency measures. There is indeed potential for probabilistic subseasonal prediction on time scales of several weeks for many extreme events. Here we provide an overview of subseasonal predictability for case studies of some of the most prominent extreme events across the globe using the ECMWF S2S prediction system: heatwaves, cold spells, heavy precipitation events, and tropical and extratropical cyclones. The considered heatwaves exhibit predictability on time scales of 3–4 weeks, while this time scale is 2–3 weeks for cold spells. Precipitation extremes are the least predictable among the considered case studies. ­Tropical cyclones, on the other hand, can exhibit probabilistic predictability on time scales of up to 3 weeks, which in the presented cases was aided by remote precursors such as the Madden–Julian oscillation. For extratropical cyclones, lead times are found to be shorter. These case studies clearly illustrate the potential for event-dependent advance warnings for a wide range of extreme events. The subseasonal predictability of extreme events demonstrated here allows for an extension of warning horizons, provides advance information to impact modelers, and informs communities and stakeholders affected by the impacts of extreme weather events.

Impact of stratospheric ozone on the subseasonal prediction in the southern hemisphere spring

Antarctic ozone has been regarded as a major driver of the Southern Hemisphere (SH) circulation change in the recent past. Here, we show that Antarctic ozone can also affect the subseasonal-to-seasonal (S2S) prediction during the SH spring. Its impact is quantified by conducting two reforecast experiments with the Global Seasonal Forecasting System 5 (GloSea5). Both reforecasts are initialized on September 1st of each year from 2004 to 2020 but with different stratospheric ozone: one with climatological ozone and the other with year-to-year varying ozone. The reforecast with climatological ozone, which is common in the operational S2S prediction, shows the skill re-emergence in October after a couple of weeks of no prediction skill in the troposphere. This skill re-emergence, mostly due to the stratosphere–troposphere dynamical coupling, becomes stronger in the reforecast with year-to-year varying ozone. The surface prediction skill also increases over Australia. This result suggests that a more realistic stratospheric ozone could lead to improved S2S prediction in the SH spring.

Improving the Forecasting of Winter Wheat Yields in Northern China with Machine Learning–Dynamical Hybrid Subseasonal-to-Seasonal Ensemble Prediction

Subseasonal-to-seasonal (S2S) prediction of winter wheat yields is crucial for farmers and decision-makers to reduce yield losses and ensure food security. Recently, numerous researchers have utilized machine learning (ML) methods to predict crop yield, using observational climate variables and satellite data. Meanwhile, some studies also illustrated the potential of state-of-the-art dynamical atmospheric prediction in crop yield forecasting. However, the potential of coupling both methods has not been fully explored. Herein, we aimed to establish a skilled ML–dynamical hybrid model for crop yield forecasting (MHCF v1.0), which hybridizes ML and a global dynamical atmospheric prediction system, and applied it to northern China at the S2S time scale. In this study, we adopted three mainstream machining learning algorithms (XGBoost, RF, and SVR) and the multiple linear regression (MLR) model, and three major datasets, including satellite data from MOD13C1, observational climate data from CRU, and S2S atmospheric prediction data from IAP CAS, used to predict winter wheat yield from 2005 to 2014, at the grid level. We found that, among the four models examined in this work, XGBoost reached the highest skill with the S2S prediction as inputs, scoring R2 of 0.85 and RMSE of 0.78 t/ha 3–4 months, leading the winter wheat harvest. Moreover, the results demonstrated that crop yield forecasting with S2S dynamical predictions generally outperforms that with observational climate data. Our findings highlighted that the coupling of ML and S2S dynamical atmospheric prediction provided a useful tool for yield forecasting, which could guide agricultural practices, policy-making and agricultural insurance.

Potential Predictability of Southwest U.S. Rainfall: Role of Tropical and High-Latitude Variability

Abstract This study explores the potential predictability of Southwest U.S. (SWUS) precipitation for the November–March season in a set of numerical experiments performed with the Whole Atmospheric Community Climate Model. In addition to the prescription of observed sea surface temperature and sea ice concentration, observed variability from the MERRA-2 reanalysis is prescribed in the tropics and/or the Arctic through nudging of wind and temperature. These experiments reveal how a perfect prediction of tropical and/or Arctic variability in the model would impact the prediction of seasonal rainfall over the SWUS, at various time scales. Imposing tropical variability improves the representation of the observed North Pacific atmospheric circulation, and the associated SWUS seasonal precipitation. This is also the case at the subseasonal time scale due to the inclusion of the Madden–Julian oscillation (MJO) in the model. When additional nudging is applied in the Arctic, the model skill improves even further, suggesting that improving seasonal predictions in high latitudes may also benefit prediction of SWUS precipitation. An interesting finding of our study is that subseasonal variability represents a source of noise (i.e., limited predictability) for the seasonal time scale. This is because when prescribed in the model, subseasonal variability, mostly the MJO, weakens the El Niño–Southern Oscillation (ENSO) teleconnection with SWUS precipitation. Such knowledge may benefit S2S and seasonal prediction as it shows that depending on the amount of subseasonal activity in the tropics on a given year, better skill may be achieved in predicting subseasonal rather than seasonal rainfall anomalies, and conversely. Significance Statement Subseasonal and seasonal predictability of precipitation over the Southwest United States (SWUS) during the wet season is challenging, and long-range forecasts from climate models still exhibit poor skill over this region. In this study we use numerical experiments with constrained tropical and/or Arctic atmospheric variability to explore how climate processes in these two regions impact the SWUS precipitation. Our results highlight how much forecast skill in SWUS precipitation may be gained from better predictions in tropical and high latitudes, from subseasonal to multiyear time scales.

From Atmospheric Waves to Heatwaves: A Waveguide Perspective for Understanding and Predicting Concurrent, Persistent, and Extreme Extratropical Weather

Abstract A notable number of high-impact weather extremes have occurred in recent years, often associated with persistent, strongly meandering atmospheric circulation patterns known as Rossby waves. Because of the high societal and ecosystem impacts, it is of great interest to be able to accurately project how such extreme events will change with climate change, and to predict these events on seasonal-to-subseasonal (S2S) time scales. There are multiple physical links connecting upper-atmosphere circulation patterns to surface weather extremes, and it is asking a lot of our dynamical models to accurately simulate all of these. Subsequently, our confidence in future projections and S2S forecasts of extreme events connected to Rossby waves remains relatively low. We also lack full fundamental theories for the growth and propagation of Rossby waves on the spatial and temporal scales relevant to extreme events, particularly under strongly nonlinear conditions. By focusing on one of the first links in the chain from upper-atmospheric conditions to surface extremes—the Rossby waveguide—it may be possible to circumvent some model biases in later links. To further our understanding of the nature of waveguides, links to persistent surface weather events and their representation in models, we recommend exploring these links in model hierarchies of increasing complexity, developing fundamental theory, exploiting novel large ensemble datasets, harnessing deep learning, and increased community collaboration. This would help increase understanding and confidence in both S2S predictions of extremes and of projections of the impact of climate change on extreme weather events.

S2S Prediction in GFDL SPEAR: MJO Diversity and Teleconnections

Abstract A subseasonal-to-seasonal (S2S) prediction system was recently developed using the GFDL Seamless System for Prediction and Earth System Research (SPEAR) global coupled model. Based on 20-yr hindcast results (2000–19), the boreal wintertime (November–April) Madden–Julian oscillation (MJO) prediction skill is revealed to reach 30 days measured before the anomaly correlation coefficient of the real-time multivariate (RMM) index drops to 0.5. However, when the MJO is partitioned into four distinct propagation patterns, the prediction range extends to 38, 31, and 31 days for the fast-propagating, slow-propagating, and jumping MJO patterns, respectively, but falls to 23 days for the standing MJO. A further improvement of MJO prediction requires attention to the standing MJO given its large gap with its potential predictability (38 days). The slow-propagating MJO detours southward when traversing the Maritime Continent (MC), and confronts the MC prediction barrier in the model, while the fast-propagating MJO moves across the central MC without this prediction barrier. The MJO diversity is modulated by stratospheric quasi-biennial oscillation (QBO): the standing (slow-propagating) MJO coincides with significant westerly (easterly) phases of QBO, partially explaining the contrasting MJO prediction skill between these two QBO phases. The SPEAR model shows its capability, beyond the propagation, in predicting their initiation for different types of MJO along with discrete precursory convection anomalies. The SPEAR model skillfully predicts the observed distinct teleconnections over the North Pacific and North America related to the standing, jumping, and fast-propagating MJO, but not the slow-propagating MJO. These findings highlight the complexities and challenges of incorporating MJO prediction into the operational prediction of meteorological variables.

Effects of spring Tibetan Plateau land temperature anomalies on early summer floods/droughts over the monsoon regions of South East Asia

Recent observational and modeling studies have demonstrated the substantial influence of the Tibetan Plateau (TP) spring land surface temperature (LST) and subsurface temperature (SUBT) on downstream summer droughts/floods events in East Asia, highlighting the potential application of LST/SUBT on sub-seasonal to seasonal prediction (S2S). In this study, we employ the National Centers for Environment Prediction—Global Forecast System/Simplified Simple Biosphere model version 2 (GFS/SSiB2) to investigate the potential role of the late spring warm LST anomaly over the TP on the extraordinary June 1998 flood in the south of the Yangtze River region. Numerical experiments indicate that the warmer (above normal) May LST over the TP may contribute to the extreme flood of 1998 over the south of the Yangtze River region, with the LST reproducing about 57% and 64% of observed above-normal rainfall anomaly over the south of the Yangtze River region and southeastern China, respectively. Further analyses reveal a possible effect of springtime TP’s LST on summer southern and eastern Asian rainfall and identify some hot spots, suggesting that the TP’s spring LST effect is not only limited to the Yangtze River region, but to a much larger scale. The imposed warm LST/SUBT over the TP triggers a strong wave activities propagating eastward along the upper-level westerly jet, associated with an increase of the atmospheric baroclinic instability as well as a strengthening and southeastward movement of the South Asian high, leading to intensified moisture convergence and convective instability favorable to the excessive rainfall in the downstream region of East Asia. The results of the 1998 case have also been compared with the results from year of 2003, which had a very cold spring LST anomaly over the TP and a severe downstream June 2003 drought (flood) in southern (northern) of the Yangtze River Basin area. Simulation results provide further evidence of the great importance of the TP spring land surface temperature anomaly in regulating summer extreme hydroclimatic events (e.g. droughts and floods) in South and East Asia. The present study suggests that consideration of LST/SUBT anomalies has a strong potential for more skillful S2S prediction of extreme hydroclimatic events such as floods, droughts and heatwaves over both Southern and Eastern Asia.

Influence of the Madden–Julian Oscillation on the Arctic Oscillation Prediction in S2S Operational Models

The connections between the Madden–Julian Oscillation (MJO) and the Arctic Oscillation (AO) are examined in both observations and model forecasts. In the observations, the time-lag composites are carried out for AO indices and anomalies of 1,000-hPa geopotential height after an active or inactive initial MJO. The results show that when the AO is in its positive (negative) phase at the initial time, the AO activity is generally enhanced (weakened) after an active MJO. Reforecast data of the 11 operational global circulation models from the Sub-seasonal to Seasonal (S2S) Prediction Project are further used to examine the relationship between MJO activity and AO prediction. When the AO is in its positive phase on the initial day of the S2S prediction, an initial active MJO can generally improve the AO prediction skill in most of the models. This is consistent with results found in the observations that a leading MJO can enhance the AO activity. However, when the AO is in its negative phase, the relationship between the MJO and AO prediction is not consistent among the 11 models. Only a few S2S models provide results that agree with the observations. Furthermore, the S2S prediction skill of the AO is examined in different MJO phases. There is a significantly positive relationship between the MJO-related AO activity and the AO prediction skill. When the AO activity is strong (weak) in an MJO phase, including the inactive MJO, the models tend to have a high (low) AO prediction skill. For example, no matter what phase the initial AO is in, the AO prediction skill is generally high in MJO phase 7, in which the AO activity is generally strong. Thus, the MJO is an important predictability source for the AO forecast in the S2S models.

Listening to Stakeholders: Initiating Research on Subseasonal-to-Seasonal Heavy Precipitation Events in the Contiguous United States by First Understanding What Stakeholders Need

AbstractHeavy precipitation events and their associated flooding can have major impacts on communities and stakeholders. There is a lack of knowledge, however, about how stakeholders make decisions at the subseasonal-to-seasonal (S2S) time scales (i.e., 2 weeks to 3 months). To understand how decisions are made and S2S predictions are or can be used, the project team for “Prediction of Rainfall Extremes at Subseasonal to Seasonal Periods” (PRES2iP) conducted a 2-day workshop in Norman, Oklahoma, during July 2018. The workshop engaged 21 professionals from environmental management and public safety communities across the contiguous United States in activities to understand their needs for S2S predictions of potential extended heavy precipitation events. Discussions and role-playing activities aimed to identify how workshop participants manage uncertainty and define extreme precipitation, the time scales over which they make key decisions, and the types of products they use currently. This collaboration with stakeholders has been an integral part of PRES2iP research and has aimed to foster actionable science. The PRES2iP team is using the information produced from this workshop to inform the development of predictive models for extended heavy precipitation events and to collaboratively design new forecast products with our stakeholders, empowering them to make more-informed decisions about potential extreme precipitation events.

Assessing the role of air-sea coupling in predicting Madden-Julian Oscillation with an atmosphere-ocean coupled model

AbstractThe Madden-Julian Oscillation (MJO) provides an important source of sub-seasonal to seasonal (S2S) predictability. Improved MJO prediction can be beneficial to S2S prediction of global climate and associated weather extremes. In this study, hindcasts based on an atmosphere-ocean coupled general circulation model (CGCM) are compared to those based on atmosphere general circulation models (AGCMs) to investigate influences of air-sea interactions on MJO prediction. Our results suggest that MJO prediction skill can be extended about one week longer in the CGCM hindcasts than AGCM-only experiments, particularly for boreal winter predictions.Further analysis suggests that improved MJO prediction in the CGCM is closely associated with improved representation of moistening processes. Compared to the AGCM experiments, the CGCM better predicts the boundary-layer moisture preconditioning to the east of MJO convection, which is generally considered crucial for triggering MJO deep convection. Meanwhile, the widely extended east-west asymmetric structure in free-tropospheric moisture tendency anomalies relative to the MJO convection center as seen in the observations is also well predicted in the CGCM. Improved prediction of MJO moisture processes in CGCM is closely associated with better representation of the zonal scale of MJO circulation and stronger Kelvin waves to the east of MJO convection, both of which have been recently suggested conductive for MJO eastward propagation. The above improvements by including air-sea coupling could be largely attributed to the realistic MJO-induced SST fluctuations through the convection-SST feedback. This study confirms a critical role of atmosphere-ocean coupling for the improvement of MJO prediction.

Memory of land surface and subsurface temperature (LST/SUBT) initial anomalies over Tibetan Plateau in different land models

This study applies three widely used land models (SSiB, CLM, and Noah-MP) coupled in a regional climate model to quantitatively assess their skill in preserving the imposed ± 5 °C anomalies on the initial land surface and subsurface temperature (LST/SUBT) and generating the 2-m air temperature (T2m) anomalies over Tibetan Plateau (TP) during May–August. The memory of the LST/SUBT initial anomalies (surface/soil memory) is defined as the first time when time series of the differences in daily LST/SUBT cross the zero line during the simulation, with the unit of days. The memory of the T2m anomalies (T2m memory) is defined in the same way. The ensemble results indicate that the simulated soil memory generally increases with soil depth, which is consistent with the results based on the observations with statistic methods. And the soil memory is found to change rapidly with depth above ~ 0.6–0.7 m and vary slowly below it. The land models have fairly long soil memories, with the regional mean 1.0-m soil memory generally longer than 60 days. However, they have short T2m memory, with the regional means generally below 20 days. This may bring a big challenge to use the LST/SUBT approach on the sub-seasonal to seasonal (S2S) prediction. Comparison between the three land models shows that CLM and Noah-MP have longer soil memory at the deeper layers (> ~ 0.05 m) while SSiB has longer T2m/surface memories and near-surface ( $$\le$$ ~ 0.05 m) soil memory. As a result, it is difficult to say which land model is optimal for the application of the LST/SUBT approach on the S2S prediction. The T2m/surface/soil memories are various over TP, distinct among the land models, and different between the + 5 °C and − 5 °C experiment, which can be explained by both changes in the surface heat fluxes and variances in the hydrological processes over the plateau.

Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction Project, Phase I (LS4P-I): organization and experimental design

Abstract. Subseasonal-to-seasonal (S2S) prediction, especially the prediction of extreme hydroclimate events such as droughts and floods, is not only scientifically challenging, but also has substantial societal impacts. Motivated by preliminary studies, the Global Energy and Water Exchanges (GEWEX)/Global Atmospheric System Study (GASS) has launched a new initiative called “Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction” (LS4P) as the first international grass-roots effort to introduce spring land surface temperature (LST)/subsurface temperature (SUBT) anomalies over high mountain areas as a crucial factor that can lead to significant improvement in precipitation prediction through the remote effects of land–atmosphere interactions. LS4P focuses on process understanding and predictability, and hence it is different from, and complements, other international projects that focus on the operational S2S prediction. More than 40 groups worldwide have participated in this effort, including 21 Earth system models, 9 regional climate models, and 7 data groups. This paper provides an overview of the history and objectives of LS4P, provides the first-phase experimental protocol (LS4P-I) which focuses on the remote effect of the Tibetan Plateau, discusses the LST/SUBT initialization, and presents the preliminary results. Multi-model ensemble experiments and analyses of observational data have revealed that the hydroclimatic effect of the spring LST on the Tibetan Plateau is not limited to the Yangtze River basin but may have a significant large-scale impact on summer precipitation beyond East Asia and its S2S prediction. Preliminary studies and analysis have also shown that LS4P models are unable to preserve the initialized LST anomalies in producing the observed anomalies largely for two main reasons: (i) inadequacies in the land models arising from total soil depths which are too shallow and the use of simplified parameterizations, which both tend to limit the soil memory; (ii) reanalysis data, which are used for initial conditions, have large discrepancies from the observed mean state and anomalies of LST over the Tibetan Plateau. Innovative approaches have been developed to largely overcome these problems.

Lagged ensembles in sub‐seasonal predictions

AbstractThe benefits of using lagged ensembles for S2S predictions have been evaluated using the ECMWF sub‐seasonal to seasonal (S2S) forecasts. The ECMWF S2S forecasts are currently produced twice a week from a 51‐member ensemble. An experiment where the ECMWF forecasts are produced daily instead of twice weekly has been performed to identify the minimum lagged‐ensemble configuration which will produce probabilistic forecasts at least as skilful as the current configuration. Results suggest that the extended‐range prediction of the Madden–Julian Oscillation (MJO) is not deteriorated with the daily lagged ensemble approach, but there is a slight degradation during the first 10 days. In the northern Extratropics, the probabilistic skill scores are improved after week 2 when using lagged ensembles, but only if the daily ensemble size exceeds 20 members. The lagged ensemble approach is more beneficial in the Tropics than in the northern Extratropics, showing even benefits in forecast week 1 over the Tropics, despite the 24 hr difference between two consecutive forecasts. As expected, the daily lagged ensemble method is more beneficial for longer lead times and the optimal lagged ensemble window increases from 2 days for week 2 to 4 days for week 4. An idealized numerical simulation, based on a Monte Carlo approach, confirms the benefits of lagged ensembles. The results obtained are generally consistent with the ECMWF forecast experiment, suggesting that such a simulation could be used as a cheap alternative to address the benefit of lagged versus burst ensembles.

Relationships between subseasonal‐to‐seasonal predictability and spatial scales in tropical rainfall

AbstractSubseasonal to seasonal (S2S) tropical rainfall predictability is assessed both from an analysis of the spatial scales of observed rainfall variability data, as well as from an S2S model reforecast skill. Observed spatial scales are quantified from gridded observed daily rainfall data, in terms of the size (area) of daily contiguous wet grid‐points (referred to as ‘wet patches’), as well as from the spatial autocorrelations of 7–91‐day running averages of rainfall. Model S2S reforecast skill is measured using the anomaly correlation coefficient between observed and simulated weekly and monthly rainfall from an 11‐member ensemble of European Centre for Medium‐Range Weather Forecasts (ECMWF) reforecasts (1998–2017). Both measures of S2S predictability are found to be systematically lower over land than sea, usually peaking at the start or end of the rainy season and decreasing during the core. Small spatial scales and low skill over equatorial/northern tropical Africa and western Amazonia coincide with small daily rainfall patch size and strong synoptic‐scale (≤7 days) variability there. Over most of South and SE Asia, daily wet patches are larger and strongly modulated by intraseasonal oscillations, boosting S2S rainfall predictability, while this is offset by large daily mean rainfall intensities that increase the noise. In consequence, S2S rainfall skill here generally remains low. Several land areas (as around Maritime Continent from the Philippines to Northern Australia, Eastern and Southern Africa, Eastern South America) exhibit larger spatial scales and skill, especially where the relative amplitude of SST‐forced interannual variations is strong. Most of the Maritime Continent illustrates such behaviour, but even here, the time‐averaged spatial scales and skill drop during the core of the rainy season.