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The prediction skill for precipitation anomalies in late spring and summer months—a significant component of extreme climate events—has remained stubbornly low for years. This paper presents a new idea that utilizes information on boreal spring land surface temperature/subsurface temperature (LST/SUBT) anomalies over the Tibetan Plateau (TP) to improve prediction of subsequent summer droughts/floods over several regions over the world, East Asia and North America in particular. The work was performed in the framework of the GEWEX/LS4P Phase I (LS4P-I) experiment, which focused on whether the TP LST/SUBT provides an additional source for subseasonal-to-seasonal (S2S) predictability. The summer 2003, when there were severe drought/flood over the southern/northern part of the Yangtze River basin, respectively, has been selected as the focus case. With the newly developed LST/SUBT initialization method, the observed surface temperature anomaly over the TP has been partially produced by the LS4P-I model ensemble mean, and 8 hotspot regions in the world were identified where June precipitation is significantly associated with anomalies of May TP land temperature. Consideration of the TP LST/SUBT effect has produced about 25–50% of observed precipitation anomalies in most hotspot regions. The multiple models have shown more consistency in the hotspot regions along the Tibetan Plateau-Rocky Mountain Circumglobal (TRC) wave train. The mechanisms for the LST/SUBT effect on the 2003 drought over the southern part of the Yangtze River Basin are discussed. For comparison, the global SST effect has also been tested and 6 regions with significant SST effects were identified in the 2003 case, explaining about 25–50% of precipitation anomalies over most of these regions. This study suggests that the TP LST/SUBT effect is a first-order source of S2S precipitation predictability, and hence it is comparable to that of the SST effect. With the completion of the LS4P-I, the LS4P-II has been launched and the LS4P-II protocol is briefly presented.

 

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 Oscillations 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. 

Despite the well‐recognized initial value nature of the subseasonal forecasts, the role of subsurface ocean initialization in subseasonal forecasts remains underexplored. Using observing system experiments, this study investigates the impact of ocean in situ data assimilation on the propagation of Madden–Julian Oscillation (MJO) events across the Maritime Continent in the European Centre for Medium‐Range Weather Forecasts (ECMWF) subseasonal forecast system. Two sets of twin experiments are analyzed, which only differ on the use or not of in situ ocean observations in the initial conditions. Besides using the Real‐time Multivariate MJO Index (RMMI) to evaluate the forecast performance, we also develop a new MJO tracking method based on outgoing longwave radiation anomalies (OLRa) for forecast evaluation. We find that the ocean initialization with in situ data assimilation, though having an impact on the forecasted ocean mean state, does not improve the relatively low MJO forecast skill across the Maritime Continent. Moist static energy budget analysis further suggests that a significant underestimation in the meridional moisture advection in the model forecast may hinder the potential role played by the ocean state differences associated with data assimilation. Bias of the intraseasonal meridional winds in the model is a more important factor for such underestimation than the mean state moisture biases. This finding suggests that atmospheric model biases dominate the forecast error growth, and the atmospheric circulation bias is one of the major sources of the MJO prediction error and should be a target for improving the ECMWF subseasonal forecast model.

 

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. 

This study examines the relationship between the Madden‐Julian oscillation (MJO) and stratospheric quasi‐biennial oscillation (QBO) in a state‐of‐the‐art global numerical weather forecast model. A set of 61‐day model integrations, with 15 ensemble members, is performed across 84 start dates during December–February of 1989–2016. For 28 of those dates—every 1 January—the stratosphere is initialized from observation, and the model simulates stronger MJO events during observed easterly QBO phases (QBOE) than westerly QBO phases (QBOW). However, in these “control experiments,” the QBO's impact on the MJO is already present in the initial conditions, and the direct influence of the model stratosphere during the simulation is unclear. To explore this more directly, the model was rerun with an artificially imposed QBOE and QBOW state, replacing the existing stratospheric initial condition above 150 hPa while leaving the troposphere unaltered. Though the imposed QBO states weaken faster in the model than in observations, their persistence is comparable to the control simulations. The MJO is stronger during imposed‐QBOE experiments than imposed‐QBOW, and differences are statistically significant by several metrics, though magnitude of the differences is smaller than in observations. Analysis suggests that the strength of the MJO response to the QBO increases for simulations with stronger upper‐tropospheric temperature differences and for simulations in which the MJO at the initialization time is strong and active over the Maritime Continent. However, tropospheric conditions still appear to have a dominant effect in explaining the apparent QBO influence in this model.

 

The impact of the quasi‐biennial oscillation (QBO) on the prediction of tropical intraseasonal convection, including the Madden Julian Oscillation (MJO) and Boreal Summer Intraseasonal Oscillation (BSISO), is assessed in the WMO Subseasonal to Seasonal (S2S) forecast database using the real‐time OLR based MJO (ROMI) index. It is shown that the ROMI prediction skill for the boreal winter MJO, measured by the maximum time at which the anomaly correlation coefficient exceeds 0.6, is higher by 5 to 10 days in the QBO easterly phase than its westerly phase. This difference occurs even in models with low tops and poorly resolved stratospheres. MJO predictability, as measured by signal to noise ratio in the S2S ensemble, also shows a similar difference between the two QBO phases, and results from a simple linear regression model show consistent behavior as well. Analysis of the ROMI index derived from observations indicates that the MJO is more coherent and stronger in the QBO easterly phase than in the westerly phase. These results suggest that the skill dependence on QBO phase results from the initial state of the MJO, the regularity of its propagation in the verifying observations, or most likely a combination of the two, but not on an actual stratospheric influence on the MJO within the model simulations. In contrast to the robust QBO‐MJO connection in boreal winter, the BSISO prediction skill exhibited by the S2S models in boreal summer is greater in the QBOwesterlyphase than in theeasterlyphase during the 1999 to 2010 period. This is consistent with the observation that BSISO OLR anomalies are stronger in the QBO westerly phase during that period. However, this relationship between the QBO and BSISO in boreal summer changes in recent decades: BSISO is weaker in QBO westerly than easterly during 1979–2000. Correspondingly, the QBO impact on BSISO prediction in boreal summer also reverses in that period as well in a statistical model, whereas this statistical model shows a consistent QBO impact on MJO prediction in boreal winter over the past four decades.

 

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 initiativecalled “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 acrucial factor that can lead to significant improvement in precipitationprediction through the remote effects of land–atmosphere interactions. LS4P focuses on process understanding and predictability, and hence it is differentfrom, and complements, other international projects that focus on theoperational S2S prediction. More than 40 groups worldwide have participated in this effort, including 21 Earth system models, 9 regionalclimate 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 ofthe Tibetan Plateau, discusses the LST/SUBT initialization, and presents thepreliminary results. Multi-model ensemble experiments and analyses ofobservational data have revealed that the hydroclimatic effect of the springLST on the Tibetan Plateau is not limited to the Yangtze River basin but may have a significant large-scale impact on summer precipitation beyond EastAsia and its S2S prediction. Preliminary studies and analysis have alsoshown that LS4P models are unable to preserve the initialized LST anomaliesin producing the observed anomalies largely for two main reasons: (i) inadequacies in the land models arising from total soil depths which are tooshallow 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 andanomalies of LST over the Tibetan Plateau. Innovative approaches have beendeveloped to largely overcome these problems.