Reliable subseasonal-to-seasonal (S2S) precipitation prediction is highly desired due to the great socioeconomical implications, yet it remains one of the most challenging topics in the weather/climate prediction research area. As part of the Impact of Initialized Land Temperature and Snowpack on Sub-seasonal to Seasonal Prediction (LS4P) project of the Global Energy and Water Exchanges (GEWEX) program, twenty-one climate models follow the LS4P protocol to quantify the impact of the Tibetan Plateau (TP) land surface temperature/subsurface temperature (LST/SUBT) springtime anomalies on the global summertime precipitation. We find that nudging towards reanalysis winds is crucial for climate models to generate atmosphere and land surface initial conditions close to observations, which is necessary for meaningful S2S applications. Simulations with nudged initial conditions can better capture the summer precipitation responses to the imposed TP LST/SUBT spring anomalies at hotspot regions all over the world. Further analyses show that the enhanced S2S prediction skill is largely attributable to the substantially improved initialization of the Tibetan Plateau-Rocky Mountain Circumglobal (TRC) wave train pattern in the atmosphere. This study highlights the important role that initial condition plays in the S2S prediction and suggests that data assimilation technique (e.g., nudging) should be adopted to initialize climate models to improve their S2S prediction.
Kilometer-scale climate model simulations are useful tools to investigate past and future changes in extreme precipitation, particularly in mountain regions, where convection is influenced by complex topography and land–atmosphere interactions. In this study, we evaluate simulations of a flood-producing mesoscale convective system (MCS) downstream of the Tibetan Plateau (TP) in the Sichuan basin from a kilometer-scale multimodel and multiphysics ensemble. The aim is to better understand the physical processes that need to be correctly simulated for successfully capturing downstream MCS formation. We assess how the ensemble members simulate these processes and how sensitive the simulations are to different model configurations. The preceding vortex evolution over the TP, its interaction with the jet stream, and water vapor advection into the basin are identified as key processes for the MCS formation. Most modeling systems struggle to capture the interaction between the vortex and jet stream, and perturbing the model physics has little impact, while constraining the large-scale flow by spectral nudging improves the simulation. This suggests that an accurate representation of the large-scale forcing is crucial to correctly simulate the MCS and associated precipitation. To verify whether the identified shortcomings systematically affect the MCS climatology in longer-term simulations, we evaluate a 1-yr WRF simulation and find that the seasonal cycle and spatial distribution of MCSs are reasonably well captured and not improved by spectral nudging. While the simulations of the MCS case highlight challenges in extreme precipitation forecasting, we conclude that these challenges do not systematically affect simulated climatological MCS characteristics.
Convective storm systems in mountain regions are not well understood, because the spatial resolution in conventional regional climate models is too coarse to resolve relevant processes. Here, we evaluate high-resolution climate model simulations of a storm system on the downwind side of the Tibetan Plateau. Understanding which models and model setups work well to represent this type of storm system is important because high-resolution models can help us understand mechanisms of storm formation in mountain regions and how climate change will affect these. A key finding is that most of the models struggle to capture the selected storm case, while a 1-yr simulation shows that the general statistics of storm systems around the Tibetan Plateau are still reasonably well captured.
- NSF-PAR ID:
- 10439609
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Journal of Climate
- Volume:
- 36
- Issue:
- 17
- ISSN:
- 0894-8755
- Page Range / eLocation ID:
- p. 5963-5987
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract 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.
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