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.
- Award ID(s):
- 1936810
- NSF-PAR ID:
- 10319700
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Date Published:
- Journal Name:
- Geoscientific Model Development
- Volume:
- 14
- Issue:
- 7
- ISSN:
- 1991-9603
- 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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Abstract Recent research has reported the great influence of springtime land surface temperature (LST) and subsurface temperature (SUBT) over the Tibetan Plateau (TP) on downstream region summer precipitation, indicating the potential application of LST/SUBT on subseasonal to seasonal (S2S) prediction. In this study, we employed both observational data and offline model simulation to explore the memory of surface and subsurface variables and assess the driving effects of snow/albedo and SUBT on the LST anomaly. Our composite analysis based on observation shows that the anomalous LST in the TP can sustain for seasons and is accompanied by persistent SUBT as well as snow and associated surface albedo anomalies. A multilayer frozen soil model reproduces the observed LST anomaly and shows surface albedo and middle‐layer SUBT have 1–3 months memory, indicating the degree of persistence or dissipation of anomaly through time with more extended memory during spring. With simulated middle‐layer SUBT as a predictor, the linear regression model produces
of 0.44 and 0.26 for 1‐ and 2‐month LST prediction, respectively. The predictability is higher during the spring. Our results also show February snowfall, May snowmelt, and aero in snow exert substantial impacts on springtime LST through snow albedo feedback. The long memory of SUBT allows it to preserve the surface thermal anomaly and release it gradually in the following months to seasons. Meanwhile, the sensitivity study indicates that the soil properties and soil column depth predominate SUBT memory, which suggests the key processes to improve LST/SUBT then downstream S2S 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.more » « less
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Abstract Analyzing a long simulation and a set of seasonal reforecasts of the Climate Forecast System version 2 (CFSv2), this study demonstrates a large model cold bias in the deep soil layer (100–200 cm) over most of North America continent during summer due to weaker seasonal change in summer. The summer subsurface temperature (SUBT) cold bias influences the land surface temperature (LST) during the summer and subsequent seasons in different ways over different geographical regions in North America: West of the Rocky Mountains, the SUBT's effect on LST is largely overruled by the stronger upstream marine influence from the Pacific. Over the central Great Plains, however, it is a major cause for severe cold LST bias during summer in the model simulation and reforecasts. As a result, model underestimates sensible heat flux into the atmosphere but overestimates latent heat flux. The latter may contribute to an excessive summer rainfall in the region. Over the northeast region, the SUBT cold bias persists to August and September, which causes an additional surface cooling in the fall and helps to bring LST to the freezing point early. This sets up the stage for a prolonged snow‐albedo feedback. In particular, the model long simulation that passes through previous summer and fall demonstrates longer persistence of snow cover over the northeast region than reforecasts initialized in late winter and spring do. A cold bias of the water temperature in the North Atlantic seems also to play a role to prolong cold bias in the northeast region.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/gmd-14-4465-2021
