[1] The parameterization of thermal roughness length z 0h plays a key role in land surface modeling. Previous studies have found that the daytime land surface temperature (LST) on dry land (arid and semiarid regions) is commonly underestimated by land surface models (LSMs). This paper presents two improvements of Noah land surface modeling for China's dry-land areas. The first improvement is the replacement of the model's z 0h scheme with a new one. A previous study has validated the revised Noah model at several dry-land stations, and this study tests the revised model's performance on a regional scale. Both the original Noah and the revised one are driven by the Global Land Data Assimilation System (GLDAS) forcing data. The comparison between the simulations and the daytime Moderate Resolution Imaging Spectroradiometer-(MODIS-) Aqua LST products indicates that the original LSM produces a mean bias in the early afternoon (around 1330, local solar time) of about −6 K, and this revision reduces the mean bias by 3 K. Second, the mean bias in early afternoon is further reduced by more than 2 K when a newly developed forcing data set for China (Institute of Tibetan Plateau Research, Chinese Academy of Sciences (ITPCAS) forcing data) is used to drive the revised model. A similar reduction is also found when the original Noah model is driven by the new data set. Finally, the original Noah model, when driven by the new forcing data, performs satisfactorily in reproducing the LST for forest, shrubland and cropland. It may be sensible to select the z 0h scheme according to the vegetation type present on the land surface for practical applications of the Noah LSM.
The Inner Tibetan Plateau (ITP; or called Qiangtang Plateau) appears to have experienced an overall wetting in summer (June, July and August) since the mid-1990s, which has caused the rapid expansion of thousands of lakes. In this study, changes in atmospheric circulations associated with the wetting process are analyzed for the years 1979 ∼ 2018. These analyses show that the wetting is associated with simultaneously weakened westerlies over the Tibetan Plateau (TP). The latter is further significantly correlated with the Atlantic Multidecadal Oscillation (AMO) on interdecadal time scales. The AMO has been in a positive phase (warm anomaly of the North Atlantic sea surface) since the mid-1990s, which has led to both a northward shift and weakening of the subtropical westerly jet stream at 200 hPa near the TP through a wave train of cyclonic and anticyclonic anomalies over Eurasia. These anomalies are characterized by an anomalous anticyclone to the east of the ITP and an anomalous cyclone to the west of the ITP. The former weakens the westerly winds, trapping water vapor over the ITP, while the latter facilitates water vapor intruding from the Arabian Sea into the ITP. Accordingly, summer precipitation over the ITP has increased since the mid-1990s.
Atmospheric heating over the Tibetan Plateau (TP) enhances the Asian summer monsoon. This study presents a state-of-the-art estimate of the heating components and their total over the TP, with the aid of high-accuracy experimental data, an updated land surface model, and carefully selected satellite data. The new estimate differs from previous estimates in three aspects: 1) different seasonality—the new estimation shows the maximum total heat source occurs in July (the mature period of the monsoon), rather than in the previously reported month of May or June (around the onset of the monsoon), because previous studies greatly overestimated radiative cooling during the monsoon season [June–August (JJA)]; 2) different regional pattern—the eastern TP exhibits stronger heating than the western TP in summer, whereas previous studies gave either an opposite spatial pattern because of overestimated sensible heat flux over the western TP or an overall weaker heat source because of overestimated radiative cooling; and 3) different trend—sensible heat, radiative convergence, and the total heat source have decreased since the 1980s, but their weakening trends were overestimated in a recent study. These biases in previous studies are due to fairly empirical methods and data that were not evaluated against experimental data.
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