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The South Asian high (SAH) location and intensity are linked with the latent heating of the Tibetan Plateau (TP) and Yangtze River basin. The relationship between SAH variability and its impact on South Asian monsoon (SAM) onset is rarely linked with TP soil moisture. This study uses remotely sensed soil moisture and reanalysis products to quantify the relationship between the TP spring (April–June) soil moisture with SAH and SAM onset during 1988–2008. The results show that the TP spring soil moisture and monsoon onset indices are negatively correlated (R < −0.65), whereas the SAH exhibits a significant positive correlation (R ≥ 0.70) with TP soil moisture. The monsoon onset shows a difference of 20–25 days between the early- and late-onset composites. Significant positive (negative) soil moisture anomalies persist over the TP during the early (late) onset followed by positive (negative) LH (SH) anomalies during early (late)-onset composites. The TP thermal forcing exhibited positive anomalies during the early (late)-onset composites implying significant soil moisture control over the diabatic heating, which favors vertical ascent over the eastern plateau. Such a pattern leads to an earlier formation and movement of the SAH toward the Bay of Bengal (BOB) and southwestward of the TP. Before the early and late monsoon onset composites, the SAH pentad evolution drives the lower-tropospheric westerlies/easterlies toward continental SA. In the Indian Ocean the wind shear and transition from prevailing easterlies into westerlies during the pre-onset, onset, and post-onset pentad results in strong/weak ascent affecting the onset timing over the Arabian Sea and continental SA with less influence over the BOB monsoon onset. Significance Statement: The Tibetan Plateau heating is one of the key drivers of the Asian monsoon precipitation in the surrounding regions, which has been previously studied in detail. This study explored the Tibetan Plateau spring soil moisture's effect on South Asian monsoon onset timing. The monsoon onset timing is calculated using changes in wind direction, atmospheric temperature, and relative precipitation magnitude. Results found that the spring soil moisture substantially affects the TP thermal heating and the SA monsoon onset timing and highlights the physical processes leading to changes in the monsoon onset timing. The inclusion of soil moisture in estimating the monsoon onset timing can provide a tangible way of improving our understanding of the monsoon and associated water resources management practices. [ABSTRACT FROM AUTHOR]

Future global temperature change would have significant effects on society and ecosystems. Earth system models (ESM) are the primary tools to explore the future climate change. However, ESMs still exist great uncertainty and often run at a coarse spatial resolution (The majority of ESMs at about 2 degree). Accurate temperature data at high spatial resolution are needed to improve our understanding of the temperature variation and for many applications. We innovatively apply the deep-learning(DL) method from the Super resolution (SR) in the computer vision to merge 31 ESMs data and the proposed method can perform data merge, bias-correction and spatial-downscaling simultaneously. The SR algorithms are designed to enhance image quality and outperform much better than the traditional methods. The CRU TS (Climate Research Unit gridded Time Series) is considered as reference data in the model training process. In order to find a suitable DL method for our work, we choose five SR methodologies made by different structures. Those models are compared based on multiple evaluation metrics (Mean square error(MSE), mean absolute error(MAE) and Pearson correlation coefficient(R)) and the optimal model is selected and used to merge the monthly historical data during 1850-1900 and monthly future scenarios data (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) during 2015-2100 at the high spatial resolution of 0.5 degree. Results showed that the merged data have considerably improved performance than any of the individual ESM data and the ensemble mean (EM) of all ESM data in terms of both spatial and temporal aspects. The MAE displays a great improvement and the spatial distribution of the MAE become larger and larger along the latitudes in north hemisphere, presenting like a 'tertiary class echelon' condition. The merged product also presents excellent performance when the observation data is smooth with few fluctuations in time series. Additionally, this work proves that the DL model can be transferred to deal with the data merge, bias-correction and spatial-downscaling successfully when enough training data are available. Data can be accessed at https://doi.org/10.5281/zenodo.5746632 (Wei et al., 2021). [ABSTRACT FROM AUTHOR]

The interaction between the land surface and the atmosphere is of significant importance in the climate system because it is a key driver of the exchanges of energy and water. Several important relations to heat waves, floods, and droughts exist that are based on the interaction of soil moisture and, for instance, air temperature and humidity. Our ability to separate the elements of this coupling, identify the exact locations where they are strongest, and quantify their strengths is, therefore, of paramount importance to their predictability. A recent rigorous causality formalism based on the Liang–Kleeman (LK) information flow theory has been shown, both theoretically and in real-world applications, to have the necessary asymmetry to infer the directionality and magnitude within geophysical interactions. However, the formalism assumes stationarity in time, whereas the interactions within the land surface and atmosphere are generally nonstationary; furthermore, it requires a sufficiently long time series to ensure statistical sufficiency. In this study, we remedy this difficulty by using the square root Kalman filter to estimate the causality based on the LK formalism to derive a time-varying form. Results show that the new formalism has similar properties compared to its time-invariant form. It is shown that it is also able to capture the time-varying causality structure within soil moisture–air temperature coupling. An advantage is that it does not require very long time series to make an accurate estimation. Applying a wavelet transform to the results also reveals the full range of temporal scales of the interactions. [ABSTRACT FROM AUTHOR]