Assessing Spatiotemporal Characteristics of Atmospheric Water Cycle Processes over the Tibetan Plateau using the WRF Model and Finer Box Model

doi:10.21203/rs.3.rs-2962563/v1

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Assessing Spatiotemporal Characteristics of Atmospheric Water Cycle Processes over the Tibetan Plateau using the WRF Model and Finer Box Model

https://doi.org/10.21203/rs.3.rs-2962563/v1

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The Tibetan Plateau (TP) is the highest and largest plateau in the world and serves as a "vanguard" for global climate change. In the context of climate warming, changes in evapotranspiration (ET) and external water vapor transport cause more uncertainty in assessing the atmospheric water cycle processes over the TP. By using the Weather Research and Forecasting (WRF) model for long-term simulations and the finer box model for the calculation of water vapor along the boundary, the external atmospheric water vapor transport and its spatiotemporal characteristics over the TP are finely described. The model-simulated precipitation and evapotranspiration are well-simulated compared with observation. Research results show that: (1) The atmospheric water storage on the TP decreases from southeast to northwest. Water vapor is mainly transported into the TP from the western and southern boundaries. The net water vapor flux transported from the western boundary to the TP by westerly wind is negative, while the net water vapor flux transported from the southern boundary to the TP by southerly wind is positive. (2) In spring and winter, water vapor is mainly transported into the TP by mid-latitude westerlies from the western boundary. In summer, water vapor transport controlled by mid-latitude westerlies weakens, and water vapor is mainly transported into the TP from the southern boundary. In autumn, water vapor controlled by mid-latitude westerlies gradually strengthens, and water vapor is mainly transported into the TP from the western boundary. In addition, the ratio of ET to precipitation on the TP is about 0.48, and the moisture recycling is about 0.37. Water vapor mainly comes from external water vapor transport.

water vapor transport

weather research and forecasting model

moisture recycling

Tibetan Plateau

The Tibetan Plateau (TP) is the highest and largest plateau in the world with an extensive distribution of rivers, lakes, glaciers, and permafrost. Together with the South Pole and the North Pole, it is known as the "three poles" of the earth (Li et al. 2020; Yao et al. 2017; Yao et al. 2012b). Due to its specific underlying surfaces and unique terrain, the TP is comprehensively influenced by multiple climatic systems, including the mid-latitude westerlies, the Indian summer monsoon, and the East Asian monsoon, and is the "vanguard" and "amplifier" of global climate change (Kang et al. 2010; Li et al. 2020; Ma et al. 2018; Yao et al. 2012b). The TP is renowned for its abundant water resources, known as the "Asian water tower", with its lake area and glaciers accounting for 52% and 80% of the total in China, respectively (Xu et al. 2008; Yao et al. 2022). The TP is also the source of major Asian rivers including the Yangtze River, Yellow River, and Lancang River, providing water resources for industry, agriculture, and production to approximately 40% of the world's population (Cao and Zhang 2015; Xu et al. 2008; Yao et al. 2012a). The water vapor budget of the TP has a direct impact on the precipitation and other hydrological cycle processes of the plateau and its surrounding regions (Guo and Tian 2022; Wang et al. 2022). Therefore, it is crucial to comprehend the spatiotemporal characteristics of the atmospheric water cycle processes on the TP.

Extensive research has been conducted on the atmospheric water cycle processes on the TP (Gao et al. 2014; He et al. 2021; Immerzeel et al. 2010; Xue et al. 2022; Yan et al. 2020) and many key datasets have been produced and stored in the National Tibetan Plateau Data Center (TPDC) (Pan et al. 2021a), such as the dataset of oceanic moisture contributions to the precipitation over the TP simulated by WAM-2 during 1979–2015 (Li et al. 2022). Generally, atmospheric water vapor originates from moisture recycling within the local region and external moisture advection (Dominguez et al. 2020; Gimeno et al. 2012). The water vapor that leads to precipitation on the TP mainly comes from the remote water vapor transport controlled by the Asian monsoon system and the mid-latitude westerlies, as well as moisture recycling on the TP (Curio et al. 2015). Feng and Zhou (2012) proposed that the majority of water vapor on the TP is transported from the southern boundary of the TP, followed by the western boundary, and the amount of water vapor transported from the western boundary is approximately 32% of that transported from the southern boundary. Based on the modified Water Accounting Model (WAM), Zhang et al. (2017) estimated that over 69% of the water vapor contributing to precipitation on the TP originates from land, while over 21% originates from the ocean, and the water vapor transported by the mid-latitude westerlies and the Indian summer monsoon contributes the most to precipitation on the TP. With 32°N as the boundary to divide the TP into southern and northern regions, water vapor contributing to precipitation in the northern region of the TP mainly comes from the northwestern source region controlled by the mid-latitude westerlies, accounting for 39–43%, and local moisture recycling of the TP, accounting for 26–30% (Zhang et al. 2019). In the southern region of the TP, water vapor contributing to precipitation mainly comes from the southeastern source region controlled by the Asian monsoon system, accounting for 51–54%, while the proportion from local moisture recycling of the TP is 14–16% (Zhang et al. 2019).

In the context of global warming, the climate of the TP has undergone significant changes, and the uncertainty of the atmospheric water cycle processes has increased. Studies have shown that the temperature of the TP rises faster than other regions at the same latitude, with an increased rate that is twice the global mean temperature increase (Duan and Xiao 2015; Moore 2012). With the increase in temperature, the near-surface state of the TP has undergone significant changes, including glacier melting, lake expansion, permafrost degradation, and vegetation changes. These changes will lead to an increase in evapotranspiration (ET) on the TP, further affecting the land-atmosphere interaction of the TP, and thus influencing the atmospheric water cycle processes, resulting in an increase in atmospheric water storage and precipitation (Guo and Wang 2014; Sun et al. 2020; Xue et al. 2022; Xue et al. 2021; Zhang et al. 2020). In addition, some studies have indicated a weakening trend in the Indian summer monsoon over the past few decades (Turner and Annamalai 2012; Yao et al. 2012a; Zhang et al. 2018), resulting in a reduction of water vapor transported from the Indian Ocean to the TP and leading to a decrease in precipitation in the southern region of the TP (Dong et al. 2016). Therefore, significant changes have occurred in the water vapor content from different sources on the TP, the spatiotemporal characteristics and mechanism of the atmospheric water cycle process on the TP need to be further studied and assessed.

The impact of global climate change on the atmospheric water cycle process leads to the redistribution of atmospheric water in time and space (Yao et al. 2022), which is crucial for precipitation, surface water resources, and the ecological environment. However, due to the limitation of data on the TP, it is difficult to accurately study the characteristics of water vapor transport on the TP and its surrounding regions. The Weather Research and Forecasting (WRF) model is a numerical weather prediction and atmospheric simulation system that has been widely used to simulate the atmospheric water cycle processes over the TP (Lin et al. 2018; Liu et al. 2019; Pan et al. 2021b; Zhang and Gao 2021). In this study, the WRF model is utilized to simulate the atmospheric water cycle processes over the TP with a high resolution of 0.05° over a long time series spanning from 2000 to 2015, and the spatiotemporal characteristics of water vapor transport on the TP are analyzed.

2.1 Study Area

The TP, which has an area of about 2.5 million km² and an average elevation of over 4000 m, is located in southwest China. It is the highest and largest plateau in the world, extending from the southern edge of the Himalayas in the south to the northern edge of the Kunlun Mountains, the Altun Mountains, and the Qilian Mountains in the north, and from the Pamir Plateau and the Karakoram Mountains in the west of the Qinling Mountains and the Loess Plateau in the east, within the longitude and latitude range of 26°00′-39°47′ N and 73°19′-104°47′ E (Fig. 1). The TP has a unique climate and weather system, which is influenced by various climate systems, including the mid-latitude westerlies, the Indian summer monsoon, and the East Asian monsoon.

2.2 WRF Model Configure

The Advanced Research WRF Model Version 3.7.1 is used in this study, which is a non-hydrostatic model, including Arakawa C-grid staggering for a horizontal grid, a fully compressible system of equations, and a third-order Runge–Kutta scheme for time-split integration (Skamarock and Klemp 2008). In this study, two nested domains are employed in this study, with the outer domain consisting of 200×200 grid points at a horizontal resolution of 0.25° and the second nested domain consisting of 300×600 grid points at a horizontal resolution of 0.05°. The Purdue Lin Scheme (Chen and Sun 2002) is the microphysics option, the Kain–Fritsch scheme (Kain 2004) is the cumulus convection parameterization option, the Yonsei University scheme (Hong et al. 2006) is the planetary boundary layer (PBL) option, the Unified Noah Land Surface Model (LSM) (Mukul Tewari et al. 2004) as the land-surface model option, and the Dudhia scheme (Dudhia 1989) as the radiation option are used in this simulation (Table 1). The outer domain is forced with the reanalysis datasets of the Final Analysis (FNL) from the National Center for Environmental Prediction (NCEP) (NCEP/NOAA 2000) and provides initial and boundary conditions for the second nested domain.

Table 1

Physical configuration of the WRF model used in this study
Physics Processes	Domain 1 (0.25°)	Domain 2 (0.05°)
Horizontal	200×200	300×600
Time interval	150 sec	30 sec
Microphysics	Lin et al.	Lin et al.
Cumulus	Kain-Fritsch	Kain-Fritsch
PBL	YSU	YSU
Radiation	Dudhia	Dudhia
Surface-Land	Noah LSM	Noah LSM
Initial and Boundary data	NCEP/FNL Reanalysis	Domain 1

2.3 Water vapor transport and finer box model

According to the Global Energy and Water Cycle Experiment (GEWEX), the TP can be considered as a box, and the water cycle process on the TP includes the net inflow of water vapor through the lateral atmospheric boundaries of the TP and the net transfer of water from the atmosphere to the surface by precipitation exceeding ET (CGIP 1998). The conservation of water mass equations for the atmosphere and land is expressed as follows:

$d({q}_{a}+{q}_{l})/dt={C}_{q}-N$	(1)
$d{q}_{a}/dt={C}_{q}-\left(P-E\right)=\frac{1}{g}{\int }_{0}^{{p}_{s}}qdp$	(2)
$d{q}_{l}/dt=\left(P-E\right)-N$	(3)
${C}_{q}= \frac{1}{g}{\int }_{0}^{{p}_{s}}\nabla \bullet \left(\stackrel{⃑}{W}q\right)dp$	(4)
$\gamma =\frac{P-{C}_{q}}{P}$	(5)

where ${q}_{a}$ and ${q}_{l}$ are the total atmospheric and land water masses per unit horizontal area; ${C}_{q}$ is the net inflow of atmospheric water; $N$ represents the outflow from the area of interest; $P$ and $E$ are the precipitation and ET fluxes at the ground level, respectively; $q$ is the specific humidity; $p$ is the pressure; and ${p}_{s}$ is the pressure at the top of the atmosphere. g is the gravitational constant. $\stackrel{⃑}{W}$ is the wind component at $x,y,z$ directions.$\gamma$ is the regional moisture recycling rate.

To account for the boundary complexity of the study area and improve the accuracy of the net inflow of water vapor through the boundary of the study area, the "finer box model" developed by Pan et al. (2021b) is employed to calculate the water vapor along the boundary grid by grid. The equation for calculating the water vapor flux for each grid is as follows:

$${q}_{(i,j)}=\frac{1}{g}{\int }_{0}^{{p}_{s}} \left({\overrightarrow{W}}_{(p,i,j)}{q}_{(p,i,j)}\cdot {\overrightarrow{n}}_{(i,j)}\right)dp$$

where $i$ and $j$ represent the location identification of the boundary grid in the whole study area, $\overrightarrow{n}$ is the unit normal vector at the boundary.

The equation for regional net water vapor flux (${\Delta }\text{q}$) is as follows:

$${\Delta }\text{q}=\sum {q}_{(i,j)} \begin{array}{c}0\le i\le M\\ 0\le j\le N\end{array}$$

where $M$ and $N$ represent the sizes of the used grids in the adopted unit of space.

2.4 Validation data and verification indicators

The Multi-Source Weighted-Ensemble Precipitation (MSWEP) (Beck et al. 2017) and Global Land Evaporation Amsterdam Model (GLEAM) (Martens et al. 2017) are employed to evaluate the simulated precipitation and evapotranspiration (ET) in this study. The MSWEP integrates gauge, satellite, and reanalysis data to evaluate the simulated precipitation, with a temporal resolution of 3 h and a spatial resolution of 0.25°, covering the period from 1979 to 2015. The GLEAM combines satellite observations to develop a global evapotranspiration product for near real-time applications, with a temporal resolution of daily and a spatial resolution of 0.25°, ranging from 1980 to 2015, which is used to evaluate the simulated ET.

The performance of the WRF simulation is assessed using the root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (CC). The equations for the evaluation metrics are as follows:

$\text{R}\text{M}\text{S}\text{E}=\sqrt{\frac{1}{n}\sum _{i=1}^{n} {\left({S}_{i}-{O}_{i}\right)}^{2}}$	(8)
$\text{M}\text{A}\text{E}=\frac{1}{n}\sum _{i=1}^{n} \left\|{S}_{i}-{O}_{i}\right\|$	(9)
$\text{C}\text{C}=\frac{cov\left(S,O\right)}{{\sigma }_{S}{\sigma }_{O}}$	(10)

where, $S$ represents the WRF simulated value. $O$ represents the observed value. $cov$is the covariance. ${\sigma }_{S}$ is the standard deviation of $S$. ${\sigma }_{O}$ is the standard deviation of $O$.

3.1 Model Validation

Figure 2 shows the comparison of precipitation and ET between the WRF simulation and observational data. The WRF-simulated precipitation is evaluated against the MSWEP precipitation observation product, yielding RMSE and MAE values of 26.064 mm/(m²•monthly) and 23.143 mm/(m²•monthly), respectively, with a correlation coefficient of 0.898. The precipitation simulated by WRF is generally higher than the MSWEP precipitation observation product. The RMSE and MAE between WRF-simulated ET and GLEAM ET product are 5.487 mm/(m²·monthly) and 4.531 mm/(m²·monthly), respectively, with a correlation coefficient of 0.977. ET simulated by the WRF model agrees well with GLEAM retrieved from satellite data. The validation results demonstrate that WRF can successfully simulate precipitation and ET which are critical components of the atmospheric water cycle on the TP. Therefore, the WRF model can be used for downscaling analysis on the TP, and the simulated data can be used to analyze the atmospheric water cycle processes and characteristics of the TP.

3.2 Annual Water Vapor Transport

Figure 3 shows the annual mean atmospheric water storage of the TP from 2000 to 2015 calculated by the mixing ratios of the water vapor, cloud vapor, rainwater, ice water, and snow water. The atmospheric water storage in the southern edge of the TP is the highest over the TP, and gradually decreases from southeast to northwest. The regions with the lowest atmospheric water storage are located in the northwestern and northeastern parts of the TP. Furthermore, the interannual variability of annual mean atmospheric water storage from 2000 to 2015 is not significant, especially in the southeastern region of the TP.

Figure 4 shows the time series of the regional mean atmospheric water storage, temperature, and precipitation from 2000 to 2015. The atmospheric water storage and temperature exhibit synchronous change trends. The changing trend of precipitation is consistent with that of the temperature and atmospheric water storage, but it is delayed in time. For example, the lowest values of atmospheric water storage and temperature appeared in 2005, followed by the lowest value of precipitation in 2006. Higher temperature increases the capacity to accommodate atmospheric water, and the precipitation will increase with the increase of atmospheric water. From 2000 to 2015, the temperature shows a fluctuating upward trend, whereas the increasing trends of atmospheric water storage and precipitation are not significant.

Figure 5 shows the annual water vapor transport from four boundaries. The western and southern boundaries are the main sources of water vapor into the TP. The amount of water vapor transported into the TP from the western boundary, influenced by the mid-latitude westerlies, is greater than that from the southern boundary influenced by the south wind. However, a large amount of water vapor transported into the plateau from the west boundary flows out of the plateau from the eastern boundary of the TP, resulting in a negative net water vapor flux transported into the TP by mid-latitude westerlies. The water vapor in the TP mainly comes from the southern boundary, which is transported by the Indian summer monsoon from the southern boundary into the TP. The net water vapor flux transported from the southern boundary to the TP is positive. From 2000 to 2015, there is a slight increasing trend in the water vapor transported into the TP from its western and southern boundaries.

3.3 Seasonal Water Vapor Transport

3.3.1 Seasonal water vapor transport distribution

Figure 6 shows that the water vapor distribution over the TP gradually decreases from southeast to northwest in different seasons. The highest water vapor content occurs in summer, while the lowest occurs in winter. In spring and autumn, the water vapor content is relatively similar. Specifically, in spring, the water vapor content ranges from 0.10×10⁹ kg to 0.15×10⁹ kg in the southeast region of the TP, while in most regions of the central and northwest parts of the TP, it ranges from 0.05×10⁹ kg to 0.10×10⁹ kg. In summer, the water vapor content is greater than 0.25×10⁹ kg in the southeast region of the TP, between 0.15–0.25×10⁹ kg in the central region of the TP, and less than 0.15×10⁹ kg in the northwest region of the TP. In autumn, the water vapor content ranges from 0.15×10⁹ kg to 0.25×10⁹ kg in the southeast of the TP, while in the northwestern of the TP, it is lowest, ranging from 0.05×10⁹ kg to 0.10×10⁹ kg. In winter, the water vapor content in most regions of the TP is less than 0.05×10⁹ kg. Overall, the water vapor content in the TP exhibits a significant decreasing trend from southeast to northwest, and the seasonal difference in water vapor content is significant.

3.3.2 Seasonal water vapor transport of four boundaries

Figure 7 illustrates the seasonal water vapor transport from four boundaries of the TP. The water vapor is predominantly transported into the TP from its western and southern boundaries, which is in agreement with previous research results, that is, the water vapor of the TP is mainly transported by the Indian summer monsoon and mid-latitude westerlies (Kang et al. 2010; Ma et al. 2018; Yao et al. 2012b). The water vapor transported into the TP mainly flows out from the eastern boundary of the plateau, with very low outflow from its northern boundary. In spring, the water vapor transported into the TP is primarily influenced by the mid-latitude westerlies through its western boundary, while the water vapor also flows out of the TP from its eastern boundary under the influence of the west wind. Therefore, the net water vapor flux impacted by the mid-latitude westerlies is negative. The water vapor content flowing into the plateau from its southern boundary is low. In summer, the influence of the mid-latitude westerlies weakens, and the water vapor content transported by the west wind decreases significantly. The impact of the Indian summer monsoon on the water vapor transport process over the TP gradually strengthens and plays a key role. The water vapor over the TP mainly comes from the southern boundary transported by the Indian summer monsoon. In autumn, the water vapor transported by the Indian summer monsoon gradually decreases, while the water vapor transported by the mid-latitude westerlies increases. Under the influence of the west wind, most of the water vapor flows out of the TP from its eastern boundary. In winter, the water vapor content transported from the southern boundary into the TP further decreases. The water vapor of the TP is mainly transported from the western boundary to the TP due to the impact of the mid-latitude westerlies, and then part of the water vapor flows out of the TP from its eastern boundary under the influence of the west wind.

3.4 Moisture Recycling over the TP

Table 2 shows the observed and simulated precipitation, ET, and the ratio of ET to precipitation. Based on the MSWEP and GLEAM, the monthly mean precipitation and ET flux over the TP are 45.10 mm/m² and 27.83 mm/m², respectively. Consequently, the ratio of ET to precipitation is 0.62. The WRF-simulated precipitation and ET fluxes over the TP are 64.53 mm/m² and 31.20 mm/m², respectively. Figure 8 shows the spatial distribution of the ratio of ET to precipitation over the TP. The ratio of ET versus precipitation over the TP is approximately 0.48. According to Eq. (5), i.e., $\gamma =\frac{P-{C}_{q}}{P}$, the regional moisture recycling rate over the TP is estimated to be approximately 0.37.

Table 2

Observed and simulated precipitation, ET, and the ratio of ET versus precipitation
Item	Monthly Flux (mm/m²)	Ratio (ET/P)
TP-Precipitation (MSWEP)	45.10	0.62
TP-ET (GLEAM)	27.83	0.62
TP-Precipitation (WRF)	64.53	0.48
TP-ET (WRF)	31.20	0.48

4.1 Water Vapor Transport on the TP

Previous studies have confirmed that the water vapor on the TP is mainly transported into the TP from its southern and western boundaries, with water vapor from the Indian Ocean and the southern edge of the Bay of Bengal dominating the summer precipitation of the southeastern TP, and the southern boundary of the TP serves as the main water vapor pathway (Feng and Zhou 2012; Wang et al. 2017). In addition, Curio et al. (2015) proposed that the mid-latitude westerlies are not completely blocked by the TP, and their contribution to summer water vapor transport is higher than previously assumed, but the mountainous terrain still significantly restricts the process of water vapor transport. There is still controversy over the water vapor transport process on the TP and the contribution of water vapor from different sources.

Based on long-term simulations using the WRF model, the research results show that the amount of water vapor transported into the TP from the western boundary controlled by the mid-latitude westerlies is greater than that transported from the southern boundary controlled by the Indian summer monsoon. However, most of the water vapor flows out of the eastern boundary of the TP due to the influence of the west wind, resulting in a negative net water vapor flux by the mid-latitude westerlies. Water vapor is primarily transported into the TP from its southern boundary, with a small amount flowing out of the northern boundary. Therefore, the net water vapor flux influenced by the south wind is positive, and water vapor transported from the southern boundary has a greater contribution to the precipitation over the TP.

The water vapor transport processes on the TP show significant differences in different seasons. In spring, the mid-latitude westerlies have a greater impact on the water vapor transport process, with water vapor mainly coming from the western boundary of the plateau and flowing out of the eastern boundary, resulting in a negative net water vapor flux. In summer, the influence of the mid-latitude westerlies on the water vapor transport process weakens, but the amount of water vapor transported into the TP from the western boundary is greater than that flowing out of the eastern boundary, resulting in a positive net water vapor flux. A large amount of water vapor is transported into the TP from the southern boundary under the influence of the Indian summer monsoon, making the TP a water vapor sink in summer. In autumn, the influence of the Indian summer monsoon on the water vapor transport process weakens, while the influence of the mid-latitude westerlies strengthens, with water vapor mainly entering the TP from the western boundary and exiting from the eastern boundary. In winter, the water vapor transport process on the TP is mainly influenced by the mid-latitude westerlies.

This study clarifies the influence of the mid-latitude westerlies and the Indian summer monsoon on the water vapor transport processes over the TP and assesses its interannual and seasonal characteristics. However, the primary water vapor sources on the TP and their contributions have not been investigated. In the future, it is necessary to combine different methods and data to further analyze the water vapor sources of the TP and their contributions.

4.2 Sources of Uncertainty

The uncertainty of the research results mainly depends on the accuracy of the WRF model simulation results. Although the WRF model has shown great simulation performance in reproducing the changes in precipitation and ET on the TP, there are still deviations between the simulation results of the WRF model and the observation data, which brings uncertainties to the research results. As precipitation is the result of complex physical and dynamic processes, similar to most regional climate models, the WRF model tends to overestimate precipitation on the TP, leading to a significant wet bias. In addition, the complex underlying surface has significant impacts on ET, which in turn affects regional moisture recycling. In the context of climate warming, the underlying surface of the TP has undergone significant changes in the past decades, and the inaccurate description of these changes in the underlying surface in the WRF model increases the uncertainty of the simulation results. Due to the high altitude and complex terrain of the TP, the horizontal resolution of the model has an important impact on the accuracy of the simulation results. Future studies need to conduct simulations with a higher horizontal resolution to fully describe the influence of complex terrain on airflow movement, reduce the uncertainty of the simulation results, and more accurately analyze the water vapor transport process and its characteristics on the TP.

The surface environment of the TP is extremely complex, including fragile cryosphere elements such as glaciers, snow, lakes, and permafrost. Climate warming has caused significant changes to the surface environment of the TP, resulting in increased uncertainty in atmospheric water cycle processes due to changes in ET. Furthermore, the TP is influenced by multiple climatic systems, including mid-latitude westerlies, Indian summer monsoons, etc. Changes in external water vapor transport also have significant impacts on the atmospheric water cycle processes over the TP. In this study, the WRF model is utilized to simulate the atmospheric water cycle processes over the TP, and the changing trends and characteristics of atmospheric water storage and water vapor transport are analyzed from inter-annual and seasonal perspectives. The following conclusions are drawn:

(1) The atmospheric water storage of the TP gradually decreases from the southeast to the northwest, with the highest atmospheric water storage located at the southern edge of the TP, and the lowest in the northwest and northeast regions. From 2000 to 2015, the temperature of the TP shows a fluctuating upward trend, and the atmospheric water storage and temperature showed synchronous changes, with higher temperatures increasing the atmospheric water vapor holding capacity. The changes in precipitation are consistent with the changing trend of atmospheric water storage but lag behind the changes in atmospheric water storage over time.

(2) The water vapor over the TP is primarily transported from the western and southern boundaries and flows out through the eastern boundary, with very little flowing out through the northern boundary. The amount of water vapor transported into the TP from the western boundary controlled by mid-latitude westerlies is higher than that transported from the southern boundary controlled by the Indian summer monsoon. However, most of the water vapor flows out of the TP through the eastern boundary under the influence of the westerlies, resulting in a negative net water vapor flux transported by mid-latitude westerlies. Water vapor transported from the southern boundary contributes more to precipitation on the TP.

(3) For seasonal changes, the water vapor content is highest in summer and lowest in winter, while the water vapor content is similar in spring and autumn. In spring, water vapor is mainly transported into the TP from the western boundary by mid-latitude westerlies, but most of the water vapor flows out through the eastern boundary, resulting in a negative net water vapor flux influenced by the westerlies. In summer, the water vapor transported by mid-latitude westerlies weakens, while the water vapor transported by the Indian summer monsoon strengthens. Water vapor is mainly transported into the TP from the southern boundary, contributing a large amount of water vapor to summer precipitation on the TP. In autumn, the water vapor transport process controlled by mid-latitude westerlies gradually strengthens, while that controlled by the Indian summer monsoon gradually weakens. Water vapor is mainly transported into the TP from the western boundary and flows out through the eastern boundary. The change process in winter is similar to that in spring.

(4) The moisture recycling rate is about 0.37, and the ratio of ET to precipitation is approximately 0.48. Therefore, external water vapor transport is the primary source of water vapor on the TP, but the contribution of locally recycled moisture cannot be ignored. Especially in the context of global climate change, significant changes in the surface environment of the TP have enhanced regional ET, increasing the uncertainty of the atmospheric water cycle process on the TP.

This study focuses on the changes in the inflow and outflow of water vapor from the four boundaries of the TP. Due to the complex terrain of the TP, it is necessary to conduct higher resolution simulations in future research to further analyze the characteristics of water vapor transport and the changes in moisture recycling on the TP.

Acknowledgments

This work was supported by the Basic Science Center for Tibetan Plateau Earth System (No. 41988101), the sub-project of the “Pan-Third Pole Environmental Change and Green Silk Road Construction” of the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA20060600), the International Talent Program of the Chinese Academy of Sciences (No. 2021PC0045), the Sichuan Government Science and Technology Department Project Titled: Environmental and Ecological Assessment of Sustainable Hydropower Infrastructure (No. 2022JDGD0039), and the National Key Scientific and Technological Infrastructure project “Earth System Science Numerical Simulator Facility” (EarthLab). The NCAR Command Language (NCL) (Version 6.1.2, 2013) is available from the UCAR/NCAR/CISL/VETS (http://dx.doi.org/10.5065/D6WD3XH5, Released 7 February 2013), Boulder, Colorado. NCL was used for the data analysis and graphs in this paper. We thank Professor Yongkang Xue of UCLA for his comments and contributions to the manuscript revisions. We also thank the anonymous reviewers and the editor for their very helpful comments.

Data Availability

The processed simulation datasets and materials used in this study are available upon request to the corresponding author ([email protected]). The input data for the WRF model were obtained from the Research Data Archive (RDA), which is maintained by the Computational and Information Systems Laboratory (CISL) at the National Center for Atmospheric Research (NCAR). The original data are available from the RDA (http://rda.ucar.edu/datasets/ds083.2/, accessed on 10 July 2019) in data set number ds083.2. The validation datasets are downloaded from open sources. MSWEP datasets are available from https://www.gloh2o.org/. GLEAM datasets are available from https://www.gleam.eu/.

Author contribution:

Conceptualization: XP

Methodology: XP

Writing – Original draft: XP, HL

Writing – Review & editing: RW, JJ

Figures: XP

Conflict of interest The authors declare that they have no conflict of interest/ competing interests.

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Assessing Spatiotemporal Characteristics of Atmospheric Water Cycle Processes over the Tibetan Plateau using the WRF Model and Finer Box Model

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Abstract

Figures

1. Introduction

2. Data and Method

2.1 Study Area

2.2 WRF Model Configure

2.3 Water vapor transport and finer box model

2.4 Validation data and verification indicators

3. Results

3.1 Model Validation

3.2 Annual Water Vapor Transport

3.3 Seasonal Water Vapor Transport

3.3.1 Seasonal water vapor transport distribution

3.3.2 Seasonal water vapor transport of four boundaries

3.4 Moisture Recycling over the TP

4. Discussion

4.1 Water Vapor Transport on the TP

4.2 Sources of Uncertainty

5. Conclusion

Declarations

References

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\(d({q}_{a}+{q}_{l})/dt={C}_{q}-N\)	(1)
\(d{q}_{a}/dt={C}_{q}-\left(P-E\right)=\frac{1}{g}{\int }_{0}^{{p}_{s}}qdp\)	(2)
\(d{q}_{l}/dt=\left(P-E\right)-N\)	(3)
\({C}_{q}= \frac{1}{g}{\int }_{0}^{{p}_{s}}\nabla \bullet \left(\stackrel{⃑}{W}q\right)dp\)	(4)
\(\gamma =\frac{P-{C}_{q}}{P}\)	(5)

\(\text{R}\text{M}\text{S}\text{E}=\sqrt{\frac{1}{n}\sum _{i=1}^{n} {\left({S}_{i}-{O}_{i}\right)}^{2}}\)	(8)
\(\text{M}\text{A}\text{E}=\frac{1}{n}\sum _{i=1}^{n} \left\|{S}_{i}-{O}_{i}\right\|\)	(9)
\(\text{C}\text{C}=\frac{cov\left(S,O\right)}{{\sigma }_{S}{\sigma }_{O}}\)	(10)