Changes in cloud amount over the Tibetan Plateau and impacts of large-scale circulation

You, Qinglong; Ma, Yujun; Cao, Yu; Zhang, Jie; Niu, Miaomiao; Zhang, Yuqing

doi:10.1016/j.atmosres.2020.105332

Cited by 24 publications

(18 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Clouds play a key role in the atmospheric energy cycle and have an important impact on the surface radiation budget [49]. The changes in cloud amount can cause radiant flux changes and then affect the change of surface temperature [50]. To explore the influence of cloud amount changes on the LST of the TP from 2000 to 2018, we obtained the cloud amount change trend of the TP showed in Fig.…”

Section: Impacting Factors On the Mast Temporal Variations 1) Cloud Amount Changesmentioning

confidence: 99%

“…There is a significant correlation between cloud amount changes and the temporal variations of LST [50]. The decrease of cloud amount in the southern part of the TP in the daytime may lead to an increase in surface downward shortwave radiation, which promoted the warming effect of the land surface [51,52].…”

Section: Impacting Factors On the Mast Temporal Variations 1) Cloud Amount Changesmentioning

confidence: 99%

See 1 more Smart Citation

Spatiotemporal Patterns of Land Surface Temperature Change in the Tibetan Plateau Based on MODIS/Terra Daily Product From 2000 to 2018

Yang

Zhao

Zhan

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Under the background of climate change, the Tibetan Plateau presents high spatial and temporal variability in land surface temperature (LST). To understand the spatiotemporal patterns of LST change, this study conducted a spatiotemporal analysis using the Mann-Kendall trend analysis method with time series of mean annual surface temperature (MAST) extracted from the MODIS/Terra daily LST product from 2000 to 2018. The analysis indicated that both daytime and nighttime MASTs show an obvious warming trend with the average rates of 0.028 K/year and 0.069 K/year, and the nighttime variation has larger spatial coverage. Areas ranging from 4500 to 5000 m exhibited the strongest warming effect. The Geodetector method was applied to detect the impacts from seven factors, including elevation, land cover type, latitude, normalized difference vegetation index (NDVI), precipitation, air temperature, and solar radiation on the spatial distribution of LST. The controlling effects of these factors were generally stronger in the nighttime than those in the daytime, and elevation was the most important factor with the contribution scores of 27.12% and 62.98% in the daytime and nighttime, respectively. In addition, the analysis revealed that the temporal changes of LST were mainly caused by surface properties (vegetation, snow cover, and water surface area) changes, radiant flux changes induced by cloud amount changes, and climate warming. In general, this study provides important insights into the spatiotemporal dynamics of LST in the TP since 2000 and helps to reveal the impact of climate change on ecoenvironmental conservation.

show abstract

Section: Impacting Factors On the Mast Temporal Variations 1) Cloud Amount Changesmentioning

confidence: 99%

Section: Impacting Factors On the Mast Temporal Variations 1) Cloud Amount Changesmentioning

confidence: 99%

Spatiotemporal Patterns of Land Surface Temperature Change in the Tibetan Plateau Based on MODIS/Terra Daily Product From 2000 to 2018

Yang

Zhao

Zhan

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Eastern China (EC) and South Asia (SA), adjacent to the TP, have significant features of the Asian monsoon climate such as remarkable seasonal variation of circulation, precipitation, and cloud fractions (Ding & Chan, 2005; Luo et al., 2009; Tao & Chen, 1987; Webster et al., 1998; Zhao et al., 2019). These three subregions are integral parts of the whole Asian monsoon region, and regional surface temperature, precipitation, and cloud‐radiation processes are very sensitive to current climate change (Ma et al., 2021; Turner & Annamalai, 2012; B. Wang et al., 2020; You et al., 2020). Understanding and predicting the Asian monsoon climate is of great scientific and societal importance owing to its huge impact on the population of a large region and sustainable socio‐economic development.…”

Section: Introductionmentioning

confidence: 99%

Top‐of‐Atmosphere Radiation Budget and Cloud Radiative Effects Over the Tibetan Plateau and Adjacent Monsoon Regions From CMIP6 Simulations

Sun

Liu

et al. 2021

JGR Atmospheres

Self Cite

View full text Add to dashboard Cite

This study investigates the top‐of‐atmosphere (TOA) radiation budget (RT) and cloud radiative effects (CREs) over the Tibetan Plateau (TP) and adjacent Asian monsoon regions including Eastern China (EC) and South Asia (SA) using the Coupled Model Intercomparison Project 6 (CMIP6) simulations. Considerable simulation biases occur but specific causes differ in these regions. Most models underestimate the intensity of annual mean RT and cloud radiative cooling effect over the TP, and the RT during the cold‐warm transition period is hard to capture. The biases in surface temperature and cloud fractions substantially contribute to cloud‐radiation biases over the western and eastern TP, respectively. Over EC, the intensity of RT and cloud radiative cooling effect is seriously underestimated especially in the springtime when the model spread is large, and their biases are closely related to less low‐middle cloud fractions and weaker ascending motion. Over SA, simulation biases mainly arise from longwave radiative components associated with less high cloud fraction and weaker convection, with the large model spread in the summertime. The annual cycles of RT and CREs over EC and SA can be well reproduced by most models, while the summertime peak of the net CRE over the TP occurs later than the observation. The RT and its simulation bias strongly depend on the cloud radiative cooling effect over EC and SA. Our results demonstrate that contemporary climate models still have obvious difficulties in representing various complex cloud‐radiation processes in Asian monsoon regions.

show abstract

“…Satellite remote sensing is the main approach for mapping croplands at large scales, but it is challenging to use in the TP considering the complicated topography, heterogeneous landscape, and insufficient satellite observations due to frequent clouds in the summer formed by warm and humid airflow raised by topography [10][11][12][13]. Until now, cropland mapping products on the plateau have been mainly produced by visual interpretation, which is time consuming, labor intensive, and difficult to apply on large spatial scales [1][2][3]14].…”

Section: Introductionmentioning

confidence: 99%

Mapping Croplands in the Granary of the Tibetan Plateau Using All Available Landsat Imagery, A Phenology-Based Approach, and Google Earth Engine

Zhang

You

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

The Tibetan Plateau (TP), known as “The Roof of World”, has expansive alpine grasslands and is a hotspot for climate change studies. However, cropland expansion and increasing anthropogenic activities have been poorly documented, let alone the effects of agricultural activities on food security and environmental change in the TP. The existing cropland mapping products do not depict the spatiotemporal characteristics of the TP due to low accuracies and inconsistent cropland distribution, which is affected by complicated topography and impedes our understanding of cropland expansion and its associated environmental impacts. One of the biggest challenges of cropland mapping in the TP is the diverse crop phenology across a wide range of elevations. To decrease the classification errors due to elevational differences in crop phenology, we developed two pixel- and phenology-based algorithms to map croplands using Landsat imagery and the Google Earth Engine platform along the Brahmaputra River and its two tributaries (BRTT) in the Tibet Autonomous Region, also known as the granary of TP, in 2015–2019. Our first phenology-based cropland mapping algorithm (PCM1) used different thresholds of land surface water index (LSWI) by considering varied crop phenology along different elevations. The second algorithm (PCM2) further offsets the phenological discrepancy along elevational gradients by considering the length and peak of the growing season. We found that PCM2 had a higher accuracy with fewer images compared with PCM1. The number of images for PCM2 was 279 less than PCM1, and the Matthews correlation coefficient for PCM2 was 0.036 higher than PCM1. We also found that the cropland area in BRTT was estimated to be 1979 ± 52 km2 in the late 2010s. Croplands were mainly distributed in the BRTT basins with elevations of 3800–4000 m asl. Our phenology-based methods were effective for mapping croplands in mountainous areas. The spatially explicit information on cropland area and distribution in the TP aid future research into the effects of cropland expansion on food security and environmental change in the TP.

show abstract

Changes in cloud amount over the Tibetan Plateau and impacts of large-scale circulation

Cited by 24 publications

References 59 publications

Spatiotemporal Patterns of Land Surface Temperature Change in the Tibetan Plateau Based on MODIS/Terra Daily Product From 2000 to 2018

Spatiotemporal Patterns of Land Surface Temperature Change in the Tibetan Plateau Based on MODIS/Terra Daily Product From 2000 to 2018

Top‐of‐Atmosphere Radiation Budget and Cloud Radiative Effects Over the Tibetan Plateau and Adjacent Monsoon Regions From CMIP6 Simulations

Mapping Croplands in the Granary of the Tibetan Plateau Using All Available Landsat Imagery, A Phenology-Based Approach, and Google Earth Engine

Contact Info

Product

Resources

About