Spatiotemporal distributions of cloud parameters and their response to meteorological factors over the Tibetan Plateau during 2003–2015 based on MODIS data

Bao, Shanhu; Letu, Husi; Jun, Zhao; Shang, Huaming; Lei, Yonghui; Duan, Anmin; Bing, Chen; Bao, Yuhai; He, Jie; Wang, Tianxing; Ji, Dabin; Tana, Gegen; Shi, Jiancheng

doi:10.1002/joc.5826

Cited by 24 publications

(16 citation statements)

References 49 publications

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“…Therefore, from 2000 to 2018 the significant increasing trend in December LST was evidently affected by the decreasing albedo and the increasing number of clear days, both of which contributed to the increase in solar radiation absorbed by the glacier surface. During the similar time period of our study, the declining trend in winter cloud cover fraction was also consistent with the increasing trend in the number of clear sky days [66,67]. The higher coefficient of determination (0.53) between albedo and LST than that of the number of clear days (0.48) indicated that the LST warming trend in December was more affected by albedo, while the increase of clear days enhanced the effect of the albedo on LST.…”

Section: Influence Of Albedo and Number Of Clear Days On Lst Of The Psupporting

confidence: 86%

Variations in Winter Surface Temperature of the Purog Kangri Ice Field, Qinghai–Tibetan Plateau, 2001–2018, Using MODIS Data

Qie

Wang

et al. 2020

Remote Sensing

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In the context of global warming, the land surface temperature (LST) from remote sensing data is one of the most useful indicators to directly quantify the degree of climate warming in high-altitude mountainous areas where meteorological observations are sparse. Using the daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST product (MOD11A1 V6) after eliminating pixels that might be contaminated by clouds, this paper analyzes temporal and spatial variations in the mean LST on the Purog Kangri ice field, Qinghai–Tibetan Plateau, in winter from 2001 to 2018. There was a large increasing trend in LST (0.116 ± 0.05 °C·a−1) on the Purog Kangri ice field during December, while there was no evident LST rising trend in January and February. In December, both the significantly decreased albedo (−0.002 a−1, based on the MOD10A1 V6 albedo product) on the ice field surface and the significantly increased number of clear days (0.322 d·a−1) may be the main reason for the significant warming trend in the ice field. In addition, although the two highest LST of December were observed in 2017 and 2018, a longer data set is needed to determine whether this is an anomaly or a hint of a warmer phase of the Purog Kangri ice field in December.

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Section: Influence Of Albedo and Number Of Clear Days On Lst Of The Psupporting

confidence: 86%

Variations in Winter Surface Temperature of the Purog Kangri Ice Field, Qinghai–Tibetan Plateau, 2001–2018, Using MODIS Data

Qie

Wang

et al. 2020

Remote Sensing

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“…The distribution characteristics and action types of monthly average CRF on MP are similar to those on the Tibetan Plateau (Su et al ., ). Although the action intensity of CRF at the surface may be weaker than that of the Tibetan Plateau (Bao et al ., ), it has a significant impact on local climate change (Allan, ; Li and Mao, ).…”

Section: Results and Analysismentioning

confidence: 99%

“…Another reliable method of understanding CRF is based on long‐term satellite observations. The results of research on cloud parameters and CRF based on satellite observations show that air temperature changes (increased) were negatively correlated with daytime CRF (cooling effect) and positively correlated with nighttime CRF (heating effect) over the Tibetan Plateau from 2003 to 2015 (Su et al ., ; Bao et al ., ). Yang et al .…”

Section: Introductionmentioning

confidence: 97%

Spatiotemporal distributions of cloud radiative forcing and response to cloud parameters over the Mongolian Plateau during 2003–2017

Bao

Letu

Jun

et al. 2019

Intl Journal of Climatology

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The Mongolian Plateau (MP) is among the most sensitive areas to global climate change, and the clouds over the MP have a greater impact on regional and global radiation budgets by altering the atmospheric and surface radiative forcing. In this study, daily Cloud and Earth Radiation Energy System data are used to investigate spatiotemporal variation of cloud radiative forcing (CRF) at the top of atmosphere (TOA), surface and atmosphere over the MP from 2003 to 2017 and then combined with Moderate Resolution Imaging Spectroradiometer level 2 atmospheric data during the same period to analyse the cloud parameter impacts on CRF over the MP. At the TOA and surface, net radiative forcing (NRF) and shortwave radiative forcing (SRF) have cooling effects and longwave radiative forcing (LRF) have heating effects in all four seasons, and the NRF cooling effect in most areas of the MP decreases in summer and autumn and increases in spring and winter. In the atmosphere, SRF in spring and summer and NRF in summer reach larger values and heat the atmosphere, and LRF plays a strong cooling role in winter. The NRF change trend in the atmosphere over Mongolia is noteworthy in spring, its reduction slope is large, and most areas of Mongolia passed a significance test. As expected, a significant negative correlation was observed between cloud cover and NRF (as well as SRF) at the TOA and surface and a positive correlation was observed with NRF/SRF in the atmosphere and all LRF. With the increase in cloud optical thickness and cloud water path, the NRF and SRF cooling effects at the TOA and surface, the LRF cooling effect in the atmosphere, the LRF heating effect at the surface, and the SRF heating effect in the atmosphere all become stronger.

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“…Besides, a late peak month (July) of NCRE in MME mainly occurs in the western TP and is caused by obviously underestimated LWCRE (not shown). As shown in Figures S10 and S11, TCF and HCF are larger over the eastern TP than those over the western TP (Bao et al, 2019), and therefore, clouds exert more influences on the intensity of CREs and their biases over the eastern TP. The correlation coefficient between TCF (HCF) and SWCRE (LWCRE) is −0.26 (0.37) over the eastern TP, and is significantly higher than the counterparts over the whole TP (Figures S12h and S12i).…”

Section: Simulation Biases Of Cloud Fractionsmentioning

confidence: 95%

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

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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.

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Spatiotemporal distributions of cloud parameters and their response to meteorological factors over the Tibetan Plateau during 2003–2015 based on MODIS data

Cited by 24 publications

References 49 publications

Variations in Winter Surface Temperature of the Purog Kangri Ice Field, Qinghai–Tibetan Plateau, 2001–2018, Using MODIS Data

Variations in Winter Surface Temperature of the Purog Kangri Ice Field, Qinghai–Tibetan Plateau, 2001–2018, Using MODIS Data

Spatiotemporal distributions of cloud radiative forcing and response to cloud parameters over the Mongolian Plateau during 2003–2017

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

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