Cloud Response Is Significantly Biased by CMIP6 Over the Tibetan Plateau

Zhao, Yang; Li, Jiming; Zhang, Weiyuan; Deng, Cong; Li, Yarong

doi:10.1029/2022gl100903

Cited by 6 publications

(5 citation statements)

References 76 publications

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“…Due to the large uncertainties in aerosol–cloud–climate interactions, it is particularly difficult to identify the impact of aerosols on precipitation processes (Y. Zhao et al., 2023, 2024). Previous studies agree that aerosols have complex and even offset effects on precipitation and precipitation extremes (Malavelle et al., 2017; Polson et al., 2014; Ramanathan et al., 2005; A. D. Zhao et al., 2019).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Differing Contributions of Anthropogenic Aerosols and Greenhouse Gases on Precipitation Intensity Percentiles Over the Middle and Lower Reaches of the Yangtze River

Luo,

Wang,

Wang

et al. 2024

JGR Atmospheres

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In June–July 2020, the middle and lower reaches of the Yangtze River (MLYR) were hit by a Meiyu event characterized by a long duration, abundant precipitation, and frequent heavy rainfall, resulting in destructive flooding. We found that the extreme cumulative precipitation in 2020 was mainly contributed by more moderate to heavy daily precipitation rather than extreme daily events. Although some previous studies have been conducted to attribute the 2020 Meiyu event in the MLYR, most of them focused on the cumulative precipitation amount. This attribution case study complements previous attribution analyses and reveals many new features. Our results show that anthropogenic climate change—primarily driven by greenhouse gas (GHG) forcing, anthropogenic aerosol (AA) forcing, and land‐use—has led to a decrease in the number of light to heavy precipitation days, while concurrently increasing the number of extreme precipitation days in the MLYR. Specifically, GHG and AA forcings decreased the frequency of light and moderate precipitation, but only GHG increased the frequency of extreme precipitation. An increasing trend in very heavy precipitation days has been observed. The competitive effects of GHG and AA forcings make it challenging to detect the signal of human activities, which could be intermingled with effects from natural variability. Under the SSP5‐8.5 scenario, the probability of experiencing both light and extreme precipitation events will significantly increase in the MLYR. By 2050–2100, these events are projected to be nearly 4 times more frequent compared to the current climate, which poses significant challenges to water security and economic development decision makers.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Differing Contributions of Anthropogenic Aerosols and Greenhouse Gases on Precipitation Intensity Percentiles Over the Middle and Lower Reaches of the Yangtze River

Luo,

Wang,

Wang

et al. 2024

JGR Atmospheres

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“…These two experiments provide good opportunities to investigate the underlying physical mechanisms of cloud feedback (Webb et al, 2017). However, clouds and their changes are complex and lack reliable and accurate representation (e.g., gray shading in Figure S3 in Supporting Information S1) even in the latest CMIP6 (Zelinka et al, 2020), especially on the TP (Li et al, 2021;Zhao et al, 2022aZhao et al, , 2022b. Therefore, we sought to reduce uncertainty in CMIP6 estimates of changes in CVD with warming (Brunner et al, 2020;Chen et al, 2020;Liang et al, 2022).…”

Section: Possible Changes In Cvd Induced By Warming Climatementioning

confidence: 99%

“…That is, the increased CO 2 first enhances downward radiation and thus land surface temperature increase, then modulating other climate variables (Kamae et al, 2015). This process could lead to weaker land and troposphere warming (Brient & Bony, 2013;Oudar et al, 2020;Qu et al, 2019), resulting in a small CF response to warming (Zhao et al, 2022a) compared with direct SST warming. Despite these discrepancies, H-layer clouds including substantial cirrus clouds are certainly expected to increase.…”

Section: Possible Changes In Cvd Induced By Warming Climatementioning

confidence: 99%

Warming Climate‐Induced Changes in Cloud Vertical Distribution Possibly Exacerbate Intra‐Atmospheric Heating Over the Tibetan Plateau

Zhao,

Li,

Wang

et al. 2024

Geophysical Research Letters

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The complex and diverse cloud vertical distribution (CVD) largely impacts radiative and precipitation properties of clouds. Using 10‐year active satellite observations, we classified CVD over the Tibetan Plateau into 12 categories and found that overlapping clouds have less frequency but stronger radiative effect, heating rate and larger precipitation (partly reflecting the seeding effect) compared with single‐layer non‐strong convective clouds. Under a warming climate due to uniform sea surface temperature increase of 4K (quadrupling CO2 increase), extremely high (>10 km) ice clouds will increase, particularly those below the tropopause will increase slightly (largely), accompanied by clear (weak) increases in stratospheric clouds. Simultaneously, a moderate to rapid decrease will occur in clouds below 10 km. Such CVD changes could further exacerbate tropopause warming. The probability of cloud overlap is also likely to increase in warmer climates, thus possibly further causing non‐convective cloud systems with stronger intra‐atmospheric heating, larger precipitation intensity and proportion.

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“…Compared to CloudSat and CALIPSO, the climate models underestimated the cloud fraction in the lower atmosphere layers [25]. Previous studies have noted that the biases in the cloud diurnal variation and cloud response of CMIP6 climate models should largely be attributed to deficiencies in cloud parameterization schemes, such as cumulus convection and boundary layer physics [58,59]. Although the patterns of RHc distribution derived from the other datasets were broadly similar to the CloudSat and CALIPSO results, there were still significant deviations in the magnitude of RHc.…”

Section: Spatial Distribution Characteristics Of Rhc Derived From Dif...mentioning

confidence: 99%

Spatio-Temporal Variation of Critical Relative Humidity Based on Multiple Datasets

Zhang,

Li,

et al. 2023

Remote Sensing

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Clouds remain an important source of uncertainty in climate simulations, in large part because subgrid processes are not well represented. Critical relative humidity (RHc) is an important metric for subgrid-scale variability in humidity in cloud parameterization. Based on CloudSat and CALIPSO satellite data, we explored the spatial and temporal distribution characteristics of RHc, assessed the ability of ERA-5 and MERRA-2 reanalysis and CMIP-6 climate models to characterise humidity subgrid variability and further explored the influence of meteorological factors and aerosols. The statistical results showed that there was significant variation in the spatial distribution of RHc, with large variations in both latitude and altitude, as well as more pronounced monthly variations, and that there were differences in monthly variations between regions. Both the reanalysis data and the climate models were able to reproduce similar spatial and temporal distribution patterns but differed significantly in their specific values. The temporal correlations with satellite observations were also relatively poor. In addition, aerosols and meteorological conditions affected the distribution of RHc by influencing the cloud fraction at a certain relative humidity level, indicating that their influence needs to be considered in future parameterization schemes.

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Cloud Response Is Significantly Biased by CMIP6 Over the Tibetan Plateau

Cited by 6 publications

References 76 publications

Differing Contributions of Anthropogenic Aerosols and Greenhouse Gases on Precipitation Intensity Percentiles Over the Middle and Lower Reaches of the Yangtze River

Differing Contributions of Anthropogenic Aerosols and Greenhouse Gases on Precipitation Intensity Percentiles Over the Middle and Lower Reaches of the Yangtze River

Warming Climate‐Induced Changes in Cloud Vertical Distribution Possibly Exacerbate Intra‐Atmospheric Heating Over the Tibetan Plateau

Spatio-Temporal Variation of Critical Relative Humidity Based on Multiple Datasets

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