Comparative study of five current reanalyses in characterizing total cloud fraction and top‐of‐the‐atmosphere cloud radiative effects over the Asian monsoon region

Li, Jiandong; Mao, Jiangyu; Wang, Fang

doi:10.1002/joc.5143

Cited by 23 publications

(13 citation statements)

References 52 publications

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“…It is also important to note that the CloudSat estimate represents total IWC, including both precipitating and cloud ice. We may therefore expect the profile maximum to be both larger in magnitude and lower in altitude than one based on cloud ice alone (Li et al, 2012(Li et al, , 2016. This expectation is supported by Fig.…”

Section: Derived Variables and Statistical Treatmentsmentioning

confidence: 52%

“…The net effect of an individual cloud on the radiation budget depends on several factors, including the type, phase, height, and microphysical characteristics of the cloud (Stevens and Schwartz, 2012). These features are difficult to parameterize so that the integrated radiative impacts of clouds remain poorly represented in global models (Bony et al, 2015), including those used to produce atmospheric reanalyses (Dolinar et al, 2016;Li et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Differences in tropical high clouds among reanalyses: origins and radiative impacts

Wright

Konopka³

et al. 2020

Atmos. Chem. Phys.

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Abstract. We examine differences among reanalysis high-cloud products in the tropics, assess the impacts of these differences on radiation budgets at the top of the atmosphere and within the tropical upper troposphere and lower stratosphere (UTLS), and discuss their possible origins in the context of the reanalysis models. We focus on the ERA5 (fifth-generation European Centre for Medium-range Weather Forecasts – ECMWF – reanalysis), ERA-Interim (ECMWF Interim Reanalysis), JRA-55 (Japanese 55-year Reanalysis), MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, Version 2), and CFSR/CFSv2 (Climate Forecast System Reanalysis/Climate Forecast System Version 2) reanalyses. As a general rule, JRA-55 produces the smallest tropical high-cloud fractions and cloud water contents among the reanalyses, while MERRA-2 produces the largest. Accordingly, long-wave cloud radiative effects are relatively weak in JRA-55 and relatively strong in MERRA-2. Only MERRA-2 and ERA5 among the reanalyses produce tropical-mean values of outgoing long-wave radiation (OLR) close to those observed, but ERA5 tends to underestimate cloud effects, while MERRA-2 tends to overestimate variability. ERA5 also produces distributions of long-wave, short-wave, and total cloud radiative effects at the top of the atmosphere that are very consistent with those observed. The other reanalyses all exhibit substantial biases in at least one of these metrics, although compensation between the long-wave and short-wave effects helps to constrain biases in the total cloud radiative effect for most reanalyses. The vertical distribution of cloud water content emerges as a key difference between ERA-Interim and other reanalyses. Whereas ERA-Interim shows a monotonic decrease of cloud water content with increasing height, the other reanalyses all produce distinct anvil layers. The latter is in better agreement with observations and yields very different profiles of radiative heating in the UTLS. For example, whereas the altitude of the level of zero net radiative heating tends to be lower in convective regions than in the rest of the tropics in ERA-Interim, the opposite is true for the other four reanalyses. Differences in cloud water content also help to explain systematic differences in radiative heating in the tropical lower stratosphere among the reanalyses. We discuss several ways in which aspects of the cloud and convection schemes impact the tropical environment. Discrepancies in the vertical profiles of temperature and specific humidity in convective regions are particularly noteworthy, as these variables are directly constrained by data assimilation, are widely used, and feed back to convective behaviour through their relationships with thermodynamic stability.

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Section: Derived Variables and Statistical Treatmentsmentioning

confidence: 52%

Section: Introductionmentioning

confidence: 99%

Differences in tropical high clouds among reanalyses: origins and radiative impacts

Wright

Konopka³

et al. 2020

Atmos. Chem. Phys.

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“…However, due to the complex terrain of the TP and the lack of measurement sites in the central and western portions of the TP, the use of such data to analyse the cloud parameters of the TP as a whole cannot produce regionally representative results (Liu, ; Rangwala et al ., ; Wang et al ., ; ). Compared with the ground‐based observational data, atmospheric reanalyses and model outputs can represent the spatial and temporal distribution characteristics of cloud parameters and the radiative effects of clouds to some extent (Weare, ; Uppala et al ., ; Zhang et al ., ; Sato et al ., ; Li et al ., ), but there is a large deviation at the national or smaller regional scale (Sato et al ., ; Flato et al ., ). The assimilation algorithms commonly used in reanalysis and the different parameters, algorithms and input data used in the models lead to deviations from the cloud parameters measured over the TP (Weare, ; Zhang et al ., ; Bao and Zhang, ; You et al ., ; Li et al ., ).…”

Section: Introductionmentioning

confidence: 99%

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

Bao

Letu

Jun

et al. 2018

Intl Journal of Climatology

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The Tibetan Plateau (TP) has important influences on regional and global climate change. Here, we perform an in-depth study of the relationship between cloud parameters and meteorological factors over the TP. The spatiotemporal variations in cloud cover and cloud optical thickness over the TP during the daytime from 2003 to 2015 are analysed using the Aqua-MODIS level 2 atmospheric product data MYD06. Results show that the annual average cloud cover over the TP decreases from the southeast to the northwest. The cloud cover of the western TP is highest in spring and lowest in autumn, while the cloud cover of the eastern TP is higher in spring and summer. The cloud covers in most areas of the TP exceed 30% in spring and summer, and the cloud optical thickness in the southeastern TP exceeds 10 in summer, with substantial cloud cover and cloud optical thickness changes. Compared to other seasons, the cooling effect of the near surface cloud net radiative forcing is greater during daytime in summer, which is likely associated with thick cloud optical thickness or large cloud cover. The results of the analysis of relationships among cloud cover, water vapour and air temperature show that positive correlations exist between cloud cover and water vapour, and that significantly negative correlations exist between cloud cover and air temperature over the TP. Combined with the analysis of variation features in the cloud parameters and meteorological factors, we find that water vapour variations during the daytime over the TP lead to cloud cover changes, which affect the air temperature variations over the TP by the cooling effects of clouds, especially in summer. K E Y W O R D Scloud cover, cloud radiative forcing, temperature, Tibetan plateau, water vapour

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“…Spectral files with 9 longwave bands and 6 shortwave bands are used, which are those used in the Unified Model's Global Atmosphere version 7 (Walters et al, 2019). The cloud fraction, effective radius of cloud particle and liquid water mixing ratio in each grid are passed to it, then the radiation fluxes under all-sky and clear-sky conditions are obtained, which are used to analyze the energy balance and to calculate the cloud radiative effect (Ramanathan et al, 1989;Li et al, 2017) at the TOA.…”

Section: Experiments and Data Setsmentioning

confidence: 99%

SimCloud version 1.0: a simple diagnostic cloud scheme for idealized climate models

Liu¹,

Collins²,

Maher³

et al. 2020

Preprint

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Abstract. SimCloud, a simple diagnostic cloud scheme for general circulation models (GCMs) is proposed in this study. The large-scale clouds, which form the core of the scheme, are diagnosed from relative humidity. In addition, marine low stratus clouds, typically found off the west coast of continents over subtropical oceans, are determined largely as a function of inversion strength. A freeze-dry adjustment based on a simple function of relative humidity may also used to reduce an excessive clouds bias in polar regions. Other cloud properties, such as the effective radius of cloud droplet and cloud liquid water content, are specified as simple functions of temperature. All of these features are user-configurable. The cloud scheme is implemented in Isca, a modeling framework designed to enable the construction of GCMs at varying levels of complexity, but could readily be adapted to other GCMs. Simulations using the scheme with realistic continents generally capture the observed structure of cloud fraction and cloud radiative effect (CRE), as well as its seasonal variation. Specifically, the explicit low cloud scheme improves the simulation of shortwave CREs over the eastern subtropical oceans by increasing the cloud fraction and cloud water path over there. The freeze-dry adjustment alleviates the longwave CRE biases in polar regions especially in winter. However, the longwave CRE in tropical regions and shortwave CRE over extratropics are still too strong compared to observations. Nevertheless, this simple cloud scheme provides a suitable basis for examining the impacts of clouds on climate in idealized modeling frameworks.

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Comparative study of five current reanalyses in characterizing total cloud fraction and top‐of‐the‐atmosphere cloud radiative effects over the Asian monsoon region

Cited by 23 publications

References 52 publications

Differences in tropical high clouds among reanalyses: origins and radiative impacts

Differences in tropical high clouds among reanalyses: origins and radiative impacts

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

SimCloud version 1.0: a simple diagnostic cloud scheme for idealized climate models

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