Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA “A‐Train” satellite observations

Jiang, Jonathan H.; Su, Hui; Zhai, Chengxing; Perun, V. S.; Genio, Anthony D. Del; Nazarenko, Larissa; Donner, Leo J.; Horowitz, Larry W.; Seman, Charles J.; Cole, Jason N. S.; Gettelman, Andrew; Ringer, Mark A.; Rotstayn, Leon; Jeffrey, Stephen; Wu, Tongwen; Brient, Florent; Dufresne, Jean‐Louis; Kawai, Hideaki; Koshiro, Tsuyoshi; Watanabe, Masahiro; L’Ecuyer, Tristan; Volodin, E. M.; Iversen, Trond; Drange, Helge; Mesquita, Michel D. S.; Read, W. G.; Waters, J. W.; Tian, Baijun; Teixeira, J.; Stephens, Graeme L.

doi:10.1029/2011jd017237

Cited by 354 publications

(374 citation statements)

References 97 publications

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“…Other aspects show no improvement in the majority of climate models such as the underestimate of cloud over middle-latitude land and ocean and an overestimate in the amount of optically thick cloud over tropical land. Incremental progress by climate models in simulating clouds has also been reported in Jiang et al [2012] and Lauer and Hamilton [2012].…”

Section: Why Are Simulations Of Clouds Improving and What Impacts MImentioning

confidence: 98%

Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator

et al. 2013

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[1] The annual cycle climatology of cloud amount, cloud-top pressure, and optical thickness in two generations of climate models is compared to satellite observations to identify changes over time in the fidelity of simulated clouds. In more recent models, there is widespread reduction of a bias associated with too many highly reflective clouds, with the best models having eliminated this bias. With increased amounts of clouds with lesser reflectivity, the compensating errors that permit models to simulate the time-mean radiation balance have been reduced. Errors in cloud amount as a function of height or climate regime on average show little or no improvement, although greater improvement can be found in individual models. Measuring Changes in the Simulations of Global Cloudiness Over Time[2] The simulation of clouds by climate models is a key ongoing challenge in the numerical representation of Earth's climate. Due to their large impact on Earth's radiation budget, clouds are important for determining aspects of current climate, such as surface air temperatures in many regions [Ma et al., 1996;Curry et al., 1996], the strength and variability of atmospheric circulations [Slingo and Slingo, 1988], and the magnitude of climate changes that result from perturbations in the chemical composition of the atmosphere [IPCC, 2007]. While important, the modeling of clouds is very difficult because most cloud processes happen at scales far smaller than can be resolved by climate models, and thus, their bulk effects must be represented with imperfect parameterizations.[3] Given the efforts of many scientists over several decades to understand cloud processes and improve their representation in models, it is important to ask are climate model simulations of clouds improving and, if so, by how much? Here, we analyze the ability of two generations of climate models to simulate the climatological distribution of clouds and judge fidelity by comparison to several decades of satellite observations. Because of the significant differences between the ways clouds are observed and the ways they are represented in models, we use a "satellite simulator" to increase the chances that differences between the models and observations represent actual model deficiencies. We find that significant progress in the ability of models to simulate clouds has occurred over the last decade, particularly in reducing the over-prediction of highly reflective clouds [Zhang et al., 2005].

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Section: Why Are Simulations Of Clouds Improving and What Impacts MImentioning

confidence: 98%

Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator

et al. 2013

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“…The CloudSat (known as Quickbeam) simulator (Haynes et al 2007;Bodas-Salcedo et al 2011) provides the ability to directly compare equivalent fields from CloudSat and reanalysis/forecast/climate models (e.g., Bodas-Salcedo et al 2008;Greenwald et al 2010;Naud et al 2010;Govekar et al 2011;Field et al 2011;Jiang et al 2012;Franklin et al 2013;Govekar et al 2014;Booth et al 2013). The simulator works by distributing model hydrometeors amongst the sub-grid columns with radar reflectivity, including attenuation, calculated at each model level.…”

Section: Cospmentioning

confidence: 99%

Using satellite and reanalysis data to evaluate the representation of latent heating in extratropical cyclones in a climate model

et al. 2016

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exhibit biases in cloud structure, with more clouds at lower altitudes than those observed in ISCCP in the warm conveyor belt region. As a result, latent heat release in ERAI is concentrated at lower altitudes. CloudSat indicates that much precipitation may be produced at too low an altitude in both HiGEM and ERAI, particularly ERAI, and neither capture observed variability in precipitation intensity. The potential vorticity structure in composite extratropical cyclones in HiGEM and ERAI is also compared. A more pronounced tropopause ridge evolves in HiGEM on the leading edge of the composite as compared to ERAI. One future area of research to be addressed is what impact these biases in the representation of latent heating have on climate projections produced by HiGEM. The biases found in ERAI indicate caution is required when using reanalyses to study cloud features and precipitation processes in extratropical cyclones or using reanalysis to evaluate climate models' ability to represent their structure.

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“…Several improvements have been reported from CMIP3 to CMIP5 (e.g., column-integrated cloud amount) [Li and Xie, 2012;Jiang et al, 2012]; however, the current GCMs still simulate subtropical clouds inadequately [Lauer and Hamilton, 2013].…”

Section: Introductionmentioning

confidence: 99%

Intermodel variances of subtropical stratocumulus environments simulated in CMIP5 models

Noda

Satoh

2014

Geophysical Research Letters

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This paper investigates simulation biases associated with the large-scale environments of subtropical marine stratocumulus (Sc) in present climate simulations from the Coupled Model Intercomparison Project Phase 5 models. Comparison of eight major variables that strongly control the Sc clouds, including jumps of temperature and vapor across the inversion layer, indicates that these models all have similar shortcomings, such as overestimation of sensible and latent surface fluxes. The differences among the biases of these major variables between the Sc regions were also evaluated. Of all Sc regions, the Namibian Sc region showed the largest biases. The modeled simulation skill of the annual variation of low cloud cover (LCC) increases when the correlation coefficient between LCC and lower atmospheric stability is higher. This paper reemphasizes the importance, suggested in previous studies, of an improved relationship between lower tropospheric stability and LCC if we are to better predict annual variations in subtropical LCC.

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Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA “A‐Train” satellite observations

Cited by 354 publications

References 97 publications

Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator

Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator

Using satellite and reanalysis data to evaluate the representation of latent heating in extratropical cyclones in a climate model

Intermodel variances of subtropical stratocumulus environments simulated in CMIP5 models

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