Intermodel variances of subtropical stratocumulus environments simulated in CMIP5 models

Noda, Akira; Satoh, Masaki

doi:10.1002/2014gl061812

Cited by 21 publications

(15 citation statements)

References 32 publications

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“…To investigate this further, we analyzed the change of low cloud cover (LCC, may also be referred as low cloud fraction) in response to global surface warming. Ideally, we would use the nonoverlapped low cloud fraction provided by the International Satellite Cloud Climatology Project (ISCCP) simulator [ Klein and Jakob , ; Webb et al , ] to calculate LCC, but it is only available for a small subset of models, so we make use of the standard vertical profile of monthly mean cloud fraction diagnosed by each model (cl) to approximate LCC as the maximum of cloud fraction between surface and 680 hPa at each grid point: ( Noda and Satoh [])

{normalL normalC normalC}_{v} = max ((), {normalcl}_{680 - 1000 hPa}),

where subscript “v” denotes LCC calculated from cloud fraction at vertical levels. Interannual anomalies of LCC v are correlated with LCC estimated from the ISCCP simulator (Figures S3 and S4), so LCC v is appropriate for studying the intermodel spread of LCC feedback.…”

Section: Intermodel Correlation Between Interannual and Long‐term Closupporting

confidence: 88%

“…To investigate this further, we analyzed the change of low cloud cover (LCC, may also be referred as low cloud fraction) in response to global surface warming. Ideally, we would use the nonoverlapped low cloud fraction provided by the International Satellite Cloud Climatology Project (ISCCP) simulator [Klein and Jakob, 1999;Webb et al, 2001] to calculate LCC, but it is only available for a small subset of models, so we make use of the standard vertical profile of monthly mean cloud fraction diagnosed by each model (cl) to approximate LCC as the maximum of cloud fraction between surface and 680 hPa at each grid point: (Noda and Satoh [2014])…”

Section: Figures 2a and 2bmentioning

confidence: 99%

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The relationship between interannual and long‐term cloud feedbacks

Zhou

Zelinka

Dessler

et al. 2015

Geophysical Research Letters

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Analyses of Coupled Model Intercomparison Project phase 5 simulations suggest that climate models with more positive cloud feedback in response to interannual climate fluctuations also have more positive cloud feedback in response to long‐term global warming. Ensemble mean vertical profiles of cloud change in response to interannual and long‐term surface warming are similar, and the ensemble mean cloud feedback is positive on both timescales. However, the average long‐term cloud feedback is smaller than the interannual cloud feedback, likely due to differences in surface warming pattern on the two timescales. Low cloud cover (LCC) change in response to interannual and long‐term global surface warming is found to be well correlated across models and explains over half of the covariance between interannual and long‐term cloud feedback. The intermodel correlation of LCC across timescales likely results from model‐specific sensitivities of LCC to sea surface warming.

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{normalL normalC normalC}_{v} = max ((), {normalcl}_{680 - 1000 hPa}),

Section: Intermodel Correlation Between Interannual and Long‐term Closupporting

confidence: 88%

“…To investigate this further, we analyzed the change of low cloud cover (LCC, may also be referred as low cloud fraction) in response to global surface warming. Ideally, we would use the nonoverlapped low cloud fraction provided by the International Satellite Cloud Climatology Project (ISCCP) simulator [Klein and Jakob, 1999;Webb et al, 2001] to calculate LCC, but it is only available for a small subset of models, so we make use of the standard vertical profile of monthly mean cloud fraction diagnosed by each model (cl) to approximate LCC as the maximum of cloud fraction between surface and 680 hPa at each grid point: (Noda and Satoh [2014])…”

Section: Figures 2a and 2bmentioning

confidence: 99%

The relationship between interannual and long‐term cloud feedbacks

Zhou

Zelinka

Dessler

et al. 2015

Geophysical Research Letters

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“…Yet, many CMIP5 model annual cycles in stratocumulus cloud amount and liquid water path are opposite of that in observations , with too much cloud during JanuaryMarch, when the atmosphere is less stable. Models with stronger correlations between low cloud cover and the LTS generally possess more realistic cloud annual cycles (see also Noda and Satoh 2014;Lin et al 2014).…”

Section: Main Regional Processes Contrib-uting To Coupled Climate Modmentioning

confidence: 99%

Challenges and Prospects for Reducing Coupled Climate Model SST Biases in the Eastern Tropical Atlantic and Pacific Oceans: The U.S. CLIVAR Eastern Tropical Oceans Synthesis Working Group

Zuidema

Chang

Medeiros

et al. 2016

Bulletin of the American Meteorological Society

135

128

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Well-known problems trouble coupled general circulation models of the eastern Atlantic and Pacific Ocean basins. Model climates are significantly more symmetric about the equator than is observed. Model sea surface temperatures are biased warm south and southeast of the equator, and the atmosphere is too rainy within a band south of the equator. Near-coastal eastern equatorial SSTs are too warm, producing a zonal SST gradient in the Atlantic opposite in sign to that observed. The U.S. Climate Variability and Predictability Program (CLIVAR) Eastern Tropical Ocean Synthesis Working Group (WG) has pursued an updated assessment of coupled model SST biases, focusing on the surface energy balance components, on regional error sources from clouds, deep convection, winds, and ocean eddies; on the sensitivity to model resolution; and on remote impacts. Motivated by the assessment, the WG makes the following recommendations: 1) encourage identification of the specific parameterizations contributing to the biases in individual models, as these can be model dependent; 2) restrict multimodel intercomparisons to specific processes; 3) encourage development of high-resolution coupled models with a concurrent emphasis on parameterization development of finer-scale ocean and atmosphere features, including low clouds; 4) encourage further availability of all surface flux components from buoys, for longer continuous time periods, in persistently cloudy regions; and 5) focus on the eastern basin coastal oceanic upwelling regions, where further opportunities for observational–modeling synergism exist.

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“…Nevertheless, important questions still remain open, e.g., regarding the influence of radiative and evaporative cooling, turbulence, large‐scale flow structures, microphysics, and in particular interactions between these phenomena (see Mellado, ; Wood, ). This lack of sufficient knowledge adds to the significant uncertainties and biases in representation of stratocumulus clouds seen in current climate models (e.g., Noda & Satoh, ). Based on the analysis of measurements from the Physics Of Stratocumulus Top (POST) field campaign (Gerber et al, ), Jen‐La Plante et al () hypothesize that deficiencies of existing entrainment parameterizations are partly due to poor understanding of turbulence in the stably stratified inversion layer capping the STBL.…”

Section: Introductionmentioning

confidence: 99%

Anisotropy of Observed and Simulated Turbulence in Marine Stratocumulus

Pedersen

Grabowski

et al. 2018

J Adv Model Earth Syst

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Anisotropy of turbulence near the top of the stratocumulus‐topped boundary layer (STBL) is studied using large‐eddy simulation (LES) and measurements from the POST and DYCOMS‐II field campaigns. Focusing on turbulence ∼100 m below the cloud top, we see remarkable similarity between daytime and nocturnal flight data covering different inversion strengths and free‐tropospheric conditions. With λ denoting wavelength and znormalt cloud‐top height, we find that turbulence at λ/znormalt≃0.01 is weakly dominated by horizontal fluctuations, while turbulence at λ/znormalt>1 becomes strongly dominated by horizontal fluctuations. Between are scales at which vertical fluctuations dominate. Typical‐resolution LES of the STBL (based on POST flight 13 and DYCOMS‐II flight 1) captures observed characteristics of below‐cloud‐top turbulence reasonably well. However, using a fixed vertical grid spacing of 5 m, decreasing the horizontal grid spacing and increasing the subgrid‐scale mixing length leads to increased dominance of vertical fluctuations, increased entrainment velocity, and decreased liquid water path. Our analysis supports the notion that entrainment parameterizations (e.g., in climate models) could potentially be improved by accounting more accurately for anisotropic deformation of turbulence in the cloud‐top region. While LES has the potential to facilitate improved understanding of anisotropic cloud‐top turbulence, sensitivity to grid spacing, grid‐box aspect ratio, and subgrid‐scale model needs to be addressed.

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Intermodel variances of subtropical stratocumulus environments simulated in CMIP5 models

Cited by 21 publications

References 32 publications

The relationship between interannual and long‐term cloud feedbacks

The relationship between interannual and long‐term cloud feedbacks

Challenges and Prospects for Reducing Coupled Climate Model SST Biases in the Eastern Tropical Atlantic and Pacific Oceans: The U.S. CLIVAR Eastern Tropical Oceans Synthesis Working Group

Anisotropy of Observed and Simulated Turbulence in Marine Stratocumulus

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