Marine Low Clouds and their Parameterization in Climate Models

Kawai, Hideaki; Shige, Shoichi

doi:10.2151/jmsj.2020-059

Cited by 13 publications

(14 citation statements)

References 189 publications

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“…Over the SE Atlantic, this albedo is arguably driven primarily by cloud fraction and liquid water path, as well as by the cloud droplet number concentration, with the latter controlled by aerosol-cloud microphysical interaction. Large-scale models have been shown to have large uncertainties and biases in their simulations of both aerosol absorption (e.g., Sand et al, 2021;Brown et al, 2021) and low marine clouds (e.g., Noda and Sato, 2014;Kawai and Shige, 2020) in this region.…”

Section: Introductionmentioning

confidence: 99%

Modeled and observed properties related to the direct aerosol radiative effect of biomass burning aerosol over the southeastern Atlantic

et al. 2022

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Abstract. Biomass burning smoke is advected over the southeastern Atlantic Ocean between July and October of each year. This smoke plume overlies and mixes into a region of persistent low marine clouds. Model calculations of climate forcing by this plume vary significantly in both magnitude and sign. NASA EVS-2 (Earth Venture Suborbital-2) ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) had deployments for field campaigns off the west coast of Africa in 3 consecutive years (September 2016, August 2017, and October 2018) with the goal of better characterizing this plume as a function of the monthly evolution by measuring the parameters necessary to calculate the direct aerosol radiative effect. Here, this dataset and satellite retrievals of cloud properties are used to test the representation of the smoke plume and the underlying cloud layer in two regional models (WRF-CAM5 and CNRM-ALADIN) and two global models (GEOS and UM-UKCA). The focus is on the comparisons of those aerosol and cloud properties that are the primary determinants of the direct aerosol radiative effect and on the vertical distribution of the plume and its properties. The representativeness of the observations to monthly averages are tested for each field campaign, with the sampled mean aerosol light extinction generally found to be within 20 % of the monthly mean at plume altitudes. When compared to the observations, in all models, the simulated plume is too vertically diffuse and has smaller vertical gradients, and in two of the models (GEOS and UM-UKCA), the plume core is displaced lower than in the observations. Plume carbon monoxide, black carbon, and organic aerosol masses indicate underestimates in modeled plume concentrations, leading, in general, to underestimates in mid-visible aerosol extinction and optical depth. Biases in mid-visible single scatter albedo are both positive and negative across the models. Observed vertical gradients in single scatter albedo are not captured by the models, but the models do capture the coarse temporal evolution, correctly simulating higher values in October (2018) than in August (2017) and September (2016). Uncertainties in the measured absorption Ångstrom exponent were large but propagate into a negligible (<4 %) uncertainty in integrated solar absorption by the aerosol and, therefore, in the aerosol direct radiative effect. Model biases in cloud fraction, and, therefore, the scene albedo below the plume, vary significantly across the four models. The optical thickness of clouds is, on average, well simulated in the WRF-CAM5 and ALADIN models in the stratocumulus region and is underestimated in the GEOS model; UM-UKCA simulates cloud optical thickness that is significantly too high. Overall, the study demonstrates the utility of repeated, semi-random sampling across multiple years that can give insights into model biases and how these biases affect modeled climate forcing. The combined impact of these aerosol and cloud biases on the direct aerosol radiative effect (DARE) is estimated using a first-order approximation for a subset of five comparison grid boxes. A significant finding is that the observed grid box average aerosol and cloud properties yield a positive (warming) aerosol direct radiative effect for all five grid boxes, whereas DARE using the grid-box-averaged modeled properties ranges from much larger positive values to small, negative values. It is shown quantitatively how model biases can offset each other, so that model improvements that reduce biases in only one property (e.g., single scatter albedo but not cloud fraction) would lead to even greater biases in DARE. Across the models, biases in aerosol extinction and in cloud fraction and optical depth contribute the largest biases in DARE, with aerosol single scatter albedo also making a significant contribution.

show abstract

Section: Introductionmentioning

confidence: 99%

Modeled and observed properties related to the direct aerosol radiative effect of biomass burning aerosol over the southeastern Atlantic

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Over the SE Atlantic, this albedo is arguably driven primarily by cloud fraction and liquid water path, as well as by cloud droplet number concentration, with the latter controlled by aerosol-cloud microphysical interaction. Large-scale models have been shown to have large uncertainties and biases in their simulations of both aerosol absorption (e.g., Sand et al, 2021;Brown et al, 2021) and low marine clouds (e.g., Noda and Sato, 2014;Kawai and Shige, 2020) in this region. Modeled direct radiative forcing across the SE Atlantic has ranged from strongly negative to strongly positive, with much of this range determined by modeled cloud fraction (e.g., see Figure 2 of Zuidema et al, 2016 andStier et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Modeled and observed properties related to the direct aerosol radiative effect of biomass burning aerosol over the Southeast Atlantic

Doherty¹,

Saide²,

Zuidema³

et al. 2021

Preprint

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Abstract. Biomass burning smoke is advected over the southeast Atlantic Ocean between July and October of each year. This smoke plume overlies and mixes into a region of persistent low marine clouds. Model calculations of climate forcing by this plume vary significantly, in both magnitude and sign. The NASA EVS-2 (Earth Venture Suborbital-2) ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) project deployed for field campaigns off the west coast of Africa in three consecutive years (Sept., 2016; Aug., 2017; and Oct., 2018) with the goal of better characterizing this plume as a function of the monthly evolution, by measuring the parameters necessary to calculate the direct aerosol radiative effect. Here, this dataset and satellite retrievals of cloud properties are used to test the representation of the smoke plume and the underlying cloud layer in two regional models (WRF-CAM5 and CNRM-ALADIN) and two global models (GEOS and UM-UKCA). The focus is on comparisons of those aerosol and cloud properties that are the primary determinants of the direct aerosol radiative effect, and on the vertical distribution of the plume and its properties. The representativeness of the observations to monthly averages are tested for each field campaign, with the sampled mean aerosol light extinction generally found to be within 20 % of the monthly mean at plume altitudes. When compared to the observations, in all models the simulated plume is too vertically diffuse, has smaller vertical gradients, and, in two of the models (GEOS and UM-UKCA), the plume core is displaced lower than in the observations. Plume carbon monoxide, black carbon, and organic aerosol masses indicate under-estimates in modeled plume concentrations, leading in general to under-estimates in mid-visible aerosol extinction and optical depth. Biases in mid-visible single scatter albedo are both positive and negative across the models. Observed vertical gradients in single scatter albedo are not captured by the models, but the models do capture the coarse temporal evolution, correctly simulating higher values in October (2018) than in August (2018) and September (2017). Uncertainties in the measured absorption Ångstrom exponent were large but propagate into a negligible (<4 %) uncertainty in integrated solar absorption by the aerosol and therefore in the aerosol direct radiative effect. Model biases in cloud fraction, and therefore the scene albedo below the plume, vary significantly across the four models. The optical thickness of clouds is, on average, well simulated in the WRF-CAM5 and ALADIN models in the stratocumulus region and is under-estimated in the GEOS model; UM-UKCA simulates significantly too-high cloud optical thickness. Overall, the study demonstrates the utility of repeated, semi-random sampling across multiple years that can give insights into model biases and how these biases affect modeled climate forcing. The combined impact of these aerosol and cloud biases on the direct aerosol radiative effect (DARE) is estimated using a first-order approximation for a sub-set of five comparison gridboxes. A significant finding is that the observed gridbox-average aerosol and cloud properties yield a positive (warming) aerosol direct radiative effect for all five gridboxes, whereas DARE using the gridbox-averaged modeled properties ranges from much larger positive values to small, negative values. It is shown quantitatively how model biases can offset each other, so that model improvements that reduce biases in only one property (e.g., single scatter albedo, but not cloud fraction) would lead to even greater biases in DARE. Across the models, biases in aerosol extinction and in cloud fraction and optical depth contribute the largest biases in DARE, with aerosol single scatter albedo also making a significant contribution.

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“…In their original concept (Mellor, 1977; Sommeria & Deardorff, 1977), the PDFs were assumed to be obtained from turbulent statistical values in turbulence schemes, but the width is too small for use in GCMs because the subgrid variabilities in GCM grid‐scales are not caused by turbulence but by mesoscale phenomena. The determination of PDFs is a complicated issue (e.g., Kawai & Shige, 2020; Kawai & Teixeira, 2012), and a lower limit of water vapor (or more precisely, total water content) variability σ min based on relative humidity has been used in practice to avoid a too‐narrow PDF; for example, 15% saturation specific humidity in Smith (1990) and 20% in the operational global model of Japan Meteorological Agency (JMA, 2019). Although this is just a lower limit, the width of the PDF is determined by this limit virtually in most cases in models.…”

Section: Examples Of Various Minor‐looking Treatmentsmentioning

confidence: 99%

“…Cloud macrophysics determines the cloud fraction and CWC in a model grid box, assuming the subgrid variabilities of water vapor and temperature (e.g., Smith, 1990; Sundqvist et al., 1989; Tiedtke, 1993; reviewed in Kawai & Shige, 2020).…”

Section: Examples Of Various Minor‐looking Treatmentsmentioning

confidence: 99%

Importance of Minor‐Looking Treatments in Global Climate Models

Kawai

Yoshida

Koshiro

et al. 2022

J Adv Model Earth Syst

Self Cite

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Global climate models (GCMs) are key tools used for climate simulations of the earth. GCMs with horizontal resolutions typically of the order of one hundred kilometers (several tens to a few hundreds of kilometers) have been used for the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5; Taylor et al., 2012) simulations and the Sixth Phase (CMIP6; Eyring et al., 2016). GCMs consist of, for instance, atmospheric models, ocean models, aerosol models, and chemical composition models. Atmospheric models contain dynamics and physics schemes including cloud, convection, turbulence, radiation, and gravity wave drag schemes.Physics schemes critically control the performance of GCMs, which can be measured in terms of biases in temperature, precipitation, radiation fluxes, and sea ice coverage and the reproduction of temperature increase in the 20 th century. Therefore, a lot of time and effort has been devoted to improving the physics schemes, and the performances of GCMs are gradually improving. In general, model developers tend to attribute the improvement of the model performance to the introduction of more advanced or sophisticated physics schemes. An example is the introduction of prognostic variables of higher moment; for instance, the introduction of prognostic number concentration of cloud particles in addition to prognostic cloud water content (CWC) (two-moment schemes) in contrast to schemes with only prognostic CWC (one-moment schemes). Another example is using more hydrometeor categories as prognostic variables: for instance, using snow, graupel, and hail in addition to the ice crystals used in simpler schemes. In turbulence schemes, the inclusion of turbulent moment terms of higher order is considered more theoretically sophisticated. Furthermore, the introduction of missing effects is also discussed in many papers; for example, the radiative effect of snow, the effect of subgrid inhomogeneity of cloud water content on radiation, the pressure gradient effect on convective momentum transport, and the influence of wind shear on the cloud overlap assumption. Modifying models in this way based on theory or observations is doubtlessly important for developing more advanced GCMs. However, such improvements do not always have major impacts.

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Marine Low Clouds and their Parameterization in Climate Models

Cited by 13 publications

References 189 publications

Modeled and observed properties related to the direct aerosol radiative effect of biomass burning aerosol over the southeastern Atlantic

Modeled and observed properties related to the direct aerosol radiative effect of biomass burning aerosol over the southeastern Atlantic

Modeled and observed properties related to the direct aerosol radiative effect of biomass burning aerosol over the Southeast Atlantic

Importance of Minor‐Looking Treatments in Global Climate Models

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