How well do climate models simulate cloud vertical structure? A comparison between CALIPSO‐GOCCP satellite observations and CMIP5 models

Cesana, G; Chepfer, Hélène

doi:10.1029/2012gl053153

Cited by 106 publications

(94 citation statements)

References 39 publications

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“…Furthermore, the impact of clouds with t < 0.3 on the top-of-atmosphere radiation budget is too small for passive sensors to detect. Assessment of optically thin clouds requires the use of observations from an active sensor such as CALIPSO and could be performed using the output of the CALIPSO simulator applied to CFMIP2 models [Cessana and Chepfer, 2012].…”

Section: Improvements As a Function Of Cloud-top Pressure And Cloud Omentioning

confidence: 99%

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: Improvements As a Function Of Cloud-top Pressure And Cloud Omentioning

confidence: 99%

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

et al. 2013

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“…The cloud scheme of our model distinguishes between stratus and cumulus cloud fractions which are calculated from relative humidity, specific humidity and an effective vertical velocity. In general, there is a significant spread even in the results of state-of-the-art models when simulating cloud cover for today's climate, and differences to satellite observations are quite large (Cesana and Chepfer, 2012). In Fig.…”

Section: Cloud Schemementioning

confidence: 99%

Albedo and heat transport in 3-D model simulations of the early Archean climate

2013

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At the beginning of the Archean eon (ca. 3.8 billion years ago), the Earth's climate state was significantly different from today due to the lower solar luminosity, smaller continental fraction, higher rotation rate and, presumably, significantly larger greenhouse gas concentrations. All these aspects play a role in solutions to the "faint young Sun paradox" which must explain why the ocean surface was not fully frozen at that time. Here, we present 3-D model simulations of climate states that are consistent with early Archean boundary conditions and have different CO₂ concentrations, aiming at an understanding of the fundamental characteristics of the early Archean climate system. In order to do so, we have appropriately modified an intermediate complexity climate model that couples a statistical-dynamical atmosphere model (involving parameterizations of the dynamics) to an ocean general circulation model and a thermodynamic-dynamic sea-ice model. We focus on three states: one of them is ice-free, one has the same mean surface air temperature of 288 K as today's Earth and the third one is the coldest stable state in which there is still an area with liquid surface water (i.e. the critical state at the transition to a "snowball Earth"). We find a reduction in meridional heat transport compared to today, which leads to a steeper latitudinal temperature profile and has atmospheric as well as oceanic contributions. Ocean surface velocities are largely zonal, and the strength of the atmospheric meridional circulation is significantly reduced in all three states. These aspects contribute to the observed relation between global mean temperature and albedo, which we suggest as a parameterization of the ice-albedo feedback for 1-D model simulations of the early Archean and thus the faint young Sun problem

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“…This is mainly due to the complexity of the responses of different types of clouds to different aerosol types and concentrations (Fan et al, 2016;Fu and Xue, 2017;Stevens et al, 2017;Zhao et al, 2018), poorly constrained aerosol concentrations (particularly in winter and beneath thick cloud cover), and confounding effects from co-varying meteorology (Gryspeerdt et al, 2016). These uncertainties contribute 30 to the large uncertainties in model CF and CP (de Boer et al, 2011;Cesana and Chepfer, 2012;Chernokulsky and Mokhov, 2012;Kay et al, 2010;Liu et al, 2011;Qian et al, 2012;Stanfield et al, 2014;Zib et al, 2012). To account for the impact of meteorological co-variability on Arctic CF, observations covering large spatial and temporal scales are required, making it difficult to estimate the regional importance of aerosol microphysical effects from in situ observations alone.…”

Section: Introductionmentioning

confidence: 99%

A satellite-based estimate of aerosol-cloud microphysical effects over the Arctic Ocean

Zamora

Kahn

Huebert

et al. 2018

Preprint

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Abstract. Climate predictions for the rapidly changing Arctic are highly uncertain, largely due to a poor understanding of the processes driving cloud properties. In particular, cloud fraction (CF) and cloud phase (CP) have major impacts on energy 10 budgets, but are poorly represented in most models, often because of uncertainties in aerosol-cloud interactions. Here we use over 10 million satellite observations coupled with aerosol transport model simulations to quantify regional-scale microphysical effects of aerosols on CF and CP over the Arctic Ocean during polar night, when direct and semi-direct aerosol effects are minimal. Combustion aerosols over sea ice are associated with very large (~25 W m -2 ) differences in longwave cloud radiative effects at the sea ice surface. However, co-varying meteorological changes on factors such as CF 15 likely explain much of this signal -for example, explaining up to 91% of the CF differences between the full dataset and the clean-condition subset. After normalizing for meteorological regime, aerosol microphysical effects have small but significant regional-scale impacts on CF, CP, and precipitation frequency. These effects indicate that dominant aerosol-cloud microphysical mechanisms are related to the relative fraction of liquid-containing clouds, with implications for a warming Arctic. 20

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How well do climate models simulate cloud vertical structure? A comparison between CALIPSO‐GOCCP satellite observations and CMIP5 models

Cited by 106 publications

References 39 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

Albedo and heat transport in 3-D model simulations of the early Archean climate

A satellite-based estimate of aerosol-cloud microphysical effects over the Arctic Ocean

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