COSP: Satellite simulation software for model assessment

Bodas‐Salcedo, Alejandro; Webb, Mark J.; Bony, Sandrine; Chepfer, Hélène; Dufresne, Jean‐Louis; Klein, Stephen A.; Zhang, Y; Marchand, Roger; Haynes, J. M.; Pincus, Robert; John, Viju O.

doi:10.1175/2011bams2856.1

Cited by 554 publications

(484 citation statements)

References 71 publications

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“…[8] A satellite simulator is a diagnostic code applied to model variables that reduces the influences of inconsistencies between the ways clouds are observed and the ways they are modeled [Bodas-Salcedo et al, 2011]. By mimicking the [9] The ISCCP simulator is the oldest of the satellite simulators used to evaluate clouds in models and has been widely used by most major climate modeling centers since its creation over 10 years ago [Klein and Jakob, 1999;Webb et al, 2001].…”

Section: Isccp Simulatormentioning

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: Isccp Simulatormentioning

confidence: 99%

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

et al. 2013

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“…The Cloud Feedback Model Intercomparison Project Observation Simulator Package (COSP, Bodas-Salcedo et al 2011) ingests a number of model fields relating to cloud and precipitation properties and uses these to simulate retrievals of satellite instruments. The COSP simulator incorporates several individual instrument simulators, including ISCCP and Cloudsat.…”

Section: Cospmentioning

confidence: 99%

“…The ISCCP (known as ICARUS) simulator (Klein and Jakob 1999;Webb et al 2001;Bodas-Salcedo et al 2011) mimics the assumptions of the ISCCP retrieval algorithm and is intended to provide ISCCP-like retrievals of a column to evaluate model output against retrievals from ISCCP itself. Many studies have used the simulator to evaluate global cloud amounts and types in climate models (e.g., Zhang et al 2005;Williams and Tselioudis 2007;Williams and Webb 2009;Kay et al 2012;Lauer and Hamilton 2013;Klein et al 2013).…”

Section: Cospmentioning

confidence: 99%

“…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%

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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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“…Clouds in nature are essentially proxies for saturated regions of the atmosphere, so the use of a high cloud fraction threshold allows for a simple comparison with observations, since it effectively identifies columns that contain a saturated or near-saturated region. This avoids the known difficulty of comparing the model cloud fraction field with observations (Bodas-Salcedo et al, 2011).…”

Section: Scaling Of Cloud Sizesmentioning

confidence: 99%

Observed Scaling in Clouds and Precipitation and Scale Incognizance in Regional to Global Atmospheric Models

O’Brien¹,

Li²,

Collins

et al. 2013

J. Climate

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We use observations of robust scaling behavior in clouds and precipitation to derive constraints on how partitioning of precipitation should change with model resolution. Our analysis indicates that 90-99% of stratiform precipitation should occur in clouds that are resolvable by contemporary climate models (e.g., with 200 km or finer grid spacing). Furthermore, this resolved fraction of stratiform precipitation should increase sharply with resolution, such that effectively all stratiform precipitation should be resolvable above scales of ∼50 km. We show that the Community Atmosphere Model (CAM) and the Weather Research and Forecasting (WRF) model also exhibit the robust cloud and precipitation scaling behavior that is present in observations, yet the resolved fraction of stratiform precipitation actually decreases with increasing model resolution. A suite of experiments with multiple dynamical cores provides strong evidence that this 'scale-incognizant' behavior originates in one of the CAM4 parameterizations. An additional set of sensitivity experiments rules out both convection parameterizations, and by a process of elimination these results implicate the stratiform cloud and precipitation parameterization. Tests with the CAM5 physics package show improvements in the resolution-dependence of resolved cloud fraction and resolved stratiform precipitation fraction.

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COSP: Satellite simulation software for model assessment

Cited by 554 publications

References 71 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

Observed Scaling in Clouds and Precipitation and Scale Incognizance in Regional to Global Atmospheric Models

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