Use of A-train satellite observations (CALIPSO–PARASOL) to evaluate tropical cloud properties in the LMDZ5 GCM

Konsta, Dimitra; Dufresne, Jean‐Louis; Chepfer, Hélène; Idelkadi, Abderrahmane; Cesana, G

doi:10.1007/s00382-015-2900-y

Cited by 25 publications

(42 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cloud reflectance sorted by cloud amount showed that models overestimate the cloud radiative effects compared to observations, even for comparable cloud amounts . Konsta et al (2015) confirmed this at the instantaneous timescale. The tropical low-level cloud properties are grouped into two clusters according to the observations.…”

Section: Discussionsupporting

confidence: 72%

“…time-step, data (e.g. Konsta et al, 2015;Suzuki et al, 2015), the motivation being to understand physical processes, as this is known to be important for understanding cloud feedbacks (e.g. Gettleman and Sherwood, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…The colour bar represents the number of points at each grid cell (cloud cover-cloud reflectance) divided by the total number of points. Reprinted with permission from Konsta et al (2015).…”

Section: Shortwave Cloud Radiative Effect (Sw Cre) and Parasol Reflecmentioning

confidence: 99%

“…3.5 A-train satellite instantaneous cloud property observations for process-oriented evaluation (CALIPSO-PARASOL; Konsta et al, 2015) This code is available at https://github.com/dimitrakonsta/ process-oriented-cloud-evaluation. These 2-D histograms correlate different cloud variables from the multi-sensor A-train observations at the instantaneous timescale, and at high spatial resolution.…”

Section: Zonal Plots Of Gcm Cloud and Hydrometeormentioning

confidence: 99%

See 3 more Smart Citations

The Cloud Feedback Model Intercomparison Project (CFMIP) Diagnostic Codes Catalogue – metrics, diagnostics and methodologies to evaluate, understand and improve the representation of clouds and cloud feedbacks in climate models

Tsushima¹,

Brient²,

Klein³

et al. 2017

Geosci. Model Dev.

View full text Add to dashboard Cite

Abstract. The CFMIP Diagnostic Codes Catalogue assembles cloud metrics, diagnostics and methodologies, together with programs to diagnose them from general circulation model (GCM) outputs written by various members of the CFMIP community. This aims to facilitate use of the diagnostics by the wider community studying climate and climate change. This paper describes the diagnostics and metrics which are currently in the catalogue, together with examples of their application to model evaluation studies and a summary of some of the insights these diagnostics have provided into the main shortcomings in current GCMs. Analysis of outputs from CFMIP and CMIP6 experiments will also be facilitated by the sharing of diagnostic codes via this catalogue.Any code which implements diagnostics relevant to analysing clouds -including cloud-circulation interactions and the contribution of clouds to estimates of climate sensitivity in models -and which is documented in peer-reviewed studies, can be included in the catalogue. We very much welcome additional contributions to further support community analysis of CMIP6 outputs.

show abstract

Section: Discussionsupporting

confidence: 72%

Section: Discussionmentioning

confidence: 99%

“…The colour bar represents the number of points at each grid cell (cloud cover-cloud reflectance) divided by the total number of points. Reprinted with permission from Konsta et al (2015).…”

Section: Shortwave Cloud Radiative Effect (Sw Cre) and Parasol Reflecmentioning

confidence: 99%

Section: Zonal Plots Of Gcm Cloud and Hydrometeormentioning

confidence: 99%

See 2 more Smart Citations

The Cloud Feedback Model Intercomparison Project (CFMIP) Diagnostic Codes Catalogue – metrics, diagnostics and methodologies to evaluate, understand and improve the representation of clouds and cloud feedbacks in climate models

Tsushima¹,

Brient²,

Klein³

et al. 2017

Geosci. Model Dev.

View full text Add to dashboard Cite

show abstract

“…This is stressed in Schmidt et al (2014), who compared cloud data from GISS-E2-R to satellite measurements and found underestimated cloud covers over mid-latitude ocean regions and a particular deficiency in subtropical low clouds. Likewise, Konsta et al (2016) compared tropical clouds in IPSL-CM5A-LR to satellite observations and found an underestimation of total cloud cover (underestimated low-and midlevel tropical clouds and overestimated high clouds) associated with a high bias in cloud optical depth. Other models have similar issues.…”

Section: Modeled Cloud Climatologiesmentioning

confidence: 99%

Response to marine cloud brightening in a multi-model ensemble

et al. 2018

View full text Add to dashboard Cite

Abstract. Here we show results from Earth system model simulations from the marine cloud brightening experiment G4cdnc of the Geoengineering Model Intercomparison Project (GeoMIP). The nine contributing models prescribe a 50 % increase in the cloud droplet number concentration (CDNC) of low clouds over the global oceans in an experiment dubbed G4cdnc, with the purpose of counteracting the radiative forcing due to anthropogenic greenhouse gases under the RCP4.5 scenario. The model ensemble median effective radiative forcing (ERF) amounts to −1.9 W m −2 , with a substantial inter-model spread of −0.6 to −2.5 W m −2 . The large spread is partly related to the considerable differences in clouds and their representation between the models, with an underestimation of low clouds in several of the models. All models predict a statistically significant temperature decrease with a median of (for years 2020-2069) −0.96 [−0.17 to −1.21] K relative to the RCP4.5 scenario, with particularly strong cooling over low-latitude continents. Globally averaged there is a weak but significant precipitation decrease of −2.35 [−0.57 to −2.96] % due to a colder climate, but at low latitudes there is a 1.19 % increase over land. This increase is part of a circulation change where a strong negative top-of-atmosphere (TOA) shortwave forcing over subtropical oceans, caused by increased albedo associated with the increasing CDNC, is compensated for by rising motion and positive TOA longwave signals over adjacent land regions.

show abstract

An investigation of microphysics and subgrid‐scale variability in warm‐rain clouds using the A‐Train observations and a multiscale modeling framework

et al. 2017

View full text Add to dashboard Cite

A common problem in climate models is that they are likely to produce rain at a faster rate than is observed and therefore produce too much light rain (e.g., drizzle). Interestingly, the Pacific Northwest National Laboratory (PNNL) multiscale modeling framework (MMF), whose warm‐rain formation process is more realistic than other global models, has the opposite problem: the rain formation process in PNNL‐MMF is less efficient than the real world. To better understand the microphysical processes in warm cloud, this study documents the model biases in PNNL‐MMF and evaluates warm cloud properties, subgrid variability, and microphysics, using A‐Train satellite observations to identify sources of model biases in PNNL‐MMF. Like other models PNNL‐MMF underpredicts the warm cloud fraction with compensating large optical depths. Associated with these compensating errors in cloudiness are compensating errors in the precipitation process. For a given liquid water path, clouds in the PNNL‐MMF are less likely to produce rain than are real‐world clouds. However, when the model does produce rain it is able to produce stronger precipitation than reality. As a result PNNL‐MMF produces about the correct mean rain rate with an incorrect distribution of rates. The subgrid variability in PNNL‐MMF is also tested, and results are fairly consistent with observations, suggesting that the possible sources of model biases are likely to be due to errors in its microphysics or dynamics rather than errors in the subgrid‐scale variability produced by the embedded cloud resolving model.

show abstract

Use of A-train satellite observations (CALIPSO–PARASOL) to evaluate tropical cloud properties in the LMDZ5 GCM

Cited by 25 publications

References 67 publications

The Cloud Feedback Model Intercomparison Project (CFMIP) Diagnostic Codes Catalogue – metrics, diagnostics and methodologies to evaluate, understand and improve the representation of clouds and cloud feedbacks in climate models

The Cloud Feedback Model Intercomparison Project (CFMIP) Diagnostic Codes Catalogue – metrics, diagnostics and methodologies to evaluate, understand and improve the representation of clouds and cloud feedbacks in climate models

Response to marine cloud brightening in a multi-model ensemble

An investigation of microphysics and subgrid‐scale variability in warm‐rain clouds using the A‐Train observations and a multiscale modeling framework

Contact Info

Product

Resources

About