The ‘too few, too bright’ tropical low‐cloud problem in CMIP5 models

By C. Nam, Sandrine Bony, J. Dufresne, H. Chepfer. 2012

343 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/c845d4e6609b258252b3f49d97af1deee078efa3

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Citations in the corpus

Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator (2012, background): “One common error is to have clouds which are too few but too bright, that is, to have lower-than-observed cloud amounts with larger-than-observed values of t, such that the average shortwave radiation budget is about right [Zhang et al., 2005; Nam et al., 2012].”
Assessment of CMIP5 climate models and projected temperature changes over Northern Eurasia (2014, background): “It is found that the CMIP5 model tends to underestimate the cloud cover (Nam et al 2012) and stratospheric water vapor (Fyfe et al 2013a), both of which allow more sun to get in and then to heat up the planet during the simulation.”
The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and GC3.1) Configurations (2017, methodology): “Despite the accurate simulation of cloud being a particular strength of HadGEM2-AO (e.g., Jiang et al., 2012; Klein et al., 2013; Nam et al., 2012), Figure 9 reveals some of the issues in terms of detrainment from deep convection not being at a sufficiently high altitude, too little thin cirrus,…”
Bounding Global Aerosol Radiative Forcing of Climate Change (2020, positive): “GCMs are known to under-represent847 low-level cloudiness (Nam et al., 2012), so may underestimate cτ by simulating the wrong848 spatial patterns of C.849 [INSERT TABLE 3 HERE]850 In summary, the radiative forcing of ari, Fari, is computed as in Eq.”
Developments in the MPI‐M Earth System Model version 1.2 (MPI‐ESM1.2) and Its Response to Increasing CO2 (2019, background): “In contrast, reflectance over parts of the subtropical and midlatitude oceans is generally too high, which is due to the commonly referred to “too few, too bright” low-level cloud bias (Nam et al., 2012).”
Evaluation of CMIP6 DECK Experiments With CNRM‐CM6‐1 (2019, background): “1, the CNRM‐CM6‐1 low‐cloud cover is improved (Figure 6) but the associated radiative effect has been tuned down to limit the so‐called “too few‐too bright” compensation error (Nam et al., 2012).”
On the Correspondence between Mean Forecast Errors and Climate Errors in CMIP5 Models (2013, background): “This suggests biases in the cloud condensate simulations (cloud liquid and ice) as the cloud optical depth is important in affecting the net radiation budget (Kay et al. 2012; Nam et al. 2012).”
Understanding Cloud and Convective Characteristics in Version 1 of the E3SM Atmosphere Model (2018, background): “This again highlights the “too few and too bright” feature of the simulated low clouds in the low latitudes (Nam et al., 2012).”