@article{nokey,

title = {Evaluation of Clouds, Radiation, and Precipitation in CMIP6 Models Using Global Weather States Derived from ISCCP-H Cloud Property Data},

author = {G. Tselioudis and W.B. Rossow and C. Jakob and J Remillard and D. Tropf and Y. Zhang},

url = {https://www.williambrossow.com/wp-content/uploads/2022/09/2021_Tselioudisetal.pdf, Download file},

doi = {10.1175/JCLI-D-21-0076.1},

year  = {2021},

date = {2021-09-01},

urldate = {2021-09-01},

journal = { Journal of Climate},

volume = {34},

number = {17},

pages = {7311-7324},

abstract = {A clustering methodology is applied to cloud optical depth (τ)\textendashcloud top pressure (TAU-PC) histograms from the new 1° resolution ISCCP-H dataset to derive an updated global weather state (WS) dataset. Then, TAU-PC histograms from current-climate CMIP6 model simulations are assigned to the ISCCP-H WSs along with their concurrent radiation and precipitation properties to evaluate model cloud, radiation, and precipitation properties in the context of the weather states. The new ISCCP-H analysis produces WSs that are very similar to those previously found in the lower-resolution ISCCP-D dataset. The main difference lies in the splitting of the ISCCP-D thin stratocumulus WS between the ISCCP-H shallow cumulus and stratocumulus WSs, which results in the reduction by one of the total WS number. The evaluation of the CMIP6 models against the ISCCP-H weather states shows that, in the ensemble mean, the models are producing an adequate representation of the frequency and geographical distribution of the WSs, with measurable improvements compared to the WSs derived for the CMIP5 ensemble. However, the frequency of shallow cumulus clouds continues to be underestimated, and, in some WSs the good agreement of the ensemble mean with observations comes from averaging models that significantly overpredict and underpredict the ISCCP-H WS frequency. In addition, significant biases exist in the internal cloud properties of the model WSs, such as the model underestimation of cloud fraction in middle-top clouds and secondarily in midlatitude storm and stratocumulus clouds, that result in an underestimation of cloud SW cooling in those regimes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Luo2017-bx,

title = {Tropical cloud and precipitation regimes as seen from  near‐simultaneous TRMM, CloudSat, and CALIPSO observations  and comparison with ISCCP},

author = {Zhengzhao Johnny Luo and Ricardo C Anderson and William B Rossow and Hanii Takahashi},

url = {https://www.williambrossow.com/wp-content/uploads/2022/03/JGR-Atmospheres-2017-Luo-Tropical-cloud-and-precipitation-regimes-as-seen-from-near‐simultaneous-TRMM-CloudSat-and.pdf, Download file},

doi = {10.1002/2017jd026569},

year  = {2017},

date = {2017-06-01},

urldate = {2017-06-01},

journal = {J. Geophys. Res.},

volume = {122},

number = {11},

pages = {5988--6003},

publisher = {\"{A}merican Geophysical Union (AGU)},

abstract = {\"{A}lthough Tropical Rainfall Measuring Mission (TRMM) and 

 CloudSat/CALIPSO fly in different orbits, they frequently cross 

 each other so that for the period between 2006 and 2010, a total 

 of 15,986 intersect lines occurred within 20 min of each other 

 from 30°S to 30°N, providing a rare opportunity to study 

 tropical cloud and precipitation regimes and their internal 

 vertical structure from near‐simultaneous measurements by these 

 active sensors. A k‐means cluster analysis of TRMM and CloudSat 

 matchups identifies three tropical cloud and precipitation 

 regimes: the first two regimes correspond to, respectively, 

 organized deep convection with heavy rain and cirrus anvils with 

 moderate rain; the third regime is a convectively suppressed 

 regime that can be further divided into three subregimes, which 

 correspond to, respectively, stratocumulus clouds with drizzle, 

 cirrus overlying low clouds, and nonprecipitating cumulus. 

 Inclusion of CALIPSO data adds to the dynamic range of cloud 

 properties and identifies one more cluster; subcluster analysis 

 further identifies a thin, midlevel cloud regime associated with 

 tropical mountain ranges. The radar‐lidar cloud regimes are 

 compared with the International Satellite Cloud Climatology 

 Project (ISCCP) weather states (WSs) for the extended tropics. 

 Focus is placed on the four convectively active WSs, namely, 

 WS1--WS4. ISCCP WS1 and WS2 are found to be counterparts of 

 Regime 1 and Regime 2 in radar‐lidar observations, respectively. 

 ISCCP WS3 and WS4, which are mainly isolated convection and 

 broken, detached cirrus, do not have a strong association with 

 any individual radar and lidar regimes, a likely effect of the 

 different sampling strategies between ISCCP and active sensors 

 and patchy cloudiness of these WSs."},

keywords = {},

pubstate = {published},

tppubtype = {article}

}