%0 Journal Article %1 Lauer2012Simulating %A Lauer, Axel %A Hamilton, Kevin %B Journal of Climate %D 2012 %I American Meteorological Society %J J. Climate %K climatechange clouds CMIP %N 11 %P 3823--3845 %R 10.1175/jcli-d-12-00451.1 %T Simulating Clouds with Global Climate Models: A Comparison of CMIP5 Results with CMIP3 and Satellite Data %U http://dx.doi.org/10.1175/jcli-d-12-00451.1 %V 26 %X Abstract Clouds are a key component of the climate system affecting radiative balances and the hydrological cycle. Previous studies from the Coupled Model Intercomparison Project phase 3 (CMIP3) showed quite large biases in the simulated cloud climatology affecting all GCMs as well as a remarkable degree of variation among the models that represented the state of the art circa 2005. Here the progress that has been made in recent years is measured by comparing mean cloud properties, interannual variability, and the climatological seasonal cycle from the CMIP5 models with satellite observations and with results from comparable CMIP3 experiments. The focus is on three climate-relevant cloud parameters: cloud amount, liquid water path, and cloud radiative forcing. The comparison shows that intermodel differences are still large in the Coupled Model Intercomparison Project phase 5 (CMIP5) simulations, and reveals some small improvements of particular cloud properties in some regions in the CMIP5 ensemble over CMIP3. In CMIP5 there is an improved agreement of the modeled interannual variability of liquid water path and of the modeled longwave cloud forcing over mid- and high-latitude oceans with observations. However, the differences in the simulated cloud climatology from CMIP3 and CMIP5 are generally small, and there is very little to no improvement apparent in the tropical and subtropical regions in CMIP5. Comparisons of the results from the coupled CMIP5 models with their atmosphere-only versions run with observed SSTs show remarkably similar biases in the simulated cloud climatologies. This suggests the treatments of subgrid-scale cloud and boundary layer processes are directly implicated in the poor performance of current GCMs in simulating realistic cloud fields.

@article{Lauer2012Simulating, abstract = {Abstract Clouds are a key component of the climate system affecting radiative balances and the hydrological cycle. Previous studies from the Coupled Model Intercomparison Project phase 3 (CMIP3) showed quite large biases in the simulated cloud climatology affecting all GCMs as well as a remarkable degree of variation among the models that represented the state of the art circa 2005. Here the progress that has been made in recent years is measured by comparing mean cloud properties, interannual variability, and the climatological seasonal cycle from the CMIP5 models with satellite observations and with results from comparable CMIP3 experiments. The focus is on three climate-relevant cloud parameters: cloud amount, liquid water path, and cloud radiative forcing. The comparison shows that intermodel differences are still large in the Coupled Model Intercomparison Project phase 5 (CMIP5) simulations, and reveals some small improvements of particular cloud properties in some regions in the CMIP5 ensemble over CMIP3. In CMIP5 there is an improved agreement of the modeled interannual variability of liquid water path and of the modeled longwave cloud forcing over mid- and high-latitude oceans with observations. However, the differences in the simulated cloud climatology from CMIP3 and CMIP5 are generally small, and there is very little to no improvement apparent in the tropical and subtropical regions in CMIP5. Comparisons of the results from the coupled CMIP5 models with their atmosphere-only versions run with observed SSTs show remarkably similar biases in the simulated cloud climatologies. This suggests the treatments of subgrid-scale cloud and boundary layer processes are directly implicated in the poor performance of current GCMs in simulating realistic cloud fields.}, added-at = {2018-06-18T21:23:34.000+0200}, author = {Lauer, Axel and Hamilton, Kevin}, biburl = {https://www.bibsonomy.org/bibtex/20c79eca542b9e82ca1b188e560ebdd12/pbett}, booktitle = {Journal of Climate}, citeulike-article-id = {11837194}, citeulike-linkout-0 = {http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-12-00451.1}, citeulike-linkout-1 = {http://dx.doi.org/10.1175/jcli-d-12-00451.1}, comment = {(private-note)I think the point is that CMPI5 clouds don't improve much on CMIP3 clouds, and both are not very good. It's all about the subgrid parameterisations.}, day = 7, doi = {10.1175/jcli-d-12-00451.1}, interhash = {300d5c6076b1c3266972094bac470059}, intrahash = {0c79eca542b9e82ca1b188e560ebdd12}, journal = {J. Climate}, keywords = {climatechange clouds CMIP}, month = dec, number = 11, pages = {3823--3845}, posted-at = {2012-12-10 10:09:11}, priority = {2}, publisher = {American Meteorological Society}, timestamp = {2020-04-15T09:30:16.000+0200}, title = {Simulating Clouds with Global Climate Models: A Comparison of CMIP5 Results with CMIP3 and Satellite Data}, url = {http://dx.doi.org/10.1175/jcli-d-12-00451.1}, volume = 26, year = 2012 }