Effectiveness and limitations of parameter tuning in reducing biases of top-of-atmosphere radiation and clouds in MIROC version 5

Abstract

Abstract. This study discusses how much of the biases in top-of-atmosphere (TOA) radiation and clouds can be removed by parameter tuning in the present-day simulation of a climate model in the Coupled Model Inter-comparison Project phase 5 (CMIP5) generation. We used output of a perturbed parameter ensemble (PPE) experiment conducted with an atmosphere-ocean general circulation model (AOGCM) without flux adjustment. The Model for Interdisciplinary Research on Climate version 5 (MIROC5) was used for the PPE experiment. Output of the PPE was compared with satellite observation data to evaluate the model biases and the parametric uncertainty of the biases with respect to TOA radiation and clouds. The results indicate that removing or changing the sign of the biases by parameter tuning alone is difficult. In particular, the cooling bias of the shortwave cloud radiative effect at low latitudes could not be removed, neither in the zonal mean nor at each latitudelongitude grid point. The bias was related to the overestimation of both cloud amount and cloud optical thickness, which could not be removed by the parameter tuning either. However, they could be alleviated by tuning parameters such as the maximum cumulus updraft velocity at the cloud base. On the other hand, the bias of the shortwave cloud radiative effect in the Arctic was sensitive to parameter tuning. It could be removed by tuning such parameters as albedo of ice and snow both in the zonal mean and at each grid point. The obtained results illustrate the benefit of PPE experiments which provide useful information regarding effectiveness and limitations of parameter tuning. Implementing a shallow convection parameterization is suggested as a potential measure to alleviate the biases in radiation and clouds.

Evaluating models' response of tropical low clouds to SST forcings using CALIPSO observations

“…However, we could only compute the LTS with the output available from other models. Note that using convective and stratiform CALIPSO-GOCCP (circles, 2007, ISCCP (triangles, 1999-2008, Rossow and Schiffer, 1999, ISCCP Q15 (squares, 1999-2008, modified to take into account the shielding effect of high clouds following Qu et al, 2015), MODIS retrieval (stars, 2003-2015), MODIS mask (diamonds, 2003-2015 partly cloudy pixels, Pincus et al, 2012) and MISR (right triangles, 2001. Cloud types are defined based on the subsidence regime for "all" clouds (ω 500 >10 hPa d −1 ) and further based on four regions described in Fig.…”

“…In contrast, we use CALIPSO-GOCCP, which has a shorter time record and poorer sampling but a greater sensitivity to trade cumulus clouds due to both its narrower horizontal footprint and its better instrument sensitivity to liquid cloud particles. However, because we are using different methods and regions in our study, we included two International Satellite Cloud Climatology Project (ISCCP) estimates (Qu et al, 2015;Pincus et al, 2012), two Moderate Resolution Imaging Spec-troradiometer (MODIS) estimates and one Multiangle Imaging Spectroradiometer (MISR) estimate of LCC/ SST for comparison. For this comparison, both night-and daytime CALIPSO-GOCCP data are used to maximize the sample size and all datasets are reprojected onto a 2.5 • × 2.5 • grid.…”

“…For this comparison, both night-and daytime CALIPSO-GOCCP data are used to maximize the sample size and all datasets are reprojected onto a 2.5 • × 2.5 • grid. The original ISCCP dataset is the same used by Pincus et al (2012), a GCM-oriented ISCCP dataset prepared to facilitate the evaluation of GCMs (consistent with the ISCCP simulator) based on a subset of ISCCP variables (Rossow and Schiffer, 1999). The second ISCCP dataset, so-called ISCCP Q15, is derived from the first one.…”

Climate Impact of Cloud Water Inhomogeneity through Microphysical Processes in a Global Climate Model

“…where a BR is a ''tunable'' constant (Ogura et al 2017) and L c is the product of q c and air density r a (kg m 23 ). In the original cloud scheme of MIROC6, the cloud inhomogeneity is not considered in the autoconversion process.…”

The End of the Wait for Climate Sensitivity?

“…Using this approach could reduce the computational burden of producing multiple structural variants and perturbed physics ensembles to explore a range of potential climate responses using fully coupled configurations, where previously groups were limited by time and computational resources to a single model configuration. Previous efforts to explore coupled GCM parameter uncertainty have been forced to make inferences from atmosphere-only simulations Ogura et al (2017), use older or cheaper model configurations Williamson et al (2015), or use flux-corrected models Frame et al (2008).…”