Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator

Abstract

[1] The annual cycle climatology of cloud amount, cloud-top pressure, and optical thickness in two generations of climate models is compared to satellite observations to identify changes over time in the fidelity of simulated clouds. In more recent models, there is widespread reduction of a bias associated with too many highly reflective clouds, with the best models having eliminated this bias. With increased amounts of clouds with lesser reflectivity, the compensating errors that permit models to simulate the time-mean radiation balance have been reduced. Errors in cloud amount as a function of height or climate regime on average show little or no improvement, although greater improvement can be found in individual models. Measuring Changes in the Simulations of Global Cloudiness Over Time[2] The simulation of clouds by climate models is a key ongoing challenge in the numerical representation of Earth's climate. Due to their large impact on Earth's radiation budget, clouds are important for determining aspects of current climate, such as surface air temperatures in many regions [Ma et al., 1996;Curry et al., 1996], the strength and variability of atmospheric circulations [Slingo and Slingo, 1988], and the magnitude of climate changes that result from perturbations in the chemical composition of the atmosphere [IPCC, 2007]. While important, the modeling of clouds is very difficult because most cloud processes happen at scales far smaller than can be resolved by climate models, and thus, their bulk effects must be represented with imperfect parameterizations.[3] Given the efforts of many scientists over several decades to understand cloud processes and improve their representation in models, it is important to ask are climate model simulations of clouds improving and, if so, by how much? Here, we analyze the ability of two generations of climate models to simulate the climatological distribution of clouds and judge fidelity by comparison to several decades of satellite observations. Because of the significant differences between the ways clouds are observed and the ways they are represented in models, we use a "satellite simulator" to increase the chances that differences between the models and observations represent actual model deficiencies. We find that significant progress in the ability of models to simulate clouds has occurred over the last decade, particularly in reducing the over-prediction of highly reflective clouds [Zhang et al., 2005].

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

“…Therefore, the biases cannot be removed by parameter tuning. We should note here that the multi-model mean ISCCP cloud amount (TAU > 1.3) from the CFMIP1 and CFMIP2 ensembles does not show such positive bias at low latitudes (Klein et al, 2013). Therefore, the bias might be a problem specific to the MIROC5 AOGCM.…”

“…If we employed a model other than MIROC5, the biases in the TOA radiation and clouds would be notably different from what we presented. Klein et al (2013) reported that the bias of having too many optically thick clouds has been reduced from CFMIP1 to CFMIP2 MME, with the best models having eliminated this bias. If we used a model with a very small bias in optically thick clouds, we might be able to change the sign of the bias by parameter tuning only.…”

“…Therefore, COSP has been widely used in previous studies, which evaluate clouds simulated by the CMIP5 MME. The studies indicate that the optical thickness of the simulated clouds tends to be overestimated compared with the observation, as in the CMIP3 (Klein et al, 2013;Nam et al, 2012;Zhang et al, 2005). In the present study, we evaluate the parametric uncertainty of this too thick (bright) bias by analyzing the COSP output of the PPE experiment, and discuss how much of the bias can be removed by parameter tuning only.…”

Using satellite and reanalysis data to evaluate the representation of latent heating in extratropical cyclones in a climate model

“…Many studies have used the simulator to evaluate global cloud amounts and types in climate models (e.g., Zhang et al 2005;Williams and Tselioudis 2007;Williams and Webb 2009;Kay et al 2012;Lauer and Hamilton 2013;Klein et al 2013). ISCCP uses an assumption that clouds are plane-parallel and homogeneous in each pixel.…”

The link between outgoing longwave radiation and the altitude at which a spaceborne lidar beam is fully attenuated

“…One reason for this uncertainty is that clouds simulated by climate models in the current climate exhibit large biases compared to observations (e.g., Zhang et al, 2005;Haynes et al, 2007;Chepfer et al, 2008;Williams and Webb, 2009;Marchand and Ackerman, 2010;Chepfer, 2012, 2013;Kay et al, 2012;Nam et al, 2012;Klein et al, 2013), leading to low confidence in the cloud feedbacks predicted by the models.…”