Variability and change of the Earth\'s climate are of fundamental importance to humankind. In particular anthropogenic climate change has been considered widely as one of the most urgent concerns for the society (United Nations, 1992, 2002). It is therefore vital to improve the understanding of the Earth\'s climate system and its variability.

Parameterization development and evaluation ideally takes a two-step approach (Lohmann et al., 2007). Insight into new processes, and initial parameterization formulation should be guided by theory, process-level observations (laboratory experiments or field studies) or, if these are unavailable, by high-resolution modelling. However, once implemented into large-scale atmospheric models, a thorough testing and evaluation is required in order to assure that the parameterization works satisfactorily for all weather situations and at the scales the model is applied to. Satellite observations are probably the most valuable source of information for this purpose, since they offer a large range of parameters over comparatively long time series and with a very large, to global, coverage. However, satellites usually retrieve parameters in a rather indirect way, and some quantities (e.g., vertical wind velocities) are unavailable. It is thus essential for model evaluation 1. to assure comparability; and, 2. to develop and apply metrics that circumvent the limitations of satellite observations and help to learn about parameterizations. In terms of comparability, the implementation of so-called \"satellite simulators\" has emerged as the approach of choice, in which satellite retrievals are emulated, making use of model information about the subgrid-scale variability of clouds, and creating summary statistics (Bodas-Salcedo et al., 2011; Nam and Quaas, 2012; Nam et al., 2014). In terms of process-oriented metrics, a large range of approaches has been developed, e.g. investigating the life cycle of cirrus from convective detrainment (Gehlot and Quaas, 2012), or focusing on the details of microphysical processes (Suzuki et al., 2011). Besides such techniques focusing on individual parameterizations, the data assimilation technique might be exploited, by objectively adjusting convection parameters and learning about parameter choices and parameterizations in this way (Schirber et al., 2013).In this chapter, we will first introduce the available satellite data, consider their limitations and the approaches to account for these, and then discuss observations-based process-oriented metrics that have been developed so far.

Variability and change of the Earth\''s climate are of fundamental importance to humankind. In particular anthropogenic climate change has been considered widely as one of the most urgent concerns for the society (United Nations, 1992, 2002). It is therefore vital to improve the understanding of the Earth\''s climate system and its variability.

Parameterization development and evaluation ideally takes a two-step approach (Lohmann et al., 2007). Insight into new processes, and initial parameterization formulation should be guided by theory, process-level observations (laboratory experiments or field studies) or, if these are unavailable, by high-resolution modelling. However, once implemented into large-scale atmospheric models, a thorough testing and evaluation is required in order to assure that the parameterization works satisfactorily for all weather situations and at the scales the model is applied to. Satellite observations are probably the most valuable source of information for this purpose, since they offer a large range of parameters over comparatively long time series and with a very large, to global, coverage. However, satellites usually retrieve parameters in a rather indirect way, and some quantities (e.g., vertical wind velocities) are unavailable. It is thus essential for model evaluation 1. to assure comparability; and, 2. to develop and apply metrics that circumvent the limitations of satellite observations and help to learn about parameterizations. In terms of comparability, the implementation of so-called \"satellite simulators\" has emerged as the approach of choice, in which satellite retrievals are emulated, making use of model information about the subgrid-scale variability of clouds, and creating summary statistics (Bodas-Salcedo et al., 2011; Nam and Quaas, 2012; Nam et al., 2014). In terms of process-oriented metrics, a large range of approaches has been developed, e.g. investigating the life cycle of cirrus from convective detrainment (Gehlot and Quaas, 2012), or focusing on the details of microphysical processes (Suzuki et al., 2011). Besides such techniques focusing on individual parameterizations, the data assimilation technique might be exploited, by objectively adjusting convection parameters and learning about parameter choices and parameterizations in this way (Schirber et al., 2013).In this chapter, we will first introduce the available satellite data, consider their limitations and the approaches to account for these, and then discuss observations-based process-oriented metrics that have been developed so far.

Land-use changes impact the energy balance of the Earth system, and feedbacks in the Earth system can dampen or amplify this perturbation. We analyze here from satellite data the response of clouds and subsequently radiation to a change of land use for the example of deforestation in the Amazon Basin. In this region, the characteristics of different cloud types over two vegetation types (forest and crop-/grasslands) were calculated for a time period of five years by using satellite data from the instruments MODIS and CERES. The cloud types are defined according to height, optical thickness, and fraction of cloud cover. For calculating the radiative forcing caused by deforestation, the dependency of spatial and temporal averages for the reflected shortwave and outgoing longwave radiation of the top of the atmosphere on vegetation types were determined as well. The results show distinct differences in cloud cover and radiative forcing over crop-/grasslands and forests for the two vegetation regimes, implying a potentially significant positive cloud feedback to deforestation.

Land-use changes impact the energy balance of the Earth system, and feedbacks in the Earth system can dampen or amplify this perturbation. We analyze here from satellite data the response of clouds and subsequently radiation to a change of land use for the example of deforestation in the Amazon Basin. In this region, the characteristics of different cloud types over two vegetation types (forest and crop-/grasslands) were calculated for a time period of five years by using satellite data from the instruments MODIS and CERES. The cloud types are defined according to height, optical thickness, and fraction of cloud cover. For calculating the radiative forcing caused by deforestation, the dependency of spatial and temporal averages for the reflected shortwave and outgoing longwave radiation of the top of the atmosphere on vegetation types were determined as well. The results show distinct differences in cloud cover and radiative forcing over crop-/grasslands and forests for the two vegetation regimes, implying a potentially significant positive cloud feedback to deforestation.