Ice clouds in satellite observations and climate models

Abstract: Ice clouds have an important role in climate. They are strong modulators of the outgoing longwave radiation and the incoming shortwave radiation and are an integral part of the hydrological cycle. However, our knowledge about them is inadequate. Climate models are far from consensus on the magnitude and spatial distribution of several cloud parameters, including the column integrated cloud ice amount, called Ice Water Path (IWP). The lack of adequate constraints from observations is a main contributor to the non-consensus. Cloud ice retrievals from satellite measurements are an important source of observations, since they are global and continuous. However, they carry large uncertainties since different sensors are sensitive to different aspects of clouds, and because clouds are largely inhomogeneous with complicated microphysical properties. Satellite observations are also notoriously difficult to use for model evaluation, due to a mismatch on how cloud parameters are defined in the models compared to what is actually observed. No satellite instrument can measure information from the entire cloud column, as desired from the model point of view. This thesis mainly concerns IWP, which is one of the key cloud parameters. By measuring clouds using different techniques at different wavelengths, the IWP retrievals are sensitive to different parts of the ice particle size distribution, and different depths in the cloud. A main aim of the PhD project is to assess the agreement of datasets based on different techniques and how they may be complementary. This investigation of IWP in observations and models starts by a comparison study of monthly averaged IWP from a climate perspective. The study shows that the differences in IWP within a group of models, and compared to observations are up to an order of magnitude. This confirmed results from previous studies, but in this study, large differences in the spatial distribution of IWP are also identified. The spatial distributions of modelled IWP indicate that they are in disagreement on where the Tropical convective regions are and how much IWP is found there in relation to the global averaged IWP. However, the observational datasets also differ by up to an order of magnitude and the uncertainties for the monthly averaged observations are almost intangibly large. This prompted a new study comparing strictly collocated observations to each other. By doing so, large uncertainties caused by spatially and temporally averaging data were removed. DARDAR, with IWP retrievals based on a combination of Radar and Lidar measurements, is regarded as the best dataset of IWP, and was therefore chosen as the reference dataset. This study determines that DARDAR has a relatively low uncertainty of between 20% to 50%. The validity ranges of the other datasets, i.e., the IWP values where data are trustworthy, are determined by comparing to DARDAR IWP. Once established for each dataset, the systematic and random errors of each dataset are quantified. It is shown that retrievals based on solar reflectance measurements are sensitive to the largest range of IWP values, from ∼30 gm-2 to ∼7000 gm-2, and have random uncertainties less than a factor of two throughout most of this range. To analyse the uncertainties further, the collocated measurements are assessed separately in different types of cloudy scenarios. It is shown that large uncertainties are attributed to the assumed cloud phase and the choice of IWP parameterisations. Further in depth studies on models were carried out using the EC-Earth climate model. A validation study of several upper tropospheric parameters showed that the model captures most large-scale features but has problems with clouds. This led to another study comparing the modelled evolution of several atmospheric variables before and after deep convection events to that of observations. A follow-up study analyses the impacts of clouds on upper tropospheric humidity (UTH) retrievals depending on if they are based on microwave or infrared measurements. By these cross-dataset comparisons we are closer to understanding how to utilise datasets that normally are not comparable due to their different sensitivities.