Assessing observed and modelled spatial distributions of ice water path using satellite data

Abstract

Abstract. The climate models used in the IPCC AR4 show large differences in monthly mean ice water path (IWP). The most valuable source of information that can be used to potentially constrain the models is global satellite data. The satellite datasets also have large differences. The retrieved IWP depends on the technique used, as retrievals based on different techniques are sensitive to different parts of the cloud column. Building on the foundation of Waliser et al. (2009), this article provides a more comprehensive comparison between satellite datasets. IWP data from the CloudSat cloud profiling radar provide the most advanced dataset on clouds. For all its unmistakable value, CloudSat data are too short and too sparse to assess climatic distributions of IWP, hence the need to also use longer datasets. We evaluate satellite datasets from CloudSat, PATMOS-x, ISCCP, MODIS and MSPPS in terms of monthly mean IWP, in order to determine the differences and relate them to the sensitivity of the instrument used in the retrievals. This information is also used to evaluate the climate models, to the extent that is possible.ISCCP and MSPPS were shown to have comparatively low IWP values. ISCCP shows particularly low values in the tropics, while MSPPS has particularly low values outside the tropics. MODIS and PATMOS-x were in closest agreement with CloudSat in terms of magnitude and spatial distribution, with MODIS being the better of the two. Additionally PATMOS-x and ISCCP, which have a temporal range long enough to capture the inter-annual variability of IWP, are used in conjunction with CloudSat IWP (after removing profiles that contain precipitation) to assess the IWP Correspondence to: S. Eliasson (s.eliasson@ltu.se) variability and mean of the climate models. In general there are large discrepancies between the individual climate models, and all of the models show problems in reproducing the observed spatial distribution of cloud-ice. Comparisons consistently showed that ECHAM-5 is probably the GCM from IPCC AR4 closest to satellite observations.

CloudSat-constrained cloud ice water path and cloud top height retrievals from MHS 157 and 183.3 GHz radiances

“…The errors in liquid drop size, surface emission/scattering, cloud layer height, and water vapor amount can all degrade the quality of the retrieved IWP. For example, the current operational IWP retrieval algorithm from Microwave Surface and Precipitation Products System (MSPPS), which is based upon a two-stream approximated radiative model solution (Zhao and Weng, 2002) at AMSU-B 89 and 150 GHz channels, was found to under-estimate IWP in comparison with other observations Waliser et al, 2009;Eliasson et al, 2011;Chen et al, 2011). Contamination of cloud ice retrievals was also found over snowy/icy surfaces .…”

“…However, observations of cloud ice have not met the requirement by climate models, showing several folds of IWP differences among various techniques Eliasson et al, 2011). Until cross-instrument consistency is achieved, current cloud ice observations will allow too much variation in cloud properties and become insufficient for constraining the model physics Li et al, 2012).…”

“…Like other A-train sun-synchronous satellites, it samples only two local solar times (01:30 and 13:30 LST) of the cloud diurnal cycle. However, CloudSat data still provide the best characterization of the vertical distribution of global cloud ice (Eliasson et al, 2011), and can be used to cross-calibrate other techniques, especially the passive sensors with limited vertical resolution. .…”

G band atmospheric radars: new frontiers in cloud physics

“…Global average ice water path varies by more than order of magnitude (0.01 and 0.2 kg m −2 ) between different climate models (Solomon et al, 2007). Unfortunately, a similar spread also exists amongst satellite estimates (Eliasson et al, 2011), which makes it difficult to make progress. Measuring snowfall, an important component of the water cycle (Mugnai et al, 2005), is an even more complicated matter because of the enormous complexity of snow crystal habit, density, and particle size distribution.…”

Introducing multisensor satellite radiance-based evaluation for regional Earth System modeling

“…This requires satellite retrievals to make unique assumptions to estimate the geophysical parameters from space. While retrieved geophysical parameters are useful to quantify geophysical parameters (often called Level 2 and Level 3 data sets), it often frustrates evaluating atmospheric models in detail due to different physical assumptions between various satellite retrievals and Earth System models [Eliasson et al, 2011;Waliser et al, 2009].…”