ALBERT

Description: [1]  This paper provides a first overview of the performance of state-of-the-art global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) in simulating climate extremes indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), and compares it to that in the previous model generation (CMIP3). For the first time, the indices based on daily temperature and precipitation are calculated with a consistent methodology across multi-model simulations and four reanalysis datasets (ERA40, ERA-Interim, NCEP/NCAR, and NCEP-DOE) and are made available at the ETCCDI indices archive website. Our analyses show that the CMIP5 models are generally able to simulate climate extremes and their trend patterns as represented by the indices in comparison to a gridded observational indices dataset (HadEX2). The spread amongst CMIP5 models for several temperature indices is reduced compared to CMIP3 models, despite the larger number of models participating in CMIP5. Some improvements in the CMIP5 ensemble relative to CMIP3 are also found in the representation of the magnitude of precipitation indices. We find substantial discrepancies between the reanalyses, indicating considerable uncertainties regarding their simulation of extremes. The overall performance of individual models is summarized by a “portrait” diagram based on root-mean-square errors of model climatologies for each index and model relative to four reanalyses. This metric analysis shows that the median model climatology outperforms individual models for all indices, but the uncertainties related to the underlying reference datasets are reflected in the individual model performance metrics.

Description: [1]  This study provides an overview of projected changes in climate extreme indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The temperature- and precipitation-based indices are computed with a consistent methodology for climate change simulations using different emission scenarios in the Coupled Model Intercomparison Project Phase 3 (CMIP3) and Phase 5 (CMIP5) multi-model ensembles. We analyze changes in the indices on global and regional scales over the twenty-first century relative to the reference period 1981–2000. In general, changes in indices based on daily minimum temperatures are found to be more pronounced than in indices based on daily maximum temperatures. Extreme precipitation generally increases faster than total wet-day precipitation. In regions, such as Australia, Central America, South Africa and the Mediterranean region, increases in consecutive dry days coincide with decreases in heavy precipitation days and maximum consecutive 5-day precipitation, which indicates future intensification of dry conditions in these regions. Particularly for the precipitation-based indices, there can be a wide disagreement about the sign of change between the models in some regions. Changes in temperature and precipitation indices are most pronounced under RCP8.5, with projected changes exceeding those discussed in previous studies based on SRES scenarios. The complete set of indices is made available via the ETCCDI indices archive to encourage further studies on the various aspects of changes in extremes.

Description: Comparisons of collocated Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) ice cloud optical thickness (τ) and effective radius (r e ) retrievals and their uncertainty estimates are described at the pixel-scale. While an estimated 27% of all AIRS fields of view contain ice cloud, only 7% contain spatially uniform ice according to the MODIS 1-km optical properties phase mask. The ice cloud comparisons are partitioned by horizontal variability in cloud amount, cloud-top thermodynamic phase, vertical layering of clouds, and other parameters. The magnitudes of τ and r e and their relative uncertainties are compared for a wide variety of pixel-scale cloud complexity. The correlations of τ and r e between the two instruments are strong functions of horizontal cloud heterogeneity and vertical cloud structure, with the highest correlations found in single-layer, horizontally homogeneous clouds over the low-latitude tropical oceans. While the τ comparisons are essentially unbiased for homogeneous ice cloud with variability that depends on scene complexity, a bias of 5–10μm remains in r e within the most homogeneous scenes identified, consistent with known radiative transfer differences in the visible and infrared bands. The AIRS and MODIS uncertainty estimates reflect the wide variety of cloud complexity, with greater magnitudes in scenes with larger horizontal variability. The AIRS averaging kernels suggest scene-dependent information content that is consistent with infrared sensitivity to ice clouds. The AIRS normalized-χ 2 radiance fits suggest that accounting for horizontal cloud variability is likely to improve the AIRS ice cloud retrievals.