Evaluation of Water Vapor Isotopologues and Humidity Bias from ECHAM4 Using TES and SCIAMACHY Satellite Observations

Abstract:

Over the last-decade, global scale datasets of atmospheric water vapor isotopologues have become available from different remote-sensing instruments. Due to the observational geometry and the spectral ranges that are used, only few satellites sample water isotopologues in the lower troposphere, where the bulk of hydrological processes within the atmosphere take place. Here we use TES and SCIAMACHY observations to evaluate the model performance especially the model humidity bias at higher altitudes, which is commonly found in most GCM models. Two methods to handle the humidity bias from the model and complexity of cross dependence of retrieved δD on atmospheric humidity (a posteriori processing) are used. The result shows that the spatial pattern of δD is in good agreement between model and observations. However, ECHAM model convoluted with Averaging Kernel (ECHAM_AK) produces unrealistically high δD values over the Tibetan plateau. This unrealistic pattern is due to a high bias in the model humidity over the Himalaya region. The problem disappears when we take into account the humidity bias correction and a posteriori analysis. We also find that ECHAM and ECHAM_AK results produce considerably higher δD values than TES and SCIAMACHY in the northern hemisphere during summer. Other features of the water isotopologue cycle such as the seasonally varying signal in the tropics due to the movement of the Inter Tropical Convergence Zone (ICTZ) are captured in the model and satellites. This ICTZ pattern is seen from TES and SCIAMACHY datasets at lower altitudes where many processes contributing to the amount effect occur. In contrast, ECHAM and ECHAM_AK show the seesaw pattern throughout the entire atmospheric column, both at the lower and high layers. It was speculated that GCMs in general show strong coherence between processes at lower altitudes (such as Sea Surface Temperature variations) and associated features at high altitudes (such as high cloud formation and the height of convective cloud). In this case the common humidity bias due to strong convection in the model or of the large-scale circulation or to excessive diffusion during the transport of water vapor might be a further consequence of these known model problems.