Response of the middle atmosphere to anthropogenic and natural forcings in the CMIP5 simulations with the Max Planck Institute Earth system model
Stenchikov, Georgiy L.
KAUST DepartmentPhysical Sciences and Engineering (PSE) Division
MetadataShow full item record
AbstractThe ECHAM6 atmospheric general circulation model is the atmosphere component of the Max Planck Institute Earth System Model (MPI-ESM) that is used in the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations. As ECHAM6 has its uppermost layer centered at 0.01 hPa in the upper mesosphere, these simulations offer the opportunity to study the middle atmosphere climate change and its relation to the troposphere on the basis of a very comprehensive set of state-of-the-art model simulations. The goals of this paper are (a) to introduce those new features of ECHAM6 particularly relevant for the middle atmosphere, including external forcing data, and (b) to evaluate the simulated middle atmosphere and describe the simulated response to natural and anthropogenic forcings. New features in ECHAM6 with respect to ECHAM5 include a new short-wave radiation scheme, the option to vary spectral irradiance independent of total solar irradiance, and a latitude-dependent gravity-wave source strength. The description of external forcing data focuses on solar irradiance and ozone. Stratospheric temperature trends simulated with the MPI-ESM for the last decades of the 20th century agree well with observations. The future projections depend strongly on the scenario. Under the high emission scenario RCP8.5, simulated temperatures are locally lower by more than 20 K than preindustrial values. Many of the simulated patterns of the responses to natural forcings as provided by solar variability, volcanic aerosols, and El Nino-Southern Oscillation, largely agree with the observations. 2013. American Geophysical Union. All Rights Reserved.
CitationSchmidt H, Rast S, Bunzel F, Esch M, Giorgetta M, et al. (2013) Response of the middle atmosphere to anthropogenic and natural forcings in the CMIP5 simulations with the Max Planck Institute Earth system model. J Adv Model Earth Syst 5: 98-116. doi:10.1002/jame.20014.
The following license files are associated with this item:
Except where otherwise noted, this item's license is described as All articles published in this journal are made available under the terms of the Creative Commons Attribution License. Permission is therefore not required for academic or commercial reuse, provided that full attribution is included in the new work.
Showing items related by title, author, creator and subject.
Assessment of Land Surface Models in a High-Resolution Atmospheric Model during Indian Summer MonsoonAttada, Raju; Kumar, Prashant; Dasari, Hari Prasad (Springer Nature, 2018-04-17)Assessment of the land surface models (LSMs) on monsoon studies over the Indian summer monsoon (ISM) region is essential. In this study, we evaluate the skill of LSMs at 10 km spatial resolution in simulating the 2010 monsoon season. The thermal diffusion scheme (TDS), rapid update cycle (RUC), and Noah and Noah with multi-parameterization (Noah-MP) LSMs are chosen based on nature of complexity, that is, from simple slab model to multi-parameterization options coupled with the Weather Research and Forecasting (WRF) model. Model results are compared with the available in situ observations and reanalysis fields. The sensitivity of monsoon elements, surface characteristics, and vertical structures to different LSMs is discussed. Our results reveal that the monsoon features are reproduced by WRF model with all LSMs, but with some regional discrepancies. The model simulations with selected LSMs are able to reproduce the broad rainfall patterns, orography-induced rainfall over the Himalayan region, and dry zone over the southern tip of India. The unrealistic precipitation pattern over the equatorial western Indian Ocean is simulated by WRF–LSM-based experiments. The spatial and temporal distributions of top 2-m soil characteristics (soil temperature and soil moisture) are well represented in RUC and Noah-MP LSM-based experiments during the ISM. Results show that the WRF simulations with RUC, Noah, and Noah-MP LSM-based experiments significantly improved the skill of 2-m temperature and moisture compared to TDS (chosen as a base) LSM-based experiments. Furthermore, the simulations with Noah, RUC, and Noah-MP LSMs exhibit minimum error in thermodynamics fields. In case of surface wind speed, TDS LSM performed better compared to other LSM experiments. A significant improvement is noticeable in simulating rainfall by WRF model with Noah, RUC, and Noah-MP LSMs over TDS LSM. Thus, this study emphasis the importance of choosing/improving LSMs for simulating the ISM phenomena in a regional model.
Correcting atmospheric effects on InSAR with MERIS water vapour data and elevation-dependent interpolation modelLi, Z. W.; Xu, Wenbin; Feng, G. C.; Hu, J.; Wang, C. C.; Ding, X. L.; Zhu, J. J. (Oxford University Press (OUP), 2012-05)The propagation delay when radar signals travel from the troposphere has been one of the major limitations for the applications of high precision repeat-pass Interferometric Synthetic Aperture Radar (InSAR). In this paper, we first present an elevation-dependent atmospheric correction model for Advanced Synthetic Aperture Radar (ASAR—the instrument aboard the ENVISAT satellite) interferograms with Medium Resolution Imaging Spectrometer (MERIS) integrated water vapour (IWV) data. Then, using four ASAR interferometric pairs over Southern California as examples, we conduct the atmospheric correction experiments with cloud-free MERIS IWV data. The results show that after the correction the rms differences between InSAR and GPS have reduced by 69.6 per cent, 29 per cent, 31.8 per cent and 23.3 per cent, respectively for the four selected interferograms, with an average improvement of 38.4 per cent. Most importantly, after the correction, six distinct deformation areas have been identified, that is, Long Beach–Santa Ana Basin, Pomona–Ontario, San Bernardino and Elsinore basin, with the deformation velocities along the radar line-of-sight (LOS) direction ranging from −20 mm yr−1 to −30 mm yr−1 and on average around −25 mm yr−1, and Santa Fe Springs and Wilmington, with a slightly low deformation rate of about −10 mm yr−1 along LOS. Finally, through the method of stacking, we generate a mean deformation velocity map of Los Angeles over a period of 5 yr. The deformation is quite consistent with the historical deformation of the area. Thus, using the cloud-free MERIS IWV data correcting synchronized ASAR interferograms can significantly reduce the atmospheric effects in the interferograms and further better capture the ground deformation and other geophysical signals.
Linear versus Nonlinear Filtering with Scale-Selective Corrections for Balanced Dynamics in a Simple Atmospheric ModelSubramanian, Aneesh C.; Hoteit, Ibrahim; Cornuelle, Bruce; Miller, Arthur J.; Song, Hajoon (American Meteorological Society, 2012-11)This paper investigates the role of the linear analysis step of the ensemble Kalman filters (EnKF) in disrupting the balanced dynamics in a simple atmospheric model and compares it to a fully nonlinear particle-based filter (PF). The filters have a very similar forecast step but the analysis step of the PF solves the full Bayesian filtering problem while the EnKF analysis only applies to Gaussian distributions. The EnKF is compared to two flavors of the particle filter with different sampling strategies, the sequential importance resampling filter (SIRF) and the sequential kernel resampling filter (SKRF). The model admits a chaotic vortical mode coupled to a comparatively fast gravity wave mode. It can also be configured either to evolve on a so-called slow manifold, where the fast motion is suppressed, or such that the fast-varying variables are diagnosed from the slow-varying variables as slaved modes. Identical twin experiments show that EnKF and PF capture the variables on the slow manifold well as the dynamics is very stable. PFs, especially the SKRF, capture slaved modes better than the EnKF, implying that a full Bayesian analysis estimates the nonlinear model variables better. The PFs perform significantly better in the fully coupled nonlinear model where fast and slow variables modulate each other. This suggests that the analysis step in the PFs maintains the balance in both variables much better than the EnKF. It is also shown that increasing the ensemble size generally improves the performance of the PFs but has less impact on the EnKF after a sufficient number of members have been used.