DM-1.3 | REGIONAL CLIMATE SYSTEM MODELLING AND BIODIVERSITY [RCM]
In project E1.3 the research focus is on the interaction between the atmosphere and the land surface, or the ocean, respectively, in the regional climate modelling system COSMO-CLM. The land surface processes have a significant impact on near-surface atmospheric phenomena. They determine, among others, near-surface sensible and latent heat fluxes and the radiation budget, and thus influence atmosphere and land characteristics, such as temperature and humidity, the structure of the planetary boundary layer, and even cloud formation processes.
Verifications have shown that the ground heat flux computed by the land surface scheme TERRA of the COSMO-CLM model is systematically overestimated under dry conditions. Since this flux is part of the surface energy balance it affects the other components like the turbulent heat fluxes and the surface temperature. This means, an overestimation of the ground heat flux during daytime leads to an underestimation of the other surface fluxes and a reduced surface warming. During afternoon and night the opposite behaviour is obtained. Data from the Meteorological Observatory Lindenberg of the German Weather Service were used to analyse this model behaviour. In sensitivity experiments with the soil model TERRA it turned out that the simulated ground heat flux is particularly influenced by the thermal conductivity of the soil and its dependence on the soil moisture, but also by the shading effect of the incoming solar radiation due to the vegetation cover. When taking these two effects in the model into account the ground heat flux and the simulated diurnal cycles of the soil temperature are considerably improved (s. Fig. 1).
Figure 1: Diurnal cycles of the soil temperature at a depth of 18 cm in Lindenberg/Falkenberg in May 2008, in the measurement (green), in the reference model version (blue) and in the improved model version (red). The combination of both model modifications with respect to shading and soil thermal conductivity gives the best results.
The vegetation in COSMO-CLM is currently described in a very simplistic way. It is prescribed by only a few parameters, like e.g. vegetation ratio, leaf area index and root depth. These parameters follow a prescribed seasonal cycle which is purely conceptional and has no interannual variability. It is the aim of this work to implement dynamic vegetation in the model which can respond to interannual atmospheric variability and therefore describe a more realistic vegetation seasonality. This is essential for simulations of climate variability and in particular of climate change. The importance of the vegetation in the climate system also in the context of land use is illustrated in Fig. 2.
Figure 2: Mean change of latent heat flux [W/m^2] in the year 1959 in the Main catchment area assuming potential vegetation (deciduous forest) instead of actual vegetation.
In project E1.3 an improved parameterisation of soil hydrology was implemented in the model system during the first funding phase of BiK-F (additional funds provided by the state of Hessen, project network INKLIM-A). Furthermore, the ocean model NEMO was coupled to the model system for the simulation of the Mediterranean Sea. This will now be extended by a sea ice module for application in the Baltic Sea.
Trang Pham van, Ph.D. student
Asharaf, S. & B. Ahrens (2013) : Soil-moisture memory in the regional climate model COSMO-CLM during the Indian summer monsoon season. - Journal of Geophysical Research - Atmospheres 118(12): 6144-6151.
Asharaf, S., Dobler, A. & B. Ahrens (2011) : Soil moisture initialization effects in the Indian monsoon system. - Advances in Science and Research 6: 161–165.
Asharaf, S., Dobler, A. & B. Ahrens (2012) : Soil moisture-precipitation feedback processes in the Indian summer monsoon season. - Journal of Hydrometeorology 13: 1461–1474.
Brinckmann, S., Trentmann J. & B. Ahrens (2014) : Homogeneity analysis of the CM SAF surface solar irradiance dataset derived from geostationary satellite observations. - Remote Sensing 6(1): 352-378.
Casanova, S. & B. Ahrens (2009) : On the Weighting of Multimodel Ensembles in Seasonal and Short-Range Weather Forecasting. - Monthly Weather Review 137(11): 3811-3822.
Dobler, A. & B. Ahrens (2011) : Four climate change scenarios for the Indian summer monsoon by the regional climate model COSMO-CLM. - Journal of Geophysical Research - Atmospheres 116: D24104: 27.
Dobler, A. & B. Ahrens (2010) : Analysis of the Indian summer monsoon system in the regional climate model COSMO-CLM. - Journal of Geophysical Research, 115, D16101, doi:10.1029/2009JD013497.
Dobler, A., Yaoming, M., Sharma, N., Kienberger, S. & B. Ahrens (2011) : Regional climate projections in two alpine river basins: Upper Danube and Upper Brahmaputra. - Advances in Science and Research 7: 11-20.
Kalinka, F. & B. Ahrens (2011) : A modification of the mixed form of Richards equation and its application in vertically inhomogeneous soils. - Advances in Science and Research 6: 123-127.
Kothe, S., Good, E., Obregón, A., Ahrens, B. & H. Nitsche (2013) : Satellite-based sunshine duration for Europe. - Remote Sensing 5(6): 2943-2972.
Krähenmann, S. & B. Ahrens (2010) : On daily interpolation of precipitation backed with secondary information. - Advances in Science and Research 4: 29-35.
Krähenmann, S. & B. Ahrens (2013) : Spatial gridding of daily maximum and minimum 2 m temperatures supported by satellite observations. - Meteorology and Atmospheric Physics 120(1-2): 87-105.
Krähenmann, S., Bissolli, P., Rapp, J. & B. Ahrens (2011) : Spatial gridding of daily maximum and minimum temperatures in Europe. - Meteorology and Atmospheric Physics 114: 151-161.
Krähenmann, S., Kothe, S., Panitz, H.-J. & B. Ahrens (2013)
Evaluation of daily maximum and minimum 2-m temperatures as simulated with the regional climate model COSMO-CLM over Africa. - Meteorologische Zeitschrift 22(3): 297-316.
Krähenmann, S., Obregon, A., Müller, R., Trentmann, J. & B. Ahrens (2013) : A satellite-based surface radiation climatology derived by combining climate data records and near-real-time data. - Remote Sensing 5(9): 4693-4718.
Kumar, P., Wiltshire, A., Mathison, C., Asharaf, S., Ahrens, B., Lucas-Picher, P., Christensen, J.H., Gobiet, A., Saeed, F., Hagemann, S. & D. Jacob (2013) : Downscaled climate change projections with uncertainty assessment over India using a high resolution multi-model approach. - Science of the Total Environment 468-469: S18-S30.
Lucas-Picher, P., Christensen, J.H., Saeed, F., Kumar, P., Asharaf, S., Ahrens, B., Wiltshire, A., Jacob, D. & S. Hagemann (2011) : Can regional climate models represent the Indian Monsoon? - Journal of Hydrometeorology 12: 849-868.
Pfeifroth, U., Hollmann, R. & B. Ahrens (2012) : Cloud cover diurnal cycles in satellite data and regional climate model simulations. - Meteorologische Zeitschrift 21(6): 551-560.
Pfeifroth, U., Müller, R. & B. Ahrens (2013) : Evaluation of satellite-based and reanalysis precipitation data in the tropical Pacific. - Journal of Applied Meteorology and Climatology 52: 634–644.
Tang, H., Eronen, J.T., Micheels, A. & B. Ahrens (2013) : Strong interannual variation of the Indian summer monsoon in the Late Miocene. - Climate Dynamics 41(1): 135-153.
Tang, H., Micheels, A., Eronen, J.T., Ahrens, B. & M. Fortelius (2013) : Asynchronous responses of East Asian and Indian summer monsoons to mountain uplift shown by regional climate modelling experiments. - Climate Dynamics 40(5-6): 1531-1549.
Tödter, J. & B. Ahrens (2012) : Generalization of the Ignorance Score: Continuous Ranked Version and Its Decomposition. - Monthly Weather Review 140: 2005-2017.
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