No abstract

No abstract

No abstract

Abstract. From the rural Global Atmosphere Watch (GAW) site Hohenpeissenberg in the pre-alpine area of southern Germany, a data set of 24 C2–C8 non-methane hydrocarbons over a period of 7 years was analyzed. Receptor modeling was performed by positive matrix factorization (PMF) and the resulting factors were interpreted with respect to source profiles and photochemical aging. Differing from other studies, no direct source attribution was intended because, due to chemistry along transport, mass conservation from source to receptor is not given. However, at remote sites such as Hohenpeissenberg, the observed patterns of non-methane hydrocarbons can be derived from combinations of factors determined by PMF. A six-factor solution showed high stability and the most plausible results. In addition to a biogenic and a background factor of very stable compounds, four additional anthropogenic factors were resolved that could be divided into two short- and two long-lived patterns from evaporative sources/natural gas leakage and incomplete combustion processes. The volume or mass contribution at the site over the entire period was, in decreasing order, from the following factor categories: background, gas leakage and long-lived evaporative, residential heating and long-lived combustion, short-lived evaporative, short-lived combustion, and biogenic. The importance with respect to reactivity contribution was generally in reverse order, with the biogenic and the short-lived combustion factors contributing most. The seasonality of the factors was analyzed and compared to results of a simple box model using constant emissions and the photochemical decay calculated from the measured annual cycles of OH radicals and ozone. Two of the factors, short-lived combustion and gas leakage/long-lived evaporative, showed winter/summer ratios of about 9 and 7, respectively, as expected from constant source estimations. Contrarily, the short-lived evaporative emissions were about 3 times higher in summer than in winter, while residential heating/long-lived combustion emissions were about 2 times higher in winter than in summer.

Abstract. A modification of the widely used SPPT (Stochastically Perturbed Parametrisation Tendencies) scheme is proposed and tested in a Convection-permitting – Limited Area Ensemble Forecasting system (C-LAEF) developed at ZAMG (Zentralanstalt für Meteorologie und Geodynamik). The tendencies from four physical parametrization schemes are perturbed: radiation, shallow convection, turbulence, and microphysics. Whereas in SPPT the total model tendencies are perturbed, in the present approach (pSPPT hereinafter) the partial tendencies of the physics parametrization schemes are sequentially perturbed. Thus, in pSPPT an interaction between the uncertainties of the different physics parametrization schemes is sustained and a more physically consistent relationship between the processes is kept. Two configurations of pSPPT are evaluated over two separate months (one in summer and another in winter). Both schemes increase the stability of the model and lead to statistically significant improvements in the probabilistic performance compared to a reference run without stochastic physics. An evaluation of selected test cases shows that the positive effect of stochastic physics is much more pronounced on days with high convective activity. Small discrepancies in the humidity analysis can be dedicated to the use of a very simple supersaturation adjustment. This and other adjustments are discussed to provide some suggestions for future investigations.

Abstract. Downstream models are often used in order to study regional impacts of climate and climate change on the land surface. For this purpose, they are usually driven offline (i.e., 1-way) with results from regional climate models (RCMs). However, the offline approach does not allow for feedbacks between these models. Thereby, the land surface of the downstream model is usually completely different to the land surface which is used within the RCM. Thus, this study aims at investigating the inconsistencies that arise when driving a downstream model offline instead of interactively coupled with the RCM, due to different feedbacks from the use of different land surface models (LSM). Therefore, two physically based LSMs which developed from different disciplinary backgrounds are compared in our study: while the NOAH-LSM was developed for the use within RCMs, PROMET was originally developed to answer hydrological questions on the local to regional scale. Thereby, the models use different physical formulations on different spatial scales and different parameterizations of the same land surface processes that lead to inconsistencies when driving PROMET offline with RCM output. Processes that contribute to these inconsistencies are, as described in this study, net radiation due to land use related albedo and emissivity differences, the redistribution of this net radiation over sensible and latent heat, for example, due to different assumptions about land use impermeability or soil hydraulic reasons caused by different plant and soil parameterizations. As a result, simulated evapotranspiration, e.g., shows considerable differences of max. 280 mm yr −1 . For a full interactive coupling (i.e., 2-way) between PROMET and the atmospheric part of the RCM, PROMET returns the land surface energy fluxes to the RCM and, thus, provides the lower boundary conditions for the RCM subsequently. Accordingly, the RCM responses to the replacement of the LSM with overall increased annual mean near surface air temperature (+1 K) and less annual precipitation (−56 mm) with different spatial and temporal behaviour. Finally, feedbacks can set up positive and negative effects on simulated evapotranspiration, resulting in a decrease of evapotranspiration South of the Alps a moderate increase North of the Alps. The inconsistencies are quantified and account for up to 30 % from July to Semptember when focused to an area around Milan, Italy.

Model error in ensemble prediction systems is often represented by either a tendency perturbation approach or a process-based parameter perturbation scheme. In this paper a novel hybrid stochastically perturbed parameterization (HSPP) scheme is proposed and implemented in the Convection Permitting Limited Area Ensemble Forecasting (C-LAEF) system developed at the Zentralanstalt für Meteorologie und Geodynamik (ZAMG). In HSPP, the individual parameterization tendencies of the physical processes radiation, shallow convection, and microphysics are perturbed stochastically by a spatially and temporally varying pattern. Uncertainties in the turbulence scheme are considered by perturbing key parameters on the process level. The proposed scheme HSPP features several advantages compared to the popular stochastically perturbed parameterization tendencies (SPPT) scheme: it considers a more physically consistent relationship between different parameterization schemes, deals with uncertainties especially adapted to the individual physical processes, respects conservation laws of energy and moisture, and eliminates the tapering function that has to be introduced to the SPPT scheme because of mainly numerical reasons. The hybrid scheme HSPP is evaluated over one summer and one winter month and compared to a reference ensemble without any stochastic physics perturbations and to two versions of the SPPT scheme. The results show that HSPP significantly increases the ensemble spread of temperature, humidity, wind speed, and pressure, especially in the lower levels of the atmosphere where a tapering function is active in the original SPPT approach. Precipitation verification yields a generally improved probabilistic performance of the HSPP scheme in summer when convection is dominating, which has also been demonstrated in a case study.

This study examines small-scale precipitation patterns in a north-Alpine region, and their dependence on the freezing level and on the crest-level (700 hPa) wind direction and speed. On the one hand, measurements from a uniquely dense operational rain-gauge network are analyzed for a period of 15 years (1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005). Information on the ambient atmospheric fields was extracted from climate-mode MM5 simulations driven with ECMWF (re-)analysis data. On the other hand, high-resolution semi-idealized MM5 simulations have been conducted, combining realistic topography with idealized atmospheric fields. The atmospheric flow parameters have been chosen to be representative of those used to classify the observational data, focusing on atmospheric conditions conducive to stratiform, orographically enhanced precipitation in the region under consideration. The results of the data analysis indicate a pronounced tendency for local precipitation maxima in the lee of individual mountain ridges, whereas the variability between stations in the centre of wider valleys and stations on the windward foot of individual ridges is comparatively small. This points towards a strong contribution of local precipitation enhancement due to the seeder-feeder mechanism, combined with downstream advection of the precipitating hydrometeors by the ambient winds. The data analysis also reveals that strong winds and high temperatures tend to shift the precipitation field towards the interior of the Alps, whereas low temperatures and weak winds favour precipitation maxima near the northern edge of the Alps. The semi-idealized simulations are consistent with these findings, but their quantitative agreement with the observed precipitation patterns depends on the ambient flow conditions. The closest agreement is found for atmospheric conditions conducive to strong orographic lifting, for which our present idealized flow fields were designed. Lower skill is obtained for conditions not dominated by orographic lifting, which implies that future work should include a generalization of the idealized flow fields. Nevertheless, precipitation patterns generated with semi-idealized simulations seem to be very promising to support the spatial interpolation of point measurements (such as are needed for precipitation climatologies), which currently is usually based on statistical methods rather than physically motivated structures.