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- ItemDIVA: An iterative method for building modular integrated models(München : European Geopyhsical Union, 2005) Hinkel, J.Integrated modelling of global environmental change impacts faces the challenge that knowledge from the domains of Natural and Social Science must be integrated. This is complicated by often incompatible terminology and the fact that the interactions between subsystems are usually not fully understood at the start of the project. While a modular modelling approach is necessary to address these challenges, it is not sufficient. The remaining question is how the modelled system shall be cut down into modules. While no generic answer can be given to this question, communication tools can be provided to support the process of modularisation and integration. Along those lines of thought a method for building modular integrated models was developed within the EU project DINAS-COAST and applied to construct a first model, which assesses the vulnerability of the world’s coasts to climate change and sea-level-rise. The method focuses on the development of a common language and offers domain experts an intuitive interface to code their knowledge in form of modules. However, instead of rigorously defining interfaces between the subsystems at the project’s beginning, an iterative model development process is defined and tools to facilitate communication and collaboration are provided. This flexible approach has the advantage that increased understanding about subsystem interactions, gained during the project’s lifetime, can immediately be reflected in the model.
- ItemAgents, Bayes, and Climatic Risks - a modular modelling approach(München : European Geopyhsical Union, 2005) Haas, A.; Jaeger, C.When insurance firms, energy companies, governments, NGOs, and other agents strive to manage climatic risks, it is by no way clear what the aggregate outcome should and will be. As a framework for investigating this subject, we present the LAGOM model family. It is based on modules depicting learning social agents. For managing climate risks, our agents use second order probabilities and update them by means of a Bayesian mechanism while differing in priors and risk aversion. The interactions between these modules and the aggregate outcomes of their actions are implemented using further modules. The software system is implemented as a series of parallel processes using the CIAMn approach. It is possible to couple modules irrespective of the language they are written in, the operating system under which they are run, and the physical location of the machine
- ItemIntegrated analysis of water quality in a mesoscale lowland basin(München : European Geopyhsical Union, 2005) Habeck, A.; Krysanova, V.; Hattermann, F.This article describes a modelling study on nitrogen transport from diffuse sources in the Nuthe catchment, representing a typical lowland region in the north-eastern Germany. Building on a hydrological validation performed in advance using the ecohydrological model SWIM, the nitrogen flows were simulated over a 20-year period (1981-2000). The relatively good quality of the input data, particularly for the years from 1993 to 2000, enabled the nitrogen flows to be reproduced sufficiently well, although modelling nutrient flows is always associated with a great deal of uncertainty. Subsequently, scenario calculations were carried out in order to investigate how nitrogen transport from the catchment could be further reduced. The selected scenario results with the greatest reduction of nitrogen washoff will briefly be presented in the paper.
- ItemTemperature sensitivity of decomposition in relation to soil organic matter pools: Critique and outlook(Göttingen : Copernicus GmbH, 2005) Reichstein, M.; Kätterer, T.; Andrén, O.; Ciais, P.; Schulze, E.-D.; Cramer, W.; Papale, D.; Valentini, R.Knorr et al. (2005) concluded that soil organic carbon pools with longer turnover times are more sensitive to temperature. We show that this conclusion is equivocal, largely dependent on their specific selection of data and does not persist when the data set of Kätterer et al. (1998) is analysed in a more appropriate way. Further, we analyse how statistical properties of the model parameters may interfere with correlative analyses that relate the Q 10 of soil respiration with the basal rate, where the latter is taken as a proxy for soil organic matter quality. We demonstrate that negative parameter correlations between Qio-values and base respiration rates are statistically expected and not necessarily provide evidence for a higher temperature sensitivity of low quality soil organic matter. Consequently, we propose it is premature to conclude that stable soil carbon is more sensitive to temperature than labile carbon.
- ItemHow tight are the limits to land and water use? - Combined impacts of food demand and climate change(München : European Geopyhsical Union, 2005) Lotze-Campen, H.; Lucht, W.; Müller, C.; Bondeau, A.; Smith, P.In the coming decades, world agricultural systems will face serious transitions. Population growth, income and lifestyle changes will lead to considerable increases in food demand. Moreover, a rising demand for renewable energy and biodiversity protection may restrict the area available for food production. On the other hand, global climate change will affect production conditions, for better or worse depending on regional conditions. In order to simulate these combined effects consistently and in a spatially explicit way, we have linked the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ) with a "Management model of Agricultural Production and its Impact on the Environment" (MAgPIE). LPJ represents the global biosphere with a spatial resolution of 0.5 degree. MAgPIE covers the most important agricultural crop and livestock production types. A prototype has been developed for one sample region. In the next stage this will be expanded to several economically relevant regions on a global scale, including international trade. The two models are coupled through a layer of productivity zones. In the paper we present the modelling approach, develop first joint scenarios and discuss selected results from the coupled modelling system.
- ItemRegional climate model simulations as input for hydrological applications: Evaluation of uncertainties(München : European Geopyhsical Union, 2005) Kotlarski, S.; Block, A.; Böhm, U.; Jacob, D.; Keuler, K.; Knoche, R.; Rechid, D.; Walter, A.The ERA15 Reanalysis (1979-1993) has been dynamically downscaled over Central Europe using 4 different regional climate models. The regional simulations were analysed with respect to 2m temperature and total precipitation, the main input parameters for hydrological applications. Model results were validated against three reference data sets (ERA15, CRU, DWD) and uncertainty ranges were derived. For mean annual 2 m temperature over Germany, the simulation bias lies between -1.1°C and +0.9°C depending on the combination of model and reference data set. The bias of mean annual precipitation varies between -31 and +108 mm/year. Differences between RCM results are of the same magnitude as differences between the reference data sets.
- ItemQuantifying the effect of vegetation dynamics on the climate of the last glacial maximum(München : European Geopyhsical Union, 2005) Jahn, A.; Claussen, M.; Ganopolski, A.; Brovkin, V.The importance of the biogeophysical atmosphere-vegetation feedback in comparison with the radiative effect of lower atmospheric CO2 concentrations and the presence of ice sheets at the last glacial maximum (LGM) is investigated with the climate system model CLIMBER-2. Equilibrium experiments reveal that most of the global cooling at the LGM (-5.1°C) relative to (natural) present-day conditions is caused by the introduction of ice sheets into the model (-3.0°C), followed by the effect of lower atmospheric CO2 levels at the LGM (-1.5°C), while a synergy between these two factors appears to be very small on global average. The biogeophysical effects of changes in vegetation cover are found to cool the global LGM climate by 0.6°C. The latter are most pronounced in the northern high latitudes, where the taiga-tundra feedback causes annually averaged temperature changes of up to -2.0°C, while the radiative effect of lower atmospheric CO2 in this region only produces a cooling of 1.5°C. Hence, in this region, the temperature changes caused by vegetation dynamics at the LGM exceed the cooling due to lower atmospheric CO2 concentrations.
- ItemForced versus coupled dynamics in Earth system modelling and prediction(Göttingen : Copernicus GmbH, 2005) Knopf, B.; Held, H.; Schellnhuber, H.J.We compare coupled nonlinear climate models and their simplified forced counterparts with respect to predictability and phase space topology. Various types of uncertainty plague climate change simulation, which is, in turn, a crucial element of Earth System modelling. Since the currently preferred strategy for simulating the climate system, or the Earth System at large, is the coupling of sub-system modules (representing, e.g. atmosphere, oceans, global vegetation), this paper explicitly addresses the errors and indeterminacies generated by the coupling procedure. The focus is on a comparison of forced dynamics as opposed to fully, i.e. intrinsically, coupled dynamics. The former represents a particular type of simulation, where the time behaviour of one complex systems component is prescribed by data or some other external information source. Such a simplifying technique is often employed in Earth System models in order to save computing resources, in particular when massive model inter-comparisons need to be carried out. Our contribution to the debate is based on the investigation of two representative model examples, namely (i) a low-dimensional coupled atmosphere-ocean simulator, and (ii) a replica-like simulator embracing corresponding components. Whereas in general the forced version (ii) is able to mimic its fully coupled counterpart (i), we show in this paper that for a considerable fraction of parameter- and state-space, the two approaches qualitatively differ. Here we take up a phenomenon concerning the predictability of coupled versus forced models that was reported earlier in this journal: the observation that the time series of the forced version display artificial predictive skill. We present an explanation in terms of nonlinear dynamical theory. In particular we observe an intermittent version of artificial predictive skill, which we call on-off synchronization, and trace it back to the appearance of unstable periodic orbits. We also find it to be governed by a scaling law that allows us to estimate the probability of artificial predictive skill. In addition to artificial predictability we observe artificial bistability for the forced version, which has not been reported so far. The results suggest that bistability and intermittent predictability, when found in a forced model set-up, should always be cross-validated with alternative coupling designs before being taken for granted.
- ItemLong-term predictability of mean daily temperature data(Göttingen : Copernicus GmbH, 2005) von Bloh, W.; Romano, M.C.; Thiel, M.We quantify the long-term predictability of global mean daily temperature data by means of the Rényi entropy of second order K2. We are interested in the yearly amplitude fluctuations of the temperature. Hence, the data are low-pass filtered. The obtained oscillatory signal has a more or less constant frequency, depending on the geographical coordinates, but its amplitude fluctuates irregularly. Our estimate of K2 quantifies the complexity of these amplitude fluctuations. We compare the results obtained for the CRU data set (interpolated measured temperature in the years 1901-2003 with 0.5° resolution, Mitchell et al., 20051) with the ones obtained for the temperature data from a coupled ocean-atmosphere global circulation model (AOGCM, calculated at DKRZ). Furthermore, we compare the results obtained by means of K2 with the linear variance of the temperature data.
- ItemTrend assessment: Applications for hydrology and climate research(Göttingen : Copernicus GmbH, 2005) Kallache, M.; Rust, H.W.; Kropp, J.The assessment of trends in climatology and hydrology still is a matter of debate. Capturing typical properties of time series, like trends, is highly relevant for the discussion of potential impacts of global warming or flood occurrences. It provides indicators for the separation of anthropogenic signals and natural forcing factors by distinguishing between deterministic trends and stochastic variability. In this contribution river run-off data from gauges in Southern Germany are analysed regarding their trend behaviour by combining a deterministic trend component and a stochastic model part in a semi-parametric approach. In this way the trade-off between trend and autocorrelation structure can be considered explicitly. A test for a significant trend is introduced via three steps: First, a stochastic fractional ARIMA model, which is able to reproduce short-term as well as long-term correlations, is fitted to the empirical data. In a second step, wavelet analysis is used to separate the variability of small and large time-scales assuming that the trend component is part of the latter. Finally, a comparison of the overall variability to that restricted to small scales results in a test for a trend. The extraction of the large-scale behaviour by wavelet analysis provides a clue concerning the shape of the trend.