Article
Refine
Year of publication
Document Type
- Article (534) (remove)
Language
- English (534) (remove)
Has Fulltext
- yes (534)
Is part of the Bibliography
- no (534)
Keywords
- climate change (11)
- Climate change (5)
- Atmospheric chemistry (4)
- Geochemistry (4)
- Biogeochemistry (3)
- COSMO-CLM (3)
- Palaeoclimate (3)
- Salinity (3)
- uncertainty (3)
- Bayesian network (2)
Institute
- Geowissenschaften (534) (remove)
Simulation of global temperature variations and signal detection studies using neural networks
(1998)
The concept of neural network models (NNM) is a statistical strategy which can be used if a superposition of any forcing mechanisms leads to any effects and if a sufficient related observational data base is available. In comparison to multiple regression analysis (MRA), the main advantages are that NNM is an appropriate tool also in the case of non-linear cause-effect relations and that interactions of the forcing mechanisms are allowed. In comparison to more sophisticated methods like general circulation models (GCM), the main advantage is that details of the physical background like feedbacks can be unknown. Neural networks learn from observations which reflect feedbacks implicitly. The disadvantage, of course, is that the physical background is neglected. In addition, the results prove to be sensitively dependent from the network architecture like the number of hidden neurons or the initialisation of learning parameters. We used a supervised backpropagation network (BPN) with three neuron layers, an unsupervised Kohonen network (KHN) and a combination of both called counterpropagation network (CPN). These concepts are tested in respect to their ability to simulate the observed global as well as hemispheric mean surface air temperature annual variations 1874 - 1993 if parameter time series of the following forcing mechanisms are incorporated : equivalent CO2 concentrations, tropospheric sulfate aerosol concentrations (both anthropogenic), volcanism, solar activity, and ENSO (all natural). It arises that in this way up to 83% of the observed temperature variance can be explained, significantly more than by MRA. The implication of the North Atlantic Oscillation does not improve these results. On a global average, the greenhouse gas (GHG) signal so far is assessed to be 0.9 - 1.3 K (warming), the sulfate signal 0.2 - 0.4 K (cooling), results which are in close similarity to the GCM findings published in the recent IPCC Report. The related signals of the natural forcing mechanisms considered cover amplitudes of 0.1 - 0.3 K. Our best NNM estimate of the GHG doubling signal amounts to 2.1K, equilibrium, or 1.7 K, transient, respectively.
Crustal structure at the western end of the North Anatolian Fault Zone from deep seismic sounding
(2001)
The first deep seismic sounding experiment in Northwestern Anatolia was carried out in October 1991 as part of the "German - Turkish Project on Earthquake Prediction Research" in the Mudurnu area of the North Anatolian Fault Zone. The experiment was a joint enterprise by the Institute of Meteorology and Geophysics of Frankfurt University, the Earthquake Research Institute (ERI) in Ankara, and the Turkish Oil Company (TPAO). Two orthogonal profiles, each 120 km in length with a crossing point near Akyazi, were covered in succession by 30 short period tape recording seismograph stations with 2 km station spacing. 12 shots, with charge sizes between 100 and 250 kg, were fired and 342 seismograms out of 360 were used for evaluation. By coincidence an M b = 4.5 earthquake located below Imroz Island was also recorded and provided additional information on Moho and the sub-Moho velocity. A ray tracing method orginally developed by Weber (1986) was used for travel time inversion. From a compilation of all data two generalized crustal models were derived, one with velocity gradients within the layers and one with constant layer velocities. The latter consists of a sediment cover of about 2 km with V p » 3.6 km/s, an upper crystalline crust down to 13 km with V p » 5.9 km/s, a middle crust down to 25 km depth with V p » 6.5 km/s, a lower crust down to 39 km Moho depth with V p » 7.0 km/s and V p » 8.05 km/s below the Moho. The structure of the individual profiles differs slightly. The thickest sediment cover is reached in the Izmit-Sapanca-trough and in the Akyazi basin. Of particular interest is a step of about 4 km in the lower crust near Lake Sapanca and probably an even larger one in the Moho (derived from the Imroz earthquake data). After the catastrophic earthquake of Izmit on 17 August 1999 this significant heterogeneity in crustal structure appears in a new light with regard to the possible cause of the Izmit earthquake. Heterogeneities in structure are frequently also heterogeneities in strength and stress that impede or even lock rupture. The Izmit earthquake is discussed in relation to a large stepover or jog at the North Anatolian Fault.
Attribution and detection of anthropogenic climate change using a backpropagation neural network
(2002)
The climate system can be regarded as a dynamic nonlinear system. Thus traditional linear statistical methods are not suited to describe the nonlinearities of this system which renders it necessary to find alternative statistical techniques to model those nonlinear properties. In addition to an earlier paper on this subject (WALTER et al., 1998), the problem of attribution and detection of the observed climate change is addressed here using a nonlinear Backpropagation Neural Network (BPN). In addition to potential anthropogenic influences on climate (CO2-equivalent concentrations, called greenhouse gases, GHG and SO2 emissions) natural influences on surface air temperature (variations of solar activity, volcanism and the El Niño/Southern Oscillation phenomenon) are integrated into the simulations as well. It is shown that the adaptive BPN algorithm captures the dynamics of the climate system, i.e. global and area weighted mean temperature anomalies, to a great extent. However, free parameters of this network architecture have to be optimized in a time consuming trial-and-error process. The simulation quality obtained by the BPN exceeds the results of those from a linear model by far; the simulation quality on the global scale amounts to 84% explained variance. Additionally the results of the nonlinear algorithm are plausible in a physical sense, i.e. amplitude and time structure. Nevertheless they cover a broad range, e.g. the GHG-signal on the global scale ranges from 0.37 K to 1.65 K warming for the time period 1856-1998. However the simulated amplitudes are situated within the discussed range (HOUGHTON et al., 2001). Additionally the combined anthropogenic effect corresponds to the observed increase in temperature for the examined time period. In addition to that, the BPN succeeds with the detection of anthropogenic induced climate change on a high significance level. Therefore the concept of neural networks can be regarded as a suitable nonlinear statistical tool for modeling and diagnosing the climate system.
Excitation functions for quasi-elastic scattering have been measured at backward angles for the systems 32,34S+197Au and 32,34S+208Pb for energies spanning the Coulomb barrier. Representative distributions, sensitive to the low energy part of the fusion barrier distribution, have been extracted from the data. For the fusion reactions of 32,34S with 197Au couplings related to the nuclear structure of 197Au appear to be dominant in shaping the low energy part of the barrier distibution. For the system 32S+208Pb the barrier distribution is broader and extends further to lower energies, than in the case of 34S+208Pb. This is consistent with the interpretation that the neutron pick-up channels are energetically more favoured in the 32S induced reaction and therefore couple more strongly to the relative motion. It may also be due to the increased collectivity of 32S, when compared with 34S.
An easy-to-use model to evaluate conductivities at high and middle latitudes in the height range 70–100 km is presented. It is based on electron density profiles obtained with the EISCAT VHF radar during 11 years and on the neutral atmospheric model MSIS95. The model uses solar zenith angle, geomagnetic activity and season as input parameters. It was mainly constructed to study the properties of Schumann resonances that depend on such conductivity profiles.
Turbulent fluxes of carbonyl sulfide (COS) and carbon disulfide (CS2) were measured over a spruce forest in Central Germany using the relaxed eddy accumulation (REA) technique. A REA sampler was developed and validated using simultaneous measurements of CO2 fluxes by REA and by eddy correlation. REA measurements were conducted during six campaigns covering spring, summer, and fall between 1997 and 1999. Both uptake and emission of COS and CS2 by the forest were observed, with deposition occurring mainly during the sunlit period and emission mainly during the dark period. On the average, however, the forest acts as a sink for both gases. The average fluxes for COS and CS2 are -93 ± 11.7 pmol m-2 s-1 and -18 ± 7.6 pmol m-2 s-1, respectively. The fluxes of both gases appear to be correlated to photosynthetically active radiation and to the CO2 and \chem{H_2O} fluxes, supporting the idea that the air-vegetation exchange of both gases is controlled by stomata. An uptake ratio COS/CO2 of 10 ± 1.7 pmol m mol-1 has been derived from the regression line for the correlation between the COS and CO2 fluxes. This uptake ratio, if representative for the global terrestrial net primary production, would correspond to a sink of 2.3 ± 0.5 Tg COS yr-1.
Turbulent fluxes of carbonyl sulfide (COS) and carbon disulfide (CS2) were measured over a spruce forest in Central Germany using the relaxed eddy accumulation (REA) technique. A REA sampler was developed and validated using simultaneous measurements of CO2 fluxes by REA and by eddy correlation. REA measurements were conducted during six campaigns covering spring, summer, and fall between 1997 and 1999. Both uptake and emission of COS and CS2 by the forest were observed, with deposition occurring mainly during the sunlit period and emission mainly during the dark period. On the average, however, the forest acts as a sink for both gases. The average fluxes for COS and CS2 are -93 ± 11.7 pmol m -2 s -1 and -18 ± 7.6 pmol m -2 s -1, respectively. The fluxes of both gases appear to be correlated to photosynthetically active radiation and to the CO2 and H2O fluxes, supporting the idea that the air-vegetation exchange of both gases is controlled by stomata. An uptake ratio COS / CO2 of 10 ± 1.7 pmol mmol -1 has been derived from the regression line for the correlation between the COS and CO2 fluxes. This uptake ratio, if representative for the global terrestrial net primary production, would correspond to a sink of 2.3 ± 0.5 Tg COS yr-1.
Measurements of OH, the sum of peroxy radicals (ROx), non-methane hydrocarbons (NMHCs) and various other trace gases were made at the Meteorological Observatory Hohenpeissenberg in June 2000. The data from an intensive measurement period characterised by high solar insolation (18-21 June) are analysed. The maximum midday OH concentration ranged between 4.5 x 106 molecules cm-3 and 7.4 x 106 molecules cm-3. The maximum total ROx mixing ratio increased from about 55 pptv on 18 June to nearly 70 pptv on 20 and 21 June. A total of 64 NMHCs, including isoprene and monoterpenes, were measured every 1 to 6 hours. The oxidation rate of the NMHCs by OH was calculated and reached a total of over 14 x 106 molecules cm-3 s-1 on two days. A simple photostationary state balance model was used to simulate the ambient OH and ROx concentrations with the measured data as input. The model was able to reproduce the main features of the diurnal profiles of both OH and ROx. The model results proved to be most sensitive to assumptions about the mixing ratio of formaldehyde (HCHO), which was included as a proxy for carbonyl compounds, and about the partitioning between HO2 and RO2. The measured OH concentration and ROx mixing ratios were reproduced well by assuming the presence of 3 ppbv HCHO and a ratio HO2/RO2 between 1:1 and 1:2. The most important source of OH, and conversely the greatest sink for ROx, was the recycling of HO2 radicals to OH. This reaction was responsible for the recycling of more than 45 x 106 molecules cm-3 s-1 on two days. The most important sink for OH, and the largest source of ROx, was the oxidation of NMHCs, in particular, of isoprene and the monoterpenes.
The climate system can be regarded as a dynamic nonlinear system. Thus, traditional linear statistical methods fail to model the nonlinearities of such a system. These nonlinearities render it necessary to find alternative statistical techniques. Since artificial neural network models (NNM) represent such a nonlinear statistical method their use in analyzing the climate system has been studied for a couple of years now. Most authors use the standard Backpropagation Network (BPN) for their investigations, although this specific model architecture carries a certain risk of over-/underfitting. Here we use the so called Cauchy Machine (CM) with an implemented Fast Simulated Annealing schedule (FSA) (Szu, 1986) for the purpose of attributing and detecting anthropogenic climate change instead. Under certain conditions the CM-FSA guarantees to find the global minimum of a yet undefined cost function (Geman and Geman, 1986). In addition to potential anthropogenic influences on climate (greenhouse gases (GHG), sulphur dioxide (SO2)) natural influences on near surface air temperature (variations of solar activity, explosive volcanism and the El Nino = Southern Oscillation phenomenon) serve as model inputs. The simulations are carried out on different spatial scales: global and area weighted averages. In addition, a multiple linear regression analysis serves as a linear reference. It is shown that the adaptive nonlinear CM-FSA algorithm captures the dynamics of the climate system to a great extent. However, free parameters of this specific network architecture have to be optimized subjectively. The quality of the simulations obtained by the CM-FSA algorithm exceeds the results of a multiple linear regression model; the simulation quality on the global scale amounts up to 81% explained variance. Furthermore the combined anthropogenic effect corresponds to the observed increase in temperature Jones et al. (1994), updated by Jones (1999a), for the examined period 1856–1998 on all investigated scales. In accordance to recent findings of physical climate models, the CM-FSA succeeds with the detection of anthropogenic induced climate change on a high significance level. Thus, the CMFSA algorithm can be regarded as a suitable nonlinear statistical tool for modeling and diagnosing the climate system.