Wolfgang Brüggemann

Identification of human-induced changes in atmospheric moisture content

Data from the satellite-based Special Sensor Microwave Imager (SSM/I) show that the total atmospheric moisture content over oceans has increased by 0.41 kg/m2 per decade since 1988. Results from current climate models indicate that water vapor increases of this magnitude cannot be explained by climate noise alone. In a formal detection and attribution analysis using the pooled results from 22 different climate models, the simulated “fingerprint” pattern of anthropogenically caused changes in water vapor is identifiable with high statistical confidence in the SSM/I data. Experiments in which forcing factors are varied individually suggest that this fingerprint “match” is primarily due to human-caused increases in greenhouse gases and not to solar forcing or recovery from the eruption of Mount Pinatubo. Our findings provide preliminary evidence of an emerging anthropogenic signal in the moisture content of earths atmosphere.

Forced and unforced ocean temperature changes in Atlantic and Pacific tropical cyclogenesis regions

Previous research has identified links between changes in sea surface temperature (SST) and hurricane intensity. We use climate models to study the possible causes of SST changes in Atlantic and Pacific tropical cyclogenesis regions. The observed SST increases in these regions range from 0.32°C to 0.67°C over the 20th century. The 22 climate models examined here suggest that century-timescale SST changes of this magnitude cannot be explained solely by unforced variability of the climate system. We employ model simulations of natural internal variability to make probabilistic estimates of the contribution of external forcing to observed SST changes. For the period 1906–2005, we find an 84% chance that external forcing explains at least 67% of observed SST increases in the two tropical cyclogenesis regions. Model “20th-century” simulations, with external forcing by combined anthropogenic and natural factors, are generally capable of replicating observed SST increases. In experiments in which forcing factors are varied individually rather than jointly, human-caused changes in greenhouse gases are the main driver of the 20th-century SST increases in both tropical cyclogenesis regions.

Incorporating model quality information in climate change detection and attribution studies

In a recent multimodel detection and attribution (D&A) study using the pooled results from 22 different climate models, the simulated “fingerprint” pattern of anthropogenically caused changes in water vapor was identifiable with high statistical confidence in satellite data. Each model received equal weight in the D&A analysis, despite large differences in the skill with which they simulate key aspects of observed climate. Here, we examine whether water vapor D&A results are sensitive to model quality. The “top 10” and “bottom 10” models are selected with three different sets of skill measures and two different ranking approaches. The entire D&A analysis is then repeated with each of these different sets of more or less skillful models. Our performance metrics include the ability to simulate the mean state, the annual cycle, and the variability associated with El Niño. We find that estimates of an anthropogenic water vapor fingerprint are insensitive to current model uncertainties, and are governed by basic physical processes that are well-represented in climate models. Because the fingerprint is both robust to current model uncertainties and dissimilar to the dominant noise patterns, our ability to identify an anthropogenic influence on observed multidecadal changes in water vapor is not affected by “screening” based on model quality.

Identification of Anthropogenic Climate Change Using a Second-Generation Reanalysis

[1] Changes in the height of the tropopause provide a sensitive indicator of human effects on climate. A previous attempt to identify human effects on tropopause height relied on information from ‘first-generation’ reanalyses of past weather observations. Climate data from these initial model-based reanalyses have well-documented deficiencies, raising concerns regarding the robustness of earlier detection work that employed these data. Here we address these concerns using information from the new second-generation ERA-40 reanalysis. Over 1979 to 2001, tropopause height increases by nearly 200 m in ERA-40, partly due to tropospheric warming. The spatial pattern of height increase is consistent with climate model predictions of the expected response to anthropogenic influences alone, significantly strengthening earlier detection results. Atmospheric temperature changes in two different satellite data sets are more highly correlated with changes in ERA-40 than with those in a first-generation reanalysis, illustrating the improved quality of temperature information in ERA-40. Our results provide support for claims that human activities have warmed the troposphere and cooled the lower stratosphere over the last several decades of the 20th century, and that both of these changes in atmospheric temperature have contributed to an overall increase in tropopause height. INDEX TERMS: 0350 Atmospheric Composition and Structure: Pressure, density, and temperature; 0370 Atmospheric Composition and Structure: Volcanic effects (8409); 1620 Global Change: Climate dynamics (3309); 1640 Global Change: Remote sensing; KEYWORDS: climate change, detection, reanalysis

The discrete lot-sizing and scheduling problem: Complexity and modification for batch availability

Abstract The discrete lot-sizing and scheduling problem (DLSP) has been suggested for the simultaneous choice of lot sizes and production schedules. In the context of computational complexity, it turns out that literature results for the DLSP are incorrect. Therefore, we prove that the decision version of the DLSP is NP-hard in the strong sense. The common assumption of instantaneous availability of the manufactured units is not satisfied in practice if the units arrive in inventory only in one batch after the whole lot has been completed. Therefore, additional constraints are presented for this case of batch availability on a single machine. The resulting modified DLSP is formulated as a mixed-integer linear program. This problem can be shown to be NP-hard again using ideas similar to the item-availability case. Hence, a two-phase simulated-annealing (SA) heuristic is suggested for solving the DLSP in the case of batch availability. Numerical results are presented for different problem classes.

DLSP for two-stage multi-item batch production

Abstract The standard mixed-integer linear model formulation for the multi-item discrete lot-sizing and scheduling problem (DLSP) is extended by additional partially nonlinear constraints for the case of two-stage batch production. The corresponding feasibility problem is NP-complete in the case of non-zero setup limes. A simulated annealing approach is suggested for computing production schedules on both stages. Numerical results are presented.

Problems, Models and Complexity. Part II: Application to the DLSP

In Part I of this study, we suggest to identify an operations research (OR) problem with the equivalence class of models describing the problem and enhance the standard computer-science theory of computational complexity to be applicable to this situation of an often model-based OR context. The Discrete Lot-sizing and Scheduling Problem (DLSP) is analysed here in detail to demonstrate the difficulties which can arise if these aspects are neglected and to illustrate the new theoretical concept. In addition, a new minimal model is introduced for the DLSP which makes this problem eventually amenable to a rigorous analysis of its computational complexity.

DLSP for multi-item batch production

The discrete lot-sizing and scheduling problem (DLSP) has been suggested for the simultaneous choice of lot sizes and production schedules. However, the common assumption of instantaneous availability of the manufactured units is not satisfied in practice if the units arrive in inventory in one batch no earlier than completion of the whole lot Therefore, we present additional constraints for the case of batch production. The resulting extended DLSP is formulated as a mixed-integer linear program. The feasibility problem of this modification to the standard DLSP is again NP-complete. A two-phase simulated-annealing approach is suggested for solving the modified DLSP. Since DLSP is a finite-time-horizon model, sensible target inventories have to be determined. Numerical results are presented for different problem classes.

Preference-based assignment of university students to multiple teaching groups

A successful approach to the student-scheduling problem is presented here. This problem arises naturally when courses and classes must be offered in such a large number of multiple teaching groups that some of these are timetabled in parallel, i.e. simultaneously. This is the case, for example, at the School of Business, Economics and Social Sciences of Universität Hamburg, Germany. Here, 3,735 students have to be assigned in a real-life scenario to 48 courses in 300 multiple teaching groups such that no student has a time clash and students’ enrolments as well as group capacities are met. Students’ satisfaction with regard to individually specified preferences for various aspects of the scheduling can be used as the objective which yields a multi-criteria decision problem. The resulting mixed-integer programme is modelled in GAMS and typical instances can be solved within minutes using the commercial CPLEX solver. Furthermore, the integration of the optimisation approach into the existing registration process using a campus-management software is discussed.

Problems, Models and Complexity. Part I: Theory

The meaning of the term ‘problem’ in operations research (OR) deviates from the understanding in the theoretical computer sciences: While an OR problem is often conceived to be stated or represented by model formulations, a computer-science problem can be viewed as a mapping from encoded instances to solutions. Such a computer-science problem turns out to be rather similar to an OR model formulation. This ambiguity may cause difficulties if the computer-science theory of computational complexity is applied in the OR context. In OR, a specific model formulation is commonly used in the analysis of the underlying problem and the results obtained for this model are simply lifted to the problem level. But this may lead to erroneous results, if the model used is not appropriate for such an analysis of the problem.To resolve this issue, we first suggest a new precise formal definition of the term problem which is identified with an equivalence class of models describing it. Afterwards, a new definition is suggested for the size of a model which depends on the assumed encoding scheme. Only models which exhibit a minimal size with respect to a ‘reasonable’ encoding scheme finally turn out to be suitable for the model-based complexity analysis of an OR problem. Therefore, the appropriate choice (or if necessary the construction) of a suitable representative of an OR problem becomes an important theoretical aspect of the modelling process.