An observing system simulation experiment for climate monitoring with GNSS radio occulation data: setup and testbed study

The long-term stability, high accuracy, all-weather capability, high vertical resolution, and global coverage of Global Navigation Satellite System (GNSS) radio occultation (RO) suggests it as a promising tool for global monitoring of atmospheric temperature change. With the aim to investigate and quantify how well a GNSS RO observing system is able to detect climate trends, we are currently performing an (climate) observing system simulation experiment over the 25-year period 2001 to 2025, which involves quasi-realistic modeling of the neutral atmosphere and the ionosphere. We carried out two climate simulations with the general circulation model MAECHAM5 (Middle Atmosphere European Centre/Hamburg Model Version 5) of the MPI-M Hamburg, covering the period 2001–2025: One control run with natural variability only and one run also including anthropogenic forcings due to greenhouse gases, sulfate aerosols, and tropospheric ozone. On the basis of this, we perform quasi-realistic simulations of RO observables for a small GNSS receiver constellation (six satellites), state-of-the-art data processing for atmospheric profiles retrieval, and a statistical analysis of temperature trends in both the “observed” climatology and the “true” climatology. Here we describe the setup of the experiment and results from a test bed study conducted to obtain a basic set of realistic estimates of observational errors (instrument- and retrieval processing-related errors) and sampling errors (due to spatial-temporal undersampling). The test bed results, obtained for a typical summer season and compared to the climatic 2001–2025 trends from the MAECHAM5 simulation including anthropogenic forcing, were found encouraging for performing the full 25-year experiment. They indicated that observational and sampling errors (both contributing about 0.2 K) are consistent with recent estimates of these errors from real RO data and that they should be sufficiently small for monitoring expected temperature trends in the global atmosphere over the next 10 to 20 years in most regions of the upper troposphere and lower stratosphere (UTLS). Inspection of the MAECHAM5 trends in different RO-accessible atmospheric parameters (microwave refractivity and pressure/geopotential height in addition to temperature) indicates complementary climate change sensitivity in different regions of the UTLS so that optimized climate monitoring shall combine information from all climatic key variables retrievable from GNSS RO data.

Uptake pathways: the potential of Bayesian belief networks to assist the management, monitoring and evaluation of development-orientated research

The effectiveness of development assistance has come under renewed scrutiny in recent years. In an era of growing economic liberalisation, research organisations are increasingly being asked to account for the use of public funds by demonstrating achievements. However, in the natural resources (NR) research field, conventional economic assessment techniques have focused on quantifying the impact achieved rather understanding the process that delivered it. As a result, they provide limited guidance for planners and researchers charged with selecting and implementing future research. In response, “pathways” or logic models have attracted increased interest in recent years as a remedy to this shortcoming. However, as commonly applied these suffer from two key limitations in their ability to incorporate risk and assess variance from plan. The paper reports the results of a case study that used a Bayesian belief network approach to address these limitations and outlines its potential value as a tool to assist the planning, monitoring and evaluation of development-orientated research.

The use of bioluminescence for monitoring in planta growth dynamics of a Pseudomonas syringae plant pathogen

The use of bioluminescence was evaluated as a tool to study Pseudomonas syringae population dynamics in susceptible and resistant plant environments. Plasmid pGLITE, containing the luxCDABE genes from Photorhabdus luminescens, was introduced into Pseudomonas syringae pv. phaseolicola race 7 strain 1449B, a Gram-negative pathogen of bean (Phaseolus vulgaris). Bacteria recovered from plant tissue over a five-day period were enumerated by counting numbers of colony forming units and by measurement of bioluminescence. Direct measurement of bioluminescence from leaf disc homogenates consistently reflected bacterial growth as determined by viable counting, but also detected subtle effects of the plant resistance response on bacterial viability. This bioluminescence procedure enables real time measurement of bacterial metabolism and population dynamics in planta, obviates the need to carry out labour intensive and time consuming traditional enumeration techniques and provides a sensitive assay for studying plant effects on bacterial cells.

Learning from risks: A tool for post-project risk assessment

Risk management (RM) comprises of risk identification, risk analysis, response planning, monitoring and action planning tasks that are carried out throughout the life cycle of a project in order to ensure that project objectives are met. Although the methodological aspects of RM are well-defined, the philosophical background is rather vague. In this paper, a learning-based approach is proposed. In order to implement this approach in practice, a tool has been developed to facilitate construction of a lessons learned database that contains risk-related information and risk assessment throughout the life cycle of a project. The tool is tested on a real construction project. The case study findings demonstrate that it can be used for storing as well as updating risk-related information and finally, carrying out a post-project appraisal. The major weaknesses of the tool are identified as, subjectivity of the risk rating process and unwillingness of people to enter information about reasons of failure.

Non-intrusive monitoring of atmospheric CO2 in analogue models of terrestrial carbon cycle

1. Closed Ecological Systems (CES) are small manmade ecosystems which do not have any material exchange with the surrounding environment. Recent ecological and technological advances enable successful establishment and maintenance of CES, making them a suitable tool for detecting and measuring subtle feedbacks and mechanisms. 2. As a part of an analogue (physical) C cycle modelling experiment, we developed a non-intrusive methodology to control the internal environment and to monitor atmospheric CO2 concentration inside 16 replicated CES. Whilst maintaining an air-tight seal of all CES, this approach allowed for access to the CO2 measuring equipment for periodic re-calibration and repairs. 3. To ensure reliable cross-comparison of CO2 observations between individual CES units and to minimise the cost of the system, only one CO2 sampling unit was used. An ADC BioScientific OP-2 (open-path) analyser mounted on a swinging arm was passing over a set of 16 measuring cells. Each cell was connected to an individual CES with air continuously circulating between them. 4. Using this setup, we were able to continuously measure several environmental variables and CO2 concentration within each closed system, allowing us to study minute effects of changing temperature on C fluxes within each CES. The CES and the measuring cells showed minimal air leakage during an experimental run lasting, on average, 3 months. The CO2 analyser assembly performed reliably for over 2 years, however an early iteration of the present design proved to be sensitive to positioning errors. 5. We indicate how the methodology can be further improved and suggest possible avenues where future CES based research could be applied.