An efficient climate forecasting method using an intermediate complexity Earth System Model and the ensemble Kalman filter

We present the implementation and results of a model tuning and ensemble forecasting experiment using an ensemble Kalman filter for the simultaneous estimation of 12 parameters in a low resolution coupled atmosphere-ocean Earth System Model by tuning it to realistic data sets consisting of Levitus ocean temperature/salinity climatology, and NCEP/NCAR atmospheric temperature/humidity reanalysis data. The resulting ensemble of tuned model states is validated by comparing various diagnostics, such as mass and heat transports, to observational estimates and other model results. We show that this ensemble has a very reasonable climatology, with the 3-D ocean in particular having comparable realism to much more expensive coupled numerical models, at least in respect of these averaged indicators. A simple global warming experiment is performed to investigate the response and predictability of the climate to a change in radiative forcing, due to 100 years of 1% per annum atmospheric CO₂ increase. The equilibrium surface air temperature rise for this CO₂ increase is 4.2±0.1°C, which is approached on a time scale of 1,000 years. The simple atmosphere in this version of the model is missing several factors which, if included, would substantially increase the uncertainty of this estimate. However, even within this ensemble, there is substantial regional variability due to the possibility of collapse of the North Atlantic thermohaline circulation (THC), which switches off in more than one third of the ensemble members. For these cases, the regional temperature is not only 3–5°C colder than in the warmed worlds where the THC remains switched on, but is also 1–2°C colder than the current climate. Our results, which illustrate how objective probabilistic projections of future climate change can be efficiently generated, indicate a substantial uncertainty in the long-term future of the THC, and therefore the regional climate of western Europe. However, this uncertainty is only apparent in long-term integrations, with the initial transient response being similar across the entire ensemble. Application of this ensemble Kalman filtering technique to more complete climate models would improve the objectivity of probabilistic forecasts and hence should lead to significantly increased understanding of the uncertainty of our future climate.     

Reliability and importance of structural diversity of climate model ensembles

We investigate the performance of the newest generation multi-model ensemble (MME) from the Coupled Model Intercomparison Project (CMIP5). We compare the ensemble to the previous generation models (CMIP3) as well as several single model ensembles (SMEs), which are constructed by varying components of single models. These SMEs range from ensembles where parameter uncertainties are sampled (perturbed physics ensembles) through to an ensemble where a number of the physical schemes are switched (multi-physics ensemble). We focus on assessing reliability against present-day climatology with rank histograms, but also investigate the effective degrees of freedom (EDoF) of the fields of variables which makes the statistical test of reliability more rigorous, and consider the distances between the observation and ensemble members. We find that the features of the CMIP5 rank histograms, of general reliability on broad scales, are consistent with those of CMIP3, suggesting a similar level of performance for present-day climatology. The spread of MMEs tends towards being “over-dispersed” rather than “under-dispersed”. In general, the SMEs examined tend towards insufficient dispersion and the rank histogram analysis identifies them as being statistically distinguishable from many of the observations. The EDoFs of the MMEs are generally greater than those of SMEs, suggesting that structural changes lead to a characteristically richer range of model behaviours than is obtained with parametric/physical-scheme-switching ensembles. For distance measures, the observations and models ensemble members are similarly spaced from each other for MMEs, whereas for the SMEs, the observations are generally well outside the ensemble. We suggest that multi-model ensembles should represent an important component of uncertainty analysis.     

Population similarity analysis of indicator bacteria for source prediction of faecal pollution in a coastal lake

Biochemical fingerprinting (BF) databases of 524 enterococci and 571 Escherichia coli isolates and an antibiotic resistance analysis (ARA) database comprising of 380 E. coli isolates from four suspected sources (i.e. dogs, chickens, waterfowls, and human sewage) were developed to predict the sources of faecal pollution in a recreational coastal lake. Twenty water samples representing four sampling episodes were collected from five sites and the enterococci and E. coli population from each site were compared with those of the databases. The degree of similarity between bacterial populations was measured as population similarity (Sp) coefficient. Using the BF-database, bacterial populations of waterfowls showed the highest similarity with the water samples followed by a sewage treatment plant (STP). Higher population similarities were found between samples from STP and water samples especially at two sites (T2 and T3) which were located near the sewerage pipes collecting wastewater from the study area. When using the ARA-database, the highest similarity was found between E. coli populations from STP and water samples at sites T2 and T4. Both faecal indicators and as well as methods predicted human faecal pollution, possibly through leakage from submerged sewerage pipes. The results indicated that the Sp-analysis of faecal indicator bacterial populations from suspected sources and water samples can be used as a simple tool to predict the source(s) of faecal pollution in surface waters.     

Questioning the virtues of pro-environmental behaviour research: Towards a phronetic approach

Drawing on Flyvbjerg’s (2001) call for the development of phronetic social science, this paper argues that much current research into pro-environmental behaviour (PEB) is misguided, and even potentially dangerous. After outlining Flyvbjerg’s argument, it reviews existing work on PEB and argues that, to date, it has predominantly sought after the Aristotelian intellectual virtues of either episteme or techne, and has neglected phronesis which Aristotle himself saw as most important. It then explores the ways in which aspects of a phronetic approach are being developed in cultural geography and environmental sociology, before offering a brief empirical case study of a PEB-change initiative to illustrate what a phronetic approach to research might look like. It concludes by calling for an improved and more reflexive dialogue between PEB researchers regarding the purpose and approach of their work, both in order to improve the relevance and impact of their research, and in order to help individuals and communities understand and confront the significant environmental challenges they currently face.     

Reliability of multi-model and structurally different single-model ensembles

The performance of several state-of-the-art climate model ensembles, including two multi-model ensembles (MMEs) and four structurally different (perturbed parameter) single model ensembles (SMEs), are investigated for the first time using the rank histogram approach. In this method, the reliability of a model ensemble is evaluated from the point of view of whether the observations can be regarded as being sampled from the ensemble. Our analysis reveals that, in the MMEs, the climate variables we investigated are broadly reliable on the global scale, with a tendency towards overdispersion. On the other hand, in the SMEs, the reliability differs depending on the ensemble and variable field considered. In general, the mean state and historical trend of surface air temperature, and mean state of precipitation are reliable in the SMEs. However, variables such as sea level pressure or top-of-atmosphere clear-sky shortwave radiation do not cover a sufficiently wide range in some. It is not possible to assess whether this is a fundamental feature of SMEs generated with particular model, or a consequence of the algorithm used to select and perturb the values of the parameters. As under-dispersion is a potentially more serious issue when using ensembles to make projections, we recommend the application of rank histograms to assess reliability when designing and running perturbed physics SMEs.