Working with the unworkable – a trainee’s case of maternal mourning and ulcerative colitis

In this paper I describe a time-limited piece of work that I undertook as a trainee with a mother whose child had died. The client had developed serious ulcerative colitis and was referred to counselling because she was refusing an operation to treat it. I have conceptualised her illness as the embodiment of her experience of disintegration in grief. As a trainee, I found working in the transference difficult in this case, as though I was betraying the client in some way. The client’s failure to make herself better both from her disease and from mourning her daughter made her continue to feel like a victim. Understanding this as transference and drawing on relational psychoanalytic theory was key to being able to work with her and to begin to make links

Altruistic kidney donation: a discourse analysis, and the client’s use of the body for unconscious communication.

In this paper I describe a time-limited piece of work with a mother whose child committed suicide. The client went on to develop serious ulcerative colitis that I have conceptualised as the embodiment of her experience of disintegration in grief. Using Freud’s theory of identification with the lost object, I suggest that her illness represented an unconscious sadistic attack on herself and the identified-with lost object. Unconscious phantasies of harming the lost loved object resulted in guilt and resistance to making a link between her disease and her grief. I experienced this as a projective identification that made it difficult for me to work in the transference. Her failure to make herself better both from her disease and from mourning her son made her continue to feel like a victim. Being believed became a central theme of the work, reflecting the trauma she had experienced and her resulting belief that she would not be understood, annihilating her ability to make meaning

The impact of structural error on parameter constraint in a climate model

Uncertainty in the simulation of the carbon cycle contributes significantly to uncertainty in the projections of future climate change. We use observations of forest fraction to constrain carbon cycle and land surface input parameters of the global climate model FAMOUS, in the presence of an uncertain structural error. Using an ensemble of climate model runs to build a computationally cheap statistical proxy (emulator) of the climate model, we use history matching to rule out input parameter settings where the corresponding climate model output is judged sufficiently different from observations, even allowing for uncertainty. Regions of parameter space where FAMOUS best simulates the Amazon forest fraction are incompatible with the regions where FAMOUS best simulates other forests, indicating a structural error in the model. We use the emulator to simulate the forest fraction at the best set of parameters implied by matching the model to the Amazon, Central African, South East Asian, and North American forests in turn. We can find parameters that lead to a realistic forest fraction in the Amazon, but that using the Amazon alone to tune the simulator would result in a significant overestimate of forest fraction in the other forests. Conversely, using the other forests to tune the simulator leads to a larger underestimate of the Amazon forest fraction. We use sensitivity analysis to find the parameters which have the most impact on simulator output and perform a history-matching exercise using credible estimates for simulator discrepancy and observational uncertainty terms. We are unable to constrain the parameters individually, but we rule out just under half of joint parameter space as being incompatible with forest observations. We discuss the possible sources of the discrepancy in the simulated Amazon, including missing processes in the land surface component and a bias in the climatology of the Amazon.This work was supported by the Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme (GA01101). Doug McNeall was supported on secondment to Exeter University by the Met Office Academic Partnership (MOAP) for part of the work. Jonny Williams was supported by funding from Statoil ASA, Norwa

Understanding uncertainty in a swan wave model using a Bayesian Emulator

This is the author accepted manuscript. The final version is available from EWTEC via the link in this recordNumerical simulation is used widely in the marine renewable energy sector. Wave and flow models are used to understand and predict the conditions experienced at offshore energy sites. Like all numerical simulations, wave models have uncertainties in their output caused by uncertainty about the various input data (which may themselves be model outputs), and uncertainty about how well the model simulates the real world. Understanding these uncertainties is important in order to hold confidence in the models accuracy. Classical Monte Carlo uncertainty analysis requires a large number of model runs which is impossible in large complex models if the computational run time is more than a few seconds. By substituting a much more computationally efficient mathematical model, known as an emulator, for the complex simulation then processing time can be decreased to a level where uncertainty analysis can be undertaken. A simple’toy’ wave model has been produced using SWAN. By using a Bayesian methodology on output from a small number of correctly designed model runs, a mathematical emulator is constructed to provide a statistical approximation of output from the model. Importantly this emulator provides not just an approximation of the output but a full probability distribution describing how close the emulator output is to the model. As this emulator provides results in a fraction of a second (compared to several seconds for the toy simulator and considerably longer for actual wave models) it can be run many thousands of times as is required for a Monte Carlo analysis. This paper describes the methodology used to construct an emulator of a simulation and provides method and results using the emulator to undertake uncertainty quantification. The methods described here can be scaled up and employed on large wave models, flow models or any deterministic numerical simulator.European Regional Development Fund (ERDF

Modeling Envisat RA-2 waveforms in the coastal zone: Case study of calm water contamination

This letter examines waveform data from the Envisat RA-2 as it passes regularly over Pianosa (a 10-km 2 island in the northwestern Mediterranean). Forty-six repeat passes were analyzed, with most showing a reduction in signal upon passing over the island, with weak early returns corresponding to the reflections from land. Intriguingly, one third of cases showed an anomalously bright hyperbolic feature. This feature may be due to extremely calm waters in the Golfo della Botte (northern side of the island), but the cause of its intermittency is not clear. The modeling of waveforms in such a complex land/sea environment demonstrates the potential for sea surface height retrievals much closer to the coast than is achieved by routine processing. The long-term development of altimetric records in the coastal zone will not only improve the calibration of altimetric data with coastal tide gauges but also greatly enhance the study of storm surges and other coastal phenomena

Correcting a bias in a climate model with an augmented emulator

This is the final version. Available from Copernicus Publications via the DOI in this record. A key challenge in developing flagship climate model configurations is the process of setting uncertain input parameters at values that lead to credible climate simulations. Setting these parameters traditionally relies heavily on insights from those involved in parameterisation of the underlying climate processes. Given the many degrees of freedom and computational expense involved in evaluating such a selection, this can be imperfect leaving open questions about whether any subsequent simulated biases result from mis-set parameters or wider structural model errors (such as missing or partially parameterised processes). Here, we present a complementary approach to identifying plausible climate model parameters, with a method of bias correcting subcomponents of a climate model using a Gaussian process emulator that allows credible values of model input parameters to be found even in the presence of a significant model bias. A previous study (McNeall et al., 2016) found that a climate model had to be run using land surface input parameter values from very different, almost non-overlapping, parts of parameter space to satisfactorily simulate the Amazon and other forests respectively. As the forest fraction of modelled non-Amazon forests was broadly correct at the default parameter settings and the Amazon too low, that study suggested that the problem most likely lay in the model's treatment of non-plant processes in the Amazon region. This might be due to modelling errors such as missing deep rooting in the Amazon in the land surface component of the climate model, to a warm-dry bias in the Amazon climate of the model or a combination of both. In this study, we bias correct the climate of the Amazon in the climate model from McNeall et al. (2016) using an "augmented" Gaussian process emulator, where temperature and precipitation, variables usually regarded as model outputs, are treated as model inputs alongside land surface input parameters. A sensitivity analysis finds that the forest fraction is nearly as sensitive to climate variables as it is to changes in its land surface parameter values. Bias correcting the climate in the Amazon region using the emulator corrects the forest fraction to tolerable levels in the Amazon at many candidates for land surface input parameter values, including the default ones, and increases the valid input space shared with the other forests. We need not invoke a structural model error in the land surface model, beyond having too dry and hot a climate in the Amazon region. The augmented emulator allows bias correction of an ensemble of climate model runs and reduces the risk of choosing poor parameter values because of an error in a subcomponent of the model. We discuss the potential of the augmented emulator to act as a translational layer between model subcomponents, simplifying the process of model tuning when there are compensating errors and helping model developers discover and prioritise model errors to target.Alan Turing Institut

Building a traceable climate model hierarchy with multi-level emulators

To study climate change on multi-millennial timescales or to explore a model’s parameter space, efficient models with simplified and parameterised processes are required. However, the reduction in explicitly modelled processes can lead to underestimation of some atmospheric responses that are essential to the understanding of the climate system. While more complex general circulations are available and capable of simulating a more realistic climate, they are too computationally intensive for these purposes. In this work, we propose a multi-level Gaussian emulation technique to efficiently estimate the outputs of steady-state simulations of an expensive atmospheric model in response to changes in boundary forcing. The link between a computationally expensive atmospheric model, PLASIM (Planet Simulator), and a cheaper model, EMBM (energy–moisture balance model), is established through the common boundary condition specified by an ocean model, allowing for information to be propagated from one to the other. This technique allows PLASIM emulators to be built at a low cost. The method is first demonstrated by emulating a scalar summary quantity, the global mean surface air temperature. It is then employed to emulate the dimensionally reduced 2-D surface air temperature field. Even though the two atmospheric models chosen are structurally unrelated, Gaussian process emulators of PLASIM atmospheric variables are successfully constructed using EMBM as a fast approximation. With the extra information gained from the cheap model, the multi-level emulator of PLASIM’s 2-D surface air temperature field is built using only one-third the amount of expensive data required by the normal single-level technique. The constructed emulator is shown to capture 93.2% of the variance across the validation ensemble, with the averaged RMSE of 1.33 °C. Using the method proposed, quantities from PLASIM can be constructed and used to study the effects introduced by PLASIM’s atmosphere

‘Not dead … abandoned’ – a clinical case study of childhood and combat-related trauma

This clinical case study examines inter-subjective processes with a counselling client who presented with symptoms of complex trauma including severe anxiety, low mood, dissociation and suicidality. Therapy lasted 12 months and the ending was unplanned. Psychoanalytic and phenomenological hermeneutic frameworks are drawn on in theorizing the work. From this perspective, loss associated with trauma is conceptualized as relational, as traumatic states threaten psychological organization and the continuing experience of relational ties that are needed for survival. Dissociation is understood as a defensive state that changes the way that temporality is experienced. The client’s capacity for dissociation appeared to have developed in early childhood in response to physical abuse, predisposing him to further ongoing and severe trauma as an adult soldier. There will be a focus on the way that dissociation and enactment in the therapeutic relationship limited the therapist’s capacity to provide the client with inter-subjective regulation of disavowed affect. The client’s unconscious experience of unbearable affect led to a breakdown of the therapeutic relationship and the termination of therapy. Detailed session and supervision notes, and correspondence received from the client were used to evaluate theory and practice links, as well as some methodological aspects of case study research