David Leedal

Explicit feedback and the management of uncertainty in meeting climate objectives with solar geoengineering

Solar geoengineering has been proposed as a method of meeting climate objectives, such as reduced globally averaged surface temperatures. However, because of incomplete understanding of the effects of geoengineering on the climate system, its implementation would be in the presence of substantial uncertainties. In our study, we use two fully coupled atmosphere–ocean general circulation models: one in which the geoengineering strategy is designed, and one in which geoengineering is implemented (a real-world proxy). We show that regularly adjusting the amount of solar geoengineering in response to departures of the observed global mean climate state from the predetermined objective (sequential decision making; an explicit feedback approach) can manage uncertainties and result in achievement of the climate objective in both the design model and the real-world proxy. This approach results in substantially less error in meeting global climate objectives than using a predetermined time series of how much geoengineering to use, especially if the estimated sensitivity to geoengineering is inaccurate.

Assessing the controllability of Arctic sea ice extent by sulfate aerosol geoengineering

In an assessment of how Arctic sea ice cover could be remediated in a warming world, we simulated the injection of SO2 into the Arctic stratosphere making annual adjustments to injection rates. We treated one climate model realization as a surrogate “real world” with imperfect “observations” and no rerunning or reference to control simulations. SO2 injection rates were proposed using a novel model predictive control regime which incorporated a second simpler climate model to forecast “optimal” decision pathways. Commencing the simulation in 2018, Arctic sea ice cover was remediated by 2043 and maintained until solar geoengineering was terminated. We found quantifying climate side effects problematic because internal climate variability hampered detection of regional climate changes beyond the Arctic. Nevertheless, through decision maker learning and the accumulation of at least 10 years time series data exploited through an annual review cycle, uncertainties in observations and forcings were successfully managed.

Are response function representations of the global carbon cycle ever interpretable

Response function models are often used to represent the behaviour of complex, high order global carbon cycle (GCC) and climate models in applications which require short model run times. Although apparently black-box, these response function models need not necessarily be entirely opaque, but instead may also convey useful insights into the properties of the parent model or process. By exploiting a transfer function (TF) framework to analyse the Lenton GCC model, this paper attempts to demonstrate that response function representations of GCC models can sometimes also provide structural information on the parent model from which they are identified and calibrated. We take a fifth-order TF identified from the impulse response of the Lenton model atmospheric burden, and decompose this to show how it can be re-expresses in a generic five-box form in sympathy with the structure of the parent model.

Identification and Representation of State Dependent Non-linearities in Flood Forecasting Using the DBM Methodology

This paper addresses the issue of identifying a state dependent input nonlinearity in a Data Based Mechanistic (DBM) flood forecasting model based on the data rather than some prior conceptualisation of nonlinearity in the system response. Four forms of nonlinear function are presented. A power law may be useful when the input non-linearity is simple. The Radial Basis Function (RBF) network method is appropriate for systems that exhibit well defined but complex input non-linearities. The Piecewise Cubic Hermite Data Interpolation (PCHIP) method also provides the flexibility to map complex input non-linearity shapes while providing the ability to maintain a natural curve. Overfit to the calibration data is a risk in both RBF and PCHIP methods when a large number of knots are used. The Takagi-Sugeno Fuzzy Inference method, together with interactive tuning, provides an alternative approach that allows human-in-the-loop interaction during the parameter estimation process but is not optimal in any statistical sense. Future work will explore the use of these methods with continuous time transfer functions and optimisation of the nonlinear function at the same time as the transfer function.

Reduced order emulation of distributed hydraulic simulation models

Abstract Water level predictions made with hydraulic models are uncertain and evaluating this uncertainty using Monte Carlo ensemble prediction is computationally very expensive. In this paper we show how a reduced order Dynamic Model Emulator (DME) can be used to reproduce, with high accuracy, the outputs of a large and complex 1-D hydraulic model (HEC-RAS) at specified cross-sections along the Montford to Buildwas reach of the River Severn in the U.K, together with estimates of uncertainty in the predictions. This emulation model is obtained by the application of Dominant Mode Analysis (DMA), involving the identification and estimation of nonlinear State-Dependent Parameter (SDP) transfer function models, using data generated by dynamic experiments conducted on the HEC-RAS model. The paper shows how this ‘nominal’ DME is able to emulate the distributed hydraulic model for a nominal set of its physically-defined parameters and it presents initial results from a complete DME that emulates the HEC-RAS model over a user-defined region of its parameter space.

Climate Decision-Making as a Recursive Process

Climate science and policy making are currently dominated by model-led forecasting as a means of informing decision-making. However, given the very significant uncertainties surrounding our understanding of both the climate and socio-economic systems and their interactions it appears more reasonable to view climate decision making as a recursive problem, led by updates based on the unfolding observed state of these systems. Not surprisingly, many aspects of the current climate decision making machinery already possess this attribute, embedded as it is in the review cycles that proliferate in this and other areas of decision making under uncertainty. In this paper we will illustrate the recursive nature of climate decision making using a geoengineering case study. Should geoengineering ever be deployed, because of the deep uncertainties surrounding these speculative technologies, it seems most likely it would be deployed sequentially within a review cycle where the magnitude of deployment is conditioned on a combination of environmental observations, model forecasts, risk assessment and cost. Such frameworks contain the essential elements of a Model Predictive Control (MPC) problem. Here we apply MPC to explore a stratospheric aerosol campaign. The experiment uses the UK Met Office Hadley Centre Global Environment Model (HadGEM2) as a surrogate for the Earth in a blind trial simulation where the objective is to define the magnitude and temporal distribution of SO2 emissions injected into the stratosphere from a northern hemisphere location equivalent to Svalbard in order to recover and then stabilize the minimum extent of the Arctic ice sheet over a period of 80 years. The control algorithm must contend with HadGEM2’s considerable internal variability and nonstationary dynamics, mismatch between the control model and HadGEM2, uncertainty in future greenhouse gas forcing and a stochastic volcanic aerosol time series. We present our results and reflect on the experiment.