The hydrological model was forced with attribution data from HadAM3p “factual” and “counter-factual” simulations. The HadAM3p is an atmospheric global climate model of the atmosphere and land surface only with SST, greenhouse gas concentrations and other conditions imposed on the model. The model is run at N96 resolution (1.25 by 1.875 degrees, 19 levels: 192 grid boxes in the longitude and 145 grid boxes in the latitude; representing 208km x 139 km at the equator) with 15-minute timesteps for dynamics and improved physics. It is a UK Met office model and has been widely used in attribution experiments and it is the standard for “weather@home” experiments in the climateprediction.net project (see https://www.climateprediction.net/). Since 2010, the model has been used by the Climate Systems Analysis Group (CSAG) as the basis for weather risk attribution forecast replacing the HadAM3 (run at N48 resolution) which had been used since CSAG started issuing seasonal forecasts in 2002. Wolski et al
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(2014) used results attribution data generated from the same model to drive the hydrological model for risk-based extreme event attribution.
The setup of the climate model and the linking up with the hydrological model follows the same approach use by Wolski et al. (2014). The model is run based on a seasonal forecasting framework “time slice” approach described in full detail in Stone et al (2014). It is an alternative experimental set up to century- long simulations of coupled models at a lower resolution. Reproducing extreme events may be difficult in the coupled model century-long experimental setup. In this case, therefore, a trade-off was made between applicability of experimental setup and climate model setup such that the experimental setup was a more desirable and feasible criterion over the climate model setup. Under this framework, the simulation of atmospheric weather pays attention particularly towards surface boundary conditions rather than their origin in which case a short spin up, enough to explore the bulk of the weather attractors sufficient for many extreme events of interests would be applicable. This is an important element of the experimental setup as such it is an important qualification to consider when interpreting the results of the modelling under such a framework. Nonetheless, interpretation must concede that “stricter” experimental setup is consistent with the improvements in coupled ocean-atmosphere models as well as feasibility in computing capabilities.
As highlighted, in section 4.3.1, the extreme precipitation leading to the flooding in 2015 was as a result of tropical cyclone Bansi and tropical storm Chedza both of which may not be realistically simulated by the HadAM3p model with the possibility of some runs not even reproducing such cyclones or depressions. Being mainly exploratory in nature, the study largely focused on integrating climate attribution simulations into the process of joint attribution of impacts in an African context where climate data are sparse too. In this view, the fitness of the model to simulate the actual meteorological phenomena leading to the extreme precipitation that caused the floods of interest to the study is not of primary importance to the study. With not too many models run in attribution mode that can actually, realistically simulate tropical depressions and cyclones, the model HadAMP3P and the context in which it is run, provided a feasible option to explore the research questions at hand.
For this experiment, the model was run specifically to generate simulations of the 2015 climate under two different emission scenarios; the factual and counter-factual. In doing so, a seasonal forecast is generated for the current climate in the factual mode while the counter-factual generates a seasonal forecast as it would have been had there been no influence of greenhouse gases on the climate system. When simulating the factual mode, the model is driven by observed monthly sea surface temperature, annually varying CO2 concentration while vegetation is fixed. The simulation also includes climatological seasonal variations in sea ice coverage. In the counterfactual mode, the greenhouse gas concentration is fixed to preindustrial times. The same observed monthly sea surface temperatures are imposed on the model but taking into account that the observed SSTs are in the context of a climate subjected to global warming from the greenhouse effect. The SST warming attributable to the GHG effect is therefore removed from the observed monthly SSTs. The warming attributable to greenhouse gas emissions (that which is removed from the SST for the counter-factual simulation) is estimated using an optimal total least squares regression analysis. This analysis is performed on data from the HadSST2 dataset of gridded observational measurements; and outputs from simulations of the HadCM3 coupled ocean-atmosphere climate model. The limitations constraints as well as the trade off and rationale for choice of approaches to this analysis are described in full detail in Stone et al (2015). The initial conditions ensemble approach was employed in this study with each mode (factual or counterfactual) run 20 times with perturbed initial conditions for each run. A total of 40 runs, 20 for each mode (i.e factual and counterfactual), were realised with each run downscaled 10 times following the procedure described in section 5.3 below. Thus the climate
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simulation and downscaling processes obtained a total of 400 realisations (200) used to drive the hydrological model.