A Bayesian stochastic generator to complement existing climate change scenarios: supporting uncertainty quantification i
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ORIGINAL PAPER
A Bayesian stochastic generator to complement existing climate change scenarios: supporting uncertainty quantification in marine and coastal ecosystems }rinc Me´sza´ros1,2 Lo
•
Frank van der Meulen2 • Geurt Jongbloed2 • Ghada El Serafy1,2
Accepted: 7 November 2020 Ó The Author(s) 2020
Abstract Available climate change projections, which can be used for quantifying future changes in marine and coastal ecosystems, usually consist of a few scenarios. Studies addressing ecological impacts of climate change often make use of a low(RCP2.6), moderate- (RCP4.5) or high climate scenario (RCP8.5), without taking into account further uncertainties in these scenarios. In this research a methodology is proposed to generate further synthetic scenarios, based on existing datasets, for a better representation of climate change induced uncertainties. The methodology builds on Regional Climate Model scenarios provided by the EURO-CORDEX experiment. In order to generate new realizations of climate variables, such as radiation or temperature, a hierarchical Bayesian model is developed. In addition, a parameterized time series model is introduced, which includes a linear trend component, a seasonal shape with varying amplitude and time shift, and an additive residual term. The seasonal shape is derived with the non-parametric locally weighted scatterplot smoothing, and the residual term includes the smoothed variance of residuals and independent and identically distributed noise. The distributions of the time series model parameters are estimated through Bayesian parameter inference with Markov chain Monte Carlo sampling (Gibbs sampler). By sampling from the predictive distribution numerous new statistically representative synthetic scenarios can be generated including uncertainty estimates. As a demonstration case, utilizing these generated synthetic scenarios and a physically based ecological model (Delft3D-WAQ) that relates climate variables to ecosystem variables, a probabilistic simulation is conducted to further propagate the climate change induced uncertainties to marine and coastal ecosystem indicators. Keywords Uncertainty quantification Climate change Bayesian inference Hierarchical models Coastal ecosystems
1 Introduction It is widely accepted that long term changes in climatic variables will cause shifts (phenological and biogeographic shifts) in species distributions, but the extent of these shifts is not yet well understood and any prediction will have a high level of associated uncertainty (Goberville et al. 2015). Climate change data in ecosystem assessments are used as forcing conditions for the numerous non-linear ecological processes. These ecological processes are & L} orinc Me´sza´ros [email protected] 1
Deltares, Boussinesqweg 1, 2629 HV Delft, The Netherlands
2
TU Delft, Mekelweg 5, 2628 CD Delft, The Netherlands
influenced by changes in extreme values, or shifts in distributions and peaks of the climate forcings. Applicable methodologies for estimating ranges and expect
