Abstract
Abstract. Data-model integration plays a critical role in assessing and improving our capacity to predict ecosystem dynamics. Similarly, the ability to attach quantitative statements of uncertainty around model forecasts is crucial for model assessment and interpretation and for setting field research priorities. Bayesian methods provide a rigorous data assimilation framework for these applications, especially for problems with multiple data constraints. However, the Markov chain Monte Carlo (MCMC) techniques underlying most Bayesian calibration can be prohibitive for computationally demanding models and large datasets. We employ an alternative method, Bayesian model emulation of sufficient statistics, that can approximate the full joint posterior density, is more amenable to parallelization, and provides an estimate of parameter sensitivity. Analysis involved informative priors constructed from a meta-analysis of the primary literature and specification of both model and data uncertainties, and it introduced novel approaches to autocorrelation corrections on multiple data streams and emulating the sufficient statistics surface. We report the integration of this method within an ecological workflow management software, Predictive Ecosystem Analyzer (PEcAn), and its application and validation with two process-based terrestrial ecosystem models: SIPNET and ED2. In a test against a synthetic dataset, the emulator was able to retrieve the true parameter values. A comparison of the emulator approach to standard brute-force MCMC involving multiple data constraints showed that the emulator method was able to constrain the faster and simpler SIPNET model's parameters with comparable performance to the brute-force approach but reduced computation time by more than 2 orders of magnitude. The emulator was then applied to calibration of the ED2 model, whose complexity precludes standard (brute-force) Bayesian data assimilation techniques. Both models are constrained after assimilation of the observational data with the emulator method, reducing the uncertainty around their predictions. Performance metrics showed increased agreement between model predictions and data. Our study furthers efforts toward reducing model uncertainties, showing that the emulator method makes it possible to efficiently calibrate complex models.
Highlights
Terrestrial ecosystems continue to be a major source of uncertainty in future projections of the global carbon cycle
The test against synthetic data showed that the emulator was able to successfully retrieve the true parameter values that were used in creating the synthetic dataset (Fig. 3)
Diagnostics showed that the chains mixed well and converged
Summary
Terrestrial ecosystems continue to be a major source of uncertainty in future projections of the global carbon cycle. Model predictions disagree on the size and nature of the ecosystem response to novel conditions expected under climate change (Friedlingstein et al, 2014) This is partly due to different assumptions and representations of ecosystem processes in models (Fisher et al, 2014; Medlyn et al, 2015) and partly due to lack of constraints on uncertainties associated with modeled processes and parameters (Dietze, 2017b). Despite having more models and data than ever before, we still have not successfully reduced the uncertainties in our predictions because of the technical difficulties of linking models and data together (Hartig et al, 2012; Fisher et al, 2014) This is true for regional- and global-scale models, which are computationally complex and need to be calibrated against large datasets. Three specific technical challenges that need to be addressed in PDA are multiple data constraints, partitioning of uncertainties, and model complexity
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