PRYSM: open code for PRoxY System Modeling

Paleoclimate data capture climate changes extending hundreds to millions of years into the past, and are thus invaluable for testing the fidelity of climate model simulations in both time and space. However, paleoclimate data are indirect measures of climate, and contain multiple sources of uncertainty. This gap can be bridged by proxy system models (PSMs, [1]), forward operators that explicitly simulate biological, chemical, and physical mechanisms by which proxies filter the input climate signal. Accounting for this transformation is crucial for facilitating multi-proxy comparisons with GCM simulations [2], and applications such as state estimation [3].

Previous studies employing PSMs have used disparate conventions, programming languages, and often apply models to site-specific research questions. Here, we present our efforts synthesizing existing models and building new ones into a public repository for PSMs, PRYSM [4]. PRYSM is archived on GitHub to facilitate user contributions and document versioning of the source code. PSMs for ice cores, corals, speleothems, tree ring cellulose/width, and lake sedimentary archives are in Python (open-source by design), with varying complexity but broadly applicable for global-scale data-model comparisons. Current and future versions of PRYSM can be utilized for parameter sensitivity testing, inter-comparison of paleoclimate data across multiple archives, comparison with climate models, generation of time series for data assimilation, and other cutting-edge applications in paleoclimatology. We highlight a number of these applications, and map the way forward for future contributions to PRYSM by the paleoclimate community.

References

[1] Evans et al., (2013). Applications of proxy system modeling in high resolution paleoclimatology. QSR, 76, 16-28

[2] Dee et al., (2017). Improved spectral comparisons of paleoclimate models and observations via proxy system modeling: Implications for multi-decadal variability. EPSL, 476, 34-46

[3] Dee et al, (2016): On the utility of proxy system modeling for estimating climate states over the Common Era. JAMES.

[4] Dee et al., (2015). PRYSM: An open-source framework for PRoxY System Modeling, with applications to oxygen-isotope systems. JAMES.