| 20 May 2026
Calibration guidelines and a runoff-isotope module for lake proxy system modeling (PRYSM v2.0)
Abstract. Lake sediments preserve information about past air temperature and precipitation, providing critical, real-world targets to validate climate models. However, these paleoclimate data encode information about multiple climate signals as well as lake dynamics, which makes comparison of paleoclimate data and model output challenging. Proxy system models (PSMs), or forward models that translate climate variables into proxy data, are tools for mechanistically interpreting paleoclimate data. PRYSM v2.0 is a PSM for paleoclimate data archived in lake sediments. Foundational to the accuracy of the PSM is a comprehensive understanding of the modern lake system supported by observations and meteorological forcing. However, two obstacles exist. First, lake water isotopes are the target of many proxies preserved in lake sediments, but the PSM does not have a built-in catchment model to aid simulation of lake water isotopes. Additionally, calibration of uncertain model parameters requires observations and expert knowledge about the lake, yet many sites are unmonitored. Here, we advance PSMs for lake sediment archives by integrating a simple runoff-isotope module that can be adapted to any lake and reproduce the seasonal timing and magnitude of two mid-latitude lakes with contrasting morphometry, hydrology, and mixing regimes. Using multi-year observational datasets from the lakes, we conduct model experiments that require the PSM to make water temperature and water δ2H profile predictions when calibrated to a single observed profile, which mimics common observation collection challenges. For these lakes, we find that observations from the warm, ice-free season are most informative and constrain uncertain parameter values that best generalize to unseen data from other depths and years. Our framework for running and calibrating the PSM is available as open-source tools in Python, aiding the application of this PSM to the vast global database of lake sediment paleoclimate time series.
This paper is an excellent approach to calibrating a proxy system model, and its method is a valuable contribution to the proxy-system model community. The extensive observational dataset used to validate the calibration is impressive. I have mostly very minor comments. My only large-scale ish comment would be that the paper felt slightly unfocused at times. That isn’t the end of the world, as it is a methods paper and likely part of a broader project to understand these lake systems throughout different past climates, but I think adding a bit more (a paragraph at most should be more than enough) discussion about a couple things could help focus this manuscript and make your results more relevant to a wider audience:
Otherwise I think this paper is pretty much ready to go! Great work.
Minor comments:
Line 36: ‘i.e.’ should be ‘e.g.’ (unless I am misunderstanding what is meant by this parenthetical)
Line 46 when you say ‘individual changes’ do you mean the changes are applied individually, and not all at once?
Line 46-47 this should either be ‘increased/decreased’ or ‘increase/decrease in’ for each of these examples
Lines 55-58 the sentence ‘resulting in a proxy time series that is translated into climate variables’ is confusing to me, because my understanding of forward models was that they translate climate variables into proxy units rather than the other way around. But more broadly I am not sure you need to go into too much detail about Bayesian modeling schema given that you are not using one. You can just say something like ‘some proxies, like terrestrial pollen, don’t require PSMs that incorporate lake dynamics but, x and y that use water isotopes, do’
Line 230 could you explain a bit more how this creek is relevant for your runoff model? Does it runoff into either of your lakes? Does the proxy system model include catchment-wide area information?
Table 2 could you include the distance of each source of observational data from to your lake sites? Or does this not apply if they are within the same grid cell for ERA5?
Line 388 are these RMSE values the average of all 1000 simulations or the 30 you selected? Could you add text about why Bear lake may have such a high error compared to red pond to the discussion?
Line 600 does the turbidity (and thus shortwave extinction coefficient) of the lakes change over the course of the year? Does the parameter related to this value also change month to month, or is it a single number?