the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Feb 2025
Effects of synoptic atmospheric variability on sub-daily precipitation δ18O-air temperature functions in reconstructions of pre-instrumental δ18O chronicles across Europe
Abstract. Stable isotopes of oxygen (O) and hydrogen (H) in streams and precipitation are cardinal tools to assess water sources, flow paths, and age. However, their spatial and temporal variability remain largely unknown – essentially due to the limited availability of long-term records of O and H isotope signatures in precipitation. These limitations have stymied their use in studies investigating catchment response to climate change. In this study, O and H isotope signatures are conjectured to be determined by the interplay of physical processes both at synoptic and local scales. Our hypothesis is that large-scale atmospheric circulation patterns (CPs) influence well-known physical effects (e.g., temperature effect) on δ 18O and δ 2H values in precipitation. To test this hypothesis, high-resolution (i.e., sub-daily) δ18O and δ2H data were collected in Belvaux (Luxembourg) from 2017 to 2022 to investigate potential relationships with large-scale atmospheric circulation patterns and local-scale meteorological variables.
Our results demonstrate CP controls on precipitation δ18O and on the relationship between δ18O and air temperature across Western Europe, with δ18O-temperature (δ18O-T) correlation slopes being larger (0.24 to 0.69) than previously reported (below 0.40). We leveraged CP-specific δ18O-T functions to build a multiple linear regression model for long-term δ18O reconstructions in Europe, based on geospatial attributes. The model was fitted on GNIP δ18O data, with a performance expressed by a root mean squared error (RMSE) of 2.3 ‰, comparing to other models focused on the regionalisation of seasonal δ18O variability over Europe. With this novel method, intramonthly δ18O in precipitation can be reconstructed for ~150 years by solely relying on historical temperature records (e.g., as obtained from WMO’s Climate Explorer web interface (https://climexp.knmi.nl)). Our model offers an assumption-lean and thus robust alternative to reconstruct pre-instrumental precipitation δ18O chronicles.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Hydrology and Earth System Sciences.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.- Preprint
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Status: open (until 30 Apr 2025)
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RC1: 'Comment on egusphere-2024-4169', Anonymous Referee #1, 28 Mar 2025
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Comments for: “Effects of synoptic atmospheric variability on sub-daily precipitation δ18O-air temperature functions in reconstructions of pre-instrumental δ18O chronicles across Europe”
OVERALL COMMENTS
This is an interesting idea and certainly a neat approach, but I am not convinced that the results are reliable in a truly useful way due to the limitations applying a calibration from a single site across continental scales. Essentially, my comments revolve around the reliability of the method and the utility compared to other possible (including previously published) methods.ATMOSPHERIC CIRCULATION PATTERNS AND PHYSICAL EFFECTS
More explanation is needed on how atmospheric climate patterns relate to physical effects. Specifically, I do no understand how you view physical effects acting differently during different kinds of atmospheric circulation patterns. I imagine that you are not arguing that, for example, the isotope fractionation occurring during condensation at a particular temperature changes with different circulation patterns (by definition it cannot). But then, I am not clear on what it means for an atmospheric circulation pattern to affect a local physical effect. Can you please clarify how you envision the relationship between synoptic weather patterns and the actual physical processes causing isotope fractionation? Perhaps I am misunderstanding your meaning due to using terminology differently, but if I have this question, others will too.SPATIAL AND TEMPORAL TIMESCALES OF MODELING
Why is it valid to extrapolate your calibration at LIST to a continental scale? It looks to me like your modeling approach basically does best at sites near the LIST field site, which aligns well with prior research showing that water isotope values exhibit spatial coherence (e.g., Bowen and Revenaugh, 2003). So wouldn’t you expect this pattern to emerge?In addition, more explanation is needed as to why your model, which I understand to ultimately be based on monthly aggregate data, eventually including monthly GNIP data, can be used to infer changes on weekly timescales. Doesn’t the data show that the modeling does best on seasonal timescales, often under/overestimating the magnitude of variability on shorter timescales?
THE BROADER CONTEXT AND OVERALL UTILITY OF THIS APPROACH
As the authors note, there are prior isoscapes for this region of the world, including approaches that make use of variables other than (or in addition to) temperature – such as precipitation amount. It would be useful to give broader reference to this prior work as well as to incorporate/argue against these prior approaches. Some thoughts:Why are other environmental variables besides local temperature not used in the modeling? What happens to model performance if they are included?
For the isoscape creation, how different are the results from using the calibration at LIST from using a continental-scale calibration? As in, instead of trying to develop a d18O-T curve for LIST and then applying that curve to the continent through time, what if you made a d18O-T curve for the continent and then applied it through time (essentially, this approach would be to update previously published d18O(weather parameters, lat, lon, elev) functions and apply them through time). Are the results materially different? Why would we prefer one approach over the other? Or would it be better to use LIST and GNIP data together to create d18O functions by aggregate month, region, etc?
How important is it to include all of the Climate Pattern information for the modeling? If including the CP information reduces RSME by 0.2 (2.8 to 2.6) and increases r2 by 0.07 (0.37 to 0.44), is this truly meaningful? Specifically, what investigations does this approach allow that were previously untenable without the CP information.
Finally, what are the uses for time-transgressive isoscapes beyond the “climate normal” versions that already exist? Accounting for the errors in developing isoscapes in each way, how different would the estimates be? What are the benefits of being able to work with estimates of precipitation isotope values from a particular year, rather than an average of many years?
LINE-BY-LINE COMMENTS
10
Qualify this statement a little further. Worldwide predictive maps of d2H and d18O exist and daily, monthly, and yearly data sets exist in many locations.11
Clarify what you mean by “long term”12-15
Clarify this further. Is this not how the community as a whole envisions what drives O and H isotope signatures? If not, what are the alternatives?14
Suggest not using the abbreviation “CP” as it is not common29
Define the sense in which stable isotopes of oxygen and hydrogen are “near-conservative”77
I do not quite understand the linkage being made here between atmospheric circulation patterns/climate patterns and physical effects on isotope values in precipitation. From what is written, I understand that you hypothesize that atmospheric circulation affects how the temperature of condensation is expressed in the isotope composition of precipitation. But how exactly does this work? By definition, if you are considering a strict temperature effect (i.e., how the temperature of condensation induces isotope fractionation between vapor and liquid water), the magnitude of the “temperature effect” must be driven by the starting temperature and overall temperature variability of the air mass induced by atmospheric circulation patterns. But the effect of temperature on isotope fractionation exists regardless of where an air mass originates, right? It is simply that different atmospheric circulation patterns may be associated with more/less temperature variability, which may mean that processes other than temperature will be responsible for the variability observed in precipitation isotope values.
100
Were internal standards used to normalize the data? What were their values?104
Why was a sin wave chosen? Were other fits considered?106
Check equation and units. Verify is consistent with results presented in tables
Kirchner (2016) is not listed in the references110
This section would benefit from a figure with a panel showing each of the climate patterns120-122
Does use of alternative classification schemes affect your results and interpretation?130
Why was temperature chosen as the sole input variable? Were other variables considered? What were their relationships with d18O?135
Were the linear regressions modeled using event-scale data or using monthly data? Line 132 says monthly, but line 136 says event-scale140
If ERA5 interannual average monthly T data is going to be used in the ultimate evaluation, should it not also be used in the initial assessment at LIST for determining coefficients of Eqn. 2?141
Suggest: “To include as many records as possible, the geospatial model…”
To emphasize that the included records do have a full year, but are still shorter than the LIST record145
How was the “climate pattern” determined for each GNIP station? Is it fair to use the Hess and Brezowsky (1952) categorization for sites outside of Germany, where it was originally designed for?The constants in Eqn. 2 are specifically for the LIST site. Why are these applicable to other sites 100s km away? Would they not have their own set of constants? In general, it would be useful to have more explanation about why you expect a calibration developed at one particular site to be broadly transferrable across continental scales.
154
Where is the DEM grid from?173
Citation needed for GMWL definition189-190
Define “considerable”224
With such a considerable difference between HCE and the other climate patterns, is fitting a curve to HCE useful? What is the physical meaning of phi = -98 months?I am not clear on how the sinusoids fitted to the data fit into the larger scope of the work. Please expand on how these were used to investigate the relationships between large-scale atmospheric circulation patterns and local-scale meteorological variables
234-235
Is this gross match between the amplitudes of d-excess and d18O meaningful?263
What is the correlation on a seasonal scale?265-267
So how different are the two approaches? Is one inherently more useful than the other?If the input data for Table 3 was at a monthly scale, why is it fair to use the model at a weekly scale?
294
I could argue that Figure 7 mostly shows that your model does best nearest to the LIST site because that is a foundation of the model. Do you agree? Why?I might further argue that your model could naturally be expected to perform more poorly closer to the coast where the temperature effect cannot emerge as important because there has not been substantial rainout yet. Other processes would be expected to be important closer to the coast. Do you agree? Why?
327-328
How significant is the slope of 7.54 in terms of identifying re-evaporation?342-344
The act of evaporation undoubtedly induces isotope fractionation and helps set “initial” isotope values of vapor. However, it is simplistic to view d-excess as a static value – consider, for example, the modeling exercises of Xia and Winnick (2021) and Xia (2023). To what degree can your data set break apart the oceanic vs. continental influences controlling d-excess values?362-363
This is confusing. So is the T-d18O relationship you infer dominantly coming from condensation reactions or is it incorporating a broader swath of processes with variable influence from temperature?364-365
I find this pretty challenging. On the one hand, you write here that the d18O-T relationships are dependent on CPs, but in the next sections (paragraphs starting lines 400 and 415), you note that the d18O-T relationships do not appear to be strongly dependent on CPs and in fact can be challenging to usefully apply outside of the region surrounding LIST. How should we reconcile these aspects of the data?375
Citations are needed here. Where has this assumption been made recently?380-381
So if changes in air temperature cannot be assumed to accurately predict d18O as the d18O-T relationship changes through time, what implications does that have for your reconstructed isoscapes? Or do you just see this as a challenge under substantially different planetary boundary conditions? Explain further385
How well, what did others find?391
Again, if the initial data focus was on monthly inputs, how does this translate into discerning sub-monthly changes in the past? Especially as you note that the model has trouble accurately capturing better than seasonal-scale variability (lines 290-305; Fig. 8)404-405
So could this instead be taken to mean that climate patterns do not have a strong control on the physical processes underpinning isotope fractionation – that they occur independent of wherever the vapor is coming from?Perhaps because the CPs tend have uneven seasonal distribution, by breaking apart different d18O-T relationships by CP you are essentially breaking out d18O-T relationships for different seasons, which is what leads to a slightly better performance.
410
If convection strength is an important parameter, why is it not included here? The ERA5 data contain this type of information419-420
This seems like a significant challenge to the use of the model presented here424-425
These sentences appear to directly contradict each other
FIGURES
Figure 1
Identify the black lines in panels (a) and (b)
In all figures with maps, clearly identify the LIST siteTABLES
Table 4
Are these coefficients and constant the same for all CPs? I thought that, as in Eqn. 2, the coefficients and constants for Eqn. 3 implied they would also be different for different CPsCitation: https://doi.org/10.5194/egusphere-2024-4169-RC1
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