the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Validation of Pan-Arctic Soil Temperatures in Modern Reanalysis and Data Assimilation Systems
- 1Department of Geography and Environmental Management, University of Waterloo, 200 University Ave., Waterloo, Ontario, Canada, N2L 3G1
- 2Environmental Studies Program, Hamilton College, 198 College Hill Road, Clinton, 13323, New York, U.S.A.
- 1Department of Geography and Environmental Management, University of Waterloo, 200 University Ave., Waterloo, Ontario, Canada, N2L 3G1
- 2Environmental Studies Program, Hamilton College, 198 College Hill Road, Clinton, 13323, New York, U.S.A.
Abstract. Reanalysis products provide spatially homogeneous coverage for a variety of climate variables in regions where observational data are limited. However, very little validation of reanalysis soil temperatures in the Arctic has been performed to date, because widespread in situ reference observations have historically been unavailable there. Here we validate pan-Arctic soil temperatures from eight reanalysis and Land Data Assimilation System (LDAS) products, using a newly-assembled database of in situ data from diverse measurement networks across Eurasia and North America. We find that most products have soil temperatures that are biased cold by 2–7 K across the Arctic, and that biases and Root Mean Square Error (RMSE) are generally largest in the cold season. Monthly mean values from most products correlate well with in situ data (R > 0.9) in the warm season, but show lower correlations (r = 0.6–0.8), in many cases, over the cold season. Similarly, the magnitude of monthly variability in soil temperatures is well captured in summer, but overestimated by 20 % to 50 % for several products in winter. The suggestion is that soil temperatures in reanalysis products are subject to much higher uncertainty when the soil is frozen and/or when the ground is snow-covered. We also validate the ensemble mean of all products, and find that when all seasons, and metrics are considered, the ensemble mean generally outperforms any individual product in terms of its correlation and variability, while maintaining relatively low biases. As such, we recommend the ensemble mean soil temperature product for a wide range of applications – such as the validation of soil temperatures in climate models, and to inform models that require soil temperature inputs, such as hydrological models, or for permafrost simulations.
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Tyler C. Herrington et al.
Status: final response (author comments only)
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RC1: 'Comment on tc-2022-5', Anonymous Referee #1, 11 Feb 2022
General comments
The authors present a comprehensive evaluation of soil temperature from eight reanalyses and LDAS products based on in-situ observations. Authors reported that the soil temperature bias is generally larger in the cold season, indicating the presence of soil freeze/thaw transition and snow layer introduced additional difficulties for soil temperature simulations. Authors also found that the ensemble generally outperforms any individual product. These findings improved the understanding of the current body of knowledge on soil temperature simulations in land surface models. However, a large part of the manuscript remained unfocused and an in-depth discussion is missing. I hence strongly suggest authors reformulate and shorten the manuscript (maybe as a brief communication) with a very specific focus on soil temperature validation.
- Manuscript structure and discussion
In Sec. 4 & 5, the authors present the evaluation results together with a large part of the discussion, and additional discussions are given in Sec. 6. This makes the manuscript very unclear and difficult to follow.
The discussion in Sec 6 is very general and superficial. Most of the part has been extensively discussed in other studies, and they are not tightly connected with the previous parts. For instance, the gab of site-scale observation and model grid (about 10–100 km), or so-called scale effects, is widely reported. P23, L392–398, this part is very confusing. Does the misclassification of permafrost affect the results? Please make sure only to present the most relevant parts here to avoid diluting your real contributions. - Soil temperature standard deviation
The authors presented and discussed the reanalysis soil temperature deviation. I am wondering why this is important here and how this could be used for validation purposes? The strong variation of soil temperature in the cold season could be expected due to the presence of a snow layer, see Figure 6 from Burke et al., (2020). - Climatology
Again, the climatology based on the ensemble results is somehow unfocused. The purpose of this study is "validation of pan-Arctic (and Boreal) soil temperatures from eight reanalyses and land data assimilation system (LDAS) products." (see P2, L53–54), rather than analyzing the climatology. To be more focused, authors could compare and evaluate the trend of ensemble results with site-scale observations. Otherwise, I do not see the necessity of this part.
Specific comments:
- P2, L24: Permafrost carbon and climate warming loop are complex, and thus ...could act as a "possibly/potentially" positive...
- P2, L31: Qinghai-Tibetan Plateau.
- P2, L45–49: Ensemble simulation has also been used for permafrost simulation, for instance, Cao et al., (2019), although these studies do not directly use the soil temperature.
- P4, L122: The variation of soil temperature is complex and typically depends on surface condition (i.e., snow layer, vegetation), soil properties (i.e., soil organic content), and soil depth. It could vary very large at the hourly and daily scales.
- P6, L135: How much the difference could be? Could you please write it down?
- P6, L141: ...2 to 12...
- P6, L142: The so-called "scale effects" has been widely reported, see Gubler el al., (2011) for the Alps and Cao et al., (2019) for high latitudes. Please cite relevant references.
- P8, L172: you have two "also" here
- P8, L180: Then why not directly use the IPA map? You could also find the global permafrost zonation index map from Gruber et al., (2012).
- P9, L192: "more" → greater/larger
- P14, L250: Qinghai-Tibetan Plateau
- P16, L256: Zero curtain period is heavily dependent on the soil moisture rather than the active layer thickness.
- P22, L357: Remove the redundant ')'.
Tables & Figures
- Table 1: Could you please also add the soil discretization information here, such as depth for each layer and the total soil column depth? Please double-check the spatial resolution of all the reanalyses, ERA5 should be 0.25°, ERA-Interim is 0.75°, and MERRA-2 is 0.5°×0.625°. Depending on the datasets you used, JRA-55 is 1.25° for the reanalysis level and 0.56° for the model level.
- Figure 4: Do you really need so many sub-plots? The inter-comparisons among different reanalyses are shown here but not discussed in the main text. Did I miss something important? Please also add the 1:1 line, so that readers could clearly see the cold/warm bias.
- Figure S3: Could you please improve the resolution of Figure S3?
References
- Burke, E. J., Zhang, Y., and Krinner, G.: Evaluating permafrost physics in the Coupled Model Intercomparison Project 6 (CMIP6) models and their sensitivity to climate change, The Cryosphere, 14, 3155–3174, https://doi.org/10.5194/tc-14-3155-2020, 2020.
- Cao, B., Quan, X., Brown, N., Stewart-Jones, E., and Gruber, S.: GlobSim (v1.0): deriving meteorological time series for point locations from multiple global reanalyses, Geosci. Model Dev., 12, 4661–4679, https://doi.org/10.5194/gmd-12-4661-2019, 2019.
- Gruber, S.: Derivation and analysis of a high-resolution estimate of global permafrost zonation, The Cryosphere, 6, 221–233, https://doi.org/10.5194/tc-6-221-2012, 2012.
- Gubler, S., Fiddes, J., Keller, M., and Gruber, S.: Scale-dependent measurement and analysis of ground surface temperature variability in alpine terrain, The Cryosphere, 5, 431–443, https://doi.org/10.5194/tc-5-431-2011, 2011.
- Manuscript structure and discussion
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RC2: 'Comment on tc-2022-5', Anonymous Referee #2, 30 Jun 2022
General comments
The authors perform a comprehensive validation of current reanalysis and LDAS model products using a set of in situ soil temperature measurements across the northern hemisphere. Validation of soil temperatures in large-scale reanalysis data products is an important task given their frequent use in global ecological and hydrology models and permafrost simulations, for example. It is crucial that the underlying biases are clearly acknowledged by the users of such products. A novelty of this study is that it validates multiple reanalysis soil temperature products at the same time and covers the entire pan-Arctic area.
The authors find that most reanalysis products are considerably cold-biased and that the biases and inter-model variability are larger during the cold season compared to the warm season. The authors also compile an ensemble from the assessed reanalysis data and show how it interestingly overperforms most/all individual models. The authors list potential future applications of the ensemble mean product, but I would wish to see a bit more discussion on its current usability, given that the recorded biases remain quite high and display some regional patterns. The underlying reasons for these are addressed in the manuscript but not how the biases would affect, e.g., permafrost simulations where a bias or RMSE of above 2° C can have notable implications.
The article is very well written and figures are great. At places the text is hard ro follow (especially Section 4.3, see detailed comments below) owing to the multiple simultaneous comparisons: near surface vs. at depth soil temperatures, cold season vs. warm season, permafrost vs. no to little permafrost, North America vs. Eurasia, and DJF vs. JJA. I suggest the authors to make sure all sections are clearly defined.
I recommend the publication of this manuscript after the authors have considered my minor suggestions and comments.
Specific comments
L104: The authors suggest that their study is "To the authors’ knowledge, this one of the first studies to compile a comprehensive set of in situ soil temperature measurements across the Eurasian and North American Arctic, from multiple diverse sparse networks". While it may be true that this is true for the “one of the first” part, it should be noted that the compilation is not totally novel, given that similar in situ temperature datasets have been compiled not only by Cao et al. (2020, in the references) but also, e.g., by Karjalainen et al. (2019) and Ran et al. (2022) who used mostly the same data sources, albeit computing temperatures averages for a much larger depth (several meters deep in permafrost but also in non-permafrost soils). Moreover, Lembrechts et al. (2020) have published a global soil temperature compilation of soil and near-surface temperatures. I suggest the authors to consider if their statement needs some elaboration, e.g., does the compiled dataset differ from previous datasets in some ways.
The authors recognize the notably different sampling size for North America but retain from explaining why no more data were collected, apart from mentioning the overall data scarcity in northern Canada, to correct the imbalance between North America and Eurasia. Based on the previous data compilations (see above), there should be suitable measurement time series available from North America. Notwithstanding, the authors satisfactorily show how the sampling imbalance does not affect the fundamental conclusions (LL378-391).
LL140-141: "Panel B of Figure 1 shows the spatial standard deviation of monthly surface soil temperatures for grid cells with more than two stations included.” However, in Figure 1b, grid cells with two stations are also shown. Also, I remain unsure whether there are any grid cells with >1 stations in Eurasia?
L236: Reference should be to Fig. S1, right?
LL 236-238: "The mean bias and RMSE are typically 1⦠C to 3⦠C smaller over North America, relative to the permafrost zone in Eurasia (see Figure S3); however with fewer grid cells over North America, the uncertainty is also larger - as evidenced by the larger error bars.” I cannot see the mentioned difference between North America and Eurasia in Figure S3 (biases in ERA5-Land) or in the associated bar graph (ERA5-Land) in Figure S2. However, all products considered the said difference between regions is visible. Consider checking and editing the text so that it corresponds to the results shown in Figure S3.
L239: What correlations, the ones between measurements and reanalysis temperatures? A slight elaboration would help the reader to see that what are compared in the sentence.
LL240-241: I also struggled with this sentence. What is the opposite situation here? It is hard to follow the comparisons between permafrost and little to no permafrost, as well as near-surface and at depth temperatures at the same time, especially since the results are not shown.
LL243-246: Are these results related to the permafrost binning? It’s fine if they are not, but overall Section 4.3 is at times hard to follow because it deals with both permafrost binning and regional comparisons.
L405: Instead of the cold season standard deviations, should you not refer here to cold stations/observations? That is, figure 6 does not distinguish between warm and cold season.
L261: The ensemble mean product is not properly addressed until deep into the results (validation) section. I suggest presenting the ensemble mean product and its calculation procedure already in the early stages (possibly inside section 2.1.).
L303: I find “coastal regions” not the ideal term here because the regions with the highest variability are far more than that. In winter, greatest variation associates with the coldest regions, yet not exclusively either. Could the variation here be related to snow cover duration / snow thickness as mentioned elsewhere in the text?
Technical corrections
L61: Please, open the abbreviation GLDAS-CLSM already here.
LL80-83: Check grammar of the sentence. Maybe delete the word "that" at line 81?
L191: Figure 2 does not have panels C and D.
Figure 3: This is a nice figure with lots of information in it. The letters in “Correlation coefficient” are clumped together and could be corrected.
Figure 4: Stratification of the values in histograms is not explained. Please add it to the caption.
Figure 5: Y-axis is a bit messy. Consider adjusting the interval at which temperatures are denoted.
Figure 8: DJF missing from Panel A label.
L286: NH à northern hemisphere
L290: Why are ensemble mean at depth temperatures not shown? Could be part of the supplement. Figure 9 also shows at depth results, so it would be interesting to see how the models reconstruct frozen ground in JJA, although it is acknowledged that this is not explicitly representative of permafrost.
L366: Please put Gruber et al. 2018 inside parentheses.
L369-370: "Moreover, the impact of snow cover on soil temperature is generally more pronounced over permafrost regions (regions of seasonal frost).“ Is something missing here? Should it be "compared to regions of seasonal frost" or what is the idea?
LL418-419: Could you elaborate, what does it mean "is being explored"?
L428: Please provide a url for the ensemble mean dataset on the ADC.
L583: Database title and url missing.
References
Karjalainen et al. 2019 Circumpolar permafrost maps and geohazard indices for near-future infrastructure risk assessments. Scientific Data https://doi.org/10.1038/sdata.2019.37
Lembrechts et al. 2020 SoilTemp: A global database of near-surface temperature. Global Change Biology https://doi.org/10.1111/gcb.15123.
Ran et al. 2022 New high-resolution estimates of the permafrost thermal state and hydrothermal conditions over the Northern Hemisphere. Earth System Science Data https://doi.org/10.5194/essd-14-865-2022
Tyler C. Herrington et al.
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Data sets
Ensemble Mean Reanalysis Soil Temperature Dataset (1981–2018) Tyler Herrington and Christopher G. Fletcher https://doi.org/10.18739/A2RN3085P
Tyler C. Herrington et al.
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