the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 18 Mar 2024
El Niño Enhances Snowline Rise and Ice Loss on the World's Largest Tropical Ice Cap
Abstract. Tropical glaciers are essential water resources in the central Andes as vital water resources and crucial climate indicators, currently undergoing rapid retreat. However, understanding their vulnerability to the combined effects of persistent warming, short-term climate phenomena, and interannual fluctuations remains limited. Here we automate mapping of key mass balance parameters on the Quelccaya Ice Cap (QIC), the world’s largest tropical ice cap. Using Landsat's near-infrared (NIR) band, we analyze snow cover area (SCA) and total area (TA) and calculate the Accumulation Area Ratio (AAR) and Equilibrium Line Altitude (ELA) over nearly 40 years (1985–2023). Between 1985 and 2022, the QIC lost ~46 % and ~34 % of its SCA and TA, respectively. We show that the QIC’s loss in SCA and rise in ELA are exacerbated by El Niño events, which are strongly correlated to the preceding wet season’s Ocean Niño Index (ONI). We observe lower levels of correlation to more recent El Niño events as anthropogenic climatic impacts overwhelm the natural forcing and continue to exacerbate loss at the QIC.
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RC1: 'Comment on egusphere-2024-676', Anonymous Referee #1, 09 Apr 2024
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This is an interesting paper analyzing the relationship between ENSO and snow-covered area and ELA on the Quelccaya ice cap in Peru. The paper, however, has a number of weaknesses that need to be improved before it is ready for publication. I have tried to outline some of the main aspects that could benefit from more attention below.
Main comments:
Title: I think the title may have to be changed – as far as I know Quelccaya is no longer the world’s largest tropical ice cap (see Kochtitzky et al., 2018). This statement is also repeated several times in the text.
Line 22-24: Statements such as this one regarding projected future loss of a glacier surface area need to include a date, as the percentage loss quoted is time-dependent. Are you referring to the year 2100?
Line 28: Yarleque et al. (2018) did not state that the QIC would disappear by 2050 under a high emission scenario. They only determined that the ELA would move above the summit by that point. Given the ice thickness of the QIC, it would likely still take several decades to fully melt all the ice in the ablation zone. Hence 2050 is a date for a ‘point of no return’ with the accumulation zone gone, but it is not a date for the complete disappearance of the QIC.
Line 32: I think what you refer to here is the actual size of the ice sheet, not the ‘magnitude of retreat’, which suggests something different (the loss of ice per unit time). Also the citation for this statement (Lamantia et al., 2023) is not included in the reference list
Line 42-44. The snowfall on Quelccaya has no dynamical connection to the ITCZ. The ITCZ is a maritime feature located north of the equator. Snowfall on Quelccaya is fueled by the South American summer monsoon, with much of the heavy snowfall associated with convective activity over the western Amazon basin, triggered via cold air incursions (see Hurley et al., 2015).
Lines 50-51: The change in the FLH is not just worrisome because of direct melt, but also because it leads to a rise of the rain-snow line, thus affecting the albedo in the ablation zone. This likely has a larger impact on the total glacier energy and mass balance than the change in the sensible heat flux alone (e.g. see discussion in Rabatel et al. (2013)).
Lines 52-53: Your statement here would imply that both La Nina and El Nino events lead to warmer SST in the tropical Pacific. Of course this is the case only for El Nino, while La Nina events are associated with colder SST.
Line 60-64: The first study to test this idea regarding end-of-the-dry-season snowline serving as an estimate of the ELA in the Andes was by Rabatel et al. (2012). This should be acknowledged. Their methodology was then applied by Yarleque et al. (2018) to estimate the interannual ELA variability on Quelccaya, equally relying on Landsat data to estimate the ELA via maximum elevation of the dry-season snowline. Hence the ELA approach used here has been applied on QIC before and the results obtained here should thus be compared to those previously published in Yarleque et al. (2018) to the extent that this is possible, especially when discussing the ELA results from your study in section 3.1 or during the discussion of the results in section 4.2.
Line 135: I am not sure you can use surface temperature from ERA5 directly to calculate days with above- or below-freezing on QIC without making some bias adjustments. What is the absolute surface elevation of Quelccaya in ERA5? I assume it is considerably lower than 5670 m, hence the ERA5 surface temperature will have a warm bias, no? Alternatively, you may want to use the free tropospheric temperature interpolated to the QIC elevation as it is less affected by a topographic bias than surface temperature.
Line 185: Snow cover, unlike ice cover, varies interannually. Hence I would not refer to the change in snow cover from one year to the next as a ‘loss’, which sounds as if it were permanent. Maybe refer to snow cover ‘change’ or ‘reduction’ instead, but it is not a permanent ’loss’.
Line 253-254: While the northern Peruvian coast indeed receives more precipitation during El Nino, the Peruvian Andes and the western Amazon are drier than normal. For example, see detailed analysis of precipitation effects of ENSO in Peru by Sulca et al. (2018). Also Hurley et al. (2019) have investigated the ENSO influence on snowfall amount and temperature anomalies on Quelccaya using on-site meteorological measurements, showing that conditions on the QIC are warmer and drier than normal during El Nino events.
Figures 2-5: I think all these bar and line graphs require uncertainty estimates. While there is some discussion of error estimates in the text, the Figures do not include any such uncertainties. Figure 5 in particular is strange. While it make sense that the total snow covered area varies from year to year in response to ENSO, why would the total area (ice cover) increase by several km^2 from one year to the next? This would imply a rapid advance of the ice cap increasing the total ice covered area by ~10% over the course of a year, which is highly unlikely. To me this rather suggests that significant uncertainties exist in the yearly estimates of total ice cover. This makes including error bars all the more important, to understand whether interannual variations of the total ice cover reside within these uncertainty estimates.
Minor suggestions for change:
Line 9: Glaciers are ‘vital water resources as vital water resources’? Something went wrong here.
Line 48: delete either ‘recording’ or ‘documenting’ (one verb too many).
Line 131: ‘European Centre for Medium Weather Range Weather Forecast’. Delete the first ‘Weather’ as it should say ’European Centre for Medium-Range Weather Forecast’.
Line 198: different => difference
Line 199: complied => compiled
Line 282: You refer to a paper by Taylor et al. (2022), yet this paper is not listed in the reference section.
Line 336: Please add the name of the journal where this article was published.
Lines 353-354: delete ‘an international journal’.
Lines 335-339: You repeat the same reference twice. Delete one of them. Also, in the text simply cite Hanshaw & Bookhagen (2014). There is no need for the labels ‘a’ and ‘b’ – it’s one and the same paper.
Line 382: delete ‘Article 3’
Line 388: This should be Pepin ‘et al.’ (the paper has many co-authors). Also note that there is an updated newer version of this paper (Pepin et al., 2022).
Line 404: delete ‘Article 1-5’.
Line 433: delete ‘Article 9’.
References:
Hurley, J.V., et al. 2015. Cold air incursions, d18O variability and monsoon dynamics associated with snow days at Quelccaya Ice Cap, Peru. J. Geophys. Res., 120, 7467-7487, doi:10.1002/2015JD023323.
Hurley, J.V., et al. 2019. On the interpretation of the ENSO signal embedded in the stable isotopic composition of Quelccaya Ice Cap, Peru. J. Geophys. Res. 124, 131-145, doi: 10.1029/2018JD029064.
Kochtitzky, W.H., et al. 2018. Improved estimates of glacier change rates at Nevado Coropuna Ice Cap, Peru. J. Glaciol., 64(244), 175-184, doi: 10.1017/jog.2018.2.
Pepin, N.C., et al. 2022. Climate changes and their elevational patterns in the mountains of the world. Rev. Geophys. 60, e2020RG000730, doi:10.1029/2020RG000730.
Rabatel, A., et al., 2012: Can the snowline be used as an indicator of the equilibrium line and mass balance for glaciers in the outer tropics? J. Glaciol., 58(212), 1027-1036. doi:10.3189/2012JoG12J027
Rabatel, A., et al. 2013. Current state of glaciers in the tropical Andes. A multi-century perspective on glacier evolution and climate change. Cryosphere, 7, 81-102, doi:10.5194/tc-7-81-2013.
Sulca, J., et al. 2018. Impacts of different ENSO flavors and tropical Pacific convection variability (ITCZ, SPCZ) on austral summer rainfall in South America, with a focus on Peru. Int. J. Climatol., 38, 420-435, doi:10.1002/joc.5185.
Yarleque, C., et al. 2018.. Projections of the future disappearance of the Quelccaya Ice Cap in the Central Andes. Sci. Rep. 8, 15564, doi:10.1038/s41598-018-33698-z.
Citation: https://doi.org/10.5194/egusphere-2024-676-RC1 -
RC2: 'Comment on egusphere-2024-676', Anonymous Referee #2, 16 Apr 2024
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The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-676/egusphere-2024-676-RC2-supplement.pdf
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