the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
27 Sep 2021
27 Sep 2021
Evaluating sources of an apparent cold bias in MODIS land surface temperatures in the St. Elias Mountains, Yukon, Canada
- 1Climate Change Institute, University of Maine, Orono, Maine, USA
- 2School of Earth and Climate Sciences, University of Maine, Orono, Maine, USA
- 3Department of Geography, Environment and Geomatics, University of Ottawa, Ottawa, Ontario, Canada
- 1Climate Change Institute, University of Maine, Orono, Maine, USA
- 2School of Earth and Climate Sciences, University of Maine, Orono, Maine, USA
- 3Department of Geography, Environment and Geomatics, University of Ottawa, Ottawa, Ontario, Canada
Abstract. Remote sensing data are a crucial tool for monitoring climatological changes and glacier response in areas inaccessible for in situ measurements. The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) product provides temperature data for remote glaciated areas where weather stations are sparse or absent, such as the St. Elias Mountains (Yukon, Canada). However, MODIS LSTs in the St. Elias Mountains have shown a cold bias relative to available weather station measurements, the source of which is unknown. Here, we show that the MODIS cold bias likely results from the occurrence of near-surface temperature inversions rather than from the MODIS sensor’s large footprint size or from poorly constrained snow emissivity values used in LST calculations. We find that a cold bias in remote sensing temperatures is present not only in MODIS LST products, but also in Advanced Spaceborne Thermal Emissions Radiometer (ASTER) and Landsat surface temperature products, both of which have a much smaller footprint (90–120 m) than MODIS (1 km). In all three datasets, the cold bias was most pronounced in the winter (mean cold bias > 8 °C), and least pronounced in the spring and summer (mean cold bias < 2 °C). We also find this enhanced seasonal bias in MODIS brightness temperatures, before the incorporation of snow surface emissivity into the LST calculation. Finally, we find the MODIS cold bias to be consistent in magnitude and seasonal distribution with modeled temperature inversions, and to be most pronounced under conditions that facilitate near-surface inversions, namely low incoming solar radiation and wind speeds, at study sites Icefield Divide (60.68° N, 139.78° W, 2,603 m a.s.l) and Eclipse Icefield (60.84° N, 139.84° W, 3,017 m a.s.l.). These results demonstrate that efforts to improve the accuracy of MODIS LSTs should focus on understanding near-surface physical processes rather than refining the MODIS sensor or LST algorithm. In the absence of a physical correction for the cold bias, we apply a statistical correction, enabling the use of mean annual MODIS LSTs to qualitatively and quantitatively examine temperatures in the St. Elias Mountains and their relationship to melt and mass balance.
Ingalise Kindstedt et al.
Status: final response (author comments only)
- RC1: 'Comment on tc-2021-211', Anonymous Referee #1, 15 Nov 2021
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RC2: 'Comment on tc-2021-211', Anonymous Referee #2, 03 Jan 2022
Overview
The manuscript describes a thorough comparison between in-situ 2m air temperature data and a MODIS LST product for two sites in the St Elias mountains. The authors use additional data from ASTER, Landsat, ERA-5 and AWSs to further support their conclusions.
The main aim of the study is to determine the cause of the difference between 2-m air temperature measurements and the MODIS LST. Three possible causes are explored: the large MODIS footprint, errors in the surface emissivity, and near-surface temperature inversion. The study finds that the latter is the most likely cause of the temperature offset. The manuscript gives insight into how well MODIS LST represents the actual surface conditions in the remote St Elias mountains, which is important for future monitoring.
The manuscript is generally well-written and the presented results are interesting. I have two areas of concern, but most of my comments are minor:
1. I think you should be a bit clearer about the fact that you can’t directly compare the 2m air temperature and the land surface temperature. In some sections, you talk about “correcting” the MODIS LST (e.g. Figure 10) – it is not necessarily that the MODIS data is biased, it is just because you are comparing two different things. For the same reason, I would also be careful calling it a “bias” in the title.
2. You mention that both AWS are situated on nunataks, but you don’t really go into detail on the effect of this. If they are placed on nunataks, and not on glacier ice, could this not also be causing part of the bias? See also my specific comments for L 136-138 and L 270.
Specific comments
L 4-5: is this referring to previous work over St Elias? Or to the current study?
L 39: “"brightness temperature", an intermediate temperature product used to produce the final surface temperature.” - I would explain what this is here, not just call it an intermediate product.
Table 1: can you add a bit more info about the different data sources here? Resolution (temporal and spatial) and maybe uncertainty.
L 109: What do you mean with “more influential”? In terms of current sea level rise?
L 111: you do not define “Divide Icefield” as “Divide” until later in the text
L 125-126: How do you know the datasets are consistent, when the time periods do not overlap? Please clarify.
Figure 2: Where is the location of the iButton?
Table 2: change “Ice core site – AWS site” to “MODIS Ice Core Site – MODIS AWS site”, to clarify it is not in situ observations.
L136-138: I am not sure I follow this. You are comparing two MODIS pixels – one with only ice, and one with ice and a nunatak, to find out the difference in temperature between the nunatak surface and the ice surface? If so, this is interesting, but should be clarified and mentioned in the discussion. In addition, I would guess that the difference between the AWS and the ice covered ground is bigger than found in this comparison, since both MODIS pixels does contain some glaciated area.
L 144: why only between 11 and 1:30?
L 145-147: Do I understand correctly, that the 700+ images at Divide span 20 years, and the 100 images at Eclipse span ~2 years of data?
L 199: How do you get the downward radiation for Eclipse/iButton?
L 200: can you provide a bit more info about the ERA-5 product you use?
Page 10, 11 and others: consider your number of significant digits – are your results really that accurate? I would stick to 1-2 significant digits. Also in e.g. L 255: if it is a simple model, it probably does not have an accuracy of 3 significant digits.
Figure 6: this is at Divide?
L 270: Why do you compare MODIS, Landsat and Aster over the ice core location and not the AWS location (if I understand figure 2 correctly). If you are investigating the cause of the difference in AWS and measured LST, it would make more sense to look at the AWS location – especially since the AWS is on a nunatak, you would be able to better investigate the effect of this.
L 302: How is the DIVIDE snowfall record measured? Maybe give some information about this in the data section.
L 314-315: Why are you using different emissivities for the two sites?
Figure 10: What happened in 2020? The AWS temperature is much lower than the MODIS temperature.
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RC3: 'Comment on tc-2021-211', Anonymous Referee #3, 06 Jan 2022
Review of: Evaluating sources of an apparent cold bias in MODIS land surface
temperatures in the St. Elias Mountains, Yukon, Canada
Kindstedt et al. submitted to the Cryosphere.
December 22, 2020
This manuscript should be rejected for publication.
General comments:
The major finding seems to be the difference in air temperature and glacier temperature is the result of an inversion. Surface temperatures of snow and ice are usually colder than air temperatures. This is particularly true during the melt season and is a pretty well established fact. I am unsure what this manuscript offers that is not already present in the literature.
The aims and goals should be refined and better described in the manuscript. The organisation of the manuscript requires substantial revision.
No lapse rates are reported, which is the typical way to identify an inversion.
Editing for clarity is required. Details need to be added to the many vague statements in the manuscript.
Typically these sorts of studies use orders of magnitude more data than what appears here. An argument needs to be presented that the small data set is adequate. Static (or literature) values need to be replaced with measurements where possible.
The placement of figures in the narrative is disjointed and not logical. Many results are being presented in the discussion.
Many references concerning the relationship between energy balance and glacier mass balance are missing.
There is substantially more meteorological data available than what has been used in this analysis.
Specific comments:
Abstract:
MODIS LST can also be sparse or absent
MODIS LSTs are offset… which each LST measurement, average LST, minimum, maximum…
Footprint usually refers to swath width, or some derivative. Is it the grid cell size you are referring to?
Snow emissivity is >0.8 and can be close to being a blackbody, so it is intuitive that brightness temperature would also contain bias.
Line 21: …with far reaching impacts. This is the kind of statement that the manuscript is peppered with and is virtually meaningless: please revise, here and throughout.
Line 23: reduced the Earth’s albedo, further accelerating warming. Please provide credible references for this statement. Most studies do exactly what you are doing which is confusing correlation and causation. Perhaps as the temperature increases more snow is melted, and the newly exposed area provides a negligible amount of atmospheric warming. For context read: https://www.nature.com/articles/s41598-018-27348-7.
Alternatively, the reduction in snow and ice causes a warming, but the amount of increase in temperature cannot be disentangled from warm air advection.
Alternatively, the snow albedo feedback melts glaciers pretty efficiently.
Line 23-24: As written this statement is not correct. Hugonnet
et al.(2021) didn’t analyse albedo, nor was it mentioned in Zemp.
Line 25: Some ink should probably be spilled on your geographical definition of the Arctic. From a climatology point of view (i.e., Arctic Amplification) Arctic is defined as north of the Arctic Circle.
Line 29: I don’t know how many crucials and criticals I have seen to this point. The writing will pack more punch if these types of words are used substantially less often.
Line 32: controlled by atmospheric warming: not necessarily true, these might simply be correlated.
Line 33: continued -> projected?
Line 34: delete “to be able”
Line 38: What does “Remote sensing temperatures include the final surface temperature” mean?
Line 44: high temporal resolution and long temporal record; they provide two decades… what resolution, which decades? Always provide dates, rates, numbers, values, colours, weights, dimensions, etc. when describing quantitative subjects.
Line 59: “Lower elevation sites receive moisture from different air masses” detail why this is important
Line 65: Not necessarily a universal phenomenon: see:https://link.springer.com/article/10.1007/s00704-012-0687-x.
Line 71: “surface itself” should be replaced with details like where the photons are being emitted e.g., from the top x nm of the snow and ice, etc.
Line 75: This paper is relevant here: https://journals.ametsoc.org/view/journals/clim/26/5/jcli-d-12-00250.1.xml
There is probably only a very minor contamination issue.
Line 77: Summit should have Greenland appended to it, here and elsewhere, when referring to the summit of GIS.
Line 80: More detail is required here: There is more forcing that downwelling solar. Air parcel advection plays a role. And why does it have to be balanced- the temperature might be changing? Provide rationale.
Line 82: efficient emitter than the atmosphere - implies the atm has a lower emissivity than snow surface. Provide details. Atmospheric emissivity is mainly dependent on water vapour concentration.
Line 92: pixel is a picture element of a computer screen, where the minimum resolution is set by the screen parameters. Using pixel to describe a remote sensing array element or grid cell is common usage, but not technically correct.
Line 96: How exactly would “disparate changes in emissivity” lead to a bias? Provide details.
Line 108: There are records longer in the Tibetan Plateau and on Greenland and very possibly elsewhere. For some context see: Global Historical Climatology Network Monthly—Version 3 (GHCN-Mv3) (www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets/global-historical-climatology-network-monthly-version-3). The GHCN-Mv3 ftp server provides a list of weather stations (ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/v3/products/ghcnm.v3.first.last) with associated country codes, station location, elevation, and data duration.
Figure 3: Landsat has different sensors (MSS, TM, ETM+, etc.) so either break these up in the figure or identify differences in the text/caption, or both.
Line 119: Is air temp. samples on the hour of hourly averages of sub-hour measurements? MODIS LSTs are essentially samples.
Line 122: Not correct. As snow level changes the Divide sensor’s height above the surface will change. It is possible that it also gets buried in some of the winter months.
Line 124: “plastic container”? Provide details. Was this vented passively? Exposed to direct sunlight?
Line 125: We combine the Eclipse AWS and iButton datasets… Why? Is this a valid method? Provide sensitivity analysis.
Line 126: consistent. - define, preferably statistically.
Line 130: “employ an improved method” provide details and why relevant here.
Line 135 (and below): It appears results are provided before methods have been described. It is not clear what is being compared. Is it daily averages of air temperature? Have temperatures (air and MODIS) been temporally matched?
Line 141: “This may be due to the inclusion of the warmer nunatak surface” - this is testable by comparing time series from grid cells which contain less (or none) exposed rock.
Line 144: What is the rationale for using only <30 degrees view angle? Is there a sensitivity analysis or a citation to confirm this?
Line 145: This temporal subset will sample somewhere below the maximum daily temperature. This also seems to be a very small amount of data from what should be available from a 20 year time series, from two sensors and multiple daily overpasses.
Line 146: “The average time between scenes” describe what this means and why it is important - as written I have no idea what it means.
Line 151: Removing these data ?
Line 160: Under development as of when?
Line 167: TOA Tb is not really a useful metric to compare to surface temperature.
Line 170-175: Provide bounding values for “low” and “high”.
Line 176: “would” -> could.
Line 176: What does “physically plausible under surface conditions” mean exactly?
Line 177: “theoretical model of temperature inversions.To” - Provide details and a space after the period.
Line 185: Typically the terms you avoid are small compared to the dominant terms you include. Provide a range of values for all the terms. This will allow the reader to evaluate the effect of removing some terms.
Line 188: It rained at the summit of Greenland Ice Sheet this year, so probably better to rephrase this sentence.
Line 195: Why assume En=0, when it will most certainly not be, either seasonally or annually?
Line 200: Provide range of values for atmospheric emissivity.
Line 200: ERA 5 Land produces a downwelling longwave variable. Why wasn’t this incorporated into the analysis?
Equation 3: Provide more information about how this equation was derived. And why use a literature value for albedo? There is considerable variation, spatially and temporally, in albedo. Why not use the coincident MODIS albedo?
Line 203: MODIS provides emissivity values. What are these for the given days sampled in this study? What are the seasonal ranges of snow emissivity?
Line 208: Differences between median values? I am unsure what “Median differences” is.
Line 209: Which is warmer, surface or air? Not clear.
Line 210: Are these distributions normally distributed? There are tests to determine this.
I gather that seasonal averages use all of the data from 2000-2020. Are air temperature and surface temperature changing at the same rate? Are inversions weakening over time? Are rates of temperature change similar between seasons? Is there a monotonic trend in emissivity? All of these things will influence your results.
Line 223: Temperature has not been measured to the precision being reported.
Line 247: R^2 =0.02 is statistically significant? How big was this data set?
Figure 5: I am not sure what the point of this figure is? The two MODIS thermal bands will differentially absorb in the atmosphere, which is the basis for the split window LST algorithm. To work out the atm. emissivity, atm. column water vapour is required.
Figure 6: Are these data temporally matched? It must be sampled data because a daily average of 1000 w/m^2 is not feasible.
Line 263: “averaging temperature” - means what?
Line 266: Air temperature scales over 100s of km, so not surprising.
Figure 7: I am skeptical about the magnitude of the p-values reported here. These should be checked.
Line 275-277: Why would you expect this? Make sure all of the expectations in this section include enough background information for a reader to evaluate.
Figure 9: Earlier in the methods you said the time of MODIS capture was between 11AM and1:30PM. Why the different diurnal time range here? Same issue with Table 6.
Line 289: “suggesting that emissivity values during these seasons may contribute to the offset” - how exactly?
If there is a trend in cloud cover change then both downwelling shortwave and longwave radiation will be altered over the course of the study period. This could add a substantial amount of error to the results. This needs to be analysed.
Line 295: which changing surface conditions?
Line 315: Why was a simple energy balance model used when radiosonde or re-analysis data can be used to determine inversion depth?
Line 314: “wintertime temperature inversion” level? Williamson et al. (2020) put inversion level at approximately 1200 masl. The two stations used here are 1000 to 2000 m above this level, and are not situated in valleys where cold air drains and collects.
Line 367: “Surface melt is primarily driven by high air temperature” - what is high? And melt is correlated with air temperature. There are many examples in the literature of melt rate being influenced by short and longwave radiation.
Line 369-370: MODIS albedo correlates very well to glacier mass balance. There are many examples of this to be found in the literature. MODIS can’t measure albedo under cloud cover. I am not sure the statement presented in the manuscript is correct.
Figure 10: Corrected is the wrong word. LST and air temperature are not the same thing and should display offsets. These offsets are important for understanding the energy transfer between surface and atmosphere. If the goal is to produce air temperature fields originating from MODIS LST, then ‘converted’ instead of ‘corrected’ might be a better option. Further, there are many examples of methods to convert LST in the literature, most of which do not appear in the manuscript. The AWS data from 2020 is suspiciously cold.
Line 376: Snow and ice melts when its temperature reaches 0C not when the air temperature above it reaches 0C. So the rationale here needs to be revisited.
Ingalise Kindstedt et al.
Ingalise Kindstedt et al.
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