the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Jul 2023
Effects of Arctic sea-ice concentration on turbulent surface fluxes in four atmospheric reanalyses
Abstract. A prerequisite for understanding the local, regional, and hemispherical impacts of Arctic sea-ice decline on the atmosphere is to quantify the effects of sea-ice concentration (SIC) on the turbulent surface fluxes of sensible and latent heat in the Arctic. We analyse these effects utilising four global atmospheric reanalyses: ERA5, JRA-55, MERRA-2, and NCEP/CFSR (CFSR and CFSv2), and evaluate their uncertainties arising from inter-reanalysis differences in SIC and in the sensitivity of the turbulent surface fluxes to SIC. The magnitude of the differences in SIC is up to 0.15, but typically around 0.05 in most of the Arctic over all four seasons. Orthogonal-distance regression and ordinary-least-square regression analyses indicate that the greatest sensitivity of both the latent and the sensible heat flux to SIC occurs in the cold season, November to April. For these months, the average sensitivity is 400 W m-2 for the latent heat flux and over 800 W m-2 for the sensible heat flux per unit of SIC (change of SIC from 0 to 1), with the differences between reanalyses as large as 300 W m-2 for the latent heat flux and 600 W m-2 for the sensible heat flux per unit of SIC. The sensitivity is highest for the NCEP/CFSR reanalysis. Comparing the periods 1980–2000 and 2001–2021, we find that the effect of SIC on turbulent surface fluxes has weakened, owing to the increasing surface temperature of sea ice and the sea-ice decline. The results also indicate signs of decadal-scale improvement in the mutual agreement between reanalyses. The effect of SIC on turbulent surface fluxes arises mostly via the effect of SIC on atmosphere-surface differences in temperature and specific humidity, whereas the effect of SIC on wind speed partly cancels out in the turbulent surface fluxes, as the wind speed increases the magnitude of both upward and downward fluxes.
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RC1: 'Comment on egusphere-2023-1131', Anonymous Referee #1, 02 Aug 2023
This study presents intercomparison of four atmosphere reanalysis models to show the influence of several key parameters including sea ice concentration, surface temperatures and wind speed on turbulent (sensible and latent) heat flux variability.
It is a well-written and performed study with a clear structure and aim. The quality of the analysis and illustrations are of high quality. Yet, some minor revisions might be needed to improve the study. In its current state it compares four reanalyzes but does not provide much of analysis which of them might be more accurate for various application. Moreover, the background information might be improved with regards to physical drivers of turbulent fluxes variability, their parameterization and validation of the key parameters including surface temperature. The same goes with sea ice concentration, snow and ice thickness. This follows by a very compact conclusion paragraph, not providing enough understanding of the advantages of such intercomparison. I believe that explaining the exact procedure of flux estimations in various reanalyzes would strengthen future discussion about results of correlation analysis and expand the potential audience of the study.
General comments:
- Despite understandable references to reanalyzes names, the less usage of abbreviations may improve readability of the study. Generally, in comparison to a few other reanalysis-related papers, this study is partially challenging to read due to abbreviations and many values without a context.
- The discussion of (simplified) representation of snow and ice in the reanalysis may be improved. For the average Arctic ice and snow thickness of 2.0 m and 0.2 m, their equivalent total thickness could be 2–3 times higher than assumed 1.5 m. These parameters are quite important for surface heat balance (for high SIC), and there are a few studies comparing surface temperatures from in-situ observations with reanalysis (showing a substantial difference in IST). It would be useful to mention, how is the difference between different models in comparison to ERA5 bias in comparison to measurements, as the study also covers analysis of areas with relatively high SIC values. This is especially vital as there are known issues of strong surface temperature biases of reanalyzes in comparison to observations (Zampieri, 10.1175/MWR-D-22-0130.1).
- The effect of cloudiness was not discussed (often reanalysis works better with clear sky conditions, for example, following Herrmannsdörfer, 10.1525/elementa.2022.00085).
- Since the largest effect on flux variability comes from the SIC, it would be useful to discuss a bit more on flux measurements above leads, lead definition, lead fraction measurements, etc.
Specific comments:
Line 8: It would be great to add the time interval for given sensitivity values.
Line 11: Is it possible to distinguish decrease from the warming and from the reanalysis improvement? Can you quantify that difference between 1980-2000 and 2001-2021?
Line 14: Can you specify what is the effect of SIC on wind speed? Is it a physical effect?
Line 25: Is it always the case, even for relatively thin ice without snow, assumed in some of the reanalysis?
Line 46: since there is no direct mention of 20 % difference in the reference, it would be helpful to explain how it was calculated. In addition, it would be great to mention the scale of those observations: when/at which conditions SIC can be as different as 20%? For the current study, SIC concentration is a key parameter, and it would be helpful to have a more detailed overview of SIC data, algorithms, scales and uncertainties. For example, one would expect that 20% difference in SIC would give around 20% difference in turbulent flux differences between various reanalysis while the actual difference is way larger.
Line 72: it would be helpful to add explanation which data in this reanalysis is based on which measurements or models in addition to Table 1. For example, more details about snow and ice thickness. Or stating that surface temperature is calculated from the surface energy budget.
Line 83: It is vital for future analysis to give a better overview of algorithms behind different models. What is calculated, what is measured among parameters important for turbulent fluxes.
Line 131: It would be helpful to comment on the background of these SIC algorithms, not only covering their labels.
Table 4: The SIC in BS and GS is close to zero, yet the average LHF are typical for ice thickness of around 0.1 m, LHF would be 2-4 times larger for open water. Despite this not being your data, can you comment on that effect? It would be useful to show LHF and SHF as a function of SIC for some average conditions to quantify the potential range of their values.
Line 177: I fully understand the reason to use some abbreviations, but some of them, lees commonly used (like MB or months) may be removed to increase readability.
Line 183: Similarly, as BS is almost ice free, one would expect SHF for open water or 0.1-0.2 m thick ice of negative 100-500 W/m2 during winter using simple parameterizations. What could be the reason for much lower values?
Figure 4: years and values/difference titles may be also added to the figure to avoid reading the full caption. It may also be possible to present both data for 1980-00 and 2001-21, and difference with a separate color bar.
Line 213: Previously it was reported that LHF for CA is negative 2-11 W/m2, while for GS it is negative 10-36 W/m2. Assuming these two regions are almost on different side of SIC range (0.9 and 0.1), the corresponding difference of 10-25 W/m2 should roughly represent a slope of monthly average LHF per unit SIC. Here the slope is one order higher, is it because of different time averaging or why is that?
Line 241: Despite this being not your assumption, it may be mentioned later that leads in winter refreeze extremely fast (Petrich, 10.1029/2006JC003466) and such surface temperature assumption could lead to flux overestimation.
Figure 7: Maybe the cells could be chosen to be larger and cover wider range of SIC and SHF.
Line 299: Great you mention the effect from snow and ice thickness. Yet, it would be very useful to present what would be an expected bias in LHF and SHF purely from snow/ice thickness inclusion. Zampieri et al. (2023) might be a useful reference 10.1175/MWR-D-22-0130.1 , especially in the context of MOSAiC expedition, mentioned in line 415. Additionally, there are products including ice and snow thickness (for example, KARRA), which may be used to reduce some of uncertainties or quantify their importance.
Line 329: Please add that NWP stands for Numerical Weather Prediction (or something else).
Line 377: It would be useful to specify for each condition this is the case as SIC directly changes surface temperature from seawater freezing point to close to air ambient. What would be the bias if the lead is refrozen (as in winter they typically do in just several tens of minutes), which gives surface temperature lower than seawater.
Line 408: The sentence should end with a dot.
Line 416: I would suggest having a bit longer and clearer conclusion, underlining what you achieved, what reanalysis capture accurately and what can be still improved. And why this type of intercomparison work is important.
Line 484: The correct link to the study is https://doi.org/10.1002/qj.3803
Citation: https://doi.org/10.5194/egusphere-2023-1131-RC1 -
AC1: 'Reply on RC1', Tereza Uhlíková, 22 Sep 2023
Dear Reviewer,
thank you very much for your time and suggestions for the improvement of our manuscript.
Please see your original comments below (in bold) and our responses (in italics):
This study presents intercomparison of four atmosphere reanalysis models to show
the influence of several key parameters including sea ice concentration, surface
temperatures and wind speed on turbulent (sensible and latent) heat flux variability.
It is a well-written and performed study with a clear structure and aim. The quality of
the analysis and illustrations are of high quality. Yet, some minor revisions might be
needed to improve the study. In its current state it compares four reanalyzes but
does not provide much of analysis which of them might be more accurate for
various application. Moreover, the background information might be improved with
regards to physical drivers of turbulent fluxes variability, their parameterization and
validation of the key parameters including surface temperature. The same goes with
sea ice concentration, snow and ice thickness. This follows by a very compact
conclusion paragraph, not providing enough understanding of the advantages of
such intercomparison. I believe that explaining the exact procedure of flux
estimations in various reanalyzes would strengthen future discussion about results
of correlation analysis and expand the potential audience of the study.
General comments:
1) Despite understandable references to reanalyzes names, the less usage of
abbreviations may improve readability of the study. Generally, in comparison to a
few other reanalysis-related papers, this study is partially challenging to read due to
abbreviations and many values without a context.
We have used fewer abbreviations in the revised manuscript (also according to
suggestions in the comment number 14).
2) The discussion of (simplified) representation of snow and ice in the reanalysis
may be improved. For the average Arctic ice and snow thickness of 2.0 m and 0.2 m,
their equivalent total thickness could be 2–3 times higher than assumed 1.5 m.
These parameters are quite important for surface heat balance (for high SIC), and
there are a few studies comparing surface temperatures from in-situ observations
with reanalysis (showing a substantial difference in IST). It would be useful to
mention, how is the difference between different models in comparison to ERA5
bias in comparison to measurements, as the study also covers analysis of areas
with relatively high SIC values. This is especially vital as there are known issues of
strong surface temperature biases of reanalyzes in comparison to observations
(Zampieri, 10.1175/MWR-D-22-0130.1).
We have added a new subsection to the revised manuscript: 3.4 Effects of thin ice on
leads and snow pack on top of sea ice. In this subsection, we have carried out calculations
on the above-mentioned effects on LHF and SHF using mean data from the SHEBA
campaign to study them, and comment on the results.
We have also added a paragraph to the Discussion section of the revised manuscript on
simplified representation of sea ice and its impact on turbulent surface fluxes (subsection
4.2).
3) The effect of cloudiness was not discussed (often reanalysis works better with
clear sky conditions, for example, following Herrmannsdörfer,
10.1525/elementa.2022.00085).
We have added a paragraph to the Discussion section of the revised manuscript on
uncertainties in paramaterization of surface turbulent fluxes including discussion on the
warm surface-temperature bias in clear-sky conditions in cold seasons in the Arctic
(subsection 4.3).
(There is possibly a typo in the reviewers comment – reanalyses (numerical weather
prediction models) usually work worse (simulate too warm surface temperatures) in winter
clear-sky conditions in the Arctic.)
4) Since the largest effect on flux variability comes from the SIC, it would be useful
to discuss a bit more on flux measurements above leads, lead definition, lead
fraction measurements, etc.
We have added a text on lead definition and flux observations over leads to the
Introduction of the revised manuscript. We have also added more discussion on
observations of the lead fraction (SIC) and freezing of leads to the Discussion (subsection
4.2).
Specific comments:
5) Line 8: It would be great to add the time interval for given sensitivity values.
Added ‘using daily means of data’ to the Abstract.
6) Line 11: Is it possible to distinguish decrease from the warming and from the
reanalysis improvement? Can you quantify that difference between 1980-2000 and
2001-2021?
We don’t have evidence that more observations assimilated in reanalyses would improve
the datasets in a way that the sensitivity of turbulent surface fluxes to SIC would decrease
between 1980-2000 and 2001-2021. The assimilation scheme and model are the same for
the entire period covered by each reanalysis.
7) Line 14: Can you specify what is the effect of SIC on wind speed? Is it a physical
effect?
We have added ‘via surface roughness and atmospheric-boundary-layer stratification’ to
the Abstract.
8) Line 25: Is it always the case, even for relatively thin ice without snow, assumed
in some of the reanalysis?
According to observations that is the case in winter, which we have clarified in the revised
manuscript.
9) Line 46: since there is no direct mention of 20 % difference in the reference, it
would be helpful to explain how it was calculated. In addition, it would be great to
mention the scale of those observations: when/at which conditions SIC can be as
different as 20%? For the current study, SIC concentration is a key parameter, and it
would be helpful to have a more detailed overview of SIC data, algorithms, scales
and uncertainties. For example, one would expect that 20% difference in SIC would
give around 20% difference in turbulent flux differences between various reanalysis
while the actual difference is way larger.
The differences are shown in Figure 7 (right panel) of Valkonen et al. (2008), which we
specify in the revised manuscript.
It was calculated as the difference in sea-ice concentration based on passive microwave
data processed applying Bootstrap and NASA Team processing algorithms (specified in
the Figure description).
The assumption of 20 % difference in SIC resulting in 20 % difference in turbulent surface
flux between reanalyses is not entirely correct – by definition, the same 20 % difference in
SIC (e.g. 0.8 vs. 1) causes already different magnitude of upward SHF and LHF. Also, the
effect of SIC on turbulent surface fluxes is larger between e.g. SIC 1 changing to 0.8 than
SIC 0.2 changing to 0, the former case having larger impact on a change in upward
turbulent flux than the latter. The results of our study (in Figures 4 and 6) show the
modelled change in turbulent surface flux if the SIC change all the way from 0 to 1 within 1
day.
10) Line 72: it would be helpful to add explanation which data in this reanalysis is
based on which measurements or models in addition to Table 1. For example, more
details about snow and ice thickness. Or stating that surface temperature is
calculated from the surface energy budget.
We have added the information about the observations (satellite input for sea-ice
concentration) in each reanalysis to Table 2. Snow and ice thickness representation of the
sea ice is presented in Table 2 in the original version of the manuscript.
Surface temperature over the water and both snow covered and bare sea ice is calculated
from the surface energy budget in each reanalysis, which we have specified in the revised
manuscript.
11) Line 83: It is vital for future analysis to give a better overview of algorithms
behind different models. What is calculated, what is measured among parameters
important for turbulent fluxes.
The turbulent fluxes are prognostic variables in each reanalysis (calculated according to
Eq.1 and Eq.2).
We have added that ‘turbulent exchange coefficients depend on the roughness lengths for
momentum, heat and moisture, and on the stratification of the atmospheric surface layer’.
12) Line 131: It would be helpful to comment on the background of these SIC
algorithms, not only covering their labels.
As addressed in the specific comment number 10, we have added the information about
the observations (satellite input for sea-ice concentration) in each reanalysis to Table 2.
The specific algorithms for obtaining information on SIC (mostly from satellite data) are
rather complex and changing/ evolving in time (during the 42-year study period of our
work) with e.g. reanalyses using different external datasets as described in the end of
section 2 Material and Methods.
We do not think that going very deep into this background information would necessarily
help us interpret the differences in results between various reanalyses (and would expand
the extent of the study considerably).
13) Table 4: The SIC in BS and GS is close to zero, yet the average LHF are typical
for ice thickness of around 0.1 m, LHF would be 2-4 times larger for open water.
Despite this not being your data, can you comment on that effect? It would be
useful to show LHF and SHF as a function of SIC for some average conditions to
quantify the potential range of their values.
Please note that the values presented in Table 4 are both time and area-averaged. The
large average upward fluxes over the Barents and Greenland Seas, in particular in cold
seasons, are due to the high sea-surface temperatures. In the case of 0.1 m ice layer, the
fluxes would be reduced in magnitude.
SHF as a function of SIC in NCEP/CFSR data is shown for example in Figure 7 in the
original manuscript (days in November–December–January 1980–2000): in Laptev Sea
(point A), Beaufort Sea (point B), and Central Arctic (point C).
14) Line 177: I fully understand the reason to use some abbreviations, but some of
them, lees commonly used (like MB or months) may be removed to increase
readability.
We have replaced the abbreviations of seasons and Mean Biases of Daily Field Means
with words (the latter as Mean Biases).
15) Line 183: Similarly, as BS is almost ice free, one would expect SHF for open
water or 0.1-0.2 m thick ice of negative 100-500 W m-2 during winter using simple
parameterizations. What could be the reason for much lower values?
In February–April, SHF over the Barents Sea is indeed -100 to -500 W m-2 during cold-air
outbreaks originating from the Arctic. However, there are also a lot of cases of warm-air
advection from the south. Due to these cases, the seasonal mean loss of sensible heat is
no more than 34 W m-2 (ERA5) or 44 W m-2 (NCEP/CFSR).
Please note that in the revised manuscript, we have decided to change the ‘reference’
data set for subsection 3.1. While before, ERA5 was chosen randomly among reanalyses
(as indicated in the original manuscript), we do think that it is more appropriate to put
NCEP/CFSR ‘in the center of the attention’ of this subsection due to the modelled sea-ice
thickness and the snow on top of sea ice in this data set, which makes the data the most
realistic in terms of physical processes (compared to the rest of reanalyses prescribing
constant sea-ice thickness and no snow on top of the sea ice).
16) Figure 4: years and values/difference titles may be also added to the figure to
avoid reading the full caption. It may also be possible to present both data for 1980-
00 and 2001-21, and difference with a separate color bar.
We have added the side titles and data from 2001–2021 to Figures 4 and 6.
17) Line 213: Previously it was reported that LHF for CA is negative 2-11 W/m2, while
for GS it is negative 10-36 W/m2. Assuming these two regions are almost on
different side of SIC range (0.9 and 0.1), the corresponding difference of 10-25
W/m2should roughly represent a slope of monthly average LHF per unit SIC. Here
the slope is one order higher, is it because of different time averaging or why is
that?
The time averaging is the same throughout our study – daily means of data.
However, there is area averaging in the presented ERA5 (NCEP/CFSR) values in Table 3
and Table 4; these are calculated as mean of values from all days from respective season
(~ 2000 days) in all the grid cells of the respective arctic basin (tens to hundreds of
thousands of grid cells in case of GS and CA in ERA5 or NCEP/CFSR). Therefore, the
slope using just these two heavily averaged values is not directly comparable to the slope
coming out of the linear bilateral ODR model using all data from all days of each season in
each grid cell (outputs of which are shown in Figure 4 and Figure 6).
18) Line 241: Despite this being not your assumption, it may be mentioned later that
leads in winter refreeze extremely fast (Petrich, 10.1029/2006JC003466) and such
surface temperature assumption could lead to flux overestimation.
We have addressed the impact of refreezing of leads in the new subsection ‘3.4 Effects of
thin ice on leads and snow pack on top of sea ice’.
19) Figure 7: Maybe the cells could be chosen to be larger and cover wider range of
SIC and SHF.
The ODR model works with SIC/SHF data from original-sized grid cells of each reanalysis
(apart NCEP/CFSR, details explained in the original manuscript) in each season and time
period (~ 2000 days) and returns slope of the regression line (and other information).
Including more grid cells to make the area larger would require averaging of the variable
values and therefore we would be showing different SIC/SHF data than those that the
model works with to compute the slope of the regression line.
As it is mentioned in the text of the original manuscript, the main purpose of Figure 7 is to
show failure cases of the regression algorithm.
20) Line 299: Great you mention the effect from snow and ice thickness. Yet, it
would be very useful to present what would be an expected bias in LHF and SHF
purely from snow/ice thickness inclusion. Zampieri et al. (2023) might be a useful
reference 10.1175/MWR-D-22-0130.1 , especially in the context of MOSAiC
expedition, mentioned in line 415. Additionally, there are products including ice and
snow thickness (for example, KARRA), which may be used to reduce some of
uncertainties or quantify their importance.
We have carried out calculations on the effect of snow pack on LHF and SHF. The results
are presented in a new subsection ‘3.4 Effects of thin ice on leads and snow pack on top
of sea ice’. In this subsection, we used mean data from the SHEBA campaign to study the
above-mentioned effects on LHF and SHF and comment on the results.
We have not added CARRA results in the present study, as CARRA only covers a small
part of the Arctic. The treatment of snow pack is at least equally good in NCEP/CFSR
(included in the study) as in CARRA.
21) Line 329: Please add that NWP stands for Numerical Weather Prediction (or
something else).
Explanation of the acronym added.
22) Line 377: It would be useful to specify for each condition this is the case as SIC
directly changes surface temperature from seawater freezing point to close to air
ambient. What would be the bias if the lead is refrozen (as in winter they typically do
in just several tens of minutes), which gives surface temperature lower than
seawater.
We have carried out calculations on the effect of thin ice on leads on LHF and SHF. The
results are presented in a new subsection 3.4 Effects of thin ice on leads and snow pack
on top of sea ice’.
23) Line 408: The sentence should end with a dot.
Period added.
24) Line 416: I would suggest having a bit longer and clearer conclusion,
underlining what you achieved, what reanalysis capture accurately and what can be
still improved. And why this type of intercomparison work is important.
We have expanded the Conclusion according to the suggestions in the revised manuscript
(Section 5).
25) Line 484: The correct link to the study is https://doi.org/10.1002/qj.3803
Link was changed.Citation: https://doi.org/10.5194/egusphere-2023-1131-AC1
-
RC2: 'Comment on egusphere-2023-1131', Anonymous Referee #2, 27 Aug 2023
The authors’ study on the impact and sensitivity of Arctic sea ice concentration on the turbulent surface fluxes of sensible and latent heat in four atmospheric reanalyses is untouched by others, thus expanding our understanding in the context of atmospheric reanalyses in Arctic. Therefore, I believe the novelty is sufficient and of significant importance to this field. The analytical methods used in this manuscript are clear and convincing, so I believe the conclusions drawn by the authors are reliable. The structure of the manuscript is also quite reasonable, and the logic is clear. I believe that this manuscript makes a significant contribution to the research in the atmospheric reanalyses intercomparison in Arctic regarding the sea ice concentration and the sensitivity on turbulent heat fluxes. Therefore, I recommend that the journal accept it for publication after the authors make minor revisions.
Comments:
Lines 51-52: “uniform spatial and temporal resolution” is unclear. Suggest rewording this.
Lines 113-117: I'm not sure why here switched back to OLSR here. Since ODR seems more appropriate, I believe it should be continued to make the subsequent text clearer and the conclusions more coherent. If the only reason for using OLSR is that it requires fewer computing resources, I think that reason is not compelling enough.
Lines 118-119: Further explanation on this point is needed.
Lines 149-150: You need to specify which figure or table is being described here.
Figure 2: “Mean biases of daily field means of sea-ice concentration between ERA5 and JRA-55 (grey), ERA5 and MERRA-2 (black), and ERA5 and NCEP/CFSR (light grey).” Which one is subtracted from which? Is it ERA5 - JRA-55 or JRA-55 - ERA5? This needs to be clarified.
Lines 154 -200: I noticed that both in the Tables/Figures and the texts, you are comparing other reanalysis data to ERA5, even though you didn't assume ERA5 to be the best in your previous description. I don't think this is appropriate. These descriptions and graphics overly emphasize ERA5 and neglect the inter-comparison between other reanalyses, for example, JRA55 vs CFSR. I believe this is neither fair nor accurate. Please modify the text description and figures to express "inter-comparison" in a more equitable and intuitive manner.
Lines 214-215: What caused the higher sensitivity of LHF to SIC in this region? It is not explained here.
Line 246: The explanation here is not clear - Why would an increase in SIC variability lead to an increased statistical relationship between SIC and LHF? And where is the literature supporting the increase in SIC variability? Please add a reference.
Line 291: There's an extra space here.
Discussion and Conclusions: This section is too verbose for me and lacks clarity in its organization. I believe the authors can add subheadings to make the structure clearer, such as 4.1, 4.2, etc. Some of the content in this section is repetitive with the previous section; I suggest simplifying it. At the same time, separating the discussion and conclusion into two parts would make the structure clearer and more specific.
Citation: https://doi.org/10.5194/egusphere-2023-1131-RC2 -
AC2: 'Reply on RC2', Tereza Uhlíková, 22 Sep 2023
Dear Reviewer,
thank you very much for your time and suggestions for the improvement of our
manuscript. Please see your original comments below (in bold) and our responses (in
italics):
The authors’ study on the impact and sensitivity of Arctic sea ice concentration
on the turbulent surface fluxes of sensible and latent heat in four atmospheric
reanalyses is untouched by others, thus expanding our understanding in the
context of atmospheric reanalyses in Arctic. Therefore, I believe the novelty is
sufficient and of significant importance to this field. The analytical methods used
in this manuscript are clear and convincing, so I believe the conclusions drawn
by the authors are reliable. The structure of the manuscript is also quite
reasonable, and the logic is clear. I believe that this manuscript makes a
significant contribution to the research in the atmospheric reanalyses
intercomparison in Arctic regarding the sea ice concentration and the sensitivity
on turbulent heat fluxes. Therefore, I recommend that the journal accept it for
publication after the authors make minor revisions.
Comments:
26) Lines 51-52: “uniform spatial and temporal resolution” is unclear. Suggest
rewording this.
We have revised this as ‘...spatial and temporal resolutions that are uniform around the
globe…’
27) Lines 113-117: I'm not sure why here switched back to OLSR here. Since ODR
seems more appropriate, I believe it should be continued to make the subsequent
text clearer and the conclusions more coherent. If the only reason for using OLSR
is that it requires fewer computing resources, I think that reason is not
compelling enough.
We do believe that our choice of using OLSR for the multilateral regression analysis is
justified. To be clearer for the reader about our choice, we have revised the respective
paragraph in section 2 Material and Methods as follows:‘For the bilateral-relationship analysis, we utilised the orthogonal-distance regression
(ODR; Boggs, 1988). Because all variables in reanalyses include uncertainties, we
theoretically considered the ordinary-least-square regression (OLSR), which assumes
no errors in the independent variable, not optimal for this case. Additionally, we carried
out tests on bilateral ODR and OLSR performance using data from several grid cells
from each reanalysis and while we found ‘nearly identical’ (at least five decimal
numbers identical) coefficients of determination (correlation coefficient squared, R2) for
both regression methods, importantly, the slopes of the regression lines varied
considerably. This is attributable to the above-mentioned OLSR's assumption of no
errors in the independent variable (x, in our case SIC) and therefore minimising the
distance only for x data to the regression line, whereas ODR minimises the orthogonal
distance between both x and y data (in our case y is LHF or SHF) and the regression
line. Utilising the same above-described tests comparing ODR and OLSR performance
for multilateral regression analysis, however, we found ‘nearly identical’ values for all
slopes of the regression lines between LHF (SHF) and SIC, Qdiff (Tdiff), and WS10m for
both ODR and OLSR. Values of R2 for all and individual components of the multilateral
regression were ‘nearly identical’ using both ODR and OLSR as well. Based on the
findings that both methods yielded ‘nearly identical’ results for the multilateral
regression analysis (using our reanalyses data), we decided to use OLSR for the
multilateral regression analysis in our work, as it requires much fewer computing
resources to perform.’28) Lines 118-119: Further explanation on this point is needed.
We have revised the sentence as follows:
‘We used linear model for both ODR and OLSR as we evaluated it as the most
applicable for our purposes, being aware of some non-linearity in the SIC effect on Q2m
(T2m) and LHF (SHF), as shown for near-surface air temperature in e.g. Lupkes at al.
(2008), their Figure 4.’29) Lines 149-150: You need to specify which figure or table is being described
here.
We have revised the sentence as follows:
‘The mean SIC in NCEP/CFSR ranged from 0.01 in Baffin Bay in August-September-
October in 2001-2021 to 0.96 in the Central Arctic in February-March-April in both
1980-2000 and 2001-2021 (Table 3).’Please note that in the revised manuscript, we have decided to change the ‘reference’
data set for subsection 3.1 to NCEP/CFSR (more details in response to comment
number 31).
30) Figure 2: “Mean biases of daily field means of sea-ice concentration between
ERA5 and JRA-55 (grey), ERA5 and MERRA-2 (black), and ERA5 and NCEP/CFSR
(light grey).” Which one is subtracted from which? Is it ERA5 - JRA-55 or JRA-55 -
ERA5? This needs to be clarified.
We have revised the part of description of Figure 2 (and analogically the description of
Figure 3 and Figure S2) as suggested, to avoid reader’s confusion:
‘Mean Biases of Daily Field Means of latent heat flux: ERA5 minus NCEP/CFSR (light
grey), JRA-55 minus NCEP/CFSR (grey), and MERRA-2 minus NCEP/CFSR (black).
Horizontal axis refers to Arctic basins as seen in Figure 1...’
Please note that in the revised manuscript, we have decided to change the ‘reference’
data set for subsection 3.1 to NCEP/CFSR (more details in response to comment
number 31).
31) Lines 154 -200: I noticed that both in the Tables/Figures and the texts, you are
comparing other reanalysis data to ERA5, even though you didn't assume ERA5
to be the best in your previous description. I don't think this is appropriate. These
descriptions and graphics overly emphasize ERA5 and neglect the intercomparison
between other reanalyses, for example, JRA55 vs CFSR. I believe this
is neither fair nor accurate. Please modify the text description and figures to
express "inter-comparison" in a more equitable and intuitive manner.
We do believe that the comparison using Mean Bias of Daily Field Means allows us
(and the reader) to compare each reanalysis to the ‘reference’ and, at the same time,
the other reanalyses between each other.
We did, however, reconsider the selection of the ‘reference’ dataset. While before, we
chose ERA5 randomly (as indicated in the original manuscript under ‘We do not
assume that ERA5 is the best reanalysis with respect to turbulent surface fluxes…’), we
do consider NCEP/CFSR the most realistic in terms of physical processes due to its
modelled sea-ice thickness and modelled snow depth on the sea ice.
Still, comparisons between other reanalyses (e.g. JRA-55 and MERRA-2) are clearly
visible in our Figure 2, 3, and S2 – e.g. in cold seasons and most basins in 1980-2000
Mean Bias in sea-ice concentration (Figure 2) JRA-55 minus NCEP/CFSR is positive,
while Mean Bias NCEP/CFSR minus ERA5 or NCEP/CFSR minus MERRA-2 is
negative, therefore we know the sea-ice concentration prescribed in JRA-55 is the
highest of all reanalyses considered.
We have also revised the respective part of the manuscript to be more clear about this
issue as follows:
‘NCEP/CFSR was chosen as the most realistic in terms of physical processes
'reference' due to its modelled sea-ice thickness and the snow on top of sea ice.
However, we do not necessarily assume that it is the best reanalysis with respect to
turbulent surface fluxes and use Mean Biases to present overview and comparison of
the typical values in all reanalyses. Mean values (temporal together with spatial) of
NCEP/CFSR variables in Arctic basins, seasons, and periods are shown in Tables 3, 4,
and S1. These Tables provide approximate look into the absolute values of variables in
the other reanalyses in respective Arctic basins (as temporal + spatial means in these
Tables cannot be directly compared to the values of Mean Biases of Daily Field Means
between NCEP/CFSR and other reanalyses presented in Figures 2, 3, and S2).’32) Lines 214-215: What caused the higher sensitivity of LHF to SIC in this
region? It is not explained here.
In the revised manuscript, we have clarified that this matter is ‘further addressed and
explained in subsection 3.4’.
This is a new subsection in our revised manuscript, where we used mean data from the
SHEBA campaign to study the effects of thin ice on leads and snow pack on top of sea
ice on LHF and SHF and comment on the results.
33) Line 246: The explanation here is not clear - Why would an increase in SIC
variability lead to an increased statistical relationship between SIC and LHF? And
where is the literature supporting the increase in SIC variability? Please add a
reference.
In our data, we clearly see the increase in SIC variability in some regions of the Arctic
(under variability in this case, we mean ‘more days in the season with SIC other than 1’,
which could have been understandably confusing without an explanation).
While it is natural, that ‘more days in the season with SIC other than 1’ increases the
statistical relationship (significance) between LHF/SHF and SIC (variability in former
possible to explain by variability in latter), and it certainly happened like that in some
regions of the Arctic, this mechanism probably more often related to ODR model not
converging in 1980-2000 but returning a value of the slope between SIC/LHF (or
SIC/SHF) in 2001-2021.
Upon further inspection of the differences of SIC/LHF or SIC/SHF relationships between
1980-2000 and 2001-2021 in single grid cells, we found that in cases where the ODR
model converged in both study periods and returned steeper slope of the regression
line between LHF/SHF and SIC in the latter period, the reason for stronger statistical
relationship wasn’t as much caused by ‘more days in the season with SIC other than 1’
but rather just values of SIC/LHF or SIC/SHF forming a steeper slope (shown in
attached Figure S 3 – NCEP/CFSR data).
We have revised the possible explanation of the larger sensitivity of LHF/SHF to SIC as
follows:
‘Mostly in the Central Arctic, however, we found some areas of increased SIC effect on
LHF between 1980–2000 and 2001–2021… This increased SIC effect on LHF may be
explained as follows. We have mentioned before that the effect of SIC on near-surface
air temperature (and specific humidity) is not linear, and it is usually the strongest with
leads opening in SIC very close to 1. As indicated in Table 3 and shown in our
representative grid cells (Figure S 3, data from NCEP/CFSR in November-December-January), SIC in some areas of the Central Arctic increased
between 1980–2000 and 2001–2021 (possible reasons discussed in section
4.5). Therefore, there has been mostly very high SIC in 2001–2021, where even very
small decrease in SIC has a strong effect on near-surface air temperature and specific
humidity. We cannot be sure, however, whether the SIC increased in larger parts of the
Central Arctic in reality in 2001–2021, and only comment on possible physical and
statistical explanations of the phenomena as represented in reanalyses data.’
34) Line 291: There's an extra space here.
The following line should have been new paragraph, we have fixed this.
35) Discussion and Conclusions: This section is too verbose for me and lacks
clarity in its organization. I believe the authors can add subheadings to make the
structure clearer, such as 4.1, 4.2, etc. Some of the content in this section is
repetitive with the previous section; I suggest simplifying it. At the same time,
separating the discussion and conclusion into two parts would make the
structure clearer and more specific.
In the revised manuscript, we have divided Discussion and Conclusion into two sections
(4 and 5), and used subdivision of the Discussion section as following:
4.1 Differences between reanalyses, their importance, and consequences,
4.2 Simplification of the sea ice in reanalyses and its impact on surface turbulent fluxes,
4.3 Other uncertainties in parameterization of surface turbulent fluxes,
4.4 Role of sea-ice concentration and meteorological variables on surface turbulent
fluxes,
4.5 Decadal changes
Some of the subsections or paragraphs were added based on the comments of the
other Reviewer, however, we have also tried to simplify the Discussion section where
possible.
-
AC2: 'Reply on RC2', Tereza Uhlíková, 22 Sep 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-1131', Anonymous Referee #1, 02 Aug 2023
This study presents intercomparison of four atmosphere reanalysis models to show the influence of several key parameters including sea ice concentration, surface temperatures and wind speed on turbulent (sensible and latent) heat flux variability.
It is a well-written and performed study with a clear structure and aim. The quality of the analysis and illustrations are of high quality. Yet, some minor revisions might be needed to improve the study. In its current state it compares four reanalyzes but does not provide much of analysis which of them might be more accurate for various application. Moreover, the background information might be improved with regards to physical drivers of turbulent fluxes variability, their parameterization and validation of the key parameters including surface temperature. The same goes with sea ice concentration, snow and ice thickness. This follows by a very compact conclusion paragraph, not providing enough understanding of the advantages of such intercomparison. I believe that explaining the exact procedure of flux estimations in various reanalyzes would strengthen future discussion about results of correlation analysis and expand the potential audience of the study.
General comments:
- Despite understandable references to reanalyzes names, the less usage of abbreviations may improve readability of the study. Generally, in comparison to a few other reanalysis-related papers, this study is partially challenging to read due to abbreviations and many values without a context.
- The discussion of (simplified) representation of snow and ice in the reanalysis may be improved. For the average Arctic ice and snow thickness of 2.0 m and 0.2 m, their equivalent total thickness could be 2–3 times higher than assumed 1.5 m. These parameters are quite important for surface heat balance (for high SIC), and there are a few studies comparing surface temperatures from in-situ observations with reanalysis (showing a substantial difference in IST). It would be useful to mention, how is the difference between different models in comparison to ERA5 bias in comparison to measurements, as the study also covers analysis of areas with relatively high SIC values. This is especially vital as there are known issues of strong surface temperature biases of reanalyzes in comparison to observations (Zampieri, 10.1175/MWR-D-22-0130.1).
- The effect of cloudiness was not discussed (often reanalysis works better with clear sky conditions, for example, following Herrmannsdörfer, 10.1525/elementa.2022.00085).
- Since the largest effect on flux variability comes from the SIC, it would be useful to discuss a bit more on flux measurements above leads, lead definition, lead fraction measurements, etc.
Specific comments:
Line 8: It would be great to add the time interval for given sensitivity values.
Line 11: Is it possible to distinguish decrease from the warming and from the reanalysis improvement? Can you quantify that difference between 1980-2000 and 2001-2021?
Line 14: Can you specify what is the effect of SIC on wind speed? Is it a physical effect?
Line 25: Is it always the case, even for relatively thin ice without snow, assumed in some of the reanalysis?
Line 46: since there is no direct mention of 20 % difference in the reference, it would be helpful to explain how it was calculated. In addition, it would be great to mention the scale of those observations: when/at which conditions SIC can be as different as 20%? For the current study, SIC concentration is a key parameter, and it would be helpful to have a more detailed overview of SIC data, algorithms, scales and uncertainties. For example, one would expect that 20% difference in SIC would give around 20% difference in turbulent flux differences between various reanalysis while the actual difference is way larger.
Line 72: it would be helpful to add explanation which data in this reanalysis is based on which measurements or models in addition to Table 1. For example, more details about snow and ice thickness. Or stating that surface temperature is calculated from the surface energy budget.
Line 83: It is vital for future analysis to give a better overview of algorithms behind different models. What is calculated, what is measured among parameters important for turbulent fluxes.
Line 131: It would be helpful to comment on the background of these SIC algorithms, not only covering their labels.
Table 4: The SIC in BS and GS is close to zero, yet the average LHF are typical for ice thickness of around 0.1 m, LHF would be 2-4 times larger for open water. Despite this not being your data, can you comment on that effect? It would be useful to show LHF and SHF as a function of SIC for some average conditions to quantify the potential range of their values.
Line 177: I fully understand the reason to use some abbreviations, but some of them, lees commonly used (like MB or months) may be removed to increase readability.
Line 183: Similarly, as BS is almost ice free, one would expect SHF for open water or 0.1-0.2 m thick ice of negative 100-500 W/m2 during winter using simple parameterizations. What could be the reason for much lower values?
Figure 4: years and values/difference titles may be also added to the figure to avoid reading the full caption. It may also be possible to present both data for 1980-00 and 2001-21, and difference with a separate color bar.
Line 213: Previously it was reported that LHF for CA is negative 2-11 W/m2, while for GS it is negative 10-36 W/m2. Assuming these two regions are almost on different side of SIC range (0.9 and 0.1), the corresponding difference of 10-25 W/m2 should roughly represent a slope of monthly average LHF per unit SIC. Here the slope is one order higher, is it because of different time averaging or why is that?
Line 241: Despite this being not your assumption, it may be mentioned later that leads in winter refreeze extremely fast (Petrich, 10.1029/2006JC003466) and such surface temperature assumption could lead to flux overestimation.
Figure 7: Maybe the cells could be chosen to be larger and cover wider range of SIC and SHF.
Line 299: Great you mention the effect from snow and ice thickness. Yet, it would be very useful to present what would be an expected bias in LHF and SHF purely from snow/ice thickness inclusion. Zampieri et al. (2023) might be a useful reference 10.1175/MWR-D-22-0130.1 , especially in the context of MOSAiC expedition, mentioned in line 415. Additionally, there are products including ice and snow thickness (for example, KARRA), which may be used to reduce some of uncertainties or quantify their importance.
Line 329: Please add that NWP stands for Numerical Weather Prediction (or something else).
Line 377: It would be useful to specify for each condition this is the case as SIC directly changes surface temperature from seawater freezing point to close to air ambient. What would be the bias if the lead is refrozen (as in winter they typically do in just several tens of minutes), which gives surface temperature lower than seawater.
Line 408: The sentence should end with a dot.
Line 416: I would suggest having a bit longer and clearer conclusion, underlining what you achieved, what reanalysis capture accurately and what can be still improved. And why this type of intercomparison work is important.
Line 484: The correct link to the study is https://doi.org/10.1002/qj.3803
Citation: https://doi.org/10.5194/egusphere-2023-1131-RC1 -
AC1: 'Reply on RC1', Tereza Uhlíková, 22 Sep 2023
Dear Reviewer,
thank you very much for your time and suggestions for the improvement of our manuscript.
Please see your original comments below (in bold) and our responses (in italics):
This study presents intercomparison of four atmosphere reanalysis models to show
the influence of several key parameters including sea ice concentration, surface
temperatures and wind speed on turbulent (sensible and latent) heat flux variability.
It is a well-written and performed study with a clear structure and aim. The quality of
the analysis and illustrations are of high quality. Yet, some minor revisions might be
needed to improve the study. In its current state it compares four reanalyzes but
does not provide much of analysis which of them might be more accurate for
various application. Moreover, the background information might be improved with
regards to physical drivers of turbulent fluxes variability, their parameterization and
validation of the key parameters including surface temperature. The same goes with
sea ice concentration, snow and ice thickness. This follows by a very compact
conclusion paragraph, not providing enough understanding of the advantages of
such intercomparison. I believe that explaining the exact procedure of flux
estimations in various reanalyzes would strengthen future discussion about results
of correlation analysis and expand the potential audience of the study.
General comments:
1) Despite understandable references to reanalyzes names, the less usage of
abbreviations may improve readability of the study. Generally, in comparison to a
few other reanalysis-related papers, this study is partially challenging to read due to
abbreviations and many values without a context.
We have used fewer abbreviations in the revised manuscript (also according to
suggestions in the comment number 14).
2) The discussion of (simplified) representation of snow and ice in the reanalysis
may be improved. For the average Arctic ice and snow thickness of 2.0 m and 0.2 m,
their equivalent total thickness could be 2–3 times higher than assumed 1.5 m.
These parameters are quite important for surface heat balance (for high SIC), and
there are a few studies comparing surface temperatures from in-situ observations
with reanalysis (showing a substantial difference in IST). It would be useful to
mention, how is the difference between different models in comparison to ERA5
bias in comparison to measurements, as the study also covers analysis of areas
with relatively high SIC values. This is especially vital as there are known issues of
strong surface temperature biases of reanalyzes in comparison to observations
(Zampieri, 10.1175/MWR-D-22-0130.1).
We have added a new subsection to the revised manuscript: 3.4 Effects of thin ice on
leads and snow pack on top of sea ice. In this subsection, we have carried out calculations
on the above-mentioned effects on LHF and SHF using mean data from the SHEBA
campaign to study them, and comment on the results.
We have also added a paragraph to the Discussion section of the revised manuscript on
simplified representation of sea ice and its impact on turbulent surface fluxes (subsection
4.2).
3) The effect of cloudiness was not discussed (often reanalysis works better with
clear sky conditions, for example, following Herrmannsdörfer,
10.1525/elementa.2022.00085).
We have added a paragraph to the Discussion section of the revised manuscript on
uncertainties in paramaterization of surface turbulent fluxes including discussion on the
warm surface-temperature bias in clear-sky conditions in cold seasons in the Arctic
(subsection 4.3).
(There is possibly a typo in the reviewers comment – reanalyses (numerical weather
prediction models) usually work worse (simulate too warm surface temperatures) in winter
clear-sky conditions in the Arctic.)
4) Since the largest effect on flux variability comes from the SIC, it would be useful
to discuss a bit more on flux measurements above leads, lead definition, lead
fraction measurements, etc.
We have added a text on lead definition and flux observations over leads to the
Introduction of the revised manuscript. We have also added more discussion on
observations of the lead fraction (SIC) and freezing of leads to the Discussion (subsection
4.2).
Specific comments:
5) Line 8: It would be great to add the time interval for given sensitivity values.
Added ‘using daily means of data’ to the Abstract.
6) Line 11: Is it possible to distinguish decrease from the warming and from the
reanalysis improvement? Can you quantify that difference between 1980-2000 and
2001-2021?
We don’t have evidence that more observations assimilated in reanalyses would improve
the datasets in a way that the sensitivity of turbulent surface fluxes to SIC would decrease
between 1980-2000 and 2001-2021. The assimilation scheme and model are the same for
the entire period covered by each reanalysis.
7) Line 14: Can you specify what is the effect of SIC on wind speed? Is it a physical
effect?
We have added ‘via surface roughness and atmospheric-boundary-layer stratification’ to
the Abstract.
8) Line 25: Is it always the case, even for relatively thin ice without snow, assumed
in some of the reanalysis?
According to observations that is the case in winter, which we have clarified in the revised
manuscript.
9) Line 46: since there is no direct mention of 20 % difference in the reference, it
would be helpful to explain how it was calculated. In addition, it would be great to
mention the scale of those observations: when/at which conditions SIC can be as
different as 20%? For the current study, SIC concentration is a key parameter, and it
would be helpful to have a more detailed overview of SIC data, algorithms, scales
and uncertainties. For example, one would expect that 20% difference in SIC would
give around 20% difference in turbulent flux differences between various reanalysis
while the actual difference is way larger.
The differences are shown in Figure 7 (right panel) of Valkonen et al. (2008), which we
specify in the revised manuscript.
It was calculated as the difference in sea-ice concentration based on passive microwave
data processed applying Bootstrap and NASA Team processing algorithms (specified in
the Figure description).
The assumption of 20 % difference in SIC resulting in 20 % difference in turbulent surface
flux between reanalyses is not entirely correct – by definition, the same 20 % difference in
SIC (e.g. 0.8 vs. 1) causes already different magnitude of upward SHF and LHF. Also, the
effect of SIC on turbulent surface fluxes is larger between e.g. SIC 1 changing to 0.8 than
SIC 0.2 changing to 0, the former case having larger impact on a change in upward
turbulent flux than the latter. The results of our study (in Figures 4 and 6) show the
modelled change in turbulent surface flux if the SIC change all the way from 0 to 1 within 1
day.
10) Line 72: it would be helpful to add explanation which data in this reanalysis is
based on which measurements or models in addition to Table 1. For example, more
details about snow and ice thickness. Or stating that surface temperature is
calculated from the surface energy budget.
We have added the information about the observations (satellite input for sea-ice
concentration) in each reanalysis to Table 2. Snow and ice thickness representation of the
sea ice is presented in Table 2 in the original version of the manuscript.
Surface temperature over the water and both snow covered and bare sea ice is calculated
from the surface energy budget in each reanalysis, which we have specified in the revised
manuscript.
11) Line 83: It is vital for future analysis to give a better overview of algorithms
behind different models. What is calculated, what is measured among parameters
important for turbulent fluxes.
The turbulent fluxes are prognostic variables in each reanalysis (calculated according to
Eq.1 and Eq.2).
We have added that ‘turbulent exchange coefficients depend on the roughness lengths for
momentum, heat and moisture, and on the stratification of the atmospheric surface layer’.
12) Line 131: It would be helpful to comment on the background of these SIC
algorithms, not only covering their labels.
As addressed in the specific comment number 10, we have added the information about
the observations (satellite input for sea-ice concentration) in each reanalysis to Table 2.
The specific algorithms for obtaining information on SIC (mostly from satellite data) are
rather complex and changing/ evolving in time (during the 42-year study period of our
work) with e.g. reanalyses using different external datasets as described in the end of
section 2 Material and Methods.
We do not think that going very deep into this background information would necessarily
help us interpret the differences in results between various reanalyses (and would expand
the extent of the study considerably).
13) Table 4: The SIC in BS and GS is close to zero, yet the average LHF are typical
for ice thickness of around 0.1 m, LHF would be 2-4 times larger for open water.
Despite this not being your data, can you comment on that effect? It would be
useful to show LHF and SHF as a function of SIC for some average conditions to
quantify the potential range of their values.
Please note that the values presented in Table 4 are both time and area-averaged. The
large average upward fluxes over the Barents and Greenland Seas, in particular in cold
seasons, are due to the high sea-surface temperatures. In the case of 0.1 m ice layer, the
fluxes would be reduced in magnitude.
SHF as a function of SIC in NCEP/CFSR data is shown for example in Figure 7 in the
original manuscript (days in November–December–January 1980–2000): in Laptev Sea
(point A), Beaufort Sea (point B), and Central Arctic (point C).
14) Line 177: I fully understand the reason to use some abbreviations, but some of
them, lees commonly used (like MB or months) may be removed to increase
readability.
We have replaced the abbreviations of seasons and Mean Biases of Daily Field Means
with words (the latter as Mean Biases).
15) Line 183: Similarly, as BS is almost ice free, one would expect SHF for open
water or 0.1-0.2 m thick ice of negative 100-500 W m-2 during winter using simple
parameterizations. What could be the reason for much lower values?
In February–April, SHF over the Barents Sea is indeed -100 to -500 W m-2 during cold-air
outbreaks originating from the Arctic. However, there are also a lot of cases of warm-air
advection from the south. Due to these cases, the seasonal mean loss of sensible heat is
no more than 34 W m-2 (ERA5) or 44 W m-2 (NCEP/CFSR).
Please note that in the revised manuscript, we have decided to change the ‘reference’
data set for subsection 3.1. While before, ERA5 was chosen randomly among reanalyses
(as indicated in the original manuscript), we do think that it is more appropriate to put
NCEP/CFSR ‘in the center of the attention’ of this subsection due to the modelled sea-ice
thickness and the snow on top of sea ice in this data set, which makes the data the most
realistic in terms of physical processes (compared to the rest of reanalyses prescribing
constant sea-ice thickness and no snow on top of the sea ice).
16) Figure 4: years and values/difference titles may be also added to the figure to
avoid reading the full caption. It may also be possible to present both data for 1980-
00 and 2001-21, and difference with a separate color bar.
We have added the side titles and data from 2001–2021 to Figures 4 and 6.
17) Line 213: Previously it was reported that LHF for CA is negative 2-11 W/m2, while
for GS it is negative 10-36 W/m2. Assuming these two regions are almost on
different side of SIC range (0.9 and 0.1), the corresponding difference of 10-25
W/m2should roughly represent a slope of monthly average LHF per unit SIC. Here
the slope is one order higher, is it because of different time averaging or why is
that?
The time averaging is the same throughout our study – daily means of data.
However, there is area averaging in the presented ERA5 (NCEP/CFSR) values in Table 3
and Table 4; these are calculated as mean of values from all days from respective season
(~ 2000 days) in all the grid cells of the respective arctic basin (tens to hundreds of
thousands of grid cells in case of GS and CA in ERA5 or NCEP/CFSR). Therefore, the
slope using just these two heavily averaged values is not directly comparable to the slope
coming out of the linear bilateral ODR model using all data from all days of each season in
each grid cell (outputs of which are shown in Figure 4 and Figure 6).
18) Line 241: Despite this being not your assumption, it may be mentioned later that
leads in winter refreeze extremely fast (Petrich, 10.1029/2006JC003466) and such
surface temperature assumption could lead to flux overestimation.
We have addressed the impact of refreezing of leads in the new subsection ‘3.4 Effects of
thin ice on leads and snow pack on top of sea ice’.
19) Figure 7: Maybe the cells could be chosen to be larger and cover wider range of
SIC and SHF.
The ODR model works with SIC/SHF data from original-sized grid cells of each reanalysis
(apart NCEP/CFSR, details explained in the original manuscript) in each season and time
period (~ 2000 days) and returns slope of the regression line (and other information).
Including more grid cells to make the area larger would require averaging of the variable
values and therefore we would be showing different SIC/SHF data than those that the
model works with to compute the slope of the regression line.
As it is mentioned in the text of the original manuscript, the main purpose of Figure 7 is to
show failure cases of the regression algorithm.
20) Line 299: Great you mention the effect from snow and ice thickness. Yet, it
would be very useful to present what would be an expected bias in LHF and SHF
purely from snow/ice thickness inclusion. Zampieri et al. (2023) might be a useful
reference 10.1175/MWR-D-22-0130.1 , especially in the context of MOSAiC
expedition, mentioned in line 415. Additionally, there are products including ice and
snow thickness (for example, KARRA), which may be used to reduce some of
uncertainties or quantify their importance.
We have carried out calculations on the effect of snow pack on LHF and SHF. The results
are presented in a new subsection ‘3.4 Effects of thin ice on leads and snow pack on top
of sea ice’. In this subsection, we used mean data from the SHEBA campaign to study the
above-mentioned effects on LHF and SHF and comment on the results.
We have not added CARRA results in the present study, as CARRA only covers a small
part of the Arctic. The treatment of snow pack is at least equally good in NCEP/CFSR
(included in the study) as in CARRA.
21) Line 329: Please add that NWP stands for Numerical Weather Prediction (or
something else).
Explanation of the acronym added.
22) Line 377: It would be useful to specify for each condition this is the case as SIC
directly changes surface temperature from seawater freezing point to close to air
ambient. What would be the bias if the lead is refrozen (as in winter they typically do
in just several tens of minutes), which gives surface temperature lower than
seawater.
We have carried out calculations on the effect of thin ice on leads on LHF and SHF. The
results are presented in a new subsection 3.4 Effects of thin ice on leads and snow pack
on top of sea ice’.
23) Line 408: The sentence should end with a dot.
Period added.
24) Line 416: I would suggest having a bit longer and clearer conclusion,
underlining what you achieved, what reanalysis capture accurately and what can be
still improved. And why this type of intercomparison work is important.
We have expanded the Conclusion according to the suggestions in the revised manuscript
(Section 5).
25) Line 484: The correct link to the study is https://doi.org/10.1002/qj.3803
Link was changed.Citation: https://doi.org/10.5194/egusphere-2023-1131-AC1
-
RC2: 'Comment on egusphere-2023-1131', Anonymous Referee #2, 27 Aug 2023
The authors’ study on the impact and sensitivity of Arctic sea ice concentration on the turbulent surface fluxes of sensible and latent heat in four atmospheric reanalyses is untouched by others, thus expanding our understanding in the context of atmospheric reanalyses in Arctic. Therefore, I believe the novelty is sufficient and of significant importance to this field. The analytical methods used in this manuscript are clear and convincing, so I believe the conclusions drawn by the authors are reliable. The structure of the manuscript is also quite reasonable, and the logic is clear. I believe that this manuscript makes a significant contribution to the research in the atmospheric reanalyses intercomparison in Arctic regarding the sea ice concentration and the sensitivity on turbulent heat fluxes. Therefore, I recommend that the journal accept it for publication after the authors make minor revisions.
Comments:
Lines 51-52: “uniform spatial and temporal resolution” is unclear. Suggest rewording this.
Lines 113-117: I'm not sure why here switched back to OLSR here. Since ODR seems more appropriate, I believe it should be continued to make the subsequent text clearer and the conclusions more coherent. If the only reason for using OLSR is that it requires fewer computing resources, I think that reason is not compelling enough.
Lines 118-119: Further explanation on this point is needed.
Lines 149-150: You need to specify which figure or table is being described here.
Figure 2: “Mean biases of daily field means of sea-ice concentration between ERA5 and JRA-55 (grey), ERA5 and MERRA-2 (black), and ERA5 and NCEP/CFSR (light grey).” Which one is subtracted from which? Is it ERA5 - JRA-55 or JRA-55 - ERA5? This needs to be clarified.
Lines 154 -200: I noticed that both in the Tables/Figures and the texts, you are comparing other reanalysis data to ERA5, even though you didn't assume ERA5 to be the best in your previous description. I don't think this is appropriate. These descriptions and graphics overly emphasize ERA5 and neglect the inter-comparison between other reanalyses, for example, JRA55 vs CFSR. I believe this is neither fair nor accurate. Please modify the text description and figures to express "inter-comparison" in a more equitable and intuitive manner.
Lines 214-215: What caused the higher sensitivity of LHF to SIC in this region? It is not explained here.
Line 246: The explanation here is not clear - Why would an increase in SIC variability lead to an increased statistical relationship between SIC and LHF? And where is the literature supporting the increase in SIC variability? Please add a reference.
Line 291: There's an extra space here.
Discussion and Conclusions: This section is too verbose for me and lacks clarity in its organization. I believe the authors can add subheadings to make the structure clearer, such as 4.1, 4.2, etc. Some of the content in this section is repetitive with the previous section; I suggest simplifying it. At the same time, separating the discussion and conclusion into two parts would make the structure clearer and more specific.
Citation: https://doi.org/10.5194/egusphere-2023-1131-RC2 -
AC2: 'Reply on RC2', Tereza Uhlíková, 22 Sep 2023
Dear Reviewer,
thank you very much for your time and suggestions for the improvement of our
manuscript. Please see your original comments below (in bold) and our responses (in
italics):
The authors’ study on the impact and sensitivity of Arctic sea ice concentration
on the turbulent surface fluxes of sensible and latent heat in four atmospheric
reanalyses is untouched by others, thus expanding our understanding in the
context of atmospheric reanalyses in Arctic. Therefore, I believe the novelty is
sufficient and of significant importance to this field. The analytical methods used
in this manuscript are clear and convincing, so I believe the conclusions drawn
by the authors are reliable. The structure of the manuscript is also quite
reasonable, and the logic is clear. I believe that this manuscript makes a
significant contribution to the research in the atmospheric reanalyses
intercomparison in Arctic regarding the sea ice concentration and the sensitivity
on turbulent heat fluxes. Therefore, I recommend that the journal accept it for
publication after the authors make minor revisions.
Comments:
26) Lines 51-52: “uniform spatial and temporal resolution” is unclear. Suggest
rewording this.
We have revised this as ‘...spatial and temporal resolutions that are uniform around the
globe…’
27) Lines 113-117: I'm not sure why here switched back to OLSR here. Since ODR
seems more appropriate, I believe it should be continued to make the subsequent
text clearer and the conclusions more coherent. If the only reason for using OLSR
is that it requires fewer computing resources, I think that reason is not
compelling enough.
We do believe that our choice of using OLSR for the multilateral regression analysis is
justified. To be clearer for the reader about our choice, we have revised the respective
paragraph in section 2 Material and Methods as follows:‘For the bilateral-relationship analysis, we utilised the orthogonal-distance regression
(ODR; Boggs, 1988). Because all variables in reanalyses include uncertainties, we
theoretically considered the ordinary-least-square regression (OLSR), which assumes
no errors in the independent variable, not optimal for this case. Additionally, we carried
out tests on bilateral ODR and OLSR performance using data from several grid cells
from each reanalysis and while we found ‘nearly identical’ (at least five decimal
numbers identical) coefficients of determination (correlation coefficient squared, R2) for
both regression methods, importantly, the slopes of the regression lines varied
considerably. This is attributable to the above-mentioned OLSR's assumption of no
errors in the independent variable (x, in our case SIC) and therefore minimising the
distance only for x data to the regression line, whereas ODR minimises the orthogonal
distance between both x and y data (in our case y is LHF or SHF) and the regression
line. Utilising the same above-described tests comparing ODR and OLSR performance
for multilateral regression analysis, however, we found ‘nearly identical’ values for all
slopes of the regression lines between LHF (SHF) and SIC, Qdiff (Tdiff), and WS10m for
both ODR and OLSR. Values of R2 for all and individual components of the multilateral
regression were ‘nearly identical’ using both ODR and OLSR as well. Based on the
findings that both methods yielded ‘nearly identical’ results for the multilateral
regression analysis (using our reanalyses data), we decided to use OLSR for the
multilateral regression analysis in our work, as it requires much fewer computing
resources to perform.’28) Lines 118-119: Further explanation on this point is needed.
We have revised the sentence as follows:
‘We used linear model for both ODR and OLSR as we evaluated it as the most
applicable for our purposes, being aware of some non-linearity in the SIC effect on Q2m
(T2m) and LHF (SHF), as shown for near-surface air temperature in e.g. Lupkes at al.
(2008), their Figure 4.’29) Lines 149-150: You need to specify which figure or table is being described
here.
We have revised the sentence as follows:
‘The mean SIC in NCEP/CFSR ranged from 0.01 in Baffin Bay in August-September-
October in 2001-2021 to 0.96 in the Central Arctic in February-March-April in both
1980-2000 and 2001-2021 (Table 3).’Please note that in the revised manuscript, we have decided to change the ‘reference’
data set for subsection 3.1 to NCEP/CFSR (more details in response to comment
number 31).
30) Figure 2: “Mean biases of daily field means of sea-ice concentration between
ERA5 and JRA-55 (grey), ERA5 and MERRA-2 (black), and ERA5 and NCEP/CFSR
(light grey).” Which one is subtracted from which? Is it ERA5 - JRA-55 or JRA-55 -
ERA5? This needs to be clarified.
We have revised the part of description of Figure 2 (and analogically the description of
Figure 3 and Figure S2) as suggested, to avoid reader’s confusion:
‘Mean Biases of Daily Field Means of latent heat flux: ERA5 minus NCEP/CFSR (light
grey), JRA-55 minus NCEP/CFSR (grey), and MERRA-2 minus NCEP/CFSR (black).
Horizontal axis refers to Arctic basins as seen in Figure 1...’
Please note that in the revised manuscript, we have decided to change the ‘reference’
data set for subsection 3.1 to NCEP/CFSR (more details in response to comment
number 31).
31) Lines 154 -200: I noticed that both in the Tables/Figures and the texts, you are
comparing other reanalysis data to ERA5, even though you didn't assume ERA5
to be the best in your previous description. I don't think this is appropriate. These
descriptions and graphics overly emphasize ERA5 and neglect the intercomparison
between other reanalyses, for example, JRA55 vs CFSR. I believe this
is neither fair nor accurate. Please modify the text description and figures to
express "inter-comparison" in a more equitable and intuitive manner.
We do believe that the comparison using Mean Bias of Daily Field Means allows us
(and the reader) to compare each reanalysis to the ‘reference’ and, at the same time,
the other reanalyses between each other.
We did, however, reconsider the selection of the ‘reference’ dataset. While before, we
chose ERA5 randomly (as indicated in the original manuscript under ‘We do not
assume that ERA5 is the best reanalysis with respect to turbulent surface fluxes…’), we
do consider NCEP/CFSR the most realistic in terms of physical processes due to its
modelled sea-ice thickness and modelled snow depth on the sea ice.
Still, comparisons between other reanalyses (e.g. JRA-55 and MERRA-2) are clearly
visible in our Figure 2, 3, and S2 – e.g. in cold seasons and most basins in 1980-2000
Mean Bias in sea-ice concentration (Figure 2) JRA-55 minus NCEP/CFSR is positive,
while Mean Bias NCEP/CFSR minus ERA5 or NCEP/CFSR minus MERRA-2 is
negative, therefore we know the sea-ice concentration prescribed in JRA-55 is the
highest of all reanalyses considered.
We have also revised the respective part of the manuscript to be more clear about this
issue as follows:
‘NCEP/CFSR was chosen as the most realistic in terms of physical processes
'reference' due to its modelled sea-ice thickness and the snow on top of sea ice.
However, we do not necessarily assume that it is the best reanalysis with respect to
turbulent surface fluxes and use Mean Biases to present overview and comparison of
the typical values in all reanalyses. Mean values (temporal together with spatial) of
NCEP/CFSR variables in Arctic basins, seasons, and periods are shown in Tables 3, 4,
and S1. These Tables provide approximate look into the absolute values of variables in
the other reanalyses in respective Arctic basins (as temporal + spatial means in these
Tables cannot be directly compared to the values of Mean Biases of Daily Field Means
between NCEP/CFSR and other reanalyses presented in Figures 2, 3, and S2).’32) Lines 214-215: What caused the higher sensitivity of LHF to SIC in this
region? It is not explained here.
In the revised manuscript, we have clarified that this matter is ‘further addressed and
explained in subsection 3.4’.
This is a new subsection in our revised manuscript, where we used mean data from the
SHEBA campaign to study the effects of thin ice on leads and snow pack on top of sea
ice on LHF and SHF and comment on the results.
33) Line 246: The explanation here is not clear - Why would an increase in SIC
variability lead to an increased statistical relationship between SIC and LHF? And
where is the literature supporting the increase in SIC variability? Please add a
reference.
In our data, we clearly see the increase in SIC variability in some regions of the Arctic
(under variability in this case, we mean ‘more days in the season with SIC other than 1’,
which could have been understandably confusing without an explanation).
While it is natural, that ‘more days in the season with SIC other than 1’ increases the
statistical relationship (significance) between LHF/SHF and SIC (variability in former
possible to explain by variability in latter), and it certainly happened like that in some
regions of the Arctic, this mechanism probably more often related to ODR model not
converging in 1980-2000 but returning a value of the slope between SIC/LHF (or
SIC/SHF) in 2001-2021.
Upon further inspection of the differences of SIC/LHF or SIC/SHF relationships between
1980-2000 and 2001-2021 in single grid cells, we found that in cases where the ODR
model converged in both study periods and returned steeper slope of the regression
line between LHF/SHF and SIC in the latter period, the reason for stronger statistical
relationship wasn’t as much caused by ‘more days in the season with SIC other than 1’
but rather just values of SIC/LHF or SIC/SHF forming a steeper slope (shown in
attached Figure S 3 – NCEP/CFSR data).
We have revised the possible explanation of the larger sensitivity of LHF/SHF to SIC as
follows:
‘Mostly in the Central Arctic, however, we found some areas of increased SIC effect on
LHF between 1980–2000 and 2001–2021… This increased SIC effect on LHF may be
explained as follows. We have mentioned before that the effect of SIC on near-surface
air temperature (and specific humidity) is not linear, and it is usually the strongest with
leads opening in SIC very close to 1. As indicated in Table 3 and shown in our
representative grid cells (Figure S 3, data from NCEP/CFSR in November-December-January), SIC in some areas of the Central Arctic increased
between 1980–2000 and 2001–2021 (possible reasons discussed in section
4.5). Therefore, there has been mostly very high SIC in 2001–2021, where even very
small decrease in SIC has a strong effect on near-surface air temperature and specific
humidity. We cannot be sure, however, whether the SIC increased in larger parts of the
Central Arctic in reality in 2001–2021, and only comment on possible physical and
statistical explanations of the phenomena as represented in reanalyses data.’
34) Line 291: There's an extra space here.
The following line should have been new paragraph, we have fixed this.
35) Discussion and Conclusions: This section is too verbose for me and lacks
clarity in its organization. I believe the authors can add subheadings to make the
structure clearer, such as 4.1, 4.2, etc. Some of the content in this section is
repetitive with the previous section; I suggest simplifying it. At the same time,
separating the discussion and conclusion into two parts would make the
structure clearer and more specific.
In the revised manuscript, we have divided Discussion and Conclusion into two sections
(4 and 5), and used subdivision of the Discussion section as following:
4.1 Differences between reanalyses, their importance, and consequences,
4.2 Simplification of the sea ice in reanalyses and its impact on surface turbulent fluxes,
4.3 Other uncertainties in parameterization of surface turbulent fluxes,
4.4 Role of sea-ice concentration and meteorological variables on surface turbulent
fluxes,
4.5 Decadal changes
Some of the subsections or paragraphs were added based on the comments of the
other Reviewer, however, we have also tried to simplify the Discussion section where
possible.
-
AC2: 'Reply on RC2', Tereza Uhlíková, 22 Sep 2023
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Tereza Uhlíková
Timo Vihma
Alexey Yu Karpechko
Petteri Juha Uotila
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