Dear editor/authors

the paper describes the TSI collected during two campaigns ACLOUD and AFLUX  (and two additional experiments) over a sea ice/open ocean transition area in the Arctic, and attempts to link the different infrared radiative regimes with air and surface  temperature, air mass advection, sea ice coverage. While I should recognize the outstanding value of the dataset, I believe the water vapour content  contribution is not highlighted sufficiently, as it is one of the main driver of the effective emissivity of the atmosphere, which in turn determines the downelling component of the LW radiation. Some specific comment on this are given in the detailed review I reported below. The author proven their in depth knoledge of the mechanisms driving the TSI, while some additional effords trying to isolate individual/relative contributions of them, might be spent (but I know it is not easy at all). I also suggest to add LW_down and LW_up (along with TSI) as separated terms to better highlight rthe relative contriobution of each component to the radiative balance. This is especially the case of Figure 9, but can be an idea for other pictures (Fig 4 for example).

As usual, please take the comments as such, as I may have misinterpreted some part of the workflow.

Looking forward to have a look at the reviewed version.

With kind regards.

page 2-9

suggest to replace "downward", "upward" as "downwelling" and "upwelling" : downward might be interpreted as a pyranometer looking towards the surface. While for flux (which is something movign towards a direction) the terms downwelling/upwelling might be more appropriate

2) Specific comments: "page number-line:[line]"

page 2-12

interwined -> concurrent?

page 2-27

MCAO and WAI water vapor content characteristics content can be at least qualitatively mentioned here, as water vapor (along with T) is the main responsible of F_downwelling atmospheric emission.

page 4-9

kippzonen.de -> kippzonen.com

page 5-1

for the KT-19 please specify the FOV and viewing nadir angle (assuming 0°).

page 5-26

remove the dot after "corrected.()"

page 6-15

"The surface albedo and sea ice fraction are roughly linearly correlated

with broader distributions..." : The sea ice fraction I_f trigs the value of the surface albedo as a weighted average of open sea and ice albedo (then the 3D multiple reflections between atm-surf adjust the value). Give an idea of this in the sentence, if the authors agree.

page6-18

"The reason is that AFLUX was conducted earlier in the year compared to ACLOUD, and thus, during..." : 

With such an amount of data probably the authors can invertigate the SZA dependence further: A_clear_sky(SZA) = A_ocean(SZA)(1-I_f) + A_ice(Ts,SZA) I_f. Goven I_f and SZA,Ts, as independent measured variables, and parametrising the A_ice and A_ocean with, for example polinomial (rather than linear) functions, it is possible to optimise the parameters providing a formulation for the albedo which can be reported and used for further studies. 

In cloudy conditions instead A_ice and A_ocean might be assumed constant (or dependent on surface temperature Ts/A_ice).

page 6-27

" ...increased the surface albedo" : increased the value of reflected  irradiance, producing an apparent increase of the surface albedo.

page 10-20

The median values are mentioned here but their values not reported either here or in Figure 4. Did I missed them?

page 10-24

"-2 to -6 Wm2, -11Wm2" : are these slight variations significant considering uncerteinties? Under overcast conditions and for thick clouds TNI should record approx ~zero either in summer or winter. The shift might be due to thin cloud/non overcast? Is it possible to understand this from your data?

page 10-26

"These strongly ... " : How the surface low RH or, better, IWV content, were excluded as a primary cause of these extreme negative values? Relatively dry air  advection causes a low incoming irradiance as the emissivity of the clear-sky depends on the WV content mostly. It would also be worth to include a picture supporting your conclusions, showing nilas (hemy camera) and high surface temperature T (KT-19) and F_up (pyrgeometer).

Figure 4

Suggestion: Despite the dotted/dashed, blue and black appears too similar. Try using a light color for the blue or a green for the black. Distinguish N-ICE with solid/dashed rather than dotted/dashed.

page 11-eq.2 (and line 5-10)

This is a major concern.

F_incoming ~ \eps \sigma T_atm^4?; what about the apparent emissivity \eps (which should probably range around 0.5-0.6) in this equation? See for example Busetto et al., 2013 (Antarctic Science). The value used for T_atm depends on the aircraft level. It is worth to use a stable value at the top of the boudary layer to represent the thermodinamic status of the atmosphere, coupled with a suitabel functio for the apparent emissivity. The above reference report a number of reference to historical parametrization.

Hence, this reviewer is not sure to agree on the discussion given below as the equation should appear as  F_down ~ eps(T,rh,...) sigma T_atm^4 with a T_atm that can be derived from the dropsondes combined with aircraft records. Did the authors really verified their eq. 2 with data collected in field?

page 12-10 

This is a major concern (given here but as a comment to the work as a whole).

"various processes" : The explanation of the values obtained through a basic statistical analysis of summer and spring periods are attributed quite arbitrarily to the combination of possible processes. It is difficult to discern between the various contribution as a multi-variate analysis approach was not conducted (if feasible). 

The arguments given in the discussion are all plausible, and the author proven to well known the processes involved, but one of the objective of this research should be to identify, for each phenomena (cloud status, atm thermodinamic status, air advection, surface properties, ...), its relative contribution. This aspect should be reinforced possibly using a robust mathematical parametrizations.

page 13-3

"It can be also interpreted as a ..." -> "It is a ..."

page 13-10

"relatively invariant cloud base height and temperature" :  give median and interquantile range values or someh=thing supporting this argument

page 13-10

"more data are available for ACLOUD" -> quantify (number of flights? matrix data dimension?)

page 13-13:14

speculative in my opinion. It could, yes, but it is not proven by the observation/analysis presented here. Suggest to remove.

page 14-5 

remove "should"

page 14-16

"..., which mostly considers the solar spectral range that is not directly relevant for the TNI" --> "..., as BT determins the upwelling component of the thermal radiation budget."

page 15-Figure 6 caption

remarks that the x-axies of panel (b) is reversed to highlight the possition of the modes in the two figures, supporting page 14-lines 8:10 discussion.

page 15-4

"that are caused during transformation processes that air masses experience during meridional transport in more detail in this section" --> "that are caused by meridional transport of air masses with different thermodynamic properties in more detail in this section"

page 16-15

The simulations can also be vaslidated w.r.t. the ground based data from the AWIPEV station BSRN (Ny Alesund, ask Maturilli/Driemel, see NYa station on BSRN site bsrn.awi.de), and/or mauro.mazzola@cnr.it for data from the Amudsen Nobile Climate Change tower (https://bo.isp.cnr.it/main/CCTower/?Home)

page 16-30 

(remark-remark-remark moisture effect) Is it possible, from data at disposal, to quantify the effect of moisture in more details?

page 17-6

"air mass transformation" --> "air mass difference/heterogeneity" ? Transformation might take longer periods to occur. Air mass flow over the area of interest with its heterogeneity.

page 18-1 

same here, not sure about the balance among the mixing air massess or just a push off the cold air by the advection of warm air. Likely both (purely philosophical/definition comment probably).

page 19-Figure 8

Add Ny-Alesund to the map.

page 21-29:33

To support this, I suggest to report downwelling and upwelling LW measurements/simulations in Figure 9 (adding panels?). Not really sure about the usefulness of the clear-sky simulations here but they might serve as a baseline for the discussion about mutual cancellation of surface effects and thermodynamic states between open ocean and sea ice.

Figure 9: add Ny Alesund to the map if it fits, ... then use a different symbols in panel (a) for each day considered, so that one can easier associate it to panel (b).

 

page 23-23:25

possibly, but a bit speculative. 

page 23-26

Processess that play ... : enumerate/mention the processes the authors have in mind, to avoid generalization

page 23-28

Are "radiative transfer and thermodynamic processess" included in the hidden list of the previous sentence?

 An extensive analysis of low-level airborne observations of near-surface thermal-infrared net irradiance (TNI) conducted in the Arctic is presented in the article “Effects of variable, ice-ocean surface properties and air mass transformation on the Arctic radiative energy budget”. The study explores the complex interplay between sea ice concentration, cloud properties, surface albedo and atmospheric thermodynamics, shedding light on the radiative transfer and thermodynamic processes occurring in the marginal ice zone (MIZ) and surrounding areas.

The article addresses the critical knowledge gap regarding the understanding of near-surface thermal-infrared net irradiance in the Arctic, particularly in the MIZ. Several noteworthy findings are presented. Firstly, the study reveals four distinct modes of near-surface TNI in the MIZ. Furthermore, the authors observe a seasonal shift in the TNI field above homogeneous sea ice, characterized by more negative net irradiances and increased occurrence of cloudy states in summer compared to winter. This shift is attributed to the non-linearity of the Planck emission. Moreover, the authors highlight the importance of considering the thermodynamic profiles and stability in both cloudy and cloud-free conditions, as these factors significantly impact the TNI. The amount and quality of data evaluated in chapter 4 (“Cloudy and cloud-free modes of thermal-infrared net irradiance (TNI)”) is impressive and the conclusions comprehensible and interesting. Naturally, there is much less data for the analysis in chapter 5 (“Impact of synoptically driven meridional air mass transports on thermal-infrared radiation at the surface”) and the conclusions on the individual case studies are more speculative here. While the publication mentions diverging and variable estimates from satellites, models, and observations, there is limited comparison or discussion of the findings in relation to existing literature. How could more robust statements be made here in the future in order to get similar TNI values from remote sensing, models and in situ observations?

The research presented in this article carries significant implications for our understanding of radiative transfer and thermodynamic processes in the Arctic. The findings provide valuable insights into the complexities of TNI in the MIZ and its relationship with sea ice dynamics. Publication is recommended and addressing the above-mentioned minor points of criticism would further strengthen the study.

With best regards  

Figure 8 change F_ter,net --> F_tin

Figure 9 – seems a bit cluttered and confusing to me. Showing simulated thermal-infrared downward irradiances for clear sky conditions adds little value.