The authors describe their work to complete the end to end modelling of GIC flow in the Swedish high voltage power grid using magnetic field measurements, the SMAP conductivity model and the RAISE representation of the power network. They investigate the October 2003 storm and then extend this to a 1-in-100 year storm using magnetic latitudinal bands of different scaling based on a recent statistical analysis of geoelectric fields.

They find the peak geoelectric field to lie between 10-30 V/km in some regions of Sweden. They use the model to identify lines that are expected to be more vulnerable to high current flow, though represented as total integrated voltage along particular lines. They discuss the implications and compare to regions where known outages have occurred before. They have generated two informative movies to illustrate the dynamic nature of the GIC flow.

This is an extremely well written paper and I could not find any issues with the logic, clarity or flow of the manuscript. The figures and tables are excellent and the results are useful for the operator. The authors are to be commended.

My only minor comments are:

1. why are the values of the line and transformer resistance under national security restrictions? They are available in other countries for example, so are probably similar in Sweden. Average values of resistance per km are given in e.g. Viljanen et al (2012) doi: 10.1051/swsc/2012017if you wanted to model GIC and use a standard 0.5 Ω for the grounding resistance.

2. Why not use Belsk, Brorfelde and Wingst observatories in the analysis to capture variations outside and to the south of Sweden in October 2003?

This is an interesting paper that studies the effects of two possible 1-in-100 years geoelectric field events on the Swedish power grid using the most advanced tools available in Sweden. However, the approach to create the 1-in-100 years geoelectric fields is not convincing at present and needs some reconsideration. The topic is worthwhile, and with some more work this should make a valuable addition to the series of Swedish publications that characterize geomagnetic activity and the power grid response in Sweden.

Major comment:

You create two possible 1-in-100 years events, Case 1 and Case 2, based on magnetic field observations during the Halloween storm. In Case 1, you calculate the geoelectric field based on the magnetic field observations and then scale the resulting values upward in southern Sweden. In Case 2, you assume spatially uniform external magnetic field with an amplitude and time development based on one magnetometer station during storm. The resulting geoelectric field is again scaled similar to Case 1. 

I find this approach problematic. Clearly, the reason for the characteristic latitude of the geoelectric field maximum, on which the scaling is based, is due to the external driver. In Case 2, you first create a spatially uniform external driver, but then change the latitude profile of the resulting geoelectric field such that it no longer corresponds to the driver. Nonetheless, you describe Case 2 to represent "an idealized scenario in which the magnetic field time series is spatially uniform" (lines 113-114). This is inconsistent. I suggest that either you drop the scaling or scale the external driver instead of the resulting geoelectric field. The latter also requires changing the way you describe the case.

In Case 1, you note that your analysis fails to explain the observed Malmö blackout. This you explain to be due to the missing magnetometer observations in the key area (lines 237-239). You also note that in northern Sweden the Halloween event is classified as a 1-in-100 years event whereas in southern Sweden it only reached 1-in-10 or 50 years (lines 108-110). Combining these, it rather sounds to me that the Halloween storm might well have been a 1-in-100 years event across Sweden, but the insufficient magnetometer coverage in southern Sweden did not allow full observation of its effects in this area. Thus, the role of the scaling in this case would not be to increase the classification of the storm but to rather correct for missing data. Such a correction should be applied to the driver rather than to the resulting geoelectric field. As this may not be straightforward, I suggest at least discussing the point in the text.

Minor comments:

Lines 11-12: "East–west-oriented power lines are especially vulnerable, as they align with the dominant orientation of the induced electric field." Do you mean this statement applies generally? Is the geoelectric field in Sweden typically oriented in the east-west direction? If not, please modify the statement. 

Line 29: "most commonly" On what is this claim based? Kp is also very widely used.

Line 43: "(NERC, 2016)" What is NERC?

Line 63: "the paper of this paper" Delete "the paper of"?

Lines 67-68: "a once in 100 year event" Shouldn't "year" be "years"? Please check throughout the manuscript.

Lines 71-80: This section does not describe either data or methods. It could be moved to section 3.

Lines 75-76: What about differences due to 1D and 3D modelling? 3D modelling is expected to give higher geoelectric field amplitudes than 1D due to the effect of lateral conductivity gradients.

Line 86: "external (Bext) produced by", "internal (Bint ) produced by" Something is missing here. Do you mean external component and internal component? 

Line 90: More detailed descriptions of GIC-SMAP and RAISE should be included. For example: What are the inputs and outputs of each model? What are the key principles and assumptions of these models? What are the key sources of uncertainty?

Line 102: Please check the style of references throughout the text.

Lines 104-105: "amplitude of the EH reaches an amplitude" Maybe write "amplitude of EH reaches a level" to avoid repetition?

Table 1: Should you add Lanabere et al. (2023, 2024) and Rosenqvist et al. (2025) as references to step 1 (cf. lines 71-80 and 108-110)?

Lines 118-120: Please explain this in more detail. Does GIC-SMAP expect Btot or Bext as input? I would have expected a first-principles model to take Bext as input and return Btot as output, but apparently this is not the case?

Figure 1: What are the arrows in panel (a)?

Figure 2: "specific sites" Are these the same locations as the stars in Fig. 1a?

Figure 3: "Magnetic field perturbations (dBH,ext /dt) at the magnetic stations along 25.42oE (solid line) and in Sweden (dashed line)." Please clarify this sentence. The stations in Finland and Estonia do not have a constant longitude. What are the arrows in panel (a)?

Line 193: "the induced electric field response is greater at HAN." Do you really mean that the induced electric field was calculated at HAN? (I understand this to mean the true location of HAN.) Or do you mean that the induced electric fields calculated using Bext from HAN at the locations marked with stars in Fig. 3a were greater than those calculated using Bext from OUJ?

Lines 194-197: I do not understand this reference. Dimmock et al. (2024, Figure 4) concerns a different event (23 Apr 2023) and they do not show the geoelectric field.

Line 211: "and the similarly response at the OUJ station" Please explain what this means.

Figure 5: What are the arrows?

Line 223: "3 V/km" 

Line 225: "1 V/km" Why were these threshold values selected?

Section 4: Due to the limitations of the power grid model you use, the analysis is limited to estimating voltages in separate transmission lines. Such an approach does not consider the power grid as an entity as proper GIC estimation would do, and hence does not provide a full picture of the response. The limitations of the chosen approach in this respect should be discussed. For example, the most intense GIC are typically observed at substations located at the edges of the power grid (https://doi.org/10.1051/swsc/2012017). According to your power grid map, both Malmö and Karlshamn appear to be located at the southern edge of the Swedish power grid. Even without exact values for the resistances, more useful results would probably be gained by estimating the GIC instead of analyzing the separate line voltages. Of course, in order to explain actual impacts, such as the Malmö blackout, you would also need to know the true grid geometry at the time of the event.

Lines 263-265: Is the number of power lines with the 100 V threshold exceeded really a relevant problem? It does not mean that there would be large GIC at the ends of all these power lines. Large GIC would be expected at the substations located at the edges of the power grid (see the point above).

Lines 345-346: "How do small-scale ionospheric currents during extreme storms influence the spatial variability of geoelectric fields and GICs?" What do you mean by small-scale? Due to the ~100 km distance between the ground and the ionosphere, magnetic field perturbations due to ionospheric current structures smaller than about 100 km in scale size are significantly attenuated before reaching the ground.

Lines 354-355: "The largest scaled EH are found within the 55◦ –58◦ MLAT band" This is a circular conclusion since you have artificially scaled the geoelectric field amplitude up particularly in this latitude band.

Line 356-357: "due to the prevailing orientation of the horizontal electric field" This conclusion sounds far more general than is probably intended or can be drawn based on you results. Please modify. (See also the similar comment on the abstract.)

Lines 365-366: "limited magnetometer coverage in southern Sweden" How accurate is the conductivity model in this area? Inaccuracies in the conductivity model can be a significant source of error for the modeled geoelectric field (https://doi.org/10.1029/2022SW003370).

Lines 368-370: This sentence is very long and difficult to follow. Please divide it into two or more shorter sentences. What is "sudden weakening of worst-case scenarios"?

This ambitious paper sets out to assess the exposure of the Swedish power network to an extreme geomagnetic disturbance (GMD).  The paper describes an approach to making the assessment and discusses the outcomes. 

The analysis is based on a single storm model, a 50-min substorm of the Halloween storm of 2003. Variability within the class of extreme events has been modelled by two cases generated from this one event.  The first case is generated by scaling upwards the magnitudes of the disturbance. The second case is generated by extending the most intense sub-region over the whole geographic region of study.  Neglecting the practical limitation of the physical disturbance that these approaches represent, the results of the simulations are presented and analysed in detail that might not be evident in a real power system response. 

In my opinion, the paper falls short of its objective because of the lack of rigour in matching the assumptions to reality.  Maybe, it is impossible to do so.  Nevertheless, the paper exposes an important aspect underlying the approach – how to model the physical form and characteristics of a 1-in-100-year extreme GMD.  Although this issue is not addressed directly, the paper has value in its clear description of the approach to the study of a large region and the patterns and discussion of some simulation results.

Specific comments: Individual scientific questions/issues

Extreme space weather events are characterised by the Dst index (-400 to beyond -1000 nT) or the maximum amplitude of E_H at some time resolution, and the authors recognise that whether an event occurs during the day or night makes a difference.  In this paper, a 1-in-100-years extreme event has been characterised using sparse data (one event), apparently without reference to the practical effects of the disturbances on power systems.

It has been postulated by others that ‘failure’ due to GMDs occurs by a process of transformer insulation degradation (such as at Salem in 1989, though even that damage did not lead to an immediate fault), by power system collapse (such as in Quebec in 1989, though that collapse was initiated by protection relay operation removing SVCs from service), or by protection relay maloperation not traced to equipment damage (such as at Malmo in 2003 and Bandsjö in 2017 and 2023).  The effect of GMDs on power systems is not a simple relationship between peak measurements of geomagnetic or geoelectrical parameters and power system degradation.  The relationship to simulated parameters of artificial GMDs is probably even weaker.

Therefore, my first question is whether the authors can justify their decision to base an ‘extreme event’ on a 10 s E_H threshold (line 118) and apparently arbitrary line voltage thresholds of 100 or 200 V (line 255) using the base parameters of a single event?  Even with the power utilities’ addition of line resistances to the calculated line voltages, is the resultant peak GIC the most useful and reliable metric of system stress likely to initiate outages? 

The authors generate two ‘plausible’ cases of GIC events using different assumptions and scaling the parameters of the Halloween storm. 

An important issue is summed up at line 108: “The E_H reached during this event has been classified as having an estimated recurrence of 1-in-100-year event in northern Sweden, but only a 1-in-10- to 1-in-50-year event in southern Sweden”.  This leads to a question that is not identified in the paper: Would the ionospheric structures retain their physical shape with scaling to higher intensities: case 1: “the actual observed magnetic field perturbation extrapolated to the whole Fennoscandian region”, or case 2: “the magnetic field time series is spatially uniform across all magnetometer stations”?

The results of the simulations are described in detail.  The basis of the scaling is consistent with approaches by others to similar modelling of the Swedish E-field profiles, so it is not surprising that the results are generally consistent with other published results.  The observations are detailed but not especially novel.  For example, at line 195, reference is made to ‘the transformer trip event’, a detail that has not yet been introduced and, relevant to line 196, several papers have shown the relationships between frequency components of the B-field, E-field, and GICs.  The difference between the results of the two cases, as depicted in Figure 5(c) surely depends on the way the cases were constructed.  To what extent do the simulation results represent plausible GMD events of 1-in-100-years severity?

The discussion of the modelled results, and the explanations in section 4, appear to support and validate the simulations.  However, the focus on voltages and voltage increase with line length (line 253) fails to mention that the line resistance also increases with length, such that longer lines might not contribute significantly higher induced currents.  Further, the comments about Malmö (line 237) and Karlshamn (line 241) suggest high susceptibility to GICs even in the south. 

Figure 9 is interesting.  A related paper [Rosenqvist et al., 2025] provides details of three GIC incidents during the Halloween storm period used in these simulations.  They occurred at 19:55, 20:03, and 20:07.  Two of the three did not coincide with the peaks of the number of lines exceeding the various voltage thresholds.  From these records, it appears that the GIC effect that caused incidents was not necessarily a peak line voltage.  This information raises questions about the validity of the presumed relationship between voltage stress and power system response.

Have the authors adequately considered the possibility that the differences between the two constructed simulations may be less significant than the types and settings of the relays that initiated tripping in the several GIC-related incidents identified in Rosenqvist’s paper?  Perhaps the detailed comparison of the two case studies is misleading and could be reduced, with advantage to the overall clarity of the paper.  What truly significant conclusions can be drawn from the simulations?

The discussion of power grid implications (section 5.2, starting at line 308) could have been used to define the extreme event parameters before the modelling started.  It appears that the argument follows the structure of:

• Extreme space weather events are linked with transformer damage. No references are given, but the examples are generally the Salem, USA transformers in March and November 1989; several Eskom generator transformers at different power stations following the GMDs of 2001-2003, and one or more transformers in New Zealand.  The records indicate that only one New Zealand transformer failed during a severe or extreme GMD, all the remainder failed or were removed from service several days, weeks or months after the GMD although techniques like oil analysis can show that the degradation was linked to GMD events.  Therefore, simultaneous transformer tripping due to physical damage appears unlikely during a 1-in-100-year event.  Transformer tripping by relays responding to waveform distortion or other parameters could cause multiple unit tripping – this is a relay/protection problem.  Apart from the ambiguity in the reported new specification for Swedish transformers to withstand GIC, it is not evident that the thermal stress is directly linked to peak exposure to line voltages.
• High geoelectric voltages on lines are given significance in this paper. However, it was not a line voltage that initiated collapse (blackout) of the Hydro Quebec system in 1989; it was a relay response to harmonics.  It is unclear whether any voltage collapse blackouts have been initiated purely by geoelectric voltages or GICs.
• The authors suggest high geoelectric voltages could “pose a significant risk to protection relays” (line 326). However, the mechanism of risk is not identified.  Considering the timing of actual relay events (see above) the problem is possibly that inappropriate relays (such as one measuring a non-physical quantity or lacking immunity to harmonics), or inappropriate relay settings, or, possibly, faulty relays or relay systems might cause ‘unnecessary’ tripping.  Collapse might be initiated by one or multiple coincident relay maloperations, each of which might remove a transformer or line circuit for several hours while its integrity is checked.  Based on Figure 9, such relay events might not occur as a direct result of high or maximum voltage but at high or changing rates of change of voltage.

What is it, then, that defines an extreme event from the perspective of the electricity system owner and operator?  Does it cause threshold maximum conditions throughout the region, could there be ‘hot spots’ of intense stress, and how big is a region?  For electricity utilities, an extreme event might focus on geomagnetic and geoelectric conditions that stress different items of the utilities’ equipment, rather than on peak (10 s) values.  (Other infrastructure, such as pipelines, might see extreme events differently.)  Would simulations directed towards identifying such conditions be more consequential? 

Though this paper does not answer these basic questions, it makes an important contribution to the field simply by its clear description of the approach that exposes these other aspects.

Technical corrections: spelling, grammar, etc

In line 204, correct ‘factos’ to factors.

At line 243, the word ‘drive’ suggests the vulnerability is directly caused by the topology or substation independently from the geoelectric voltage.  I suggest instead that ‘… grid topology and substation characteristics may influence vulnerability as much as does the regional E_H strength’ – or similar.

At line 252, would ‘dominant’ be more suitable than ‘preferred’?

At line 267, it appears MLT should be MLAT.

At line 311, the units of line resistance are probably ohm/km values, not simply ohms.

The details of many references are incorrect.  Many take the form of https://doi.org/https://doi.org/10.1029/2022SW003304 (line 395).  Another does not return a page.

Reviewer Report, Paper #egusphere-2025-4900

General Comments

This paper extends previous investigations into understanding the impacts of induced electric fields and GICs on Sweden's power grid for extreme geomagnetic storm scenarios. The study utilises results generated from models described and published in previous works. The text and figures are generally of high standard, however, given the reliance of models cited in previous works, the paper could benefit from a little more detail regarding these to make this paper more self-contained. There are also some statements/concepts that require further qualification and/or clarification. The following general comments are followed by specific comments that should be considered by the authors to improve the overall readability.

Although the authors do state the following with regards to Case 2, "In the second case (Case 2), we construct an idealized scenario in which the magnetic field time series is spatially uniform across all magnetometer stations.", the paper could benefit from a little more qualification early in the paper of why this approach was taken. It is acknowledged that the benefit becomes apparent later in the paper, particularly for southern Sweden, however, early qualification would improve the readability.

The authors use multiple terminology for storms as "strong", "intense", "severe", and "extreme". When using these different terms it should be made clear that it is with respect to a particular metric, or alternatively, use consistent terminology throughout the paper. Although there is no consistent international standard definition of "extreme" storms, as noted by the authors in lines 272-273, the terms should be applied with a consistent approach within the paper.

Specific Comments

Lines 7-8 and throughout: The term "electric field" is used and then interchanged with the term "geoelectric field". Perhaps better to be consistent.

Line 8: Suggest removing "s" from "lines".

Line 31: Is the ordering of references alphabetical or chronological? A consistent approach should be used.

Line 33: Suggest moving the "Bergin" reference to after "May 1921 storm".

Lines 37-40: As per general comment, the Halloween storm (Dst minimum = -383nT) is referred to as an "extreme" storm whereas the May 2024 storm (Dst = -412nT) is referred to as "severe". Consistent terminology should be used as much as possible.

Line 58: Suggest adding "at high latitudes" after "attributed to substorms" and replace "of" with "for".

Lines 73-73: Please clarify if this is electric field calculated using 1D or 3D conductivity structures.

Lines 76-78: As per general comment regarding the use of multiple terminology for storms as "strong", "intense", "severe", and "extreme".

Line 102: Suggest citing reference as "Rosenqvist and Hall (2019)".

Lines 111-112: Can the authors provide some detail as to the extrapolation/interpolation technique used for Case 1?

Line 116: The "SECS" acronym should be defined in the paper.

Lines 120-121: "However, using the external field allows estimation at locations where measurements were not collected, since it is independent of the ground conductivity." This statement needs more qualification as the paper suggests the interpolation/extrapolation is used for Case 1 and Case 2 to provide time series where measurements are not collected. What are the measurements being referred to?

Lines 123-125: These sentences to explain "plausible" are somewhat confusing. Please consider rewording.

Line 144: "The magnetic field components Bx and By time series are presented in Figure 1b,c." Although the caption of Figure1 indicates the interpolated time series are for the locations indicated by stars, this is not obvious in the text. Please indicate this in the text to assist the readability.

Line 146: Insert spacing prior to hyphen in "20:02:00– 20:08:40 UT".

Line 149: Insert "a" before "few".

Line 153: Insert "in" before "Figure" and remove "s" from "occurs".

Line 164: Suggest replacing "is illustrated" with "are provided".

Figure 1a: The brown star is heavily obscured.

Figure 1 caption, second line: Insert "Interpolated" before "Time series"?

Lines 180-181: This sentence, and the time series plots of Figure 3b (and their max values indicated by black dashed lines), are somewhat inconsistent with the statement in lines 113-114, "In the second case (Case 2), we construct an idealized scenario in which the magnetic field time series is spatially uniform across all magnetometer stations". Please clarify and/or qualify with respect to lines 113-114.

Lines 183-185: Same comment as for 180-181, "Due to the latitudinal differences….". Please clarify and/or qualify with respect to lines 113-114.

Line 204: "factos" should be "factors".

Figure 4 caption: Is the term "ground-based observations" correct? The paper states that Case 2 uses B_ext from the SECS method which suggests its not directly an observation. Please clarify.

Line 211: "similarly"?

Figure 5 caption, second line: "Shows" should be "shows"?

Line 224 and throughout: The power line voltage is described as voltage per km which is effectively electric field. Is this the average electric field over the length of the power line? Some information regarding how this is calculated would help with readability. Further, how were the thresholds of 1 V/km and 3 V/km determined to indicate hazardous conditions?

Figures 6c and 7c: The figures appear to omit lines between 1-3 V/km? Is this correct? Would it have been better to use a different colour for lines between these thresholds?

Lines 241-244: This needs a little more qualification here by referring to Figures 6 and 7. The results of this paper suggest that Karlshamn is not vulnerable to the extreme scenario in Case 1 and appears to be missing from Case 2, perhaps as per the previous comment. It is acknowledged that these statements are somewhat qualified later in the paper (lines 363-364) but qualification here would help with readability.

Figure 7 caption: Should be "(b-d)"?

Lines 263-265: The authors acknowledge that other parameters are needed to convert the estimated voltages to current flowing through the transmission lines and transformer windings in the network to understand the impact of GICs. Although the estimation of voltages is of value, longer lines in general will have higher total resistances (an analogous plot to Figure 8c of line length to total resistance would be similarly linear). The nominal operating voltage of these lines are hundreds of kV with operating tolerances typically of 5-10%. Statements such as those in lines 263-265 and lines 326-327 should be qualified more definitively using industry standards in terms of line tripping and network impact.

Line 291: Suggest replacing "area" with "range".

Line 294: Suggest using "cables" or inserting "the" before "telecommunication".

Line 301: Although the MLAT threshold for the Pulkkinen et al study was provided early in the paper, perhaps it would be useful to provide again for context here.

Lines 310-313: The numbers appear to be quoted as resistances in Ohms but their low values would suggest they are resistivity. Please clarify. If they are resistance values then it would not seem appropriate to apply for all transmission lines as their resistance is a function of resistivity, length and area. Are these values used in the RAISE model or are they determined from applying these resistance values to the obtained voltages? Please provide more information on how the currents were calculated to qualify the estimates of 200 A.

Lines 365-367: Should this statement be clarified for Case 1 as Case 2 does suggest Malmo is vulnerable?

Line 372: Should the reference citation be "Vanhamaki and Juusola, (2020)"?

References: Some reference titles use capitalisation and others don’t. Should this be consistent?