# Brief communication: Intercomparison study reveals pathways for improving the representation of sea-ice biogeochemistry in models

Letizia Tedesco, Giulia Castellani, Pedro Duarte, Meibing Jin, Sebastien Moreau, Eric Mortenson, Benjamin Tobey Saenz, Nadja Steiner, Martin Vancoppenolle

<figure><img src="image_1.png" /></figure>

## To cite this version:

Letizia Tedesco, Giulia Castellani, Pedro Duarte, Meibing Jin, Sebastien Moreau, et al. Brief communication: Intercomparison study reveals pathways for improving the representation of sea-ice biogeochemistry in models. 2025. hal-05391850

HAL Id: hal-05391850

https://hal.science/hal-05391850v1

Preprint submitted on 1 Dec 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_3.png" /></figure>

1 **Brief communication: Intercomparison study reveals pathways for**
2 **improving the representation of sea-ice biogeochemistry in models**

3 Letizia Tedesco¹, Giulia Castellani², Pedro Duarte³, Meibing Jin⁴, Sebastien Moreau³, Eric Mortenson⁵,

4 Benjamin Tobey Saenz⁶, Nadja Steiner⁷,⁸,⁹, Martin Vancoppenolle¹⁰

5 ¹Marine and Freshwater Solutions, Finnish Environment Institute, Helsinki, Finland

6 ²Institute of Environmental Physics (IUP), MARUM – Center for Marine Environmental Sciences, University of Bremen,

7 Bremen, Germany

8 ³Norwegian Polar Institute, Tomsø, Norway

9 ⁴University of Alaska Fairbanks, AK, USA

10 ⁵University of Miami, Cooperative Institute for Marine and Atmospheric Studies (CIMAS)

11 ⁶Biota.Earth, Berkeley, CA, USA

12 ⁷Institute of Ocean Sciences, Fisheries and Oceans Canada, Sidney, BC, Canada

13 ⁸Canadian Center for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, BC, Canada

14 ⁹School of Earth and Ocean Sciences, University of Victoria, Victoria, BC, Canada

15 ¹⁰Laboratoire d'Océanographie et du Climat, CNRS/IRD/MNHN, Sorbonne Université, Paris, France

16

17 Correspondence to: Letizia Tedesco (letizia.tedesco@environment.fi)

18

19 **Abstract.** Sea-ice biogeochemical models are key to understanding polar marine ecosystems. We present an intercomparison

20 of six one-dimensional sea-ice biogeochemical models, assessing their ability to simulate algal phenology and nutrient

21 dynamics by comparing them with sea-ice physical-biogeochemical data collected during the N-ICE2015 expedition. While

22 no model fully captured observed bloom dynamics without tuning, adjustments improved biomass simulations but had a

23 limited impact on nutrient representation. Variability in tuning strategies underscores key knowledge gaps and the need for

24 further model development in more harmonised ways. Our findings can inform future efforts to enhance the reliability and

25 predictive capacity of sea-ice biogeochemical models.

26 **1 Introduction**

27 Sea ice is home to an active microbial community, with ice algae displaying some of the highest Chlorophyll-a (Chl-a)

28 concentrations of any aquatic environment (Arrigo, 2017). Ice algae play multiple pivotal roles in polar oceans, representing

29 the largest biomass fraction in sea ice (Poulin et al., 2011), contributing to overall marine primary production (Dalman et al,

30 2025), acting as a critical food source for the marine food web, especially during winter (Schaafsma et al., 2017), and

31 efficiently contributing to the ocean carbon sink (Boetius et al., 2013). Together with phytoplankton, ice algae form the

32 foundation of the polar marine food web, supporting key under-ice foraging species such as Arctic cod (Boreogadus saida)

33 in the Arctic Ocean (Geoffroy et al., 2023) and Antarctic krill (Euphausia superba) in the Southern Ocean (Kohlbach et al.,
<figure><img src="image_4.png" /></figure>

34 2017). These species depend on the presence of sea ice and play a crucial role in transferring carbon to higher trophic levels, including humans (Steiner et al., 2021).

36

37 Current environmental changes are placing considerable pressure at the base of the food web, triggering significant effects throughout trophic levels. Despite the recognised importance of the sea-ice ecosystem (Lannuzel et al., 2020), our knowledge remains limited due to its remote location and extreme weather conditions, which restrict observational data - particularly biological observations - to sparse spatial and temporal distributions. As a result, the representation of sea-ice biological and ecological processes in numerical models has historically been limited. However, in recent decades, significant advances have been made in modelling sea-ice habitats and the evolution of sea-ice biological communities (Castellani et al., In press). Progress includes improved representation of physical processes, greater biodiversity, and enhanced ecosystem complexity.

45

46 An intercomparison of three-dimensional models has already been conducted to understand similarities and differences in simulated ice algae abundance and distribution (IAMIP1, Watanabe et al., 2019). This study investigated the seasonal-to-decadal variability in ice-algal primary productivity across four Arctic regions during 1980–2009, as simulated by five participating models. Its conclusions indicated that, despite the ongoing reduction in Arctic sea ice, the decadal trend in ice-algal productivity remained unclear. The vernal bloom shifted towards an earlier onset and shorter duration over the simulated period, and the choice of maximum growth rate was identified as a key driver of inter-model differences in simulated ice-algal primary productivity. A second phase, expanding the study's scope to global coverage and centennial timescales following CMIP6 protocols, is currently underway (IAMIP2, Hayashida et al., 2021). However, given the numerous limitations and uncertainties associated with these large-scale models, they are more useful for deriving bulk properties than for investigating detailed ecological processes.

56

57 To this end, one-dimensional (1D) process models become essential for addressing knowledge gaps in sea-ice biogeochemistry and ecological dynamics, as they provide a level of detail that large-scale models lack. They also allow for direct comparisons with in-situ observations, improving the ability to validate results. However, existing process models have been developed independently during periods of limited observations and incomplete process understanding, validated by observations at different locations, leading to substantial differences across models. These differences make an intercomparison of models performances challenging. To address this, the BEPSII (Biogeochemical Exchange Processes at Sea-Ice Interfaces, https://www.bepsii.org) expert group initiated an intercomparison of 1D sea-ice biogeochemical models, presented here, aiming at understanding variability among models' results and drivers of model sensitivity, testing transferability, and with the final goal to promote harmonisation for future model developments. The focus has been on understanding the similarities and differences in simulated ice algae dynamics and investigating the controlling factors responsible for the temporal variability of ice-algal productivity among existing 1D models.
<figure><img src="image_5.png" /></figure>

68

69 We present in this study an intercomparison of 1D sea-ice biogeochemical models (briefly described in Sect. 2.1), focusing on their ability to simulate ice algal dynamics and nutrient cycling. Using a refrozen lead time series (described in Sect 2.2)
70
71 as a test case, we assess model performance through a structured comparison of simulated and observed biogeochemical
72 variables. Two experiments—*no tuning* and *tuning*—were conducted (Sect 2.3) to evaluate the baseline model configurations
73
74 as well as the impact of targeted parameter adjustments on model accuracy. We analyse differences in model outputs,
75 identify key sources of variability, and discuss the challenges associated with simulating ice algal growth and nutrient fluxes
76 (Sect 3). Finally, we highlight the implications of our findings for future model development and propose directions for
improving the representation of biogeochemical processes in sea-ice models (Sect. 4).

## 2 Methods

### 2.1 Sea-ice biogeochemical models

79 1D process models are typically designed to represent only vertical processes, assuming little horizontal advection. Since
80 they are computationally efficient, these models can incorporate a high level of ecosystem complexity, such as representing
81 multiple functional groups of organisms and providing high vertical resolution by discretising sea ice into several layers.

82

83 1D sea-ice biogeochemical models vary in vertical resolution, ecosystem complexity, and whether they are coupled to the
84 ocean and/or atmosphere (Castellani et al., In press). The biogeochemically active part of sea ice, also known as the
85 Biologically Active Layer (BAL) (Tedesco et al., 2010), is represented either as a single layer near the ice-ocean interface of
86 prescribed or variable thicknesses depending on sea-ice permeability, or as multiple layers spanning the vertical range of the
87 sea ice with an active brine network (e.g., Jeffery et al., 2016). Single-layer approaches are computationally more efficient,
88 with dynamic layering providing a more realistic representation of bottom community dynamics (Tedesco et al., 2010).
89 Multi-layer models, on the other hand, capture the vertical variability of biogeochemical variables and allow simulating
90 surface and infiltration communities.

91

92 As in ocean models, the structure of sea-ice microbial ecosystems is represented using a set of Chemical Functional Families
93 (CFF). These can be inorganic, such as sea-ice micro- and macronutrients, or organic, further divided into living CFF (e.g.,
94 ice algae) and non-living CFF (e.g. sea-ice detritus). The simplest models are N-P models, which include only one nutrient
95 (N) and one algal functional type (P). The elemental composition of ice algae is typically fixed based on prescribed Redfield
96 Carbon, Nitrogen, Silicon, Phosphorous ratios (106:16:16:1), along with fixed Chlorophyll:Carbon ratios. The more
97 comprehensive N-P-Z-D models also include grazers (Z) (such as ice fauna) and/or sea-ice detritus (D). In the simplest
98 version of these models, only one limiting nutrient is considered. More complex models may represent multiple nutrients and
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

99 different CFFs for ice algal communities, as well as bacteria and grazers. In simpler models, the processes associated with 100 bacterial remineralisation or grazer feeding are often implicitly parameterised using constant rates.

101

102 The intercomparison included five modelling teams and a total of six model configurations. These models varied in several 103 aspects, encompassing differences in physical and biogeochemical process complexity, radiation schemes, vertical 104 resolution, choice of limiting nutrient(s), and coupling to an interactive sea-ice physical model and/or ocean biogeochemical 105 model. Table 1 summarises the main commonalities and differences among the models. For more details on a specific model, 106 we refer to the model's original reference (Table 1).

107

108 Table 1: Sea-ice biogeochemical models participating in the 1D intercomparison project. BGC stands for biogeochemistry. Please see the 109 main text for the remaining nomenclature used in the table.

<table><thead><tr><th>Model</th><th>Ice Physics</th><th>Transport</th><th>Radiation</th><th>Grid for sea ice BGC</th><th>Sea-ice functional groups</th><th>Cell quotas/Chl:C</th><th>Limiting elements</th><th>Ocean BGC</th><th>Reference</th></tr></thead><tbody><tr><td>BFM-SI</td><td>SM 0L</td><td>Growth/melt</td><td>1 band; BL</td><td>1L bottom dynamic</td><td>3N-2P-2D-1B-1Z</td><td>Quota/prognostic</td><td>Nitrogen, Phosporous, Silicon</td><td>1D slab</td><td>Tedesco et al (2010)</td></tr><tr><td>BFM-SI-Clim</td><td>SM 0L</td><td>Growth/melt</td><td>1 band; BL</td><td>1L bottom dynamic</td><td>3N-1P-2D</td><td>Quota/prognostic</td><td>Silicon</td><td>1D</td><td>Tedesco and Vichi (2014)</td></tr><tr><td>CICE 5.1</td><td>EC ML</td><td>Melt, Brine drainage</td><td>1 band; DE</td><td>Multi-layer</td><td>2N-1P-1D</td><td>RFD/constant</td><td>Nitrogen, Silicon</td><td>n.a.</td><td>Duarte et al (2017)</td></tr><tr><td>CSIB-1D</td><td>SM 0L</td><td>Melt</td><td>1 band; BL</td><td>1L bottom static</td><td>2N-3P-2Z-2D</td><td>RFD/constant</td><td>Nitrogen, Silicon</td><td>1D</td><td>Mortenson et al (2017)</td></tr><tr><td>SIESTA</td><td>EC ML</td><td>Desalination</td><td>32 bands; DE</td><td>Multi-layer</td><td>3N-1P-1D</td><td>RFD/constant</td><td>Nitrogen, Phosporous, Silicon</td><td>n.a.</td><td>Saenz and Arrigo (2014)</td></tr><tr><td>SIMBA</td><td>Prescribed</td><td>Melt</td><td>1 band; BL</td><td>1L bottom static</td><td>1N-1P-1D</td><td>RFD/constant</td><td>Nitrogen</td><td>n.a.</td><td>Castellani et al (2017)</td></tr></tbody></table>

110

111 Most of the models had interactive physical components, while only one required prescribed ice physics. Additionally, only 112 half of the models were coupled to an interactive ocean biogeochemical model. Among the sea-ice physical models, 113 complexities ranged from a Semtner 0-layer scheme (SM 0L) to multi-layer energy-conserving models (EC ML). All 114 models, except one, used a single-band radiation transfer scheme, with many assuming Beer-Lambert (BL) light attenuation, 115 while only two employed a Delta-Eddington (DE) scheme. The majority of the models simulated ice algae only in the 116 bottom sea-ice layer, either as a static or dynamic system, while two models were multi-layer models, simulating ice algae 117 along the entire ice column. In terms of ecosystem complexity, models varied from simple Redfield-based models (RFD)
<figure><img src="image_8.png" /></figure>

118 with a single limiting nutrient, one algal group, and a detritus compartment to more comprehensive quota models with
119 several functional groups, including ice bacteria, ice fauna, and multi-nutrient limitations.

## 120 2.2 The N-ICE2015 Dataset

121 The refrozen lead time series monitored during the N-ICE2015 expedition (Granskog et al., 2018) was selected as a test case
122 for the model intercomparison due to the high frequency of available physical and biogeochemical measurements. The
123 N-ICE expedition was a field campaign conducted aboard the RV *Lance*, which was frozen into pack ice north of Svalbard,
124 drifting from 83° to 83°N to follow the direction of the drift, in the southern Nansen Basin of the Arctic Ocean from January
125 to June 2015. Among the four ice floes monitored during the study period, the refrozen lead data were derived from Floe 3,
126 which was studied from mid-April to early June 2015 between 81.8 and 80.5°N.

127

128 The lead, approximately 400 m wide, opened on 23 April, began refreezing on 26 April, and was fully refrozen by 1 May.
129 The newly formed young ice in the lead was sampled from 6 May along a 100 m-long transect extending from the edge of
130 the lead toward its centre every 2-3 days until it broke up on 4 June (Kauko et al., 2017). The algal growth period occurred
131 in April and May. While the ice algal community was initially highly mixed, pennate diatoms of the genus *Nitzschia* became
132 dominant later in the season.

133

134 The N-ICE2015 refrozen lead time series was chosen for this intercomparison based on two key factors:

135

136 • Observational data availability: It provides sufficient observations (Kauko et al., 2017) for comparison with model
137 simulations of physico-biogeochemical variables.

138 • Ancillary data availability: It includes detailed time series of atmosphere and ocean data, necessary to force model
139 runs, and has been successfully used in a previous 1D modelling study (Duarte et al., 2017).

140

## 141 2.3 Experimental setup

142 A strict protocol was developed and followed by all modelling groups. To accommodate the diversity of models, a minimum
143 set of variables was selected for comparison with observations. These included sea-ice season timing, ice thickness, and
144 snow thickness for coupled physical-biogeochemical models, as well as sea-ice nutrient concentrations and algal biomass
145 (represented by Chl-a) for all models.

146

147 Two distinct experiments were conducted to assess model performance. The first experiment, labelled *no tuning*, aimed to
148 run each model in its default configuration. The primary objective was to analyze the differences between model outputs and
149 observational data and quantify the extent of biases. The intercomparison within this experiment sought to identify potential
<figure><img src="image_9.png" /></figure>

150 reasons for deviations from observations, such as the omission of key processes or inadequate parameterisations. The second experiment, labelled *tuning*, involved adjusting the models to better align with observed physical and biogeochemical properties. This experiment aimed to identify which processes needed to be modified or added, as well as the specific parameterisations or parameters that were adjusted and fine-tuned to improve agreement with observations.

154
155 Both experiments were carried out independently by each modelling group, without prior knowledge of the work undertaken by others. This approach was adopted to eliminate potential biases, whether conscious or unconscious, during the implementation phase. To ensure a standardized comparison across models, all simulations used the same atmospheric forcing, as well as identical initial and boundary conditions. For the sea-ice biogeochemical model without a thermodynamic component, observed ice and snow thickness data were provided. This standardised approach improved the comparability of the models, allowing for a robust evaluation of model performance. In the final phase, results were presented by each modelling group, and teams collaboratively discussed challenges, adjustments, and tuning choices.

162 3. Results and discussion

163 Although the N-ICE refrozen lead resembles a typical ice season, in the *no tuning* experiment, none of the models accurately captured the observed algal phenology and bloom magnitude (Fig. 1, top left). Most models exhibited deviations in either phenology or bloom magnitude, underestimating or overestimating Chl-a and producing either an earlier or delayed bloom onset. Notably, most models underestimated algal biomass, except SIMBA, though performance varied across diagnostic measures.

168
169 Due to limited nutrient data, few considerations can be drawn about simulated nutrient dynamics beyond an assessment of the potential model error's order of magnitude. Here, models underestimated nitrate and silicate concentrations, except BFM-SI and BFM-SI Clim, which overestimated nutrient concentrations (Fig. 1, mid and bottom left), though all remained within a reasonable range.

173
174 In the *tuning* experiment, all models were able to reasonably simulate the ice algal phenology, though performance still varied across models (Fig. 1, top right). However, little improvement was achieved in the simulation of nitrate and silicate dynamics. Interestingly, tuning focused on different processes and parameters among models (Table 2), including:

177
178 • Change in the algal growth rate,
179 • The possibility of downward vertical migration during melting,
180 • Change in the initial simulation date, and
181 • Magnitude of silic acid limitation by changing the half saturation constant and/or the Nitrogen: Silicon ratio of ice
182 algae.
<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /><figcaption>184 **Figure 1:** Experiment with *no tuning* (left) and *tuning* (right). Model results for ice algae Chl-a (top), nitrate (middle), and silicate (bottom). Observations are shown as dots for the mean of the entire ice core or the bottom 10 cm, while associated bars indicate the variability of the measurements between their maximum and minimum values.</figcaption></figure>

188 Despite tuning efforts, none of the models significantly improved the simulation of nitrate magnitude, except for BFM-SI
189 (Fig. 1, mid right), which was also the only model that did not underestimate nitrate and silicate before tuning (Fig. 1, mid
190 and bottom left). After tuning, BFM-SI's simulated nitrate concentrations quite closely matched the observed values (Fig. 1,
191 mid right), attributed to increased nutrient uptake via enhanced algal growth. When comparing nutrient parameterisations

183

187
<figure><img src="image_12.png" /></figure>

192 across models (Table 1), BFM-SI stands out as the only model in which the variability of the dynamic sea-ice BAL 193 modulates the upward fluxes of dissolved inorganic matter. Instead, CSIB-1D was the best model to simulate the silicate 194 magnitude, matching observations before and after tuning. Overall, it remains unclear what element limited mostly algal 195 growth since models show to disagree, either structurally, by not including the limiting element of interest, or by using 196 different parameterisations. While all models may reasonably fit the chlorophyll observations, some models might achieve 197 good results for the wrong reasons. In general, models performed more poorly when simulating sea-ice nutrient dynamics. 198 Despite the scarcity of available data, the simulation of nutrient processes appears poorly constrained, pointing to the need 199 for more in-depth observational and experimental work.

200

201 The tuning experiment highlights the diversity of tuning parameters across models (Table 2), prompting critical questions 202 about model functionality and calibration. While models can be adjusted to align with observations, there is a risk of 203 achieving accurate results for the wrong reasons. This risk arises when tuning compensates for a missing process in the 204 model, leading to incorrect adjustments of parameters or processes. As a result, intercomparison modelling projects like this 205 play a crucial role in identifying and addressing missing or misrepresented processes, ultimately contributing to the 206 improvement of model performance and reliability.

207

208 Table 2. Comparison among models’ performances before and after tuning.

209

<table><thead><tr><td>MODEL</td><td>Ice algal phenology before tuning</td><td>Tuning strategy</td><td>Sea-Ice biogeochemistry after tuning</td></tr></thead><tbody><tr><td>BFM-SI</td><td>Good algal growth timing but lower algal biomass</td><td>Lower silica limitation and higher algal biomass in seawater</td><td>Algal phenology and magnitude within observed range; Nitrate within range; Higher silicate</td></tr><tr><td>BFM-SI Clim</td><td>Good algal growth timing but lower algal biomass</td><td>Lower silica limitation and higher algal biomass in seawater</td><td>Algal phenology and magnitude within observed range; Higher silicate</td></tr><tr><td>CICE5.1</td><td>Good algal growth timing but lower algal biomass</td><td>Lower silica limitation and reduced recruitment</td><td>Algal phenology and magnitude within observed range; Lower nitrate, Silicate within range</td></tr><tr><td>CSIB-1D</td><td>Delayed algal growth and lower algal biomass</td><td>Higher algal growth rate</td><td>Delayed phenology; Peak magnitude within observed range; Lower nitrate; Silicate within range</td></tr><tr><td>SIESTA</td><td>Good algal growth timing but lower algal biomass and earlier algal loss</td><td>Algal migration (possibility to keep position and lower Si half-saturation constant</td><td>Algal phenology within observed range; Earlier decay; Lower nutrients</td></tr><tr><td>SIMBA</td><td>Earlier algal growth and higher algal biomass</td><td>Lower algal growth rate and removal of winter drainage of nutrients</td><td>Algal phenology and magnitude mainly within observed range; Lower nitrate</td></tr></tbody></table>

210
<figure><img src="image_13.png" /></figure>

## 211 4. Conclusions

212 This study presents an intercomparison of one-dimensional sea-ice biogeochemical models, evaluating their ability to simulate algal phenology, bloom magnitude, and nutrient dynamics in a refrozen lead environment. The results highlight significant disparities in model performance, with most models struggling to accurately reproduce the observed algal biomass and nutrient concentrations, the latter also after tuning. While adjustments improved the representation of ice algal phenology, they had a limited impact on nutrient dynamics, emphasizing the challenges of parameterizing key processes such as nutrient fluxes and reinforcing the need for continued model development and validation supported by dedicated field and experimental observations.

219 The intercomparison highlights the unexpected challenges encountered in simulating a refrozen lead, primarily attributed to the short ice season and the difficulty most models faced in accumulating sufficient sympagic biomass. In a future Arctic Ocean characterized by increased lead openings, refreezing events, and young ice formation, there is an urgent need for models to be able to represent such a dynamic environment. This study underscores the importance of understanding and addressing the complexities involved in simulating specific and dynamic environmental scenarios.

225 The diversity of adjustments across models highlights both the range of tuning options available and the persisting knowledge gaps. The insights gained contribute valuable knowledge to ongoing efforts aimed at refining and improving numerical models, ensuring their accuracy and reliability in capturing complex interactions. To further advance this field of science, collaborative and harmonized modelling developments are recommended. A *Phase 2* of the intercomparison is auspicabile, potentially extending the study to the variability of habitats that characterizes Antarctic sea ice. Collaborative sensitivity tests could be conducted, with all models evaluating biological responses to the same tuning adjustments, tuning options could be expanded, and standard parameter ranges could be revisited based on newer data collected in recent years. Increased clarity of model sensitivities will improve future model robustness and enhance confidence in simulations of biogeochemical processes in ice-covered oceans.

## 235 Code and data availability

236 All relevant data, model code and numerical simulations presented in this work will be publicly made available upon manuscript's acceptance.
<figure><img src="image_14.png" /></figure>

238 Author contributions

239 LT and MV conceived the study. LT, GC, PD, EM, and BS produced the model runs. LT merged results from different
240 models and wrote the first draft of the ms. All authors contributed to the analysis of results, discussion, and editing of the
241 manuscript.

242 Competing interests

243 The authors declare no competing interests.

244 Acknowledgements

245 This work was written under the auspices of BEPSII (Biogeochemical Exchange Processes at the Sea-Ice Interfaces) Expert
246 Group (www.bepsii.org).

247 Financial support

248 LT and PD received funding from the European Union's Horizon 2020 research and innovation programme under grant
249 agreement No 101003826 via project CRiceS (Climate relevant interactions and feedbacks: the key role of sea ice and snow
250 in the polar and global climate system). GC, PD, and SM received funding from the RCN BREATHE project (Bottom sea ice
251 Respiration and nutrient Exchanges Assessed for THE Arctic, #325405). PD and SB also received funding from the iC3
252 Center of Excellence (Centre for ice, Cryosphere, Carbon and Climate, #332635). EM received funding through ArcticNet
253 and the National Science and Engineering Council in Canada. NS acknowledges Fisheries and Oceans Canada.

254 References

255 Arrigo, K.R.: Sea ice as a habitat for primary producers, In D.N. Thomas (Ed.), *Sea Ice* (3rd ed., pp. 352–369).
256 Wiley-Blackwell, doi:10.1002/9781118778371.ch14, 2017.

257
258 Boetius, A., Albrecht, S., Bakker, K., Bienhold, C., Felden, J., Fernández-Méndez, M., Hendricks, S., Katlein, C., Lalande, C., Krumpen, T., Nicolaus, M., Peeken, I., Rabe, B., Rogacheva, A., Rybakova, E., Somavilla, R., and Wenzhöfer, F.: Export of algal biomass from the melting Arctic sea ice. Science, 339, 6126, 1430–1432, doi:10.1126/science.1231346, 2013.

261
262 Castellani, G., Losch, M., Lange, B. A., and Flores, H.: Modeling Arctic sea-ice algae: Physical drivers of spatial distribution and algae phenology, J. Geophys. Res. Oceans, 122, 7466–7487, doi:10.1002/2017JC012828, 2017.
<figure><img src="image_15.png" /></figure>

264

265 Castellani, G., Tedesco, L., Steiner, N., Vancoppenolle, M.: Numerical model of sea-ice biogeochemistry, In D.N. Thomas

266 (Ed.), *Sea Ice* (4th ed.). Wiley-Blackwell, In press.

267

268 Dalman L. A., Meiners K. M., Thomas D. N., Deman F., Bestley S., Moreau S., Arrigo K. R., Campbell K., Corkill M.,

269 Cozzi S., Delille B., Fransson A., Fraser A. D., Henley S. F., Janssens J., Lannuzel D., Munro D. R., Nomura D., Norman L.,

270 Papadimitriou S., Schallenberg C., Tison J.-L., Vancoppenolle M., van der Merwe P., Fripiat F.: Observation-based estimate

271 of net community production in Antarctic sea ice. Geophysical Research Letters, 52, e2024GL113717, doi:

272 10.1029/2024GL113717, 2025.

273

274 Duarte, P., Meyer, A., Olsen, L. M., Kauko, H. M., Assmy, P., Rosel, A., Itkin, P., Hudson, S. R., Granskog, M. A., Gerland,

275 S., Sundfjord, A., Steen, H., Hop, H., Cohen, L., Peterson, A. K., Jeffery, N., Elliott, S. M., Hunke, E. C., and Turner, A. K.:

276 Sea ice thermohaline dynamics and biogeochemistry in the Arctic Ocean: Empirical and model results, J. Geophys.

277 Res.-Biogeo., 122, 1632–1654, doi: 10.1002/2016JG003660, 2017.

278

279 Geoffroy, M., Bouchard, C., Flores, H., Robert, D., Gjøsæter, H., Hoover, C., Hop, H., Hussey, N. E., Nahrgang, J., Steiner,

280 N., Bender, M., Berge, J., Castellani, G., Chernova, N., Copeman, L., David, C. L., Deary, A., Divoky, G., Dolgov, A. V.,

281 Duffy-Anderson, J., Dupont, N., Durant, J. M., Elliott, K., Gauthier, S., Goldstein, E. D., Gradinger, R., Hedges, K., Herbig,

282 J., Laurel, B., Loseto, L., Maes, S., Mark, F. C., Mosbech, A., Pedro, S., Pettitt-Wade, H., Prokopchuk, I., Renaud, P. E.,

283 Schembri, S., Vestfals, C., Walkusz, W.; The circumpolar impacts of climate change and anthropogenic stressors on Arctic

284 cod (Boreogadus saida) and its ecosystem. Elementa: Science of the Anthropocene, 11, 1: 00097. doi:

285 10.1525/elementa.2022.00097, 2023.

286

287 Granskog, M. A., Fer, I., Rinke, A., and Steen, H.: Atmosphere-Ice-Ocean-Ecosystem Processes in a Thinner Arctic Sea

288 Ice Regime: The Norwegian Young Sea ICE (N-ICE2015) Expedition, J. Geophys. Res.-Oceans, 123, 1586–1594,

289 doi: 10.1002/2017jc013328, 2018.

290

291 Hayashida, H., Jin, M., Steiner, N. S., Swart, N. C., Watanabe, E., Fiedler, R., Hogg, A. McC., Kiss, A. E., Matear, R. J., and

292 Strutton, P. G.: Ice Algae Model Intercomparison Project phase 2 (IAMIP2), Geosci. Model Dev., 14, 6847–6861,

293 https://doi.org/10.5194/gmd-14-6847-2021, 2021.

294

295 Jeffery, N., Elliott, S., Hunke, E. C., Lipscomb, W. H. and Turner, A. K.: Biogeochemistry of CICE: The Los Alamos Sea

296 Ice Model, Documentation and User's Manual. Zbgc_colpkg modifications to Version 5, Los Alamos National Laboratory,

297 Los Alamos, N. M., 2016.
<figure><img src="image_16.png" /></figure>

298

299 Kauko, H. M., Taskjelle, T., Assmy, P., Pavlov, A. K., Mundy, C. J., Duarte, P., Fernández-Méndez, M., Olsen, L. M., Hudson, S. R., Johnsen, G., Elliot, A., Wang, F., Granskog, M. A.: Windows in Arctic sea ice: light transmission and ice algae in a refrozen lead, J. Geophys. Res.-Biogeo., 122, doi:10.1002/2016JG003626, 2017.

302

303 Kohlbach D., Lange B. A., Schaafsma, F. L., David, C., Vortkamp, M., Graeve, M., van Franeker, J. A., Krumpen, T., and Flores, H.: Ice Algae-Produced Carbon Is Critical for Overwintering of Antarctic Krill *Euphausia superba*, Front. Mar. Sci. 4:310, doi:10.3389/fmars.2017.00310, 2017.

306

307 Lannuzel, D, Tedesco, L., van Leeuwe, M., Campbell, K., Flores, H., Delille, B., Miller, L., Stefels, J., Assmy, P., Bowman, J., Brown, K., Castellani, G., Chierici, M., Crabeck, O., Damm, E., Else, B., Fransson, A., Fripiat, F., Geilfus, N.-X., Jacques, C., Jones, E., Kaartokallio, K., Kotovitch, M., Meiners, K., Moreau, S., Nomura, D., Peeken, I., Rintala, J.-M., Steiner, N., Tison, J.-L., Vancoppenolle, M., der Linden, F. V., Vichi, M., Wongpan, P.: The future of Arctic sea-ice biogeochemistry and ice-associated ecosystems, Nature Climate Change, doi:10.138/s41558-020-00940-4, 2020.

312

313 Mortenson, E., Hayashida, H., Steiner, N., Monahan, A., Blais, M., Gale, M. A., Galindo, V., Gosselin, M., Hu, X., Lavoie, D., Mundy, C. J.: A model-based analysis of physical and biological controls on ice algal and pelagic primary production in Resolute Passage, Elementa: Science of the Anthropocene Sci Anth, 5, 39, doi:10.1525/elementa.229, 2017.

316

317 Poulin, M., Daugbjerg, N., Gradinger, R., Ilyash, L. V., Ratkova, T. N., von Quillfeldt, C.: The pan-Arctic biodiversity of marine pelagic and sea-ice unicellular eukaryotes: a first-attempt assessment, Mar. Biodiv., 41, 13–28, doi:10.1007/s12526-010-0058-8, 2011.

320

321 Saenz, B. T., and Arrigo, K. R.: Annual primary production in Antarctic sea ice during 2005–2006 from a sea ice state estimate, J. Geophys. Res. Oceans, 119, 3645–3678, doi:10.1002/2013JC009677, 2014.

323

324 Schaafsma, F. L., D. Kohlbach, C. David, B. A. Lange, M. Graeve, H. Flores, and J. A. van Franeker: Spatio-temporal variability in the winter diet of larval and juvenile Antarctic krill, *Euphausia superba*, in ice-covered waters, Marine Ecology Progress Series, 580, 101–115, doi: doi.org/10.3354/meps12309, 2017.

327

328 Steiner, N., Bowman, J., Campbell, K., Chierici, ...M., Eronen-Rasimus, Falardeau, E. M., Flores, H., Fransson, A., Herr, H., Insley, S. J., Kauko, H., Lannuzel, D., Loseto, L., Lynnes, A., Majewski, A., Meiners, K., Miller, L., Michel, L., Moreau, S., Nacke, M., Nomura, D., Tedesco, L., van Franeker, J. A., van Leeuwe, M. A., Wongpan, P.: Climate change
<figure><img src="image_17.png" /></figure>

331 impacts on sea-ice ecosystems and associated ecosystem services, Elem Sci Anth, 9 (1): 00007, doi:10.1525/elementa.2021.00007, 2021.

333

334 Tedesco, L., Vichi, M., Haapala, J., and Stipa, T.: A dynamic Biologically-Active Layer for numerical studies of the sea ice ecosystem, Ocean Modelling, 35(1-2):89-104, doi:10.1016/j.ocemod.2010.06.008, 2010.

336

337 Tedesco, L., Vichi, M.: Sea ice biogeochemistry: a guide for modellers, PLOS ONE, doi:10.1371/journal.pone.0089217, 2014.

339

340 Watanabe, E., Jin, M., Hayashida, H., Zhang, J., & Steiner, N.: Multi-Model Intercomparison of the Pan-Arctic Ice-Algal Productivity on Seasonal, Interannual, and Decadal Timescales, Journal of Geophysical Research: Oceans, 124, 9053–9084.

342 doi:10.1029/2019JC015100, 2019.