Poleward shifts in marine fisheries under Arctic warming

As global warming makes the Arctic Ocean more accessible, concerns have been raised about the environmental consequences of a possible expansion of commercial fisheries into pristine marine ecosystems. Using a recently released global dataset, we quantify for the first time how fishing activities are responding to diminishing sea ice and a warmer Arctic Ocean. We show that trawling dominates Arctic fisheries and that this activity penetrates rapidly into Arctic shelf areas previously protected by extensive ice-cover as a response to interannual sea ice loss. We model the development of trawling activity under a climate change scenario and use the model to identify areas with high risk of increased trawling activity and estimate the amount of trawling avoided in recently established fishery protection zones. Stronger responsibility must be undertaken by Arctic coastal states to regulate increased fishing pressure and protect vulnerable Arctic shelf ecosystems.publishedVersio

Implementation and evaluation of open boundary conditions for sea ice in a regional coupled ocean (ROMS) and sea ice (CICE) modeling system

The Los Alamos Sea Ice Model (CICE) is used by several Earth system models where sea ice boundary conditions are not necessary, given their global scope. However, regional and local implementations of sea ice models require boundary conditions describing the time changes of the sea ice and snow being exchanged across the boundaries of the model domain. The physical detail of these boundary conditions regarding, for example, the usage of different sea ice thickness categories or the vertical resolution of thermodynamic properties, must be considered when matching them with the requirements of the sea ice model. Available satellite products do not include all required data. Therefore, the most straightforward way of getting sea ice boundary conditions is from a larger-scale model. The main goal of our study is to describe and evaluate the implementation of time-varying sea ice boundaries in the CICE model using two regional coupled ocean–sea ice models, both covering a large part of the Barents Sea and areas around Svalbard: the Barents-2.5 km, implemented at the Norwegian Meteorological Institute (MET), and the Svalbard 4 km (S4K) model, implemented at the Norwegian Polar Institute (NPI). We use the TOPAZ4 model and a Pan-Arctic 4 km resolution model (A4) to generate the boundary conditions for the sea ice and the ocean. The Barents-2.5 km model is MET’s main forecasting model for ocean state and sea ice in the Barents Sea. The S4K model covers a similar domain but it is used mainly for research purposes. Obtained results show significant improvements in the performance of the Barents-2.5 km model after the implementation of the time-varying boundary conditions. The performance of the S4K model in terms of sea ice and snow thickness is comparable to that of the TOPAZ4 system but with more accurate results regarding the oceanic component because of using ocean boundary conditions from the A4 model. The implementation of time-varying boundary conditions described in this study is similar regardless of the CICE versions used in different models. The main challenge remains the handling of data from larger models before its usage as boundary conditions for regional/local sea ice models, since mismatches between available model products from the former and specific requirements of the latter are expected, implying case-specific approaches and different assumptions. Ideally, model setups should be as similar as possible to allow a smoother transition from larger to smaller domains.Implementation and evaluation of open boundary conditions for sea ice in a regional coupled ocean (ROMS) and sea ice (CICE) modeling systempublishedVersio

Implementation and evaluation of open boundary conditions for sea ice in a regional coupled ocean (ROMS) and sea ice (CICE) modeling system

The Los Alamos Sea Ice Model (CICE) is used by several Earth system models where sea ice boundary conditions are not necessary, given their global scope. However, regional and local implementations of sea ice models require boundary conditions describing the time changes of the sea ice and snow being exchanged across the boundaries of the model domain. The physical detail of these boundary conditions regarding, for example, the usage of different sea ice thickness categories or the vertical resolution of thermodynamic properties, must be considered when matching them with the requirements of the sea ice model. Available satellite products do not include all required data. Therefore, the most straightforward way of getting sea ice boundary conditions is from a larger-scale model. The main goal of our study is to describe and evaluate the implementation of time-varying sea ice boundaries in the CICE model using two regional coupled ocean–sea ice models, both covering a large part of the Barents Sea and areas around Svalbard: the Barents-2.5 km, implemented at the Norwegian Meteorological Institute (MET), and the Svalbard 4 km (S4K) model, implemented at the Norwegian Polar Institute (NPI). We use the TOPAZ4 model and a Pan-Arctic 4 km resolution model (A4) to generate the boundary conditions for the sea ice and the ocean. The Barents-2.5 km model is MET’s main forecasting model for ocean state and sea ice in the Barents Sea. The S4K model covers a similar domain but it is used mainly for research purposes. Obtained results show significant improvements in the performance of the Barents-2.5 km model after the implementation of the time-varying boundary conditions. The performance of the S4K model in terms of sea ice and snow thickness is comparable to that of the TOPAZ4 system but with more accurate results regarding the oceanic component because of using ocean boundary conditions from the A4 model. The implementation of time-varying boundary conditions described in this study is similar regardless of the CICE versions used in different models. The main challenge remains the handling of data from larger models before its usage as boundary conditions for regional/local sea ice models, since mismatches between available model products from the former and specific requirements of the latter are expected, implying case-specific approaches and different assumptions. Ideally, model setups should be as similar as possible to allow a smoother transition from larger to smaller domains.Implementation and evaluation of open boundary conditions for sea ice in a regional coupled ocean (ROMS) and sea ice (CICE) modeling systempublishedVersio

Arctic amplification under global warming of 1.5 and 2 °C in NorESM1-Happi

Differences between a 1.5 and 2.0 ∘C warmer climate than 1850 pre-industrial conditions are investigated using a suite of uncoupled (Atmospheric Model Intercomparison Project; AMIP), fully coupled, and slab-ocean experiments performed with Norwegian Earth System Model (NorESM1)-Happi, an upgraded version of NorESM1-M. The data from the AMIP-type runs with prescribed sea-surface temperatures (SSTs) and sea ice were provided to a model intercomparison project (HAPPI – Half a degree Additional warming, Prognosis and Projected Impacts; http://www.happimip.org/, last access date: 14 September 2019). This paper compares the AMIP results to those from the fully coupled version and the slab-ocean version of the model (NorESM1-HappiSO) in which SST and sea ice are allowed to respond to the warming, focusing on Arctic amplification of the global change signal. The fully coupled and the slab-ocean runs generally show stronger responses than the AMIP runs in the warmer worlds. The Arctic polar amplification factor is stronger in the fully coupled and slab-ocean runs than in the AMIP runs, both in the 1.5 ∘C warming run and with the additional 0.5 ∘C warming. The low-level Equator-to-pole temperature gradient consistently weakens more between the present-day climate and the 1.5 ∘C warmer climate in the experiments with an active ocean component. The magnitude of the upper-level Equator-to-pole temperature gradient increases in a warmer climate but is not systematically larger in the experiments with an active ocean component. Implications for storm tracks and blocking are investigated. We find considerable reductions in the Arctic sea-ice cover in the slab-ocean model runs; while ice-free summers are rare under 1.5 ∘C warming, they occur 18 % of the time in the 2.0 ∘C warming simulation. The fully coupled model does not, however, reach ice-free conditions as it is too cold and has too much ice in the present-day climate. Differences between the experiments with active ocean and sea-ice models and those with prescribed SSTs and sea ice can be partially due to ocean and sea-ice feedbacks that are neglected in the latter case but can also in part be due to differences in the experimental setup

Arctic amplification under global warming of 1.5 and 2 °C in NorESM1-Happi

Differences between a 1.5 and 2.0 ∘C warmer climate than 1850 pre-industrial conditions are investigated using a suite of uncoupled (Atmospheric Model Intercomparison Project; AMIP), fully coupled, and slab-ocean experiments performed with Norwegian Earth System Model (NorESM1)-Happi, an upgraded version of NorESM1-M. The data from the AMIP-type runs with prescribed sea-surface temperatures (SSTs) and sea ice were provided to a model intercomparison project (HAPPI – Half a degree Additional warming, Prognosis and Projected Impacts; http://www.happimip.org/, last access date: 14 September 2019). This paper compares the AMIP results to those from the fully coupled version and the slab-ocean version of the model (NorESM1-HappiSO) in which SST and sea ice are allowed to respond to the warming, focusing on Arctic amplification of the global change signal. The fully coupled and the slab-ocean runs generally show stronger responses than the AMIP runs in the warmer worlds. The Arctic polar amplification factor is stronger in the fully coupled and slab-ocean runs than in the AMIP runs, both in the 1.5 ∘C warming run and with the additional 0.5 ∘C warming. The low-level Equator-to-pole temperature gradient consistently weakens more between the present-day climate and the 1.5 ∘C warmer climate in the experiments with an active ocean component. The magnitude of the upper-level Equator-to-pole temperature gradient increases in a warmer climate but is not systematically larger in the experiments with an active ocean component. Implications for storm tracks and blocking are investigated. We find considerable reductions in the Arctic sea-ice cover in the slab-ocean model runs; while ice-free summers are rare under 1.5 ∘C warming, they occur 18 % of the time in the 2.0 ∘C warming simulation. The fully coupled model does not, however, reach ice-free conditions as it is too cold and has too much ice in the present-day climate. Differences between the experiments with active ocean and sea-ice models and those with prescribed SSTs and sea ice can be partially due to ocean and sea-ice feedbacks that are neglected in the latter case but can also in part be due to differences in the experimental setup

Arctic amplification under global warming of 1.5 and 2 °C in NorESM1-Happi

Differences between a 1.5 and 2.0 ∘C warmer climate than 1850 pre-industrial conditions are investigated using a suite of uncoupled (Atmospheric Model Intercomparison Project; AMIP), fully coupled, and slab-ocean experiments performed with Norwegian Earth System Model (NorESM1)-Happi, an upgraded version of NorESM1-M. The data from the AMIP-type runs with prescribed sea-surface temperatures (SSTs) and sea ice were provided to a model intercomparison project (HAPPI – Half a degree Additional warming, Prognosis and Projected Impacts; http://www.happimip.org/, last access date: 14 September 2019). This paper compares the AMIP results to those from the fully coupled version and the slab-ocean version of the model (NorESM1-HappiSO) in which SST and sea ice are allowed to respond to the warming, focusing on Arctic amplification of the global change signal. The fully coupled and the slab-ocean runs generally show stronger responses than the AMIP runs in the warmer worlds. The Arctic polar amplification factor is stronger in the fully coupled and slab-ocean runs than in the AMIP runs, both in the 1.5 ∘C warming run and with the additional 0.5 ∘C warming. The low-level Equator-to-pole temperature gradient consistently weakens more between the present-day climate and the 1.5 ∘C warmer climate in the experiments with an active ocean component. The magnitude of the upper-level Equator-to-pole temperature gradient increases in a warmer climate but is not systematically larger in the experiments with an active ocean component. Implications for storm tracks and blocking are investigated. We find considerable reductions in the Arctic sea-ice cover in the slab-ocean model runs; while ice-free summers are rare under 1.5 ∘C warming, they occur 18 % of the time in the 2.0 ∘C warming simulation. The fully coupled model does not, however, reach ice-free conditions as it is too cold and has too much ice in the present-day climate. Differences between the experiments with active ocean and sea-ice models and those with prescribed SSTs and sea ice can be partially due to ocean and sea-ice feedbacks that are neglected in the latter case but can also in part be due to differences in the experimental setup

An inter-comparison of the mass budget of the Arctic sea ice in CMIP6 models

We compare the mass budget of the Arctic sea ice for 15 models submitted to the latest Coupled Model Intercomparison Project (CMIP6), using new diagnostics that have not been available for previous model intercomparisons. These diagnostics allow us to look beyond the standard metrics of ice cover and thickness to compare the processes of sea ice growth and loss in climate models in a more detailed way than has previously been possible. For the 1960–1989 multi-model mean, the dominant processes causing annual ice growth are basal growth and frazil ice formation, which both occur during the winter. The main processes by which ice is lost are basal melting, top melting and advection of ice out of the Arctic. The first two processes occur in summer, while the latter process is present all year. The sea ice budgets for individual models are strikingly similar overall in terms of the major processes causing ice growth and loss and in terms of the time of year during which each process is important. However, there are also some key differences between the models, and we have found a number of relationships between model formulation and components of the ice budget that hold for all or most of the CMIP6 models considered here. The relative amounts of frazil and basal ice formation vary between the models, and the amount of frazil ice formation is strongly dependent on the value chosen for the minimum frazil ice thickness. There are also differences in the relative amounts of top and basal melting, potentially dependent on how much shortwave radiation can penetrate through the sea ice into the ocean. For models with prognostic melt ponds, the choice of scheme may affect the amount of basal growth, basal melt and top melt, and the choice of thermodynamic scheme is important in determining the amount of basal growth and top melt. As the ice cover and mass decline during the 21st century, we see a shift in the timing of the top and basal melting in the multi-model mean, with more melt occurring earlier in the year and less melt later in the summer. The amountof basal growth reduces in the autumn, but it increases in the winter due to thinner sea ice over the course of the 21st century. Overall, extra ice loss in May–June and reduced ice growth in October–November are partially offset by reduced ice melt in August and increased ice growth in January– February. For the individual models, changes in the budget components vary considerably in terms of magnitude and timing of change. However, when the evolving budget terms are considered as a function of the changing ice state itself, behaviours common to all the models emerge, suggesting that the sea ice components of the models are fundamentally responding in a broadly consistent way to the warming climate. It is possible that this similarity in the model budgets may represent a lack of diversity in the model physics of the CMIP6 models considered here. The development of new observational datasets for validating the budget terms would help to clarify this

Arctic Sea Ice in CMIP6

International audienceWe examine CMIP6 simulations of Arctic sea‐ice area and volume. We find that CMIP6 models produce a wide spread of mean Arctic sea‐ice area, capturing the observational estimate within the multi‐model ensemble spread. The CMIP6 multi‐model ensemble mean provides a more realistic estimate of the sensitivity of September Arctic sea‐ice area to a given amount of anthropogenic CO2 emissions and to a given amount of global warming, compared with earlier CMIP experiments. Still, most CMIP6 models fail to simulate at the same time a plausible evolution of sea‐ice area and of global mean surface temperature. In the vast majority of the available CMIP6 simulations, the Arctic Ocean becomes practically sea‐ice free (sea‐ice area 2) in September for the first time before the year 2050 in each of the four emission scenarios SSP1‐1.9, SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 examined here