Understanding differences in North Atlantic poleward ocean heat transport and its variability in global climate models

The ocean heat transport from the North Atlantic to the Barents Sea impacts the sea ice extent and the energy budget of the Arctic. The analyzed climate models from the fifth (CMIP5) and sixth Coupled Model Intercomparison Project phase 6 (CMIP6) phases of the Coupled Model Intercomparison Project show large intermodel differences in the ocean heat transport with biases of several Terawatts at the Iceland-Scotland Ridge and Barents Sea Opening (BSO). While both model generations show a large spread in mean volume transports, in CMIP6 temperatures are more homogeneous and realistic, yielding heat transports closer to observations. On all time scales, changes in heat transport reflect changes in volume transport, while temperature changes affect the heat transport variability on longer time scales, especially at the BSO. The temporal variability of heat and volume transports is linked to wind forcing south of Iceland and along the Norwegian coast in all models but has different magnitudes.publishedVersio

Variability along the Atlantic water pathway in the forced Norwegian Earth System Model

The growing attention on mechanisms that can provide predictability on interannual-to-decadal time scales, makes it necessary to identify how well climate models represent such mechanisms. In this study we use a high (0.25° horizontal grid) and a medium (1°) resolution version of a forced global ocean-sea ice model, utilising the Norwegian Earth System Model, to assess the impact of increased ocean resolution. Our target is the simulation of temperature and salinity anomalies along the pathway of warm Atlantic water in the subpolar North Atlantic and the Nordic Seas. Although the high resolution version has larger biases in general at the ocean surface, the poleward propagation of thermohaline anomalies is better resolved in this version, i.e., the time for an anomaly to travel northward is more similar to observation based estimates. The extent of these anomalies can be rather large in both model versions, as also seen in observations, e.g., stretching from Scotland to northern Norway. The easternmost branch into the Nordic and Barents Seas, carrying warm Atlantic water, is also improved by higher resolution, both in terms of mean heat transport and variability in thermohaline properties. A more detailed assessment of the link between the North Atlantic Ocean circulation and the thermohaline anomalies at the entrance of the Nordic Seas reveals that the high resolution is more consistent with mechanisms that are previously published. This suggests better dynamics and variability in the subpolar region and the Nordic Seas in the high resolution compared to the medium resolution. This is most likely due a better representation of the mean circulation in the studied region when using higher resolution. As the poleward propagation of ocean heat anomalies is considered to be a key source of climate predictability, we recommend that similar methodology presented herein should be performed on coupled climate models that are used for climate prediction.publishedVersio

Context matters when using climate model projections for aquaculture

At present, specific guidance on how to choose, assess and interpret climate model projections for the aquaculture sector is scarce. Since many aspects of aquaculture production are influenced by the local farm-level environment, there is a need to consider how climate model projections can be used to predict potential future farming conditions locally. This study compared in-situ measurements of temperature and salinity from Norwegian salmon farms and fixed monitoring stations to simulations from a regional ocean climate model for multiple locations and depths in southern Norway. For locations considered in this study, a similar seasonal cycle in terms of phasing was visible for modelled and measured temperatures. For some depths and times of the year the modelled and measured temperatures were similar, but for others there were differences. The model tended to underestimate temperature. On occasion there were differences between average modelled and measured temperatures of several degrees and aquaculture users would need to consider the implications of using the modelled temperatures. As for salinity, the model does not include localized freshwater inputs, so the model overestimated salinity for locations close to shore and was not able to represent more brackish water conditions in shallower depths. It was not possible to draw a general conclusion as to whether the model was suitable for aquaculture purposes, as the similarities and differences between the modelled and measured values varied by variable, area, depth, and time. These findings made it clear that aquaculture users would have to implement a process to determine whether they could use climate model outputs for their specific purpose. A model vetting framework is presented that can be used to support decisions on the use of climate model projections for aquaculture purposes. The vetting framework describes four stages that can be used to establish the necessary context regarding the aquaculture requirements and model capabilities, and then check how the model is simulating the conditions of interest at farm sites. Although the focus was aquaculture, the findings are relevant for other sectors and the framework can guide use of climate models for more local-scale assessment and management in coastal locations

Climate change and new potential spawning sites for Northeast Arctic cod

In this study we investigate both historical and potential future changes in the spatial distribution of spawning habitats for Northeast Arctic cod (NEA cod) based on a literature study on spawning habitats and different physical factors from a downscaled climate model. The approach to use a high resolution regional ocean model to analyze spawning sites is new and provides more details about crucial physical factors than a global low resolution model can. The model is evaluated with respect to temperature and salinity along the Norwegian coast during the last decades and shows acceptable agreement with observations. However, the model does not take into consideration biological or evolutionary factors which also have impact on choice of spawning sites. Our results from the downscaled RCP4.5 scenario suggest that the spawning sites will be shifted further northeastwards, with new locations at the Russian coast close to Murmansk over the next 50 years, where low temperatures for many decades in the last century were a limiting factor on spawning during spring. The regional model gives future temperatures above the chosen lower critical minimum value in larger areas than today and indicates that spawning will be more extensive there. Dependent on the chosen upper temperature boundary, future temperatures may become a limiting factor for spawning habitats at traditional spawning sites south of Lofoten. Finally, the observed long-term latitudinal shifts in spawning habitats along the Norwegian coast the recent decades may be indirectly linked to temperature through the latitudinal shift of the sea ice edge and the corresponding shift in available ice-free predation habitats, which control the average migration distance to the spawning sites. We therefore acknowledge that physical limitations for defining the spawning sites might be proxies for other biophysically related factors.publishedVersio

Barents Sea plankton production and controlling factors in a fluctuating climate

The Barents Sea and its marine ecosystem is exposed to many different processes related to the seasonal light variability, formation and melting of sea-ice, wind-induced mixing, and exchange of heat and nutrients with neighbouring ocean regions. A global model for the RCP4.5 scenario was downscaled, evaluated, and combined with a biophysical model to study how future variability and trends in temperature, sea-ice concentration, light, and wind-induced mixing potentially affect the lower trophic levels in the Barents Sea marine ecosystem. During the integration period (2010–2070), only a modest change in climate variables and biological production was found, compared to the inter-annual and decadal variability. The most prominent change was projected for the mid-2040s with a sudden decrease in biological production, largely controlled by covarying changes in heat inflow, wind, and sea-ice extent. The northernmost parts exhibited increased access to light during the productive season due to decreased sea-ice extent, leading to increased primary and secondary production in periods of low sea-ice concentrations. In the southern parts, variable access to nutrients as a function of wind-induced mixing and mixed layer depth were found to be the most dominating factors controlling variability in primary and secondary production.publishedVersio

Multidisciplinary perspectives on living marine resources in the Arctic

Many areas in the Arctic are vulnerable to the impacts of climate change. We observe large-scale effects on physical, biological, economic and social parameters, including ice cover, species distributions, economic activity and regional governance frameworks. Arctic living marine resources are affected in various ways. A holistic understanding of these effects requires a multidisciplinary enterprise. We synthesize relevant research, from oceanography and ecology, via economics, to political science and international law. We find that multidisciplinary research can enhance our understanding and promote new questions and issues relating to impacts and outcomes of climate change in the Arctic. Such issues include recent insights on changing spawning migrations of the North-east Arctic cod stock that necessitates revisions of socioeconomic estimates of ecosystem wealth in the Barents Sea, better integrated prediction systems that require increased cooperation between experts on climate prediction and ecosystem modelling, and institutional complexities of Arctic governance that require enhanced coordination.publishedVersio

Context matters when using climate model projections for aquaculture

At present, specific guidance on how to choose, assess and interpret climate model projections for the aquaculture sector is scarce. Since many aspects of aquaculture production are influenced by the local farm-level environment, there is a need to consider how climate model projections can be used to predict potential future farming conditions locally. This study compared in-situ measurements of temperature and salinity from Norwegian salmon farms and fixed monitoring stations to simulations from a regional ocean climate model for multiple locations and depths in southern Norway. For locations considered in this study, a similar seasonal cycle in terms of phasing was visible for modelled and measured temperatures. For some depths and times of the year the modelled and measured temperatures were similar, but for others there were differences. The model tended to underestimate temperature. On occasion there were differences between average modelled and measured temperatures of several degrees and aquaculture users would need to consider the implications of using the modelled temperatures. As for salinity, the model does not include localized freshwater inputs, so the model overestimated salinity for locations close to shore and was not able to represent more brackish water conditions in shallower depths. It was not possible to draw a general conclusion as to whether the model was suitable for aquaculture purposes, as the similarities and differences between the modelled and measured values varied by variable, area, depth, and time. These findings made it clear that aquaculture users would have to implement a process to determine whether they could use climate model outputs for their specific purpose. A model vetting framework is presented that can be used to support decisions on the use of climate model projections for aquaculture purposes. The vetting framework describes four stages that can be used to establish the necessary context regarding the aquaculture requirements and model capabilities, and then check how the model is simulating the conditions of interest at farm sites. Although the focus was aquaculture, the findings are relevant for other sectors and the framework can guide use of climate models for more local-scale assessment and management in coastal locations

Key physical processes and their model representation for projecting climate impacts on subarctic Atlantic net primary production: A synthesis

Oceanic net primary production forms the foundation of marine ecosystems. Understanding the impact of climate change on primary production is therefore critical and we rely on Earth System Models to project future changes. Stemming from their use of different physical dynamics and biogeochemical processes, these models yield a large spread in long-term projections of change on both the global and regional scale. Here we review the key physical processes and biogeochemical parameterizations that influence the estimation of primary production in Earth System Models and synthesize the available projections of productivity in the subarctic regions of the North Atlantic. The key processes and modelling issues we focus on are mixed layer depth dynamics, model resolution and the complexity and parameterization of biogeochemistry. From the model mean of five CMIP6 models, we found a large increase in PP in areas where the sea ice retreats throughout the 21st century. Stronger stratification and declining MLD in the Nordic Seas, caused by sea ice loss and regional freshening, reduce the vertical flux of nutrients into the photic zone. Following the synthesis of the primary production among the CMIP6 models, we recommend a number of measures: constraining model hindcasts through the assimilation of high-quality long-term observational records to improve physical and biogeochemical parameterizations in models, developing better parameterizations for the sub-grid scale processes, enhancing the model resolution, downscaling and multi-model comparison exercises for improved regional projections of primary production.publishedVersio