RMetS National Meeting – Arctic prediction in a changing climate: understanding key processes and challenges
The Arctic is the most rapidly warming region on Earth, and it is facing considerable environmental change. These changes are beginning to alter the way humanity uses and exploits the Arctic region, with commercial activities such as tourism, fishing, mineral and oil extraction, and shipping on the increase. Increased human activity in the Arctic has implications for safety and environmental conservation, and can present difficulties for local communities who rely on subsistence hunting and fishing, and community resupply. Environmental prediction for the Arctic is therefore becoming increasingly important, but our ability to accurately predict the Arctic atmosphere–sea-ice–ocean system is relatively immature, with short-term forecasts significantly less accurate than those produced for the mid-latitudes. There are numerous challenges with respect to forecasting systems in the Arctic: there is a relatively sparse in-situ observing network; some satellite-based observations are hard to use because of the snow and ice-covered surfaces; it is difficult and expensive to make detailed research-quality observations; and the dominant physical processes are different to those at mid-latitudes and over the tropics. To address these issues, the Year of Polar Prediction (YOPP1) was developed as one of the key elements of the WMO's Polar Prediction Project2. On Wednesday 21 November 2018, the Royal Meteorological Society hosted a meeting at the Society of Chemical Industry (SCI) in London to discuss recent advances and challenges associated with the understanding and prediction of key Arctic processes using numerical models and observations – including new insights provided by several field campaigns and modelling experiments carried out under the YOPP umbrella. The first talk was entitled: What are the challenges and priorities for improved prediction and climate monitoring of the Arctic? (Irina Sandu, ECMWF). Irina explained that in order to improve Arctic predictions and reanalyses, which constitute a great tool for Arctic climate monitoring, work is needed in three areas: (i) enhanced coupled modelling, (ii) data assimilation methods and (iii) the effective use of observations in the numerical weather prediction systems. Arctic regions pose specific challenges for each of these three areas because model errors are large, model uncertainty is not necessarily well represented, and in-situ observations are sparse. Moreover, while the Arctic is very rich in terms of remote sensing observations from polar orbiting satellites, some of these observations are difficult to use in data assimilation because of ambiguous signal properties, particularly over snow and sea-ice and in cloudy situations. Irina introduced the challenges and priorities in each of these three key areas and described recent and ongoing efforts to evaluate and improve predictions in the Arctic, and beyond, made in the framework of the YOPP modelling activities and the EU-APPLICATE3 project. She highlighted the fact that, compared to previous similar initiatives, YOPP puts an additional emphasis on numerical experimentation, in a concerted effort to exploit observations for model improvement and drive developments in data assimilation and the design of observing systems. The next talk presented was: What are the limitations of Arctic sea ice remote sensing products, and what opportunities can they provide for improving predictive skill of Arctic forecasts? (Ed Blockley, Met Office). Ed explained that, in response to declining sea-ice cover, human activity in the Arctic is increasing, and that accurate forecasts of Arctic sea ice – on a variety of different timescales – are therefore becoming increasingly important for the safety of human activities in the region. He then provided a brief overview of how sea ice has been traditionally initialised in short-range forecasting and seasonal prediction systems, and explained that most operational systems initialise sea ice purely using passive microwave sea ice concentration products. He gave an overview of the passive microwave remote sensing products typically used for initialisation of sea ice concentration, as well as the (relatively new) satellite-derived sea ice thickness products currently available. Information was provided about how these observations are derived, what the uncertainties and limitations are, and what new avenues of research are currently underway to improve measurements of Arctic sea ice from space. Ed also illustrated the potential impact of assimilating satellite sea ice thickness products into our operational prediction systems. Using the Met Office GloSea seasonal prediction system, he showed how sea ice thickness values inferred from the CryoSat-2 satellite altimetry could be used within the initialisation process to considerably improve forecasts of Arctic summer sea ice on seasonal timescales. Next up was: Chasing the source of the AMOC (Atlantic Meridional Overturning Circulation) atmosphere–ocean coupling in the Iceland and Greenland Seas (Ian Renfrew, University of East Anglia) Ian described a coordinated meteorological and oceanographic field campaign over the Iceland and southern Greenland Seas that took place in February and March 2018. The aim of the campaign was to characterise the atmospheric forcing and the ocean response of coupled atmosphere–ocean processes – in particular, cold-air outbreaks in the vicinity of the marginal-ice-zone, and their triggering of oceanic heat loss and the generation of dense waterbody body masses. He described how the team observed the spatial structure and variability of surface flux fields in the region, and the weather systems that dictate these fluxes, through the first meteorological field campaign in the Iceland Sea. This was done as part of a coupled atmosphere–ocean field campaign in winter 2018 involving a rare wintertime research cruise, airborne observations and a host of ocean and atmosphere observing systems. In-situ observations of air–sea interaction processes were made from several platforms. Ian presented some highlights from the field campaign and introduced some early findings from the post-campaign analysis – most notably a comprehensive set of observations of a marine cold air outbreak which were obtained via radiosonde releases and the research aircraft. Mixing it up: What connects Arctic clouds and sea ice? was a talk presented by Ian Brooks (University of Leeds). Ian explained that the single largest source of uncertainty in climate models arises from the representation of clouds; this is particularly true in the Arctic where, for much of the year, low-level boundary-layer clouds dominate the surface energy budget. The time-evolution and properties of these clouds are intimately linked their interactions with the surface and boundary layer structure. The properties of both clouds and boundary layer (and indeed the surface) are essentially sub-grid-scale in climate and weather forecast models, and thus many important features are poorly represented by current parameterisations. Ian emphasised that observations show that the summertime boundary-layer over sea ice is often decoupled below cloud base, inhibiting turbulent mixing – and hence the transport of aerosols and water vapour – between cloud and the surface. This decoupling is not reproduced by models, and the controls on this turbulent structure are poorly understood. He provided a review of the state of knowledge, alongside some of the recent research on Arctic boundary layer processes and their effects on clouds and the surface energy budget. A unique feature of polar boundary layers is the occurrence of a humidity inversion, where absolute humidity increases across the top of the boundary layer. This allows boundary-layer clouds to extend into the temperature inversion and results in a weakening of turbulence generated by cloud-top radiative cooling. Finally, he showed how recent field measurements have improved the parameterisation of surface turbulent fluxes over marginal sea ice, which will ultimately affect the model representation of the evolution of sea ice. The view from above Arctic snow at 89-325GHz: What can surface emissivity on these channels tell us about snowpack stratigraphy? was the next talk by Chawn Harlow ( Met Office). Chawn introduced Measurements of Arctic Cloud, Snow and Sea Ice in the Marginal Ice ZonE (MACSSIMIZE) – an airborne campaign carried out by the Met Office in March 2018 that focussed on evaluation of snow microwave emission models in the 89–325GHz frequency range. He described how the campaign included a ground-based component to collect snow pit measurements co-located with the airborne radiometric measurements, and that these were now being used to improve snow physical models for use in future Met Office numerical weather prediction systems. An explanation of the main motivation for MACSSIMIZE followed, both from the point of view of snow remote sensing and for improving atmospheric data assimilation, along with a description of the emissivity retrievals obtained during the campaign at 89, 118, 157, 183.3, 243 and 325GHz. Chawn then described how the MACSSIMIZE observations will be used to evaluate the snow thermodynamic and emissivity models used at the Met Office in the short term, and how they will eventually be used to improve assimilation of microwave atmospheric sounding data over snow-covered surfaces in the polar regions. Finally, Richard Essery (University of Edinburgh) presented: Why tundra snow is upside down in models, and why it matters?. Richard described how Earth System and Numerical Weather Prediction models are beginning to use more sophisticated representations of terrestrial snow, drawing on snow physics models that were first developed for forecasting avalanche risk. He explained that, having been developed for deep mid-latitude mountain snow, these models neglect important physical processes occurring in shallow tundra snow subjected to high winds and low temperatures. Understanding these processes is important for representing the thermal properties of Arctic snow and exploiting information from microwave remote sensing over snow-covered surfaces. Richard provided an overview of the ground-based component of the YOPP-endorsed MACSSIMIZE campaign, carried out at Trail Valley Creek in northwestern Canada. He presented results obtained from the snow pit measurements within MACSSIMIZE and drew attention to the existence of a granular depth hoar layer located below the wind-slab, which is a common feature of Arctic tundra snow. The resulting vertical density profile – with low-density depth hoar below a high-density wind-slab layer – is not reproduced by current snow models. Field instruments that can replace visual and subjective measurements of snow structure are now becoming available and can provide data for improving models. With speakers focussing on operational forecasting, Earth observation, and on observation and modelling of key Arctic processes such as clouds, sea ice and snow, this meeting illustrated the broad range of the UK's expertise and interest in Arctic prediction, and highlighted the significant contribution that scientists in the UK are making to YOPP. This RMetS meeting was convened by Ed Blockley and Chawn Harlow (Met Office) and was chaired by Ed Blockley (Met Office), with Jonathan Day (ECMWF) as rapporteur. The event was sponsored by the Centre for Polar Observation and Modelling (CPOM).
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