PRE-MELT: Preconditioning the trigger for rapid Arctic ice melt

Lead Research Organisation: University of Bristol
Department Name: Geographical Sciences

Abstract

The oldest, thickest sea ice in the 'last ice area' of the Arctic - a region thought to be most resilient to climate warming - unexpectedly broke up twice in the past year. Our current theories assume that the end-of-summer ice-covered area will steadily retreat into the Central Arctic Basin as global warming accelerates over coming decades. However, the dynamic break-up events witnessed in 2018 challenge this prevailing view. Here we hypothesise that a weaker, increasingly mobile Central Arctic ice pack is now susceptible to dynamic episodes of fragmentation which can precondition the ice for rapid summer melt. This mechanism of dynamic seasonal preconditioning is unaccounted for in global climate models, so our best current projections are overlooking the possibility for rapid disintegration of the Arctic's last ice area.

Our team has demonstrated that seasonal preconditioning is already responsible for the neighbouring Beaufort Sea becoming ice-free twice in the past five years. Even ten years ago this region contained thick perennial sea ice, mirroring the Central Arctic Ocean, but it has now transitioned to a marginal Arctic sea. Could the processes responsible for the decline of the Beaufort Sea ice pack start to manifest themselves in the Central Arctic? Currently, a shortfall in satellite observations of the Arctic pack ice in summer prevents us from testing our hypothesis. We desperately require pan-Arctic observations of ice melting rates, but so far satellite observations of sea ice thickness are only available during winter months. Our project will therefore deliver the first measurements of Arctic sea ice thickness during summer months, from twin satellites: ESA's Cryosat-2 & NASA's ICESat-2. We have designed a new classification algorithm for separating ice and ocean radar altimeter echoes, regardless of surface melting state, providing the breakthrough required to fill the existing summer observation 'gap'. Exploiting the recent launch of multiple SAR missions for polar reconnaissance, our project will integrate information on ice-pack ablation, motion and deformation to generate a unique year-round sea ice volume budget in the High Arctic.

This record will inform high-resolution ice dynamics simulations, performed with a suite of state-of-the-art sea ice models from stand alone (CICE), ocean-sea ice (NEMO/CICE), to fully coupled regional high resolution (RASM), and global coarser resolution (HadGEM) models, all now equipped with the anisotropic (EAP) sea ice rheology developed by our team. Using the regional and stand-alone models we will analyse the role of mechanics in this keystone region north of Greenland to scrutinise the coupling and preconditioning of winter breakup events - such as those witnessed in 2018 - to summer melting rates. Using the coupled models, we will quantify the likelihood of the Arctic's last ice area breaking up much sooner than expected due to oceanic and atmospheric feedbacks and how this will affect the flushing of ice and freshwater into the North Atlantic.

Planned Impact

We anticipate that four broad categories of user group will benefit from the results and activities of the project, in addition to scientists working directly on Arctic sea ice, climate and oceanography. We aim to particularly engage with UK and Canadian stakeholders based on the locations and experience of the project team.

(i) Climate change policy community
Our novel sea ice thickness budget analysis combining model and observation sea ice thickness budget in the Central Arctic Ocean will provide an objective diagnose of the errors in climate models contributing to IPCC reports. The conclusions of this project will therefore be of critical importance to bodies charged with summarizing (IPCC, Met Office) and directing (NERC, European Space Agency) climate science. Our team's research has previously been represented in NGO climate change reviews (such as the recent AMAP: SWIPA assessment, but also IPCC AR5 reports and ArcticNet Hudson Bay IRIS), and in Davos at the Arctic Science Forum.

(ii) Marine transportation industry
The Northeast and Northwest Arctic passages have been sea ice-free for a longer summer season in many recent years, providing a quicker cheaper alternative to traditional shipping routes between Europe and Asia. Tourist cruise ships, such as the Crystal Serenity in 2016, are also beginning to navigate Arctic waterways during summer, and ship traffic through the Canadian Arctic has more than doubled over the past 40 years. We anticipate that our sea ice thickness intialised forecasts could be used to predict least-cost (i.e. viable, lowest risk) routes for both ice-reinforced and non-reinforced ships through Arctic passages, helping to identify zones of high vulnerability to ice hazards. Moreover, these data would be valuable for marine insurance risk & exposure management and for international shipping regulatory authorities, e.g. the International Maritime Organization (IMO), to support polar transportation conventions. In recent years, private industry stakeholders such as this have been an active participant in SIPN and will be able to easily access our data through this platform.

(iii) Oil and gas industry
Anticipated decline in output from existing oil and LPG resources will potentially require the development of nearly 50% new worldwide energy production capacity by 2035. Incidentally, the Arctic is thought to hold 30% of the world's undiscovered gas and 13% of its undiscovered oil (including 84% offshore). Oil and gas companies have begun to perform initial prospecting and drilling tests, for example by Shell in the Chukchi Sea in 2015. However, it is crucial that these operations are only executed in a sustainable way, with particular attention paid to the risk of sea and glacial ice hazards on infrastructure. Our project will inform forecasts of the probability and timing of ice-free zones over oil and gas leasing areas, such as those in the Chukchi Sea, as well as over active British leases in the Norwegian Arctic. Results will be communicated to private sector firms in marine transportation and O&G industries through a dedicated user workshop towards the end of the project.

(iv) General public
In addition to a large media exposure by our team members (radio, TV, newspapers, YouTube), we intend to develop sea ice products that are accessible to beneficiaries at a variety of levels, including primary and secondary school students via our UCL @GeoBus and through SIPN. We will also host with NASA colleagues and IASC fellows a Hackathon on Arctic risks and extremes. The forthcoming upgraded SIPN-2 platform and our CPOM website will be capable of providing straightforward and visually appealing maps of our products, along with links to non-technical web pages explaining how the satellite data are acquired and processed. In addition, we will produce a set of free public-outreach posters, for use in secondary schools, museums, outreach events in the UK, Germany, Canada etc.

Publications

10 25 50
 
Description The first dataset of satellite sea ice thickness observations for the Arctic summer months: May-October, from ESA's CryoSat-2 mission.
Exploitation Route We expect the new data, to be made publicly available, will be used by many others in the climate sciences for studies including: sea ice and climate modelling, sea ice and weather forecasting, polar energy budgets, and biogeochemical.
Sectors Environment

 
Description Press around Nature paper https://nature.altmetric.com/details/135925193 including 40+ news articles and 300+ tweets
 
Title Arctic sea ice and physical oceanography derived from CryoSat-2 Baseline-C Level 1b waveform observations, Oct-Apr 2010-2018 
Description This dataset presents monthly gridded sea ice and ocean parameters for the Arctic derived from the European Space Agency's satellite CryoSat-2. Parameters include sea ice freeboard, sea ice thickness, sea ice surface roughness, mean sea surface height, sea level anomaly, and geostrophic circulation. Data are provided as monthly grids with a resolution of 25 km, mapped onto the NSIDC EASE2-Grid, covering the Arctic region north of 50 degrees latitude, for all winter months (Oct-Apr) between 2010 
Type Of Material Database/Collection of data 
Year Produced 2020 
Provided To Others? Yes  
Impact N/A 
URL https://data.bas.ac.uk/metadata.php?id=GB/NERC/BAS/PDC/01257
 
Title Arctic sea ice and physical oceanography derived from CryoSat-2 Baseline-C Level 1b waveform observations, Oct-Apr 2010-2018 
Description This dataset presents monthly gridded sea ice and ocean parameters for the Arctic derived from the European Space Agency's satellite CryoSat-2. Parameters include sea ice freeboard, sea ice thickness, sea ice surface roughness, mean sea surface height, sea level anomaly, and geostrophic circulation. Data are provided as monthly grids with a resolution of 25 km, mapped onto the NSIDC EASE2-Grid, covering the Arctic region north of 50 degrees latitude, for all winter months (Oct-Apr) between 2010 and 2018. CryoSat-2 Level 1b Baseline C observed waveforms have been retracked using a numerical model for the SAR altimeter backscattered echo from snow-covered sea ice presented in Landy et al. (2019), which offers a sophisticated physically-based treatment of the effect of ice surface roughness on retracked ice and ocean elevations. Methods for optimizing echo model fits to observed CryoSat-2 waveforms, retracking waveforms, classifying returns, deriving sea ice freeboard, and converting to thickness are detailed in Landy et al. (In Review). This dataset contains derived sea ice thicknesses from two processing chains, the first using the conventional snow depth and density climatology from Warren et al. (1999) and the second using reanalysis and model-based snow data from SnowModel (Stroeve et al., In Review). Sea surface height and ocean topography grids were derived from only those CryoSat-2 samples classified as leads. Both the random and systematic uncertainties relevant for each parameter have been carefully estimated and are provided in the data files. NetCDF files contain detailed descriptions of each derived parameter. Funding was provided by ESA Living Planet Fellowship Arctic-SummIT grant ESA/4000125582/18/I-NS and NERC Project PRE-MELT grant NE/T000546/1. 
Type Of Material Database/Collection of data 
Year Produced 2020 
Provided To Others? Yes  
URL https://data.bas.ac.uk/full-record.php?id=GB/NERC/BAS/PDC/01257
 
Title Year-round Arctic sea ice thickness from CryoSat-2 Baseline-D Level 1b observations 2010-2020 
Description This dataset presents biweekly gridded sea ice thickness and uncertainty for the Arctic derived from the European Space Agency's satellite CryoSat-2. An associated 'developer's product' also includes intermediate parameters used or output in the sea ice thickness processing chain. Data are provided as biweekly grids with a resolution of 80 km, mapped onto a Northern Polar Stereographic Grid, covering the Arctic region north of 50 degrees latitude, for all months of the year between October 2010 and July 2020. CryoSat-2 Level 1b Baseline-D observed radar waveforms have been retracked using two different approaches, one for the 'cold season' months of October-April and the second for 'melting season' months of May-September. The cold season retracking algorithm uses a numerical model for the SAR altimeter backscattered echo from snow-covered sea ice presented in Landy et al. (2019), which offers a physical treatment of the effect of ice surface roughness on retracked ice and ocean elevations. The method for optimizing echo model fits to observed CryoSat-2 waveforms, retracking waveforms, classifying returns, and deriving sea ice radar freeboard are detailed in Landy et al. (2020). The melting season retracking algorithm uses the SAMOSA+ analytical echo model with optimization to observed CryoSat-2 waveforms through the SARvatore (SAR Versatile Altimetric Toolkit for Ocean Research and Exploitation) service available through ESA Grid Processing on Demand (GPOD). The method for classifying radar returns and deriving sea ice radar freeboard in the melting season are detailed in Dawson et al. (2022). The melting season sea ice radar freeboards require a correction for an electromagnetic range bias, as described in Landy et al. (In Review). After applying the correction, year-round freeboards are converted to sea ice thickness using auxiliary satellite observations of the sea ice concentration and type, as well as snow depth and density estimates from a Lagrangian snow evolution scheme: SnowModel-LG (Stroeve et al., 2020; Liston et al., 2020). The sea ice thickness uncertainties have been estimated based on methods described in Landy et al. (In Review). NetCDF files contain detailed descriptions of each parameter. Funding was provided by the NERC PRE-MELT grant NE/T000546/1 and the ESA Living Planet Fellowship Arctic-SummIT grant ESA/4000125582/18/I-NS. 
Type Of Material Database/Collection of data 
Year Produced 2022 
Provided To Others? Yes  
Impact New opportunities for Arctic sea ice forecasting on seasonal timescales. Colleagues at several institutions, notably the Geophysical Fluid Dynamics Laboratory at NOAA in the US, have started using the dataset for assimilation into dynamical ice-ocean model prediction systems. 
URL https://data.bas.ac.uk/full-record.php?id=GB/NERC/BAS/PDC/01613