Deliverable 2.10 Synthesis of gap analysis and exploitation of the existing Arctic observing systems

This report presents a synthesis of the substantial assessment of Arctic observations within INTAROS.Since the assessed systems mainly belong to the European partners in the project, the assessment is unavoidably biased towards the European sector of the Arctic. The detailed results of the assessment can be found in previous deliverables (D2.1, D2.2, D2.4, D2.5, D2.7, D2.8 and D2.12). Also some higher-level recommendations for future improvements of Arctic observing are taken into account. The assessment addresses a substantial subset of Arctic observing systems, data collections and satellite products across scientific disciplines, also including some data repositories and a brief scientific gap analysis. In the assessment we analyzed sustainability, including funding, technical maturity and data handling for the entire chain from observation to users, including metadata procedures and availability of data. The gap analysis includes both technical characteristics, such as spatial and temporal coverage and resolution or accuracy, and a smaller set of scientific gap analyses where models and observations were used synergistically.

Each characteristic of the observing systems were ranked from maturity 1 (lowest score) to maturity 6 (highest score) based on the results of the survey. In the synthesis wefirst ranked the systems according to general sustainability and then other characteristics were used. The range in maturity of sustainability varied from 1 to 6, and so did the other characteristics. A noteworthy result was that systems with high sustainability scores tended to score high also on other characteristics, such as data handling and technical maturity. Moreover, many systems with high maturity in sustainability, as well as in data handling and data availability, are supported by national or international monitoring or infrastructure programs. It is also noteworthy that several of these are mostly present at mid-latitudes, but poorly represented in the Arctic.

For observations over Arctic land, the quality of some existing systems would benefit from being enhanced by new instruments or improved methods. As example, adequate observing of snow properties is problematic due to the high spatial variability of snow cover. While this also applies to hydrological observations, the situation improves as a result of large overarching international programmes. Observations of aerosols and some trace gases are also lacking in some specific regions. For the Arctic Ocean there is a lack of in-situ observing systems across all disciplines, which is connected to limited infrastructure provided by ships,icebreakers, and various types of autonomous observing platforms operating on sea ice with capacity to transfer data in near real-time.Subsurface observing systems such as bottom-anchored moorings and sea floor installations are robust and can operate autonomously over several year, but the data can only be delivered in delayed mode.In the atmosphere, icebreaker-based summer science expeditions provide the only reliable information on atmospheric vertical structure. While scientific expeditions likely provide the highest quality observations available for the Arctic Ocean region, the scores for almost all other aspects, sustainability as well as for data handling, in general and especially for atmospheric observations are among the lowest for vessel-based observations.

Satellite observations provide the only possibility to obtain data with sufficient spatial and temporal coverage as well as resolution. Satellite data products have generally high score on data handling aspects, but for some data products the score on quality and uncertainty estimation is low. While retrieved temperature, and to a lesser extent, humidity at levels in the atmosphere is generally adequate for monitoring, satellite profiling of the atmosphere suffers from significant and seasonally varying biases and errors. Passive satellite sensing of clouds is also problematic; while some bulk products, such as cloud fraction, are useful during the sunlit season, more precise information, such as liquid water path, has high uncertainty as indicated by comparing different retrievals from the same set of sensors. In the dark season, when visible radiation channels vanish, most satellite cloud products are very unreliable. Regarding sea ice observations, there is significant uncertainty in the estimation of thickness and snow layer. There is also uncertainty in ice concentration in the summer season with melt-ponds on top of the ice.

Traditionally, observation network assessments build on the network concept with a “comprehensive” level including all observations, a “baseline” level of an agreed subset of sustained observations, and a “reference” level, with observations adhering to specific calibrations and traceability criteria. An atmospheric example is the “comprehensive” global GCOS radiosounding network, and the “baseline” GUAN (GCOS Upper Air Network) and “reference” GRUAN (GCOS Reference Upper Air Network) networks. With the lack of in-situ observations and the logistical difficulties to deploy new stations, this concept does not work well in the marine part of the Arctic.

In summary, we recommend to

•Advance Arctic observing systems under national, international or regional programs that provide more sustainable funding than short-term research projects

•Coordinate better between operational monitoring systems and research-funded observations, since both systems often use the same data and will have mutual benefit of collaboration

•Improve the utilization of existing infrastructures on land and sea for more cost-effective collection of in situ data across multiple disciplines

•Deploy more autonomous observing platforms in the sea ice areas for year-round operation and implement data collection from all types of ships operating in the Arctic ocean

•Enhance the observing system on existing stations, including supersites, by use of new sensors and methods to validate satellite observations and support modelling and forecasting systems

•Improve the exploitation of satellite data and coordinate better in situ and satellite observations for use in data assimilation, modelling and reanalyses

•Clarify roles and responsibilities between data producers and managers and establish adequate funding mechanisms to support a functional data management system for multidisciplinary Arctic data.