Florence Fetterer’s research while affiliated with University of Colorado and other places

The creation and curation of environmental data present numerous challenges and rewards. In this study, we reflect on the increasing amount of freely available glacier data (inventories and changes), as well as on related demands by data providers, data users, and data repositories in-between. The amount of glacier data has increased significantly over the last two decades as remote sensing techniques have improved and free data access is much more common. The portfolio of observed parameters has increased as well, which presents new challenges for international data centers, and fosters new expectations from users. We focus here on the service of the Global Terrestrial Network for Glaciers (GTN-G) as the central organization for standardized data on glacier distribution and change. Within GTN-G, different glacier datasets are consolidated under one umbrella, and the glaciological community supports this service by actively contributing their datasets and by providing strategic guidance via an Advisory Board. To assess each GTN-G dataset, we present a maturity matrix and summarize achievements, challenges, and ambitions. The challenges and ambitions in the democratization of glacier data are discussed in more detail, as they are key to providing an even better service for glacier data in the future. Most challenges can only be overcome in a financially secure setting for data services and with the help of international standardization as, for example, provided by the CoreTrustSeal. Therefore, dedicated financial support for and organizational long-term commitment to certified data repositories build the basis for the successful democratization of data. In the field of glacier data, this balancing act has so far been successfully achieved through joint collaboration between data repository institutions, data providers, and data users. However, we also note an unequal allotment of funds for data creation and projects using the data, and data curation. Considering the importance of glacier data to answering numerous key societal questions (from local and regional water availability to global sea-level rise), this imbalance needs to be adjusted. In order to guarantee the continuation and success of GTN-G in the future, regular evaluations are required and adaptation measures have to be implemented.

Three passive microwave-based sea ice products archived at the National Snow and Ice Data Center (NSIDC) are compared: (1) the NASA Team (NT) algorithm product, (2) Bootstrap (BT) algorithm product, and (3) a new version (Version 4) of the NOAA/NSIDC Climate Data Record (CDR) product. Most notable for the CDR Version 4 is the addition of the early passive microwave record, 1979 to 1987. The focus of this study is on long-term trends in monthly extent and area. In addition to hemispheric trends, regional analysis is also carried out, including use of a new Northern Hemisphere regional mask. The results indicate overall good consistency between the products, with all three products showing strong statistically significant negative trends in the Arctic and small borderline significant positive trends in the Antarctic. Regionally, the patterns are similar, except for a notable outlier of the NT area having a steeper trend in the Central Arctic, likely related to increasing surface melt. Other differences are due to varied approaches to quality control, e.g., weather filtering and correction of mixed land-ocean grid cells. Another factor, particularly in regards to NT trends with BT or CDR, is the inter-sensor calibration approach, which yields small discontinuities between the products. These varied approaches yield small differences in trends. In the Arctic, such differences are not critical, but in the Antarctic, where overall trends are near zero and borderline statistically significant, the differences are potentially important in the interpretation of trends.

Across High Asia, the amount, timing, and spatial patterns of snow and ice melt play key roles in providing water for downstream irrigation, hydropower generation, and general consumption. The goal of this paper is to distinguish the specific contribution of seasonal snow versus glacier ice melt in the major basins of High Mountain Asia: Ganges, Brahmaputra, Indus, Amu Darya, and Syr Darya. Our methodology involves the application of MODIS-derived remote sensing products to separately calculate daily melt outputs from snow and glacier ice. Using an automated partitioning method, we generate daily maps of (1) snow over glacier ice, (2) exposed glacier ice, and (3) snow over land. These are inputs to a temperature index model that yields melt water volumes contributing to river flow. Results for the five major High Mountain Asia basins show that the western regions are heavily reliant on snow and ice melt sources for summer dry season flow when demand is at a peak, whereas monsoon rainfall dominates runoff during the summer period in the east. While uncertainty remains in the temperature index model applied here, our approach to partitioning melt from seasonal snow and glacier ice is both innovative and systematic and more constrained than previous efforts with similar goals.

Basic statistical metrics such as autocorrelations and across-region lag correlations of sea ice variations provide benchmarks for the assessments of forecast skill achieved by other methods such as more sophisticated statistical formulations, numerical models, and heuristic approaches. In this study we use observational data to evaluate the contribution of the trend to the skill of persistence-based statistical forecasts of monthly and seasonal ice extent on the pan-Arctic and regional scales. We focus on the Beaufort Sea for which the Barnett Severity Index provides a metric of historical variations in ice conditions over the summer shipping season. The variance about the trend line differs little among various methods of detrending (piecewise linear, quadratic, cubic, exponential). Application of the piecewise linear trend calculation indicates an acceleration of the winter and summer trends during the 1990s. Persistence-based statistical forecasts of the Barnett Severity Index as well as September pan-Arctic ice extent show significant statistical skill out to several seasons when the data include the trend. However, this apparent skill largely vanishes when the data are detrended. In only a few regions does September ice extent correlate significantly with antecedent ice anomalies in the same region more than 2 months earlier. The springtime “predictability barrier” in regional forecasts based on persistence of ice extent anomalies is not reduced by the inclusion of several decades of pre-satellite data. No region shows significant correlation with the detrended September pan-Arctic ice extent at lead times greater than a month or two; the concurrent correlations are strongest with the East Siberian Sea. The Beaufort Sea's ice extent as far back as July explains about 20 % of the variance of the Barnett Severity Index, which is primarily a September metric. The Chukchi Sea is the only other region showing a significant association with the Barnett Severity Index, although only at a lead time of a month or two.

September open water fraction in the Arctic is analyzed using the satellite era record of ice concentration (1979–2017). Evidence is presented that three breakpoints (shifts in the mean) occurred in the Pacific sector, with higher amounts of open water starting in 1989, 2002, and 2007. Breakpoints in the Atlantic sector record of open water are evident in 1971 in longer records, and around 2000 and 2011. Multiple breakpoints are also evident in the Canadian and Russian halves. Statistical models that use detected breakpoints of the Pacific and Atlantic sectors, as well as models with breakpoints in the Canadian and Russian halves and the Arctic as a whole, outperform linear trend models in fitting the data. From a physical standpoint, the results support the thesis that Arctic sea ice may have critical points beyond which a return to the previous state is less likely. From an analysis standpoint, the findings imply that de-meaning the data using the breakpoint means is less likely to cause spurious signals than employing a linear detrend.

Barry, a giant in climate and cryospheric sciences, pioneered the archival of computer data and traveled the world to share his vision with others.

Basic statistical metrics such as autocorrelations and across-region lag correlations of sea ice variations provide benchmarks for the assessments of forecast skill achieved by other methods such as more sophisticated statistical formulations, numerical models, and heuristic approaches. However, the strong negative trend of sea ice coverage in recent decades complicates the evaluation of statistical skill by inflating the correlation of interannual variations of pan-Arctic and regional ice extent. In this study we provide a quantitative evaluation of the contribution of the trend to the predictive skill of monthly and seasonal ice extent on the pan-Arctic and regional scales. We focus on the Beaufort Sea where the Barnett Severity Index provides a metric of historical variations in ice conditions over the summer shipping season. The variance about the trend line differs little among various methods of detrending (piecewise linear, quadratic, cubic, exponential). Application of the piecewise linear trend calculation indicates an acceleration of the trend during the 1990s in most of the Arctic subregions. The Barnett Severity Index as well as September pan-Arctic ice extent show significant statistical predictability out to several seasons when the data include the trend. However, this apparent skill largely vanishes when the data are detrended. No region shows significant correlation with the detrended September pan-Arctic ice extent at lead times greater than a month or two; the concurrent correlations are strongest with the East Siberian Sea. The Beaufort Sea’s ice extent as far back as July explains about 20% of the variance of the Barnett Severity Index, which is primarily a September metric. The Chukchi Sea is the only other region showing a significant association with the Barnett Severity Index, although only at a lead time of a month or two.

This poster summarizes some progress made on addressing user questions about different sea ice products; Why they exist and the purpose of each product.