Thermodynamics and dynamics of polar ice sheets, glaciers and ice shelves under changing climate

Polar ice sheets, glaciers, and ice shelves, referred to as land ice in this document, are under transition in the changing climate. Observations show that glaciers have retreated and melt water discharge from land ice has increased together with the warming climate. Decreasing volume of land ice is reflected to the whole Earth system via changes in surface radiation balance, sea level rise, and water balance in inland watersheds. Warming and melting land ice creates positive feedback loops that further increase melting. The study of polar land ice in changing climate is challenging due to lack of observations from the remote areas. Large interannual variability of climate, rapid changes in temperature and ice conditions, and short observational timeseries further complicate the research. This Ph.D. thesis concentrates on the aspects of melting land ice in the polar regions. The focus is on the interannual variability of surface melt and weather conditions on two of the Antarctic Peninsula Ice Shelves (Larsen C and Wilkins) and the Greenland Ice Sheet. Surface melt is addressed through the surface energy balance and other direct measures of melt. Furthermore, the changes in surface accumulation and ablation patterns have been modelled on a small Arctic valley glacier, Midtre Lovénbreen. This modelling study presents for the first time a method that allows to produce high resolution maps of accumulation and ablation, using only a glacier flow model and the digital elevation models of the glacier surface. Surface energy balance controls the melt of snow and ice. The effect of atmospheric moisture or clouds on the surface energy balance was important in the Antarctica Peninsula region and on the Greenland Ice Sheet. Cloud cover fraction was related to the wintertime surface net heat flux on Larsen C Ice Shelf. A multi-regression model, including the cloud cover fraction as one of the explanatory variables, explained up to 80% of the interannual variation of the surface net heat flux in June-August. On the Greenland Ice Sheet the vertically integrated total column water was positively correlated with the summertime surface melt. Local near surface winds (at 10 m height above ground level) were important in explaining the surface net heat flux on Larsen C Ice Shelf in summer, autumn, and winter. In Greenland, the wind components correlated locally with the number of melt days. The positive correlation was likely related to the adiabatic heating of descending air on the lee side of the ice sheet and, in other locations, to the advection of warm air from lower latitudes. The number of melt days on the Greenland Ice Sheet were also correlated with positive North Atlantic Oscillation Index and Greenland Blocking Index, indicating that these large scale patterns contribute in creating conditions that favour melt. The large scale atmospheric conditions that increase humidity or advect warm air to the polar regions are likely to increase surface melt in Greenland or Antarctic Peninsula region. Nevertheless, explaining the high resolution melt patterns requires understanding of the local conditions and topographic features. This Ph.D. thesis contributes in understanding local surface melt as part of the large scale climate system

Assessment of Atmospheric Reanalyses with Independent Observations in the Weddell Sea, the Antarctic

Surface layer and upper-air in situ observations from two research vessel cruises and an ice station in the Weddell Sea from 1992 and 1996 are used to validate four current atmospheric reanalysis products: ERA-Interim, CFSR, JRA-55, and MERRA-2. Three of the observation data sets were not available for assimilation, providing a rare opportunity to validate the reanalyses in the otherwise datasparse region of the Antarctic against independent data. All four reanalyses produce 2 m temperatures warmer than the observations, and the biases vary from +2.0 K in CFSR to +2.8 K in MERRA-2. All four reanalyses are generally too warm also higher up in the atmospheric boundary layer (ABL), with biases up to +1.4 K (ERA-Interim). Cloud fractions are relatively poorly reproduced by the reanalyses, MERRA-2 and JRA-55 having the strongest positive and negative biases of about +30 % and -17 %, respectively. Skill scores of the error statistics reveal that ERA-Interim compares generally the most favorably against both the surface layer and the upper-air observations. CFSR compares the second best and JRA-55 and MERRA-2 have the least favorable scores. The ABL warm bias is consistent with previous evaluation studies in high latitudes, where more recent observations have been applied. As the amount of observations has varied depending on the decade, season, and region, the consistency of the warm bias suggests a need to improve the modeling systems, including data assimilation as well as ABL and surface parameterizations.Peer reviewe

Summer predictions of Arctic sea ice edge in multi-model seasonal re-forecasts

In this study, the forecast quality of 1993–2014 summer seasonal predictions of five global coupled models, of which three are operational seasonal forecasting systems contributing to the Copernicus Climate Change Service (C3S), is assessed for Arctic sea ice. Beyond the Pan-Arctic sea ice concentration and extent deterministic re-forecast assessments, we use sea ice edge error metrics such as the Integrated Ice Edge Error (IIEE) and Spatial Probability Score (SPS) to evaluate the advantages of a multi-model approach. Skill in forecasting the September sea ice minimum from late April to early May start dates is very limited, and only one model shows significant correlation skill over the period when removing the linear trend in total sea ice extent. After bias and trend-adjusting the sea ice concentration data, we find quite similar results between the different systems in terms of ice edge forecast errors. The highest values of September ice edge error in the 1993–2014 period are found for the sea ice minima years (2007 and 2012), mainly due to a clear overestimation of the total extent. Further analyses of deterministic and probabilistic skill over the Barents–Kara, Laptev–East Siberian and Beaufort–Chukchi regions provide insight on differences in model performance. For all skill metrics considered, the multi-model ensemble, whether grouping all five systems or only the three operational C3S systems, performs among the best models for each forecast time, therefore confirming the interest of multi-system initiatives building on model diversity for providing the best forecasts.This study was partly funded by the H2020-APPLICATE project, EU grant number 727862. JCAN acknowledges the Spanish Ministry of Science, Innovation and Universities for the personal grant Juan de la Cierva FJCI-2017-34027, PRACE for awarding access to MareNostrum at Barcelona Supercomputing Center (BSC), and ESA/CMUG-CCI3 for financial support. PO work was funded by the Ramon y Cajal grant RYC-2017-22772.Peer ReviewedPostprint (author's final draft

Advancing global & regional reanalyses

This report outlines the structure of and summarizes the recommendations made at the 5th International Conference on Reanalysis (ICR5), attended by 259 participants from 37 countries, in Rome (Italy), on 13-17 November 2017. It first summarizes the conference structure. Then, the key recommendations of ICR5 are given for the five main conference topics: production; observations (data rescue and preparation); data assimilation methods; quality assurance of reanalysis; and applications in science, services, and policymaking. Lastly, five high-level recommendations are proposed to managing agencies on how best to advance the field of reanalyses, which serves tens of thousands of users, via enhanced research, development, and operations

Improving Arctic Weather and Seasonal Climate Prediction: Recommendations for Future Forecast Systems Evolution from the European Project APPLICATE

International audienceThe Arctic environment is changing, increasing the vulnerability of local communities and ecosystems, and impacting its socio-economic landscape. In this context, weather and climate prediction systems can be powerful tools to support strategic planning and decision-making at different time horizons. This article presents several success stories from the H2020 project APPLICATE on how to advance Arctic weather and seasonal climate prediction, synthesizing the key lessons learned throughout the project and providing recommendations for future model and forecast system development