The impact of variable sea ice roughness on changes in Arctic Ocean surface stress: a model study

The Arctic sea ice cover is thinning and retreating, causing changes in surface roughness that in turn modify the momentum flux from the atmosphere through the ice into the ocean. New model simulations comprising variable sea ice drag coefficients for both the air and water interface demonstrate that the heterogeneity in sea ice surface roughness significantly impacts the spatial distribution and trends of ocean surface stress during the last decades. Simulations with constant sea ice drag coefficients as used in most climate models show an increase in annual mean ocean surface stress (0.003 N/m2 per decade, 4.6%) due to the reduction of ice thickness leading to a weakening of the ice and accelerated ice drift. In contrast, with variable drag coefficients our simulations show annual mean ocean surface stress is declining at a rate of -0.002 N/m2 per decade (3.1%) over the period 1980-2013 because of a significant reduction in surface roughness associated with an increasingly thinner and younger sea ice cover. The effectiveness of sea ice in transferring momentum does not only depend on its resistive strength against the wind forcing but is also set by its top and bottom surface roughness varying with ice types and ice conditions. This reveals the need to account for sea ice surface roughness variations in climate simulations in order to correctly represent the implications of sea ice loss under global warming.

A regionally informed abundance index for supporting integrative analyses across butterfly monitoring schemes

1. The rapid expansion of systematic monitoring schemes necessitates robust methods to reliably assess species' status and trends. Insect monitoring poses a challenge where there are strong seasonal patterns, requiring repeated counts to reliably assess abundance. Butterfly monitoring schemes (BMSs) operate in an increasing number of countries with broadly the same methodology, yet they differ in their observation frequency and in the methods used to compute annual abundance indices. 2. Using simulated and observed data, we performed an extensive comparison of two approaches used to derive abundance indices from count data collected via BMS, under a range of sampling frequencies. Linear interpolation is most commonly used to estimate abundance indices from seasonal count series. A second method, hereafter the regional generalized additive model (GAM), fits a GAM to repeated counts within sites across a climatic region. For the two methods, we estimated bias in abundance indices and the statistical power for detecting trends, given different proportions of missing counts. We also compared the accuracy of trend estimates using systematically degraded observed counts of the Gatekeeper Pyronia tithonus (Linnaeus 1767). 3. The regional GAM method generally outperforms the linear interpolation method. When the proportion of missing counts increased beyond 50%, indices derived via the linear interpolation method showed substantially higher estimation error as well as clear biases, in comparison to the regional GAM method. The regional GAM method also showed higher power to detect trends when the proportion of missing counts was substantial. 4. Synthesis and applications. Monitoring offers invaluable data to support conservation policy and management, but requires robust analysis approaches and guidance for new and expanding schemes. Based on our findings, we recommend the regional generalized additive model approach when conducting integrative analyses across schemes, or when analysing scheme data with reduced sampling efforts. This method enables existing schemes to be expanded or new schemes to be developed with reduced within-year sampling frequency, as well as affording options to adapt protocols to more efficiently assess species status and trends across large geographical scales.