ARCTIC, ANTARCTIC, AND ALPINE RESEARCH © 5 
during the interviews and later coded thematically in 
Word roughly following the thematic framework pro- 
vided by Macusi et al. (2020), which includes the four 
major thematic areas of threats, vulnerabilities, pro- 
blems, and adaptations. 
Results 
Climate modeling wind speed extremes 
Analyzing SMHI-LENS for potential future changes in 
wind speed extremes (comparing the thirty-year period 
2071-2100 to 1981-2010) yields heterogeneous results 
around Iceland (Figure 2). We chose the 1981 
2010 period because this is the thirty-year period in 
the historical simulations of the model that is closest to 
present day. Though wide areas of the North Atlantic 
west, south, and east of Iceland show fewer storm days 
(according to the proxy definition described in the 
Methods section) in a warmer future climate, the 
:egion north to northeast of Iceland features an 
increase in storm days. The general pattern of these 
changes is robust across different future scenarios in 
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Figure 2. Climate change signal in number of storm days 
per year (see definition in the Methods section) for the period 
2071-2100 relative to 1980-2010 according to SMHI-LENS and 
SS$SP3-7.0; results that are statistically insignificant (p > .05 
according to 1,000-fold bootstrapping) are hatched. The rough 
outline of Iceland is center, with the east coast of Greenland on 
the left. We define storm days as days exceeding the ninety-fifth 
percentile of daily wind speeds of the fifty members of the 
historical data experiment (reference period 1970-2014), calcu- 
lated individually for every grid box of the climate model. For the 
future scenarios we keep these thresholds and analyze whether 
exceedances occur more or less frequently in the climate model. 
SMHI-LENS. The positive response of 4.5 more storm 
days in S$SP3-7.0 northeast of Iceland essentially means 
approximately 25 percent more days with disruptions 
of fishing activities in this area. Conversely, west, south, 
and east of Iceland, 15 to 35 percent fewer storm days 
are diagnosed in SMHI-LENS for SSP3-7.0 in our 
proxy analysis. Given the large sample size of SMHI- 
LENS (fifty members times thirty years for this analy- 
sis) most of these results are statistically significant 
(p < .05). Only comparably small regions along the 
zero line of the climate change signal are insignificant 
(hatched areas in Figure 2; based on 1,000-fold boot- 
strapping). We assume that the pronounced signals 
over land areas are mainly related to changes in land 
ıse or vegetation used for the climate scenarios and— 
also given their very localized character—refrain from 
discussing these in the context of changes relevant for 
fishing activities. 
It should be noted that the results presented here 
from the SMHI-LENS should only be seen as one poten- 
tial outcome of future change in storminess and that 
different climate models might provide different 
answers on future storminess changes. A recent study 
by Blackport, Fyfe, and Screen (2022) showe that CMIP6 
models simulate a wide range of trends for sea-level 
pressure (SLP) and wind trends in the North Atlantic 
:egion. However, they generally strongly underestimate 
or even fail to simulate the observed increasing trend 
toward stronger zonal winds in the North Atlantic and 
a more positive NAO index between 1950 and 2020. 
Though it is not entirely clear whether these observed 
trends are a response to increased greenhouse gas for- 
cing or only part of potential centennial variability 
(IPCC 2021), it might indicate that climate models 
anderestimate future wind changes in the North 
Atlantic region. 
Further, Fuentes-Franco et al. (2023) showed, based 
on CMIP® future projections, that the variability of the 
NAO will decrease in the future and that the SLP 
pattern connected to the NAO changes as well. Across 
all CMIP6 models, SLP increases in the Nordic Seas 
and Barents Sea and decreases toward the Labrador 
Sea. Fuentes-Franco et al. (2023) also showed 
a linkage between the simulated changes in NAO varia- 
bility and precipitation extremes. For the Iceland area, 
more precipitation extremes are occurring in all sce- 
aarios linked to the changing NAO variability. Models 
with a stronger reduction in NAO variability show 
a particularly strong increase in precipitation extremes. 
"hough the process behind this has not fully been 
antangled yet, it seems to be linked to changes in the 
sea surface temperature gradients in the North 
Atlantic.