Warming

Annual average temperature in the Barents area increased by 1–2°C over the period 1954–2003, with warming strongest in winter (Callaghan et al., 2005). The observed rate of increase in Ny Ålesund (Svalbard) for the period 1994–2013 was 1.3°C per decade (Maturilli et al., 2014), again with the increase greatest in winter (3°C per decade).

A new set of temperature and precipitation projections has been generated for the Barents area by empirical-statistical downscaling of results from the CMIP5 ensembles of General Circulation Models (GCMs) driven by the RCP2.6, RCP4.5 and RCP8.5 emission scenarios (Benestad et. al., 2016). Large ensembles are likely to capture the effect of natural variations (Deser et al., 2012) and by comparing the downscaled results and observations it is clear that the range in model results is similar to that for the observed interannual variability (Figure 4.1). The downscaled data indicate that the warmest (95th percentile) winter temperatures over the land-area surrounding the Barents Sea are likely to

Figure 4.1 Comparison of observed temperature and computed winter-mean temperature for the period 1900–2100 at Svalbard airport for the RCP2.6, RCP4.5, and RCP8.5 emission scenarios (Benestad et al., 2016).

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increase by 3–10°C over the period 2015–2080 under the RCP4.5 scenario (Figure 4.2). The warming shows regional variability, with the greatest warming expected to occur over Svalbard and the High Arctic. Temperatures are projected to be even higher under a regional climate model (RCM) that downscaled data from four GCMs (CMIP5) and the RCP8.5 scenario (Koenigk et al., 2015).

According to these results, warming over the Barents Sea may be 8–15°C by mid-century and up to 20°C by the end of the century in winter, largely driven by the disappearance of sea ice from the Baltic Sea. Warming over the Barents Sea in summer is projected to be 3–4°C by mid-century and 6–8°C by the end of the century under RCP8.5, with a warming over the adjacent land of 4–6°C by the end of the century. The drawback to this study is the small number of GCM simulations on which the regional data are based.

However, these data are comparable with an atlas based on 81 RCP8.5 simulations, and corresponding results from empirical-statistical downscaling of larger ensembles indicates a similar range, albeit with strongest warming in the western part of the Barents area (Benestad et al., 2016). Recent analysis of projections from another RCM (COSMO-CLM, Steppeler et al., 2003), downscaling RCP2.6, RCP4.5 and RCP8.5 scenario runs from the Earth System Model MPI-ESM-LR to a resolution of about 25 km in the Barents and Scandinavian region, are in agreement with results reported by Koenigk et al. (2015) (Figure 4.3).

There was little difference between the projected summer temperatures for the different emission scenarios (i.e. the patterns were similar), although a temperature increase of several degrees is expected to bring the winter conditions closer to freezing over the Nordic countries, where winter temperatures already are moderate. Present winters are extremely cold in northern Russia and are expected to remain well below freezing even by the end of the century (Benestad, 2011). Future winters on Svalbard, however, are more likely to rise above freezing, and extreme high winter temperatures in the future are expected

to have a range of effects with consequences for other parts of the cryosphere (Førland et al., 2011; Hansen et al., 2014;

Vikhamar-Schuler et al., 2016). Winter warming may increase the risk of ROS events.

Projections for future warming in the Arctic were also generated for the fifth assessment of the Intergovernmental Panel on Climate Change (IPCC AR5), with the extent of warming dependent on time horizon and emission scenario. Changes in temperature are projected with higher confidence than for many other aspects of the climate system (such as precipitation and wind), even at high latitudes. However, the IPCC AR5 also pointed to the increased spread in model projections for temperature in the polar regions. “The zonal means ... show good agreement of models and scenarios over low and mid-latitudes for temperature, but higher spread across models and especially across scenarios for the areas subject to polar amplification...” (Collins et al., 2013). This increased spread in model results for high latitudes may be a bit deceptive, as explained by Benestad (2005), because the polar regions involve smaller surface areas with different geometry to the latitude bands at lower latitudes, and so represent a smaller statistical sample and fewer real degrees of freedom. Projections of future temperature suggest that model spread does not change much over time (Hansen et al., 2014). On the other hand, the presence of additional factors in the Arctic (such as sea ice) and different representation in the models, also gives rise to a wider range of results (Stramler et al., 2011).

It is important to note that pronounced natural variations, associated with shifts in ocean currents, sea ice, storm tracks, and wind can diminish or amplify the estimates of future temperature by roughly 5°C (Benestad et al., 2016). The Arctic climate responds to changes in ice and snow, heat transport through ocean currents and storms, and heat loss to space, but is also characterized by its marine environment; especially the strong Coriolis effect, frontal systems, energy and moisture Figure 4.2 Projected change in a typical warm winter mean temperature (DJF; left) and annual total precipitation (right) for a typical wet year between 2015 and 2080 based on empirical-statistical downscaling of the 95-percentile of CMIP5 ensemble following the RCP4.5 emissions scenario. The results are based on Benestad et al. (2016).

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exchange between surface and the atmosphere, and near-surface processes. For instance, Woods and Caballero (2016) suggested that an increase in intense moisture injections across 70°N may explain a substantial fraction of the trends in winter temperature and sea ice over the past two decades. Recent studies

have also examined the magnitude of day-to-day variation and the persistence (one-day auto-correlation) of the temperature, and found some support for reduced day-to-day variation in temperature in a warmer world, but little change in persistence in terms of day-to-day autocorrelation (Benestad et al., 2016).

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Figure 4.3 Projected change in seasonal temperature across the Barents and Scandinavian region using the regional climate model COSMO-CLM driven by the global MPI-ESM-LR Earth System Model under three emission scenarios. RCP2.6 (left), RCP4.5 (middle) and RCP8.5 (right) for the period 2071–2100 relative to 1971–2000. The dark and light blue lines indicate the northern extent of an ice-free sea for at least 20% of the time in the future and historical period, respectively. The biggest temperature changes occur between the two lines, i.e. where the sea ice is retreating regularly. Numbers at the lower right of each plot give the mean climate change signal over the domain shown (Dobler et al., 2016).