Climate change and projections for the Barents region: what is expected to change and what will stay the same?

• Published in: Environmental Research Letters
• Main Authors: Rasmus E Benestad, Kajsa M Parding, Ketil Isaksen, Abdelkader Mezghani
• Format: Article in Journal/Newspaper
• Language: English
• Published: IOP Publishing 2016
• Subjects: the Barents region; climate change; temperature; precipitation; storms; downscaling; Environmental technology. Sanitary engineering; TD1-1066; Environmental sciences; GE1-350; Science; Q; Physics; QC1-999; Arctic; Barents Sea; barents region; Climate change; Fennoscandia
• Online Access: https://doi.org/10.1088/1748-9326/11/5/054017; https://doaj.org/article/0ddca0467e41463b840b62c6a60042e8

We present an outlook for a number of climate parameters for temperature, precipitation, and storm statistics in the Barents region. Projected temperatures exhibited strongest increase over northern Fennoscandia and the high Arctic, exceeding 7 °C by 2099 for a typical ‘warm winter’ under the RCP4.5 scenario. More extreme temperatures may be expected with the RCP8.5, with an increase exceeding 18 °C in some places. The magnitude of the day-to-day variability in temperature is likely to decrease with higher temperatures. The skill of the downscaling models was moderate for the wet-day frequency for which the projections indicated both increases and decreases within the range of −5–+10% by 2099. The downscaled results for the wet-day mean precipitation was poor, but for the warming associated with RCP 4.5, it could result in wet-day mean precipitation being intensified by as much as 70% in 2099. The number of synoptic storms over the Barents Sea was found to increase with a warming in the Arctic, however, other climate parameters may not change much, such as the persistence of the temperature and precipitation. These climate change projections were derived using a new strategy for empirical-statistical downscaling, making use of principal component analysis to represent the local climate parameters and large ensembles of global climate model (GCM) simulations to provide information about the large scales. The method and analysis were validated on three different levels: (a) the representativeness of the GCMs, (b) traditional validation of the downscaling method, and (c) assessment of the ensembles of downscaled results in terms of past trends and interannual variability.