Simulation of factors affecting E.huxleyi blooms in arctic and subarctic seas by CMIP5 climate models: model validation and selection

• Main Authors: Gnatiuk, Natalia, Radchenko, Iuliia, Davy, Richard, Morozov, Evgeny, Bobylev, Leonid
• Format: Text
• Language: English
• Published: 2019
• Subjects: Arctic; Greenland; Global warming; Subarctic
• Online Access: https://doi.org/10.5194/bg-2019-177; https://www.biogeosciences-discuss.net/bg-2019-177/

The coccolithophore E.huxleyi plays an essential role in the global carbon cycle. Therefore, considering the ongoing global warming, the assessment of future changes in coccolithophore blooms is very important. Our paper aims to provide a framework for selecting the optimum combination of global climate models to conduct such an assessment. To do this we analyse the forcing factors influencing present and future blooms using climate model projections. Then, based on the projected changes in the forcing factors, future changes in the dynamics of coccolithophore E.huxleyi blooms can be determined. Here we describe the complex methodology used for the validation of 34 CMIP5 climate models, and the selection of models that best represent the regional features of the oceanographic and meteorological factors affecting E.huxleyi blooms in arctic and subarctic seas: sea surface (i) temperature and (ii) salinity; (iii) wind speed at a height of 10 m above the surface; (iv) ocean surface current speed; and (v) surface downwelling shortwave radiation. The validation of the CMIP5 Atmosphere-Ocean General Circulation Models against reanalysis data includes analysis of the interannual variability, seasonal cycle, spatial biases and temporal trends of the simulated forcing factors. Here we propose a percentile score-based model ranking method for the selection of the best models from the CMIP5 ensemble. The selection of the best models was performed separately for each study area in the Barents, Bering, Greenland, Labrador, North and Norwegian Seas and for each of the five forcing factors affecting the coccolithophore blooms. In total, 30 combinations of most-skilful models were selected. The results show that there is no common optimal combination of models, nor is there one top-model, that has high skill in reproducing regional features across the combination of the five considered forcing factors and all arctic and subarctic seas. However, some climate models consistently show good skill for many of these combinations e.g. ACCESS1-3; ACCESS1-0; HadGEM2-AO; HadGEM2-CC; HadGEM2-ES; GFDL-CM3; INMCM4; GISS-E2-R; GISS-E2-R-CC. The models that have the smallest skill for the majority of the study regions are CMCC-CM; FGOALS-g2; IPSL-CM5A-LR; IPSL-CM5A-MR; IPSL-CM5B-LR; MIROC5; MRI-ESM1.