Fundación General CSIC (Programa ComFuturo); TURBOMIX project (CGL2015-73100-EXP). With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), • lat: latitude • lon: longitude • x, x coordinate • y: y coordinate • time: time • sst: Sea Surface Temperature • hsst: Singularity exponents of Sea Surface Temperature, Peer reviewed

8 pages, 3 figures.-- Data Availability Statement: The details of the model configuration, as well as, the simulated Sea Surface Temperatures generated for this study, and the singularity analysis described in Section 3 are available in https://doi.org/10.20350/digitalCSIC/14487 (Isern-Fontanet et al., 2022), The contribution of ocean fronts to the properties and temporal evolution of Sea Surface Temperature (SST) structure functions have been investigated using a numerical model of the California Current system. First, the intensity of fronts have been quantified by using singularity exponents. Then, leaning on the multifractal theory of turbulence, we show that the departure of the scaling of the structure functions from a straight line, known as anomalous scaling, depends on the intensity of the strongest fronts. These fronts, at their turn, are closely related to the seasonal change of intensity of the coastal upwelling characteristics of this area. Our study points to the need to correctly reproduce the intensity of the strongest fronts and, consequently, properly model processes such as coastal upwelling in order to reproduce SST statistics in ocean models, This work was supported by the Ministry of Economy and Competitiveness, Spain, and FEDER EU through the EXPLORA Ciencia National R + D Plan under TURBOMIX project (CGL2015-73100-EXP) and through the projects PROMISES (ESP2015-67549-C3-1-R) and COSMO (CTM2016-79474-R). This work was supported in part by the Spanish R&D project L-BAND (ESP2017-89463-C3-1-R), which is funded by MCIN/AEI/10.13039/501100011033 and ERDF. A way of making Europe, and project INTERACT (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033. We also acknowledge support from Fundación General CSIC (Programa ComFuturo). This work acknowledges the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S). This work is a contribution to CSIC PTI Teledetect, Peer reviewed