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The sea surface temperature (SST) is one of the essential parameters needed to understand the climate change in the Arctic. Saildrone, an advanced autonomous surface vehicle, has proven to be a useful tool for providing accurate SST data at high latitudes. Here, data from two Saildrones, deployed in the Arctic in the summer of 2019, are used to investigate the diurnal variability of upper ocean thermal structure. An empirical cool skin effect model with dependence on the wind speed with new coefficients was generated. Several local large diurnal warming events were observed, the amplitudes of warming in the skin layer >5 K, rarely reported in previous studies. Furthermore, the warming signals could persist beyond 1 day. For those cases, it was found surface warm air suppressed the surface turbulent heat loss to maintain the persistence of diurnal warming under low wind conditions. Salinity also plays an important role in the formation of upper ocean density stratification during diurnal warming at high latitudes. A less salty and hence less dense surface layer was likely created by precipitation or melting sea ice, providing favorable conditions for the formation of upper ocean stratification. Comparisons with two prognostic diurnal warming models showed the simulations match reasonably well with Saildrone measurements for moderate wind speeds but exhibit large differences at low winds. Both schemes show significant negative biases in the early morning and late afternoon. It is necessary to improve the model schemes when applied at high latitudes.

As one of the most significant oceanic parameters, the skin sea surface temperature (SST skin ) is an important factor in heat exchanges at the air-sea interface. SST skin can be retrieved by infrared (IR) radiometers mounted on satellites (

Infrared (IR) radiometers onboard satellites provide global coverage and frequent skin sea surface temperature (SST) retrievals, such as the Moderate Resolution Imaging Spectroradiometer (MODIS; Kilpatrick et al., 2015) and the Visible Infrared Imaging Radiometer Suite (VIIRS; Minnett et al., 2020). The current retrieval algorithms for both MODIS and VIIRS, using the channels centered at 11 and 12 μm, the longwave IR atmospheric "window," are applicable for daytime and nighttime measurements and are a modification of the nonlinear SST algorithm (NLSST) of C. C. Walton et al. (1998) with the following form:where BT 11 and BT 12 are brightness temperatures (BTs) in the 11 and 12 μm channels. T sfc is a reference SST. θ is the sensor zenith angle, and mirror relates to different sides of the MODIS scan mirror. Coefficients a 0 -a 6 are derived by regression of matchups between the in situ and satellite measurements, monthly with latitude-band dependence. The algorithm is described in detail by Kilpatrick et al. (2015) and Jia and Minnett (2020).The NLSST algorithm mainly accounts for the atmospheric effects of water vapor (H 2 O); but satellite retrievals are highly affected in aerosol-contaminated regions (Nalli & Stowe, 2002;Vázquez-Cuervo et al., 2004), for example, caused by tropospheric dust from the Saudi Arabian and Sahara deserts (Luo et al., 2021;Merchant et al., 2006). Luo et al. (2019) demonstrated the aerosol effect on MODIS retrieved SST and introduced an improved algorithm for nighttime data in the Saharan dust outflow area. Beyond dust aerosols, dramatic volcanic explosions inject large amounts of aerosol into the stratosphere, such as Mt. Pinatubo (Philippines, 1991) and El Chichón (Mexico, 1982), the two largest volcanic eruptions in the 20th century. Reynolds et al. (1989) showed a