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Our study forms the oceanic counterpart to numerous observational studies over land considering the sensitivity of extreme precipitation to a change in air temperature. We explore the sensitivity of oceanic precipitation to changing sea surface temperature (SST) by exploiting two novel datasets at high resolution. First, we use the Ocean Rainfall And Ice-phase precipitation measurement Network (OceanRAIN) as an observational along-track shipboard dataset at 1-minute resolution. Second, we exploit the most recent European Re-Analysis version 5 (ERA5) at hourly resolution on 31 km grid. Matched with each other, ERA5 vertical velocity allows to constrain OceanRAIN precipitation. Despite the inhomogeneous sampling along ship tracks, OceanRAIN agrees with ERA5 on the average latitudinal distribution of precipitation with fairly good seasonal sampling. However, the 99th percentile of OceanRAIN precipitation follows a super-Clausius-Clapeyron scaling with SST that exceeds 8.5 % K−1 while ERA5 precipitation scales with 4.5 % K−1. The sensitivity decreases towards lower precipitation percentiles while OceanRAIN keeps an almost constant offset to ERA5 due to higher spatial resolution and temporal sampling. Unlike over land, we find no evidence for decreasing precipitation event duration with SST. ERA5 precipitation reaches a local minimum at about 26 °C that vanishes when constraining vertical velocity to strongly rising motion and excluding areas of weak correlation between precipitation and vertical velocity. This indicates that instead of moisture limitations as over land, circulation dynamics rather limit precipitation formation over the ocean. For strongest rising motion, precipitation scaling converges to a constant value at all precipitation percentiles. Overall, high resolution in observations as well as climate models is key to understand and predict the sensitivity of oceanic precipitation extremes to a change in SST. [ABSTRACT FROM AUTHOR]

Representation of the drop size distribution (DSD) of rainfall is a key element of characterizing precipitation in models and retrievals, with a functional form necessary to calculate the precipitation flux and the drops' interaction with radiation. With newly available oceanic disdrometer measurements, this study investigates the validity of commonly used DSDs, potentially useful a priori constraints for retrievals, and the forward model errors caused by DSD variability. These data are also compared to leading satellite-based estimates of oceanic DSDs. Forward model errors due to DSD variability are shown to be significant for both active and passive sensors. The modified gamma distribution is found to be generally adequate to describe rain DSDs, but may cause systematic errors for high latitude or stratocumulus rain retrievals; depending on the application, an exponential or generalized gamma function may be preferable for representing oceanic DSDs. An unsupervised classification algorithm finds a variety of DSD shapes that differ from commonly used DSDs, but does not find a singular set that best describes the global variability. Finally, DSD shapes are found to be not particularly distinctive of regional or large-scale environments, but rather occur at varying frequencies over the global oceans. [ABSTRACT FROM AUTHOR]

A unique data set was sampled by aircraft, ship, drift buoys and satellites in a strong storm event which occurred over the Norwegian Sea in March 2005 during the LOFZY field experiment. The atmospheric characteristics and the impact on the upper ocean are investigated. The storm field with winds up to 27 m s−1 was situated on the west-side of a low-pressure trough along the Norwegian coast and was marked by a sharp wind front. The entire system of trough, front and storm field was about 200 km wide, had a lifetime of 2 d in the area and advanced at 1–3 m s−1 SE-wards. Temperature contrast across the system was small, but wind contrast was remarkable and concentrated in a step-like front with 5–15 m s−1 difference over a distance of 3 km. The entire system was restricted to the lowest 2 km. It was accompanied by wide-spread rain and snow. Precipitation slightly surpassed evaporation (0.22 mm h−1). Surface heat fluxes amounted up to 400 W m−2. However, they cannot account for the observed sea surface temperature changes between −0.8 and +0.1 K within 6 h during the frontal passage. This is attributed to vertical mixing in the ocean caused by the frontal wind impact and superimposed to pre-existing mesoscale eddy circulations. The mixing acted on a temperature stratification in the uppermost 20–100 m which was opposite on both sides of the Norwegian Current. [ABSTRACT FROM AUTHOR]

A systematic investigation and comparison of near-surface marine wind speed obtained from in situ and satellite observations, a reanalysis, and a reanalysis-driven regional climate model (RCM) are presented for the eastern North Atlantic and the North Sea. Wind-speed retrievals from QuikSCAT Level 2B 12.5 km and HOAPS-3-S are analyzed. The root-mean-square error (rmse) between QuikSCAT and buoy 10-m equivalent neutral wind (EQNW) is 1.50 (1.87) m⋅s-1 using a colocation criteria of 0.1° and 0.06° (0.3° and 0.2°) in longitudinal and latitudinal distances from buoy locations and within 10 (20) min, demonstrating that QuikSCAT's mission requirement of providing wind speed with an rmse of 2 m⋅s-1 is met for the investigated area. The influence of three different stability and anemometer height correction algorithms for buoy wind speed on the buoy/QuikSCAT error assessed: EQNW gives the best agreement with QuikSCAT data; however, differences are smaller than the buoy measurement error. The rmse between HOAPS and buoy wind converted to 10 m the logarithmic wind profile is 2.27 (2.36) m⋅s-1 using a collocation 0.1° x 0.06° (0.3° x 0.2°) and within 10 (20) min. QuikSCAT shows good agreement with buoy wind for speeds up to 20 m⋅s-1 HOAPS shows an underestimation of high wind speeds beyond 15-20 m⋅s-1 probably due to a saturation of the return signal. The rmse between buoy wind speed and the National Centers of Environmental Prediction/National Center for Atmospheric Research Reanalysis (NRA R1) and the spectrally nudged RCM REMO SN-REMO) are 2.2 and 2.5 m⋅s-1, respectively. [ABSTRACT FROM AUTHOR]