Abstract. Latent heat flux (LHF) is one of the main contributors to
the global energy budget. As the density of in situ
LHF measurements over the global oceans is generally poor, the potential of remotely sensed LHF for meteorological
applications is enormous. However, to date none of the available satellite products have included estimates of systematic, random, and
sampling uncertainties, all of which are essential for assessing their quality. Here, the challenge is taken on by matching
LHF-related pixel-level data of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS) climatology
(version 3.3) to in situ measurements originating from a high-quality data archive of buoys and selected ships. Assuming the ground
reference to be bias-free, this allows for deriving instantaneous systematic uncertainties as a function of four atmospheric
predictor variables. The approach is regionally independent and therefore overcomes the issue of sparse in situ data densities over
large oceanic areas. Likewise, random uncertainties are derived, which include not only a retrieval component but also contributions
from in situ measurement noise and the collocation procedure. A recently published random uncertainty decomposition approach is
applied to isolate the random retrieval uncertainty of all LHF-related HOAPS parameters. It makes use of two combinations
of independent data triplets of both satellite and in situ data, which are analysed in terms of their pairwise variances of
differences. Instantaneous uncertainties are finally aggregated, allowing for uncertainty characterizations on monthly to
multi-annual timescales. Results show that systematic LHF uncertainties range between 15 and 50 W m−2 with
a global mean of 25 W m−2. Local maxima are mainly found over the subtropical ocean basins as well as along the western
boundary currents. Investigations indicate that contributions from qa (U) to the overall LHF uncertainty are
on the order of 60 % (25 %). From an instantaneous point of view, random retrieval uncertainties are specifically large over the
subtropics with a global average of 37 W m−2. In a climatological sense, their magnitudes become negligible, as do
respective sampling uncertainties. Regional and seasonal analyses suggest that largest total LHF uncertainties are seen
over the Gulf Stream and the Indian monsoon region during boreal winter. In light of the uncertainty measures, the observed
continuous global mean LHF increase up to 2009 needs to be treated with caution. The demonstrated approach can easily be
transferred to other satellite retrievals, which increases the significance of the present work.