Four state-of-the-art satellite-based estimates of ocean surface latent heat fluxes (LHFs) extending over three decades are analyzed, focusing on the interannual variability and trends of near-global averages and regional patterns. Detailed inter-comparisons are made with other datasets including: (i) reduced observation reanalyses (RedObs) whose exclusion of satellite data renders them an important independent diagnostic tool; (ii) a moisture budget residual LHF estimate using reanalysis moisture transport, atmospheric storage and satellite precipitation; (iii) the ECMWF Reanalysis 5 (ERA5); (iv) Remote Sensing Systems (RSS) single-sensor passive microwave and scatterometer wind speed retrievals, and (v) several sea-surface temperature (SST) datasets. Large disparities remain in near-global satellite LHF trends and their regional expression over the 1990-2010 period, during which time the Interdecadal Pacific Oscillation changed sign. The budget residual diagnostics support the smaller RedObs LHF trends. The satellites, ERA5 and RedObs are reasonably consistent in identifying contributions by the 10m wind speed variations to the LHF trend patterns. However, contributions by the near-surface vertical humidity gradient from satellites and ERA5 trend upward in time with respect to the RedObs ensemble and show less agreement in trend patterns. Problems with wind speed retrievals from Special Sensor Microwave Imager / Sounder satellite sensors, excessive upward trends in trends in Optimal Interpolation Sea Surface Temperature (OISST AVHRR-Only) data used in most satellite LHF estimates and uncertainties associated with poor satellite coverage before the mid-1990s are noted. Possibly erroneous trends are also identified in ERA5 LHF associated with the onset of scatterometer wind data assimilation in the early 1990s.
Abstract. The development of algorithms for the retrieval of water cycle components from satellite data – such as total column water vapor content (TCWV), precipitation (P), latent heat flux, and evaporation (E) – has seen much progress in the past 3 decades. In the present study, we compare six recent satellite-based retrieval algorithms and ERA5 (the European Centre for Medium-Range Weather Forecasts' fifth reanalysis) freshwater flux (E−P) data regarding global and regional, seasonal and interannual variation to assess the degree of correspondence among them. The compared data sets are recent, freely available, and documented climate data records (CDRs), developed with a focus on stability and homogeneity of the time series, as opposed to instantaneous accuracy. One main finding of our study is the agreement of global ocean means of all E−P data sets within the uncertainty ranges of satellite-based data. Regionally, however, significant differences are found among the satellite data and with ERA5. Regression analyses of regional monthly means of E, P, and E−P against the statistical median of the satellite data ensemble (SEM) show that, despite substantial differences in global E patterns, deviations among E−P data are dominated by differences in P throughout the globe. E−P differences among data sets are spatially inhomogeneous. We observe that for ERA5 long-term global E−P is very close to 0 mm d−1 and that there is good agreement between land and ocean mean E−P, vertically integrated moisture flux divergence (VIMD), and global TCWV tendency. The fact that E and P are balanced globally provides an opportunity to investigate the consistency between E and P data sets. Over ocean, P (nearly) balances with E if the net transport of water vapor from ocean to land (approximated by over-ocean VIMD, i.e., ∇⋅(vq)ocean) is taken into account. On a monthly timescale, linear regression of Eocean-∇⋅(vq)ocean with Pocean yields R2=0.86 for ERA5, but smaller R2 values are found for satellite data sets. Global yearly climatological totals of water cycle components (E, P, E−P, and net transport from ocean to land and vice versa) calculated from the data sets used in this study are in agreement with previous studies, with ERA5 E and P occupying the upper part of the range. Over ocean, both the spread among satellite-based E and the difference between two satellite-based P data sets are greater than E−P, and these remain the largest sources of uncertainty within the observed global water budget. We conclude that, for a better understanding of the global water budget, the quality of E and P data sets needs to be improved, and the uncertainties more rigorously quantified.
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