Bosilovich, Michael G.

Roberts, Jason B.

Robertson, Franklin R.

NASA Technical Reports Server

Narrowing Ocean Latent Heat Flux Uncertainties: 
Perspectives from Reanalysis and Satellite Estimates
Franklin R. Robertson1 (pete.Robertson@nasa.gov) , Jason B. Roberts1 and Michael G. Bosilovich2
1NASA / Marshall Space Flight Center Huntsville, AL 35805, USA   2NASA / GSFC Global Modeling and Assimilation Office Greenbelt, MD 20771
Issues & Challenge:
Latent heat flux (LHF) is a major component of the net surface energy exchange
governing ocean heat content and storage rate. Reanalyses using reduced
observational input: NOAA 20th Century Reanalysis (Ps); CERA-20C (Marine
wspd, Ps) and JRA-55C (conventional data only), avoid discontinuities induced
by assimilating broader satellite record. But how useful are they? LHF retrievals
using passive microwave satellite data (e.g. SeaFlux, JOFURO-3, IFREMER4 and
HOAPS4) are maturing but still suffer from inadequate validation data, lack of
direct sensitivity to near-surface moisture, and ambiguities in surface wind
stress / wind speed relationships. Can the complementary aspects of these
data sets help quantify and reduce ocean LHF / Evaporation uncertainties?
• Interannual variability (strongly influenced by ENSO events) is similar among all data sets,
especially post 1992 when passive microwave satellite coverage is more robust.
• Low-frequency behavior and trends are much greater in IFREMER4 and HOAPS4 but
JOFURO3 is generally closer to reanalyses (including the partial record of the emerging
ERA5 reanalysis.
• SeaFlux algorithm extreme sensitivity to: (1) Earth Incidence Angle variability and (2) single
sensor algorithm training data coverage is currently being corrected.
• An independent global ocean evaporation estimate (•••) made by combining GPCP ocean
precipitation estimates and land / ocean moisture transport, P-ET, from observationally-
driven land surface models: supports much smaller reanalysis
LHF/E trends and suggests that larger satellite trends are due to algorithm / data issues.
Time–Dependent Global Mean LHF Variability
LHF Trends 1992/2010 LHF (Wm-2 decade-1)
Qs-Qa Biases Wind Speed Biases
• (Left) Climatological biases
relative to ICOADS ship data
portrayed in q900/TPW; SST
space. (Right) Mean differences
(product minus in situ
observations) over the common
period 1999-2008.
• All qa retrievals tend to be too
moist in well-mixed convecting
regions and too dry in stable ,
descending regions.
• HOAPS4, which uses an older
Bentamy (2006) qa algorithm, is
most extreme.
• Red (blue) contours outline the
15% relative frequency of
occurrence regions for the
subtropical descent and deep
convective dynamical regimes.
• Though pattern-wise similar, reanalyses are
typically up to 20% larger than satellite
retrievals, with the latter averaging ~ 81 Wm-2.
• Mean annual cycle amplitudes are smaller in
reanalyses, particularly off east coasts of NH
continents.
• Satellite-derived LHF trends (SeaFlux omitted) are
systematically higher than reanalyses equatorward of 40o,
especially over the Atlantic and Indian Oceans. Reanalysis
reductions in eastern Tropical Pacific LHF is consistent
with negative trend of Pacific Decadal Variability index
during this period.
• Satellite qs-qa trends (expressed as % of climatological
values) are substantially larger than in reanalyses and
correlate strongly with the LHF trend outside the E Pacifc.
• Reanalysis fractional wind speed trend patterns agree
reasonably over the Pacific basin, consistent with changes
in PDV, but they differ in the western Indian Ocean.
Fractional trend
qs-qa (% decade-1)
Fractional trend 
10m wspd (% decade-1)
Sat
• HOAPS4, JOFURO3 and IFREMER4
winds speeds track reanalyses after
robust SSMI coverage in mid-1990s.
• Independent single sensor 10m wind
speed retrievals confirm F16 time
dependent biases (likely due to sensor
antenna degradation).
Page 3
What are the causes of 
water cycle variations?
Are variations in the global 
and regional water cycle predictable?
How are water and 
nutrient cycles linked?
NEWS Integrated Water and Energy Cycle Research Challenge:
Document and enable improved, observationally-based, predictions of water and energy 
cycle consequences of Earth system variability and change.
The NEWS challenge is global in scale and requires the integration of NASA system components to make 
decisive progress toward the NEWS challenge in an end-to-end program
HOAPSv4
Climatologies and State / Time Dependent qa Biases
Summary Points:
(1) Regime dependent biases associated with large-scale ynamics and SST distributions
control vertical moisture stratification and uncertai ty in satellite q retrievals;
however, the effect on global mean LHF variations is mall.
(2) SST trend differences between OISST-AVHRR (used by HOAPS4, SeaFlux2 and
IFREMER4) and reanalyses whose SST record includes cooler passive microwave-
derived SSTs post-1992 are a significant source of larger satellite qs-qa trends
compared to those of reanalyses.
(3) Global mean LHF / E estimated independently from GPCP P and ocean à land
moisture transport inferred from LSM P-ET suggests further improvement to satellite
derived near-surface meteorology will reduce decadal scale global LHF trends.
SeaFlux 2, https://cclayson.whoi.edu/seaflux/
J-OFURO3, https://j-ofuro.scc.u-tokai.ac.jp/en/
IFREMER4, https://wwz.ifremer.fr/oceanheatflux/ 
HOAPS4,https://wui.cmsaf.eu/safira/action/viewD
oiDetails?acronym=HOAPS_V002
JRA55C, http://jra.kishou.go.jp/JRA-55/
index_en.html
NOAA/ESRL 20CRv2c,https://www.esrl.noaa.gov/
psd/data/20thC_Rean/
CERA-20C, https://www.ecmwf.int
ERA5,https://www.ecmwf.int
Ancillary passive / active microwave wind speed 
retrievals from Remote Sensing Systems (RSS) 
http://www.remss.com/.
P-ET from GPCP, https://precip.gsfc.nasa.gov/ and 
seven LSM systems, see Robertson et al, (2014 J. 
Climate)
All data are monthly mean quantities and have 
been interpolated to a 1.0 x 1.0 latitude / 
longitude grid.
E 'OC = P 'OC + P − ET( )'LANDarea∫ δa.
Sensitivities !LHF / !""#
(1990-2010)
SeaFlux 2 -8.0 (5.0 30oN/S)
HOAPS4 20.0
IFREMER4 18.7
JOFURO3 9.7
CERA-20C 4.5
JRA-55C 6.8
NOAA/C20cr   2.3
ERA5 6.8 (2000-2010)
GPCP/LSM 6.0
• Differences in qs(SST) used in satellite retrievals
(HOAPS4 and IFREMER) vs reanalysis values (ooo) is
a significant contribution to qs-qa trend
differences.
• IFREMER qs(OISST) is 0.5 gKg-1 larger than that of
SeaFlux or HOAPS qs(OISST) for Jan2007-Oct2011.
Sat Sat
Rean Rean Rean
Data Sets
Ensemble Annual Cycle RMS  (Wm-2) 
Sat Retrievals (shaded); Sat - Reanalyses (contoured)
Ensemble Mean LHF Climatologies (Wm-2) 
Sat Retrievals (shaded); Sat - Reans (%, contoured)
SST anomaly
!SST oC!LHF W
m
-2
https://ntrs.nasa.gov/search.jsp?R=20180008637 2019-08-31T17:40:05+00:00Z


Narrowing Ocean Latent Heat Flux Uncertainties: Perspectives from Reanalysis and Satellite Estimates

Abstract

Similar works

Full text

Available Versions

NASA Technical Reports Server