Bosilovich, Michael G.

Roberts, Jason B.

Robertson, Franklin R.

English

NASA Technical Reports Server

Inter-annual to Inter-decadal Variability in Ocean / Land Moisture Transport: 
Estimates from Reanalyses, Satellites and Land Surface Models
Franklin R. Robertson1 , Michael G. Bosilovich2 and Jason B. Roberts1
1NASA / Marshall Space Flight Center, Earth Science Branch, 320 Sparkman Dr., Huntsville, AL 35805, USA 2NASA / GSFC Global Modeling and Assimilation Office, 880 Greenbelt, Rd, Greenbelt, MD 20771 
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
Time–Dependent Flux Variability over Ocean / Land Domains
Area-averaged monthly anomalies over 60oN/S Land and Ocean Regions
(units: kg m-2, base climatology 1990/2010, running 3-month smoothing)
Summary Points:
(1) Reanalysis vertically-integrated moisture flux divergence variability shows strong
consistency with quasi-independent, observationally driven LSM P-ET estimates.
(2) ENSO warm and cold SST events provide the primary interannual signal modulating land
ocean moisture exchange.
(3) Satellite P - E estimates over ocean exhibit significa tly stronger interannual signals and
trends than either reanalyses or LSMs. Known retrieval issues with satellite evaporation
(SSMIS calibration, input SST) drive trends. Satellite precipitation monthly variations are
larger than reanalyses.à Satellite P-E differs from reanalyses and obs driven LSMs.
(4) Future work to optimally blend these and other water and energy cycle fluxes to
minimize error depends on establishing error covariance structures of quantities and
accounting for time and weather regime dependence of these errors.
Issues & Challenge:
Vertically-integrated atmospheric transport of moisture between
ocean and land is a fundamental component of the physical
climate system linking the hydrologic and energy cycles of the
planet as well as determining fresh water fluxes to the ocean, and
water availability to the biosphere.
For land / ocean domains and monthly time scales, vertically-
integrated moisture convergence ∫"−∇ % 𝑞𝑽 𝑑𝑚 ~ P-E; thus, (i)
direct estimates of this transport from reanalysis wind and
moisture fields, (ii) E and P from satellite retrievals and, (iii) P and
ET from observationally constrained land surface models yield
largely independent estimates on land/ocean moisture exchange.
To what degree are variations in the fluxes mutually consistent?
Global Ocean Precipitation (mm d-1)
GPCP  RSS V7  GPROF2010.2  TRMM GPROF TRMM 3A25
• Strong correlation between oceanic P and SST on ENSO
time scales with more (less) P sequestered over oceans
during warm (cold) events.
• PR 3A25 (Orbit boost & bias corrected) indicates GPROF
biases related to stratiform / convective detection.
Global Ocean Evaporation (mm d-1)
Global SST anomalies     
(rhs scale; shaded, K-1)
Global SST anomalies     
(rhs scale; shaded, K-1)
*+,*"∇ % 𝑞𝑽 𝑑𝑚𝛿𝑎+, = *0123*"−∇ % 𝑞𝑽 𝑑𝑚𝛿𝑎0123
𝐸56 − 𝑃56 = *1891 𝑃 − 𝐸𝑇 0123 𝛿𝑎
𝜕𝑊𝜕𝑡 = *"−∇ % 𝑞𝑽 𝑑𝑚 + 𝐸 − 𝑃
Atmospheric 
Reanalyses
•Atmospheric Reanalyses
•Passive Microwave Satellite (Ocean)
•Observationally Constrained Land 
Surface Models
Atmospheric Moisture Budget and
Complementary Data Sources
Moisture 
Budget
Flux Divergence 
Moisture Vanishes 
Globally
Complementarity of Ocean 
and Land Area average Data
• Evaporation low-frequency behavior and trends are
much greater and likely exaggerated in IFREMER4,
HOAPS4 and ERA5 but JOFURO3 and SeaFlux v3 are
generally closer to Reduced Observation Input
Reanalyses (RedObs) which use no satellite inputs.
• Uncertainties in (1) SSMIS sensor wind speeds and (2)
input qs(SST) are significant contributions to satellite
retrievals.
Reanalyses 
*"𝛻 % 𝑞𝑉 𝑑𝑚 Zero-lag Regressions on Nino3.4  (mm d-1 K-1)
E-P   JOF3, Wentz v7 (Oc), LSM (Land) 
Wentz v7 – GPCP    PJOF3 – HOAPS4    E LSM-Reanalysis     ET-P 
• Well-known regional patterns of moisture export / import in reanalyses are replicated by 
satellite retrievals and LSM syntheses of E-P.
• Satellite E retrieval differences have longitudinal variations, P differences are more meridional.
Reanalysis Global Ocean              
and Land Surface Model P-ET (mm d-1)
• Both reanalyses and LSMs show systematic anomalous export of
moisture from ocean to land (land to ocean) during transition to
ENSO cold (warm) events. Anomalous moisture export to land and P-
ET decreases over land tend to lead global SST maxima.
• Significant agreement exists between reanalysis export of moisture
from ocean to land and land P-ET diagnosed from observationally-
constrained Land Surface Models.
• Satellite retrievals of E-P over the global oceans, while sign-
consistent in terms of interannual signals, exhibit greater amplitudes
than reanalysis div(qV) or RedObs.
Global SST anomalies     
(rhs scale; shaded, K-1)
*𝒎𝛁 % 𝒒𝑽 𝒅𝒎
*"∇ % 𝑞𝑽 𝑑𝑚
Land Surface Model P-ET  WaterGAP (GPCC precip) 
Princeton MEaSURES GLEAM / MSWEP Earth2Observe
Consistency Check: Atmospheric  
Moisture Transport
Global SST anomalies     
(rhs scale; shaded, K-1)
MERRA-2 ERA5 JRA-55 (Adjusted for changes in satellites) 
SeaFlux3, https://cclayson.whoi.edu/seaflux/
J-OFURO3, https://j-ofuro.scc.u-tokai.ac.jp/en/
FREMER4, https://wwz.ifremer.fr/oceanheatflux/ 
HOAPS4, https://wui.cmsaf.eu/
JRA55C, http://jra.kishou.go.jp/JRA-55/index_en.html
NOAA/ESRL 
20CRv3,https://www.esrl.noaa.gov/psd/data/20thC_Rean/
CERA-20C, https://www.ecmwf.int
ERA5,https://www.ecmwf.int
Remote Sensing Systems (RSS) http://www.remss.com/.
P-ET from GPCP, https://precip.gsfc.nasa.gov/ 
http://www.watergap.de/
http://earth2observe.github.io/water-resource-reanalysis-v1/
https://www.gleam.eu/ (GLEAM 3.2 ET)
https://sealevel.nasa.gov/
Humphrey et al, 2017 GRL
Reanalysis Ensemble                  , E-P from Satellite, 
LSM and  Reduced  Observation Input Reanalyses (RedObs)
Evap 60 N/S mm d-1
Satellite Evap
Reanalyses
Precipitation
Land Surface 
Models
GMSL
GRACE Reconst.
LSM Ensemble P-ET (as ocean equivalent E-P)
Reanalysis Ensemble Ocean 
60N/S  ∫"∇ % 𝑞𝑽 𝑑𝑚
We update the Syed et al (2010) estimate of global 
land discharge variations solved as the residual of 
rate of GMSL change  +  either reanalysis                   , 
or E-P anomalies from LSMs (scaled for land / ocean 
relative area coverage). 
Tendencies of both TOPEX / Poseidon / JASON 
Altimeter (https://sealevel.nasa.gov/ ) and GRACE 
Reconstructed Storage data (Humphrey et al, 2017 
GRL) are used to estimate GMSL rate of change.
*"∇ % 𝑞𝑽 𝑑𝑚
•Atmospheric Reanalysis
•Satellite ocean P, E retrievals, LSM P, E   
-∫" ∇ % 𝑞𝑽 𝑑𝑚
𝑑𝐺𝑀𝑆𝐿𝑑𝑡 = P − E + Discharge
TOPEX/Poseidon / 
JASON & GRACE 
dGMSL/dt  Anomalies from de-trended 
global means.  (3-mo smoothing, mm d-1) 
Altimeter
GRACE storage reconstruction re-scaled 
for ocean area equivalent  
GRACE Recon+LSM P-ET    Rescaled 
to ocean area equiv
Altimeter-Reanalysis div(qV)
Ensemble LSM
Discharge / Runoff 
(ocean area equiv. mm d-1)
Note: ALT/Rean estimate independent of LSMs
Consistency Check: Interannual Variability in Global Mean Sea Level
https://ntrs.nasa.gov/search.jsp?R=20190034089 2020-03-11T16:10:50+00:00Z


Inter-Annual to Inter-Decadal Variability in Ocean / Land Moisture Transport: Estimates from Reanalyses, Satellites and Land Surface Models

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