P1253296783SQlOf

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The 29th CDPW, Madison, Wisconsin, Oct. 18-22,
2004
Global Land Surface Memory and Its Impacts Yun
Fan, Huug van den Dool Peitao Peng CPC/NCEP/NOAA
http//www.cpc.ncep.noaa.gov/soilmst/index.htm
Motivation Background
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Land Surface Memory its impacts
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Temporal spatial evolution
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The lower boundary condition of the atmosphere,
such as SST, soil moisture and snow cover often
have a longer memory than weather itself. Land
surface processes play an important role in
weather and climate prediction. In order to
improve our understanding of land
surface-atmosphere interaction on different time
scales, we need to improve our understanding of
the land surface hydrological energy cycles.
Also to further improve weather forecast
climate prediction, we need more accurate land
initial conditions.
Auto correlations of the global land surface soil
moisture,which show that cold seasons and dry
areas often have longer memory than those in warm
seasons and wet areas.
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Model Data
Difference of March September soil moisture
climatology, which shows two kinds of climates
and annual range of land surface soil water
variability.
The maximum minimum land surface soil moisture
and the months of these extremes take place.
The soil moisture budget (Huang et al 1996) over
an area A Where W(t) is soil water content
P(t) precipitation E(t)
evapotranspiration R(t) net
streamflow divergence G(t) net groundwater
loss Forcing Data -CPC Monthly Global
Precipitation Over Land -Monthly Global
Reanalysis/CDAS 2m Temperature Data Coverage
56 years (1948-present)
Local correlations of the land surface soil
moisture and the land surface air temperature in
next month show local impacts of the soil
moisture on the land surface air temperature.

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CPC soil moisture monitor prediction activities

• Drought flood monitoring
• Empirical forecast tools (Constructed Analog)
• GFS forecast climate prediction
• Recent CPC global soil moisture status

The annual cycle (right) anomalies (left) of
the observed (black) and simulated (green) top 1
meter column soil moisture in north, central
south Illinois from 1984 to 2004.
Non-local correlations of the land surface soil
moisture (southeast China or Bengal) and the
global land surface precipitation and air
temperature show the non-local impacts of the
land surface soil moisture.
Summary Future Work
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An accurate and homogeneous global lower boundary
condition is very important for weather and
climate predictions. The preliminary results
show that the CPC global (near real time) soil
moisture dataset can reasonably reproduce the
annual cycle interannual variability of
the observed soil moisture, when compared to
Illinois observations and GRACE satellite data.
The simulated land surface hydrologic cycle and
extreme events also look realistic. Future Work
Need more detailed data validation
analysis. Add a simple cold season physics in
near future. Model comparisons (e.g. With RR,
Noah LSM and VIC LSM etc. ). Demonstrate more
detailed non-local impacts of soil moisture
prediction experiments etc. Reference Fan, Y.,
H. van den Dool, 2004 Climate Prediction Center
global monthly soil moisture data set at 0.5
degree resolution for 1948 to present.
J.Geophys.Res. 109, D10102, doi10.1029/2003JD0043
45 H. van den Dool, J. Huang, and Y. Fan, 2003
Performance and analysis of the constructed
analogue method applied to U.S. soil
moisture over 1981-2001. J. Geophys. Res.,
108(D16), 8617, doi10.1029/2002JD003114 Huang,
J., H. van den Dool, and K. G. Georgakakos, 1996
Analysis of model-calculated soil moisture over
the US (1931- 1993) and applications to long
range temperature forecasts. J.Climate, 9,
1350-1362. Also see the poster by Huug van den
Dool et al for the latest GRACE data
comparisons. Acknowledgment This project was
supported by the GCIP Grant (GC00-095) and GAPP
Grant (GC04-039).
Seasonal variation of the land surface water
recharge or discharge. Monsoon is the dominant
feature.
EOFs PCs of the global lower boundary
(normalized SST and soil moisture) for March
1950-2003. Both the El-Nino Southern Oscillation
and long term trend modes are clearly seen in the
leading EOFs PCs of combined SST and soil
moisture..
Simulated extreme hydrological events