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LAND-ATMOSPHERE INTERACTION:
IS THERE ANY OBSERVATIONAL EVIDENCE?
Findell and Eltahir (1997) provide
evidence that soil moisture variations
in Illinois affect precipitation, though
the evidence is disputed by Salvucci
et al. (2002).
Evidence that the nature of the boundary
layer over land is influenced by variations
in soil moisture include the analysis of
Betts and Ball (1995):
dry soil
wet
wet soil
dry
dry
dry
wet
wet
Impacts on precipitation are much more difficult to identify.
Problem: The search for evidence of feedback in nature is limited by scant
soil moisture and evaporation data – we have no observational evidence of
feedback on precipitation at the large scale.
Question: Can we uncover evidence of feedback at the large scale in the
observational precipitation record?
Precipitation
Calculate: Lag-2 autocorrelation
between precipitation pentads
1
6
10
16
July
21
26
Observational data set:
“Unified Precipitation Database”, put together by Higgins et al.
Daily, ¼ o over the U.S. for 1948-1997
Based on 12000 stations/day (on average)
Assembled from: NCD Coop.; RFC daily;
NCDC hourly, accumulated to daily
Aggregation: Data aggregated to pentads (5 day totals) at 2o x 2.5o.
AGCM strategy for interpreting the observations:
1.
2.
3.
4.
Identify a feature of interest in the autocorrelation field (or other field).
See if the AGCM reproduces this behavior.
If so, determine what causes the behavior in the AGCM.
Infer that the same mechanisms apply in nature.
something of
a leap of faith…
The observations show a pattern of autocorrelation that is similar
in location and timing, though not in magnitude, to that produced
by the GCM.
Possible reasons:
1. Statistical fluke
2. The pattern is a reflection of something unrelated to land-atmosphere feedback, such
as monsoon dynamics, long-term precipitation trends, or SST variability.
3. The pattern does reflect land-atmosphere feedback.
Note: if #3 is correct, then an analysis of what controls feedback in the GCM could shed
further light on the observations.
What might be going on?
In the west: high evaporation sensitivity yields low soil moisture memory,
and low evaporation yields low impact on rainfall.
In the east: consider the
evaporation-versus-soil moisture
curve:
Where things are wet,
E
evaporation is not
sensitive to soil
moisture.
W
Can we explain
what controls ac(P)
in the GCM?
GCM
Pn
correlates
with
obs
Pn+2
means that
Pn
Pn+2
correlates
with
En+2
wn
wn+2
correlates
with
Breaks down in western US
Breaks down in western US
correlates
with
correlates
with
Breaks down in eastern US
Another study: Evidence of Feedback in Observational PDFs
Dataset: GPCP monthly precipitation, 1979-2000.
Approach: Rank precipitation for a given month into pentiles; determine
conditional PDFs of rainfall in the following month for each pentile.
Standardize and assume ergodicity to generate the PDFs.
years with highest June rainfall
June rainfall: 4th level
Does July rainfall for these
years tend to be higher than
normal?
June rainfall: 3rd level
June rainfall: 2nd level
years with lowest June rainfall
Does July rainfall for these
years tend to be lower than
normal?
The AGCM reproduces this observed behavior…
…but only when land-atmosphere feedback in the model is enabled:
Note that the
broadness of the
PDFs implies that
while feedback exists,
the prediction skill
associated with the
feedback may be
quite limited.
More studies...
Some AGCM studies examine the impact of “perfectly forecasted” soil moisture on
the simulation of observed extreme events. Examples:
Schubert et al. (see fig. 1 of Entekhabi et al.,
BAMS, 80, 2043-2058, 1999) demonstrated that
their AGCM could only capture the 1988 Midwest
drought and the 1993 Midwest flood if soil moistures
were maintained dry and wet, respectively.
Hong and Kalnay (Nature, 408, 842-844, 2000)
studied the impact of dry soil moisture
conditions on the maintenance of the 1998
Oklahoma-Texas drought.
Key test: Impact of land initialization on forecast skill
Other studies have examined the impact of “realistic” soil moisture initial conditions on the
evolution of subsequent model precipitation.
Studies include: Viterbo and Betts, JGR,
104, 19361-19366, 1999. Also:
Fennessy and Shukla, J. Climate,
12, 3167-3180, 1999.
Douville and Chauvin, Clim. Dyn.,
16, 719-736, 2000.
Detailed description of another recent study of this type (Koster and Suarez, J. Hydromet., 2003)
POOR MAN’S LDAS:
A study of the impacts of soil moisture initialization on seasonal forecasts
At every time step in a GCM simulation, the land surface model is forced with observed precipitation
rather than GCM-generated precipitation. The observed global daily precipitation data comes from
GPCP and covers the period 1997-2001 at a resolution of 1o X 1o (George Huffman, pers. Comm.)
The daily precipitation is applied evenly over the day.
ATMOSPHERIC
CALCULATIONS
Time step n
E,H
ATMOSPHERIC
CALCULATIONS
Time step n+1
Precip.
Rad. T,q,…
E,H
Precip.
Rad. T,q,…
Observed
Precip.
LAND
CALCULATIONS
Time step n
Observed
Precip.
LAND
CALCULATIONS
Time step n+1
Note: for the “soil moisture initialization”
runs, some scaling is required to ensure
an initial condition consistent with the
AGCM:
Essentially, a dry condition
for the GPCP forcing run…
…is converted to an equivalently
dry condition for the AGCM
forecast simulation.
Key finding from this
study: soil moisture
initialization has an impact
on forecasted precipitation
only when three conditions
are satisfied:
1. Strong year-to-year
variability in initial soil
moisture.
2. Strong sensitivity of
evaporation to soil
moisture (slope of
evaporative-fractionversus-soil-moisture
relationship).
3. Strong sensitivity of
precipitation to
evaporation (convective
fraction).
On average, there is
a hint of improvement
associated with land
moisture initialization
Illustration of point 6:
The ensemble mean is off,
but some of the ensemble
members do give a
reasonable forecast
A more “statistically complete” experiment was tried next....
Approach:
GLDAS project (NASA/GSFC)
using Berg et al. (2003) data
Observed
precipitation
Observed
radiation
Wind speed, humidity,
air temperature, etc.
from reanalysis
Initial conditions
for subseasonal
forecasts
Mosaic LSM
The resulting initial conditions:
(1) Reflect observed antecedent atmospheric forcing, and
(2) Are consistent with the land surface model used in the
AGCM.
1-Month Forecasts Performed
Atmosphere not “initialized”. Land initializatized on:
May 1
June 1
July 1
Aug. 1
Sept. 1
1979
1980
1981
1992
1993
75 separate 1-month forecasts, each of which can be
evaluated against observations.
(Note: each forecast is an average over 9 ensemble
members.)
We compare all results to a parallel set of forecasts that do not utilize
land initialization: the “AMIP” forecasts. The AMIP forecasts do
not rely on atmospheric initialization, either.
In essence, the AMIP forecasts derive skill only from the
specification of SST.
Before we evaluate the forecasts, we ask a critical question: what is the
maximum predictability possible in this forecasting system?
To answer this, we perform an idealized analysis:
STEP 1: For each of the 75 forecasted months, assume that the first ensemble
member represents “nature”.
STEP 2: For each of these months, assume that the remaining 8 ensemble
members represent the forecast.
STEP 3: Determine the degree to which the “forecast” agrees with the
assumed “nature”.
STEP 4: Repeat 8 times, each ensemble member in turn taken as “nature”.
Average the resulting skill diagnostics.
Regress “forecast”
against “observations” to
retrieve r2, our measure
of forecast skill.
The idealized analysis effectively determines the degree to which atmospheric chaos
foils the forecast, under the assumptions of “perfect” initialization, “perfect”
validation data, and “perfect” model physics. In other words, it provides an estimate
of “maximum possible predictability”.
Where we look for skill is also limited by quality of observations
Areas with adequate idealized predictability and adequate rain gauge density
Precipitation Forecast Areas
Temperature Forecast Areas
Breadth of areas
that can be tested
will increase
with future
improvements in
data collection
and analysis.
FORECAST EVALUATION: PRECIPITATION
With initialization
Differences
Without initialization
Idealized differences
FORECAST EVALUATION: TEMPERATURE
With initialization
Differences
Without initialization
Idealized differences
What happens when the atmosphere is initialized (via reanalysis) in addition to the
land variables? Supplemental 9-member ensemble forecasts, for June only (1979-1993):
1. Initialize atmosphere and land
2. Initialize atmosphere only
Warning: Statistics are based on only 15 data pairs!
June r2 values, averaged over area of focus
AMIP runs:
SSTs only
GLDAS runs:
SSTs + land
initialization
SSTs +
atmosphere
initialization
SSTs + land
initialization
+ atmosphere
initialization
June r2 values, averaged over area of focus
AMIP runs:
SSTs only
GLDAS runs:
SSTs + land
initialization
SSTs +
atmosphere
initialization
SSTs + land
initialization
+ atmosphere
initialization
Outlook
Presumably, skill associated with land initialization can only increase with:
-- improvements in model physics
-- improved data for initialization
satellite sensors (HYDROS, GPM, …)
ground networks
data assimilation
-- improved data for validation
In other words, we’ve demonstrated only a “minimum” skill associated with
land initialization.
Current
increase in skill
Idealized potential
increase in skill
We have a lot
of untapped
potential!
DATA ASSIMILATION: THE OPTIMAL MERGING OF
OBSERVATIONS AND MODEL RESULTS
strengths
Key motivation: observations and
model results have their own
strengths and weaknesses. By
combining them optimally, we get the
best of both worlds.
Combined
model/
observational
state at time t
Integrate
model
forward in
time
obs
model
Model
state at
time t+1
Observation
at time t+1
weaknesses
Measures of real-world
states. “What we’re after
in the first place. ”
Inadequate coverage,
significant measurement
error.
Based on trustworthy
physics. Complete space/
time coverage, including
unmeasurable states.
Results subject to myriad
inadequacies of model
parameterizations
Decide how
much you
believe model
result (estimate
model error)
Combined
model/
observational
state at time t+1
Decide how much
you believe observation (estimate
observational error)
The actual mathematics involved here can be very complicated...
Retrospective period
SMMR data
assimilation
Corrected ECMWF
reanalysis
forcing
(scaling)
Climatology /
interannual variability
of land surface states
LSM
Real time forecasts
scaling
AMSR data
assimilation
GLDAS
forcing
(scaling)
LSM
Initial conditions
for forecasts