Land-Atmosphere Feedback in the Sahel
Randal Koster
Global Modeling and Assimilation Office
NASA/GSFC
Greenbelt, MD
randal.d.koster@nasa.gov
Organization of Talk
1. Overview of the processes that control land-atmosphere
feedback. (Case study: North America)
2. Application of these ideas to the Sahel: do the
observations support the existence of feedback there?
3. Model study of the controls on Sahelian rainfall
variability.
Warm season precipitation variance is often high
in transition zones between dry and wet areas.
Example: North America
July Rainfall:
Mean
[mm/day]
July Rainfall:
Variance
[mm2/day2]
Observations
(Higgins,
50-yr dataset)
8.0
5.0
3.2
2.0
1.3
0.8
0.5
0.32
0.20
0.13
0.
Koster et al., GRL, 40, 3004
More evidence: tree ring data!
(360 years of proxy
precipitation data put
together by H. Fritts,
U. Arizona)
Jul/Aug precipitation
variances at each
tree ring site
Shading: Mean annual precipitation (GPCP)
White dots: Locations
of tree ring sites with
Jul/Aug precipitation
variances in top half of
range
Q: Do we have any reason to suspect that precipitation
variances should be amplified in transition zones?
A: Yes. Transition zones are more amenable to landatmosphere feedback.
Precipitation
wets the
surface...
…causing soil
moisture to
increase...
…which causes
evaporation to
increase during
subsequent days
and weeks...
…which affects the overlying
atmosphere (the boundary layer
structure, humidity, etc.)...
…thereby (maybe)
inducing additional
precipitation
Observed s2P
s2
Feedback enhances P through the
enhancement of P autocorrelation (on
timescales of days to weeks).
Pn
correlates
with
Pn+2
means that
Pn
Pn+2
correlates
with
En+2
wn
wn+2
correlates
with
correlates
with
correlates
with
Observed s2P
s2
Feedback enhances P through the
enhancement of P autocorrelation (on
timescales of days to weeks).
Pn
correlates
with
Pn+2
means that
Pn
correlates
with
Pn+2
Breaks down in
western US: low
soil moisture
memory
wn
En+2
wn+2
correlates
with
Breaks down in western US:
correlates
low evaporation
with
correlates
with
Observed s2P
s2
Feedback enhances P through the
enhancement of P autocorrelation (on
timescales of days to weeks).
Pn
correlates
with
Pn+2
means that
Pn
Pn+2
correlates
with
En+2
wn
wn+2
correlates
with
correlates
with
correlates
with
Breaks down in eastern
US: low sensitivity of
evaporation to soil
moisture
Observed s2P
s2
Feedback enhances P through the
enhancement of P autocorrelation (on
timescales of days to weeks).
Pn
correlates
with
Pn+2
means that
Pn
Only in the center of the
country (in the wet/dry
transition zone) are all
conditions ripe for feedback
Pn+2
correlates
with
En+2
wn
wn+2
correlates
with
correlates
with
correlates
with
We therefore have reason to believe that landatmosphere feedback can help explain the patterns of
observed precipitation variances.
Note: up to this slide, we haven’t looked at any model
results!
What can AGCMs tell us?
July Rainfall:
Mean
[mm/day]
July Rainfall:
Variance
[mm2/day2]
Correlations
(pentads, twice
removed)
[dimensionless]
AGCM
AGCM, no
feedback
Observations
(Higgins,
50-yr dataset)
0.50
0.24
0.16
0.12
0.08
0.
-0.08
-0.12
-0.16
-0.50
-0.24
8.0
5.0
3.2
2.0
1.3
0.8
0.5
0.32
0.20
0.13
0.
same plots
as before
July Rainfall:
Mean
[mm/day]
July Rainfall:
Variance
[mm2/day2]
Correlations
(pentads, twice
removed)
[dimensionless]
AGCM
bulls-eye in
model is
definitely
induced by
feedback!
AGCM, no
feedback
Observations
(Higgins,
50-yr dataset)
0.50
0.24
0.16
0.12
0.08
0.
-0.08
-0.12
-0.16
-0.50
-0.24
8.0
5.0
3.2
2.0
1.3
0.8
0.5
0.32
0.20
0.13
0.
The observations show statistics that are similar in location and timing, though
not in magnitude, to those produced by the GCM. This is either a coincidence or
evidence of feedback in nature.
Central North America, of course,
is just one of the Earth’s wet/dry
transitions zones.
Annual Precipitation
Another is the Sahel…
Does nature allow land-atmosphere
feedback to affect rainfall statistics in
the Sahel?
Precipitation Variances (mm2/day2)
AGCM
The comparison
between model results
and observations isn’t
as clear-cut as it is in
North America, but it is
suggestive…
AGCM with no land feedback
Observations
Precipitation Variances (mm2/day2)
AGCM
The comparison
between model results
and observations isn’t
as clear-cut as it is in
North America, but it is
suggestive…
AGCM with no land feedback
Observations
The dots show where
precipitation itself is maximized
Another observational study
If land-atmosphere feedback operates in the Sahel, then realistic
land initialization there should lead to improved monthly
forecasts.
Test with comprehensive forecast study:
75 start dates (first days of each month:
May to September)
9 ensemble members per forecast
Compare
In one set of forecasts, utilize realistic land ICs
In other set, don’t utilize realistic land ICs
Forecast skill resulting from realistic land surface initialization appears
negligible for precipitation…
Skill from knowing
SST distribution and
realistic land ICs
Skill from knowing
SST distribution
Differences: Added
forecast skill from
realistic land ICs
Precipitation
Precipitation
Precipitation
Temperature
Temperature
Temperature
HOWEVER, locations for
which the rain gauge density
is adequate enough to
properly initialize the model
are arguably very limited.
Regions w/adequate
raingauge density
and model
predictability
Added forecast skill from
land initialization
Precipitation
Temperature
So, for the feedback question, observations are limited.
Consider now a pure model study...
# of
Exp. simulations
Length
Total
years
A
4
200 yr
800
Prescribed,
climatological
land; climatological ocean
AL
4
200 yr
800
Interactive
land, climatological ocean
AO
16
45 yr
720
Prescribed,
climatological
land, interannually varying
ocean
ALO
16
45 yr
720
Description
Interactive
land, interannually varying
ocean
Evaporation efficiency (ratio of
evaporation to potential evaporation)
prescribed at every time step to
seasonally-varying climatological
means
SSTs set to seasonally-varying
climatological means (from obs)
SSTs set to interannually-varying
values (from obs)
LSM in model allowed to
run freely
Koster et al., J. Hydromet., 1, 26-46, 2000
Simulated precipitation variability can be described in terms of a simple linear
system:
Total precipitation variance
Precipitation variance in the absence of land feedback
s2ALO = s2AO [ Xo + ( 1 - Xo ) ]
Fractional contribution
of ocean processes to
precipitation variance
Fractional contribution of chaotic
atmospheric dynamics to
precipitation variance
s2ALO
s2AO
Land-atmosphere
feedback factor
The above tautology isolates the relative
contributions of SSTs, soil moisture, and
chaotic atmospheric dynamics to
precipitation variability.
Contributions to Precipitation Variability
Idealized “predictability” (for 1-month forecasts, MJJAS) deduced from
aforementioned forecast experiment. (“Ability of model to predict itself.”)
Predictability from
SST distribution and
realistic land ICs
Predictability from
SST distribution
Differences: Added
predictability from
realistic land ICs
Precipitation
Precipitation
Precipitation
Temperature
Temperature
Temperature
Temperature
More AGCM results: The GLACE multi-model experiment.
In GLACE, land-atmosphere feedback was quantified independently in 12
AGCMs. While the models differ in their feedback strengths, certain features
of the coupling patterns are common amongst them. These features are
brought out by averaging over all of the model results:
More AGCM results: The GLACE multi-model experiment.
In GLACE, land-atmosphere feedback was quantified independently in 12
AGCMs. While the models differ in their feedback strengths, certain features
of the coupling patterns are common amongst them. These features are
brought out by averaging over all of the model results:
The AGCMs tend to agree:
land-atmosphere feedback
operates in the Sahel.
To summarize:
Organization of Talk
1. Overview of the processes that control land-atmosphere
feedback. (Case study: North America)
2. Application of these ideas to the Sahel: do the
observations support the existence of feedback there?
3. Model study of the controls on Sahelian rainfall
variability.
To summarize:
Organization of Talk
1. Overview of the processes that control land-atmosphere
feedback. (Case study: North America)
We think we understand the impact
2. Application of these ideas
to the Sahel: do
the on the
of land-atmosphere
feedback
observations support thestatistics
existence
of feedback
there?
of precipitation
in North
America. Through feedback,
precipitation
and variance
3. Model study of the controls
on the memory
West African
are increased in the transition zones
monsoon.
between wet and dry areas. The
observations appear to support this.
To summarize:
Organization of Talk
1. Overview of the processes that control land-atmosphere
feedback. (Case study: North America)
2. Application of these ideas to the Sahel: do the
observations support the existence of feedback there?
Observations are too sparse in the Sahel
3. Model study of the controls
ontothe
West
African
(relative
North
America)
for an equally
monsoon.
clear indication that land atmosphere
feedback operates there. Nevertheless, the
available observations are not inconsistent
with feedback.
To summarize:
Organization of Talk
1. Overview of the processes that control land-atmosphere
feedback. (Case study: North America)
2. Application of these ideas to the Sahel: do the
observations support the existence of feedback there?
3. Model study of the controls on Sahelian rainfall
variability.
The NSIPP model (and indeed most of the
models participating in GLACE) show the
Sahel to be a region of strong landatmosphere feedback.
The above modeling results may, of course, be model dependent. A new,
upcoming experiment may provide a clearer look at the controls on monsoon
dynamics…
WAMME
West African Monsoon
Modeling and
Evaluation
See website: http://wamme.geog.ucla.edu/
A Spring AGU (Acapulco) session addresses the experiment…
Experiment Design
W Simulations: Establish a time series of surface conditions
time step n
Step forward the
coupled AGCM-LSM
time step n+1
Step forward the
coupled AGCM-LSM
Write the values
of the land surface
prognostic variables
into file W1_STATES
Write the values
of the land surface
prognostic variables
into file W1_STATES
(Repeat without writing to obtain simulations W2 – W16)
All simulations are run from June through August
Experiment Design (cont.)
R(S) Simulations: Run a 16-member ensemble, with each member forced to
maintain the same time series of surface (deeper) prognostic variables
time step n
Step forward the
coupled AGCM-LSM
Throw out updated
values of land surface
prognostic variables;
replace with values for
time step n from
file W1_STATES
time step n+1
Step forward the
coupled AGCM-LSM
Throw out updated
values of land surface
prognostic variables;
replace with values for
time step n+1 from
file W1_STATES
Participating Groups
Model
1. BMRC with CHASM
2. U. Tokyo w/ MATSIRO
Contact
McAvaney/Pitman
Kanae/Oki
Country
3. COLA with SSiB
4. CSIRO w/ 2 land schemes
5. NCAR
Dirmeyer
Kowalczyk
Oleson
USA
Australia
USA
6. Env. Canada with CLASS
7. GFDL with LM2p5
8. GSFC(GLA) with SSiB
9. Hadley Centre w/ MOSES2
10. NCEP/EMC with NOAH
11. NSIPP with Mosaic
12. UCLA with SSiB
Verseghy
Gordon
Sud
Taylor
Lu/Mitchell
Koster
Xue
Canada
Australia
Japan
USA
USA
UK
USA
USA
USA
W: GFDL
Scale goes from 0 to 1
S: GFDL
Scale goes from 0 to 1
Differences: GFDL
Scale goes from -0.5 to 0.5
Another pure model study (no observations): monsoon rainfall
What controls the timing of the monsoon? Quantify importance of:
1. Average solar cycle.
2. Interannual SST variations
3. Interannual soil moisture variations
Region considered
Illustration of W diagnostic
(not for African monsoon region)
Precipitation time series
produced by different
ensemble members under
the same forcing
All simulations in ensemble
respond similarly to
boundary forcing
W is high
Simulations in ensemble
have no coherent response
to boundary forcing
W is low
The contributions of the different boundary forcings to the
agreement (between ensemble members) of monsoon structure is
established by analyzing the outputs of various experiments…
NSIPP model
W
solar,
solar,
SSTs
solar, SSTs,
soil moisture
solar,
SSTs
(Middle two bars differ because they were
derived from different experiments, with
different assumptions.)
The contributions of the different boundary forcings to the
agreement (between ensemble members) of monsoon structure is
established by analyzing the outputs of various experiments…
NSIPP model
W
In this model, soil
moisture variations
have a major impact
on monsoon evolution
solar,
solar,
SSTs
solar, SSTs,
soil moisture
solar,
SSTs
(Middle two bars differ because they were
derived from different experiments, with
different assumptions.)