Download - Land-Atmosphere Feedback in the Sahel
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Land-Atmosphere Feedback in the Sahel
Randal KosterGlobal Modeling and Assimilation OfficeNASA/GSFCGreenbelt, [email protected]
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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.
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Warm season precipitation variance is often high in transition zones between dry and wet areas.
Example: North America
Observations(Higgins,
50-yr dataset)
July Rainfall:Mean
[mm/day]
July Rainfall:Variance[mm2/day2]
0.320.200.13
8.0
3.22.0
5.0
1.30.80.5
0.
Koster et al., GRL, 40, 3004
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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
White dots: Locations of tree ring sites with Jul/Aug precipitation variances in top half of range
Shading: Mean annual precipitation (GPCP)
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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 land-atmosphere feedback.
Precipitation wets thesurface...
…causing soilmoisture toincrease...
…which causesevaporation to increase duringsubsequent daysand weeks...
…which affects the overlying atmosphere (the boundary layer structure, humidity, etc.)...
…thereby (maybe) inducing additional precipitation
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Feedback enhances 2P through the
enhancement of P autocorrelation (on timescales of days to weeks).
Pn Pn+2
correlateswith
means that
correlateswith
Pn Pn+2
wn
En+2
wn+2correlates
with
correlateswith
correlateswith
Observed 2P
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Pn Pn+2
correlateswith
means that
correlateswith
Pn Pn+2
wn
En+2
wn+2correlates
with
correlateswith
correlateswithBreaks down in
western US: low soil moisture memory
Breaks down in western US:low evaporation
Observed 2P Feedback enhances 2
P through the enhancement of P autocorrelation (on
timescales of days to weeks).
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Pn Pn+2
correlateswith
means that
correlateswith
Pn Pn+2
wn
En+2
wn+2correlates
with
correlateswith
correlateswith
Breaks down in eastern US: low sensitivity of evaporation to soil moisture
Observed 2P Feedback enhances 2
P through the enhancement of P autocorrelation (on
timescales of days to weeks).
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Pn Pn+2
correlateswith
means that
correlateswith
Pn Pn+2
wn
En+2
wn+2correlates
with
correlateswith
correlateswith
Observed 2P
Only in the center of the country (in the wet/dry transition zone) are all conditions ripe for feedback
Feedback enhances 2P through the
enhancement of P autocorrelation (on timescales of days to weeks).
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We therefore have reason to believe that land-atmosphere 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?
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0.320.200.13
8.0
3.22.0
5.0
1.30.80.5
0. -0.50
0.50
0. 0.120.160.24
-0.24
-0.16-0.12-0.08
0.08
AGCM
AGCM, nofeedback
Observations(Higgins,
50-yr dataset)
July Rainfall:Mean
[mm/day]
July Rainfall:Variance[mm2/day2]
Correlations (pentads, twice
removed)[dimensionless]
same plots as before
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0.320.200.13
8.0
3.22.0
5.0
1.30.80.5
0. -0.50
0.50
0. 0.120.160.24
-0.24
-0.16-0.12-0.08
0.08
AGCM
AGCM, nofeedback
Observations(Higgins,
50-yr dataset)
July Rainfall:Mean
[mm/day]
July Rainfall:Variance[mm2/day2]
Correlations (pentads, twice
removed)[dimensionless]
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.
bulls-eye in model is definitely induced by feedback!
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Central North America, of course, is just one of the Earth’s wet/dry transitions zones.
Another is the Sahel…
Annual Precipitation
Does nature allow land-atmosphere feedback to affect rainfall statistics in the Sahel?
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The comparison between model results and observations isn’t as clear-cut as it is in North America, but it is suggestive…
Precipitation Variances (mm2/day2)
AGCM
AGCM with no land feedback
Observations
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The comparison between model results and observations isn’t as clear-cut as it is in North America, but it is suggestive…
Precipitation Variances (mm2/day2)
AGCM
AGCM with no land feedback
Observations
The dots show where precipitation itself is maximized
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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 forecastIn one set of forecasts, utilize realistic land ICs
In other set, don’t utilize realistic land ICsCompare
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Forecast skill resulting from realistic land surface initialization appears negligible for precipitation…
Temperature
Precipitation
Temperature
Precipitation
Temperature
Precipitation
Differences: Added forecast skill from realistic land ICs
Skill from knowing SST distribution and
realistic land ICsSkill from knowing SST distribution
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Precipitation
Temperature
Added forecast skill from land initialization
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
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So, for the feedback question, observations are limited. Consider now a pure model study...
# of TotalExp. simulations Length years Description
A 4 200 yr 800
AL 4 200 yr 800
AO 16 45 yr 720
ALO 16 45 yr 720
Prescribed, climatologicalland; climato-logical ocean
Interactive land, climato-logical ocean
Prescribed, climatologicalland, interan-nually varyingocean
Interactive land, interan-nually varying ocean
SSTs set to seasonally-varyingclimatological means (from obs)
SSTs set to interannually-varyingvalues (from obs)
LSM in model allowed torun freely
Evaporation efficiency (ratio of evaporation to potential evaporation) prescribed at every time step to seasonally-varying climatologicalmeans
Koster et al., J. Hydromet., 1, 26-46, 2000
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Simulated precipitation variability can be described in terms of a simple linear system:
ALO =
AO [ Xo + ( 1 - Xo ) ]
ALO
AO
Total precipitation variancePrecipitation variance in the absence of land feedback
Fractional contribution of ocean processes to precipitation variance
Fractional contribution of chaoticatmospheric dynamics to precipitation variance
Land-atmospherefeedback factor
The above tautology isolates the relative contributions of SSTs, soil moisture, and chaotic atmospheric dynamics to precipitation variability.
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Contributions to Precipitation Variability
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Idealized “predictability” (for 1-month forecasts, MJJAS) deduced from aforementioned forecast experiment. (“Ability of model to predict itself.”)
Temperature Temperature
Precipitation
Temperature
Precipitation
Temperature
Precipitation
Differences: Added predictability from realistic land ICs
Predictability from SST distribution and
realistic land ICsPredictability from SST distribution
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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:
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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.
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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.
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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 the West African monsoon.
We think we understand the impact of land-atmosphere feedback on the statistics of precipitation in North America. Through feedback, precipitation memory and variance are increased in the transition zones between wet and dry areas. The observations appear to support this.
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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 the West African monsoon.
Observations are too sparse in the Sahel (relative to North America) for an equally clear indication that land atmosphere feedback operates there. Nevertheless, the available observations are not inconsistent with feedback.
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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 land-atmosphere feedback.
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WAMMEWest African Monsoon
Modeling and Evaluation
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…
See website: http://wamme.geog.ucla.edu/A Spring AGU (Acapulco) session addresses the experiment…
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W Simulations: Establish a time series of surface conditions
Step forward thecoupled AGCM-LSM
Write the valuesof the land surface prognostic variablesinto file W1_STATES
Step forward thecoupled AGCM-LSM
Write the valuesof the land surface prognostic variablesinto file W1_STATES
time step n time step n+1
(Repeat without writing to obtain simulations W2 – W16)
Experiment Design
All simulations are run from June through August
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Experiment Design (cont.)
R(S) Simulations: Run a 16-member ensemble, with each member forced tomaintain the same time series of surface (deeper) prognostic variables
Step forward thecoupled AGCM-LSM
Throw out updated values of land surfaceprognostic variables; replace with values for
time step n fromfile W1_STATES
Step forward thecoupled AGCM-LSM
time step n time step n+1
Throw out updated values of land surfaceprognostic variables; replace with values for
time step n+1 fromfile W1_STATES
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Oleson5. NCAR
Kanae/Oki2. U. Tokyo w/ MATSIRO
Xue12. UCLA with SSiBKoster11. NSIPP with MosaicLu/Mitchell10. NCEP/EMC with NOAH Taylor9. Hadley Centre w/ MOSES2Sud8. GSFC(GLA) with SSiBGordon7. GFDL with LM2p5 Verseghy6. Env. Canada with CLASS
Kowalczyk4. CSIRO w/ 2 land schemesDirmeyer3. COLA with SSiB
McAvaney/Pitman1. BMRC with CHASMContactModel
Participating GroupsCountry
USA
USA
UK
USA
Australia
USA
USA
USA
USA
Japan
Australia
Canada
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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
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Region considered
What controls the timing of the monsoon? Quantify importance of:
Another pure model study (no observations): monsoon rainfall
1. Average solar cycle.2. Interannual SST variations3. Interannual soil moisture variations
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All simulations in ensemblerespond similarly to boundary forcing is high
Simulations in ensemblehave no coherent responseto boundary forcing is low
Precipitation time seriesproduced by different ensemble members under the same forcing
Illustration of diagnostic(not for African monsoon region)
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solar,SSTs
solar, SSTs, soil moisture
NSIPP model
solar,
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…
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solar,SSTs
solar, SSTs, soil moisture
NSIPP model
solar,
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…
In this model, soil moisture variations have a major impact on monsoon evolution