Land-Atmosphere Feedback in the Sahel

Randal KosterGlobal Modeling and Assimilation OfficeNASA/GSFCGreenbelt, MDrandal.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

Observations(Higgins,

50-yr dataset)

July Rainfall:Mean

[mm/day]

July Rainfall:Variance[mm2/day2]

0.32

0.20

0.13

8.0

3.2

2.0

5.0

1.3

0.8

0.5

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

White dots: Locations of tree ring sites with Jul/Aug precipitation variances in top half of range

Shading: Mean annual precipitation (GPCP)

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

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

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).

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).

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).

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?

0.32

0.20

0.13

8.0

3.2

2.0

5.0

1.3

0.8

0.5

0. -0.50

0.50

0. 0.12

0.16

0.24

-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

0.32

0.20

0.13

8.0

3.2

2.0

5.0

1.3

0.8

0.5

0. -0.50

0.50

0. 0.12

0.16

0.24

-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!

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?

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 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

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

In one set of forecasts, utilize realistic land ICs

In other set, don’t utilize realistic land ICsCompare

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

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

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

Simulated precipitation variability can be described in terms of a simple linear system:

ALO =

AO [ Xo + ( 1 - Xo ) ]

ALO

AO

Total precipitation variance

Precipitation 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.

Contributions to Precipitation Variability

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

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)

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.

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.

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.

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…

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

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

Oleson5. NCAR

Kanae/Oki2. U. Tokyo w/ MATSIRO

Xue12. UCLA with SSiB

Koster11. NSIPP with Mosaic

Lu/Mitchell10. NCEP/EMC with NOAH

Taylor9. Hadley Centre w/ MOSES2

Sud8. GSFC(GLA) with SSiB

Gordon7. GFDL with LM2p5 Verseghy6. Env. Canada with CLASS

Kowalczyk4. CSIRO w/ 2 land schemes

Dirmeyer3. COLA with SSiB

McAvaney/Pitman1. BMRC with CHASM

ContactModel

Participating Groups

Country

USA

USA

UK

USA

Australia

USA

USA

USA

USA

Japan

Australia

Canada

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

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

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)

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…

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