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Lecture 9 Land and Climate: Modeling Studies. What is land-atmosphere feedback on precipitation?. …which affects the overlying atmosphere (the boundary layer structure, humidity, etc.). …causing soil moisture to increase. Precipitation wets the surface. …which causes evaporation to

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slide1

Lecture 9

Land and Climate: Modeling Studies

What is land-atmosphere feedback on precipitation?

…which affects the overlying atmosphere (the boundary layer structure, humidity, etc.)...

…causing soil

moisture to

increase...

Precipitation

wets the

surface...

…which causes

evaporation to

increase during

subsequent days

and weeks...

…thereby (maybe)

inducing additional precipitation

slide2

Perhaps such feedback contributes to predictability.

Short-term weather prediction with numerical models (e.g., those shown on the news every night) are limited by chaos in the atmosphere.

Initialize model

with that state;

integrate into

future

Establish

atmospheric

state

Short-term

(~several days)

weather

prediction

Decay reflects short

timescale of atmospheric

“memory”

Relevance

of initial

conditions

Atmosphere

Saturday’s forecast for Tuesday (March 23, 2004):

sunny, high of 46F (8C).

days

slide3

Relevance

of initial

conditions

Ocean

Land

months

For longer term prediction, we must rely on slower moving components of the Earth’s system, such as ocean heat content and soil moisture.

Associated

prediction of

weather, if

weather

responds to

these states

Establish

ocean state,

land moisture

state

Initialize model

with those states;

integrate into

future

Long-term

(~weeks to years)

prediction of ocean

and/or land states

For soil moisture to contribute to precipitation predictability, two things must happen:

1. A soil moisture anomaly must be “remembered” into the forecast period.

2. The atmosphere must respond in a predictable way to the remembered soil moisture anomalies

slide4

Part 1: Soil Moisture Memory

Observational soil moisture measurements give some indication of soil moisture memory.

Vinnikov and Yeserkepova, 1991

Soil moisture timescales of several months are possible. “The most important part of upper layer (up to 1 m) soil moisture variability in the middle latitudes of the northern hemisphere has … a temporal correlation scale equal to about 3 months.” (Vinnikov et al., JGR, 101, 7163-7174, 1996.)

Vinnikov and Yeserkepova, 1991

slide5

Delworth and Manabe (1988) analyzed soil moisture memory in the GFDL GCM and came up with a Markovian framework for characterizing it.

We will discuss Delworth and Manabe’s soil moisture memory analysis further during the round-table discussion.

D&M’s memory analysis was recently furthered at Goddard...

slide6

s

s

Koster and Suarez, 2001

slide9

Evaporation term: cRn/Cs

(and equivalently, the

runoff term: aPn/Cs)

slide11

This analysis allows us to examine soil moisture memory in terms of both large-scale forcing and inherent LSM behavior (e.g., through a and c terms, which describe the sensitivity of evaporation and runoff to soil moisture). The memory equation reduces to that of Delworth and Manabe under several simplifying assumptions.

slide12

Recent idealized experiment to analyze soil moisture memory (Sarith Mahanama, GSFC)

-- A perpetual July experiment was performed to investigate the effect of precipitation and net radiation on soil moisture memory. Two different LSMs (the Mosaic LSM and the NSIPP-Catchment LSM) were given identical water holding capacities, vegetation type, soil type etc. and were forced under a variety of artificially generated climates.

-- The imposed climates had:

    • average monthly precipitations ranging from 15 to 500mm
    • average monthly net radiation ranging from 20 to 400mm (Water equivalent)
  • -- Essentially, within the idealized framework, the intermodel differences of soil moisture memory result solely from intermodel differences in the sensitivity of evaporation and runoff to soil moisture variations.
  • -- A total of 400 different “climates” were imposed on each LSM. The simulation associated with each climate was a 200-month perpetual July simulation. Sub-monthly distributions of the variables followed those of the PILPS2c 1979-July forcing data for a chosen region.
slide13

Idealized Experiment to analyze soil moisture memory

Autocorrelation of soil moisture (r) in different climates:

slide14

Idealized Experiment to analyze soil moisture memory

Differences in autocorrelation of soil moisture (r) in different climates:

slide15

Superposition of ISLSCP-I July net radiation and precipitation on memory difference plot:

slide16

Supplemental analysis of globally simulated soil moisture memory with the two different models.

When the Mosaic and Catchment LSMs are given the same soil moisture holding capacities, the Catchment LSM indeed shows higher memory for intermediate dryness index.

When the Mosaic and Catchment LSMs are given their own, model-specific soil moisture holding capacities, the memory differences are seen to be largely a func-tion of the difference in capacity. (I.e., to some extent, a larger water holding capacity implies a larger memory.)

slide17

Part 2: Atmosphere’s Response to Soil Moisture Anomalies

Three ways of looking for evidence of atmospheric response:

2. Simple analytical models.

3. AGCM studies.

1. Examine observational data.

Very difficult. (See next lecture.)

Advantage: feedbacks can be quantified and easily understood. Disadvantage: ignores some nonlinearities and complexities of system.

Useful for several reasons: (a) full set of diagnostic out-puts, (b) inclusion of nonlinearities, and (c) ability to do sensitivity studies.

Examples:

Rodriguez-Iturbe et al., WRR, 27, 1899-1906,

1991.

Brubaker and Entekhabi, WRR, 32, 1343-1357,

1996.

Liu and Avissar, J. Clim, 12, 2154-2168, 1999.

slide18

AGCM evidence goes way back...

Shukla and Mintz (1982) provide one of the first AGCM studies demonstrating the impact of land moisture anomalies on precipitation:

Questions that can be addressed with an AGCM: How large is the impact of a land anomaly on the atmosphere? What are the relative roles of ocean variability, land variability, and chaotic atmospheric dynamics in determining precipitation over continents?

slide19

Studies examining the impact of “perfectly forecasted” soil moisture on the simulation of

non-extreme interannual variations.

Some examples: Delworth and Manabe, J. Climate, 1, 523-547, 1988.

Dirmeyer, J. Climate, 13, 2900-2922, 2000.

Round-table discussion

See GSWP lecture

Douville et al., J. Climate, 14, 2381-2403,

2001.

Koster et al., J. Hydromet., 1,26-46,

2000.

Dry conditions

Wet conditions

Simulations used:

Ensemble 1: Sixteen 45-year simulations

at 4oX5o with

Interactive land surface processes

Prescribed interannual-varying SST

Ensemble 2: Sixteen 45-year simulations

at 4oX5o with

Fixed land surface processes (but with

realistic interannual variations)

Prescribed interannual-varying SST

1987 conditions

1988 conditions

slide20

Description of this last study...

# of Total

Exp. simulations Length years Description

Evaporation efficiency (ratio of

evaporation to potential evaporation)

prescribed at every time step to

seasonally-varying climatological

means

A 4 200 yr 800

AL 4 200 yr 800

AO 16 45 yr 720

ALO 16 45 yr 720

Prescribed,

climatological

land; climato-

logical ocean

Interactive

land, climato-

logical ocean

Prescribed,

climatological

land, interan-

nually varying

ocean

SSTs set to seasonally-varying

climatological means (from obs)

SSTs set to interannually-varying

values (from obs)

Interactive

land, interan-

nually varying

ocean

LSM in model allowed to

run freely

Koster et al., J. Hydromet., 1, 26-46, 2000

slide21

Analysis of the simulation output shows that land and ocean contribute differently to continental precipitation variability.

Annual precipitation variances

Seasonal precipitation variances

(from a similar 1995 study)

slide22

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

s2ALO= s2AO[ Xo + ( 1 - Xo ) ]

s2AO

Fractional contribution

of ocean processes to

precipitation variance

Land-atmosphere

feedback factor

Fractional contribution of chaotic

atmospheric dynamics to

precipitation variance

slide24

More from Koster et al. (2001)

In an additional ensemble, every member of the ensemble is subject to the same time series of evaporation efficiency. Does the precipitation respond coherently to this signal?

Results for SST control over precipitation coherence:

A variable W is defined that describes the

coherence between the different precipitation time

series.

slide25

Koster et al. (2001) (cont.)

Boreal summer

Boreal winter

Results for SST and

soil moisture control

over precipitation

coherence

Differences: an

indication of the

impacts of soil

moisture control

alone

slide26

Koster et al. (2001) (cont.)

Why does land moisture have an effect where it does? For a large effect, two things are needed:

a large enough evaporation signal

a coherent evaporation signal – for a given soil moisture anomaly, the resulting evaporation

anomaly must be predictable.

Both conditions can be related to

relative humidity:

The dots show where the land’s signal is strong.

From the map, we see a strong signal in the

transition zones between wet and dry climates.

Evap.

coherence

slide27

Why does land-atmosphere

feedback occur where it

does?

One control: Budyko’s

dryness index

variance

amplification

factor

The results of this study could be highly model-dependent. A critical question about a critical issue: how does the atmosphere’s response to soil moisture anomalies vary with AGCM? We address this with...

slide28

time step n

time step n

Part 1: Establish a time series of surface conditions (Simulation W1)

time step n+1

Step forward the

coupled AGCM-LSM

Step forward the

coupled AGCM-LSM

time step n+1

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

Step forward the

coupled AGCM-LSM

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

Throw out updated

values of land surface

prognostic variables;

replace with values for

time step n+1 from

file W1_STATES

(Repeat without writing to obtain simulations W2 – W16)

… the GLACE Experiment

Coupled large scale

Ongoing experiment: GLACE, a follow-on to a pilot coupled model intercomparison experiment. (K02: Koster et al., Comparing the degree of land-atmosphere interaction in four atmospheric general circulation models, J. Hydromet., 3, 363-375, 2002.)

Part 2: Run a 16-member ensemble, with each member forced to maintain

the same time series of surface prognostic variables (Simulations R1 – R16)

Part 3: Same as Part 2, but only reset the deep soil moisture variables.

slide29

How does GLACE build on K02?

Participation from a wider range of models. The idea is to generate

a comprehensive “table” of coupling strengths, a table that can help in

the interpretation of the published results of a wide variety of models.

Separation of the effects of “fast” and “slow” reservoirs. The K02 results

largely reflect the specification of the “fast” reservoirs (e.g., surface

temperature). They thus may have little relevance to issues of seasonal

prediction.

Effect on air temperature. Ignored in the K02 study is the effect of the

specification of surface variables on the evolution of air temperature.

(This is a particularly interesting issue when only the “slow” soil moisture

reservoirs are specified.)

Correction of miscellaneous technical issues. Lessons learned from the

K02 study can be applied immediately to GLACE.

slide30

Participating Groups

Model

Contact

Status

1. BMRC with CHASM

McAvaney/Pitman

submitted

2. U. Tokyo w/ MATSIRO

Kanae/Oki

submitted

3. COLA with SSiB

Dirmeyer

submitted

4. CSIRO w/ 2 land schemes

Kowalczyk

submitted

5. NCAR

submitted

6. Env. Canada with CLASS

Verseghy

submitted

7. GFDL with LM2p5

Gordon

submitted

8. GSFC(GLA) with SSiB

Sud

submitted

9. Hadley Centre w/ MOSES2

Taylor

submitted

10. NCEP/EMC with NOAH

Lu/Mitchell

submitted

11. NSIPP with Mosaic

Koster

submitted

12. UCLA with SSiB

Xue

submitted

slide31

All simulations in ensemble

respond to the land surface

boundary condition in the

same way

W is high

Simulations in ensemble

have no coherent response

to the land surface

boundary condition

W is high

slide32

Impact of all land prognostic variables on precipitation

Ωp (R - W):

GFDL

COLA

UCLA

HadAM3

BMRC

NCEP

CCCma

NSIPP

CSIRO-CC3

CSIRO-CC4

GEOS

slide33

Ωp (S - W):

Impact of sub-surface soil moisture on precipitation

GFDL

COLA

UCLA

HadAM3

BMRC

NCEP

CCCma

NSIPP

CSIRO-CC3

CSIRO-CC4

GEOS

slide34

In principle, imposing land surface boundary states should decrease

the intra-ensemble variance of the atmospheric fields.

corresponding

pdf when land

boundary

is specified

Idealized pdf of precipitation at

a given point, across

ensemble members

s2P (R)

s2P (S)

We look at the variance ratios:

and

s2P (W)

s2P (W)

slide35

Variance(R) / Variance(W):

Impact of all land prognostic variables on precipitation

GFDL

COLA

UCLA

HadAM3

BMRC

NCEP

CCCma

NSIPP

CSIRO-CC3

CSIRO-CC4

GEOS

slide36

Impact of sub-surface soil moisture on precipitation

Variance(S) / Variance(W):

GFDL

COLA

UCLA

HadAM3

BMRC

NCEP

CCCma

NSIPP

CSIRO-CC3

CSIRO-CC4

GEOS

slide37

ΩT (S - W):

Impact of sub-surface soil moisture on temperature

GFDL

COLA

UCLA

HadAM3

BMRC

NCEP

CCCma

NSIPP

CSIRO-CC3

CSIRO-CC4

GEOS

slide41

How about AGCM studies that only initialize the soil moisture? (I.e., studies that don’t prescribe soil moisture throughout the simulation period?)

Such studies include Oglesby and Erickson,

J. Climate, 2, 1362-1380, 1989. Also:

Beljaars et al., Mon. Weather Rev.,

124, 362-383….

Wet

initial-

ization

Rind, Mon. Weather Rev., 110, 1487-1494.

June 1

initialized

dry

Dry

initial-

ization

Differ-

ences

slide42

Impact of Soil Moisture Predictability on Temperature Prediction

(darker shades of green denote

higher soil-moisture impact)

…and a study by Schlosser and Milly (J. Hydromet., 3, 483-501, 2002), in which

the divergence of states in a series of parallel simulations was studied in detail:

Predictability Timescale

Estimate (via memory)

for soil

moisture

Actual Predictability Timescale

(diagnostics of precipitation show a much weaker soil-moisture impact)

Some recent studies have examined the impact of soil moisture initialization on forecast skill (relative to real observations). These will be discussed in the next lecture.