What is land-atmosphere feedback on precipitation?

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

Lecture 9

Land and Climate: Modeling Studies

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.

Establishatmospheric

state

Initialize modelwith that state;integrate into

future

Short-term(~several days)

weatherprediction

days

Relevanceof initialconditions

Decay reflects shorttimescale of atmospheric “memory”

Atmosphere

Saturday’s forecast for Tuesday (March 23, 2004): sunny, high of 46F (8C).

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

Establishocean state,

land moisturestate

Initialize modelwith those states;

integrate intofuture

Long-term(~weeks to years)

prediction of oceanand/or land states

Associatedprediction ofweather, if

weatherresponds tothese states

months

Relevanceof initialconditions

Ocean

Land

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

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

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

Vinnikov and Yeserkepova, 1991

Part 1: Soil Moisture Memory

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...

Koster and Suarez, 2001

The autocorrelation equation effectively relates soil moisture memory to four separate controls:

1. seasonality in the statistics of the atmospheric forcings,

2. the variation of evaporation with soil moisture,

3. the variation of runoff with soil moisture,

4. correlation between the atmospheric forcings and antecedent soil moisture.

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2

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11 nn

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Memory equation:

Seasonality term: wn/wn+1

Evaporation term: cRn/Cs

(and equivalently, therunoff term: aPn/Cs)

(the covariance term)

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.

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.

Idealized Experiment to analyze soil moisture memory

Autocorrelation of soil moisture () in different climates:

Idealized Experiment to analyze soil moisture memory

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

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

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.)

Part 2: Atmosphere’s Response to Soil Moisture Anomalies

Three ways of looking for evidence of atmospheric response:

1. Examine observational data.

Very difficult. (See next lecture.)

2. Simple analytical models.

3. AGCM studies.

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

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

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.

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?

Studies examining the impact of “perfectly forecasted” soil moisture on the simulation ofnon-extreme interannual variations.

Some examples: Delworth and Manabe, J. Climate, 1, 523-547, 1988. Dirmeyer, J. Climate, 13, 2900-2922, 2000. See GSWP lecture

Douville et al., J. Climate, 14, 2381-2403,2001.

Dry conditions

1988 conditions1987 conditions

Wet conditions

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

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

Round-table discussion

Description of this last 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

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)

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

Contributions to Precipitation Variability

A variable is defined that describes the coherence between the different precipitation timeseries.

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?

More from Koster et al. (2001)

Results for SST control over precipitation coherence:

Koster et al. (2001) (cont.)

Boreal summer Boreal winter

Results for SST andsoil moisture control over precipitationcoherence

Differences: an indication of theimpacts of soilmoisture controlalone

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.

Koster et al. (2001) (cont.)

Evap.coherence

Why does land-atmospherefeedback occur where itdoes?

One control: Budyko’sdryness index

varianceamplificationfactor

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...

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

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)

Part 2: Run a 16-member ensemble, with each member forced to maintainthe same time series of surface prognostic variables (Simulations R1 – R16)

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

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.)

… the GLACE Experiment

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

How does GLACE build on K02?

Participation from a wider range of models. The idea is to generatea comprehensive “table” of coupling strengths, a table that can help inthe interpretation of the published results of a wide variety of models.

Separation of the effects of “fast” and “slow” reservoirs. The K02 resultslargely 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 thespecification of surface variables on the evolution of air temperature. (This is a particularly interesting issue when only the “slow” soil moisturereservoirs are specified.)

Correction of miscellaneous technical issues. Lessons learned from theK02 study can be applied immediately to GLACE.

5. 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

Status

submitted

submitted

submitted

submitted

submitted

submitted

submitted

submitted

submitted

submitted

submitted

submitted

All simulations in ensemblerespond to the land surface boundary condition in thesame way is high

Simulations in ensemblehave no coherent responseto the land surface boundary condition is high

Ωp (R - W):

GFDL

GEOS

CSIRO-CC3NSIPPCCCma

NCEPBMRCHadAM3

UCLACOLA

CSIRO-CC4

Impact of all land prognostic variables

on precipitation

Ωp (S - W):

GFDL

GEOS

CSIRO-CC3NSIPPCCCma

NCEPBMRCHadAM3

UCLACOLA

CSIRO-CC4

Impact of sub-surface soil moisture on precipitation

In principle, imposing land surface boundary states should decrease the intra-ensemble variance of the atmospheric fields.

Idealized pdf of precipitation ata given point, acrossensemble members

correspondingpdf when land boundaryis specified

We look at the variance ratios: 2

P (S)

2P (W)

2P (R)

2P (W) and

Variance(R) / Variance(W): Impact of all land prognostic variables on

precipitation

GFDL

GEOS

CSIRO-CC3NSIPPCCCma

NCEPBMRCHadAM3

UCLACOLA

CSIRO-CC4

Variance(S) / Variance(W):

GFDL

GEOS

CSIRO-CC3NSIPPCCCma

NCEPBMRCHadAM3

UCLACOLA

CSIRO-CC4

Impact of sub-surface soil moisture on

precipitation

ΩT (S - W): Impact of sub-surface soil moisture on temperature

GFDL

GEOS

CSIRO-CC3NSIPPCCCma

NCEPBMRCHadAM3

UCLACOLA

CSIRO-CC4

Do models show any agreement regarding where land-atmosphere interaction is important?

Experiment website: http://glace.gsfc.nasa.gov

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

June 1initializeddry

Beljaars et al., Mon. Weather Rev., 124, 362-383….

Wet initial-ization

Dry initial-ization

Differ-ences

Such studies include Oglesby and Erickson,J. Climate, 2, 1362-1380, 1989. Also:

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

Impact of Soil Moisture Predictability on Temperature Prediction

(darker shades of green denotehigher soil-moisture impact)

Predictability TimescaleEstimate (via memory)

Actual Predictability Timescale

(diagnostics of precipitation show a much weaker 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:

for soil moisture

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.