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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.
2__
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1 ),cov(
2
2
),cov(
11 nn
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nn w
nn
s
n
s
n
s
n
s
n
w
w
ww
nn Fw
CPa
CRc
CPa
CRc
ww
2__
__
1 ),cov(
2
2
),cov(
11 nn
n
nn w
nn
s
n
s
n
s
n
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CPa
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CRc
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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.