precipitation wets the surface

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 10

Land-Atmosphere Feedback: Observational Studies

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?)

First, some slides from last week…

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.

Evidence that the nature of the boundarylayer over land is influenced by variationsin soil moisture include the analysis of Betts and Ball (1995):

dry

drydry

wet

wet

wet

wet soil

dry soil

Findell and Eltahir (1997) provideevidence that soil moisture variationsin Illinois affect precipitation, though the evidence is disputed by Salvucciet al. (2002).

LAND-ATMOSPHERE INTERACTION:IS THERE ANY OBSERVATIONAL EVIDENCE?

Calculate: Lag-2 autocorrelationbetween precipitation pentads

July

1 166 10 21 26

Pre

cipi

tatio

nQuestion: Can we uncover evidence of feedback at the large scale in the

observational precipitation record?

Impacts on precipitation are much more difficult to identify.Problem: The search for evidence of feedback in nature is limited by scant soil moisture and evaporation data – we have no observational evidence of feedback on precipitation at the large scale.

Observational data set:

Aggregation: Data aggregated to pentads (5 day totals) at 2o x 2.5o.

“Unified Precipitation Database”, put together by Higgins et al.Daily, ¼ o over the U.S. for 1948-1997Based on 12000 stations/day (on average)Assembled from: NCD Coop.; RFC daily; NCDC hourly, accumulated to daily

AGCM strategy for interpreting the observations:

1. Identify a feature of interest in the autocorrelation field (or other field).2. See if the AGCM reproduces this behavior.3. If so, determine what causes the behavior in the AGCM.4. Infer that the same mechanisms apply in nature.

something of a leap of faith…

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

Obser-vations

July Rainfall:Mean

[mm/day]

July Rainfall:Variance[mm2/day2]

July Rainfall:Variance

(normalized)[dimensionless]

Correlations (pentads, twice

removed)[dimensionless]

The observations show a pattern of autocorrelation that is similar in location and timing, though not in magnitude, to that produced by the GCM.

Possible reasons:1. Statistical fluke2. The pattern is a reflection of something unrelated to land-atmosphere feedback, such as monsoon dynamics, long-term precipitation trends, or SST variability.3. The pattern does reflect land-atmosphere feedback.Note: if #3 is correct, then an analysis of what controls feedback in the GCM could shed further light on the observations.

What might be going on? In the west: high evaporation sensitivity yields low soil moisture memory, and low evaporation yields low impact on rainfall.In the east: consider the evaporation-versus-soil moisture curve:

W

E Where things are wet, evaporation is not sensitive to soil moisture.

Can we explainwhat controls ac(P)

in the GCM?

Pn Pn+2

correlateswith

means that

correlateswith

Pn Pn+2

wn

En+2

wn+2correlateswith

correlateswith

correlateswith

Breaks down in western US

Breaks down in eastern US

Breaks down in western US

GCM obs

Another study: Evidence of Feedback in Observational PDFs

Dataset: GPCP monthly precipitation, 1979-2000.

Approach: Rank precipitation for a given month into pentiles; determine conditional PDFs of rainfall in the following month for each pentile. Standardize and assume ergodicity to generate the PDFs.

years with lowest June rainfall

years with highest June rainfall

June rainfall: 4th level

June rainfall: 3rd level

June rainfall: 2nd level

Does July rainfall for these years tend to be higher than normal?

Does July rainfall for these years tend to be lower than normal?

The AGCM reproduces this observed behavior…

…but only when land-atmophere feedback in the model is enabled:

Note that the broadness of the PDFs implies that while feedback exists, the prediction skill associated with the feedback may be quite limited.

1. What lies behind the distribution of soil moisture variance?

More Evidence: Historical Temperature Distributions

mean soil moisture (degree of saturation)

Low rain case: lower limit, soil wants to stay really dry low variance.

High rain case: upper limit, soil wants to stay really wet low variance

Intermediate case: no limits, no “absorbing state” high variance.

Soil moisture variance versus mean soil moisture, as simulated by a GCM

mean soil moisture (degree of saturation)

0 1

0 1

Evaporation as a function of soil moisture: simplified picture

E/Rnet

mean soil moisture (degree of saturation)0 1

In this regime, evaporation is no longer sensitive to soil moisture variations

In this regime, evaporation increases with soil moisture.

Evaporation as a function of soil moisture: simplified picture

Impact on Evaporation Variance

E/Rnet

mean soil moisture (degree of saturation)0 1

In this regime, soil moisture variance is translated to a zero evaporation variance

In this regime, soil moisture variance is translated to a nonzero evaporation variance

2: W

Soil Moisture

Evaporation2: E

Mean soil moisture

Variance as a function of mean soil moisture: midlatitude land (AGCM results)

E/Rnet

mean soil moisture (degree of saturation)0 1

In this regime, soil moisture variance is translated to a zero evaporation variance

In this regime, soil moisture variance is translated to a nonzero evaporation variance

2: W

Soil Moisture

Temperature

Evaporation2: E

2: T

Mean soil moisture

Variance as a function of mean soil moisture: midlatitude land (AGCM results)

The surface temperature distribution is strongly correlated with the evaporation distribution. Why? Because more evaporation means more latent cooling of the land surface.

2. What lies behind the distribution of soil moisture skew?

Soil moisture skew versus mean soil moisture, as simulated by a GCM

mean soil moisture (degree of saturation)0 1

mean soil moisture (degree of saturation)

Low rain case: lower limit, positive skew.

High rain case: upper limit, negative skew

Intermediate case: no limits, zero skew.

0 1

Positive skews are emphasized because precipitation itself is positively skewed.

Evaporation as a function of soil moisture: simplified picture

E/Rnet

mean soil moisture (degree of saturation)0 1

Impact on Evaporation Skew

Zero Eskew promoted

Negative Eskew

promoted

Skew: W

Skew: E

Soil Moisture

Evaporation

Mean soil moisture

Skew as a function of mean soil moisture: midlatitude land (AGCM results)

E/Rnet

mean soil moisture (degree of saturation)0 1

Impact on Evaporation Skew

Zero Eskew promoted

Negative Eskew

promoted

Skew: W

Skew: E

Negative of Skew: T

Soil Moisture

Temperature

Evaporation

Mean soil moisture

Skew as a function of mean soil moisture: midlatitude land (AGCM results)

Again, the surface temperature distribution follows the evaporation distribution. (A strong negative correlation.)

dryer wetter

large continental region

maximum of temperature

variance

positive temperature skew

negative temperature skew

maximum of soil moisture variance

Idealized schematic of soil moisture/evaporation impacts on temperature moments

This behavior is seen clearly in the AGCM. Is it seen in the observations?

dryer wetter

large continental region

maximum of temperature

variancepositive

temperature skew

negative temperature

skew

maximum of soil moisture

variance

The U.S. is one such place to look for these features:1) Relatively clean west-to-east moisture gradient2) GHCN temperature data spanning close to a century3) Soil moisture proxies: derived from GSWP2 modeling study, but based

on observed precipitation, radiation, etc.

soil moisture distribution(degree of saturation)

Temperature Variance (GHCN observations)

Dots: estimated high soil moisture variance, from independent GSWP2 analysis

dryer wetter

large continental region

maximum of temperature

variance

positive temperature skew

negative temperature skew

maximum of soil moisture variance

Idealized schematic of soil moisture/evaporation impacts on temperature moments

Temperature Skew (observations)

dots: high temperature variance (observations)

Mean soil moisture

Binned Results

Mean soil moisture

MODEL RESULTS OBSERVATIONS

Summary of Temperature Analysis

Soil moisture boundaries and the shape of the evaporation function have a first-order effect on the distributions of the second and third moments of evaporation – and thus temperature – in the AGCM.

In the AGCM, these effects place the maximum of temperature variance on the dry side of the maximum of soil moisture variance. They place the maximum of the temperature skew on the wet side of this variance maximum, and they place negative temperature skew on the dry side of this variance maximum.

Analysis of observational temperature fields (spanning 100 years, from GHCN) show strong hints of these same features.

The geographical distribution of temperature moments in nature appear to be hydrologically controlled at seasonal timescales. Either that, or the agreement with the model results is pure coincidence.

dryer wetter

large continental region

maximum of temperature

variancepositive

temperature skew

negative temperature

skew

maximum of soil moisture

variance

Some AGCM studies examine the impact of “perfectly forecasted” soil moisture on the simulation of observed extreme events. Examples:

Hong and Kalnay (Nature, 408, 842-844, 2000)studied the impact of dry soil moisture conditions on the maintenance of the 1998 Oklahoma-Texas drought.Schubert et al. (see fig. 1 of Entekhabi et al.,

BAMS, 80, 2043-2058, 1999) demonstrated thattheir AGCM could only capture the 1988 Midwestdrought and the 1993 Midwest flood if soil moistureswere maintained dry and wet, respectively.

More studies...

Other studies have examined the impact of “realistic” soil moisture initial conditions on theevolution of subsequent model precipitation.

Studies include: Viterbo and Betts, JGR, 104, 19361-19366, 1999. Also:

Fennessy and Shukla, J. Climate,12, 3167-3180, 1999.

Douville and Chauvin, Clim. Dyn.,16, 719-736, 2000.

Key test: Impact of land initialization on forecast skill

ATMOSPHERICCALCULATIONS

Time step n

ATMOSPHERICCALCULATIONS

Time step n+1

LANDCALCULATIONS

Time step n

LANDCALCULATIONS

Time step n+1

ObservedPrecip.

ObservedPrecip.

Rad. T,q,…

Precip.

Rad. T,q,…

Precip.

E,H E,H

POOR MAN’S LDAS: A study of the impacts of soil moisture initialization on seasonal forecasts

At every time step in a GCM simulation, the land surface model is forced with observed precipitationrather than GCM-generated precipitation. The observed global daily precipitation data comes from GPCP and covers the period 1997-2001 at a resolution of 1o X 1o (George Huffman, pers. Comm.) The daily precipitation is applied evenly over the day.

Detailed description of another recent study of this type (Koster and Suarez, J. Hydromet., 2003)

Note: for the “soil moisture initialization”runs, some scaling is required to ensurean initial condition consistent with theAGCM:

Essentially, a dry conditionfor the GPCP forcing run…

…is converted to an equivalentlydry condition for the AGCM forecast simulation.

Key finding from this study: soil moisture initialization has an impact on forecasted precipitation only when three conditions are satisfied:1. Strong year-to-year variability in initial soil moisture.2. Strong sensitivity of evaporation to soil moisture (slope of evaporative-fraction-versus-soil-moisture relationship).3. Strong sensitivity of precipitation to evaporation (convective fraction).

On average, there isa hint of improvementassociated with landmoisture initialization

Illustration of point 6:The ensemble mean is off,but some of the ensemblemembers do give areasonable forecast

Approach:

Observedprecipitation

Wind speed, humidity, air temperature, etc.

from reanalysis

Observedradiation

Mosaic LSM

Initial conditions for subseasonal forecasts

The resulting initial conditions:(1) Reflect observed antecedent atmospheric forcing, and(2) Are consistent with the land surface model used in the

AGCM.

GLDAS project (NASA/GSFC)using Berg et al. (2003) data

A more “statistically complete” experiment was tried next....

1-Month Forecasts Performed

Atmosphere not “initialized”. Land initializatized on:

May 1 June 1 July 1 Aug. 1 Sept. 1

197919801981

19921993

75 separate 1-month forecasts, each of which can be evaluated against observations.

(Note: each forecast is an average over 9 ensemble members.)

We compare all results to a parallel set of forecasts that do not utilize land initialization: the “AMIP” forecasts. The AMIP forecasts do not rely on atmospheric initialization, either.

In essence, the AMIP forecasts derive skill only from the specification of SST.

Before we evaluate the forecasts, we ask a critical question: what is the maximum predictability possible in this forecasting system?To answer this, we perform an idealized analysis:

For each of the 75 forecasted months, assume that the first ensemble member represents “nature”.

STEP 1:

For each of these months, assume that the remaining 8 ensemble members represent the forecast.

STEP 2:

STEP 3: Determine the degree to which the “forecast” agrees with the assumed “nature”.

STEP 4: Repeat 8 times, each ensemble member in turn taken as “nature”.Average the resulting skill diagnostics.

Regress “forecast” against “observations” to retrieve r2, our measure of forecast skill.

The idealized analysis effectively determines the degree to which atmospheric chaos foils the forecast, under the assumptions of “perfect” initialization, “perfect” validation data, and “perfect” model physics. In other words, it provides an estimate of “maximum possible predictability”.

Where we look for skill is also limited by quality of observations

Areas with adequate idealized predictability and adequate rain gauge density

Precipitation Forecast Areas

Temperature Forecast Areas

Breadth of areas that can be tested will increase with future improvements in data collection and analysis.

FORECAST EVALUATION: PRECIPITATION

With initialization Without initialization

Differences Idealized differences

FORECAST EVALUATION: TEMPERATURE

With initialization Without initialization

Differences Idealized differences

June r2 values, averaged over area of focus

AMIP runs: SSTs only

GLDAS runs:SSTs + landinitialization

SSTs + landinitialization + atmosphereinitialization

SSTs + atmosphereinitialization

What happens when the atmosphere is initialized (via reanalysis) in addition to the land variables? Supplemental 9-member ensemble forecasts, for June only (1979-1993):1. Initialize atmosphere and land2. Initialize atmosphere only Warning: Statistics are based on only 15 data pairs!

June r2 values, averaged over area of focus

AMIP runs: SSTs only

GLDAS runs:SSTs + landinitialization

SSTs + landinitialization + atmosphereinitialization

SSTs + atmosphereinitialization

Outlook

Presumably, skill associated with land initialization can only increase with:-- improvements in model physics-- improved data for initialization

satellite sensors (HYDROS, GPM, …)ground networksdata assimilation

-- improved data for validation

In other words, we’ve demonstrated only a “minimum” skill associated with land initialization.

Current increase in skill

Idealized potential increase in skill

We have a lotof untappedpotential!