do global models properly represent the feedback between land and atmosphere? an observational study...

Do Global Models Properly Represent the Feedback Between Land and Atmosphere?An Observational Study from GLACE

Paul Dirmeyer1, Randy Koster2 and Zhichang Guo1

1Center for Ocean-Land-Atmosphere Studies (COLA), Calverton, Maryland, USA 2NASA Goddard Space Flight Center, Greenbelt, Maryland, USA

20 Sep 2005 Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL 3

GLACE Informing GABLS/GLASS

• How little we can do to verify coupled land-atmosphere behavior in global weather/climate models

• Evidence for the need for co-located observations of land (subsurface and surface), atmosphere (near-surface through PBL) and fluxes between them

• The power of the plural – the value multi-model approaches

Things to watch for in this presentation:


Participating Modeling Groups

Not all models

listed are part of this

study. Some are

latecomers or did not supply all necessary

output fields.

Institute GCM Land Model

BMRC - Australia BMRC CHASM

U. Tokyo - Japan CCSR MATSIRO

Env. Canada CCCma CLASS

COLA – USA COLA SSiB

CSIRO – Australia CSIRO -CC3 & -CC4

NASA/GSFC/CRB – USA GEOS-CRB HySSiB

GFDL – USA GFDL LaD

Hadley Centre – UK HadAM3 MOSES2

SNU – Korea SNU LSM

NCAR – USA CAM3 CLM2

NOAA/NCEP – USA GFS OSU & NOAH

NASA/GSFC/GMAO – USA NSIPP Catchment

UCLA – USA UCLA SSiB


Experiment Design• All groups

integrated their global models for June-August with specified SST.

• In the control case (W), land surface state variables evolve freely and initial conditions for each ensemble member vary widely (e.g., from 1 June of different years of an AMIP simulation).

• One ensemble member is used as the source of land state variables to be specified in every member of the test cases…

W Simulations: Control integrations - establish a time series of surface conditions

(Repeat without writing to obtain simulations W2 –16)

16-member ensembles for June through August

time step n

Step forward thecoupled AGCM-LSM


Write the valuesof the land surface prognostic variablesinto file W1_STATES

Write the valuesof the land surface prognostic variablesinto file W1_STATES

time step n+1


Test Cases

• In case R, all land state variables are replaced at each time step of integration.

• In case S, only sub-surface soil moisture is replaced.

R Simulations: Run a 16-member ensemble, with each member forced to maintain the same time series of land surface prognostic variables.S Simulations: Run a 16-member ensemble, with each member forced to maintain the same time series of subsurface soil moisture prognostic variables

time step n



Throw out prognostic soil moisture; replace with values from W1_STATES

time step n+1

Throw out prognostic soil moisture; replace with values from W1_STATES


All simulations in ensemble respond to the land surface boundary condition in the same way

(coupling strength) is high intra-ensemble variance is small

Simulations in ensemble have no coherent response to the land surface boundary condition

is low intra-ensemble variance is large

We defined a diagnostic variable Ω that describes the impact of the surface boundary on the generation of precipitation.

Diagnostic Analysis

Ω = (16σ2<X> - σ2

X ) / 15 σ2X ,

where σ2X is the intra-ensemble

variance of X and σ2<X> is the

corresponding variance of the ensemble-mean time series – averaged over six-day intervals.


Global Land-Atmosphere Coupling Experiment

Koster, R. D., P. A. Dirmeyer, Z. Guo, G. Bonan, E. Chan, P. Cox, H. Davies, T. Gordon, S. Kanae, E. Kowalczyk, D. Lawrence, P. Liu, S. Lu, S. Malyshev, B. McAvaney, K. Mitchell, T. Oki, K. Oleson, A. Pitman, Y. Sud, C. Taylor, D. Verseghy, R. Vasic, Y. Xue, and T. Yamada, 2004: Regions of strong coupling between soil moisture and precipitation. Science, 305, 1138-1140.

The GLACE project showed that while the 12 participating models differ in their land-atmosphere coupling strengths (the change in , or between cases S and W), certain features of the coupling patterns are common to many of the models. These features are brought out by averaging over all of the model results.


Arid Humid

W→ET ET→P

W2

W1

P

ET

Arid regime: ET (mostly surface evaporation) very sensitive to soil wetness variations, but the dry atmosphere is unresponsive to small inputs of water vapor.

Humid regime: Small variations in

ET affect the conditionally

unstable atmosphere (high

moist static energy), but deep-rooted

vegetation (transpiration) is not

responsive to nominal soil wetness

variations.

Coupled Feedback Loop

In between, soil wetness sensitivity and conditional instability both have some

effect.


The Current Study

We have, in the results of GLACE, a multi-model-based estimate of the strength and spatial variation of land-atmosphere coupling, and its relationship to state variables and fluxes within global models. Can we confirm or refute the GLACE results using the observational record?


The Observational Quandary

Three major impediments to validating the GLACE results:

• The parameter is a handy construct for model comparisons and analysis, but is not a physical quantity. It is an artifact of ensemble model simulations. There is no direct way to calculate a field of , never mind , from observations.

• It is very difficult to infer feedbacks from the observational record. This is one of the main reasons we use models, where we can control the parameters of experiments, generate very large sample sizes for statistical testing and separate signal from noise.

• We lack global measurements of soil moisture & surface fluxes, which are key elements of the coupling pathway. Thus, at best, we can only validate the behavior of global models over a small number localities.


What in situ Data Are There?

In order to compare the model representation of land-atmosphere coupling strength to the real world, we need:

• Complete observations of land surface state variables, near surface atmospheric states, and fluxes between land and atmosphere.

• A long enough period of record to provide a large sample that both spans the range of variability of these variables and provides for adequate statistical significance of the results.

• Data in the same season as the GLACE experiments: June, July and August.

There are very few sources of observational data that can meet all these requirements. Two are identified for this study.


ARM/CART

• DOE operates the Atmospheric Radiation Measurement (ARM) program; in particular, the Southern Great Plains site consists of a Central Facility and a number of Extended Facilities

Elevation (m

MSL)

across a large area of Oklahoma and southern Kansas (map at right - nine stations have sufficient data for comparison with the models).

Kansas

Oklahoma


FLUXNET

• The FLUXNET network of micrometeorological tower sites (table right). We have drawn upon the long-term archive at the Oak Ridge National Laboratory DAAC.

• European sites do not measure soil moisture – of limited use.

FLUXNET Sites

Latitude

Longitude

Surface

Bondville40.006

N88.292

W

Corn/soybean rotation

Little Washita

34.960 N

97.979 W

Grass, rangeland

Bayreuth50.161

N11.882 E

Needleleaf evergreen

Hyytiala61.847

N24.295 E


Loobos52.168

N5.744 E


Tharandt50.964

N13.567 E



Closed Energy Balance

FLUXNET

ARM/CART

No GHF


Coupling Strength ~ Goodness of Fit For the coupling of soil moisture to ET, we hypothesize that the goodness of fit of a curve relating soil wetness and evaporative fraction should be proportional to ∆NLH (the change in coherence of normalized latent heat flux (NLH) from case W to case S). The goodness of fit parameter g = s/R where:

2/1)( 2

ii

i iniin

n

NLHNLH

s

)min()max( ii NLHNLHR

Best fit through 20 bins (i) with equal number of points (blue lines in next slide).

Range in y of the best fit.

Make no a priori assumption about the functional relationship of NLH on SW.Low g means good fit, high g poor fit.

NLH = LH/NetRad


Goodness of Fit (Case W)

NLH vs. SW for nine models for the ARM Central Facility. The points are 6-day means from all ensemble members. The red dots are for the member of W chosen as the basis for the test cases R and S. The relationship from observations is above.

Globallyr2 for ∆NLH vs. g(NLH) =

0.33but

r2 for ∆LHF vs. g(LHF) = 0.53

g=0.319

g=0.102 g=0.058

g=0.247 g=0.151 g=0.221

g=0.280

g=0.855g=0.194

g=0.201


Models Don’t Behave Like Obs.

Models rarely have these attributes.

•Obs show better fit for NLH than LHF, only 3½ models do.

•Obs show better fit for SHF than LHF, 1 model does.

•Obs: SHF has better fit than NSH, only 1½ models do.

g(*,SW) LHF SHF NLH NSH

Obs (ARM) 0.427 0.232 0.201 0.248

CCCma 0.253 0.337 0.319 0.342

COLA 0.186 0.260 0.194 0.204

CSIRO-CC3 0.988 0.743 0.855 1.045

GEOS-CRB 0.085 0.176 0.102 0.118

GFDL 0.103 0.151 0.058 0.126

HadAM3 0.284 0.371 0.280 0.308

CAM3 0.291 0.335 0.221 0.228

GFS/OSU 0.223 0.295 0.247 0.245

NSIPP 0.146 0.219 0.151 0.153


Why the Differences Between Models and Obs?

One possible explanation is that the GCMs emphasize a different factor controlling surface heat flux than does the real world. For example the Penman-Monteith equation and similar relationships have two main terms; • One based on potential evapotranspiration (effectively net radiation)• One based on the humidity gradient between the land surface and near-surface air.We lack complete information (namely aerodynamic resistance) that would allow us to directly compare the relative magnitudes of each term for each model and for observations. We can, however, compare the main components of each term among the models and observations.


Cause of Model Behavior

• All of the models suffer from a tendency to simulate excessively warm temperatures and unrealistically low daytime relative humidity at least over the ARM region.

Categorical frequency of occurrence of net radiation (top), the difference between actual and saturation specific humidity (middle) and temperature (bottom) over the ARM region for observations (bars), and the mean of the GCMs (markers). Vertical lines span the range of models for each bin.

Net Radiation

Temperature

q Deficit


Reversed Relationship Is GlobalThe stronger dependence on soil wetness of latent heat than evaporative fraction predominates in models over most of the globe (blue areas in map of multi-model g(LHF,SW)/g(NLH,SW) below). All models have a global mean value of this ratio <1, and 7 of 9 models have a majority of the land surface covered by values <1. Thus, according to this analysis, most models appear to have a “reversed” relationship between soil wetness and surface fluxes – in contrast to nature, soil moisture in models appears to be tied more strongly to evaporation than to evaporative fraction.

ARM SiteARM Site


Betts Analysis

Betts (2004) found a strong relationship between surface properties and lifting condensation level (LCL) in ERA40.

GLACE model relationships vary – the table on the next slide shows r2 between SHF and LCL and estimated mean PBL heating rates. European FLUXNET sites are included since soil wetness is not needed for these calculations.


Betts Analysis

OB

S (S

HF)

OB

S

(SH

F+

GH

F)

BM

RC

CC

Cm

a

CO

LA

CS

IRO

-CC

3

GEO

S-C

RB

GFD

L

Had

AM

3

CA

M3

GFS

/O

SU

NS

IPP

Mod

el A

vera

ge

AR

M

r2 0.59 0.63 0.03 0.70 0.72 0.21 0.37 0.74 0.65 0.78 0.36 0.49 0.51Htg rate

3.7 4.1 0.7 6.1 3.2 1.9 5.2 3.7 2.1 3.2 2.8 3.3 3.2

Bon

d-

ville

r2 0.27 0.22 0.03 0.58 0.89 0.60 0.55 0.73 0.70 0.69 0.57 0.00 0.53

Htg rate

4.1 4.8 -0.4 3.9 4.9 2.2 3.6 3.2 3.1 3.4 3.4 3.0

Little

W

ash

ita

r2 0.59 0.65 0.03 0.70 0.58 0.16 0.37 0.72 0.69 0.76 0.37 0.53 0.49

Htg rate

2.9 4.1 0.7 6.1 2.5 1.6 5.7 4.0 2.7 3.7 3.2 3.6 3.4

Bayre

ut

h

r2 0.40 0.51 0.01 0.31 0.09 0.52 0.00 0.00 0.60 0.61 0.18 0.15 0.25

Htg rate

5.8 5.8 0.4 5.8 -2.0 2.2 4.0 3.9 5.3 3.5 2.9

Hyytia

la

r2 0.55 N/A 0.46 0.30 0.61 0.81 0.58 0.60 0.76 0.58 0.25 0.36 0.53

Htg rate

4.5 N/A 3.7 4.9 8.6 4.1 7.7 5.4 5.7 7.0 6.2 7.3 6.1

Loob

os

r2 0.51 0.57 0.27 0.38 0.63 0.39 0.46 0.32 0.65 0.62 0.21 0.17 0.41Htg rate

6.0 7.2 -5.3 7.6 4.0 3.5 6.8 10.7 3.2 5.3 7.8 -14. 2.9Th

a-

ran

dt

r2 0.39 0.49 0.01 0.66 0.49 0.38 0.00 0.23 0.45 0.61 0.14 0.15 0.31

Htg rate

3.1 3.7 0.4 7.5 3.6 2.2 5.5 2.9 3.4 4.3 3.5 3.7

Models usually underestimate the strength of the relationship between SHF and LCL. PBL heating rates are rarely within 0.5° of observed mean rates. Low values of r2 suggest models that do not represent the relative importance of SHF as a source of boundary layer heating (or cooling) compared to other thermodynamic processes.


LCL vs. Soil Wetness

Observed ARM relationship agrees with Betts’ theory of soil wetness controls on SHF. The models are all over the place.


W2

W1

P

ET

Coupled Feedback Loop

Everything so far has concerned the terrestrial branch of the loop – what about observational validation of the behavior of GLACE models’

precipitation?


Links to Precipitation

Potential evidence for land-atmosphere coupling over the central U.S. has been found in the observational record of precipitation, based on lagged autocorrelation of pentad precipitation (map below; Koster et al. 2003) and categorical monthly precipitation (Koster & Suarez 2004).Is there a similar relationship in theGLACE models?

July1

166 10 21 26

Pre

cipi

tatio

n


Pentad Model PrecipitationAveraged over the conterminous U.S.

and grouped by month, some

models, especially the multi-model average, show a magnitude and

time evolution of lagged auto-

correlation similar to observations

(dotted lines).


Month-to-Month Persistence

(Koster & Suarez 2004) showed a tendency for persistence of anomalous precipitation in NH mid-latitudes that using pentile rankings (wettest 20% of months were usually followed by wet months, etc.). We repeat the investigation with quartiles (right) and find the models are slightly weaker than observations at showing persistence of wettest (purple) and driest (hatched) 25% of cases. Other influences (e.g. SST impacts) may also play a role.


Summary

• There exist few locations with long records of observations of the necessary data to verify weather and climate models’ coupling behavior between land and atmosphere.

• In these locations, GCMs show stronger dependence of LHF on soil moisture than observations suggest, and weaker links to SHF or evaporative fraction.

• Systematic errors in surface temperature and humidity may contribute to the incorrect dependencies.

• These problems may also lead to excessive boundary layer growth and incorrect PBL heating rates.

• Nevertheless, the models (averaged together) capture observed lagged relationships of monthly rainfall.


Summary

• GLACE results cannot be disproved by the poor validation of individual models, but there is certainly room for improvement in the parameterization of model “physics”.

• The multi-model approach is further supported by the results of this validation study – the multi-model mean performs better than most models in all circumstances, and is often best.

• Long-term co-located measurements of soil wetness, surface fluxes and near-surface meteorology should be distributed around the globe in order to aid model development and assess the potential for SW as a predictor for climate via land-atmosphere feedback.


• Thank you!

This work was conducted under support from National Aeronautics and Space Administration grant NAG5-11579.

do global models properly represent the feedback between land and atmosphere? an observational study...

Documents

ensemble member

land surface state variables

source of land state

intraensemble variance

ensemblemean time series

low intraensemble variance

high intraensemble variance

member ensembles