Transcript Drought Working Group analysis of model- produced Zhichang Guo (COLA)

Drought Working Group analysis of model- produced
soil moisture as an index of agricultural drought
Randal D. Koster (GMAO, NASA/GSFC)
Zhichang Guo (COLA)
Paul A. Dirmeyer (COLA)
Rongquian Yang (NCEP, NOAA)
Ken Mitchell (NCEP, NOAA)
Cindy Wang (Chinese Academy of Sciences)
Dennis Lettenmaier (U. Washington)
Kingtse Mo (NCEP, NOAA)
Wanru Wu (NCEP, NOAA)
One of the goals of the U.S. CLIVAR drought working
group:
Develop a working definition of drought (onset and
demise) that is useful to both the prediction/research and
applications communities.
In this talk, we focus on “agricultural” drought: deficits
in soil water availability for vegetation (e.g., crop)
growth. What quantifiable index can we use to
characterize agricultural drought?
Some Potential Agricultural Drought Indices
Index
In-situ soil
moisture
measurements
Strengths
Direct quantification of
soil moisture.
Weaknesses
Available networks limited
in most parts of the world;
‘point’ measurements.
Some Potential Agricultural Drought Indices
Index
Strengths
Weaknesses
In-situ soil
moisture
measurements
Direct quantification of
soil moisture.
Available networks limited in
most parts of the world;
‘point’ measurements.
Satellite-based
soil moisture
measurements
Global estimates of
areally-averaged soil
moisture.
Measures only top several
mm (cm) of soil, and not
under dense vegetation.
Some Potential Agricultural Drought Indices
Index
Strengths
Weaknesses
In-situ soil
moisture
measurements
Direct quantification of
soil moisture.
Available networks limited in
most parts of the world;
‘point’ measurements.
Satellite-based
soil moisture
measurements
Global estimates of
areally-averaged soil
moisture.
Measures only top several
mm (cm) of soil, and not
under dense vegetation.
Palmer
drought index
Global estimates of
drought state; long
history of use.
An empirical estimate;
ignores some aspects of
antecedent meteorology.
Some Potential Agricultural Drought Indices
Index
Strengths
Weaknesses
In-situ soil
moisture
measurements
Direct quantification of
soil moisture.
Available networks limited in
most parts of the world;
‘point’ measurements.
Satellite-based
soil moisture
measurements
Global estimates of
areally-averaged soil
moisture.
Measures only top several
mm (cm) of soil, and not
under dense vegetation.
Palmer drought
index
Global estimates of
drought state; long
history of use.
An empirical estimate;
ignores some aspects of
antecedent meteorology.
Model-derived
soil moisture
Global estimates of
areally-averaged soil
moisture; reflects all
prior meteorology.
Not a direct
measurement;
soil moisture estimates
are model-dependent.
Some Potential Agricultural Drought Indices
Index
Strengths
In-situ soil
moisture
measurements
Direct quantification of
soil moisture.
Weaknesses
Available networks limited in
most parts of the world;
‘point’ measurements.
Satellite-based
Global estimates of
soil moisture
areally-averaged soil
Remainder
of talk:
measurements
moisture.
Measures only top several
mm (cm) of soil, and not
under dense vegetation.
index
drought state; long
history of use.
An empirical estimate;
ignores some aspects of
antecedent meteorology.
Model-derived
soil moisture
Global estimates of
areally-averaged soil
moisture; reflects all
prior meteorology.
Not a direct
measurement;
soil moisture estimates
are model-dependent.
examine this weakness.
Palmer drought
estimatesit?
of
Can weGlobal
get around
Study #1: Analysis of GSWP-2 Data
nd
Global Soil Wetness
Project
This phase of the project takes advantage of:
• The10-year ISLSCP Initiative 2 data set
• The ALMA data standards developed in GLASS
• The infrastructure developed in the pilot phase of GSWP
GSWP-2 represents an evolution in multi-model large-scale land-surface modeling
with the following goals:
• Produce state-of-the-art global data sets of soil moisture, surface fluxes, and related
hydrologic quantities.
• Develop and test in situ and remote sensing validation, calibration, and assimilation
techniques over land.
• Provide a large-scale validation and quality check of the ISLSCP data sets.
• Compare LSSs, and conduct sensitivity analyses of specific parameterizations.
www.iges.org/gswp/
[email protected]
nd
Global Soil Wetness
Project
This phase of the project will take advantage of:
• The10-year ISLSCP Initiative 2 data set
In GSWP-2,
• The ALMA data standards
developeda innumber
GLASS of land surface
• The infrastructure developed
the pilot
phase of
GSWP
modelsinwere
driven
with
the same
observations-based meteorological
GSWP-2 representsforcing.
an evolution
in multi-model
land-surface
What
we willlarge-scale
demonstrate
here modeling
with the following goals:
is that the different models produce a
• Produce state-of-the-art global data sets of soil moisture, surface fluxes, and related
similar soil moisture product, when the
hydrologic quantities.
is suitably
scaled...
• Develop and test inproduct
situ and remote
sensing validation,
calibration, and assimilation
techniques over land.
• Provide a large-scale validation and quality check of the ISLSCP data sets.
• Compare LSSs, and conduct sensitivity analyses of specific parameterizations.
www.iges.org/gswp/
[email protected]
GSWP-2 Models (as of March 2005)
Name
Institute
Nation
Time
step
Vertical
Structure
Soil,
Vegetation,
Other
Most Recent
Reference(s)
CLM2-TOP
U. Texas
USA
1hE
5mS
10W 10T 5S
Sd Vd *
Bonan et al. (2002), Niu
and Yang (2003)
HySSiB
GSFC
USA
1h
3W 2T 2S
Sg Vs
Mocko and Sud (2001)
France
5m
3W 2T 1S
Sd Vi
Etchevers et al. (2001)
USA
30m
3W 2T 1S
Sg Vs
Koster and Suarez (1992)
UK
30m
4W 4T 1S
Sd Vd
Cox et al. (1999), Essery
et al. (2003)
USA
15m
4W 4T 1S
Sg Vd
Ek et al. (2003)
ISBA
Mosaic
MOSES2
Météo France /
CNRM
NASA / GSFC /
HSB
Met Office
NSIPPCatchment
NOAA / NCEP /
EMC
NASA / GSFC /
GMAO
SiBUC
Kyoto U.
SSiBCOLA
IGES / COLA
SWAP
Russian Academy
of Sciences / IWP
VISA
U. Texas
USA
3hE
5mS
10W 10T 5S
Sd Vd *
LaD
NOAA / GFDL
USA
30m
1W 18T 1S
Sd Vs
ORCHIDEE
IPSL
France
1h
4W 7T 1S
Sd Vd *
Krinner et al. (2005)
Sland
U. Maryland
USA
20m
1W 2T 0S
Sd Vdyn
Zeng et al. (2005)
BucketIIS
U. Tokyo
Japan
3h
1W 1T 1S
n/a
Manabe (1969)
NOAH
USA
Japan
USA
Russia
This
page
showsSgthe
Koster et al. (2000),
20m
3W 6T 3S
Vs *
Ducharne et al (2000)
international
in online
1h
3W 2T 1S participation
Sg Vd
Dirmeyer and Zeng
30m
6W 6T The
1S
Sg Vs
GSWP-2.
models
(1999)
Gusev and Nasonova
3h
2W 1T
1S
Sg Vi
*
analyzed
here
are
circled.
(2003)
Yang and Niu (2003),
Niu and Yang (2003)
Milly and Shmakin
(2002a,b)
Vertical structure shows soil layers for water (W) and temperature (T), and the maximum number of snow layers (S). Soil
data sets are either supplied by GSWP-2 (g) or the model’s default (d). For vegetation distributions, GSWP-2 supplied
datasets include IGBP (i) and SiB (s) categories; Sland has dynamic vegetation. Two models have different time steps for
energy (E) and soil (S).
Southern U.S.
Europe
Ostensibly, the modelderived soil moistures
produced in GSWP (with
the same atmospheric
forcing) are very
different.
Sahara
Root zone soil moistures
(degrees of saturation)
produced by the 7 land
surface models at five sites.
Sahel
Amazon
Such inter-model differences have long been documented in
the literature. They reflect a simple and often overlooked
fact:
For various reasons, mostly related to model limitations,
a land model’s “soil moisture” variable is best
interpreted as an “index” of soil moisture state, one that
increases as the soil gets wetter and decreases as it gets
drier.
In general, a model’s soil moisture should not be considered
an absolute quantity that can be compared between models
or against direct observations. It’s MODEL DEPENDENT!
Scaling the data, to isolate temporal variability
Let w(j,n) = model’s total soil moisture for day j of year n.
Define:
where
w(j,n) – mw(j)
WI(j,n) = ---------------------------sw(j)
mw(j) = Mean (over many years) of w on day j.
sw(j) = Standard deviation of w on day j.
Note: given the non-Gaussian nature of soil moisture, there
are better ways to scale the data, particularly if a long data
history is available…
Soil moisture cdf at 46N, 100W
CDF matching:
map percentiles.
For the GSWP2 analysis, with only 10 years of data, we use
the simpler “standard normal deviate” approach. The use of
the simpler approach can only make things more difficult for
us, so if we still succeed…
Raw model soil moistures
Scaled model soil moistures
Southern U.S.
Europe
Sahara
Sahel
Amazon
(31-day smoother applied)
Average r2 between models (degree to which the models produce the same
soil moisture information, in terms of temporal variability, with no smoothing)
Note: When scaling the
soil moisture, the seasonal
cycle is subtracted out
before statistics are
computed, making it that
much more difficult to get
a high r2.
0. .05
.10 .15 .20 .25 .30 .35 .40 .45 .50 .55 .60 .65 .70 .75 .80 .85 .90 .95 1.0
Scaled model soil moistures
If an agricultural drought were defined as, say, a soil moisture
falling 0.5s below its climatological mean for that time of year,
then all of the models would capture the 1988 Midwest drought.
Model dependence of soil moisture values may not be
such a big issue…
Study #2: Study of North American Drought
Lead: U. Washington.
Slides adapted from originals by
Dennis Lettenmaier and Cindy Wang.
Models
• VIC: Variable Infiltration Capacity Model
(Liang et al. 1994)
• CLM3.5: Community Land Model version 3.5
(Oleson et al. 2007)
• NOAH LSM: NCEP, OSU, Air Force, Hydrol. research lab
(Mitchell et al. 1994, Chen and Mitchell 1996)
• Catchment LSM: NASA/GSFC Global Modeling and
Assimilation Office
(Koster et al. 2000; Ducharne et al. 2000)
Data
• All models driven with observations-based met forcing.
Daily precipitation and temperature max-min, other
land surface variables (downward solar and longwave
radiation, near-surface humidity, and wind) derived via
index methods. Methods as described in Maurer et al.
(2002).
• Period of analysis: 1920-2003 (after 5-year spinup).
• Spatial resolution: 0.5 (3322 land grid cells)
• Domain: conterminous United States.
•
Soil and vegetation parameters differ for different
models (generally NLDAS), as provided by model
developers.
The challenge: Different land schemes
have different soil moisture dynamics
Model simulated total soil moisture at cell
(40.25N, 112.25W)
Solution: Normalized total column soil moisture
Soil moisture cdf at 46N, 100W
Recall: there are more valid
ways of scaling soil moisture
than using standard normal
deviates…
• For each model, total column soil moisture was
expressed as percentiles.
• Percentiles were estimated for each model by month,
using simulated total column soil moisture for the
period 1920-2003.
• Percentiles were computed using the Weibull plotting
position formula.
Averaged soil moisture percentiles 1932-38
Averaged soil moisture percentiles 1950-57
Spatial distribution of average (monthly) between-model
correlations of soil moisture percentiles
Study #3: Objective Climate Drought Monitoring
over the United States
Lead: NCEP.
Slides adapted from originals by
Kingtse Mo and Wanru Wu.
Agricultural drought (SM percentiles, June 2008)
EMC/NCEP
All models capture the same basic features:
– Drought in SE, southern Texas and California.
– Wetness in Great Plains.
But details differ.
Uncertainties of the NLDAS: Compare VIC and
Noah over 1948-2003.
Soil moisture percentiles
Corr
RMS
•Differences are regionally dependent
•Over the areas east of 90W, differences are small.
•Over the areas west of 90W, differences are large.
•The RMS error is larger than 25%: the difference between one
drought class to another
Thanks: Yun Fan and Andy Wood!!
Note similar result from these three studies:
Between-model correlations are smallest in driest
areas.
Average (monthly) between-model
correlations of soil moisture
percentiles: U. Washington study
correlation values
from NCEP EMC study
r2 values from GSWP2 study
Key Question: Why is the model-dependence
of a soil moisture index larger (and thus the
potential usefulness of this index smaller) in
drier areas?
One major reason: the potential for correlation is tied to precipitation
variance. A larger year-to-year rainfall variability implies a larger year-toyear soil moisture signal that all models can more easily capture. If
precipitation variance is small, the model states aren’t controlled as much by a
large forcing signal, and differences in model physics manifest themselves
more easily.
Correlation between models
(GSWP2)
s 2P
A key difference in model physics that can manifest itself in the absence of
strong interannual precipitation forcing: the model’s water holding capacity.
e-folding time of soil moisture
autocorrelation (months)
– U. Washington study
Soil water holding capacity
of six models (cm)
Differences between
VIC and Noah
(NCEP study)
Total SM anomaly percentile for
selected River Forecast Center
areas
Vic(Blue), Noah (black)
From 1950-2001
1. For RFCs east of 90-95W, VIC and
Noah agree. e.g. the lower
Mississippi , Arkansas RFCs.
2. There are large differences over
the western region. e. g. the
Missouri , Colorado RFCs
3. VIC has more high frequency
components than the Noah.
3 month running mean
Another measure of agreement: average standard deviation of
soil moisture values between models. (GSWP2 study)
Before mapping
After mapping
Summary and Discussion
Land surface models use physically-based formulations to
integrate (over time) the effects of meteorological forcing on soil
moisture.
The models may provide information on soil moisture state
for evaluating agricultural drought.
But: simulated soil moistures are model-dependent.
Nevertheless, we find that, when interpreted in the context of
their own climatology, the seemingly different model products
are in fact consistent – they provide largely the same information
on the time variability of soil moisture at a point.
The model-dependence of a simulated soil moisture product may
not greatly limit its use in characterizing agricultural drought.
Summary and Discussion (cont.)
This is particularly true over regions with large interannual
precipitation variance.
The use of a multi-model average of the scaled values could
help “average out” any model-specific behavior that does
remain after scaling:
Scaled model soil moistures
Multi-model average
A particularly useful index for agricultural drought? Something to consider!