PPT-22 - Climate Science: Roger Pielke Sr.

Climate Impacts of Agriculture
Related Land Use Change in the US
Jimmy Adegoke 1 , Roger Pielke Sr. 2 , Andrew M. Carleton 3
1 Dept. Of Geosciences, University of Missouri-Kansas City
2 Dept. of Atmospheric Sciences, Colorado State University
3 Dept. of Geography, The Pennsylvania State University
World Meteorological Organization (WMO) Committee on
Agricultural Meteorology (CAgM ( CAgM) ) Expert Team Meeting
on the Contribution of Agriculture to the State of Climate
Ottawa, Canada, 27-30 27 30 September 2004

Presentation Outline
Cropland/Forest Impacts on Convective Cloud
Development in the US Midwest : Empirical studies
Agriculture-related land use change impacts on
seasonal climate in the Central US: Modeling studies
Crop-climate modeling Issues, challenges & questions

Current & Potential Natural Vegetation
[Copeland et al., 1996]

Agriculture - Human Imprint on the Land
Surface
Spring Summer

Land Surface-Atmosphere Interactions
Schematic of the differences in surface heat energy budget
and planetary boundary layer over a forest and cropland.

Temp, Water
Trace Gases
Pollutants
Community
Composition &
Structure
Atmosphere
Heat, Moisture
Radiation
Light, Temp
Moisture, Wind
Surface Physiology &
Hydrology
Water & Nutrients
Agriculture
Deforestation, etc.
Biogeochemical
& Hydrological
Cycles
Landscape Modification Anthropogenic Activities
Trace Gases &
Pollutants
Sec - Hours
>1yr - 100yrs
>1000yrs

Focus of Land Surface-Climate Work:
Empirical Studies
Impacts of changes in US Midwest land cover
parameters (e.g. land cover, surface roughness,
zones of land cover transitions) on convective
cloudiness (Carleton et al., 2001 GRL Vol. 28, 1679-1684)
Sensitivity of the AVHRR derived Normalized
Difference Vegetation Index (NDVI) and the
Fractional Vegetation Cover (FVC) to growing
season surface moisture conditions (Adegoke and
Carleton, 2002 JHM 4, 24-41).

Wetland
0%
Prairie
2%
Wetland
5%
Prairie
29%
water
3%
Land Use Changes in Illinois
Forest
93%
River
2%
Agriculture
68%
Urban
3%
1820 1980
water
5%
River
0%
Forest
61%
Jackson County
Lake County
Forest
9%
Urban
34%
Prairie
0%
Forest
25%
Wetland
2%
Agriculture
52%
Prairie
0%
Barren
1%
Wetland
2%
Water
1%
Water
2%
Barren
1%
[From Iverson & Risser, 1987]

Land Use/Land Cover Map of The Midwest
Showing Sampling Locations of Convective
Cloud Parameters
…

GOES INFRA RED & VISIBLE IMAGES
11 JUNE 1997 16:00 UTC

Stratification of Case Study Days: June-Aug. 1981-98
Strong Flow / Weak Flow : 500 mb Vector Winds

a) Mean BWF VIS: Crop MI
Albedo
1.00
0.80
0.60
0.40
0.20
0.00
7am
9am
11am
b) Mean BWF VIS: Boundary MI
Albedo
1.00
0.80
0.60
0.40
0.20
0.00
7am
9am
11am
c) Mean BWF VIS: Forest MI
Albedo
1.00
0.80
0.60
0.40
0.20
0.00
7am
9am
11am
1pm
1pm
1pm
3pm
3pm
3pm
5pm
5pm
5pm
295.00
290.00
285.00
280.00
275.00
270.00
265.00
300.00
290.00
280.00
270.00
260.00
250.00
295.00
290.00
285.00
280.00
275.00
270.00
265.00
260.00
Mean BWF IR TEMP: Crop MI
6am
8am
10am
12Noon
2pm
4pm
6pm
Mean BWF IR TEMP: Boundary MI
6am
8am
10am
12Noon
2pm
4pm
6pm
Mean BWF IR TEMP: Forest MI
6am
8am
10am
12Noon
2pm
4pm
6pm
8pm
8pm
8pm

a) Mean BWF VIS: Crop IN
Albedo
1.00
0.80
0.60
0.40
0.20
0.00
7am
9am
11am
b) Mean BWF VIS: Boundary IN
Albedo
1.00
0.80
0.60
0.40
0.20
0.00
7am
9am
11am
c) Mean BWF VIS: Forest IN
Albedo
1.00
0.80
0.60
0.40
0.20
0.00
7am
9am
11am
1pm
1pm
1pm
3pm
3pm
3pm
5pm
5pm
5pm
d) Mean BWF VIS: Crop MO
Albedo
1.00
0.80
0.60
0.40
0.20
0.00
7am
9am
11am
e) Mean BWF VIS: Boundary MO
Albedo
1.00
0.80
0.60
0.40
0.20
0.00
7am
9am
11am
f) Mean BWF VIS: Forest MO
Albedo
1.00
0.80
0.60
0.40
0.20
0.00
7am
9am
11am
1pm
1pm
1pm
3pm
3pm
3pm
5pm
5pm
5pm

a) Mean WF VIS: Crop MI
Albedo
1.00
0.80
0.60
0.40
0.20
0.00
7am
9am
11am
b) Mean WF VIS: Boundary MI
Albedo
1.00
0.80
0.60
0.40
0.20
0.00
7am
9am
11am
c) Mean WF VIS: Forest MI
Albedo
1.00
0.80
0.60
0.40
0.20
0.00
7am
9am
11am
1pm
1pm
1pm
3pm
3pm
3pm
5pm
5pm
5pm
d) Data Range WF VIS: Crop MI
Albedo
1.00
0.80
0.60
0.40
0.20
0.00
7am
9am
11am
e) Data Range WF VIS: Boundary MI
Albedo
f) Data Range WF VIS: Forest MI
Albedo
1.00
0.80
0.60
0.40
0.20
0.00
1.00
0.80
0.60
0.40
0.20
0.00
7am
7am
9am
9am
11am
11am
1pm
1pm
1pm
3pm
3pm
3pm
5pm
5pm
5pm

160
140
120
100
80
60
40
20
0
140
120
100
80
60
40
20
0
140
120
100
80
60
40
20
0
Mean SF VIS Brightness: Crop MI
Mean SF VIS Brightness: Boundary
MI
Mean SF VIS Brightness: Forest MI
120.00
100.00
80.00
60.00
40.00
20.00
0.00
100.00
80.00
60.00
40.00
20.00
0.00
100.00
80.00
60.00
40.00
20.00
0.00
Mean BWF VIS Brightness: Crop MI
6am
6am
8am
10am
12Noon
2pm
4pm
6pm
Mean BWF VIS Brightness:
Boundary MI
8am
10am
12Noon
2pm
4pm
6pm
Mean BWF VIS Brightness: Forest
MI
6am
8am
10am
12Noon
2pm
4pm
6pm
8pm
8pm
8pm

Average Cloud Top Brightness Temperature: MI & MO
102.00
100.00
98.00
96.00
94.00
92.00
90.00
88.00
110.00
108.00
106.00
104.00
102.00
100.00
98.00
BWF Average Maximun Brightness Temp
(MI)
Crop Boundary Forest
BWF Average Maximun Brightness Temp
(MO)
Crop Boundary Forest

Thermodynamic indices for selected “weak flow” days:
12Z Radiosonde Data: MI vs MO
Michigan
Missouri
CCL
(mb)
749
827
CCL-EL
(mb)
480
528
K-Index
(mb)
12
29
Tc ( o C)
32
26.8
Mean
Tv ( o C)
16
20.5
Mean
T-Tv
( o C)
3.7
2.7
RH o / o
68
78
Water
Content
.18
.33

Sensible and Latent Heating of the Atmosphere Required for
Initiation of Convective Clouds vs. Bowen Ratio [Rabin et al., 1990]

Summary of Cloud Research Findings
Analyses of Visible and IR GOES cloud data for
contrasting circulation regimes indicate some
cloud-land cover associations across major
crop-forest boundaries.
Land cover boundary zones are shown to be
favored areas for enhanced cloud development
under moderate mid-tropospheric (< 30 m/s) flow
conditions. The boundary zones tend to behave
like regions of differential vertical circulations (i.e.,
NCMCs)

Focus of Land Surface-Climate Work:
Modeling Studies
Improving the representation of land surface
heterogeneity (land cover; soil moisture; soil type) in
the Colorado State University Regional Atmospheric
Modeling System (RAMS)
(Adegoke et al. 2003; Strack et al. 2003; Rozoff et al., 2003)
Developing protocols for a more realistic description
of seasonally and interannually varying vegetation
cover and growth rates in regional climate models
(Adegoke et al. 2004; Eastman et al. 2001; Lixin Lu et al., 2002).

Lessons Learned
Realistic representation of spatial heterogeneity of land
surface parameters improves model simulation of
regional-scale effects of agriculture-related land use
changes on climate and terrestrial biophysical
processes.
Key Parameters:
- Land Cover
- Soil Moisture
-LAI
-Soil Type
- Soil Temperature

Over High Plains, if
model soil moisture is
low
– Forecast CAPE too
low, about half the
observed CAPE
– Forecast of instability
insufficient
Soil Moisture Impacts

Soil Moisture Simulation with Different Soil (a);
Matching Soil (b)
(a)
LDAS Evaluation Team: Alan Robock et al., 2004
(b)

Soil Moisture
LDAS Evaluation Team: Alan Robock et al., 2004

Recent Improvements in RAMS-LEAF2
1. Protocols for ingesting variable soil moisture
2. Incorporation of high-resolution land cover data
(30 m) from the USGS NLCD database
3. Specification of variable soil type from the FAO soil
type database
4. Protocols for ingesting NDVI and derivation of LAI
from NDVI
5. RAMS-Century coupling for explicit modeling of the
seasonal evolution of vegetation in the simulation
of seasonal climate.

Map of U.S. High Plains Aquifer

Acreage of Rain fed & Irrigated Corn Farming in
Nebraska (1950-1988)
Area (ha)
3000000
2500000
2000000
1500000
1000000
500000
0
1950-51
1954-55
1958-59
1962-63
1966-67
1970-71
Rainfed
Irrigated
1974-75
Year
1978-79
1982-83
1986-87
1990-91
1994-95
1998-99

Nebraska Irrigation Modeling Project
Complex changes in the lower atmosphere (PBL)
radiation budget can result from large-scale land use
changes of this magnitude (e.g., vapor flux CAPE)
This study was designed to evaluate the changes in
the summertime surface energy budget & convective
rainfall parameters due to irrigation in Nebraska using
RAMS.

RAMS Modeling Domain
Coarse Grid: 40 km ; Fine Grid:10 km; Domain Height: 20km

a) Kuchler Potential Vegetation b) OGE – Dry Run
c) OGE + Current Irrigation – Control Run
(a)
(b) (c)

Summary of Model Results
Significant inner domain area-averaged difference
between the Control and Dry runs:
- 36% increase in surface latent heat flux
- 15% decrease in surface sensible heat flux
- 28% increase in water vapor flux at 500m
-2.6 o C elevation in dew point temperature
-1.2 o C decrease in near surface temperature
Greater differences observed between the Control
and Natural Vegetation runs e.g.,
- Near ground temperature was 3.3 o C warmer &
surface sensible heat 25% higher in the Natural run.
[Adegoke et al., 2003 Monthly Weather Review 131(3), 556-564.]

ClimRAMS Coupled with CENTURY
(Lu et al., 2001, Journal of Climate)
RAMS{temp, swin, prcp, rh, (u,v,w)}
CENTURY{LAI, roots, Zo,
evap, transp, vegfrac, vegalb}

Satellite-derived Leaf Area Index
Derived from AVHRR 10day
composite NDVI
NDVI ⇒ LAI following
Sellers et al. (1996) and
Nemani et al. (1996)
Lu et al., 2001
Derived LAI for Central U.S. in dry (1988),
average(1989), and wet (1993) years.
Average JJA NDVI for Central U.S.

STRONG DIFFERENCES
Magnitude of LAI
Heterogeneity of LAI
SOME DIFFERENCE
Seasonality of LAI
Comparison of LAI Forcing
Lu et al., 2001
Default LAI in Inner (50 km) Grid NDVI-derived LAI in Inner (50 km) Grid

Good Agreement between Model
Predictions & Observations
e.g. Domain-average
maximum and
minimum air
temperature and
precipitation for inner
grid during 1989
(selected as an
“average” year) for
the run with NDVIderived
LAI
Lu et al., 2001

Runs Broadly Agree With Observations
e.g. Distribution of maximum and minimum temperature and
precipitation for inner grid for the run with NDVI-derived LAI
January-March 1989 June-August 1989
Lu et al., 2001

Physical Mechanism for Precipitation Increase
• Lower domainaveraged
LAI allows
more solar radiation
to reach the surface,
increasing CAPE
• Spatial variability in
LAI triggers
mesoscale
circulations.

RAMS-Century Coupling Strategy and Design
• Differences in spatial and
temporal resolutions:
RAMS: 3-D, CENTURY: 1-D
time step: minute vs. day
• Internet Stream Socket
Client/Server mechanism
• Both atmospheric forcings and
biospheric parameters are
prognostic variables
Lu et al., 2001

Coupled Model Captures 2-way Feedbacks
Default
Coupled
LAI response of
CENTURY is different
after harvest when run in
coupled mode. The
coupled model gives a
response in modeled
precipitation.
Lu et al., 2001

Coupled Model Simulated Climate
Lu et al., 2001

Conclusions From Lu et al. 2001
Both satellite-derived and model-calculated LAI
produce a significant impact on the modeled
seasonal climate
In both cases, the climate is cooler and produces
more precipitation relative to using RAMS default LAI
The effect of heterogeneity in LAI appears to be the
dominant factor in producing these differences
Including realistic description of heterogeneous
vegetation phenology influences the prediction of
seasonal climate.

Looking Ahead
The challenge: Fully coupled crop-climate model
capable of investigating the 2-way interactions of cropclimate
system under a wide range of conditions.
Must include feedbacks of crop growth on surface
climate
Will require much stronger cross-disciplinary
interaction/collaboration between Agricultural and
Atmospheric Sciences

Discussion Issues/Questions
Crop models tend to operate at the field/plot
spatial scale while climate models typically have
horizontal resolutions of a few km to 100~200 km.
Addressing this spatial scale disparity is not trivial.
Are there additional local terrain and
surface/vegetation characteristics that should be
considered in crop-climate simulations that may
not currently reflected in climate models?
Crop growth parameterization issues: Century vs
CERES-maize model vs General Large Area Model
for annual crops (GLAM)