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CLIM 714 Land-Climate Interactions
Natural Variability of the Land Surface - Phenology
Lecture 12CLIM 714
Paul Dirmeyer
CLIM 714 Land-Climate Interactions
Main Climate Drivers for Natural Land Cover
• Climatic Constraints– Temperature
• annual and seasonal means, extremes, timing of first frost, ice free days, growing degree days
– Moisture• annual and seasonal means, extreme
events (floods and droughts), precipitation, actual and potential evapotranspiration
CLIM 714 Land-Climate Interactions
Regional Patterns• Climate Zones
– Köppen (1928) - map coauthored by student Geiger (P & T)
– Holdridge’s “Life Zones” (P & T)– USDA “Hardiness” Zones (minimum T)
• Potential Natural Vegetation– climax vegetation– A.W. Kuchler (1964)– EPA and other groups have updated
CLIM 714 Land-Climate Interactions
A Tropical hum id Af Tropical w et No dry season
Am Tropical monsoonalShort dry season; heavy monsoonal rains in other
Aw Tropical savanna Winter dry season
B Dry BWh Subtropical desert Low -latitude desert
BSh Subtropical steppe Low -latitude dry
BWk Mid-latitude desert Mid-latitude desert
BSk Mid-latitude steppe Mid-latitude dry
C M ild M id-Latitude Csa Mediterranean Mild w ith dry, hot summer
Csb MediterraneanMild w ith dry, w arm summer
Cfa Humid subtropicalMild w ith no dry season, hot summer
Cw a Humid subtropicalMild w ith dry w inter, hot summer
Cfb Marine w est coastMild w ith no dry season, w arm summer
Cfc Marine w est coastMild w ith no dry season, cool summer
DSevere M id-Latitude Dfa Humid continental
Humid w ith severe w inter, no dry season, hot summer
Dfb Humid continental
Humid w ith severe w inter, no dry season, w arm summer
Dw a Humid continentalHumid w ith severe, dry w inter, hot summer
Dw b Humid continentalHumid w ith severe, dry w inter, w arm summer
Dfc SubarcticSevere w inter, no dry season, cool summer
Dfd Subarctic
Severe, very cold w inter, no dry season, cool summer
Dw c SubarcticSevere, dry w inter, cool summer
Dw d SubarcticSevere, very cold and dry w inter, cool summer
E Polar ET TundraPolar tundra, no true summer
EF Ice Cap Perennial ice
H Highland
Köppen-GeigerClimateClassificationSystem
CLIM 714 Land-Climate Interactions
Köppen Classification Scheme
CLIM 714 Land-Climate Interactions
In this system, vegetation iscategorized by basic climate statealone (precipitation, temperature,and thus potential evapotranspi-ration). There is no competition,succession, variability, or sensitivityto soils, nutrients, disturbances, etc. Schemes like Holdridge’s are
analogous to the climateclassification schemes (e.g.,Köppen’s), with theassumption that climatedetermines the biome.
They are only moderately accurate, and are generallythought to be too crude forstudies of climate change.
Holdridge’s Life Zones
CLIM 714 Land-Climate Interactions
USDA Plant HardinessGardeners in the U.S. are familiar with the USDA classification scheme, which is based solely on minimum temperature extremes (assuming precipitation is irrelevant because gardeners can water their plants during dry weather).
CLIM 714 Land-Climate Interactions
Geographic Controls on Land Cover
• Geomorphology– Landforms are a product of the
interaction of geology and climate– Topography can modify climate– Soils develop as a result of weathering
of geologic substrates
CLIM 714 Land-Climate Interactions
Geologic map modified fromKing and Biekman (1974)
http://tapestry.usgs.gov/
CLIM 714 Land-Climate Interactions
Terrain of the Conterminous United Statesshaded relief map
Dry adiabatic lapse rate:
Γa = g / cp
≈ 9.7 K / 1000 m
Observed lapse rate:
Γ ≈ 6.5 K / 1000 m
This “correction” can be used to estimate local temperatures near observation stations that are at a different altitude.
CLIM 714 Land-Climate Interactions
A Statistical Solution: A Neural Data-Driven Assessment of Global
Vegetation Classes
Global distribution of feature types obtained after simulation with a SOM (Self-Organizing Map) and the topological arrangement of the categories on the network. As training data the monthly means of temperature, precipitation, insolation and the water storage capacity of soils are used. (Kropp 1999)
Neural nets are “black boxes”. They don’t tell
“why” or “how”.
Neural nets are “black boxes”. They don’t tell
“why” or “how”.
CLIM 714 Land-Climate Interactions
Based on observed 1971-2000 climatology, along with soils and other information (e.g. native species habitats).
CLIM 714 Land-Climate Interactions
Predicting current vegetation
Henderson-Sellers (1990) used a GCM and Holdridge’s “Life Zones” to estimate the distribution of vegetation that would be consistent with GCM climate.
CLIM 714 Land-Climate Interactions
Today’s models
• 2001 - a great deal of variation in coupled GCM-DVM ability to simulate current vegetation, as driven by climate.
Cramer, W., A. Bondeau, F.I. Woodward, I.C. Prentice, R.A. Betts, V. Brovkin, P.M. Cox, V. Fisher, J.A. Foley, A.D. Friend, C. Kucharik, M.R. Lomas, N. Ramankutty, S. Sitch, B. Smith, A. White, and C. Young-Molling, 2001: Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models. Global Change Biology, 7, 357-373.
CLIM 714 Land-Climate Interactions
Monitoring Vegetation Variations
• Satellite are appropriate to a range of dynamic monitoring tasks – monitoring vegetation dynamics over
course of a year – link to (crop) growth models to provide
yield estimates – distinguish cover types (classification)
CLIM 714 Land-Climate Interactions
Issues
• temporal sampling – reconcile requirements of monitoring
task with sensor characteristics and external influences• repeat cycle of sensor • spatial resolution of sensor • lifespan of mission / historical data • cloud cover & aerosol effects on optical /
thermal data
CLIM 714 Land-Climate Interactions
Issues
• discriminating surface changes from external influences on remote sensing data – Viewing and illumination conditions can
change over time • Viewing:
– wide field of view sensors – pointable sensors
• Illumination: – variations in Sun position (visible and near=IR channels)
• variations in atmospheric conditions
CLIM 714 Land-Climate Interactions
Issues
• cloud cover
Composite image from U. Wisconsin SSEC
CLIM 714 Land-Climate Interactions
Issues• Sensor calibration
– degradation over time – variations between instruments
• Co-registration of data – effects of mis-registration (practical)
• Quantity of data – volume of data can be very large – preprocessing requirements can be very large– move towards formation of databases of
remote-sensing derived 'products' (e.g., EOS Earth Observing System)
CLIM 714 Land-Climate Interactions
Mean Annual Cycles - Snow
CLIM 714 Land-Climate Interactions
Magnitude of annual cycle (top) and standard deviation of April soil wetness (bottom).
Mean Annual Cycles – Soil Wetness
CLIM 714 Land-Climate Interactions
Albedo movie
CLIM 714 Land-Climate Interactions
PAR movie
CLIM 714 Land-Climate Interactions
LAI movie
CLIM 714 Land-Climate Interactions
Green-up and climate
Is the green-up causing a cool spell, or is this an artifact of weather variability?
CLIM 714 Land-Climate Interactions
Dates of start and length of growing season
CLIM 714 Land-Climate Interactions
Climate regimes reflected in growing seasons
CLIM 714 Land-Climate Interactions
First green-up
DMA = delayed moving average
Detects the time of the first increase in NDVI from base level.
Year-to-year variations are caused by climate variations (mainly temperature anomalies, but many factors play a role, including precipitation/soil moisture, and solar radiation).
1995
1996
Schwartz et al. (2002 Int. J. Climatol.)
CLIM 714 Land-Climate Interactions
Peak greening
SMN = Seasonal Midpoint NDVI
Detects the time of the crossing of NDVI over the (max+min)/2 value. 1995
1996
Schwartz et al. (2002 Int. J. Climatol.)
Satellite NDVI measurements
Fit a spline curve
Determine midpoint
Remove cloud-contaminated points
DMA looks for this first positive trend.
CLIM 714 Land-Climate Interactions
Hysteresis means you can’t just map weather to vegetation – physical and biological processes
must be modeled
CLIM 714 Land-Climate Interactions
Soil moisture and diurnal cycle
Low soil moisture (solidlines) leads to greaterdiurnal temperatureranges (via lower heatcapacity of soil), changedpartitioning of latent andsensible heat fluxes, anddrier afternoon air.
Where extremes control vegetation (e.g. freezes), this can be a factor.
CLIM 714 Land-Climate Interactions
Dealing with issues
• Vegetation Indices (VIs)– measured reflectance / radiance
sensitive to variations in vegetation amount
– BUT also sensitive to external factors – want contiguous data (clouds) – Typically take VI compositing approach
• Assume highest measured VI is actual VI.• Interpolate across missing data.
CLIM 714 Land-Climate Interactions
Use of VIs• no one ideal VI - NDVI used historically • empirical relationships will vary spatially
and temporally • direct:
– attempt to find (empirical) relationship to biophysical parameter (e.g. LAI)
• indirect:– look at timing of vegetation events (phenology)
• VI can still be sensitive to external factors (Especially bi-directional reflectance distribution function (BRDF) effects)
CLIM 714 Land-Climate Interactions
BRDF explainedBRDF gives the reflectance of a target as a function of illumination geometry and viewing geometry. The BRDF depends on wavelength and is determined by the structural and optical properties of the surface, such as shadow-casting, multiple scattering, mutual shadowing, transmission, reflection, absorption and emission by surface elements, facet orientation distribution and facet density.However, it should not be overlooked that the BRDF simply describes what we all observe every day: that objects look differently when viewed from different angles, and when illuminated from different directions.
CLIM 714 Land-Climate Interactions
Real-world examples of BRDF variations
This is a black spruce forest in the BOREAS experimental region in Canada. Left: backscattering (sun behind observer), note the bright region (hotspot) where all shadows are hidden. Right: forward-scattering (sun opposite observer), note the shadowed centers of trees and transmission of light through the edges of the canopies.
A barren field with rough surface Left: backscattering (sun behind observer), note the bright region (hotspot) where all shadows are hidden. Right: forward-scattering (sun opposite observer), note the specular reflection.
CLIM 714 Land-Climate Interactions
VI Issues• IDEAL:
– Attempt to make VI sensitive to vegetation amount but not to external factors:
• atmospheric variations • topographic effects • BRDF effects (view and illumination) • soil background effects
– SAVI, ARVI etc.
• PRACTICE:– VIs maintain some sensitivity to external
factors – Be wary of variations in satellite calibration etc.
for time series
CLIM 714 Land-Climate Interactions
VI Issues
CLIM 714 Land-Climate Interactions
VI Issues
CLIM 714 Land-Climate Interactions
Examples/Techniques
• land cover change detection
• Vegetation Indices eg:– change in VI - infer
change in vegetation state
– NDVI variation in Mozambique (UN World Food Programme)
CLIM 714 Land-Climate Interactions
CLIM 714 Land-Climate Interactions
Variance of Boreal Summer LAI over 8 years (1987-1994)
CLIM 714 Land-Climate Interactions
NPP(gC m-2 yr-1)
Mean annual NPP (1981-2000) estimated
with a DVM at 8km resolution
CLIM 714 Land-Climate Interactions
58
60
62
64
66
68
70
1981 1983 1985 1987 1989 1993 1995 1997 1999
Glo
bal
NP
P (
Gt
C /
yr)
Year
Inter-annual variation in global total NPP(Gt C/ yr)
CLIM 714 Land-Climate Interactions
K =0.55%* R2 = 0.29
K = 0.50%*R2 = 0.34
K = 0.40%*R2 = 0.33
0.32
0.36
0.4
0.44
1980 1984 1988 1992 1996 2000
50- 90N 20- 50N GlobeA1 K = 0.24%
R2 = 0.13
K = 0.68%**R2 = 0.72
K= 0.27%R2 = 0.07
0.32
0.38
0.44
0.5
0.56
1980 1984 1988 1992 1996 2000
0- 20N 0- 20S 20- 90SA2
K = 0.52%**R2 = 0.67
K = 0.66%**R2 = 0.64
K = 0.53%*R2 = 0.50
4
8
12
16
1980 1984 1988 1992 1996 200060
62
64
66
68
7050- 90N 20- 50N GlobeB1 K = 0.15%
R2 = 0.09
K = 0.50%R2 = 0.21
K = 0.88%**R2 = 0.85
4
8
12
16
20
1980 1984 1988 1992 1996 2000
0- 20N 0- 20S 20- 90SB2
Trends in mean land NDVI
Trends in total land NPP
Annual growth rate K, significance * 95%,** 99%. 1991-92 omitted because of the Mount Pinatubo eruption.
Interannual trends in mean land NDVI and in total land NPP
CLIM 714 Land-Climate Interactions
Trends in length of growing season
• Evidence exists both for both an earlier spring green-up and later autumn senescence over the later half of the 20th Century.
Sparks, T. H., and A. Menzel, 2002: Observed changes in seasons: An overview. Int. J. Climatol., 22, 1715-1725.
CLIM 714 Land-Climate Interactions
GLO-PEM estimate of changes in annual
NPP 1982-2000
CLIM 714 Land-Climate Interactions
-6
-4
-2
0
2
4
1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001
NPP
Ano
mal
y
0-20S20-50S
P inatubo Eruption
NOAA 11NOAA 7 NOAA 9 NOAA 14
-4
-3
-2
-1
0
1
2
3
4
1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001
NPP
Ano
mal
y an
d M
EI
6
10
14
18
22
10-d
ay a
nd a
nnua
l NPP
Anomaly MEI 10-day Annual
P inatubo Eruption
NOAA 7 NOAA 9 NOAA 11 NOAA 14
-6
-4
-2
0
2
4
1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001
NPP
Ano
mal
y
50-90N20-50N 0-20N
NOAA 7 NOAA 9 NOAA 11 NOAA 14
P inatubo Eruption
Global
Northern hemisphere Southern hemisphere
Interannual variability and trend in global terrestrial net primaryproductivity: satellite analysis 1980-2000
M. Cao, S.D. Prince, J. Small, S.J. GoetzDepartment of Geography, University of Maryland
MEI is an indicator of the intensity of El Niño (+ve) and La Niña (-ve). The 10-day anomaly is the deseasonalized change in NPP. NPP anomalies in g C m-2 per 10 days.
Temporal changes in NPP for the globe and each hemisphere 1981-2000.
CLIM 714 Land-Climate Interactions
La Niña - 1998-99
Normal - 1995-96
El Niño - 1982-83
El Niño - 1986-87
El Niño – 1993-94
El Niño – 1997-98
Anomalies.Yr. x-(mean of
1981-2000) gCm-2 per 10 d
Difference between NPP for the stated year and mean value for whole period 1981-2000.
The responses of NPP to ENSO
CLIM 714 Land-Climate Interactions
-1.5
-1
-0.5
0
0.5
1
1.5
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000
NP
P A
nom
aly
(Gt C
/ M
onth
)
-3
-2
-1
0
1
2
3
EN
SO
Ind
ex
NPP ENSO
Pinatubo Eruption
-15
-10
-5
0
5
10
15
20
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000
Rai
nfal
l Ano
mal
y (m
m/m
onth
)
-1.5
-1
-0.5
0
0.5
1
1.5
2
Tem
pera
ture
Ano
mal
y (o
C)
Rainfall Temperature
AFRICAWhole continent
CLIM 714 Land-Climate Interactions
6.4
6.6
6.8
7
7.2
7.4
1981 1983 1985 1987 1989 1991 1993 1995 1997 1999
NP
P (
g C
m-2
10d
ays)
-1
-0.5
0
0.5
1
1.5
2
EN
SO
Ind
ex
NPP ENSO
Interannual Variation in NPP and ENSO Cycles for the Whole African ContinentNPP vs ENSO in Africa
CLIM 714 Land-Climate Interactions
19831987 1997
Negative anomaly1990
Positive anomaly
No change
19891999
1996
El Niño
Normal
La Niña
CLIM 714 Land-Climate Interactions
Low
High
Low
High
Coefficient of variation Mean Annual NPP
Coefficient of Variation and Mean Annual NPP 1982 - 1999
CLIM 714 Land-Climate Interactions
Areas exhibiting model sensitivity to phenologyNumber of months per yearLAI-Phen statistically different from LAI-Mean
Latent Heat FluxSoil Moisture Content
PrecipitationDaily Max 2m Air Temperature
HadAM3+MOSES2
In GLACE, HadAM ranked 11th out of 12 models for sensitivity of precipitation to land surface.
CLIM 714 Land-Climate Interactions
Simulating disturbancesHistorical fire-return intervals describe the approximate time interval before a new firewould occur at the same site, here simulated by a DVM at 0.5°x0.5° longitude/latituderesolution (averaged over the period 1900 to 1995, driven by climatology).
Fire frequency inecosystems with stronghuman influence onthe fire regime, suchas tropicalsavannas andMediterraneantype eco-systems, areunderestimated by the global firemodule in this DVM.
CLIM 714 Land-Climate Interactions
Fire in Boreal Forests• Many ecosystems have
fire as a natural element
• One of less obvious is the boreal forests, where despite typically high soil moisture, most areas outside Northeast Asia have a 50-200y recurrence of fire.
• Outside the growing season, the forest crown and undergrowth can be very dry – fueling fires started by lightning.
CLIM 714 Land-Climate Interactions
BOREAS Southern Study Area• BOREAS was a forerunner to the GEWEX CSEs
• Recent burns are red in classification map; Young, medium and old regenerating forests are shaded as olive, tan and brown respectively.
• TMI satellite image (lower right)
CLIM 714 Land-Climate Interactions
Key to Global Change Forecasts
• One must be able to simulate observed (historical and present) variations in vegetation phenology before one can believe “true” predictions.
• The same is true for modeling vegetation distribution, as we will see next week.