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How Likely Is a Large Terrestrial
Feedback to Climate Change?
Mike Goulden et al
Transect Surveys Experimental Manipula;on Eddy Covariance Drier Warmer We6er Colder Probability of a change
Impact of that change (for
example, radiative forcing)
Climate
?
Weather
(means, extremes)
Occurrence of
fire
?
?
Physiology
Growth
Population size
Species reordering
?
?
Dominant PFT//
biome shift
Structure
?
Ecosystem goods and services
Mass, energy flux in and out (feedbacks)
?
Framework for comparing climate feedback risks Analogous to probabilis;c risk assessment – (insurance, engineering, etc) Radiative forcing
Probability of
Feedback
caused
by
that
=
X
an event
risk
event
•  Probability of event – landscape issue that accounts for the frac;on of landscape that is vulnerable (spa;al probability) and the likelihood of an event at a vulnerable loca;on (temporal probability) •  An event may have a low probability because only a small frac;on of the landscape is vulnerable or because the event occurs infrequently Increased
probability of
fire
X
Radiative forcing
caused by a fire
=
Climate
feedback risk
via fire
•  Long-­‐term goal to determine rela;ve importance (risk) of various feedbacks -­‐ iden;fy, understand, and focus on high probability, high consequence feedbacks GL
CSS
1938
1983
1977
2002
California sagebrush,
black sage still
dominate; Rhus/
Malosma have
increased
Why so stable?
•  Differences in soil? Nope – same texture across border – same
texture to 2-m depth
Loma Ridge - Soil Texture Analysis
80
70
Percentage
60
50
Grassland
Shrubland
40
30
20
10
0
Clay
Silt
Soil Texture
Sand
•  Constant environment? Nope – at least 4 wildfires (1948, 67, 98, 07), an order
of magnitude interannual precip variation
•  Lots of ecological resiliency (at least to normal fire and precip patterns of
management since 1930)
Resiliency to wildfire –
an unfortunate dataset
Air T
*
65 mph gusts
Water input for the various treatments
300
Cumulative ambient
Cumulative dry
Cumulative wet
1/3 of the plots get
extra water
250
1/3 of the plots get
normal water
200
150
1/3 of the plots get
reduced water
100
50
0
10/1/2008
11/20/2008
1/9/2009
2/28/2009
4/19/2009
6/8/2009
1000
Manipulation 2007
Manipulation 2008
Manipulation 2009
Manipulation 2010
Manipulation 2011
Tustin 1927-2003
Water input (mm)
800
600
400
200
0
0
10
20
30
40
Days with at least 2.54 mm water input
50
N addition
Seedling Addition /
Sampling Subplots
SDL
add
S
R
Nbag
sRes0
Harv07
Harv07
Harv07
Lit
Harv07
07
SDL
add
Harv09
Harv09
Nbag
sRes0
9
9
616/HS
Permanent Monitoring
Subplots
LWS
505 T LWS
229
NDVI
215
416
TDR
616/HS
505
T
NDVI
616/HS
616/HS
Harv08,0
9
616/HS
Seed Addition
Subplots
Harv08,0
9
TDR
616/HS
T
T
616/HS
616/HS
Harv08 Resin
L
Resin Harv08
R
DOWNHILL
S
R
Tab 7. Timeline and responsibility
Task
Establish or construct sites
Clean up fire debris
Repair fire damaged towers
Repair damaged Grassland manipulations
Repair damaged CSS manipulations
Build PJ manipulations
Build Pine/Oak manipulations
Establish wet side Santa Ana transect
Dismantle and restore sites
Field manipulations
Operate Grassland manipulations
Operate CSS manipulations
Operate PJ manipulations
Operate Pine manipulations
Collect and apply seeds
Nitrogen fertilization
Field measurements GL and CSS plots
Field measurements PJ and PO plots
Natural Gradient Observations
Operate eddy covariance towers
Field measurements at tower sites
Field measurements at transect sites
Data analysis and synthesis
Who*
5/1/08
1/1/08
ALL
4
GW
2
GW,MG,UG,CK 44
GW,MG,UG,CK
3333
GW,MG,UG,CK
333
GW,MG,UG,CK
333
AK,UG
GW,JS,CK,UG
1/1/09
1/1/10
1/1/11
4/30/13
1/1/13
1/1/12
22
3333333
GW,MG,CK
2222211111222222111112222221111122222211111222222211
GW,MG,CK
211111222222111112222221111122222211111222222211
GW,MG,CK
11111111111111111111111111111111111111111111111111
GW,MG,CK
222211111222222111112222221111122222221111
KS,JS,UG
1111112
1111112
1111112
1111112
1111112
KS,JS,UG
1
1
1
1
1
1
1
1
ALL (mainly JS,CK) 333331111133333311111333333111113333331111133333311
ALL (mainly JS,CK)
22222222222222222222222222222222222222222222222222
GW
AK,JS,CK,UG
AK,UG
MG,KS,AK
111111111111111111111111111111111111111111111111111111111111
2
2
2
2
2
2
2
2
2
22
22
22
22
22
111111111111111111222222222222222222222333333333333333333333
*Tasks performed by the following people (see Budget Justification for details on expertise): MG (Michael
Goulden), KS (Katie Suding), GW (Greg Winston), JS (Jane Smith), CK (Chris Kopp), AK (Anne Kelly),
UG (Undergraduate Field Assistants), ALL (all members of research team). 4 indicates a very large effort
is required (2-3 people full time); 3 indicates a large effort is required (1-2 people full time); 2 indicates a
medium effort is required (1-0.5 people full time); 1 indicates a modest or background effort is required 510 hours a week).
2009-10
600
Ambient treatment
Dry treatment
Wet treatment
400
600
(d)
400
200
200
0
0
(e)
(b)
0.3
0.3
0.2
0.2
0.1
0.1
0.0
0.6
0.0
(c)
(f)
0.6
0.4
0.4
0.2
0.2
0.0
Nov
Jan
Mar
May
Nov
Jul
Date
Jan
Mar
May
Jul
0.0
EVI
EVI
(a)
Cumulative water input (mm) Soil water (cm3 cm-3)
Soil water (cm3 cm-3) Cumulative water input (mm)
2008-09
Water input
0
100
200
300
400
500
600
700
800
(a)
ANPP
600
400
2007
2008
2009
2010
2011
200
0
(b)
ANPP
600
400
200
0
0.0
0.2
0.4
0.6
Fraction water increasing species
0.8
1.0
Ripgut Brome (Bromus diandrus)
0.6
0.5
r 0.4
ev
o
c la
n 0.3
io
tc
ar
F 0.2
0.1
0
0
100
200
300
400
500
600
500
600
Water input (mm yr -­‐1 )
Filaree (Erodium sp.)
0.6
0.5
r 0.4
e
v
o
cl
a 0.3
n
o
tic
ar
F 0.2
0.1
0
0
100
200 Water input 300(mm yr -­‐1 ) 400
(a) Erodium sps.
(d) Lolium multiflorum
(g) Bromus diandrus
(b) Calandrinia ciliata
(e) Lupine sps.
(h) Vicia villosa
(f) Nasella pulchra
(i) Avena sps.
0.4
0.3
0.2
0.1
Fractional species cover
0.0
0.4
0.3
0.2
0.1
0.0
(c) Hirschfeldia incana
0.4
0.3
2008
2009
2010
2011
0.2
0.1
0.0
0
200
400
600
0
200
400
600
Water input (mm)
0
200
400
600
• Rapid changes in relative abundance – Species reordering
• Species coexist at climatological precipitation (median 236 mm /yr)
• Species reordering tied to traits
• Favored with drought - Forbs and shorter time to flowering
• Favored in wet treatments - Grasses and N fixers
Fractional species cover
0.4
Bromus diandrus
Lolium multiflorum
Avena sps.
Nasella pulchra
Calandrinia ciliata
Erodium sps.
Hirschfeldia incana
Vicia villosa
Lupine sps.
0.3
0.2
1929-2010 median
236 mm
0.1
0.0
0
100
200
300
Water input (mm yr-1)
400
500
600
•  Species coexist at climatological precipitation (median 236 mm /yr)
•  Grasses have trouble at > ~100 mm
•  A series of very dry years might eliminate grasses
Fractional species cover
0.4
Bromus diandrus
Lolium multiflorum
Avena sps.
Nasella pulchra
Calandrinia ciliata
Erodium sps.
Hirschfeldia incana
Vicia villosa
Lupine sps.
0.3
0.2
0.1
0.0
0
100
200
300
Water input (mm yr-1)
400
500
600
A consecutive series of unusually wet or dry years is probably
necessary to either completely wipe out a PFT or allow a new PFT to
establish – this is very infrequent
Average 3-­‐year precipitation, 1930-­‐2010 climate
s 600
d
o
ir
e
p
r 500
a
e
y-­‐
3
400
m
o
d
n
ar
300
0
0
0
0
1
r 200
e
p
s
e
c
n 100
e
r
u
cc
O
3 years with <= 10 cm yr-­‐1
0.1 percentile
3 years with >= 50 cm yr-­‐1
3.7 percentile
0
0
50
100 150 200 250 300 350 400 450 500 550 600 650 700
3-­‐year mean precipitation (mm)
•  The dry plots received ~100 mm yr-1 for the first 3 years of the study
•  These plots became forb dominated
•  But this was an extreme treatment (<1 percentile)
•  And the grasses have returned in the most recent 1.5 yrs, which have
been wetter than average
•  Species composition shifts rapidly but changes may be reversible and
community type is generally stable
•  What required to shift community type (for example, CSS to AGS)
•  CSS plots recovering from 2007 fire – increase in shrubs and decrease in
herbaceous plants
•  Shrubs markedly set back in the dry treatments
100
100
90
90
90
80
80
80
70
70
70
60
60
60
Sage
50
Sage
50
Grass
Grass
40
Forb
Vine
30
40
Forb
Vine
30
Percent Cover
100
Percent Cover
Percent Cover
-­‐H2O, +N
Ambient H2O, +N
+H2O, +N
Grass
40
20
20
10
10
10
2010
Year
2011
Vine
0
0
2009
Forb
30
20
0
Sage
50
2009
2010
Year
2011
2009
2010
Year
2011
Probability of a change
Impact of that change (for
example, radiative forcing)
Climate
Very high
Weather
(means, extremes)
Occurrence of
fire
Moderate
Very high
Physiology
Growth
Population size
Species reordering
Small
Very
low
Dominant PFT//
biome shift
Structure
Large
Ecosystem goods and services
Mass, energy flux in and out (feedbacks)
Small
How Likely Is a Large Terrestrial Feedback to
Climate Change?
•  Changes in fire frequency appear likely.
But most fire adapted ecosystems recover rapidly from fire,
limiting the integrated effects. And the albedo effect may
partially or completely offset the carbon effect – a moderately
high probability, moderately low consequence mechanism.
•  Changes in Physiology, Growth, Population sizes, and Species
reordering are very likely – these responses are fast – a natural
mode of variability.
But many of these changes may not have large consequences
for climate – a very high probability, low consequence
mechanism .
•  Changes in dominant plant functional type and biome shifts may
have larger consequences for climate.
But these responses appear less likely – they are slow and very
poorly understood – a low probability, very high consequence
mechanism.