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Land-sea interaction and dynamic vegetation modelling
Ben Smith
Dept of Physical Geography and Ecosystem Science, Lund University, Sweden
• Land-sea-atmosphere connectivity in the global Earth system
• Carbon export from land to sea – possible impacts on coastal ecosystems and biogeochemistry
• Terrestrial vegetation and carbon cycling – recent trends and underlying causes
• Modelling climate change effects on vegetation and carbon cycling – introducing dynamic global vegetation models (DGVMs)
• Case study modelling land-sea carbon export and possible impacts on biogeochemistry of the Baltic Sea
• Future outlook: coupled models of regional Earth system dynamics
Lecture outline
Supporting exercise (in own time)
• Modelling ecosystem response to climate change using the LPJ-GUESS DGVM
Compartments and fluxes of the global carbon cycle
IPCC-AR5 2014
Emissions = 9 PgC/yr
Land-atmosphere = -3 PgC/yr
Ocean-atmosphere = -2 PgC/yr
Land-ocean = 1 PgC/yr
(data for 2000-2009)
Effects of riverine C input to coastal seas*
• Browner, more turbid water, steeper light attenuation
• Substrate for bacteria, increased respiration and pCO2
• Lower pH, decreased carbonate saturation state, represses calcifying organisms
• Cascading effects on overall biogeochemistry and ecology
• Few detailed system-level studies
*Riesebell 2004 J. Oceanography 60: 719-729
Effects of a BaU future emission scenario on global ocean CO2, carbonate and pH (Houghton et al. 1995)
Increased water CO2 concentration enhances production of some phytoplankton species
10.90 river input
point sources
fisheries
CO2 exchange with atmosphere
atmospheric deposition
input from North Sea
export to North Sea
input to sediment
return flux from sediment to water column
−11.58
−3.87 1.14
−1.05
−0.06
0.04 3.91
0.57
*Kulinski & Pempkoviak 2011 Biogeosciences 8: 3291-3230
Riverine input (all sources) 10 Tg/year 40% organic C
Ope
n oc
ean
Land Atmosphere
Sediments
River runoff is the main source of C to the Baltic Sea*
Likely causes: • increased primary production • longer growing season • expanded distribution of trees and shrubs
*Tucker et al. (2001) Int. J. Biometeorol. 45: 184
Change in land surface greenness 1982-1999*
increasing ’greenness’ →
normalised difference vegetation index
NDVI = NIR – R NIR + R
Vegetation cover and productivity have increased in concert with recent decades’ climate warming
*Ahas & Aasa (2004)
Trends in 644 plant phenological time series for Estonia 1948-1999*
non-significant trend
significant trend
Num
ber o
f spe
cies
/ ph
ases
Change in days
earlier spring later autumn
Earlier and longer growing season
*Walther et al. 2005 Proceedings of the Royal Society B 272: 1427
previous distribution
recent distribution
recent observations
0°C january isotherm
1931-1960 1981-2000
Changing distribution of holly, Ilex aquifolium*
Species distributions track climate shifts
*Harsch et al. 2009, Ecology Letters 12: 1040
1912 2009
Treeline advance, Mt Nuolja, Sweden (Van Bogaert et al. 2011. J. Biogeog.)
Par
amet
er e
stim
ate
3
2
1
0
–1
–2
Temperature trend
diffuse abrupt treeline
type
Effect of temperature change on tree limit elevation shift for 166 near-treeline sites + = warming associated with upslope shift*
Treelines are rising on average across the globe
Woody biomass is increasing in many shrublands, savannahs and extra-tropical forests
Eastern Cape Province South Africa (Welz 2013)
Above-ground biomass change 1993-2012
Satellite passive microwave measurements Liu et al. 2015 Nature Climate Change 5: 470-474
Likely causes include increased water-use efficiency under elevated CO2
Duke forest FACE experiment in North Carolina
NPP at ca. 350 ppm CO2 (gC m−2 år−1)
NP
P a
t 550
ppm
CO
2(g
C m
−2år
−1)
25% increase in net primary productionat 550 ppm CO2
NPP at ca. 350 ppm CO2 (gC m−2 år−1)
NP
P a
t 550
ppm
CO
2(g
C m
−2år
−1)
25% increase in net primary productionat 550 ppm CO2
CO2 is food for plants Increased atmospheric CO2 → higher NPP, at least in short term ...
NPP at 350 ppm (gC m−2 yr−1)
NP
P at
500
ppm
(gC
m−2
yr−1
)
Net reaction of photosynthesis
CO 2 + H 2 O + sunlight CH 2 O + O 2 carbon water dioxide
carbohydrate oxygen
CO 2 + H 2 O + sunlight CH 2 O + O 2 carbon water dioxide
carbohydrate oxygen
Norby et al. 2005 PNAS 102: 18052-18056
H2O
CO2
H2O
CO2
Reduced stomatal conductance under elevated CO2 may enhance water-use efficiency (WUE)
reduced stomatal conductance in response to higher ambient CO2 reduces
transpirational water loss
WUE = CO2 uptake by photosynthesis
H2O loss by transpiration
*Keenan et al. 2013 Nature 499: 324-372
mean of 13 models W
UE
ano
mal
y (g
C /
kg H
2O h
Pa)
Forests around the world show a positive trend in water-use efficiency of growth*
Carbon balance of woody ecosystems responds slowly to changes in structure, cover
or distribution
uptake
release
*Hyvönen et al. 2007 New Phytologist 173: 463-480
Net
eco
syst
em e
xcha
nge
of C
O2
(gC
m−2
yr−1
)
Young forests are a sink for CO2, storing assimilated carbon in the stems of growing trees. Over time, phenology and mortality augment soil carbon pools, causing decomposition losses to balance CO2 uptake
*Ciais et al. 2013. IPCC-AR5
Glo
bal C
flux
(Pg
C y
r−1)
25% of C emissions are taken up by terrestrial ecosystems due to surplus of photosynthesis relative to respiration and emissions from wildfires
Land ecosystems are a sink for atmospheric CO2 — a temporary anomaly not expected at steady state
atmosphere → plant
GPP
plants (→ heterotrophs) → soil
litter production
soil → atmosphere RH
plant → atmosphere
RA
net flux from atmosphere
to plants
Soil organic matter
NPP = GPP−RA net flux from atmosphere
to ecosystem
NEE = NPP−RH
Climate response of terrestrial ecosystems is intricately tied to their role in the global carbon cycle
CO2 CO2
CO2
net ecosystem exchange
net primary production
gross primary production
heterotrophic respiration
autotrophic respiration
chloroplast, mitochondrion
resp
onse
dis
tanc
e (1
0y m
)
response time (10x years) 1 sec 1 hour 1 min 1 day 1 year 100 years 10,000 years
tree seedling
stomate
adult tree
1 km
1000 km
mesophyll cell
Scales of terrestrial ecosystem processes
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5
photosynthesis respiration
gas exchange
growth
population dynamics
competition phenology
speciation genetic adaptation
disturbance
soil organic matter turnover
management
global change impacts research
• link structure to function, accounting for
feedbacks between them
• link ’fast’ (physiology, biogeochemistry)
and ’slow’ (demography, composition) ecosystem processes
• account for transient ecosystem dynamics when driving conditions (climate, CO2) are changing rapidly
StructuresFunctions
space
time
Dynamic Global Vegetation Model (DGVM) globally-applicable coupled model of
terrestrial vegetation dynamics and ecosystem biogeochemistry
Soil organic matter
Soil organic matter
soil bio- geochemistry
population dynamics
& disturbance
plant biogeography
primary production & growth
Vegetation
climate CO 2 CO 2
• link structure to function, accounting for
feedbacks between them
• link ’fast’ (physiology, biogeochemistry)
and ’slow’ (demography, composition) ecosystem processes
• account for transient ecosystem dynamics when driving conditions (climate, CO2) are changing rapidly
• based on generalised global (or biome-specific) parameterisations and plant functional types (PFTs)
StructuresFunctions
space
time
Average individual for plant functional type cohort in patch
Modelled area (stand) 10 ha - 2500 km2
replicate patches in various stages of development
Patch 0.1 ha
tree or shrub grass
crown area
height
fine roots
leaves
LAI
sapwood heartwood
0 - 50 cm 50 - 100 cm
leaves / LAI
fine roots
stem diameter
crown area
height
fine roots
leaves
LAI
sapwood heartwood sapwood heartwood
0 - 50 cm 50 - 100 cm
leaves / LAI
fine roots
stem diameter
Vegetation representation in the LPJ-GUESS DGVM*
*Smith et al. 2001 Global Ecology & Biogeography 10: 621-637
Parameter
max establishment rate (ha−1 yr−1)
max longevity (yr)
survival in shade
optimal temp for photosynthesis (°C)
bioclimatic distribution
allocation to stem growth
leaf:sapwood area ratio (m2 cm−2)
leaf phenology
crown spreading
boreal
10-25
evergreen
0.3
150
0.05
high
900
1250
temperate
15-25
summergreen
0.4
250
0.05
high
900
1250
boreal-temperate
10-25
summergreen
0.4
250
0.1
low
300
2500
no limits
10-30
summergreen-raingreen
-
-
-
low
-
-
Trait differences influence functioning and interactions among plant functional types (groups of similar species)
Smith et al. 2014 Biogeosciences 11: 2027-2054
Potential vegetation change in Sweden*
Rel
ativ
e co
ver (
leaf
are
a in
dex)
Norway spruce Scots pine other conifer beech elm ash oak alder birch other broadleaved herbaceous
* Smith et al. 2007 SOU 2007:60
LPJ-GUESS
RCA3
A2 emissions
ECHAM4
Conifer forest NPP 1996-2000*
*Smith et al. 2008. Forest Ecology & Management 255: 3985-3994
T+P+R+C T+P+C T P
T = change i temperature P = change in precipitation R = change in incoming SW radiation C = change in CO2
Scenario
R C
Year in climate model scenario RCA3-ECHAM4/OPYC3-A2
Net
prim
ary
prod
uctio
n (k
gC m
−2 y
r−1)
N Sweden
S Sweden
* Smith et al. 2007 SOU 2007:60
Climate and CO2 effects on NPP*
*Yurova et al. 2007 J. Geophysical Research 112
Net ecosystem CO2 exchange (NEE) Degerö mire, northern Sweden*
Water table depth (cm)
observed modelled
−2.0 −1.5 −1.0 −0.5 0.0
2.0 1.5 1.0 0.5
1900 1920 1940 1960 1980 2000
Glo
bal N
EC
B (G
tC)
flux to atmosphere
flux to biosphere
IPCC-AR4 estimates
Globally, past net ecosystem C balance (NECB) reflects land use and CO2 trends
atmospheric [C
O2 ]
cropland cover
Future land ecosystem carbon balance*
Eco
syst
em C
poo
l (G
tC)
LPJ-GUESS
Business-as-usual emissions (RCP8.5)
GCM
1900 2100 23000
8.5
W/m2
1900 2100 23000
8.5
W/m2
sink
source
neutral
* Ahlström et al. 2012 Environmental Research Letters 7
increased sink / reduced source
reduced sink / increased source
Robust sign of change for some regions
kgC m−2 yr−1
−0.150
0.150
0
∆ Net biome C exchange (2071-2100)−(1961-1990)
latitude
earlier leaf-out → ↑photosynthesis
milder autumn → ↑respiration
intensified seasonality in some
tropical regions
Some robust seasonal and regional trends
month
increased sink / reduced source
reduced sink / increased source
J F M A M J J A S O N D
increased sink / reduced source
reduced sink / increased source
Department of Earth Sciences
www.oceanclimate.se
Carbon in runoff from land is a major input to Baltic Sea biogeochemistry
Organic carbon (dissolved, particulate)
Dissolved CO2 (mainly respiration)
Alkalinity (mainly weathering products)
• Dissolved organic carbon (DOC) comprises a relevant part of the carbon transported to the Baltic Sea
• Main source: wetlands • DOC is generated by decay of soil
organic matter and transported by runoff and rivers to the Baltic Sea
Urban areas
Bare areas
Cultivated land
Pastures and natural grassland
Open herbaceous vegetation with shrubs
Lichens and mosses
Cropland-woodland mosaic
Wetlands
Snow and ice
Sparse vegetation
Broadleaved deciduous closed forest
Broadleaved deciduous open forest
Mixed closed forest
Mixed open forest
Needleleaved closed forest
Needleleaved open forest
Water
Land cover of the Baltic Sea Basin
Ledwith (2003), GLC2000 project
Urban areas
Bare areas
Cultivated land
Pastures and natural grassland
Open herbaceous vegetation with shrubs
Lichens and mosses
Cropland-woodland mosaic
Wetlands
Snow and ice
Sparse vegetation
Broadleaved deciduous closed forest
Broadleaved deciduous open forest
Mixed closed forest
Mixed open forest
Needleleaved closed forest
Needleleaved open forest
Water
Land cover of the Baltic Sea Basin
Urban areas
Bare areas
Cultivated land
Pastures and natural grassland
Open herbaceous vegetation with shrubs
Lichens and mosses
Cropland-woodland mosaic
Wetlands
Snow and ice
Sparse vegetation
Broadleaved deciduous closed forest
Broadleaved deciduous open forest
Mixed closed forest
Mixed open forest
Needleleaved closed forest
Needleleaved open forest
Water
Land cover of the Baltic Sea Basin
Urban areas
Bare areas
Cultivated land
Pastures and natural grassland
Open herbaceous vegetation with shrubs
Lichens and mosses
Cropland-woodland mosaic
Wetlands
Snow and ice
Sparse vegetation
Broadleaved deciduous closed forest
Broadleaved deciduous open forest
Mixed closed forest
Mixed open forest
Needleleaved closed forest
Needleleaved open forest
Water
Land cover of the Baltic Sea Basin
Urban areas
Bare areas
Cultivated land
Pastures and natural grassland
Open herbaceous vegetation with shrubs
Lichens and mosses
Cropland-woodland mosaic
Wetlands
Snow and ice
Sparse vegetation
Broadleaved deciduous closed forest
Broadleaved deciduous open forest
Mixed closed forest
Mixed open forest
Needleleaved closed forest
Needleleaved open forest
Water
Land cover of the Baltic Sea Basin
high veg cover (more conifers & wetlands) high veg cover (less conifers & wetlands) large lakes/reservoirs open highland alpine + subalpine birch forest glacier
Mechanisms?
• Vegetation effect on water residence time
• Decomposition influence on soil water CO2 concentration and pH
• Root exudate effect on soil pH
• Direct weathering by DOC
Weathering reaction (plagioclase):
2CO2 + 3H2O + Ca2Al2Si2O8 → Al2Si2O5(OH)4 + Ca2+ + 2HCO3−
alkalinity
Vegetation cover/type affects export of organic carbon and weathering products*
* Humborg et al. 2004 Limnology & Oceanography 49: 1871-1883.
Dissolved organic carbon (DOC) production and export in the LPJ-GUESS DGVM*
*Yurova et al. 2008 Water Resources Res. 44
DOC production and export Degerö mire, N Sweden*
*Yurova et al. 2008 Water Resources Research 44
modelled DOC storage
modelled DOC export
observed DOC export
modelled DOC production
modelled DOC export
Production Export gm−2
Export mg l−1
Storage gm−2
runoff basis mg/l
catchment area basis g/m2
Simulated DOC production 1961-2005 Wetland sources
DOC in runoff gC m−2 yr−1 Wetland fraction of landscape
−0.1 0.0 0.1 0.2 0.3
Råne älv Niemisel Torne älv Mattila
Töre älv Infl.Bölträsket Kalix älv Karlsborg
Lule älv Luleå Skellefte älv Slagnäs
Alterälven Norrfjärden Pite älv Bölebyn
Rickleån Utl Ume älv Stornorrfors
Öre älv Torrböle Lögde älv Lögdeå
Gide älv Gideåbacka Ångermanälven Sollefteå
Ljusnan Funäsdalen Indalsälven Bergeforsen Ljungan Skallböleforsen
Delångersån Iggesund Gavleån Gävle
Dalälven Älvkarleby Forsmarksån Johannisfors
Norrström Stockholm Nyköpingsån Spånga
Motala Ström Norrköping Göta Älv Trollhättan
Botorpström Brunnsö Viskan Åsbro
Emån Emsfors Ätran Falkenberg Nissan Halmstad
Ljungbyån Ljungbyholm Lagan Laholm
Lyckebyån Lyckeby Mörrumsån Mörrum
Rönneån Klippan Helgeån Hammarsjön
TOC concentration trend (mgC l−1 yr−1)
Ljungbyån
0
5
10
15
20
25
30
1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006
TOC
(mgC
l−1)
∆ DOC production (1996-2005)−(1976-1988)
LPJ-GUESS
gC m−2 yr−1
∆ runoff DOC concentration (1996-2005)−(1976-1988)
LPJ-GUESS
mgC l−1
∆ precipitation (1996-2005)−(1976-1988)
mm yr−1
Simulated versus observed DOC concentration trends*
*Measurements: T. Wällstedt Modelling: Guy Schurgers (wetlands only)
Scaling up: root zone to catchment
CSIM
climate atmospheric CO2 acid deposition ...
LPJ-GUESS
vegetation DOC
DOC, DIC alkalinity
Forest
Herbacous
Lake and streams
Cultivated areas
Ground water compartment 1
Ground water compartment 2
PrecipitationEvapotranspiration
Forest
Herbacous
Lake and streams
Cultivated areas
Ground water compartment 1
Ground water compartment 2
PrecipitationEvapotranspiration
runoff
• CSIM catchment hydrochemistry model*
• based on generalised watershed loading functions (GWLF)
• basic model assumption → water flow path is the most
important factor regulating river chemistry
*Mörth et al. 2007. Ambio 36: 124
y = 0,9991x R² = 0,9712
0
1
2
3
4
0 1 2 3 4
Obs
erve
d
Modelled
DIC (CT, mmol/L)
y = 1,0154x R² = 0,8997
0
0,5
1
1,5
2
2,5
0 0,5 1 1,5 2 2,5
Obs
erve
d
Modelled
DOC (mmol/L)
y = 0,9978x R² = 0,9719
0
1
2
3
4
0 1 2 3 4
Obs
erve
d
Modelled
Alk (AT, mmol/L)
y = 0,986x R² = 0,8341
0
20
40
60
80
0 20 40 60 80
Obs
erve
d
Modelled
Runoff (mm)
Calibration results 1996-2000 point = watershed
boreal forest and peatlands of the north
cultivated south
south
north
*T. Wällstedt, C.M. Mörth, C. Humborg et al. unpublished
~ +15%
~ +25%
~ +25%
Future climate scenarios
1975 2000 2025 2050 2075 2100
mean scenario (A1B) business-as-usual (A2) best case (B1)
DO
C
Alk
alin
ity
DIC
mol yr−1
CSIM
LPJ-GUESS
RCA3
ECHAM5
emissions scenario
Runoff to Bothnian Sea
*T. Wällstedt, C.M. Mörth, C. Humborg et al. unpublished
BB
BS
BP
GF
GR
DS
KA
runoff
DIC
alkalinity
DOC
Change in riverine export × sub-basin (2069-2098)-(1996-2005)
business-as-usual (A2) scenario
flux
chan
ge (%
)
*Omstedt et al. 2012 Tellus 64B: 19586
n.s.
Future impacts on biogeochemical export to the Baltic Sea*
Northern basins: DOC increases more than runoff
Southern basins: fluxes mainly follow runoff
pH c
hang
e (2
069-
2098
)−(1
971-
2000
) total pH change additional pH change
S = surface water - summer W = surface water - winter D = deep water
atm CO2
+ climate
+ nutrient loads
+ land use
atm CO2
+ climate
+ nutrient loads
+ land use
atm CO2
+ climate
+ nutrient loads
+ land use
*Omstedt et al. 2012 Tellus 64B: 19586
Change factor combination
Simulated effects on seawater pH* PROBE-Baltic oceanography-biogeochemistry model
Soil organicmatter
Soil organicmatter
soil C dynamics
populationdynamics
& disturbance
migrationphenology& growth
Vegetation
climateCO2CO2
Rossby Centre Atmospheric-Ocean
Model RCAO
temperature radiation soil water
Future prospect: regional Earth system model coupling land-atmosphere-ocean*
albedo leaf area index
fractional cover - broadleaved forest - needleleaved forest - open land vegetation
daily
LPJ-GUESS dynamic vegetation model
*Smith et al. 2011 Tellus 63A: 87-106
CO2 CH4
climate at domain boundaries
CO2 CH4
open loop for biogeochemical
feedback
grass – tundra IBS BNS BINE BNE
forest
100 yrs →
rela
tive
cove
r (%
LA
I) Vegetation change across the boreal zone
and Arctic*
LPJ-GUESS
RCA4
RCP forcing
EC-EARTH
*W. Zhang et al. in prep.
• Seasonality shift – longer growing season, earlier temperature peak • Evaporative cooling evens out growing season temperature profile
→ favours further shrub encroachment and treeline advance
-3
-2
-1
0
1
2
3
J F M A M J J A S O N D
(b)∆T
(°C
) albedo feedback
evapotranspiration feedback
Additional temperature change (2071-2100)−(1961-1990)
*W. Zhang et al. in prep.
max +1σ mean among yrs −1σ min
RCP 2.6 RCP 4.5 RCP 8.5
Albedo warming in spring, evapotranspiration cooling in summer
Vegetation-atmosphere feedback reduces sea ice cover*
*W. Zhang et al. in prep.
−25 −20 −15 −10 −5 0 5 10 15 20 25 %
Change in SIC 1992-2011 (feedback)−(no-feedback)
∆SLP (Pa)
(fb)−(no-fb)
∆LR (Wm−2)
(fb)−(no-fb)
DJF MAM JJA SON
more trees/ shrubs
more grass/ tundra
Veg. change
(2007-11) −(1992-96)
Causality?
• Land, sea and atmosphere are part of the Earth system, coupled via biogeochemical (especially carbon), hydrological and energy fluxes. River runoff carbon fluxes are in the ballpark 10% of anthropogenic emissions.
• Vegetation patterns and ecosystem functions are changing in response to climate change and elevated CO2
• Models that resolve processes at a wide range of scales are needed to describe potential future changes. DGVMs are built for this purpose.
• Increasing temperatures and CO2 will likely lead to vegetation distributional shifts, effects on carbon cycling will vary by climate zone and uncertainties due to forcing are large.
• System models accounting for land-sea carbon exports and impacts on marine biogeochemistry are emerging. One example for the Baltic Sea suggests 21st Century changes in climate, vegetation and CO2 concentrations will lead to lower pH in the Baltic Sea
• Regional Earth system models that fully couple land-sea-atmosphere matter and energy fluxes may be needed to resolve complex responses to multiple drivers
Summary of main points