Transcript CLM-CN update: Sensitivity to CO2, temperature, and precipitation in C-only vs. C-N mode Peter Thornton, Jean-Francois Lamarque, Mariana Vertenstein, Nan Rosenbloom, Jeff Lee.

CLM-CN update: Sensitivity to
CO2, temperature, and
precipitation in C-only vs. C-N
mode
Peter Thornton, Jean-Francois
Lamarque, Mariana Vertenstein,
Nan Rosenbloom, Jeff Lee
CLM-CN: Summary Model Structure and Fluxes
Plant
Pools
Current
Storage
Leaf
Live
Stem
Live
Coarse Root
Previous
Storage
Fine
Root
Dead
Stem
Dead
Coarse Root
Wood Litter
(CWD)
Litter
Pools
Soil Organic
Matter Pools
Litter 1
(Labile)
Litter 2
(Cellulose)
Litter 3
(Lignin)
SOM 1
(fast)
SOM 2
(medium)
SOM 3
(slow)
Atmospheric
CO2
Atmospheric
N species
Legend
Vegetation
Biomass
C flux
N flux
Temp
sensitivity
Soil
Organic
Matter
Coupled Carbon-Nitrogen dynamics
• Strong feedback between decomposition and plant growth:
soil mineral N is the primary source of N for plant growth.
• Can result in a shift from C source to C sink under warming.
P.E. Thornton, NCAR
CLM-CN spinup summary: global total C pools (CAM drivers)
using accelerated decomposition method of Thornton and Rosenbloom, Ecol Mod (2005)
GPP
(CO2+Ndep)
offline CLM-CN
(CAM drivers)
coupled
(CAM – CLM-CN)
transient, control
(transient-control)
NEE
(CO2+Ndep)
offline CLM-CN
(CAM drivers)
coupled
(CAM – CLM-CN)
transient, control
(transient-control)
TotC
(CO2+Ndep)
offline CLM-CN
(CAM drivers)
coupled
(CAM – CLM-CN)
transient, control
(transient-control)
Cumulative land carbon uptake and net ecosystem exchange, 1850-2100
with constant climate (25-yr cycle), prescribed [CO2]atm
Total C uptake (PgC)
(land fraction)
Mean NEE (PgC/y)
Expt
1850-2000
2000-2100
1980-2000
2080-2100
N dep
16 (6%)
50 (3%)
-0.24
-0.73
CO2 fert
61 (22%)
220 (13%)
-0.98
-2.56
CO2+Ndep
79 (29%)
301 (17%)
-1.31
-4.13
CLM-C
223 (81%)
843 (49%)
-3.80
-10.75
Land fractions referenced against cumulative fossil fuel emissions of
276 PgC for 1850-2000 and 1732 PgC for 2000-2100 (SRES A2)
Land biosphere sensitivity to increasing atmospheric CO2 (L)
CLM-C
CLM-CN (CO2,Nfix,dep)
CLM-CN (CO2,Nfix)
CLM-CN (CO2)
 C4MIP models
 C4MIP mean
Results from offline CLM-CN,
driven with CAM climate, in
carbon-only (CLM-C) and
carbon-nitrogen (CLM-CN)
mode, from present to 2100.
Using SRES A2 scenario
assumed CO2 concentrations.
Land biosphere sensitivity to increasing atmospheric CO2 (L)
CLM-CN (CO2,Nfix,dep)
CLM-CN (CO2,Nfix)
CLM-CN (CO2)
Evidence that increasing Nlimitation under rising CO2 has
an important effect on the
transient behavior of L, and
that consideration of
anthropogenic N deposition
reverses this trend by around
2060.
5
0
4
-5
3
-10
CLM-C
CLM-CN
2
-15
1
-20
0
-25
Tair
Prcp
Coupling C-N cycles buffers the interannual variability of
NEE due to variation in temperature and precipitation
(global means, control simulations).
NEE sensitivity to Prcp (PgC / mm d -1)
NEE sensitivity to Tair (PgC / K)
NEE sensitivity to Tair and Prcp (interannual variability)
NEE sensitivity to Tair and Prcp (CLM-CN vs CLM-C)
Tair
Prcp
CLM-CN
CLM-CN
CLM-C
CLM-C
Components of NEE
temperature response
NPP
NEE
NPP dominates NEE response to
temperature in most regions.
Exceptions include Pacific
Northwest, Scandanavia.
HR
FIRE
Dissection of NPP
temperature response
GPP
NPP
Warmer temperatures lead to drying
in warm soils (increased evaporative
demand), and wetting in cold soils
(less soil water held as ice).
Btran
Soil ice
Components of NEE
precipitation response
NPP
NEE
NPP dominates NEE response to
precipitation in tropics,
midlatitudes, HR dominates in
arctic and coldest climates.
HR
FIRE
Dissection of HR
precipitation response
HR
NEE
Higher Precip in arctic/cold climate
produces deeper snowpack, warmer
soils, increased HR.
Snow depth
Tsoil
Potential for complex climate
feedbacks depending on the
spatial patterns of changing
temperature and precipitation.
NPP variability dominates the
Tair and Prcp response in most
locations, but HR dominates
the Prcp response in cold
climates, due to feedback
between snowpack, soil
warming, and enhanced HR.
NEE sensitivity to Tair and Prcp: effects of rising CO2 and
anthropogenic N deposition
60
% change from control
40
20
0
-20
CLM-C: +CO2
CLM-CN: +CO2
CLM-CN: +CO2 +Nmin
-40
Tair
Prcp
Carbon-only model has increased sensitivity to Tair and
Prcp under rising CO2. CLM-CN has decreased sensitivity
to both Tair and Prcp, due to increasing N-limitation.
2.
Nitrogen coupling
reduces sensitivity to
CO2. This effect
increases with increasing
CO2.
Anthropogenic nitrogen
deposition alleviates this
effect.
5
0
4
-5
3
-10
CLM-C
CLM-CN
2
-15
1
-20
0
Nitrogen coupling
reduces global mean
sensitivity to temperature
and precipitation.
4.
Complex spatial patterns
of NEE response to T
and P. These responses
would be in tension
under warmer-wetter
climate.
5.
CO2 increases T and P
sensitivities in carbononly model, decreases
sensitivities in carbonnitrogen model.
Prcp
60
40
% change from control
3.
-25
Tair
20
0
-20
CLM-C: +CO2
CLM-CN: +CO2
CLM-CN: +CO2 +Nmin
-40
Tair
Prcp
NEE sensitivity to Prcp (PgC / mm d -1)
1.
NEE sensitivity to Tair (PgC / K)
CLM-CN Summary: C-cycle response to nitrogen coupling,
CO2, temperature, and precipitation
CLM-CN summary contd:
Nitrogen cycle buffers land carbonclimate feedbacks
•
•
•
•
Nitrogen coupling…
reduces CO2 fertilization (reduces a
negative feedback on climate system)
reduces T sensitivity (reduces a positive
feedback)
reduces P sensitivity (uncertain feedback
sign)
So, not a simple result with respect to total
carbon-climate system gain.
CLM-CN development path
• 3-pool vs 4-pool switch implemented and tested, with
expected results.
• Landcover change effects (product pools) underway.
• 13C now incorporated and tested, transient runs
underway, collaboration with CSU.
• Collaboration with LLNL to add 14C on the same
framework.
• Implement age-class distributions.
• Introduce N speciation and direct effects of ozone on
physiology, in collaboration with CCSM Chem-Climate
WG.
Speciation of land N emissions
Nitrification vs.
denitrification depends
on aerobic state of soil,
probably at the
microscopic scale.
Sophisticated models
already exist, and it
should be possible to
adapt them for use in
CLM-CN.
Agricultural emissions
could be tied to new
efforts with crop
modeling.