Sensitivity analysis for climate change impacts, adaptation and mitigation projection with pasture models, B Gianni, E Fiona, S Jean

Tags: temperate grassland, adaptation, annual temperature, climate change impacts, Clermont Ferrand, Bellocchi Gianni1, climate adaptation, Institute of Biological and Environmental Sciences, Potsdam Institute for Climate Impact Research, mitigation services, Dairy research institute, UK, Rothamsted Research, University of Aberdeen, Scotland, UK, Climate change, AgResearch, France, Colorado State University, INRA, global models, Modeling Project, N2O emissions, precipitation changes, Daily Daycent United Kingdom, Site specific, Australia, Illustrative example, Grassland Ecosystem Research, surface response, annual precipitation, Daycent United Kingdom, CO2 concentration
Content: Session: L3.1 Climate adaptation and mitigation services sensitivity analysis for climate change impacts, adaptation & mitigation projection with pasture models. Bellocchi Gianni1, Ehrhardt Fiona2, Conant Rich3, Fitton Nuala4, Harrison Matthew5, Lieffering Mark6, Minet Julien7, Martin Raphaлl1,Moore Andrew8, Myrgiotis Vasileios9, Rolinski Susanne10, Ruget Franзoise11, Snow Val12, Wang Hong13, Wu Lianhai14, Ruane Alex15, Soussana Jean-Franзois2. 1 INRA, Grassland Ecosystem Research (UR874), Clermont Ferrand, France; 2 INRA, Paris, France; 3 NREL, Colorado State University, Fort Collins, USA; 4 Institute of Biological and Environmental Sciences, University of Aberdeen, Scotland, UK; 5 Tasmanian institute of Agriculture, Burnie, Australia; 6 AgResearch Grasslands, Palmerston North, New Zealand; 7 Universitй de Liиge, Arlon, Belgium; 8 CSIRO, Australia; 9 SRUC Edinburgh Campus, Scotland, UK; 10 Potsdam Institute for Climate Impact Research, Germany; 11 INRA, UMR EMMAH, Avignon, France; 12 AgResearch, Lincoln Research Centre, Christchurch, New Zealand; 13 Agriculture and Agri-Food Canada, Saskatoon, Canada; 14 Department of Sustainable Soil Science and Grassland System, Rothamsted Research, UK. 15NASA Goddard Institute for Space Studies, Climate, Impacts Group, New York, NY, United States of America. Context - A study for temperate grassland systems The development of climate adaptation services requires an improved accuracy in model projections for climate change impacts on pastures. Moreover, changes in grassland management need to be tested in terms of their adaptation and mitigation potential. Within AgMIP (Agricultural Model Intercomparison and Improvement Project), based on the C3MP protocol for crops, we explore climate change impacts on grassland production, future greenhouse gas emissions and removals in temperate grassland systems. Key words: Climate change, Pasture, Carbon, Greenhouse gas, Soil, Livestock.
Methodology - An exercise adapted from C3MP (Coordinated Climate-Crop Modeling Project), AgMIP
· 16 contrasted grassland sites were used covering a large climate gradient over 3 continents (mean annual temperature T from 7 to 14°C; mean annual precipitation P from 380 to 1380 mm) · Model simulations of grassland systems using 99 combinations of climate drivers {Temperature, Precipitation, CO2} to explore yield and GHG responses to climate change. · Calibrated models on specific sites & global models on a set of common sites. · Grassland management considered for simulation: no N-limitation, no irrigation, cutting events. · Target outputs : Yields, GPP, NPP, net above-ground primary production, net herbage accumulation, SOC stock, SON stock, N2O emissions
Combinations site/model : 16 sites ­ 8 models
MODEL
COUNTRY
T
DairyMod v5.3.0
Australia
13,7
DairyMod v5.3.0
Australia
11,7
APSIM-GRAZPLAN v7.5
Australia
12,9
APSIM-GRAZPLAN v7.5
Australia
16,5
DairyMod v5.3.0
Australia
13,5
PaSim
Switzerland
10,3
LPJmL
Switzerland
10,3
PaSim
France
6,7
LPJmL
France
6,7
STICS v8.3.1
France
12,0
STICS v8.3.1
France
9,7
STICS v8.3.1
France
13,9
APSIM v7.6
New Zealand
13,4
APSIM-SWIM
New Zealand
11,1
Daily Daycent
United Kingdom
8,8
Daily Daycent
United Kingdom
9,8
Daily Daycent
United Kingdom
9,8
Daycent
United States
8,4
Daycent
United States
8,4
P 1024 1134 756 491 750 1150 1150 1071 1071 784 879 648 910 714 1383 1343 1343 378 378
Calculation of an emulator for yield & N2O emissions 1/Determination of a global equation for yield by using a regression and {T, P, CO2} terms (= emulator): Y (CO2, T, P) = a + b(T) + c(T)І + d(P) + e(P)І+ f(CO2) + g(CO2)І + h(T*P)+ i(T*CO2)+ j(P*CO2)+ k(T*P*CO2) 2/Determination of the final equation for temperate grassland yield estimation by using a backward stepwise regression 3/Projection of the surface equation in {T, P, CO2} dimensions
Yield (t DM.ha -1 .yr-1 ) P (mm) Yield (t DM.ha -1 .yr-1 ) T (°C) Yield (t DM.ha -1 .yr-1 ) T(°C)
Results ­ Site specific sensitivity of grasslands annual production to climatic drivers (Illustrative example for site Ellinbank Dairy research institute, Australia, DairyMod). Site specific emulator calculated with 99 scenarios. Temperature changes from -1°C to +8°C, precipitation changes from -50% to +50%, CO2 concentration from 380 ppm to 900 ppm. Each dot represents an individual run. The mesh curve was fitted with the emulator calculated as described (n=99 ; r2 =0.995, P<0.001).
1/ T*P*Yield ; CO2 = 380 ppm
2/ T* CO2 *Yield ; 3/ P*CO2 *Yield ; local mean P (mm) local mean T (°C)
22
20
18
16
14
12
10
8
6
4
2
800
700
600
500
CO 2
(ppm)
400
1800 1600 1400 1200 1000 800 600 400 200
P2 vs CO2 vs Yield 2 4 6 8 10 12 14 16 18 20 22
20
15
10
5
0
-5
1400 1200
1000
800
P (mm)
600
26 24 22 20 18 16 14 12 10
T2 vs P2 vs Yield -5 0 5 10 15 20
20
15
10
5
0
-5
800
700
600
500
CO 2
(ppm)
400
26 24 22 20 18 16 14 12 10
T2 vs CO2 vs Yield -5 0 5 10 15 20
Relative Yield (2050s RCP 4.5/actual)
Sensitivity of temperate grasslands production & N2O emissions to climatic
drivers from an ensemble of models
380 ppm
900 ppm
Yield Mean annual precipitation (mm)
1600 1400 1200 1000 800 600 400 Fig.a
Fig.c
1600
1400
Mean annual precipitation (mm)
-0.2
0.0
1200
0.2
0.3
0.4 0.5
1000
0.6
0.7
0.8
800
0.9
1.0
600
400 Fig.b
Mean surface response of temperate grasslands annual production to climatic drivers (n=1089 ; r2 = 0.64, P<0.001). Production changes were calculated relative to climate conditions leading to maximum production, under 380 ppm (Fig.a) and 900 ppm (Fig.b).
6 8 10 12 14 16 18 20 22 24 Mean annual temperature (°C)
6 8 10 12 14 16 18 20 22 24 Mean annual temperature (°C)
N2O Mean annual precipitation (mm)
1600
1400
1200
1000
800
-1,5
-1,0
600
-0,5
Fig.c 400
0,0 0,5 1,0
1,5
6
8
10
12
14
16
18
20
22
24
Mean annual temperature (°C)
Mean annual precipitation (mm)
1600
Mean surface response of temperate
1400
grasslands N2O emissions to climatic drivers
1200
(n=891 ; r2 = 0,296, P<0.001).
1000
Production changes were calculated relative to
800 600 Fig.d 400
climate conditions leading to maximum N2O emissions, under 380 ppm (Fig.c) and 900 ppm (Fig.d).
6
8
10
12
14
16
18
20
22
24
Mean annual temperature (°C)
Extension to global maps for grassland systems Relative changes in grassland production and N2O emissions under future climatic scenarios (2050s RCP 4.5, 2050s RCP 8.5) compared to current climate (1980-2009) were projected from the emulator for all sites. Results show that without N limitation, production and N2O emissions mostly increases by the 2050's. Elevated CO2 (ca. 600 ppm in both RCPs) plays a large role in this increase and this questions the extent to which models may overestimate this CO2 effect (e.g. for P limited grasslands). The use of an emulator provides a fast-track to upscale simulations for global grasslands and test a large range of climate scenarios, as well as adaptation and mitigation options. By extending the methodology to tropical grassland systems and rangelands, global maps for the variation of production, Carrying Capacity and GHG emissions could be generated for the grassland biome.
Relative Yield (2050s RCP 8.5/actual) Relative N2O emissions (2050s RCP 4.5/actual) Relative N2O emissions (2050s RCP 8.5/actual)

B Gianni, E Fiona, S Jean

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