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This study works as a test for the data that should be used as reference in future analyses. The Climate Research Unit database (CRU), which covers the period 1900-2006) is used as reference. A experimental 20th Century Reanalysis (R20C) dataset that uses CRU as inputs is evaluated using a previously tested benchmarking procedure. This d a t a s e t c o v e r s t h e p e r i o d 1870-2008. An array of LPX outputs using both R20C and CRU datasets are tested against previous published results (CRU Ref). The results of the benchmarking are shown (the higher the values the worse the approach is).

RESULTS Effect of fire on LGM biome

distribution

Vegetation and fire simulations for the Last Glacial Maximum: A study of fire and CO2 effects

Maria Martin Calvo (1), Colin Prentice (1,2), Sandy Harrison (2,3)

(1) Imperial College of London, (2) Macquarie University, (3) University of Reading

For this experiment the ignitions were set to zero for the simulations, which were compared for both preindustrial (CRU) and LGM periods with the equivalent simulations considering ignitions.

Benchmarking Effect of productivity on fire during the LGM

Introduction and objectives

FGOALS-1.0g

MIROC3.2

CNRM-CM33

HadCM3M2

CRU version 3.0

R20C run with CRU data

CRU Ref CRU R20C

Fire NME

AA 0.846 0.87 1.02 AA-MR 0.907 0.91 1.05

AA-MVR 0.987 1 1.09 IAV 0.693 0.85 1.2

IAV-VR 0.773 0.97 1.05 SC 1.38 1.38 1.32

SC-MR 1.37 1.37 1.3

SC-MVR 1.26 1.26 1.25

MPD P 0.101 0.1 0.12

fAPAR NME

AA 0.565 0.6 0.56 AA-MR 0.557 0.56 0.55

AA-MVR 0.579 0.6 0.58 IAV 1.47 1.57 2.5

IAV-VR 1.33 1.25 1.14 SC 1.14 1.15 1.19

SC-MR 1.05 1.06 1.13 SC-MVR 1.06 1.05 1.06

MPD P 0.184 0.12 0.12

Veget. cover MM

LF 0.76 0.74 0.76

T 0.557 0.53 0.48

H 0.669 0.65 0.65

BG 0.295 0.3 0.39

LT 0.935 0.4 0.39

LP 0.921 0.49 0.5

NPP NME AA 0.874 0.86 0.83

AA-MR 0.722 0.76 0.79

AA-MVR 0.694 0.72 0.75

Canopy Height NME

AA 1.04 0.89 0.96

AA-MR 0.727 0.72 0.74

AA-MVR 0.682 0.73 0.76

GPP NME AA 0.975 1.5 1.39

AA-MR 1.02 1.11 1.33

AA-MVR 1.33 1.36 1.22

CO

2-LG

M

CO

2-PI

HadCM3M2 CNRM- CM33

MIROC FGOALS CRU

CO

2-LG

M

CO

2-PI

CO

2-LG

M

CO

2-PI

CO

2-LG

M

CO

2-PI

CO

2-LG

M

CO

2-PI

With

fire

W

ithou

t fire

LGM CRU-CO2PI

Bibliography Acknowledgements

Historic data

LGM data

CLIMATE INPUTS

LPX MODEL Understanding the relations between different components of the earth system and how present and future climate change will affect them is complex. For this reason, it is useful to complement direct observations with additional indirect data (records) and model reconstructions. Furthermore, this helps to understand how the system reacted under different environmental constraints in the past. The aim of this project reflects this need, and uses a dynamic global vegetation model (LPX), previously tested, to evaluate the behaviour of fire and vegetation under different conditions. The project can be separated into different studies: •  Historical test: evaluation of model simulation against different sets of data

(benchmarking). Test of experimental climate database (20th Century Reanalysis)

•  Effect of CO2 on vegetation distribution and fire under Last Glacial Maximum (LGM) climate. Study of CO2 vs. Temperature as fire constraints

•  Role of fire on biome distribution and primary production

Model and data used The Land Processes and eXchanges Dynamic Global Vegetation Model is the main tool of the project. The LPX model is an evolution of the LPJ DGVM with an improvement in the fire module.

The maps above show a clear effect on tropical forests, which seems to be less important on boreal areas. CO2 has an important effect on biomass production, that globally leads to a slight reduction on area burned, but an increase on biomass burning. Looking at different latitude regions, area burned seems to be stimulated by CO2 on the extra-tropics, but that’s globally compensated by the reduction in the tropics, while biomass burning increases in all cases, specially in the extra-tropics.

This experiment aims to better understand the effect of CO2 on biomass production and biome distribution, and how this changes may affect the fire response. To perform the study LGM runs were performed under two different CO2 scenarios: CO2-LGM (185 ppm) and CO2-PI (280 ppm).

The comparisons show the fire effect on regulating biome distribution, with a larger forested area under no-fire conditions. Hence, when fire is not present tropical forest grows where savannah and shrubland would be, and boreal parkland and boreal forests replace the tundra.

LPX uses climate and non climatic data as inputs (yellow squares on the chart). The first part of the project compares two historic datasets by using a benchmarking procedure. The rest of the experiments use detrended data from four PMIP 2 climate models for the Last Glacial Maximum. Preindustrial snapshots for compar i sons were done us ing detrended CRU under preindustrial CO2 levels (280 ppm).

AA Annual Average

AA-MR mean removed

AA-MVR mean & var. removed

IAV Inter-annual variability

IAV-VR variance removed

SC Seasonal Concentration

SC-MR mean removed

SC-MVR mean & var. removed

P Phase

LF Life Form

T Tree

H Herb

BG Bare Ground

LT Leaf Type

LP Leaf Phenology

•  The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7 2007-2013) under grant agreement n° 238366

•  The 20th century reanalysis data based on CRU was kindly provided by Patrick Bartlein

•  The benchmarking procedure uses the data and formulations from Kelley et al. 2012, whose simulations where also used as reference

•  Prentice et al. (2011). Modeling fire and the terrestrial carbon balance. Global Biogeochemical Cycles, 25(3), 1–13 •  Kelley et al. (2012). A comprehensive benchmarking system for evaluating global vegetation models. Biogeosciences Discussions, 9(11), 15723–15785 •  Daniau et al. (2012). Predictability of biomass burning in response to climate changes. Global Biogeochemical Cycles, 26(4), 1–12 •  Harrison and Prentice (2003). Climate and CO2 controls on global vegetation distribution at the last glacial maximum: analysis based on

palaeovegetation data, biome modelling and palaeoclimate simulations. Global Change Biology, 9(7), 983–1004. •  Van der Werf et al. (2008). Climate controls on the variability of fires in the tropics and subtropics. Global Biogeochemical Cycles, 22(3), 1–13 •  Bond et al. (2005). The global distribution of ecosystems in a world without fire. New Phytologist, 165(2), 525–537 •  Wang et al. (2010). Large Variations in Southern Hemisphere Biomass Burning During the Last 650 Years. Science, 17(6011), 1663–1666.

Future work •  Biome correction and statistical analysis •  Model- data comparisons using charcoal data (based on previous

studies for the different latitude regions) against biomass burning •  Comparisons with CO records from Antarctica in collaboration with

the CNRS in Grenoble, France for the Last Millenium •  Future simulations based on IPCC scenarios •  Transient simulation for the deglaciation period

Unfortunately, as shown on the red-framed part of the table, the R20C run had worse results than CRU for fire and for inter-annual variability. Therefore, the subsequent comparisons are done using the CRU database.

The response of biomass burning to CO2 changes is highly dependant on the biome, being the tropical forest the most affected. The increase on CO2 leads to an increase in biomass burning in tropical forests and temperate and boreal parklands, with a less clear response from the other biomes (as shown in the chart above these lines).

Despite the net primary production is reduced both globally and by latitude regions, fire actually stimulates NPP for all the forested biomes under LGM climate. The effect on non-forested areas is less clear and even negative in some cases. The reduction in forested area, however, compensates this effect from a global perspective. The effect on the preindustrial run is different. NPP for dry grass/shrub, sclerophyll woodland and tropical savannah is negatively affected, other biomes don’t have a clear response.