Biosphere-climate interactions in current and future climate over North America

Transcription

1 Biosphere-climate interactions in current and future climate over North America Camille Garnaud, Laxmi Sushama Centre ESCER, UQÀM Montréal, Canada Regional climate change can strongly impact on local surface vegetation characteristics These can in turn modulate the regional climate by modifying key surface characteristics: albedo, latent and sensible heat flux, evapotranspiration, CO 2 emission, roughness length Biosphere-atmosphere interactions and feedbacks have been shown to alter the climate So they are likely to modify the magnitude of climate change 2 1

2 Dynamic vegetation models Most studies use Global Climate Models, and thus can overlook regional to local interactions Regional climate model framework for higher resolution Wramneby et al., 2010: Used RCA- GUESS to study the impact of dynamic vegetation on future climate. No such study has been performed over North America Vegetation feedback contribution to changes in temperature ( vs ) (Wramneby et al., 2010) Warm colours: amplification of future warming with vegetation dynamics Cold colours: dampening of future warming with vegetation dynamics 3 Models CTEM, version 1.0: Canadian Terrestrial Ecosystem Model (Arora, 2003; Arora and Boer, 2003, 2005) Dynamic vegetation 9 types of vegetation(pfts) No competition, fire or land-use change CTEM structure showing the 5 carbon pools: leavess (L), stem (S), roots (R), litter (D), and soil organic carbon (H). (Arora, 2003) 4 2

3 Models CRCM, version 5: Canadian regional climate model (Zadra et al., 2008) K. Winger CLASS, version 3.5: Canadian Land Surface Scheme (Verseghy, 1991, 1993, 2011) CLASS schematic diagram (Verseghy, 2011) 5 Part I: current climate Objectives: To assess the impact of dynamic vegetation on the CRCM5 simulated climate over North America ( ) To study the nature and variability of biosphere-atmosphere interactions over North America in current climate 2 simulations: STAT: CRCM5/CLASS = static vegetation DYN: CRCM5/CLASS/CTEM = dynamic vegetation Simulations ( ): LAM configuration Boundary conditions: : ERA40 (Uppala et al., 2005) : ERA-Interim (Dee et al., 2011) Δt: 20 min Δx: 0.5 x0.5 Both simulations are intialised with the same biosphere state 6 3

4 Static vs. Dynamic Static Dynamic Garnaud, C., L. Sushama, and D. Verseghy, Impact of dynamic vegetation on the Canadian RCM simulated climate over North America. Climate Dynamics (under revision) 7 Static vs. Dynamic Canopy resistance and photosynthesis Phenology Canopy mass STAT: CRCM/CLASS Function of incoming solar radiation, air temperature, soil moisture suction, vapour pressure deficit. (no photosynthesis) Function of air and soil temperature. Linear growth of the LAI between prescribed min and max values in predefined transition periods. Function of a prescribed maximum canopy mass value. No seasonal variations. DYN: CRCM/CLASS/CTEM Also depends on the CO 2 atmospheric concentration Carbon benefit approach (day length, temperatures and soil moisture) Depends on NPP and carbon allocation to leaves, stem and roots. Possible daily variations. 8 4

5 STAT vs. DYN: Leaf Area Index STAT ISLSCP II DYN LAI: biosphere characteristic that has most impact on interactions with atmosphere STAT: overestimation in West and underestimation in East during summer DYN: Good spatial distribution, underestimation in Northwest ISLSCP II (Los et al., 2000): tends to overestimate, particularly over forests (Gibelin et al., 2006) 9 CTEM impact on climate (JJA) DYN - STAT STAT -CRU DYN -STAT Albedo SHF Temperature ( C) W/m 2 North: decrease in albedo = increase in sensible heat flux Northern Canada: DYN creates a warm bias Southern Canada: DYN decreases the cold bias in STAT SW: decrease of the warm bias(higher albedo, lower SHF) CRU TS 2.10 observational data (Mitchell and Jones, 2005) 10 5

6 CTEM impact on climate (JJA) DYN - STAT STAT -CRU DYN -STAT LAI LHF Precipitation (mm/day) m 2 /m 2 W/m 2 Lower LAI in DYN Lower LHF in DYN The differences have a strong effect due to the convective activity in summer 11 CTEM impact on climate The impact of dynamic vegetation on the climate as simulated by CRCM5 can be strong, but varies seasonally and spatially The potential improvement brought by CTEM is limited by: Its own biases The biases of the CRCM5 simulated climate However, CTEM introduces biosphere-climate interactions 12 6

7 Biosphere-climate interactions STAT DYN STAT: Weak correlations Precipitation does not affect phenology High-latitudes: Temperature determines growing season length DYN: Precipitation: positive correlations Temperature: latitudinal line of demarcation North: Plants benefit from high temperatures and higher LAI increases surface temperature. South: Plants suffer from high temperatures and higher LAI lowers surface temperature. 13 Biosphere memory STAT DYN 1yr-lagged correlations : Does the climate (precipitation or temperature) have an impact on the biosphere the following year? Confidence level: 10% STAT: not significant DYN: yes, but locally 14 7

8 Atmosphere memory STAT DYN 1yr-lagged correlations : Does the biosphere have an impact on the climate (precipitation or temperature) the following year? Confidence level: 10% STAT: not significant DYN: Locally Weaker than biosphere memory 15 Summer 1988 STAT OBS DYN Number of hot days: number of days with T max exceeding long term ( ) 90 th percentile daily T max in summer Observations: Hopkinson et al. (2011) and Maurer et al. (2002) 16 8

9 Part I: CTEM improves the model (CRCM5) in certain regions, introduces new biases in others, due to large differences in LAI between DYN and STAT, which greatly affects biosphere-atmosphere interactions with respect to water and energy fluxes. Nonetheless, CTEM enhances biosphere-atmosphere interactions, which are reflected in the higher values of correlation between atmosphere and biosphere variables. These lead to improved simulations of extremes. 17 Part II: future climate Objective: To evaluate the impact of climate change on vegetation in terms of phenology and productivity To explore the resulting impact of vegetation dynamics on climate change 2 simulations: DYN: CRCM5/CLASS/CTEM = dynamic vegetation STAT: CRCM5/CLASS = static vegetation Simulations ( ) corresponding to RCP8.5 scenario: Dynamical downscaling of CanESM2 LAM configuration Δt: 20 min Δx: 0.5 x

10 Response of the biosphere (DYN) Leaf onset (julian days) ( ) - ( ) Net Primary Productivity (kgc/(m 2.yr)) Growing season lengthens Productivity increases Max Leaf Area Index (m 2 /m 2 ) Reflected in biomass 19 Response of the biosphere (DYN) gc/(m 2.yr 2 ) Net primary productivity (NPP) trends: Sen's slope method (Sen, 1968) 5% significance level: Mann-Kendall test (Kendall, 1975; Khaliq et al., 2009) : positive trends Reaches a plateau by the end of the century, with negative trends in southeast US 20 10

11 Impact of biosphere on climate SPRING Projected changes: ( ) ( ) Maximum temperature ( C) Albedo Sensible heat flux (W/m 2 ) Δ STAT Δ DYN Δ DYN -Δ STAT Spring (MAM) season Lower albedo in DYN, particularly in areas covered by needleleaf evergreen trees Warming enhancement over most of NA due to an increase in SHF Albedo-mediated warming 21 Impact of biosphere on climate SUMMER Projected changes: ( ) ( ) Maximum temperature ( C) Sensible heat flux (W/m 2 ) Latent heat flux (W/m 2 ) Δ STAT Δ DYN Δ DYN -Δ STAT Summer (JJA) season Northern latitudes: warming enhancement More southern regions: warming attenuation Due to hydrological feedbacks 22 11

12 Impact of biosphere on climate Projected changes: ( ) ( ) Δ STAT Δ DYN Δ DYN -Δ STAT JJA: Notable decrease in precipitation Precipitation MAM Attenuated decrease in DYN Increased water use efficiency Precipitation JJA Δ DYN : Max LAI m 2 /m 2 23 The extension of the growing season in future climate in the dynamic vegetation simulation leads to higher annual vegetation productivity and biomass. Dynamic vegetation allows the biosphere to respond to climate change through feedbacks and interactions at the land-atmosphere interface Thus, it significantly contributes to climate change through modification of the climate, particularly with respect to temperature, depending on the region. In most parts of North America, the vegetation enhances its water use efficiency due to rising CO 2 concentrations in the atmosphere

13 Thank you for your attention! Related publications: Garnaud, C., L. Sushama, and V. Arora (2014) The effect of driving climate data on the simulated terrestrial carbon pools and fluxes over North America. International Journal of Climatology, 34: DOI: /joc.3748 Garnaud, C., L. Sushama, and D. Verseghy. Impact of dynamic vegetation on the Canadian RCM simulated climate over North America. Climate Dynamics, accepted (under revision) Garnaud, C., and L. Sushama. Biosphere-climate interactions in a changing climate over North America. Journal of Geophysical Research, accepted (under revision) Contact info: camille.garnaud@gmail.com 25 13