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Vegetation modelling at 1/24th degree - comparison of CLM 4.5_TM
results driven by CMIP5 Zhenlin Yang, Bev Law
College of Forestry, Oregon State University CESM annual workshop, Breckenridge
June, 2014
FMEC: Forest Mortality, Economics and Climate change in western North America
NIFA-funded, usd 4 million+, 5 yr project. Started 1st May, 2013.
Study Area
RCP8.5, to 2085, Annual
• selection criteria: span range of delta_T, delta_PPT
• one near bubbled multimodel mean
bold dashed circles = first-round selection
bold circles = final
selection
Plant functional types of CLM 4.5
Sub-PFT Eco-region
Ponderosa pine KM
Douglas fir and other firs
KM
Ponderosa pine WC
Douglas fir WC
Firs EC
Ponderosa pine EC
Douglas fir CR
Conifers BM
Sub-Plant functional types of CLM 4.5_TM (only trees)
Species
Lodgepole pine Western white pine
Douglas-fir Western red cedar
Pinyon pine Western hemlock
Subalpine fir Mountain hemlock
Engelmann spruce
Ponderosa pine
Whitebark pine Quaking aspen
Western larch Chapman oak
Pacific silver fir Blue oak
White fir Garden pea
Grand fir Jeffrey pine
Noble fir California black oak
Alaska yellow cedar
Sugar pine
Western juniper Black spruce
Sitka spruce California black oak
Species of CLM 4.5_TM (only trees)
Forest distribution in USA
1/24th from NALCMS 1/2th default LC
Nested Land Cover – from 1/2th to 1/24th
Next step: current species map, Mathys, et al. (2014)
IPSL_MR
IPSL_LR
Predicted Annual Gross Primary Production – BAU Management (2020-2029), unit: g C/m2/yr
The first three map categories are static classes which are consistent throughout the time series: persistent non-forest, persistent forest, and water. Forest change pixels are classified according to the year in which change occurred.
Goward et al. 2012 Full data coverage expected August, 2014
Forest age distribution in North America (Pan et al., 2011)
Opportunity I : mortality-stand age development in CLM 4.5
Opportunity II: Sensitivity analysis in mortality-related modules of CLM 4.5
CNFireInterp CNGapmortality CNGapPftToColumn CNFirearea CNFireFluxes
Stressor: Foliar damage Climate index: 7+ consecutive days of maximum daily temperature > 40oC
More stress
Less stress
RCP8.5 MACA-downscaled
Opportunity III – Mortality algorithm development
Stressor: Drought Climate index: 2+ consecutive years of wet season (NDJFMA) precip. < 50% of normal
RCP8.5 MACA-downscaled
Needs to be improved. Default constant mortality rate for all PFT’s across the globe – 2% Option 1 - Calibrate mortality rates to specific sites based on inventory data M=μB μ=constant Other answers for generating a more dynamic, stochastic mortality algorithm? μ=b0*f(ETp) μ=b0*f(ETp)*f(species)*f(age) • Mortality FIA + Remote Sensing (RK) • Climate-dependent analysis Accumulative annual precip. be 50% of long-term average (MACA) Summer temperature above 40oC for one week (MACA) VPDaso+VPDmjj (FDSI; Williams et al.2013) Pndjfm (FDSI) • Species-dependent analysis Species (FIA), average wood density (FIA), specific gravity (FIA), biomass allocation (FIA; LA:SA surrogate)
• Age-dependent analysis Stand Age (FIA) Diameter at Breast Height (FIA)
Simulated monthly GPP versus observed GPP at Metolius Mature Pine site, OR, USA.
0
50
100
150
200
250
300
350
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
GPP
(gC
m-2
mon
th-1
)
Time
GPP_CLM4_5 GPP_Observed
GPP_CLM4_5_TM GPP_CLM4_0
Site-level Modelling
Domain OR CA WA
Grid cells 25920 57519 15876
Time period (1950-2005)+3*(2006-2099)
(1950-2005)+3*(2006-2099)
(1950-2005)+3*(2006-2099)
Climate forcings MIROC5/IPSL-CM5A-LR/IPSL-CM5A-MR
MIROC5/IPSL-CM5A-LR/IPSL-CM5A-MR
MIROC5/IPSL-CM5A-LR/IPSL-CM5A-MR
Harvesting scenarios BAU/Harvesting BAU/Harvesting BAU/Harvesting
Current status carbon2 (FES carbon cluster) Done: MIROC5 (BAU), IPSL-CM5A-MR (BAU), IPSL-CM5A-LR (BAU, Harvest) Ongoing: MIROC5 (Harvest)
Azure, 256 cores with 1TB storage Done: Input data layers Ongoing: PFTs, climate data
Azure, 256 cores with 1TB storage (valued at 320k USD) Done: Settings ready Ongoing: PFTs, climate data
Regional Modeling Status - 1/24th, 3-hourly
0
500
1000
1500
2000
2500
3000
2007 2008 2009 2010 2011
Burn
ed a
rea
(squ
are
km)
Time
MTBS GFED CLM4_5
Historical annual area burned in Oregon (km-2) Will redo with new MACA and new fire mortality algorithms
Evaluation of CLM burned area vs MTBS & GFED remote sensing products
• Observation: 1.4 Tg (2002-2006) • CLM 4.0: 2.4 Tg (2002-2006) • CLM 4.5_TM: 1.08 Tg (2002-2012),0.97 Tg
(2002-2006)
cc_dstem cc_leaf cc_lstem cc_other croot_stem
CLM 4.5 0.22 0.8 0.22 0.45 0.3
CLM 4.5_TM 0.07 0.6 0.07 0.45 0.1
Combustion completeness factors for fir
Evaluation of CLM fire emission
Results to date Predicted Annual non-peat fire c loss, IPSL_LR, unit: g C/m2/yr
2020-2029, BAU
2020-2029, Harvest 2070-2079, Harvest
2070-2079, BAU
00.10.20.30.40.50.60.70.80.9
0 500 1000 1500 2000 2500
Frac
tion
of a
rea
burn
t
GPP (gC /m2/yr)
Comparison of GPP vs area burnt – year 2005
0
20
40
60
80
100
120
0 500 1000 1500 2000 2500
Fire
C lo
ss(g
C/m
2 /yr
)
GPP (gC/m2/yr)
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0 20 40 60 80 100 120
Frac
tion
of a
rea
burn
ed
Fire C loss (gC /m2/yr))
Comparison of average fire C loss and Gross primary production (2001-2029)
Comparison of average fire C loss and fire burned area (2001-2029)
3
3.2
3.4
3.6
3.8
4
4.2
4.4
4.6
4.8
5
0.054 0.056 0.058 0.06 0.062 0.064 0.066
GPP
incr
ease
per
yea
r (gC
m-2
) Temperature increase per year (oC)
Annual ecoregion-level GPP, precipitation and VPD changes comparing 2070-2099 to 2020-2049(unit: GPP: g C/m2/year, Precip_ET: mm/year, VPD:kpa)
Annual ecoregion-level GPP and temperature increase comparing 2070-2099 to 2020-2049
wv
-3
-2
-1
0
1
2
3
4
5
6
km bm cr ec wc wv nb cp
Ecoregions
Precp_ET_r
VPD_2020
VPD_2070
GPP_r
An example of Regional Model Run with MIROC5 & BAU Management
km, bm, cr, ec, wc are the abbreviations of ecoregions in Oregon. KM=Klamath Basin in S OR, BM= Blue Mountains in NE OR, CR=Coast Range, EC=East Cascades, WC=West Cascades
CR NB
KM WC
EC
CP
BM
Acknowledgement
Thanks to David Lawrence, Mariana Vertenstein, Lianhong Gu, Dali Wang, Jeff Hicke, David Rupp et al..