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..