Modeling Two-Way Land/Atmosphere/Ocean Interactions Yongkang Xue 1, F. De Sales, Z. Zhang, Y. Wang, Y. Liu, H.-Y. Ma, L. Marx, M. Ek, R. Yang, J.-W. Lee.

Modeling Two-Way Land/Atmosphere/Ocean Interactions

Yongkang Xue1, F. De Sales, Z. Zhang, Y. Wang, Y. Liu, H.-Y. Ma, L. Marx, M. Ek, R. Yang, J.-W. Lee

1. University of California, Los Angels

NOAA Climate Test Bed SeminarSeptember 3, 2015

PI: Yongkang Xue; CO-PI: W. Parton, S. Shen, T. Gillespie, F. De Sale, Y. Gu

Surface-Induced Forcing and Decadal Variability and Change of East Asian Climate, Hydrology and Agriculture

This research is supported by U.S. NSF EASM-3 Project

ATMOSPHERE

Courtesy of Prof Y. Xue

LAND

OCEAN-ATMOSPHERE INTERACTIONS

Source: Woods Hole Oceanographic Institution

OCEANprecipitation, clouds, temperature

Traditionally, L/A interaction and O/A interaction studies separately.

ATMOSPHERE


LAND




?


?

Vegetation Biogeophysical Process Effect on JJA precipitation

Soil Moisture JJA coupling strength

Koster et al., 2004, Science

Xue et al., 2010, J. Climate

Effect of L/A Interaction is of the 1st order process in Climate system

VBP: vegetation biogeophysical processes

Terrestrial ecosystems

Distribution Structure

Bio

phys

ical

fee

dbac

k

Radiation balance

Momentum transfer

Heat transfer

Water cycle

PhotosynthesisRespiration

2nd generation model

Temperature, humidity, precipitation, downward SW and LW radiation, Greenhouse Gas (CO2) and atmospheric circulation

The Climatic System

Terrestrial ecosystems

Distribution Structure

Bio

phys

ical

fee

dbac

k

Radiation balance

Momentum transfer

Heat transfer

Water cycle

3nd generation model

Temperature, humidity, precipitation, downward SW and LW radiation, Greenhouse Gas (CO2) and atmospheric circulation

The Climatic System

PhotosynthesisRespirationDecompositionNutrient cyclesBiomass burning

Annual T change due to 2 x air CO2With no photosynthesis effects

Betts, Cox, Lee, Woodward, 1997, NatureSellers et al., 1996, Science

Annual T change due to 2 x air CO2With no photosynthesis effects

Additional T change due to 2 x air CO2 plus photosynthesis effects

Betts, Cox, Lee, Woodward, 1997, NatureSellers et al., 1996, Science

Additional 0.2K warming

Additional T change due to 2 x air CO2 plus photosynthesis effects

Additional T change due to 2 x air CO2 plus photosynthesis effects & LAI change

ONLY LAI change under 2xCO2

+7.2%

Betts, Cox, Lee, Woodward, 1997, Nature

Leaf Area Index (LAI): the area of leaf surface per unit area of ground

Additional 0.2K warming

Additional 0.1K Cooling

Anav, et al., 20131986-2005 Mean Annual LAI

1986

-200

5 L

AI T

rend

1986

-200

5 L

AI S

tand

ard

devi

ation

Coupled Model Intercomparison Project Phase 5 (CMIP5) Simulations


1986

-200

5 L

AI T

rend

1986

-200

5 L

AI S

tand

ard

devi

ation


Similar results shown in off line DVG intercomparison ( Murray-Tortarolo, Anav et al., 2013)


1986

-200

5 L

AI T

rend

1986

-200

5 L

AI S

tand

ard

devi

ation


1986-2005 Mean Annual Precipitation

1986

-200

5 P

reci

pita

tion

Tren

d


Merray-Tortarolo, Anav, et al., 2013

Offline Model Intercomparison Project Simulations

Observation

(1). models based on observed vegetation perform better than dynamic models. (2). models that include a wider range of PFTs are more similar to the satellite observations. However, using many PFTs leads to an increased uncertainty due to their parameterizations.

Merray-Tortarolo, Anav, et al. (2013) found that

litterdtvdC )1(

),,( soillitter TSMCssRdtsdC

Energy balance:(1-α) SW + LW - εσ T4= LE + SE + GH

Water balance:P=E + Runoff +SM +snow +canopy

Carbon Balance:

HYPOTHESIS: Improper simulations in Energy, Water and Carbon balances cause the deficiencies in producing carbon exchange then interactions between L/A interaction

(SM, Tcanopy)

Plant Functional Type Competition

Reanalysis and Observational data

Satellite data products

Field data

(10 days)

(1-3hours)

15 min

SSiB4Hour, day, intraseasonal, interannual, decadal

Intraseasonal, interannual, decadal

TRIFFID

15-30days

Intraseasonal, Interannual,Decadal, decadal

intraseasonal, interannual, decadal


RCM

CFS6hours


Initial Conditions

T, Q, u, SW↓, LW ↓, Pr

Tskin, α,LH, SH, u*

Tc, Td, Sm, Anet, Rdc, Wilt RD,RB,

D0,Z0

Earth System Models

DAYCENT

Tg, Td, Sm

N

N

C

FPFT, Z2,LAI, Stem, Cs

(10 days)

(1 days)



Field data

(10 days)

(1-3hours)

15 min



TRIFFID

15-30days




RCM

CFS6hours


Initial Conditions




D0,Z0

Earth System Models

DAYCENT

Tg, Td, Sm

N

N

C


(10 days)

(1 days)NOAA Climate Program Office provides the first support

Fig.4. Comparison of simulated and satellite-derived dominant PFTs . (a) GLC2000; (b) MODIS IGB vegetation types based on the 2001-2010 mean; (c) SSiB4 simulation based on the 1998-2008 mean.

1. Needle Leaf 2. Broad Leaf 3. C3 Grass 4. C4 Plants (Savanna) 5. Shrub 6. Tundra shrubs 7. Bare lands 8. Crop lands 9. Mixed forest10. Ice and Snow

Zhang et al., 2015, JGR

SSiB4/TRIFFID GIMMS FASIR

Correlations: with GIMMS: 0.59; with FASIR: 0.67

DJF

MAM

JJA

Simulated & Satellite LAI temporal evolutions from 1948-2008

Wang, Yu, Pal, Mei, Bonan, Levis, Thornton, 2015, Climate Dynamics

Broad leaf treesC4 plants

Bare groundShrubs

SSiB4/TRIFFID Simulated PFT Fractions(1996-2006) &GLC2000 Dominant Land Cover Classifications

GLC2000 Land CoversavannaGrassforest

Crops

desert

C3 Grass

C4 Plant

Shrubs

C4 plants

Shrubs

TRIFFID/SSiB4 (2001-2010)-(1979-1988)

Summary for Part I

1). Integrated biogeophysical and biogeochemical processing and dynamic vegetation modelling are important for climate variability and change studies at interannual-decadal-century scales.

2). Current dynamic vegetation models show significant bias in producing LAI, which have important implication in future climate prediction

3). A water/energy/carbon balance approach and validation as did in PILPS are essential to produce the valuable scientific and societal information for interannual to decadal scales

ATMOSPHERE


LAND




?


?

New evidence shows the connection between the tropical Pacific climate and the land surface processes

Ma, Mechoso, Xue, Xiao, Neelin, Ji, (2013) J Climate

Ma, et al., 2013 J. Climate

UCLA CGCM Without L/O/A Interaction

UCLA CGCM With L/O/A Interaction

NINO3 Spectrum



Field data

(10 days)

(1-3hours)

15 min



TRIFFID

15-30days




RCM

CFS6hours


Initial Conditions




D0,Z0

Earth System Models

DAYCENT

Tg, Td, Sm

N

N

C


(10 days)

(1 days)

Adjust the lowest model layer height

Sea Surface Temperature climatology30-yr DJF average (°C)

Sea Surface Temperture climatology30-yr JJA average (°C)

Precipitation climatology30-yr average (mm/day)

Annual mean JJA mean

Surface temperature climatology30-yr average (C)

Annual mean JJA mean

Bias:0.57RMSE:2.34Scor:0.98

Sea-level pressure climatology30-yr average (hPa)

Nino 3.4 index

Wavelet spectrum

year

CFS/SSiB2

Hadsst

MEAN SOIL WATER POTENTIAL SCHEMES

O

Mean soil water potential affects photosynthesis process then transpiration and carbon flux, and phenological process and vegetation growth.

MEAN SOIL WATER POTENTIAL SCHEMES

Sahel precipitation recovery(2000-2008) minus (1980-1988)

JJAS mean (mm/day)

Observation CFS/SSiB2-I CFS/SSiB2-II

CFS/SSiB2-I

CFS/SSiB2-II

Summary for Part II

1). A 30-year continuous simulation was initialized from a 10-year spin-up run in which atmospheric aerosol concentrations, solar constant and CO2 emissions were maintained at 1979 levels, while ocean and land were allowed to run freely.

2). Results show the model is able to simulate the mean state of atmosphere and ocean climatology but with substantial biases in some aspects. Positive precipitation biases are observed over the equatorial Atlantic and a split ITCZ pattern is simulated in the Pacific ocean. In terms of surface temperature, positive biases were simulated over semi-arid regions, predominantly in the summer month. 3) The model reproduced well the climatological SST and warm pool but with biases over different regions. The model also reproduced well the SST anomaly variation well expect the southern ocean SST after the 2000.

Summary (Continue)

4) A very preliminary sensitivity test with two different mean soil water potential scheme revealed that the land surface processes have impact on the atmospheric and ocean’s processes and their decadal variability .

5) With adequate soil water potential parameterizations, the model simulated the decadal variability of precipitation and surface temperature in several areas, notably over parts of Africa and North and South Americas. Decadal variability results are better during the summer season than during the winter. Model captured the increase in summer temperatures over land between 1980s and 2000s in most areas

Modeling Two-Way Land/Atmosphere/Ocean Interactions Yongkang Xue 1, F. De Sales, Z. Zhang, Y. Wang, Y. Liu, H.-Y. Ma, L. Marx, M. Ek, R. Yang, J.-W. Lee.

Documents

surface energy

photosynthesis effectsbetts

air co2with

change of east asian

greenhouse gas co2

climate system vbp

area of leaf surface

water budgets