Noah-MP: A New Paradigm

for Land Surface Modeling

Zong-Liang YANG,Fei Chen, Mike Barlage, Mike Ek,

Guo-Yue Niu, Xitian Cai, RongqianYang, et al.

1Presentation at the Workshop on Land Surface Modeling in Support of NWP and

Sub-Seasonal Climate Prediction, George Mason University, 5-6 December, 2013

Two schools of thought in LSM development and evaluation

Atmospheric Forcing

Model Structure

Augments (gw, dv, …)

Model Evaluation Pyramid

Land Surface Model (CLM,

Noah, Vic, …)

LSM developers consider

1. Increasing realism in representing key processes

2. Understanding feedbacks and interactions

3. Maintaining synergism between LSM and other modules in the host GCM

4. Aiming for past, present, and future climate applications & operational weather/climate predictions

5. Generalizing parameter-zations across sites

LSM evaluators consider

1. Uncertainty in many subsurface parameters and other non-measurable parameters

2. Uncertainty in atmospheric forcing and observations used for evaluation

3. Calibration of the parameters for the augmented part only or for the entire LSM

4. Evaluation in all dimensions

5. Equifinality?

LSM developers do not use automated, sophisticated

evaluation tools.

LSM evaluators calibrate/evaluate LSMs

that already exist.

How Can We Use Sophisticated Evaluation Methods To Guide LSM Development?

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An Effort to Reconcile Both1) Gulden, L. E., E. Rosero, Z.-L. Yang, M. Rodell, C.S. Jackson, G.-Y. Niu, P. J.-F.

Yeh, and J. Famiglietti, 2007: Improving land-surface model hydrology: Is an

explicit aquifer model better than a deeper soil profile? Geophys. Res. Lett., 34,

L09402, doi:10.1029/2007GL029804.

2) Gulden, L.E. et al., 2008: Model performance, model robustness, and model

fitness scores: A new method for identifying good land-surface models, Geophys.

Res. Lett., 35, L11404, doi:10.1029/2008GL033721.

3) Jiang, X., G. Niu, and Z.-L. Yang, 2009, Impacts of vegetation and groundwater

dynamics on warm season precipitation over the Central United States, J.

Geophys. Res., 114, D06109, doi:10.1029/2008JD010756.

4) Rosero, E., Z.-L. Yang, L. E. Gulden, G.-Y. Niu, and D. J. Gochis, 2009:

Evaluating enhanced hydrological representations in Noah-LSM over transition

zones: Implications for model development, J. Hydrometeorology, 10, 600-622.

DOI:10.1175/2009JHM1029.1

5) Rosero, E., Z.-L. Yang, T. Wagener, L. E. Gulden, S. Yatheendradas, and G.-Y.

Niu, 2010: Quantifying parameter sensitivity, interaction and transferability in

hydrologically enhanced versions of Noah-LSM over transition zones, J. Geophys.

Res., 115, D03106, doi:10.1029/2009JD012035.

2010 NOAA/NCEP Land Modeling Workshop at Austin, Texas

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Noah-MP

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• Exchange processes with the atmosphereo Momentumo Energy (reflected shortwave, emitted longwave, latent/sensible

heat)o Water (precipitation, evapotranspiration)o Trace gases (CO2, CH4, N2O, BVOCs)/dusts/aerosols/pollutants

• Exchange processes with the oceano Fresh watero Sediments/nutrientso Salinity

• Land-memory processeso Vegetation phenologyo Snow/ice covero Soil moistureo GroundwaterIntraseasonal to Interannual Variability

• Human activitieso Land use (agriculture, afforestation, deforestation,

urbanization, …)o Water use (irrigation, human withdraws, dams, …)o Air pollution / Water pollutiono Environmental degradation

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Generalized Land Surface Modeling

Status Quo in Land Surface ModelingA land surface model represents, mathematically and numerically, the land surface’s

• characteristics (e.g., land cover parameters),

• states (e.g., soil moisture, temperature, snow, leaf area),

• fluxes (e.g., runoff, evapotranspiration, outgoing longwave radiation, photosynthesis), as a function of space and time.

There is a rich body of literature in modeling each of the biogeophysical and biogeochemical processes.

How to choose parameterizations (parts) and assemble them into an LSM (whole) depends on the developer’s experience, judgment, and understanding.

Examples: BATS, SiB, SSiB,

VIC, Noah, CLM, JULES, CABLE, … 7

Science Questions

• Is there a synergy among the different parameterizations?

• Is there an optimal combination of these parameterizations?

• Do existing parameterizations possess typical signatures or exhibit typical patterns in simulating energy, water, and carbon balances?

• Is there a modular framework that o Allows for freely assembling of these parameterizations,

o Diagnose differences,

o Identifies structural errors,

o Improve understanding,

o Enhances data/model fusion and data assimilation,

o Facilitate ensemble forecasts and uncertainty quantification?

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What is Noah-MP?

• Augmented Noah LSM with Multi-Parameterization options (Noah-MP): o Key references: (Niu et al., JGR, 2011; Yang et

al., JGR, 2011)

o Recoded based on the standard Noah LSM

o Well documented and highly modular

o Improved biophysical realism (land memory processes): separate vegetation canopy and ground temperatures; a multi-layer snowpack; an unconfined aquifer model for groundwater dynamics; an interactive vegetation canopy layer

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Interactive Vegetation Canopy

Carbon gain rate: photosythesis * fraction of carbon partition to leaf

Carbon loss rate: leaf turnover (proportional to leaf mass)

respiration: maintenance & growth (proportional to leaf mass)

death: temperature & soil moisture

LAI = Mleaf * Carea where Carea is area per leaf mass (m2/g).

The model includes a set of carbon mass

(g C/m2) balance equations for:1. Leaf mass

2. Stem mass

3. Wood mass

4. Root mass

5. Soil carbon pool (fast)

6. Soil carbon pool (slow)

Processes include:1. Photosynthesis (S↓, T, θ, eair, CO2,O2, N…)

2. Carbon allocation to carbon pools

3. Respiration of each carbon pool (Tv,θ, Troot)

lossgain

leafRR

t

M

Dickinson et al. (1998),

Yang and Niu (2003)

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Comparison of Noah-MP and Satellite

LAI and % Vegetation Cover (GVF)

Yang et al. (2011)

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Noah-MP water storage change

compares well with GRACE

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Niu et al. (2011)

Yang et al. (2011)

Improved biophysical realism:

a multi-layer snowpack

an unconfined aquifer model for groundwater dynamics

an interactive vegetation canopy layer

Modeled and Obs SWE at Niwot Ridge (2005-2007)

Noah

VIC

LEAF

SAST

Noah-MP

CLM

Modeled and Obs SWE at GLEES (2005-2007)* All models melt snow too fast * Larger differences among models* SAST and LEAF largest melt rate * CLM and Noah-MP closest to obs* Noah, LEAF, VIC: lowest SWE during accumulation

Noah-MP is unique among LSMs

• A new paradigm in land-surface, environmental, and hydrological modeling (Clark et al., 2007; 2011)

• In a broad sense,o Multi-parameterization ≡ Multi-physics ≡ Multi-

hypothesis

• A modular & powerful framework foro Diagnosing differences

o Identifying structural errors

o Improving understanding

o Enhancing data/model fusion and data assimilation

o Facilitating ensemble forecasts and uncertaintyquantification

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Noah-MP1. Leaf area index (prescribed; predicted)2. Turbulent transfer (Noah; NCAR LSM)3. Soil moisture stress factor for transpiration (Noah; SSiB; CLM)4. Canopy stomatal resistance (Jarvis; Ball-Berry)5. Snow surface albedo (BATS; CLASS)6. Frozen soil permeability (Noah; Niu and Yang, 2006)7. Supercooled liquid water (Noah; Niu and Yang, 2006)8. Radiation transfer:

Modified two-stream: Gap = F (3D structure; solar zenith angle; ...) ≤ 1-GVF

Two-stream applied to the entire grid cell: Gap = 0Two-stream applied to fractional vegetated area: Gap = 1-GVF

9. Partitioning of precipitation to snowfall and rainfall (CLM; Noah)10. Runoff and groundwater:

TOPMODEL with groundwaterTOPMODEL with an equilibrium water table (Chen&Kumar,2001)Original Noah schemeBATS surface runoff and free drainage

More to be added Niu et al. (2011)

Collaborators: Yang, Niu (UT), Chen (NCAR), Ek/Mitchell (NCEP/NOAA), and others15

Maximum # of Combinations 1. Leaf area index (prescribed; predicted) 22. Turbulent transfer (Noah; NCAR LSM) 23. Soil moisture stress factor for transp. (Noah; SSiB; CLM) 34. Canopy stomatal resistance (Jarvis; Ball-Berry) 25. Snow surface albedo (BATS; CLASS) 26. Frozen soil permeability (Noah; Niu and Yang, 2006) 27. Supercooled liquid water (Noah; Niu and Yang, 2006) 28. Radiation transfer: 3

Modified two-stream: Gap = F (3D structure; solar zenith angle; ...) ≤ 1-GVF

Two-stream applied to the entire grid cell: Gap = 0Two-stream applied to fractional vegetated area: Gap = 1-GVF

9. Partitioning of precipitation to snow- and rainfall (CLM; Noah) 210. Runoff and groundwater: 4

TOPMODEL with groundwaterTOPMODEL with an equilibrium water table (Chen&Kumar,2001)Original Noah schemeBATS surface runoff and free drainage

2x2x3x2x2x2x2x3x2x4 = 4608 combinationsProcess understanding, probabilistic forecasting, quantifying uncertainties

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Global 36 Ensemble Experiments

Yang et al. (2011)

GLDAS forcing, global 1°× 1°resolution

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Global 36 Ensemble Experiments

Runoff options as the dominant player in forming clusters:

SIMTOP (bottom sealed) produces the wettest soil and greatest ETBATS (greatest surface runoff) produces the driest soil and smallest ETNoah lies between SIMTOP and BATSSIMGM results in best soil moisture simulations

Yang et al. (2011)

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Static Data

Forcing Data

Validation Data

Lat-Lon mask, land mask, soil type, soil color, land use, greenness vegetation fraction (GVF)

NLDAS2 forcing: precipitation, temperature, specific humidity, air pressure, downward longwave and shortwave radiation, wind

USGS streamflow and groundwater, GRACE water storage change, CMC snow, MODIS LAI, ……

Mississippi River Basin

6 HUC2 regions

~3.3 million km2

0.125° × 0.125°

~22,378 grid cells

Regional High Resolution Experiments

USGS Gage Stations

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Regional 14 Ensemble Experiments

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Basin Mean Monthly Runoff (2000-2009)

For the entire Mississippi Basin

All EXP1-14 capture seasonal and interannual variations.

BATS and Noah runoff schemes produce largest runoff amplitudes.

TOPMODEL and SIMGM runoff schemes show smallest amplitudes, in best agreement with observations.

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Sub-Basin Mean Monthly Runoff

For Missouri Sub-basin, TOPMODEL and SIMGM runoff schemes produce best simulations. For Ohio-Tennessee Sub-basin, BATS and Noah runoff schemes produce best simulations.

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Statistics of Monthly Runoff (2000-2009)

For the entire Mississippi Basin

All EXP1-14 show ~90% correlation

BATS and Noah runoff schemes produce largest runoff amplitudes

TOPMODEL and SIMGM runoff schemes shows smallest amplitudes

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Statistics of Monthly Runoff (2000-2009)

Different sub-basins have different best performing combinations.

Runoff options dominate over other options.

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Mississippi River Basin (2000-2009)

Runoff options as the dominant player in forming clusters:

SIMTOP produces wetter soil and greater ETBATS produces drier soil and smaller ETNoah performs in a way similar to BATSSIMGM simulations produce wetter soil and greater ET

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GPP–Soil Water in Sub-Basins

Runoff options as the dominant player in forming clusters:

SIMTOP produces wetter soil and greater GPPBATS produces drier soil and smaller GPPNoah performs in a way similar to BATSSIMGM simulations produce wetter soil and greater GPP

Ohio-TennesseeMissouri

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Taylor Plots for Different Options (36 experiments)

DV: on is more realistic than off; Ball-Berry exhibits more variability than Jarvis; β shows least difference; Runoff options are the dominant options influencing energy, water, and carbon balances.

Coupled WRF/Noah-MP Seasonal

Climate Simulations

• Pairs of six-month

30-km simulations

starting Feb 25

• 2002 and 2010

• Spin-up soil for one

year using offline

HRLDAS

• IC/BC from NARR

• CAM radiation;

YSU; Thompson

Barlage et al. (2013)

LSM Setup• Default Noah LSM

• Noah-MP with OPT_RUN=3: free drainage

comparable to Noah runoff scheme

• Noah-MP with OPT_RUN=5: Miguez-Macho & Fan

groundwater with equilibrium water table

• Noah-MP with OPT_BTR=1(Noah) and 2 (CLM)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

Tran

sp

irati

on

Eff

icie

ncy

Soil Saturation

Noah

CLM

Barlage et al. (2013)

Equations:

Mass balance in groundwater storage:

Darcy’s Law for groundwater – river exchange:

Darcy’s Law for lateral groundwater flow:

s

hhwQ

nwtdwtd n dzKdzKn

n

2

width of flow cross section

TransmisivityHead difference divided by distance (water table slope)

conductivity

wi,j

i, j

Q1

Q2 Q3

Q4

Q5Q6

Q7

Q8

Plan view

h i,j

Mean sea level

Sg

Q8

Q4

R Qr

Cell ij

Cross section view

Miguez-Macho & Fan water table dynamics in NOAH-MP

 

Qr = rc × wtd - riverbed( )

rQQyRx

dt

dSn

g

8

1

Water table depth (wtd)

Fan et al, JGR 2007Miguez-Macho et al., JGR 2007

Regional Groundwater Recharge: 2002

Noah Noah-MP R3 Noah-MP R5 Barlage et al. (2013)

Regional Deep Soil Moisture: 2002

Noah Noah-MP R3 Noah-MP R5 Barlage et al. (2013)

Regional Root Soil Moisture: 2002

Noah Noah-MP R3 Noah-MP R5 Barlage et al. (2013)

Regional Latent Heat Flux: 2002

Noah-MP R3 Noah-MP R5 Barlage et al. (2013)

Noah-MP Reduces Seasonal 2-m

Temperature Bias in WRF

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Barlage et al. (2013)

Noah-MP Improves Seasonal Temperature

Forecasts in CFS

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Rongqian Yang et al. (2013)

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WRF Simulated & Observed Monthly and Seasonal

Mean Precipitation in Central Great Plains

WRF with interactive canopy (DV) improves summertime rainfall in the central Great Plains.

DV + dynamic water table = DVGW improving the simulation even more.

Reason: improved coupling between soil moisture and precipitation through lowered lifting condensation level (see next slide).

Incorporating vegetation and groundwater dynamics into a regional climate model would be beneficial for seasonal precipitation forecast in the transition zones.

Jiang et al. (2009) J. Geophys. Res.

Lifting condensation level (LCL) height versus

soil moisture index (SMI) in the soil layers

Jiang et al. (2009) J. Geophys. Res.

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Summary• Improved realism in snowpack, groundwater, and

vegetation phenology improves performance.

• The multi-parameterization (MP) framework allows for multi-hypothesis testing and understanding of parameterization interaction.

• Runoff parameterizations are the dominant options influencing energy, water, and carbon balances.

• Different sub-basins have different best performing combinations.

• The groundwater-climate interaction improves seasonal prediction of 2-m temperature and precipitation.

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Summary (continued)

• More studies, both offline and coupled, covering multi-scales (meters to tens of km and daily through seasonal to interannual), are warranted to realize the full potential of the MP framework.

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Path Forward: Terrestrial Hydrological

Model Intercomparison Testbed: Multi-

Everything

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Coupler

CLM JULES Noah-MP

CATHY ParFlow OGS

LSMs

SSMs

RTMsData Assimila-tion

Scaling

Data

Cal/Val

SiB VIC …

PAWS PIHM …

Bench-marking

High-performanceComputing

Cloud Modeling

Thank you!

http://www.geo.utexas.edu/climate

NSF

NASA

NOAA

KAUST

TACC

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