land surface key issues and glass progress land-surface and the key issues bart van den hurk...

Land surface key issues and GLASS progress

Land-surface and the key issues

Land-surface and the key issues

Bart van den Hurkco-chair GLASS


Overview of key-issuesOverview of key-issues

• Model development (parameterization and model structure)

• Surface characteristics/parameters• Land-atmosphere interaction, feedbacks

and climate change attribution• Data assimilation


General developments in the past decades

General developments in the past decades

• Increases in– number of key components

• ’80s: surface energy• ’90s: surface hydrology• ’00s: surface biochemistry• ’10s: surface feedbacks (?)

– spatial detail• more surface types become relevant (urban,

lakes)• more types of land-atm interaction become

relevant– contribution to predictability at various time scales


Outstanding parameterization issues

(1)


(1)• Hydrology

P-E > 0Often it’s not!We need:– groundwater

redistribution– overland flow (river

routing) and wetland formation

– dynamic calibration procedures

• Snow

Okavanga river Botswana:floodplain extent f(local P)

rms of basin discharge

old schemenew scheme

snow dominatedbasins



(2)


(2)• Biogeochemistry

– Photosynthesis and carbon allocation used to be a “climate application”, but…

– phenology (LAI) and dependence on nutrients and droughts

– (temperature sensitivity of) soil respiration

– releases CH4 from wetlands, BVOC from forest, aerosol from arid or urban areas

anomaly fAPAR summer 2003 (obs and modelled); Ciais et al 2005



(3)


(3)• Human land use and hydrology

management– urban “tiles”– lakes– irrigation

Siebert et al, 2005


Outstanding issues in land-atmosphere

interaction

Outstanding issues in land-atmosphere

interaction• To what extent is land

responsible for land-atm.coupling variability?

• Studies with CRM and LES underway– sign of soilm-precip.feedback

depends on convective parameterizationPCTL

(Pwet-Pdry)

PCTL

positive feedback

negative feedback

Hohenegger et al, in press


Outstanding Land Data Assimilation issues (1)Outstanding Land Data Assimilation issues (1)

• Multiple systems around (GLDAS, NWP systems) or coming up (Land Flux)

• Offline systems rely heavily on precip/rad forcing, coupled (“nudging”) systems on assumed dSM/dRH

• Land Flux from GRP maybe should merge concepts

Science question: willDA reduce the spread in current ET estimates?


Outstanding Land Data Assimilation issues (2)Outstanding Land Data Assimilation issues (2)

• Data assimilation is also developing into new observational types– (SMOS) soil moisture– LAI from NDVI (requires long assimilation

window)

• Data assimilation and parameter estimation become increasingly intertwined– parameterization is increasingly relying on

calibration rather than model structure

• The suite of systems requires a coordinated Land Data Assimilation Intercomparison Project (LDAIP)


Outstanding feedback/ attribution issues (1)


• Diagnostics for land atmosphere coupling vary widely– (Koster’s , P/sm, LCL/sm, diurnal T/RH)

• …but refer to different processes• …and are not always observable• Integrated framework is being developed

in GLASS




• Experiments address land use in climate signal– Land use 1870 and 1992 provided to 7 GCMs

• …but LUCID learns that parameter transfer is far from straightforward– GCMs have different tile structures and model

philosophies (e.g. yes/no phenology, yes/no prognostic albedo, …)

JJA temp responseof land use change


Outstanding application/ operation issues (1)


• Surface Tile structure– debate on “types of heterogeneity” is not as active

anymore, but will increase again when spatial resolution increases (land param. in LES)

– some land surface types demand complex tiling structure (e.g. floodplains/wetlands with coupling to groundwater)

– tile structure determines interpretation of land use data to a large extent (see LUCID example)

• Coupled/offline operation– e.g. routing/groundwater need horizontal network– data assimilation of LAI needs long time window




• Land Information System (LIS)– LIS is increasingly suited for complex

modelling/data assimilation experiments• multiple LSMs coupled to WRF or using

offline forcings data• data assimilation capabilities

– but its portability can probably be improved

• Also ECMWF is integrating offline surface driver into Integrated Forecasting System


Conclusions on outstanding issues

Conclusions on outstanding issues

• Parameterization– new components (groundwater, lakes, urban,

…)– new diagnostics and/or experimental set-up:

• Data assimilation– combination of scheme structures desired– trade-off data assimilation/parameter

calibration revisited

• Infrastructure– need for flexible LIS-type systems will increase

“What is the true contribution of the land component to the climate system?”“Does my land surface model describe this contribution sufficiently well?”


Progress report of GLASS

Progress report of GLASS

• The current structure


Some issues concerning GLASS structure

Some issues concerning GLASS structure

• PILPS-type studies are still quite active (Urban, Snow, Radiation) but does not answer the question: how good should a model be? What benchmark should be considered?

• Data-assimilation is not well visible in structure

• Difference between local and global coupling is artificial


Proposal for new structure (under discussion)


benchmarking

land-atmospherecoupling

model datafusion

metricsGLACE2 LandFlux

LoCo

current projectsnew projects

PILPS-UrbanSnowMIP

LUCID

Coordinated LDAIP

RAMI4PILPSPILPS-Carbon 2




benchmarking


model datafusion

metricsGLACE2

LoCo

PILPS-UrbanSnowMIP

LUCID


{

Global Land Atmosphere Coupling Exp 2: GLACE2Global Land Atmosphere Coupling Exp 2: GLACE2

• Adressing potential seasonal predictability from land surface state

• Integration into WCRP/TFSP project


Perform ensembles of retrospective

seasonal forecasts

Initialize land stateswith “observations”,

using GSWP approach

Prescribed SSTs or the use of a coupled ocean

model

Initialize atmosphere with “observations”, via

reanalysis

Evaluate forecasts against

observations

Step 1:

Experiment overviewExperiment overview


Perform ensembles of retrospective

seasonal forecasts

Initialize land stateswith “observations”,

using GSWP approach

Initialize atmosphere with “observations”, via

reanalysis

Evaluate forecasts against

observations

Step 2:

“Randomize” land

initializatio

n!


Prescribed SSTs or the use of a coupled ocean

model


Step 3: Compare skill; isolate contribution of realistic land initialization.

Forecast skill obtain in experiment using

realistic land initialization

Forecast skill obtained in identical experiment,

except that land is not initialized to realistic

values

Forecast skill due to land initialization



Group/Model Points of Contact

1. NASA/GSFC (USA): GMAO seasonal forecast system (old and new)

2. COLA (USA): COLA GCM, NCAR/CAM GCM

3. Princeton (USA): NCEP GCM

4. IACS (Switzerland): ECHAM GCM

5. KNMI (Netherlands): ECMWF

6. GFDL (USA): GFDL system

7. U. Gothenburg (Sweden): NCAR

8. CCSR/NIES/FRCGC (Japan): CCSR GCM

# models

S. Seneviratne, R. Andreas

E. Wood, L. Luo

P. Dirmeyer, Z. Guo

R. Koster, T. Yamada2

B. van den Hurk, H. Camargo, G. Balsamo

T. Gordon

J.-H. Jeong

T. Yamada

2

1

1

2

1

1

1

11 models

Confirmed participant list

Confirmed participant list


Generation of SST Boundary Conditions

Generation of SST Boundary Conditions

• Needed: SST conditions for forecasts that do not include measurements of SSTs during the forecast period.

• Approach: Determine persistence timescales from observational record (with data exclusion) and reduce initial (measured) SST anomalies with time into the forecast, using those timescales.


“Persisted” SST boundary conditions have been constructed and are now available online. (T. Yamada)


Many groups started simulations

Many groups started simulations


Original timelineOriginal timeline

• Summer 2007:– Finish identifying interested modeling groups

• Summer 2007– Provide data to participants (meteorological

forcing data, atmospheric initialization, SST conditions)

• Summer/Fall 2008– Simulations due

• Fall/Winter 2008– First analyses performed


New timelineNew timeline

• Summer 2007:– Finish identifying interested modeling groups

• Summer 2007– Provide data to participants (meteorological

forcing data, atmospheric initialization, SST conditions)

• Summer/Fall 2008– Simulations due

• Fall/Winter 2008– First analyses performed

Spring 2008

Fall/Winter 2008

FirstSpring 2009


http://gmao.gsfc.nasa.gov/research/GLACE-2/

http://gmao.gsfc.nasa.gov/research/GLACE-2/




benchmarking


model datafusion

metricsGLACE2

LoCo

PILPS-UrbanSnowMIP

LUCID


Land Use and Climate – IDentification of robust impacts (LUCID)

Land Use and Climate – IDentification of robust impacts (LUCID)

• Designed to separate land use change and GHG signal

• Multiple phases (prescribed SST coupled models)

• Phase 1:– Fixed SSTs– 4 ensembles (30 yrs, 5 members)

• pre-industrial/present-day vegetation• pre-industrial/present-day GHG & SST

– 7 GCMs– First results presented during sep 2008 Paris

w/s


Major outcomeMajor outcome

• Transfer of common landuse maps to model parameters quite variable– tiling structure– assignment of PFTs

– parameter treatment (fixed/prognostic albedo, z0)

– phenology (prescribed/prognostic LAI)

• Result– Response between models not very uniform– No reason to suspect significant teleconnection

patterns from land use– GRL paper (Pitman et al) being drafted


LUCID JJA LHFLUCID JJA LHF

phenology of LAI

Small difference between forest/crop LAI




benchmarking


model datafusion

metricsGLACE2

LoCo

PILPS-UrbanSnowMIP

LUCID


LoCoLoCo

• Multiple processes

• Multiple diagnostics being explored• Proposal for hierarchy of land-atm coupling

maps (see GEWEX News November’08)• Proof of concept to be applied to SGP with

LIS

direct PBL feedback on surface fluxes

Triggering of convection

Fueling convection

rememberinganomalies

PBL cloudformation




benchmarking


model datafusion

metricsGLACE2

LoCo

PILPS-UrbanSnowMIP

LUCID


PILPS-Urban (Sue Grimmond, KCL)

http://geography.kcl.ac.uk/micromet/ModelComparison

PILPS-Urban (Sue Grimmond, KCL)

http://geography.kcl.ac.uk/micromet/ModelComparison

• Aimed at evaluation of urban models– range in structure (simple

plane parallel canopies complex multicomponent canyon models)

• Big group (22 models!)• Initial data-set (Vancouver)

released for testing• Presently: models are

running an anonymous city– hierarchy of data releases

(characteristics, calibration, validation)




benchmarking


model datafusion

metricsGLACE2

LoCo

PILPS-UrbanSnowMIP

LUCID


SnowMIP (Richard Essery)

SnowMIP (Richard Essery)

• BAMS overview paper submitted• Main results:

– focus on forest– 33 models, 11 countries– sites Canada, US, Switzerland– results:

• accumulation and ablation ~ ok• albedo ~ ok• mass and difference forest/open land vary

widely• soil temperatures too low


SWESWE

accumulation at open sites


Soil TemperatureSoil Temperature


Final remarksFinal remarks

• Parameterization focuses on new components

• Systematic benchmarking of both models and data is not well embedded but needs attention

• Data assimilation will benefit from LDAIC• New infrastructure inspired on LIS should

combine offline modelling, data fusion and benchmarking

• GLASS panel will get new membership

land surface key issues and glass progress land-surface and the key issues bart van den hurk...

Documents

land surface key issues

glass progress landsurface

types of land

surface hydrology

surface types

land atmosphere interaction

surface feedbacks

land atmosphere coupling