GLASS Panel Meeting August 2003 Tucson, Arizona, USA SAHRA

GLASS Framework 2 x 2 Matrix: Spatial scale vs. Land-atmosphere coupling strategy 2 x 2 Matrix: Spatial scale vs. Land-atmosphere coupling strategy hydro.iis.u-tokyo.ac.edu/GLASS/

Current GLASS Actions Local Offline –PILPS C1 (finishing) –San Pedro (starting) –Isotope PILPS (proposed) Large-Scale Offline –GSWP2 (underway) Large-Scale Coupled –GLACE (underway) Local Coupled –LOCO (proposed) Other issues

Summary of the first phase of the PILPS C-1 project Comparison of both « biophysical » and « biogeochemical » flux from different types of models with observations at one EUROFLUX site: Loobos The site: -Temperate « mature(100 years) » coniferous forest -Climate: 700 mm precipitation, 9.8 °C mean temperature - Planted on a sand  no soil carbon at the beginning of the plantation - Measured fluxes: NEE, LE,H, Rn - Meteorological parameters: incoming SW rad., precipitation, temperature, wind speed, relative humidity, pressure -Period covered: Models: Including SVAT with and without carbon cycle Simulations: Free equilibrium simulations: -Models are run until equilibrium of state variables using years « in loop » Free 100 years run: -simulation of « realistic scenario »: Beginning with a soil with no carbon, the models are run for 1906 (plantation of the forest) to 1998 using observed climate.

Main preliminary conclusion Taking into account that models was not calibrated, the models reproduce relatively well the observations Sensible heat flux is overestimated at night. High net CO2 and latent heat fluxes are underestimated The 100 years simulation was very interesting since if all models give relatively similar NEE and are all able to reproduce the difference of sink between 1997 and 1998, trajectories of models carbon fluxes and pools are very different ! For more details on results go to:

CLASS-MCMCLASS-UA ORCHIDEE-1ORCHIDEE-2 SWAP IBIS VISA Total Soil carbon (Kg C/m-2) MC AVIM Main preliminary conclusion Taking into account that models was not calibrated, the models reproduce relatively well the observations Sensible heat flux is overestimated at night. High net CO2 and latent heat fluxes are underestimated The 100 years simulation was very interesting since if all models give relatively similar NEE and are all able to reproduce the difference of sink between 1997 and 1998, trajectories of models carbon fluxes and pools are very different! For more details on results go to:

PILPS Semi Arid Experiment (PILPS San Pedro) USA ArizonaNew Mexico Phoenix Albuquerque Kendall Lucky Hills Tucson Sevilleta Sponsors SAHRA

Locations Site Longitude West Latitude North Elevation [m.a.s.l.] Precipitation [mm/year] Temperature [  C] Lucky Hills Shrubland 110  03’05’’31  44’37” Kendall Grassland 109  56’28”31  44’10” Tucson Shrub/cacti 111  49’48”32  13’01” Sevilleta Grassland 106  43’30”34  20’30” Sevilleta Shrubland 106  44’39”34  20’05”

Forcings Outputs Spin up Period Data Supplied Calibration Period Data Supplied Evaluation Period Data NOT supplied Forcings2000 Outputs Forcings Outputs San Pedro Shrub & Grass Sevilleta Shrub & Grass Tucson Mixed Shrub & Cacti Split Sample Test

Science Questions What is the ability of the models to reproduce the water, energy, and carbon exchanges in semi-arid environments? This is the first PILPS study in a semi-arid region, with study sites in Arizona and New Mexico. Does model calibration reduce the among-model range in the model simulations? Code for performing a multi-criteria optimization procedure developed by Gupta and Bastidas will be made available to all model participants for calibration of their schemes. Are the current (usually single) representations of semi-arid lands in the models enough to reproduce the different environments that exist in those areas? The experiment will be a direct test of spatial transferability of surface parameters, as is commonly practiced but not validated in weather, climate and hydrologic models.

Implementation What is the ability of the models to reproduce the water, energy, and carbon exchanges in semi-arid environments? Uncalibrated simulation of 3 Arizona sites (validation data withheld) Does model calibration reduce the among-model range in the model simulations? Validation data provided for Arizona sites – modelers can calibrate their models to improve simulations Are the current (usually single) representations of semi- arid lands in the models enough to reproduce the different environments that exist in those areas? Use calibrated parameters from grassland and shrub sites in Arizona for similar sites in New Mexico (NM validation data withheld) Validation data provided for Arizona sites.

Isotopes in PILPS : IPILPS Stable and radioactive isotopes are valuable for both tracing and dating Stable water isotope measurements –Demonstrated that the Amazon recycles its water (3 or 4 times) –Prompted early GCM deforestation experiments (e.g. Henderson- Sellers & Gornitz, 1984) Carbon isotopes differentiate C3 and C4 photosynthesis Off-line (PILPS) inter-comparison is timely e.g. – “Heavy” water (HD 16 O & H 2 18 O) at the land surface –River routing schemes tested against GNIR – the Global Network for Isotopes in Rivers QUESTION: Are the stores and fluxes of two different “heavy” water isotopes (HD 16 O & H 2 18 O) correctly characterised at the land surface?

Basin Isotope Modelling Isotopes provide an independent climate and water resource validation tool Weakened signal in central Amazon (Manaus) indicates increased water recycling Logarithmic enhancement in Darling reveals irrigation impacts H-S, Stone, McGuffie etc GRL, 2003, Manaus Enhanced D/H ratio Darling

nd Global Soil Wetness Project Multi-model investigations into variability and predictability of the global surface water and energy cycles This phase of the project takes advantage of: The10-year ISLSCP Initiative 2 data set ( ) The ALMA data standards developed in GLASS The infrastructure developed in the pilot phase of GSWP

nd Global Soil Wetness Project GSWP-2 represents an evolution in multi-model large-scale land-surface modeling with the following features: The ten year length of the ISLSCP Initiative 2 allows for a better investigation of interannual land surface climate variability. The ISLSCP Initiative 2 data set contains more than one rendition of many global fields, produced by different methods and scientists. This gives us a straightforward means to investigate LSS sensitivity to the choice of forcing data sets. Application and further development of the methods of calibration and validation of LSSs with in situ and remote sensing data, including direct simulation of brightness temperatures as observed by satellite.

GSWP-2 data sets GSWP-2 data sets for parameter specification, meteorological forcing, and validation have been produced. The data sets are based on the ISLSCP-Initiative II data, but many of the fields represent additional processing, such as the production of “hybrid” data sets combining gridded observations (low temporal resolution) with model reanalysis (high time resolution). This hybridization removes systematic errors in the reanalysis data, providing a superior set of forcing data for the land surface models. Hybrid minus reanalysis

Data servers The data sets have been posted online for community access. There are three DODS servers for accessing the data directly over the internet: North America: Europe: Asia: The North American and Asian servers are GDS. There is also FTP access to the individual files at: ftp://monsoondata.org/ (password required) and direct HTTP access to files at:

AMS 2004 There will be a GSWP-2 session at the AMS 18th Conference on Hydrology, AMS Annual Meeting (Seattle, Washington, USA, January 2004) to present preliminary results of the experiment. 12 Abstracts submitted to GSWP-2 session. 24 November deadline for baseline runs. Start on QC, comparison, and sensitivity test cases. Public presentation of preliminary results at AMS (Seattle, Jan 2004). Progress

GRP/GMPP exchange via GSWP-2 Plans are being made for a collaborative effort between GRP and GMPP. Bill Rossow has been communicating with Paul Dirmeyer and Jan Polcher about using GSWP estimates of surface (latent and sensible) heat fluxes over land for helping to close the global surface energy budget. GSWP will also pursue sensitivity studies using ISCCP estimates of surface radiation for model forcing, compared to SRB and reanalysis estimates, to understand how uncertainty in our estimates of radiation propagate into the terrestrial hydrologic cycle.

K02 strategy, part 1: Establish a time series of surface conditions (Simulation W1) Step forward the coupled AGCM-LSM Write the values of the land surface prognostic variables into file W1_STATES Step forward the coupled AGCM-LSM Write the values of the land surface prognostic variables into file W1_STATES time step ntime step n+1 (Repeat without writing to obtain simulations W2 – W16) GLACE: Global Land-Atmosphere Coupling Experiment This experiment is a broad follow-on to the four-model intercomparison study described by Koster et al. (2002)*, hereafter referred to as K02. *J. Hydrometeorology, 3, , 2002 glace.gsfc.nasa.gov/

K02 strategy, part 2: Run a 16-member ensemble, with each member forced to maintain the same time series of surface prognostic variables (Simulations R1 – R16) Step forward the coupled AGCM-LSM Throw out updated values of land surface prognostic variables; replace with values for time step n from file W1_STATES Step forward the coupled AGCM-LSM time step ntime step n+1 Throw out updated values of land surface prognostic variables; replace with values for time step n+1 from file W1_STATES glace.gsfc.nasa.gov/

All simulations in ensemble respond to the land surface boundary condition in the same way  is high Simulations in ensemble have no coherent response to the land surface boundary condition  is low Define a diagnostic  that describes the impact of the surface boundary on the generation of precipitation. glace.gsfc.nasa.gov/

Plan for GLACE K02 Step 1: 16-member ensemble, with prognostic states written out at each time step by one of the members. Step 2: 16-member ensemble, with all members forced to use the same time series of surface prognostic states. All simulations are run over July. NEW GLACE Step 1: 16-member ensemble, with prognostic states written out at each time step by one of the members. Step 2: 16-member ensemble, with all members forced to use the same time series of surface prognostic states. Step 3: 16-member ensemble, with all members forced to use the same time series of deeper (root zone and below) soil moisture states. All simulations are run from June through August glace.gsfc.nasa.gov/

Bonan14. NCAR with CLM2 Kanae/Oki13. U. Tokyo w/ MATSIRO Xue12. UCLA with SSiB Koster11. NSIPP with Mosaic Lu/Mitchell10. NCEP/EMC with NOAH Taylor9. Hadley Centre w/ MOSES2 Sud8. GSFC(GLA) with SSiB Gordon7. GFDL with LM2p5 Verseghy6. Env. Canada with CLASS Viterbo5. ECMWF with TESSEL Kowalczyk4. CSIRO w/ 2 land schemes Dirmeyer3. COLA with SSiB Hahmann2. CCM3 with BATS McAvaney/Pitman1. BMRC with CHASM ContactModel Participating Groups Status Dropped out 15. LMD w/ ORCHIDEE Polcher Not yet submitted

Prelim. Results Large variety coupling strengths evident among models, with no systematic patterns. glace.gsfc.nasa.gov/

In principle, imposing land surface boundary states should decrease the intra-ensemble variance of the atmospheric fields. pdf of precipitation at a given point, across ensemble members corresponding pdf when land boundary is specified We are examining this in GLACE by looking at the variance ratio:  2 P (S)  2 P (W) glace.gsfc.nasa.gov/

Strawman Proposal: LO cal CO upled Phase 1: Synthetic column – extract column boundary conditions from atmospheric model –Proof of concept Phase 2: Case Studies with Past Field Campaigns –PILPS sites: Cabauw, FIFE, Boreas, others? –GABLS sites: which? –Others: SGP/ARM/IHOP, LBA, Wangara, others? Phase 3: Future Field Campaigns –Upcoming Sites: HEAT (Houston, TX)-Urban; others? –New Site(s): Design “local coupled testbed(s)” Local-Coupled modeling experiments – GABLS and GLASS? Multi-LSS + Common-SCM to understand land-PBL feedbacks

Field Site for Local-Coupled Testbed GOAL: Collect a specifically targeted dataset to meet the objectives of the GLASS local-coupled action. –A well-defined mesoscale hydrologic catchment (for surface water balance observations) –Observations from groundwater table through PBL –Include heterogeneity in elevation, vegetation, soils, climate (inc. snow), etc. –Observation platforms: In-situ: weather, fluxes, snow, soil moisture, groundwater, vegetation Aircraft: fluxes, temperature, moisture, microwave, etc. Soundings: tethered Satellite: various available Questions: What should the spatial scale and heterogeneity be? When, where, and how long? Is this experiment of interest? Parameters Soil Properties Vegetation Properties Elevation & Topography Subgrid Variation Catchment Delineation River Connectivity Forcing Precipitation Wind profiles Humidity profiles Radiation Air Temperature profiles Fluxes Evapotranspiration Sensible Heat Flux PBL fluxes Radiation Runoff Drainage Isotopes/carbon States Soil Moisture Groundwater Surface Water Temperature (soil, veg, air) Humidity Wind Pressure Snow Carbon Nitrogen Biomass

Other Issues Initialization of soil wetness in climate models –Lack of transferability of soil moisture data sets exists from one model to another. –GLASS is preparing a summary paper to educate the broader modeling community as to the pitfalls of treating soil moisture as a uniformly defined quantity across models Application of LSSs in regional models. –The regional modeling community is not well connected to the global modeling community who seem to do much of the land model development work. –LSSs in regional models are often not well initialized, and the panel perceives that the regional modeling community underestimates the severity of this problem. –A workshop on the topic is being considered to bring the land surface and regional modeling communities together. A potential stage for such a workshop may be the African Monsoon Multidisciplinary Analysis (AMMA). Urban modeling iLEAPS ISLSCP