Ensemble Forecasting from LPJ simulations over the Mediterranean basin: Preliminary Results Dr. Monia Santini University of Tuscia and CMCC CMCC Annual Meeting Marina di Ugento (LE) June 9th-11st, 2010 1

Aim To analyze uncertainties in projections of Med-basin natural vegetation trends for the 21st century from the DGVM-LPJ, based on future global and regional climate simulations, on model parameterization and formulations.

Ensemble Forecasting (Araujo and New, 2007) “…each individual outlook to the future in a scenario-study will not necessarily contain the most likely prospects, but, as a whole, the simulations provide the bandwidth of possible changes.” (Dammers, 2000). Initial condition perturbation The ensemble spread gives information about the prediction errors

LPJ inputs/outputs Spatialized inputs Soil: texture Climate: monthly temperature, precipitation, cloud coverage, number of wet days Aggregated inputs CO2 concentration Average grid-cell basis outputs: Monthly: carbon and water fluxes Annually: carbon pools , fire return period, and competition among 9 Plant Functional Types (PFTs) + barren areas http://www.pik-potsdam.de/lpj/

Sources of LPJ uncertainties Model inputs: future climate projections Representation of driving processes and/or model formulation Parameters within the model (e.g. Zaehle et al. 2005; Anav, 2009) regarding soil and/or PFTs

Sources of LPJ uncertainties Future climate projections 1971-2050 A1B

Sources of LPJ uncertainties Model formulation Daily precipitation generation: Interpolation from monthly data Random generation from precipitation amount and number of wet days DAY OF MONTH PRECIPITATION (mm) Monthly precipitation =256 mm Number of wet days = 12 Monthly precipitation next month =20mm

Sources of LPJ uncertainties PFT parameters TNE = Temperate Needleleaved Evergreen TBE = Temperate Broadleaved Evergreen TBS = Temperate Broadleaved Summergreen BNE = Boreal Needleleaved Evergreen C3 = C3 grass

Input data processing Soil (HSWD) 0.0083333° (≈1 km) LPJ soil parameterization COARSE MEDIUM COARSE MEDIUM FINE COARSE FINE MEDIUM FINE MEDIUM COARSE FINE NONVERTISOL FINE VERTISOL ORGANIC (Harmonized Soil World Database; IIASA, 2009)

Input data processing Soil (HSWD) 0.0083333° (≈1 km)  0.083333° (≈10 km) Climate interpolated to aggregated soil grid (natural neighbouring) CMCC grid

Input data processing Soil (HSWD) 0.0083333° (≈1 km)  0.083333° (≈10 km) Climate interpolated to aggregated soil grid (natural neighbouring) ENEA grid

Input data processing Soil (HSWD) 0.0083333° (≈1 km)  0.083333° (≈10 km) Climate interpolated to aggregated soil grid (natural neighbouring) CNRS-IPSL grid

Input data processing Soil (HSWD) 0.0083333° (≈1 km)  0.083333° (≈10 km) Climate interpolated to aggregated soil grid (natural neighbouring) METEOFRANCE grid (regular)

Input data processing Soil (HSWD) 0.0083333° (≈1 km)  0.083333° (≈10 km) Climate interpolated to aggregated soil grid (natural neighbouring) METEOFRANCE grid (irregular)

Input data processing Soil (HSWD) 0.0083333° (≈1 km)  0.083333° (≈10 km) Climate interpolated to aggregated soil grid (natural neighbouring) MPI grid

Input data processing Soil (HSWD) 0.0083333° (≈1 km)  0.083333° (≈10 km) Climate interpolated to aggregated soil grid (natural neighbouring) CO2-5 years interpolated to 1 year IMAGE Team, 2001

LPJ simulation protocol Perform 24 separated runs of LPJ Spin-up period of 1000 years (from 971 to 1970) Transient simulation from 1971 to 2050

Whole-domain climate input anomalies ‘Temperature’ anomaly YEARS ANOMALY Relative to 1971-2000 (all model average) ‘Number of wet days’ anomaly YEARS ANOMALY

LPJ outputs For each grid cell LPJ produces annual values for: Plant Functional Types Vegetation carbon Soil carbon Litter carbon Carbon loss by fires Fire return period ...and monthly values for: Net Primary Production Heterotrophic Respiration Run-off Evaporation Interception Transpiration Soil moisture (2 layers) NEE Blue water fluxes White water fluxes (unproductive) Green water fluxes (productive)

PFT percent distribution TEMPERATE NEEDLELEAVED EVERGREEN 2000

PFT percent distribution TEMPERATE NEEDLELEAVED EVERGREEN 2000 2050

PFT percent distribution C3 PERENNIAL GRASS 2000

PFT percent distribution C3 PERENNIAL GRASS 2050 2000

Litter C - aggregated average gC/m2 YEARS YEARS

Soil C - aggregated average gC/m2 YEARS YEARS

Vegetation C - aggregated average gC/m2 YEARS YEARS

C loss by fires - aggregated average gC/m2 YEARS YEARS

Fire probability - aggregated average YEARS YEARS

Spatial “litter C” anomalies 2021-2050 minus 1971-2000, whole ensemble average

Spatial “soil C” anomalies 2021-2050 minus 1971-2000, whole ensemble average

Spatial “vegetation C” anomalies 2021-2050 minus 1971-2000, whole ensemble average

Spatial “C loss by fires” anomalies 2021-2050 minus 1971-2000, whole ensemble average

Spatial yearly Blue Water anomalies 2021-2050 minus 1971-2000, whole ensemble average

Spatial yearly Green Water anomalies 2021-2050 minus 1971-2000, whole ensemble average

Spatial yearly NPP anomalies gC/m2 2021-2050 minus 1971-2000, whole ensemble average

First conclusions At aggregated level, different ensemble members have a variable influence in function of the considered model output. At spatialized level: two different behaviors between N and S Med-basin for C-related fields Med-surrounding areas the most critical in terms of water resources and fires

in progress and future work Validation (last decade) at eddy sites (other EU-projects)

in progress and future work Forcing with land use change simulations to separate natural vegetation from croplands focusing on fires Input to Historical analysis of LUC Land use change scenarios from coupled ICES+CLUE@CMCC Input to Climate projections drive Input to LPJ model result LU/LC maps: URBAN CROPLAND detail from ICES+CLUE@CMCC FORESTLAND/GRASSLAND detail from LPJ model RUI = Rural-Urban Interface Carbon loss by fires Fire return interval Output for Impacts at the RUI

in progress and future work Ensemble updating using other GCMs-RCMs and/or emission scenarios (e.g. AR4 and AR5) e.g. COSMO-CLM domain (14 km res.) Temporal coverage 2000-2100

in progress and future work Forcing with land use change simulations to separate natural vegetation from croplands focusing on White, Green & Blue water fluxes in SSA WGB = White Green Blue SSA = Sub-Saharian Africa CLIMAFRICA

Thank you! monia.santini@unitus.it