Summary Terrestrial ECV’s Alexander Loew, Silvia Kloster Max-Planck-Institute for Meteorology.

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Presentation transcript:

Summary Terrestrial ECV’s Alexander Loew, Silvia Kloster Max-Planck-Institute for Meteorology

Loew et al., 2013

CCI - SM as a good proxy for soil moisture & rainfall dynamics Soil moisture vs. precipitation anomalies „ECV_SM a good proxy for precipitation anomalies“ MPI-ESM soil moisture vs. ECV_SM 1 „ECV_SM good proxy for global SM dynamics“ 1 precipitation impact removed Loew et al., 2013

Brocca et al., 2014, JGR Soil moisture as a raingauge Correlation between 5-day precipitation estimates from soil moisture and GPCC reference precipitation

S O L V E D ! Effect of sampling bias on global mean soil moisture fields Communication to modellers matters!

Suitability for SM dynamics? Is CCI SM suitable to evaluate the general soil moisture dynamics of an ESM?

MPI-ESM:JSBACH

Vegetation model From Burned area to fire emissions 10 Fuel Load [gC/m 2 ] Carbon Emissions [gC/(m 2 year)] * Combustion Completeness Carbon Emissions JSBACH FIRE CO, NO2 HCHO … *Mortality Burned area [m 2 /year] Algorithm A&B, SPITFIRE (Bonnan et al., 2001) (Arora and Boer, 2005) (Thonicke et al 2001) Carbon Emissions GFEDv3 (van der Werf et al, 2010) JSBACH ESA CCI FIRE (GFED) 1 Fract. Burned Tree 2 CCI LC (GFED) BA satellites

Integration Pathway: Burned Area in JSBACH Equal distribution of burned fraction GFED 1.87 ° grid cell grid cell burned A Observed fraction of burned trees versus burned grass GFED 1.8 7° grid cell grid cell burned B

Results: Carbon emissions Carbon Emissions JSBACH Fire Carbon Emissions GFEDv3 Difference Carbon Emissions JSBACH Fire minus GFEDv PgC/y 2.02 PgC/y EXP4 GFED JSBACH - GFED

Using the GFED BA Uncertainty EXP4 + Unc - Unc EXP4 + Unc - Unc The relation between the uncertainty in the Burned Area and calculated Carbon Emissions is non-linear.

Global multiyear records consistent with landcover are a prerequesite for this kind of analysis

Questions How does an integration of ESA CCI LC data affect the energy and water fluxes at global scales? Does the integration of ESA CCI LC data improve the skill of MPI-ESM in simulating present day climate? Is the usage of ESA CCI LC data superior compared to precursor data? Added value of CCI?

PFT distribution (JSBACH)

Input Landcover data SourcePeriod CTRL- Globcover2005 CCI LC v1.2 (Nov) Forcing data Nameonline/offline CRU/NCEPoffline WATCHoffline MPI-ESMonline

Protocol agreed with CRG of CCI LC

Effect on model prognostic variables Change in LC = change in prognostic variables

CTRL-CCI What effect has CCI data compared to CTRL model?

Globcover - CCI In which aspects does CCI differ from precursor?

WATCH - CRU What is the effect of different forcings?

Independent model benchmarking CMIP5 (ESG) Your model Observations Standard diagnostics Your script ctrl simulation

Skill scores

Benchmarking: offline

Benchmarking: online Global 2m temperature simulations slightly better with ESA CCI LC data Note: small changes only and significance of results still would need to be assessed

Summary CCI LC slightly improves global 2m temperature estimates (robustness?)... however changes small compared to forcing uncertainty  high resl. LC for better process understanding (phase 2) Large potential for joint fire and LC data usage for improvement of global fire emission estimates No suitable CCI fire record available so far. Unique first multidecadal data record; good proxy for prec. dynamics Documentation of caveats needed; no CDF matching to reference model if possible