Assessment of ESA GlobAlbedo for climate model applications - examples using MPI-ESM
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1 Assessment of ESA GlobAlbedo for climate model applications - examples using MPI-ESM Alexander Loew, Thomas Raddatz, Swantje Preuschmann Max-Planck-Institute for Meteorology alexander.loew@mpimet.mpg.de
2 Surface albedo... of major importance for climate local, regional (water & energy) P SW net LW net LE H G Q s Q g
3 Surface albedo... of major importance for climate local, regional (water & energy) global Snowball Earth
4 Surface albedo... of major importance for climate local, regional (water & energy) global Small numbers How small is small enough (accuracy requirements, sensitivities)?
5 Small effect big impact...
6 Sahelian drought largest climate anomaly observed so far (recent times) Severe impact on humans Vegetation/surface albedo feedback as amplifier
7 Meteosat surface albedo
8 Surface albedo feedback + precipitation + LE + + SST Positive vegetation-feedback (amplifier) surface albedo Charney, 1977; Schnitzler et al., 2001, Zheng & Yoon, 2009, Taylor et al., vegetation
9 Reliable albedo data helps for improved rainfall simulations Meteosat surface albedo (EUMETSAT) With albedo prescribed Loew, A., Raddatz, T., Bader, J.: Annual to decadal Sahelian rainfall variability: the role of surface albedo. in preparation
10 Earth system models Energy Momentum ATMOSPHERE (ECHAM6) Water Carbon Energy Momentum COUPLER (OASIS) LAND (JSBACH) OCEAN (MPI-OM) Sea ice OCEAN Biochemistry (HAMOCC) MPI-ESM Typical Resolutions T31 T127 ( km)
11 Globalbedo & MPI-ESM 1.Usability of GlobAlbedo for climate model evaluation? 2.How does usage of GlobAlbedo alter the simulated climate? (feedback)
12 Climate model evaluation Objective: Value of GlobAlbedo for climate model benchmarking?
13 Automated model evaluation Simulations Spatial, temporal means OBS Regridding Land/sea mask Metric / Statistics, maps pycmbs = python Climate Model Benchmarking Suite
14 Automated model evaluation Simulations Spatial, temporal means OBS Regridding Land/sea mask Metric / Statistics, maps pycmbs = python Climate Model Benchmarking Suite
15
16 Work performed Integration of GlobAlbedo (BHR, DHR) data into pycmbs framework Use GlobAlbedo for evaluation of CMIP5 model simulations Use results as part of CMIP5 surface radiation assessment paper
17 Monthly mean surface albedo Surface albedo model: F Observations: CLARA-SAL (CM-SAF) = BSA MODIS WSA CERES Globalbedo (BHR, DHR) F Blue sky albedo product would be probably usefull
18 CMIP5 radiation assessment Multimodel mean land surface albedo Climatological difference with MODIS... GA BHR... GA DHR... CLARA SAL
19 Model ranking HISTORICAL AMIP
20 Loew et al., 2014, submitted, JC CMIP5 radiation assessment Surface solar radiation flux components Whole CMIP5 archive (AMIP, HISTORICAL)
21 Towards integrated model evaluation CMIP6 Meehl et al., 2014
22 Summary evaluation GlobAlbedo provides a useful and competetive data product that is usefull for climate model benchmarking similar to other existing datsets. Recommendation: study value for regional climate studies (interannual anomalies) Integration of GlobAlbedo in ESMValTool for continuous model benchmarking in CMIP6
23 GlobAlbedo as boundary condition in MPI-ESM
24 Does it matter? Objective: Using GlobAlbedo as model boundary condition. GlobAlbedo MPI-ESM Impact assessment
25 Tasks Integrate GlobAlbedo as boundary condition in MPI-ESM Perform dedicated (AMIP like) model simulations Validate model results and assess impact on climate
26 Data and methods MPI-ESM GlobAlbedo 0.5 product CMIP5 simulations (as independent reference + internal variability) 1. Perform CTRL simulation with standard model setup (AMIP) 2. Perform EXPERIMENT with GlobAlbedo as boundary condition
27 Results GA improves Antarctic temperature simulations
28 Effect on temperature
29 Effect on temperature Motivation to improve ice albedo parameterization!
30 Temperature analysis Where are larger differences between CTRL and EXPERIMENT? IPCC regions
31 Temperature analysis Where are larger differences between CTRL and EXPERIMENT? IPCC regions
32 Do we get improvement? RMSD Difference between temperature observations and simulations
33 Summary impact assessment Impact mainly on regional scale Mainly slight improvement of T2m simulations using GlobAlbedo Improvement of ice albedo required in MPI-ESM
34 Conclusions GlobAlbedo turned out to be a very usefull dataset for different aspects of climate modelling More regional analyis required More longterm data record required (temporal anomalies); QA4ECV
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