2 nd CCliCS Workshop, April 1 3, 2013, Taipei, Taiwan

Transcription

1 2 nd CCliCS Workshop, April 1 3, 2013, Taipei, Taiwan LLNL-PRES This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA Lawrence Livermore National Security, LLC LLNL-PRES

2 Large systematic errors still present in the latest version of climate models AGCMs JJA Precipitation Errors CMIP5 CMIP3 Ma, Xie, Bolye, Klein, Zhang (2013) J Climate LLNL-PRES

3 Large systematic errors still present in the latest version of climate models Recent studies with ih CAM5 showed that many systematic climate biases are present in just a few days of hindcasts (fast processes) Is this correspondence robust in other climate models? AGCMs JJA Precipitation Errors CMIP5 CMIP3 Ma, Xie, Bolye, Klein, Zhang (2013) J Climate LLNL-PRES

4 Allows to evaluate the nature of parameterization errors before longer time scale feedbacks develop The U.S. US Department tof Energy CAPT project at both thllnl and NCAR (Phillips et al. 2004) CAPT: Cloud Associated dparameterizations ti Testbed Initial tendency errors (e.g., Rodwell and Palmer 2007) Seamless Prediction across weather and climate time scales (e.g., Palmer et al. 2008; Williams and Brooks 2008; Martin et al. 2010) LLNL-PRES

5 It provides essential clues to the origin of climate errors: A good correspondence may indicate that the errors are likelythe results of parameterization errors as the largescale flow remains close to observations in the first few days of hindcasts. If the correspondence is robust across all climate models, improving biases in one model may also improve all models. LLNL-PRES

6 An international model intercomparison project endorsed by WMO WGNE and WGCM (Williams et al. 2012) Run climate models in short range weather forecast mode (same models as those used in CMIP5 ) Models are run for selected periods during YOTC (May 2008 April 2010) The goal is to better understand and to yield significant insights into the cause of CMIP5 model errors LLNL-PRES

7 Short term hindcasts: Transpose AMIP II Jul (tamip experiments, 16 hindcasts) Long term climate simulations: CMIP5 AMIP CMIP5 AMIP simulations (JJAof ) The analysis will focus on typical model errors in precipitation, clouds and radiation from five climate models. LLNL-PRES

8 Model Name Modeling Group(s) AGCM Resolutions (Lon x Lat, Lev) Contact CCSM4 (CAM4) National Center for Atmospheric Research 288x192, L26 D. Williamson IPSL Institute Pierre Simon Laplace 96x96, L39 S. Fermepin HadGEM2-A Met Office Hadley Centre 192x145, L38 K.Williams CNRM-CM5 CNRM/ Centre Europeen de Recherche et Formation Avancees en Calcul Scientifique 256x128, L31 H. Douville MIROC5 Atmosphere and Ocean Research Institute -The University of Tokyo 256x128, L40 M. Watanabe LLNL-PRES

9 T AMIP II (hindcast) approach Initial Conditions: ECMWF analysis CMIP5 Models 5 days hindcast LLNL-PRES

10 Multi-Model Model Mean Bias LLNL-PRES

11 Many systematic biases in the CMIP5/AMIP runs can be identify through short term term hindcasts (T AMIP II) LLNL-PRES

12 Strong similarity il it bt between hindcast and climate bias patterns LLNL-PRES

13 Strong correlation to cloud biases Negative biases mayimplypoor poor simulationsof of cloud condensate LLNL-PRES

14 Bias correlations generally saturated fast after Day 2 Small impacts of atmosphericinitialinitial conditions on the correspondence LLNL-PRES

15 Are the biases in precipitation, total cloud fraction and net shortwave flux at TOA due to errors in model dynamics or physics? Given the very similar bias patterns in the climate runs and hindcasts, we believe that most biases arelikely due to deficiencies in model physics! LLNL-PRES

16 Strong similarity between hindcast and climate bias patterns Strong warm biases over mid latitude land LLNL-PRES

17 Strong similarity between hindcast and climate bias patterns Strong warm biases b over mid latitude l land l LLNL-PRES

18 Why T2m biases also show strong correspondence? Initial biases in the precipitation (less) or clouds (less) can affect the surface energy budget (more SWAbs) and in turn modify the boundary layer property (higher T2m) Biases in the initial conditions for land surface models LLNL-PRES

19 Understanding over what time scale systematic errors develop yields significant insights into the origin of the errors. Transpose AMIP II models indicate many systematic climate errors, particular those associated with moist processes, are present in just a few days of hindcasts ( fast processes ) ). Different atmospheric initial conditions seem to have small impacts on the bias correspondence. Thank you and questions? LLNL-PRES