Avoiding Gotchas in CMIP5 Data, Metadata, and Analysis

Transcription

1 Avoiding Gotchas in CMIP5 Data, Metadata, and Analysis Karl E. Taylor Program for Climate Model Diagnosis and Intercomparison () Lawrence Livermore National Laboratory Presented to the Workshop Boulder, Colorado 16 August 2016

2 Outline CMIP5 data specifications and access The data aren t perfect The metadata aren t perfect Some analysis tips Summary NOTE: Links to nearly all the URL s provided in this talk can be found via

3 CMIP5 model output specifications Output request (see ): Variables Frequencies Reporting periods File structure One variable per file Filename template (see cmip-pcmdi.llnl.gov/cmip5/docs/cmip5_data_reference_syntax.pdf) e.g., tas_amon_hadcm3_ historical_r1i1p1_ nc Time samples per file vary Data structure Dimension order: time, plev, lat, lon with consecutive longitudes occupying contiguous memory (i.e., row-major ordering as in C, num-py, mathematica) Pressure levels are standardized, but some exceptions Horizontal grids differ from one model to another (most are cartesian latxlon)

4 CMIP5 metadata specifications Metadata conventions: CF (cfconventions.org) Standardizes certain netcdf attributes, so that files are self-describing and are machine-interpretable Enables you to write code that can process data stored on different grids CMIP5 imposes additional standards, including Certain global attributes, e.g., Model names: cmip-pcmdi.llnl.gov/cmip5/docs/cmip5_modeling_groups.docx Experiment names: cmip-pcmdi.llnl.gov/cmip5/docs/ cmip5_data_reference_appendix1-1.doc Variable naming Specific units For further info, see cmip-pcmdi.llnl.gov/cmip5/docs/ CMIP5_output_metadata_requirements.pdf

5 Example of info. in the DRS specifications: What is the meaning of rip? Used, e.g., in file name: tas_amon_hadcm3_ historical_r1i1p1_ nc Some models performed an ensemble of closely related simulations These are distinguished in the filenames and global attributes by different values assigned to: r = realization : simulations started from equally likely initial conditions that lead to equally likely realizations of the true climate trajectory i = initialization : only used in decadal predictions, to distinguish among different initialization procedures p = physics : to identify simjulations that are very closely related (e.g., perturbed physics ensemble members or simulations forced by slightly modified parameterizations)

6 How to obtain CMIP5 model output Original data available via ESGF CoG web interface ( Grid ftp THREDDS (e.g., opendap) Also may be available via secondary repositories

7 Warning: There are flaws in the metadata, e.g.: Forcing (global attribute forcing not always consistent with model documentation) Branch_time (see jgregory_cmip5ancestry.txt) Realization numbering

8 There are flaws too in the data itself Check errata page: Examine output before using it. For example: Discontinuities in time series? Sign or units errors? Reasonable looking maps

9 Sample errata entry: CSIRO Mk3.6.0 ocean surface temperature

10 There are flaws too in the data itself Check errata page: Examine output before using it. For example: Discontinuities in time series? Sign or units errors? Reasonable looking maps

11 Sanity check on wind stress field: global RMS error Courtesy of Ji-Woo Lee

12 Sanity check on wind stress field: global RMS error Courtesy of Ji-Woo Lee

13 Easy to identify problem with BNU and FIO models Observed wind stress Access 1.0 BNU-ESM FIO-ESM Courtesy of Ji-Woo Lee

14 Analysis tip 1: Model drift PI control run may not represent an equilibrium state Usually after a few decades surface conditions stabilize and are near equilibrium Subsequently, climate change simulations are initialized (e.g., historical, abrupt4xco2, 1pctCO2) spawned from control. Remove residual drift: perturbed minus control Especially important for slowly adjusting quantities (e.g., ocean heat content, carbon reservoirs, ice volume) branch_time (global attribute) indicates time that the run was spawned by the parent simulation, but these are are not always correctly recorded.

15 Analysis tip 1: Model drift PI control run may not be true equilibrium Control run drift in deep water heat content is evident in many models Drift-corrected heat content changes in historical runs are not large compared to the corrections themselves Gleckler et al., Nature Climate Change, 2016

16 Analysis tip 2: Consider all contributors to differences in model results Unforced variability ( climate noise ) Differences in imposed conditions Differences in model treatments of various physical processes, which lead to differences in responses Instantaneous radiative forcing fast adjustments slow responses

17 Interpretation pitfall 2: Spread in model results cannot be rigorously interpreted as uncertainty Unforced variabilty scenario model response Hawkins & Sutton, BAMS, 2009

18 Forced changes and unforced variability in global mean tropospheric temperature (TLT) in CMIP3 runs Single simulation Ensembles of equally likely outcomes Courtesy of B.Santer

19 Not all models included the same set of forcing in historical runs. IPCC AR5 Table 12.1:

20 The spread in model responses are due to several factors Different forcing leads to different climate responses Models forced similarly exhibit a range of responses Unforced variability contributes too. IPCC AR5 16 August 2016 K. E. Taylor

21 There may be discontinuities in forcing between historical and future (RCP) simulations Synthetic Lower Stratospheric Temperature in BNU Historical+RCP8.5 Simulation 6 Spatial average over 82.5 o N-82.5 o S. Synthetic MSU channel Anomaly ( o C) Time (years) Courtesy of B. Santer

22 Understand limits to using spread of model results as a rigorous measure of uncertainty It doesn t include possibility of a common bias across models If the bias is not zero, the truth may lay outside model results It assumes that existing models constitute a representative sample of all possible models that are equally consistent with physical laws and observations. If some of the models are inconsistent with observations, then eliminating/ down-weighting those models should improve uncertainty estimation If social pressures decrease the spread of model results, model uncertainty will be unjustifiably perceived as being reduced The common (but not rigorously grounded) aspects of model formulation may (misleadingly) limit the spread

23 Structural uncertainty may be underestimated in perturbed physics ensembles (perhaps also in multi-model ensembles) CMIP3 ensemble Sea level rise pattern (with global mean removed) Perturbed physics ensemble Pardaens, Gregory, and Rowe, Clim. Dyn., 2010

24 Analysis tip 3: In decadal prediction runs, learn about bias adjustment before attempting to interpret Initializing a model from observations invariably introduces an initial, artificial transient in the response Caused by a discrepancy between a model s climatology and the observed. Various methods have been developed to remove this transient (i.e., bias correct ), but Available CMIP5 model output has been generally reported without bias correction.

25 Summary of tips: be skeptical; assume flaws in data and metadata Check errata page: Perform checks on all output files you process Discontinuities in time series? Sign or units errors? Reasonable looking maps? Don t blindly trust metadata. Confirm what/how forcing was included Branch-times may be incorrect Understand limitations of experiment design Learn how to distinguish forced response, drift, and unforced variability Remember: differences between models or between models and observations may not be statistically significant.

26 Helpful resources: CMIP5 website Information of most use by analysts Experiment design Experiment names Forcing Model output specifications Acknowledgement guidance Models and modeling group names CMIP5-based publications CMIP5 errata esdocs (model documentation ESGF CoG interface to CMIP5 data

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