Summary of MDTF process-oriented diagnostics

The MDTF diagnostics package is a portable framework for running process-oriented diagnostics (PODs) on climate model data. Each POD script targets a specific physical process or emergent behavior, with the goals of determining how accurately the model represents that process, ensuring that models produce the right answers for the right reasons, and identifying gaps in the understanding of phenomena.

The scientific motivation and content behind the framework was described in E. D. Maloney et al. (2019): Process-Oriented Evaluation of Climate and Weather Forecasting Models. BAMS, 100 (9), 1665–1686, doi:10.1175/BAMS-D-18-0042.1.

Convective Transition Diagnostics

J. David Neelin (UCLA) neelin@atmos.ucla.edu

This POD computes statistics that relate precipitation to measures of tropospheric temperature and moisture, as an evaluation of the interaction of parameterized convective processes with the large-scale environment. Here the basic statistics include the conditional average and probability of precipitation, PDF of column water vapor (CWV) for all events and precipitating events, evaluated over tropical oceans. The critical values at which the conditionally averaged precipitation sharply increases as CWV exceeds the critical threshold are also computed (provided the model exhibits such an increase).

Variables

Frequency

Precipitation rate

6-hourly or higher

Column water vapor

6-hourly or higher

References:

  • Kuo, Y.-H., K. A. Schiro, and J. D. Neelin (2018): Convective transition statistics over tropical oceans for climate model diagnostics: Observational baseline. J. Atmos. Sci., 75, 1553-1570, https://doi.org/10.1175/JAS-D-17-0287.1.

Extratropical Variance (EOF 500hPa Height)

CESM/AMWG (NCAR) bundy@ucar.edu

This POD computes the climatological anomalies of 500 hPa geopotential height and calculates the EOFs over the North Atlantic and the North Pacific.

Variables

Frequency

Surface pressure

Monthly

Geopotential hegiht

Monthly

MJO Propagation and Amplitude

Xianan Jiang (UCLA) xianan@ucla.ecu

This POD calculates the model skill scores of MJO eastward propagation versus winter mean low-level moisture pattern over Indo-Pacific, and compares the simulated amplitude of MJO over the Indian Ocean versus moisture convective adjustment time-scale.

Variables

Frequency

Precipitation rate

Daily or higher

Specific humidity

Daily or higher

References:

  • Jiang, X. (2017): Key processes for the eastward propagation of the Madden‐Julian Oscillation based on multimodel simulations, JGR‐Atmos, 122, 755–770, https://doi.org/10.1002/2016JD025955.

  • Gonzalez, A. O., and X. Jiang (2017): Winter mean lower tropospheric moisture over the Maritime Continent as a climate model diagnostic metric for the propagation of the Madden‐Julian oscillation, Geophys. Res. Lett., 44, 2588–2596, https://doi.org/10.1002/2016GL072430.

  • Jiang, X., M. Zhao, E. D. Maloney, and D. E. Waliser, (2016): Convective moisture adjustment time scale as a key factor in regulating model amplitude of the Madden‐Julian Oscillation. Geophys. Res. Lett., 43, 10412‐10419, https://doi.org/10.1002/2016GL070898.

MJO Spectra and Phasing

CESM/AMWG (NCAR) bundy@ucar.edu

This PDO computes many of the diagnostics described by the WGNE MJO Task Force and developed by Dennis Shea for observational data. Using daily precipitation, outgoing longwave radiation, zonal wind at 850 and 200 hPa and meridional wind at 200 hPa, the module computes anomalies, bandpass-filters for the 20-100 day period, calculates the MJO Index as defined as the running variance over the bandpass filtered data, performs an EOF analysis, and calculates lag cross-correlations, wave-number frequency spectra and composite life cycles of MJO events.

Variables

Frequency

Precipitation rate

Daily

OLR

Daily

U850

Daily

U200

Daily

V200

Daily

References:

MJO Teleconnections

Eric Maloney (CSU) eric.maloney@colosate.edu

The POD first compares MJO phase (1-8) composites of anomalous 250 hPa geopotential height and precipitation with observations (ERA-Interim/GPCP) and several CMIP5 models (BCC-CSM1.1, CNRM-CM5, GFDL-CM3, MIROC5, MRI-CGCM3, and NorESM1-M). Then, average teleconnection performance across all MJO phases defined using a pattern correlation of geopotential height anomalies is assessed relative to MJO simulation skill and biases in the North Pacific jet zonal winds to determine reasons for possible poor teleconnections. Performance of the candidate model is assessed relative to a cloud of observations and CMIP5 simulations.

Variables

Frequency

Precipitation rate

Daily

OLR

Daily

U850

Daily

U250

Daily

Z250

Daily

References:

  • Henderson, S. A., Maloney, E. D., & Son, S. W. (2017): Madden–Julian oscillation Pacific teleconnections: The impact of the basic state and MJO representation in general circulation models. Journal of Climate, 30 (12), 4567-4587 https://doi.org/10.1175/JCLI-D-16-0789.1.

Diurnal Cycle of Precipitation

Rich Neale (NCAR) bundy@ucar.edu

The POD generates a simple representation of the phase (in local time) and amplitude (in mm/day) of total precipitation, comparing a lat-lon model output of total precipitation with observed precipitation derived from the Tropical Rainfall Measuring Mission.

Variables

Frequency

Precipitation rate

3-hourly or higher

References:

  • Gervais, M., J. R. Gyakum, E. Atallah, L. B. Tremblay, and R. B. Neale (2014): How Well Are the Distribution and Extreme Values of Daily Precipitation over North America Represented in the Community Climate System Model? A Comparison to Reanalysis, Satellite, and Gridded Station Data. Journal of Climate, 27, 5219–5239, https://doi.org/10.1175/JCLI-D-13-00320.1.

  • Gettelman, A., P. Callaghan, V. E. Larson, C. M. Zarzycki, J. T. Bacmeister, P. H. Lauritzen, P. A. Bogenschutz, and R. B. Neale, (2018): Regional Climate Simulations With the Community Earth System Model. Journal of Advances in Modeling Earth Systems, 10, 1245–1265, https://doi.org/10.1002/2017MS001227.

Coupling between Soil Moisture and Evapotranspiration

Alexis M. Berg (Princeton) ab5@princeton.edu

This POD evaluates the relationship between soil moisture and evapotranspiration. It computes the correlation between surface (0~10 cm) soil moisture and evapotranspiration during summertime. It then associates the coupling strength with the simulated precipitation.

Variables

Frequency

Soil moisture

Monthly

Evapotranspiration

Monthly

Precipitation rate

Monthly

References:

  • Berg, A and J. Sheffield. (2018): Soil Moisture–Evapotranspiration Coupling in CMIP5 Models: Relationship with Simulated Climate and Projections, J. Climate, 31 (12), 4865-4878, https://doi.org/10.1175/JCLI-D-17-0757.1.

Wavenumber-Frequency Spectra

CESM/AMWG (NCAR) bundy@ucar.edu

This POD performs wavenumber frequency spectra analysis (Wheeler and Kiladis) on OLR, Precipitation, 500hPa Omega, 200hPa wind and 850hPa wind.

Variables

Frequency

Precipitation rate

Daily

OLR

Daily

U850

Daily

U200

Daily

W250

Daily

References: