MJO Teleconnection Diagnostic Package¶
Last Update: 2/1/2019
The teleconnection diagnostics first generate maps of MJO phase composites of 250 hPa geopotential height and precipitation for observations and several CMIP5 models, putting behavior of the candidate model within this cloud of models and observations. Then, average teleconnection performance across all MJO phases defined using a pattern correlation of geopotential height anomalies is assessed relative to 1) MJO simulation skill and 2) 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.
PI: Eric D. Maloney (email@example.com), Colorado State University
Current Developer: Bohar Singh (firstname.lastname@example.org), Colorado State University
Contributors: Stephanie Henderson (University of Wisconsin–Madison), Bohar Singh (CSU)
Open source copyright agreement¶
This package is distributed under the LGPLv3 license (see LICENSE.txt).
Calculation of RMM indices for a new model will be saved in
wkdir/casename/MJO_teleconnection/model/netCDFin txt format (mjo_diag_RMM_MDTF.ncl)
Z250 phase composite for all MJO phases (mjo_diag_geop_hgt_comp_MDTF.ncl)
Pattern correlation with observation (ERA-I Z250) (mjo_diag_Corr_MDTF.ncl)
Precipitation (30S-30N) phase composite for all MJO phases (mjo_diag_prec_comp_MDTF.ncl)
Extended winter wave number-frequency power spectrum of precipitation to get the ratio of eastward and westward propagation power (mjo_diag_EWR_MDTF.ncl)
Area averaged DJF mean U250 error (model-observation) over Pacific Ocean (15N80N,120E-120W) (mjo_diag_U250_MDTF.ncl)
ncl script to plot teleconnection skill v/s MJO skill (mjo_diag_fig1_MDTF.ncl)
ncl script to plot teleconnection skill v/s basic state skill (mjo_diag_fig1_MDTF.ncl)
All scripts can be found at:
Required Programing Language and libraries¶
Required input data to the module¶
The following five 3-D (lat-lon-time) model fields are required:
precipitation rate (units: mm/s = kg/m2/s) or mm/day with appropriate conversion, daily avg
Outgoing Longwave radiation (units: W/m2)
U850 wind (units: m/s)
U250 wind (units: m/s) (Note: U250 wind is used instead of u200 for RMM index calculation)
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.
More About the Diagnostic¶
Henderson et al (2017) documented reasons for MJO midlatitude teleconnection errors in CMIP5 models. Since MJO teleconnections have significant impacts on atmospheric rivers, blocking, and other extreme events in the midlatitudes, teleconnection errors in models have important implications for the subseasonal prediction of midlatitude weather extremes (e.g. Henderson et al. 2016; Mundhenk et al. 2018; Baggett et al. 2017). In addition to extended analyses of stationary wavenumber biases and use of a linear baroclinic model to diagnose CMIP model biases, Henderson et al (2017) developed diagnostics linking teleconnection biases to biases in the position and extent of the North Pacific jet.
The first diagnostic in this POD presents MJO composite 250 hPa geopotential height anomalies for ERA-I, the candidate model (upper right), and six other CMIP5 models assessed to have good MJO performance. All composites are generated as a function of MJO phase as defined according to Wheeler and Hendon (2004). An example of this diagnostic is presented in Figure 1 for phase 1 of the MJO.
The diagnostic next assesses teleconnection performance versus measures of basic state fidelity and MJO skill. Figure 2 from Henderson et al (2017) contains two panels, each having MJO teleconnection performance during December-February on the y-axis. In Figure 2a, the x axis represents an MJO skill metric. While Figure 2a shows a relationship between MJO skill and teleconnection performance, even models with a good MJO can have poor teleconnection performance. For only the models assessed to have a sufficiently good MJO, Figure 2b assesses the relationship between teleconnection performance and biases in the North Pacific zonal flow. Plus signs are a measure of the total root mean squared (RMS) error of the 250 hPa zonal flow over the region 15°N – 60°N, 110°E – 120°W, and the filled circle provides a measure of the RMS error in the length of the North Pacific subtropical jet. Both measures are correlated with MJO teleconnection performance, although biases in the jet provides a somewhat better metric (r =-0.7 versus -0.6 for the total RMS). Subsequent analysis showed that models with a jet that extends too far east tend to have degraded teleconnection performance. Model physics appears to play a key role in the extent of the Pacific jet, as was demonstrated by Neelin et al. (2016) in diagnosing projected California precipitation changes between CMIP3 and CMIP5 models into the late 21st Century. The Pod developed here places the candidate model in question into the cloud of other models on Figure 2, with separate links on the POD site for left and right panels of Figure 2.