Challenges in forecasting the MJO
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1 Challenges in forecasting the MJO Augustin Vintzileos University of Maryland ESSIC/CICS-MD Jon Gottschalck NOAA/NCEP/CPC
2 Outline Forecasting at the interface between weather and climate Multi-scale impacts of subseasonal predictability Scientific basis for subseasonal predictability The DYNAMO campaign The NCEP models during DYNAMO: Confronting the atmospheric model to observations Confronting GFS to CFS at subseasonal time scales Confronting the oceanic model to observations Conclusions
3 Tropical Subseasonal Variability Weather forecasting Modulation of Tropical Cyclone probability of formation Extreme precipitation events in the western CONUS Seasonal to Interannual forecasting Affecting predictability of ENSO Modulating amplitude of ENSO Day 0 - Day 7 Week 2 Week 4 Month 1 Month 2 Season 1 Year 1
4 EL NIÑO/SOUTHERN OSCILLATION (ENSO) DIAGNOSTIC DISCUSSION issued by CLIMATE PREDICTION CENTER/NCEP/NWS and the International Research Institute for Climate and Society 6 September 2012 ENSO Alert System Status: El Niño Watch Status of the MJO in August September 2012 In early September 2012 the Forecaster faced a collapsing MJO event over the Indian Ocean EL NIÑO/SOUTHERN OSCILLATION (ENSO) DIAGNOSTIC DISCUSSION issued by CLIMATE PREDICTION CENTER/NCEP/NWS and the International Research Institute for Climate and Society 8 November 2012 ENSO Alert System Status: Not Active However, a few days later the MJO came back roaring, crossed the Maritime Continent and entered the western Pacific: MJO Forecast Challenges Need for MJO to ENSO research!!
5 Forecasting ENSO with the NASA model Observed value Forecast values (Vintzileos et al., 2005) Observed intraseasonal activity modified the forecast from La Nina to neutral in just one month
6 Scientific basis for Subseasonal forecasting: Kelvin, Rossby and MJO modes D Y N A M O Courtesy C. Schreck
7 The DYNAMO campaign: Observations help to answer science questions and face modelling challenges
8 CINDY/DYNAMO DYNAMO Field Campaign Field Campaign 1/10/2011 to 31/3/2012
9 DYNAMO Radiosondes: Relative humidity MJO-1 MJO-2 MJO-3 Gan R/V Revelle Diego Garcia R/V Mirai
10 From NCEP to DYNAMO to NCEP CPO funded CPC and ESSIC to provide monitoring and forecast support to DYNAMO GFS - High Res. Weekly CPC - GTH Outlook product (human forecaster) GDAS CFS - R GEFS - Ensemble CFS - Coupled DYNAMO Tailored Products Data Catalog Earth Observing Laboratory (EOL), NCAR Global Telecommunication System DYNAMO and to evaluate the NCEP models during DYNAMO
11 Confronting NCEP models to DYNAMO observations
12 On the physics of the MJO Lagged correlations: -RMM2 index (MJO entering Indian Ocean) vs. DYNAMO OBS. (RH and Wind) at Gan Island Observations are indicative of a moisture recharge process as e.g. in Benedict and Randall (2007)
13 Decoupling of the dynamics and thermodynamics in the GFS DYNAMO Obs. GFS at 24 hours GFS at 168 hours
14 200 hpa Relative Humidity at Gan: DYNAMO (blue) and GFS at fcst=12h (red) MJO1 MJO2 MJO3
15 Forecast of Anomalous OLR (GFS) for the November DYNAMO MJO event Week 1 Week 2 Forecast Verification
16 SST Satellite Observations during DYNAMO OLR (1) (2) DYN1 DYN2 (3) DYN - Last (1) Uncoupled period (2) Weekly Coupled period (3) Strongly Coupled period
17 RMM1 (cont.) and RMM2 (dash) forecast skill for CFS (blue) and GFS (red) (Correlation period is the same calendar period for each lead time) Uncoupled DYNAMO-1 event both CFS and GFS similar skill Weakly coupled DYNAMO-2 event GFS RMM2 (Indian Ocean) skill drops quickly Strongly coupled DYNAMO-3 event CFS clearly beats GFS
18 Synopsis of DYNAMO moorings D1 and D2 (courtesy Ren-Chieh Lien ) DYN1 DYN2 DYN1 DYN2
19 Correlation between observed and CFS forecast Temperature fields DYNAMO subsurface data were not sent to the GTS Mooring D1 Mooring D2 Initialization D e p t h CFS-Reanalysis vs. DYNAMO Daily data Forecast D e p t h CFS-Forecast vs. DYNAMO Weekly data Very important drop in skill at the depth of the mixed layer may affect forecast for > week 2
20 Conclusions: In the NCEP atmospheric model there is a fast decoupling between the dynamics and thermodynamics A working hypothesis for this decoupling is that relative humidity in the upper troposphere is quite high in the model This decoupling leads to the incapacity of the model to propagate eastward organized large scale convection There are times when convection and SST are evolving in a coherent way. During these times the coupled CFS presents a better skill than the uncoupled GFS. The ocean model presents a very fast decline of forecast skill at the base of the mixed layer with possible impacts to the forecast of SST for Week-3 and Week-4
21 Work to follow: Systematic investigation of the capacity of models to represent the individual physical sources of subseasonal predictability i.e., Kelvin, Rossby and MJO modes and their couplings. Test the hypothesis that higher than observed humidity in the upper troposphere is responsible for the decay of eastward propagating organized convection. Investigate predictability of SST at Week-3 and Week-4 and examine the ocean mixed layer as source of errors. Systematic investigation of the impacts of the MJO on the predictability of ENSO Use the finding to enhance forecast skill of the Global Tropics Hazards and Benefits Outlook (GTH) tool. Extend the GTH to Week-3 and Week-4.
22 Example of GTH Outlook issued October 16 th : Tropical Cyclone Sandy Sandy
23 Questions?
24
25
26 Forecast of Anomalous OLR (GFS) for the first DYNAMO MJO event Week 1 Forecast Week 2 Verification
27
28 The Global Tropical Hazards and Benefits Outlook (GTH) Developed at NOAAs Climate Prediction Center: in operations since 2006 Dynamical models (ECMWF, NCEP, UKMet, CAN etc) Statistical tools & Composites of tropical subseasonal modes (MJO, Rossby, Kelvin) GTH discussion every Monday at 2:30pm led by a CPC forecaster CPC Forecasters NWS centers Academia Preliminary GTH Outlook followed by the final product one day later
29 DYNAMO was a lucky campaign! Review of DYNAMO through the RMM index October to December 2011 January to March 2012 Western Pacific Western Pacific Africa Maritime Continent Africa Maritime Continent Indian Ocean Indian Ocean
30 In the lower troposphere mean humidity and wind fields are such that negative wind anomalies should increase humidity Gan
31 Lagged correlations: -RMM2 index (MJO over Indian Ocean) vs. OBS and FCST OLR (day 7)
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