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Climate Test Bed

This feature column is for publicizing science-related forecast challenges identified by operational forecasters and end-users, inviting our partners in the research community to work together on improvement of national climate prediction services.

Fig. 1 Anomaly correlation skill (%) of 5-month lead NDJ SST forecast (ICs through MAM by NMME (top) and CA (bottom).

Geographical separation of seasonal prediction skill between statistical tool and dynamical model

Statistical tool: Constructed Analog (CA) (van den Dool 1992, 2007)

i) Data: HAD SST (45°S-45°N, 1948-1980)

ii) Ensemble size: 24 members (1-4 seasons data in ICs, 6 EOF cutoffs (35, 40, 45, 50, 55, 60)

Dynamical model: North American Multi-Model Ensemble (NMME) (Kirtman et al. 2014)

Initial condition (IC) season: MAM, JJA, SON, DJF

Forecast lead-time: 1 and 5 months

Skill metrics: Anomaly Correlation (AC)

Assessment time period: CA ~ 1981-2015; NMME ~ 1981-2010

Verification data: NOAA-OI-v2 SST

Puzzle: It was found distinct geographical separation of seasonal prediction skill with decent skill shown over the tropical western Pacific and Indian Ocean by CA and that over the tropical central-eastern Pacific by NMME (e.g. Fig. 1). A summary of all cases (varied initial seasons and lead times) is given by

Puzzle: It was found distinct geographical separation of seasonal prediction skill with decent skill shown over the tropical western Pacific and Indian Ocean by CA and that over the tropical central-eastern Pacific by NMME (e.g. Fig. 1). A summary of all cases (varied initial seasons and lead times) is given by Table 1.

Challenges: The Constructed Analog (CA), a statistical tool, clearly revealed appreciable predictability over the tropical western Pacific and Indian Ocean, where dynamical models had little skill; pointing to possibly missing of important process(es) common to dynamical models, whose development efforts more focused on improving ENSO forecast historically.

(More details ...)

NOAA CTB Seminar

May 9, 2016

A CTB seminar on "Linkage between the Summer Indian Ocean SST and the Decay of ENSO Events" was presented by Dr. Rong-Cai Ren of State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China. The talk, focusing on seasonal and interannual variability in the tropical Indian Ocean (TIO) affected by ENSO through an ENSO-induced ‘atmospheric-bridge’ and subsequent air-sea coupling processes, revealed the physical linkage between the summer SST anomalies over the TIO and the timing of ENSO decay phase, i.e. significant warmer/colder TIO SST anomalies can persist from spring into summer only in later-decay El Niño/La Niña years. It further demonstrated most of the ENSO-induced atmospheric-bridge-related processes contributed positively to the TIO SST changes in summer due to later-decay of ENSO, as they did in spring during normal-delay ENSO year. Nevertheless, the surface wind-evaporation-mechanism and sensible heat-flux anomalies in summer always contributed negatively to the summer SST anomalies over most parts of the TIO no matter whether there was a weakening or strengthening surface wind due to later-decay of ENSO events. Discussions between Dr. Ren and forecasters and scientists from CPC, EMC and Florida State University in audience were throughout the seminar and afterwards. Future potential cooperation on studying the interaction of TIO with ENSO between LASG and NCEP scientists was also explored.

 Abstract      Abstract     Presentation ppt Presentation ppt

North American Multi-Model Ensemble (NMME) Teleconference

May 5, 2016

    I) NCAR/CESM 1.0 skill evaluation and more

   II) Sea ice data of NMME reforecast

A progress report on skill and bias evaluations of the NCAR Community Earth System Model (CESM 1.0) was presented by Dr. Huug van den Dool with co-investigator Dr. Li-Chuan Chen of NCEP/CPC in the May NMME teleconference. The simple metrics, anomaly correlation, was used and the assessments were made from various angles, e.g. as functions of prediction lead-time, start/target month, relationship between skill and fidelity etc. The comparison of NMME performances with and without CESM, and using CESM compared to CCSM3 were also conducted. The bottom line showed CESM 1.0 with no glaring errors was slightly better than CCSM3. Though a marginal improvement obtained, it is a good sign for CESM to get on board, since CESM 1.0 is more advanced than its predecessor CCSMs with new physical and chemical climate system components. Continuing collaboration to push NMME to a new height was earnestly discussed. Following the telecom agenda, Dr. Qin Zhang of CPC gave a brief talk about the effort of collecting sea ice model output from the NMME models, which is part of the NOAA CPO/CVP funded project of understanding Arctic sea ice mechanisms and predictability.