The 2016/2017 La Niña and S2S prediction for South-East Asia Font: Calibri, Bold Size: 46pt Colour: Green Thea Turkington Acknowledge colleagues: Raizan Rahmat, Bertrand Timbal, and Jia Liu Font: Calibri, Regular Size: 25pt Colour: Black Font: Calibri, Regular Size: 20pt Colour: Black FOCRAII - 2017 April 25th 2017

Part A: The 2016/2017 La Niña (?)

Warming in the tropics Warming trend in the tropics (0.008-0.010 K/month) Just not in the Nino3.4 region Temperature anomaly SST anomaly The pattern of the trend is La nina like (partly at least due to the IPO flip positive to negative) CPC: La Niña conditions are present, with transition to ENSO-neutral favoured during Jan Mar SST OLR anomalies Enhanced low level easterlies * BOM: La Niña Watch Only cloudiness shows La Niña characteristics SOI negative since Oct, but in neutral range SST in neutral range APCC: Expected ENSO neutral phase to prevail Only weak negative SSTs (Nino3.4 & IOD) *partly masked by intra-seasonal activity ERSST v4.0 SST dataset, trend 1977-2016 Black box = Nino3.4, green boxes = Tri-pole index (IPO), blue box = Nino.West Red = global temperature (NOAA) Blue =SST trend 30°S – 30°N (ERSST)

Nino 3.4 2016 with/without removing trend   Nino 3.4 Without removing trend Removing trend Min Max 5% 95% Jan-16 2.17 2.48 1.94 2.33 Feb-16 1.91 2.10 1.69 1.96 Mar-16 1.55 1.63 1.31 1.48 Apr-16 1.00 1.09 0.78 0.89 May-16 0.42 0.61 0.20 0.41 Jun-16 -0.11 0.13 -0.33 -0.08 Jul-16 -0.40 -0.26 -0.62 -0.43 Aug-16 -0.56 -0.78 -0.60 Sep-16 -0.68 -0.94 -0.77 Oct-16 -0.79 -0.67 -1.03 -0.83 Nov-16 -1.05 -0.84 Dec-16 -0.76 -1.00 -0.71 Using 3 SST datasets (ERSST v4.0, HadISST v1, COBE)

2014-2016 Neutral El Niño La Niña

Removing tropical trend Nino 3.4 index 2016/17 and past events 2016 Removing tropical trend Nino 3.4 index Nino 3.4 index La Niña events El Niño events Using ERSST v4.0 dataset 2015/2016

DETRENDING IS IMPORTANT Part A: Summary DETRENDING IS IMPORTANT Major events are still the with/without removing trend Marginal events differ In 2016, more aligned with other atmospheric indicators What does this mean for 2017?

Part B: S2S prediction for South-East Asia -S2S-SEA Workshop 1 Font: Calibri, Bold Size: 46pt Colour: Green

S2S-SEA Capability Building Programme (Objectives & Activities) Establish a project to investigate the usefulness of these forecasts in the database for our region. A research project to prepare for release of real time products. Involve all ASEAN NMSs who are interested and engage S2S international experts. (Li and Robertson, 2015, MWR) JJAS correlation score

S2S-SEA Workshop Series S2S-SEA I S2S-SEA II S2S-SEA III S2S-SEA IV AIMS: Understand S2S better Know what’s available What skill assessment methodologies to use How to move forward in subsequent workshops Recent workshop 27 Feb – 3 Mar Research

Overview of S2S-SEA I Lectures: Andrew Robertson (IRI), Frederic Vitart (ECMWF), From Meteorological Services Singapore: Raizan Rahmat, Thea Turkington, Jia Liu Regional Participants: One from each 10 countries + 6 extra Topics: Introduction: S2S Background, database, ensemble generation etc. Tools: Virtual box, NetCDF, python Observation datasets Rainfall :TRMM Temperature: ERA-I Downloading and processing data ECMWF – various lead times (+1 week to +4 weeks) Model verification Deterministic: CORA (relationship), MSSS (bias)

Case Study: 03 – 09 Nov ‘11

Temperature CORA and MSSS (1998-2014) ECMWF verified against ERA-Interim

Precipitation CORA and MSSS (1998-2014) ECMWF verified against TRMM

What next? S2S-SEA II Possibilities: Probabilistic verification Downscaling to station Diagnostic studies Product development Challenges: Continuity (same participants?) A lot to cover within short period Availability of data

Part B: Summary https://github.com/S2S-SEA/workshop1/wiki