No. 45

Summer 2016
Contents

P a geIncorporation of ENSO Forecast Probabilities into the TCC El Niño Outlook

1

Summary of the 2014–16 El Niño event

2

Sea Ice in the Sea of Okhotsk in the 2015/2016 Winter Season

6

Summary of Kosa (Aeolian dust) Events over Japan in 2016

7

TCC Experts Visit Cambodia

9

Incorporation of ENSO Forecast Probabilities into the TCC El Niño Outlook
With the new forecast probability information, the lead
would be:
 The likelihood of La Niña conditions developing
during boreal summer is 40%, which is lower than in
the previous month’s prediction.
 Despite a 40% likelihood that ENSO-neutral conditions will persist until boreal autumn, it is more likelythat a La Niña event will begin to develop in autumn(60%).
Figure 2 on the “Figures and Tables” page of the outlook shows probability values from two months before thecurrent month to four months after. An example is shownin Figure 1, which suggests that the probability of La Niña
onset will become gradually higher during boreal summerand autumn.
Using probability values in the ENSO outlook supports
expression of the likelihood of the onset, persistence andENSO forecast probabilities were incorporated into theTCC El Niño Outlook in August 2016.
Forecast probabilities for the onset, persistence and endof ENSO events (El Niño, La Niña and ENSO-neutral periods) were incorporated into the TCC El Niño Outlook inAugust 2016 for added clarity.
Specifically, information on ENSO event outlooks contained in the lead part and the main body now include
probability values regarding ENSO event onset and otherconsiderations. By way of example, the second lead part ofthe El Niño Outlook in July (before the update) was asfollows:
 La Niña conditions are less likely to develop during
boreal summer than in the previous month’s prediction; La Niña onset is more likely to occur in boreal
autumn.

Figure 1
ENSO forecast probabilities based on JMA/MRI-CGCM2 (Fig. 2 in the El Niño Outlook)
Red, yellow and blue bars indicate probabilities that the five-month running mean of the NINO.3 SST deviation from the sliding 30-year mean will be +0.5°C or above (El Niño), between +0.4 and -0.4°C (ENSO-neutral) and -0.5°C or below (La Niña), respectively. Regular text indicates past months, and bold text
indicates current and future months.

TCC News1

No. 45 | Summer 2016

end of ENSO events with greater precision than simple expressions such as, “It is likely that…” Users are also provided with information on probability changes or tendencies compared to the previous month’s outlook.
These probabilities are based on output from the JMA’sENSO prediction model (JMA/MRI-CGCM2), which isalso used for three-month prediction and warm-/cold-season
prediction. The model is operated as a 51-member ensemble prediction system, and probabilities are essentially calculated as ratios of variables used to predict each event (ElNiño, La Niña or ENSO-neutral conditions) before being
calibrated using data from the 30-year (1981 – 2010)
re-forecast experiment.
The accuracy of ENSO forecast probability data fromthis procedure is evaluated using re-forecast experimentresults. Table 1 shows the performance of the ENSO probprobability forecast for four months ahead. The hit rate forEl Niño prediction (i.e., [No. of cases in which El Niño
was predicted and occurred (70)] / [Total number of El
Niño predictions (104)]) is 67%, and the miss rate (i.e.,[No. of cases in which El Niño was not predicted but occurred (27)] / [Total number of El Niño events (97)]) is
28%. For La Niña, the hit rate is 70% (68 / 97) and themiss rate is 26% (24 / 92). Thus, it is considered that whena probability of 50% or higher is predicted, the use ofphrases such as, “It is likely that El Niño conditions willdevelop” is appropriate.
(Ichiro Ishikawa, Climate Prediction Division)

El Niño

not El Niño

Total

Predict El Niño (Prob≥50%)

70

34

104

Predict not El Niño (Prob<50%)

27229

256Total

97263

360

La Niña

not La Niña

Total

Predict La Niña (Prob≥50%)

68

29

97

Predict not La Niña (Prob<50%)

24239

263

Total

92268

360

Table 1

Contingency tables for ENSO probability forecasts based on 30-year re-forecast experiment data
The top panel shows El Niño probability for four months ahead, and the bottom panel shows La Niña
probability. The rows correspond to cases in which the model predicts high probability (≥ 50%), lowprobability (< 50%) and the total of both. The columns correspond to whether El Niño/La Niña events
actually occurred. The figures indicate numbers of cases corresponding to each row and column. The
total number of cases is 360 (30 years x 12 months).

Summary of the 2014–16 El Niño event
The El Niño conditions observed from boreal summer 2014 real spring 2016, having lasted eight seasons (since borealto spring 2016 were the first since 2009–10. During this event, summer 2014), which is the longest among the 15 El Niño
the maximum monthly mean sea surface temperature (SST) events that have occurred since 1949.
deviation in the NINO.3 region (5°S–5°N, 150°W–90°W) was+3.0°C, which was the third highest after the El Niño events of
1997–98 and 1982–83.
1. Outline of the 2014-16 El Niño event
The Japan Meteorological Agency (JMA) monitors sea
surface temperature (SST) in the NINO.3 region (5°S–5°N,150°W–90°W), where interannual variability is the largest in
the equatorial Pacific, to identify El Niño/La Niña events(http://ds.data.jma.go.jp/tcc/tcc/products/elnino/index.html).
JMA defines El Niño (La Niña) events as periods during
which the five-month running mean of NINO.3 SST deviation from the latest 30-year average is +0.5 (–0.5)°C or higher (lower) for at least six consecutive months. Based on thisdefinition, the 2014–16 El Niño event started to develop in
boreal summer 2014 (Figure 2). However, the five-monthrunning mean of NINO.3 SST deviation remained slightly
Figure 2 Time series of NINO.3 SST deviation from theabove the El Niño threshold of +0.5°C until boreal winterlatest 30-year average (°C).
2014/2015 (December 2014 – February 2015). The event
Black (red) line indicates monthly mean (five-month runbegan to strengthen in boreal spring 2015, and the NINO.3ning mean).
SST deviation reached its peak value of +3.0°C in December
2015. The event then decayed rapidly and terminated in bo-TCC News

2No. 45 | Summer 2016

This event was the strongest for 18 years since that of
1997–98. The monthly mean NINO.3 SST of +3.0°C
above the latest 30-year average recorded in December
2015 was the third highest among the 15 occurring since1949, behind those of 1997–98 and 1982–83 (+3.6 and+3.3°C, respectively; Figure 3).
2. Development of the 2014–16 El Niño event
Figure 4 shows shows time-longitude representations ofanomalies in zonal wind stress, SSTs and vertically averaged temperature over the top 300-m layer along theequatorial Pacific. In 2014, two strong westerly windevents (WWEs) occurred over the western equatorial Pacific during the period from January to March. In response, warmer-than-normal subsurface water propagatedeastward in the equatorial Pacific, resulting in increased
SST anomalies in the eastern equatorial Pacific aroundMay – June 2014. However, further SST increases weresubsequently suppressed due to easterly wind anomalies
in the central part of the equatorial Pacific in association
with the Madden-Julian Oscillation (MJO) in early summer. In February and March 2015, two strong WWEs inthe western part of the equatorial Pacific excited warmer-than-normal subsurface water in the central part. As aresult, SSTs in the eastern part began to increase in borealspring 2015. Although these processes were also seen in
the first half of 2014, the El Niño event developed due tosubsequent significant atmosphere-ocean interaction

Figure 3 Peak values of NINO.3 SST deviation during
each El Niño event since 1949 (°C).
The deviation is a departure from the latest 30-year averagein each event.

throughout 2015. In early 2015, colder-than-normal subsurface water that had built up during the El Niño event
migrated to the eastern part of the equatorial Pacific, leading to a rapid decrease in SST anomalies there. The2014–16 El Niño event ended in boreal spring 2016.

Figure 4 Time-longitude distributions along the equator in the Pacific Ocean from August 2013 to July 2016.
(a) Zonal surface wind stress anomalies (0.01N/m2). Positive values indicate westerly anomalies. (b) Vertically averaged
temperature anomalies over the top 300m layer (VAT300m, °C). (c) SST anomalies (°C). The base period for the normal is
1981–2010.

TCC News

3No. 45 | Summer 2016

Figure 5a shows SST anomalies for November 2015
during the mature phase of the 2014–16 event. Large positive SST anomalies of up to 3.5°C were observed in centraland eastern parts of the equatorial Pacific. The subsurface
temperature anomalies along the equatorial Pacific shownin Figure 5b exhibit large zonal contrast, and exceed 7°C
around 100°W. For comparison, Figure 6a presents SST
anomalies for November 1997 during the mature phase ofthe 1997–98 El Niño event (one of the strongest since1949). As in 2015, large positive SST anomalies in thecentral and eastern equatorial Pacific are observed. How-ever, the center of the anomalies was located farther east in1997 than in 2015. SST anomalies in the central part of theequatorial Pacific were higher in 2015 than in 1997, while
those in the eastern part were lower in 2015 (Figure 7a).
Subsurface temperature anomalies in the eastern part of theequatorial Pacific in 1997 (Figure 6b) exceeded 10°C,
which is significantly higher than the corresponding figurefor 2015 (Figure 5b).

(a)(b)
Figure 5 Anomalies of (a) SST and (b) upper ocean temperature along the equatorial Pacific in November 2015.
Contour intervals are 0.5°C for (a) and 1°C for (b). The base period for the normal is 1981–2010.

(a)(b)
Figure 6 Same as Figure 5 but for November 1997.

(a)(b)
Figure 7 Same as Figure 5 but for anomalies in November 2015 relative to those in November 1997.

TCC News4

No. 45 | Summer 2016

3. Climatic impacts of the 2014–16 El Niño event
The annual anomaly of the global average surface temperature in 2015 was +0.42°C above the 1981–2010 average, and was the highest since 1891. The global averagesurface temperature is affected by natural climate variability on interannual to interdecadal time scales in addition toglobal warming caused by increased concentrations ofgreenhouse gases such as CO2. As the anomaly of the
global average surface temperature follows the NINO.3
SST anomaly with a time lag of several months, the record
temperatures of 2015 may have been influenced by the El
Niño event that began in boreal summer 2014 and developed from boreal spring 2015.
El Niño can also be considered to have affected theAsia-Pacific climate from boreal summer 2015 to boreal
winter 2015/2016, bringing high temperatures at low latitudes and low precipitation amounts in and around Indonesia. India and Pakistan experienced heatwaves in May andJune 2015, respectively. The six-month mean temperaturefor July – December 2015 at Hyderabad in southern India
was 27.4°C (2.2°C higher than the normal). The total precipitation amount for September – November 2015 at Ban-jarmasin on Borneo Island in Indonesia was 113 mm (19%of the normal). These figures were consistent with typicalanomaly patterns observed in past El Niño events (Figure8).
Below-normal temperatures and above-normal precipitation amounts were observed in western Japan during boreal
summer 2015, while above-normal temperatures and precipitation amounts were observed in Okinawa/Amami andin western/eastern Japan during boreal winter 2015/2016.
These characteristics are likely to have been related to atmospheric circulation anomalies, which were mostly consistent with those observed in past El Niño years.
(Tamaki Yasuda and Kenji Kamiguchi,
Climate Prediction Division)

Figure 8 The schematic charts of typical anomaly patterns of surface temperature and precipitation for boreal summer (left) and autumn (right) in past El Niño events.

TCC News5No. 45 | Summer 2016

Sea Ice in the Sea of Okhotsk in the 2015/2016 Winter Season
The maximum sea ice extent in the Sea of Okhotsk forwinter 2015/2016 was similar to the normal.
The sea ice extent in the Sea of Okhotsk for winter2015/2016 was below or near the normal (Figure 9). The
seasonal maximum of 1.165 x 106 km2 was reached on 29
February (Figures 9 and 10), and was similar to the normalof 1.169 x 106 km2 (based on the 30-year average from1980/1981 to 2009/2010). Figure 11 shows the overall trendof maximum sea ice extent from 1971 to 2016. Although
values for the Sea of Okhotsk show large interannual variations, there is a long-term downward trend of 0.067 [0.033– 0.101] x 106 km2 per decade (the numbers in square
brackets indicate the two-sided 95% confidence interval),
which equates to a loss of 4.3 [2.1 – 6.4]% per decade.
(Ayako Takeuchi, Office of Marine Prediction)

Figure 9 Seasonal variation of sea ice extent at five-day
intervals in the Sea of Okhotsk from November 2015 to July 2016.
The normal is the 30-year average from 1980/1981 to2009/2010).

Figure 11 Interannual variation of maximum sea ice extent
in the Sea of Okhotsk from 1971 to 2016.
Maximum sea ice extent: the greatest amount of sea ice extent
observed during the year

Figure 10 Sea ice situation on 29 February 2016.
The white area shows the observed sea ice extent, andthe red line indicates the extent of normal coverage(the 30-year average from 1980/1981 to 2009/2010).

TCC News6No. 45 | Summer 2016

Summary of Kosa (Aeolian dust) Events over Japan in 2016
Characteristics of Kosa events in 2016
Kosa (Aeolian dust) is a meteorological phenomenon inwhich fine dust is blown up to an altitude of several thousand meters by cyclonic or other wind systems from deserts
or cropland in semi-arid areas of the Asian continent, and istransported over long distance by westerly winds, resulting
in haze or dustfall in downstream areas. It is often observed
between March and May in Japan and makes the sky yellow and hazy. Heavy Kosa can affect transportation by obstructing visibility.
In Japan, 59 JMA meteorological stations (as of 1 August2016) perform Kosa monitoring based on visibility. FromJanuary to June 2016, the number of days on which Kosawas observed at any of these stations (referred to heresimply as “the number of Kosa days”) was 11, which wasapproximately half the 1981 – 2010 normal of 23.1. The
number of Kosa days in April corresponded to the normal,
but was lower in other months (Figure 12, top).
The total number of stations observing Kosa (referred tohere simply as “the total number of Kosa stations”) over thesame period was 96, which was also around half the normalof 208.1. The monthly total number of Kosa stations was
lower than the normal from January to June (Figure 12,bottom).
Significant Kosa event in late April
Kosa was extensively observed at stations in Japan from23 to 26 April 2016 (Figure 13).
Due to a large dust storm in an area of the Gobi Desert
around 21 April, massive volumes of dust were blown up
into the atmosphere. The surface weather charts in Figure
14 indicate that a low-pressure system moving eastward at
50 degrees north latitude carried dust over to Japan.

Figure 13

Figure 12

Monthly number of days when meteorological stations in Japan observed Kosa(top), and the monthly total number ofstations observing Kosa (bottom) fromJanuary to June 2016
The red and yellow bars show the values for 2016
and the 1981 – 2010 normal, respectively.

Meteorological stations observing Kosaand minimum visibility on 24 April

Figure 14

TCC News

7

Surface weather analysis chart at 09 JST(00 UTC) on 21, 22, 23, and 24 April

No. 45 | Summer 2016

Based on results from JMA’s Kosa prediction model, theKosa was expected to prevail over western Japan on 23April (Figure 15).
Dust RGB* images created since 2015 using data from anew sensor called the Advanced Himawari Imager (AHI)on board Himawari-8 showed that Kosa particles were
blown up by the Gobi Desert storm and carried over to Japan by westerly winds (Figure 16).
In consideration of the forecast results as well as satellite
imagery and surface observation reports from meteorological stations, JMA released information on the Kosa event tothe public on 23 April in order to call attention to potential
traffic hazards relating to visibility degradation.

* Dust RGB is a satellite product synthesized from three
infrared bands (B11 (8.6 µm), B13 (10.4 µm) and B15(12.4 µm)) of Japan’s new Himawari-8 geostationary satellite. In RGB images created with the assignment of [B15 –B13] (i.e., the differential of B15 and B13) to red, [B13 –B11] to blue and B13 to green, Kosa is highlighted in pinkor magenta. This product is very useful for Kosa monitoring based on the detection of heavy dust areas over theAsian continent.

(Toshiyuki Kitajima and Toshinori Aoyagi,
Atmospheric Environment Division)

Figure 15 Forecasts of surface dust concentration by JMA’s Kosa prediction model at 09 JST (00 UTC) on 22, 23,and 24 April (initial time: 21 JST (12 UTC) on 21 April)

Figure 16 Dust RGB images from Himawari-8 taken at 18 JST (09 UTC) on 21 April, and at 09 JST (00 UTC) on22, and 23 April. Kosa area appears in pink (magenta).

TCC News8No. 45 | Summer 2016

TCC Experts Visit Cambodia
As part of its support for the capacity-building activities,
TCC held annual training seminars on one-month forecasting in 2015 (see TCC News No.42 and TCC website fordetails of the seminar). The Center also dispatches experts
to NMHSs in Southeast Asia and elsewhere for seminarfollow-up activities.
From 2 to 4 August 2016, TCC experts paid a visit toCambodia’s Department of Meteorology and held a follow-up seminar on the generation of one-month forecasts
using the statistical downscaling technique and on the basic
operation of TCC’s Interactive Tool for Analysis of theClimate System (iTacs).
The TCC visitors outlined the formulation of statisticalguidance for temperature and precipitation with on-site
observation data, JMA’s one-month re-forecast (hindcast)and operational forecast data. Seminar attendees produced

one-month forecasts for regional districts in Cambodia using JMA’s guidance tool and iTacs, and made presentations
on their achievements on the final day. An introductory
presentation on iTacs also provided a good opportunity forthe attendees to formulate physical interpretations of thefigures created and to acquaint themselves with the operation of the application.
The visit provided outstanding opportunities for the attendees to deepen their understandings of one-month forecasting and to discuss future collaborative work with TCC.
TCC will continue to arrange expert visits to NMHSs in theAsia-Pacific region and elsewhere as necessary to assist
with operational climate services.

Any comments or inquiry on this newsletter and/or the TCC
website would be much appreciated. Please e-mail totcc@met.kishou.go.jp.
(Editors: Kiyotoshi Takahashi, Atsushi Gotoand Yasushi Mochizuki)

Tokyo Climate Center (TCC), Japan Meteorological Agency
Address: 1-3-4 Otemachi, Chiyoda-ku, Tokyo 100-8122, Japan
TCC Website: http://ds.data.jma.go.jp/tcc/tcc/index.html

TCC News(Masayuki Hirai and Kazuto Takemura,Tokyo Climate Center)

9No. 45 | Summer 2016