<figure><img src="image_135.png" /></figure>

FIG. 3.27. Eighteen-year, multimission record of Chla averaged over the PSO (see Fig. 3.25) for (black) SeaWiFS, (blue) MODISA, and (red) VIIRS. (a) Independent records from each mission, with the multimission mean Chla concentration for the region (horizontal black line). (b) Monthly anomalies for SeaWiFS, MODISA, and VIIRS after subtraction of the 9-year MODISA monthly climatological mean (2003–11), with the averaged difference between SeaWiFS and MODISA over the common mission lifetime (gray region). The MEI (green diamonds, see text) inverted and scaled to match the range of the Chla anomalies.

2015). This difference is not due to a change in global phytoplankton abundances but rather is a consequence of the R2014.0 reprocessing that includes calibration updates and a revised chlorophyll algorithm (Hu et al. 2012). The time series demonstrates the high level of consistency between the overlapping periods of the SeaWiFS and MODISA missions. Beyond 2012, the MODISA time series becomes increasingly erratic (not shown), reflecting a growing uncertainty in the calibration of that instrument (Franz et al. 2015). Consistency between MODISA and VIIRS in 2012, however, provides confidence for extension of the multimission trends into 2015.

Chla monthly anomalies within the PSO (Fig. 3.27b) exhibit variations of ~15% over the multimission time series, with climatic events such as El Niño and La Niña clearly delineated. In 2015, consistent with a strong El Niño, Chla trends in the PSO approached the lowest levels measured since the 1997/98 El Niño. Furthermore, mean Chla concentrations in the PSO declined by approximately 20% from the peak observed during the 2010/11 La Niña, con-

sistent with expectations based on multivariate ENSO index variations (MEI; Wolter and Timlin 1998).

Distinguishing the different drivers of Chla variability is important for interpreting the satellite record. Light-driven decreases in chlorophyll are associated with constant or even increased rates of photosynthesis, while nutrient-driven decreases are associated with decreased photosynthesis. An analysis of photoacclimation and nutrient-driven changes in growth rate and biomass from the MODIS record shows that the inverse relationship between SST and Chla anomalies is overwhelmingly due to light- and division rate-driven changes in cellular pigmentation, rather than changes in biomass (Behrenfeld et al. 2016). This study also shows that photoacclimation contributed 10%-80% of the variability in cellular pigmentation, suggesting the 2015 anomaly patterns in Chla for the PSO (Fig. 3.26c) were largely driven by photoacclimation. An additional contributor to the anomaly patterns in Chla is the misrepresentation of Chla changes due to colored dissolved organic matter (cDOM) signals (Siegel et al. 2005). Sunlight degrades cDOM, and this degradation is more extensive for shallow MLDs, yielding in the PSO an inverse relationship between cDOM and SST (Nelson and Siegel 2013) that may be mistakenly attributed to Chla changes (Siegel et al. 2013).

j. Global ocean carbon cycle—R. A. Feely, R. Wanninkhof, B. R. Carter, J. N. Cross, J. T. Mathis, C. L. Sabine, C. E. Cosca, and J. A. Tirnanes

The global ocean is a major sink for anthropogenic carbon dioxide ($CO_2$) that is released into the atmosphere from fossil fuel combustion, cement production, and land-use changes. Over the last decade, the global ocean has continued to take up a substantial fraction of anthropogenic carbon ($C_{anth}$) emissions and is therefore a major mediator of global climate change. Air-sea flux studies, general ocean circulation models including biogeochemistry, and data-constrained inverse models suggest the ocean absorbed approximately 46 Pg C (1 Pg C ≡ $10^{15}$ grams of carbon) of $C_{anth}$ between 1994 and 2014 (Le Quéré et al. 2015; DeVries 2014), with an increase in the rate of $C_{anth}$ uptake from $2.2 \pm 0.5$ Pg C yr$^{-1}$ during the 1990s to approximately $2.6 \pm 0.5$ Pg C yr$^{-1}$ during the most recent decade from 2005 to 2014 (Table 3.1). A summary of the air-sea exchange and ocean inventory of $C_{anth}$ based on both observations and model results through 2014 is presented. Data for 2015 are not available owing to the need for careful scientific quality control of ocean carbon data prior to analysis.
## I) AIR-SEA CARBON DIOXIDE FLUXES

Ocean CO₂ uptake can be estimated from air-sea differences in CO₂ partial pressure (pCO₂) and gas transfer velocity, which is mainly a function of wind speed. Significant improvement in global and regional CO₂ flux estimates have been made in the past year as part of Surface Ocean pCO₂ Mapping Intercomparison (SOCOM), comparing 13 independent data-based methods of global interpolation of pCO₂ (Rödenbeck et al. 2015). Recent research has also decreased uncertainty on the equations used to estimate CO₂ exchange from air-sea pCO₂ differences (Wanninkhof 2014; Ho and Wanninkhof 2016). Large increases in autonomous pCO₂ measurements over time have been achieved with ships of opportunity (SOOP-CO₂) and moorings. The third update of the Surface Ocean CO₂ Atlas (SOCAT) with over 14 million data points was released to the public in 2015 (Bakker et al. 2016). Subsequent data releases will occur annually such that the data can inform the annual assessment of global CO₂ sources and sinks provided by the Global Carbon Project (www.globalcarbonproject.org). The increased data coverage and new mapping techniques make it possible to obtain air-sea CO₂ fluxes at monthly time scales, allowing investigation of variability on subannual to decadal time scales and the causes thereof. An important recent result illuminated by these improved approaches is the reinvigoration of the Southern Ocean carbon sink since 2002 (Landschützer et al. 2015), which had previously been found to be decreasing (Le Quéré et al. 2007).

The newly released datasets have been used to verify the magnitude of the anthropogenic air-sea CO₂ fluxes over the last decade and in 2014. The ocean sink in 2014 was 10% above the 2005–14 average of 2.6 ± 0.5 Pg C yr⁻¹ (Table 3.1). In 2014, the ocean and land carbon sinks removed 27% and 37% of total CO₂ emissions, respectively, leaving 36% of emissions in the atmosphere, compared to 44% as a decadal average (Le Quéré et al. 2015).

Ocean uptake anomalies (Fig. 3.28b) in 2014 relative to the 2005–14 average (Fig. 3.28a) are attributed to several climate reorganizations. The lower CO₂ effluxes in the equatorial Pacific are attributed to anomalously high regional SST and reduced upwell-ing of CO₂-rich subsurface waters due to a weak Modoki-like near-El Niño in 2014. Stronger effluxes are evident in the northeast Pacific due to the warm “Blob” (Bond et al. 2015) as well as warm conditions offshore of the California coast (Fig. 3.29). A cold anomaly in the southern Labrador Sea and adjacent regions (Josey et al. 2015) associated with deep mix-

<figure><img src="image_136.png" /></figure>

**FIG. 3.28.** (a) Average annual air-sea CO₂ flux for 2005–14 based on the AOML-EMP approach (Park et al. 2010). Positive values are effluxes and negative values are influxes. The SST anomaly interpolation method used for this analysis is less robust than more recent and sophisticated approaches (Rödenbeck et al. 2015), but faithfully reproduces the major anomaly features, especially in the highly data-constrained equatorial Pacific. (b) Air-sea CO₂ flux anomaly in 2014 compared to ten-year average (2005–14). Positive values are increased effluxes (or decreased influxes) and negative values are increased influxes (or decreased effluxes).

<figure><img src="image_137.png" /></figure>

**FIG. 3.29.** CO₂ measurement from a ship of opportunity (SOOP) from New Zealand to Long Beach, CA, show-ing anomalously high surface water partial pressure of CO₂ (pCO₂) values in 2014 and 2015 in the anomalously warm surface water offshore of the California coast. Equatorial pCO₂ values are depressed in the boreal spring of 2014 and 2015 compared to climatological values.
<table><caption>Table 3.1. Global ocean C<sub>anth</sub> uptake rates. All uncertainties are reported as ±1σ.</caption><thead><tr><td>Years</td><td>Mean C<sub>anth</sub> Uptake (Pg C yr<sup>-1</sup>)</td><td>Reference</td></tr></thead><tbody><tr><td>1960–69</td><td>1.1 ± 0.5</td><td>Le Quéré et al. 2015</td></tr><tr><td>1970–79</td><td>1.5 ± 0.5</td><td>Le Quéré et al. 2015</td></tr><tr><td>1980–89</td><td>2.0 ± 0.5</td><td>Le Quéré et al. 2015</td></tr><tr><td>1990–99</td><td>2.2 ± 0.5</td><td>Le Quéré et al. 2015</td></tr><tr><td>1994–2006</td><td>2.6 ± 0.5</td><td>Sabine and Tanhua 2010</td></tr><tr><td>2000–09</td><td>2.3 ± 0.5</td><td>Le Quéré et al. 2015</td></tr><tr><td>1994–2010</td><td>2.3 ± 0.5</td><td>Khatiwala et al. 2013</td></tr><tr><td>2000–10</td><td>2.9 ± 0.4</td><td>Kouketsu and Murata 2014</td></tr><tr><td>2005–14</td><td>2.6 ± 0.5</td><td>Le Quéré et al. 2015</td></tr><tr><td>2014</td><td>2.9 ± 0.5</td><td>Le Quéré et al. 2015</td></tr></tbody></table>

ing led to larger effluxes in the northwest Atlantic. A large negative anomaly in the northwest Pacific, perhaps related to a shift in the PDO, contributed to the higher-than-average 2014 ocean CO₂ uptake.

A recent synthesis of pCO₂ data in the western Arctic showed that the Arctic biogeochemical seaspcape is in rapid transition. An analysis of nearly 600 000 surface seawater pCO₂ measurements from 2003 to 2014 found 0.0109 ± 0.0057 Pg C yr⁻¹ entered the ocean in the western Arctic coastal ocean (north of the Bering Strait) during this period, and that this uptake would be expected to increase by 30% under decreased sea ice cover conditions expected with Arctic warming (Evans et al. 2015). Reductions in ice cover may have a more moderate impact on other areas of the western Arctic, such as south of Bering Strait (Cross et al. 2014).

## 2) CARBON INVENTORIES FROM THE GO-SHIP SURVEYS

The CLIVAR/CO₂ Repeat Hydrography Global Ocean Ship-Based Hydrographic Investigations Program (GO-SHIP; www.go-ship.org/) collects high-quality surface-to-bottom water property measurements along transoceanic sections at decadal intervals. These data are essential for estimating decadal C<sub>anth</sub> storage changes within the ocean interior. The extended multiple linear regression

method (eMLR) distinguishes these changes from large natural decadal changes in dissolved inorganic carbon (DIC) concentrations between cruises (e.g., Friis et al. 2005; Sabine et al. 2008). The method has recently been modified to permit basinwide estimates of C<sub>anth</sub> trends by utilizing data from repeat hydrography cruises and climatological data from World Ocean Atlas 2013 (Sabine and Tanhua 2010; Locarnini et al. 2013; Zweng et al. 2013; Williams et al. 2015). Global-scale results from this modified eMLR approach indicate a C<sub>anth</sub> uptake rate of ~2.6 Pg C yr⁻¹ (1994–2006). This estimate is consistent (within uncertainties) with model-based (Khatiwala et al. 2013; Talley et al. 2016) and data-based estimates (Table 3.1) for this period.

C<sub>anth</sub> storage rates vary widely regionally (Fig. 3.30), ranging from 0.1 ± 0.02 to 2.2 ± 0.7 mol C m⁻² yr⁻¹ (Williams et al. 2015). For comparison, the 2.3–2.9 Pg C yr⁻¹ global mean uptake rate estimates above correspond to a global mean C<sub>anth</sub> storage rate between 0.53 and 0.67 mol C m⁻² yr⁻¹. Updating regional storage estimates with measurements from the most recent GO-SHIP hydrographic surveys is an ongoing effort. Recent estimates (Fig. 3.30b) suggest greater storage in the Atlantic in the recent decade than in the preceding decade (Woosley et al. 2016), but consistent storage between the two decades in the Pacific.
<figure><img src="image_138.png" /></figure>

<figure><img src="image_139.png" /></figure>

FIG. 3.30. Regional C<sub>anth</sub> (anthropogenic carbon) storage rate estimates in literature as colored dots with positions corresponding to the approximate centers of the broad regions considered. Estimates are from: A. Williams et al. (2015), B. Sabine et al. (2008), C. Sabine et al. (2008), D. Peng et al. (2003), E. Peng et al. (2003), F. Murata et al. (2009), G. Wakita et al. (2010), H. Sabine et al. (2008), I. Waters et al. (2011), J. Waters et al. (2011), K. Waters et al. (2011), L. Sabine et al. (2008), M. Matear and McNeil (2003), N. Murata et al. (2007), O. Murata et al. (2010), P. Peng et al. (1998), Q. Peng et al. (1998), R. Murata et al. (2008), S. Peng and Wanninkhof (2010), T. Friis et al. (2005), U. Tanhua et al. (2007), V. Olsen et al. (2006), W. Wanninkhof et al. (2010), and X. Quay et al. (2007). Storage rate estimates that use data from cruises in the year 2011 or afterward are mapped in (b), and all other estimates are mapped in (a). Atlantic estimates in (b) are from Woosley et al. (2016). Colored lines are provided representing preliminary storage rate estimates along the labeled P16 and P02 sections in the decades spanning the (a) 1990s to 2000s and (b) 2000s to 2010s occupations. The similar line in (b) for S4P is from Williams et al. (2015).
## 4. THE TROPICS—H. J. Diamond and C. J. Schreck, Eds.

a. Overview—H. J. Diamond and C. J. Schreck

From the standpoint of the El Niño–Southern Oscillation (ENSO), 2015 featured one of the three strongest El Niño episodes (1982/83, 1997/98, and 2015) since 1950. The end of 2014 was characterized by borderline El Niño conditions, and 2015 began with above-average SSTs across the central and east- central equatorial Pacific, with the largest anomalies (>1°C) confined to the region around the internation- al date line. However, this warmth was accompanied by little-to-no atmospheric response, indicating that El Niño had not fully developed. SST anomalies then increased across the central and eastern equatorial Pacific during March–May. This evolution, combined with a coupling of the SST anomalies to the atmo- spheric wind and convection patterns, resulted in the development of El Niño conditions during March 2015. El Niño's strengthening accelerated during June–August, and again during September–Novem- ber, when SST anomalies increased sharply across the eastern half of the equatorial Pacific.

Globally, 101 named tropical storms were observed during 2015. This overall tropical cyclone (TC) activ- ity is well above the 1981–2010 global average of 82 storms and 10% higher than the 91 TCs recorded in 2014 (Diamond 2015). The eastern/central Pacific experienced significantly above-normal activity in 2015, and the western north Pacific and north and south Indian Ocean basins were also above normal; all other basins featured either near or below-normal TC activity. These levels of activity are consistent with the El Niño conditions in place. The 26 named storms in the eastern/central Pacific basin was the highest count in that basin since 1992 and was four more than the previous record of 22 named storms recorded in 2014, as documented in the International Best Tracks Archive for Climate Stewardship (IBTrACS; Knapp et al. 2010). Globally, eight TCs reached the Saffir– Simpson hurricane wind scale Category 5 intensity level—five in the western North Pacific basin, one in the southern Indian Ocean, one in the eastern North Pacific, and one in the southwest Pacific. This was three more than were recorded in 2013 and one more than recorded in 2014 (Diamond 2014, 2015). In terms of accumulated cyclone energy (ACE), the North Atlantic basin season was below normal, also consistent with the El Niño conditions in place. The actual number of storms, on the other hand, was close to normal due to a large number of weak and short- lived storms. Following a near-normal hurricane season in 2014 and a below-normal season in 2013, this marked the first time since 1992–94 in which

three consecutive seasons in the North Atlantic were not above normal in terms of ACE (Bell et al. 2015).

The editors of this chapter would like to insert a personal note recognizing Dr. William M. (Bill) Gray, emeritus professor of atmospheric science at Colorado State University. Dr. Gray, who pioneered the develop- ment of seasonal tropical cyclone outlooks and was one of the most influential meteorologists of the past 50 years, passed away on 16 April 2016 in Fort Collins, Colorado, at the age of 86. Speaking on behalf of the entire community, we will always be indebted to, and benefit from, the accomplishments made during his incredibly long and outstanding career.

b. ENSO and the tropical Pacific—G. D. Bell, M. Halpert, and M. L'Heureux

The El Niño–Southern Oscillation is a coupled ocean–atmosphere phenomenon over the tropical Pacific Ocean. Two indices used to monitor and as-

<figure><img src="image_140.png" /></figure>

FIG. 4.1. The evolution of three strong El Niño events (1982, 1997, and 2015) is compared using time series of (a) the oceanic Niño index (ONI; °C), and (b) the standardized 3-month running equatorial Southern Oscillation index (EQ–SOI, std. dev.). Each time series starts with the JAS season in the year prior to the event (year–1) and ends with the OND season in the year that the event formed (year). For the 1982, 1997, and 2015 El Niños, “year–1” corresponds to 1981, 1996, and 2014, respectively. ONI values are derived from the ERSST.v4 dataset (Huang et al. 2014). EQ–SOI values are derived from the monthly EQ–SOI index based on the Climate Forecast System Reanalysis (CFSR) (Saha et al. 2010b). The EQ–SOI is calculated as the standardized anomaly of the difference between the area-average monthly sea level pressure over the eastern equatorial Pacific (5°N– 5°S, 80°–130°W) and Indonesia (5°N–5°S, 90°–140°E). [Data available at: www.cpc.ncep.noaa.gov/data/indices /reqsoi.for and discussed by Barnston (2015).]
sess the strength of ENSO are the oceanic Niño index (ONI) and the equatorial Southern Oscillation index (EQ-SOI). The ONI (Fig. 4.1a) is the seasonal running average of sea surface temperature (SST) anomalies in the Niño-3.4 region (5°N–5°S, 170°–120°W) using ERSST.v4 data (Huang et al. 2015). NOAA's Climate Prediction Center classifies ENSO events historically using the ONI. At the end of 2015 ONI values were +2.25°C, comparable to the strongest El Niño (1997/98) in the 1950–2015 record.

The EQ-SOI measures the difference in surface air pressure anomalies between Indonesia and the eastern equatorial Pacific Ocean, two large areas along the equator (Barnston 2015). Therefore, the EQ-SOI is a more robust measure of ENSO than the traditional SOI, which is based on measurements at only two stations, both of which are off-equatorial (Tahiti at 18°S, Darwin at 12°S; Troup 1965; Trenberth 1984). Large negative values as seen during 2015 typify El Niño (Fig. 4.1b), and reflect the combination of decreased surface air pressure over the eastern equatorial Pacific and increased air pressure over Indonesia. Overall, the combined time series of the EQ-SOI and ONI suggest that the global climate during 2015 was af-

fected by one of the three strongest El Niño episodes (1982/83, 1997/98, and 2015/16) dating back to 1950.

### 1) Oceanic conditions

The SST evolution across the Pacific basin during 2015 (Figs. 4.2, 4.3) is shown based on OISST data (Smith and Reynolds 1998). In 2015, the year began with above-average SSTs across the central and east-central equatorial Pacific, with the largest anomalies (>1°C) confined to the region around the date line (Fig. 4.2b). The corresponding weekly SST indices for the Niño-4 (Fig. 4.3a) and Niño-3.4 (Fig. 4.3b) regions were above 0.8°C and 0.5°C, respectively (regions shown in Fig. 4.3e). The ONI for December–February 2014/15 (DJF) was +0.52°C, which is near the NOAA threshold for El Niño conditions (ONI ≥ 0.5°C). However, this warmth was accompanied by little-to-no atmospheric response (Figs. 4.4a, 4.5a), indicating that El Niño had not fully developed.

SST anomalies then increased across the central and eastern equatorial Pacific during March–May (MAM; Figs. 4.2d, 4.3). This evolution, combined with a coupling of the SST anomalies to the atmospheric wind and convection patterns (Figs. 4.4b,

<figure><img src="image_141.png" /></figure>

**Fig. 4.2.** Seasonal SST (left) and anomaly (right) for (a, b) DJF 2014/15, (c, d) MAM 2015, (e, f) JJA 2015, and (g, h) SON 2015. Contour interval for total SST is 1°C. For anomalous SST, contour interval is 0.5°C for anomalies between ±1°C, and interval is 1°C for anomalies > 1°C or < -1°C. Anomalies are departures from the 1981–2010 seasonal adjusted OI climatology (Smith and Reynolds 1998).
<figure><img src="image_142.png" /></figure>

FIG. 4.3. Time series during 2015 of weekly area-averaged SST anomalies (°C) in the four Niño regions: (a) Niño-4 region [(5°N–5°S, 160°E–160°W, yellow box in (e)], (b) Niño-3.4 region [(5°N–5°S, 170°–120°W, thick black box in (e)], (c) Niño-3 region [5°N–5°S, 150°–90°W, red box in (e)], and (d) Niño-1+2 region [0°–10°S, 90°–80°W, blue box in (e)]. Values are departures from the 1981–2010 weekly adjusted OI climatology (Smith et al. 1998).

4.5b), resulted in the development of fully-coupled El Niño conditions during March 2015. The presence of El Niño during MAM was also indicated by an eastward shift of the 30°C isotherm to the date line, along with a weaker and reduced westward extent of the equatorial cold tongue (Fig. 4.2c). In fact, the SSTs were nearly uniformly warm (above 27°C) throughout the eastern half of the cold tongue, indicating that the normal east–west SST gradient in that region had nearly disappeared.

El Niño's strengthening accelerated during June–August (JJA; Figs. 4.2e,f) and September–November (SON; Figs. 4.2g,h), as SST anomalies increased sharply across the eastern half of the equatorial Pacific. The ONI for JJA was 1.23°C, increased to 2.04°C during SON, and reached 2.25°C for the last three months of the year (October–December;

<figure><img src="image_143.png" /></figure>

FIG. 4.4. Anomalous 850-hPa wind vectors and speed (contour interval is 2 m s⁻¹) and anomalous OLR (shaded, W m⁻²) during (a) DJF 2014/15, (b) MAM 2015, (c) JJA 2015, and (d) SON 2015. Anomalies are departures from the 1981–2010 period monthly means.

Fig. 4.1a). These values are comparable to the strongest El Niño episodes in the 1950–2015 record.

This evolution is reflected by large SST index values for all four Niño regions, with the weekly Niño-4 index reaching +1.8°C in November and +1.7°C in December (Fig. 4.3a). The average Niño-4 index values for November and December 2015 were 1.75°C and 1.64°C, surpassing the previous record highs of 1.28°C and 1.2°C set in November and December 2009, respectively. Also, the weekly Niño-3.4 (Fig. 4.3b) and Niño-3 (Fig. 4.3c) indices reached +3.0°C by the end of 2015, while the weekly Niño-1+2 index remained near +2.5°C (Fig. 4.3d).

During the last half of the year, the anomalous warming largely reflected a weakening of the annual cycle in SSTs across the Pacific basin, with actual temperatures remaining nearly constant instead of cooling off as they would in a typical year, in association with a strengthening and expanding equatorial cold tongue. This cold tongue, which normally intensifies during JJA and SON, was nearly absent in 2015
<figure><img src="image_144.png" /></figure>

**FIG. 4.5.** Anomalous 200-hPa wind vectors and speed (contour interval is 4 m s⁻¹) and anomalous OLR (shaded, W m⁻²) during (a) DJF 2014/15, (b) MAM 2015, (c) JJA 2015, and (d) SON 2015. Anomalies are departures from the 1981–2010 period monthly means.

(Figs. 4.2e,g), as was the typical westward advection of cooler waters toward the date line. Consistent with these conditions, the normal westward migration of the +30°C isotherm to New Guinea did not occur during JJA and SON. Instead, these exceptionally warm temperatures actually migrated eastward, further strengthening El Niño and its associated atmospheric response.

Consistent with the evolution of the equatorial SSTs, positive subsurface temperature anomalies increased east of the date line throughout the year (Fig. 4.6). A significant temperature increase occurred during MAM (Fig. 4.6b) in response to the combination of the evolving El Niño and the downwelling phase of a strong equatorial oceanic Kelvin wave (section 4c) that was initiated by a westerly wind burst.

Subsequent westerly wind bursts in late June/early July, early August, and early October also initiated downwelling equatorial oceanic Kelvin waves, which helped to maintain well-above-normal subsurface ocean temperatures through the end of the year (Figs. 4.6c,d). In contrast, in the western Pacific,

<figure><img src="image_145.png" /></figure>

**FIG. 4.6.** Equatorial depth–longitude section of ocean temperature anomalies (°C) averaged between 5°N and 5°S during (a) DJF 2014/15, (b) MAM 2015, (c) JJA 2015, and (d) SON 2015. The 20°C isotherm (thick solid line) approximates the center of the oceanic thermocline. The data are derived from an analysis system that assimilates oceanic observations into an oceanic general circulation model (Behringer et al. 1998). Anomalies are departures from the 1981–2010 period monthly means.

subsurface temperature anomalies decreased during the year. These conditions reflected a progressive flattening of the oceanic thermocline (indicated by the 20°C isotherm, thick solid line), which is typical of a strong El Niño pattern of anomalous downwelling (upwelling) in the eastern (western) equatorial Pacific (Wang et al. 1999; Wang and Weisberg 2000).

## 2) ATMOSPHERIC CIRCULATION: TROPICS AND SUBTROPICS

During DJF 2014/15, the atmospheric circulation across the tropical Pacific reflected ENSO-neutral conditions, with near-average low-level (850-hPa) winds (Fig. 4.4a) and no consistent El Niño signal in the upper-level winds (Fig. 4.5a). Also, convection was slightly suppressed over the east-central equatorial Pacific in the area of anomalously warm SSTs, indicating a lack of oceanic–atmospheric coupling.
In March, the atmospheric pressure, wind, and convection patterns became coupled to the increasingly warm SST anomalies, signifying the development of El Niño. The atmospheric response to El Niño was evident through the remainder of the year, intensifying as El Niño strengthened.

The tropical atmospheric response to El Niño during MAM through SON featured an east-west dipole pattern of anomalous convection, with convection expanding and strengthening over the central and east-central equatorial Pacific while becoming more suppressed over Indonesia and the eastern Indian Ocean (Figs. 4.4b-d, 4.5b-d). This pattern reflected 1) a pronounced eastward extension of the primary area of tropical convection to well east of the date line and, at times, an actual shift of the main region of tropical convection to the eastern half of the tropical Pacific (not shown), and 2) a strengthening and equatorward shift of the intertropical convergence zone (ITCZ) in the Northern Hemisphere.

A key El Niño-related feature of the low-level (850-hPa) winds during JJA through SON was an extensive area of anomalous westerlies that strengthened and expanded along the equator as the year progressed (Figs. 4.4b-d). This anomaly pattern reflected a marked weakening (3-6 m s⁻¹ below normal) of the easterly trade winds, with departures exceeding 6 m s⁻¹ near the date line in SON (Fig. 4.4d).

An El Niño-related upper-level wind pattern also became established during MAM and strengthened as the year progressed. This pattern featured an extensive area of easterly wind anomalies across the central and east-central tropical Pacific (Figs. 4.5b-d), along with near-average winds over both the eastern equatorial Pacific and Indonesia.

The overall circulation also featured a combination of anomalous upper-level convergence and low-level divergence over Indonesia and the western tropical Pacific, and a combination of anomalous upper-level divergence and low-level convergence over the central and east-central equatorial Pacific. The resulting vertical motion pattern was consistent with the observed east-west dipole pattern of tropical convection, as was also noted by Bell and Halpert (1998) for the 1997/98 El Niño. Collectively, these wind, convection, and vertical motion patterns reflect a markedly reduced strength of the equatorial Walker circulation typical of El Niño (Bjerknes 1969).

In the subtropics, the upper-level winds during JJA-SON 2015 featured anticyclonic anomalies in both hemispheres straddling the area of enhanced equatorial convection. This anticyclonic couplet is a typical feature of El Niño (Arkin 1982). In the winter

hemisphere, anomalous westerly winds along the poleward flank of the anomalous anticyclonic circulation reflect major dynamical and kinematic changes in the jet stream over the Pacific basin. As seen during JJA and SON in the Southern Hemisphere (Figs. 4.5c,d), the westerly wind anomalies between 20° and 30°S reflected a strengthening and eastward extension of the wintertime jet steam to well east of the date line, along with an eastward shift of that jet's exit region to the eastern South Pacific. This wintertime jet stream pattern represents a fundamental manner in which El Niño's circulation impacts are communicated downstream and poleward into the extratropics.

### 3) RAINFALL IMPACTS

Because of the rapid strengthening and expansion of the El Niño-related convection and circulation anomalies during MAM and JJA, many typical El Niño rainfall impacts (Ropelewski and Halpert 1987) were evident during the year. The accumulated precipitation deficits and surpluses during June-December, along with time series of area-averaged monthly precipitation totals and percentiles during the year, highlight these impacts (Fig. 4.7).

Two main regions with above-average precipitation during June-December 2015 were the central equatorial Pacific and within the Pacific ITCZ. The enhanced rainfall for both regions began in March and subsequently intensified with area-averaged monthly totals during May-December (red line, Fig. 4.7b) all being in the upper 10th percentile of occurrences (black bars). For the June-December period, rainfall surpluses in both areas exceeded 800 mm, with the largest surpluses exceeding 1200 mm. During June-October, these conditions were associated with strong hurricane seasons for both the central and eastern Pacific hurricane basins (see section 4e3).

Two other regions that typically record above-average precipitation during El Niño include southeastern South America and the Gulf Coast region of the United States. The extended South Pacific jet stream contributed to precipitation surpluses of 100-200 mm in southeastern South America during June-December, with above-average precipitation recorded in nearly every month from July to December (Fig. 4.7c; section 7d). Along the U.S. Gulf Coast, above-average precipitation was recorded from October to December, with area-averaged totals above the 90th percentile of occurrences during November-December (Fig. 4.7d).

Many other areas typically record below-average precipitation during El Niño. One such region is Indonesia, where cumulative deficits during June-
December 2015 exceeded 1000 mm. The most significant deficits occurred during July–October, when monthly totals of less than 100 mm were generally half of normal and in the lowest 10th percentile of occurrences (Fig. 4.7e). Other regions with below-average precipitation during the period from June to December included:

* The South African monsoon season (October–April) is typically suppressed during El Niño, and from October–December precipitation totals were well below average, with monthly totals in the lowest 10th percentile of occurrences in all three months (Fig. 4.7f).

* The Amazon basin recorded significantly below-average rainfall throughout the year, with monthly totals generally in the lowest 10th percentile of occurrences (Fig. 4.7g). During June–December 2015, much of the region recorded deficits of 400–600 mm.

* The Central America/Caribbean Sea region (Fig. 4.7h) and the tropical Atlantic (Fig. 4.7i) had rainfall that was below average during almost every month from April to December, with monthly totals in the lowest 20th percentile of occurrences in most months. Below-average

totals across the tropical Atlantic were also consistent with the overall below-average strength of the 2015 Atlantic hurricane season (see section 4e2).

c. *Tropical intraseasonal activity*—S. Baxter, C. J. Schreck, and G. D. Bell

Tropical intraseasonal variability was prominent during 2015 in both the atmosphere and ocean, even in the presence of strong lower-frequency variability associated with El Niño. In the atmosphere, two aspects of this intraseasonal variability were the Madden–Julian oscillation (MJO; Madden and Julian 1971, 1972, 1994; Zhang 2005) and convectively coupled equatorial waves, which include equatorial Rossby waves and atmospheric Kelvin waves (Wheeler and Kiladis 1999; Kiladis et al. 2009;

Roundy 2012a,b). There were three distinct periods of MJO activity during 2015 affecting a total of six months (Figs. 4.8–4.10), which were interspersed with the convectively coupled waves. Between these three periods, the intraseasonal variability was dominated by atmospheric Kelvin waves and tropical cyclone activity. Within the Pacific Ocean, strong intraseasonal variability throughout the year was reflected in a series of upwelling and downwelling equatorial oceanic Kelvin waves (Fig. 4.11).

The MJO is a leading intraseasonal climate mode of tropical convective variability. Its convective anomalies often have the same spatial scale as ENSO, but differ in that they exhibit a distinct eastward propagation and generally traverse the globe in 30–60 days. The MJO impacts weather patterns around the globe

<figure><img src="image_146.png" /><figcaption>**FIG. 4.7.** Precipitation during 2015: (a) Accumulated precipitation departures during (b–i) Jun–Dec (mm), Time series of area-averaged monthly precipitation for regions indicated with red boxes in (a). Bars show monthly percentile percentiles (left y-axis), and red and blue lines show monthly observed and climatological mean precipitation (right y-axis), respectively. Rainfall amounts are obtained by merging rain gauge observations and satellite-derived precipitation estimates (Janowiak and Xie 1999). Precipitation percentiles are based on a gamma distribution fit to the 1981–2010 base period. Anomalies are departures from the 1981–2010 means.</figcaption></figure>
<figure><img src="image_147.png" /></figure>

**FIG. 4.8.** Time–longitude section for 2015 of 5-day running anomalous 200-hPa velocity potential (× 10⁶ m² s⁻¹) averaged for 5°N–5°S. For each day, the period mean is removed prior to plotting. Green (brown) shading highlights likely areas of anomalous divergence and rising motion (convergence and sinking motion). Red lines and labels highlight the main MJO episodes. Anomalies are departures from the 1981–2010 base period daily means.

(Zhang 2013), including monsoons (Krishnamurti and Subrahmanyam 1982; Lau and Waliser 2012), tropical cyclones (Mo 2000; Frank and Roundy 2006; Camargo et al. 2009; Schreck et al. 2012), and extratropical circulations (Knutson and Weickmann 1987; Kiladis and Weickmann 1992; Mo and Kousky 1993; Kousky and Kayano 1994; Kayano and Kousky 1999; Cassou 2008; Lin et al. 2009; Riddle et al. 2012; Schreck et al. 2013; Baxter et al. 2014). The MJO is often quite variable in a given year, with periods of moderate-to-strong activity sometimes followed by little or no activity. The MJO tends to be most active during ENSO neutral and weak El Niño periods, and is often absent during strong El Niño events (Hendon et al. 1999; Zhang and Gottschalck 2002; Zhang 2005). Given a background El Niño rivaling one of the strongest on record during 2015, the MJO events observed during the year are remarkable.

Common metrics for identifying the MJO include time–longitude plots of anomalous 200-hPa velocity potential (Fig. 4.8) and outgoing longwave radiation (OLR, Fig. 4.9), as well as the Wheeler–Hendon (2004)

<figure><img src="image_148.png" /></figure>

**FIG. 4.9.** Time–longitude section for 2015 of anomalous outgoing longwave radiation (OLR; W m⁻²) averaged for 10°N–10°S. Negative anomalies indicate enhanced convection and positive anomalies indicate suppressed convection. Contours identify anomalies filtered for the MJO (black) and atmospheric Kelvin waves (red) as in Kiladis et al. (2006) and Straub and Kiladis (2002), respectively. Purple shaded ovals indicate hurricanes named on figure. Red labels highlight the main MJO episodes. Contours are drawn at ±10 W m⁻², with the enhanced (suppressed) convective phase of these phenomena indicated by solid (dashed) contours. Anomalies are departures from the 1981–2010 base period daily means.

Real-time Multivariate MJO (RMM) index (Fig. 4.10). In the time–longitude plots, the MJO exhibits eastward propagation. In the RMM, the MJO propagation and intensity are seen as large, counterclockwise circles around the origin. These diagnostics point to three main MJO episodes during 2015. MJO #1 was a strong episode from March into early April. MJO #2 was a strong event that began in late May and lasted through mid-July. MJO #3 was a moderately strong event that lasted from October through the end of the year.

MJO #1 featured a zonal wave-1 pattern of strong convective anomalies, with a periodicity of approximately 40 days (Figs. 4.8, 4.9, 4.10a,b). The plot of anomalous velocity potential shows that this event circumnavigated the globe once (Fig. 4.8). The RMM index achieved record amplitude of 4.03 standard deviations on 16 March (Fig. 4.10a). Historically, the
only prior MJO event to eclipse 4.0 occurred 30 years ago in February 1985 (4.02). The 2015 event ended in April when the convective anomalies became dominated by a series of fast-propagating atmospheric Kelvin waves (Fig. 4.9).

One of the largest impacts from MJO #1 was the interaction with a high-amplitude downwelling equatorial oceanic Kelvin wave (Fig. 4.11b). This oceanic Kelvin wave was triggered during March by a westerly wind burst associated with enhanced convection over the western Pacific (Fig. 4.11a). This wave reached the eastern Pacific in May and produced a significant increase in the upper ocean heat content while El Niño was developing. MJO #1 also impacted the extratropical circulation, mainly during mid- to late March, when suppressed convection and anomalous upper-level convergence were present over the eastern Indian Ocean, and enhanced convection and anomalous upper-level divergence were present over the western and central Pacific Ocean (Fig. 4.8). These conditions contributed to an eastward extension of the East Asian jet stream and a subsequent cold air outbreak over the continental United States.

MJO #2 began in late May and lasted through mid-July, with its wave-1 signal also making a full trip

around the globe. Its convective anomalies masked the strengthening El Niño in early and mid-June, then accentuated the El Niño signal during late June and early July (Fig. 4.9). The RMM index showed remarkable amplitude in early July, again approaching four standard deviations (Fig. 4.10c). As is common with many MJO episodes (Straub et al. 2006; Sobel and Kim 2012), the convective signal of MJO #2 was partially masked by atmospheric Kelvin wave activity (Fig. 4.9). This MJO provided especially conducive conditions for producing tropical cyclones. Twelve storms, spanning from the Arabian Sea to the North Atlantic, developed in association with this event. These storms included “twin” tropical cyclones Raquel and Chan-hom that straddled the equator in the western Pacific and contributed to a particularly strong westerly wind burst (Fig. 4.11a).

Following MJO #2, enhanced tropical cyclone activity across the central and eastern North Pacific from August through October contributed to the atmospheric intraseasonal variability. Some of these storms (named and purple shaded ovals, Fig. 4.9) can be identified as westward-moving patterns of anomalous upper-level divergence and enhanced OLR (storm names).

MJO #3 lasted from mid-October through the end of the year. The periodicity of this event is difficult to assess, though it likely exceeded 60 days and is at the slower end of the MJO spectrum (Fig. 4.10d). After being initiated over the western Pacific, the area of enhanced convection associated with MJO #3 propagated over the Indian Ocean, where it then became quasi-stationary for most of November. It could be argued that this event did not begin in earnest until its eastward propagation resumed in early December. Similar to MJO #2, this event at times masked the El Niño convection pattern and at other times accentuated it. Across the Pacific Ocean, intraseasonal variability associated with equatorial oceanic Kelvin wave activity was seen throughout the year (Fig. 4.11b). All three MJO events featured westerly wind bursts (Fig. 4.11a) that triggered downwelling Kelvin waves. Overall, downwelling Kelvin waves tended to be strong, helping to strengthen and maintain the anomalous warmth associated with El Niño.

In contrast, the upwelling Kelvin waves tended to be weak throughout the year and had little net impact on the surface and subsurface warmth associated with El Niño. This suppression of the upwelling waves is linked to sustained anomalous westerly winds over the central and western equatorial Pacific in association with El Niño (see Figs. 4.4b-d).

<figure><img src="image_149.png" /><figcaption>**FIG. 4.10.** Wheeler-Hendon (2004) Real-time Multivariate MJO (RMM) index for (a) Jan-Mar, (b) Apr-Jun, (c) Jul-Sep, and (d) Oct-Dec 2015. Each point represents the MJO amplitude and location on a given day, and the connecting lines illustrate its propagation. Amplitude is indicated by distance from the origin, with points inside the circle representing weak or no MJO. The 8 phases around the origin identify the region experiencing enhanced convection, and counterclockwise movement is consistent with eastward propagation.</figcaption></figure>
<figure><img src="image_150.png" /></figure>

FIG. 4.11. (a) Time-longitude section for 2015 of anomalous 850-hPa zonal wind (m s⁻¹) averaged for 10°N–10°S. Black contours identify anomalies filtered for MJO. Red labels highlight the main MJO episodes. Significant westerly wind bursts (WWB) are labeled. (b) Time-longitude section for 2015 of the anomalous equatorial Pacific Ocean heat content, calculated as the mean temperature anomaly between 0 and 300 m depth. Yellow/red (blue) shading indicates above- (below-) average heat content. The relative warming (solid lines) and cooling (dashed lines) due to downwelling and upwelling equatorial oceanic Kelvin waves are indicated. Anomalies are departures from the 1981–2010 base period pentad means.

### d. Intertropical convergence zones

#### 1) PACIFIC—A. B. Mullan

The broad-scale patterns of tropical Pacific rainfall are dominated by two convergence zones, the inter-tropical convergence zone (ITCZ) and the South Pacific convergence zone (SPCZ). The ITCZ lies between 5° and 10°N and is most active during the August to December period, when it lies at its northernmost position. The SPCZ extends diagonally from around the Solomon Islands (10°S, 160°E) to near 30°S, 140°W, and is most active in the November–April half-year. Both convergence zones are strongly influenced by the state of ENSO.

During 2015, an El Niño event that had established itself in March continued to intensify through the end of the year. The monsoon of the western North Pacific extended far to the east to bring unusually strong and persistent westerly winds to the date line and beyond. Sea surface and subsurface temperatures were much warmer than normal, and the convergence zones were more active. For most months from May to December, the NASA ENSO Precipitation index (ESPI; Curtis and Adler 2000) was close to +2 or more, well above the +1 threshold associated with El Niño conditions.

Figure 4.12 summarizes the convergence zone behavior for 2015 and allows comparison of the 2015 seasonal variation against the longer term (1998–2014) climatology. Rainfall transsects over 20°N to 30°S are presented for each quarter of the year, averaged across successive 30-degree longitude bands, starting in the western Pacific at 150°E–180°.

With the demise of the TRMM satellite in mid-2015, the rainfall data for this year's chapter are taken from NOAA's “CMORPH” global precipitation analysis (Joyce et al. 2004). This dataset, derived from low orbiter satellite microwave observations (as is TRMM 3B43), is available at the same 0.25° resolution as the TRMM 3B43 used previously (e.g., Mullan 2014).

Although not identical, CMORPH and TRMM 3B43 rainfall are similar in pattern and magnitude at the broad scale discussed here.

In the western North Pacific, rainfall generally exceeded climatology from early in the year. The second quarter bulletin of the Pacific ENSO Applications Climate Center (www.weather.gov/media/peac/PEU/PEU_v21_n2.pdf) commented that: “In eastern Micronesia [5°–10°N, 140°–160°E,] ... extraordinary amounts of rainfall occur[ed] in March and April.” As a result of the El Niño event, from March to December convection was greatly enhanced over climatology from the date line eastward, especially in the Northern Hemisphere for the ITCZ (Figs. 4.12b–d). Not only was the ITCZ closer to the equator, but the region of convection also had a broader latitude extent with a larger rainfall maximum. Convection at the equator itself was typically about double the climatological value for sectors 150°E–180° and 180°–150°W. Figure 4.13 gives the 2015 annual average precipitation in the Pacific and clearly shows the broader ITCZ: rainfall is twice the climatology along a line a few degrees north of the equator and again near 15°N, while rainfall is close to climatology along 10°N.
<figure><img src="image_151.png" /></figure>

**FIG. 4.12.** Rainfall rate (mm day⁻¹) from CMORPH analysis for (a) Jan–Mar, (b) Apr–Jun, (c) Jul–Sep, and (d) Oct–Dec 2015. Each panel shows the 2015 rainfall cross section between 20°N and 30°S (solid line) and the 1998–2014 climatology (dotted line), separately for four 30° sectors from 150°E–180° to 120°–90°W.

<figure><img src="image_152.png" /></figure>

**FIG. 4.13.** Annual-average CMORPH precipitation for 2015, as a percentage of the 1998–2014 average. The white areas have precipitation anomalies within 25% of normal.

<figure><img src="image_153.png" /></figure>

**FIG. 4.14.** CMORPH rainfall rate (mm day⁻¹) for Oct–Dec period for each year 1998 to 2015, averaged over the longitude sector 180°–150°W. The cross sections are color-coded according to NOAA's ONI, except for 2015 (an El Niño year) shown in black.

Enhanced convection near the equator, around and east of the date line, is typical of El Niño conditions. However, the degree of enhancement was quite extreme in 2015, as was the extent of warming in equatorial sea surface temperatures. Figure 4.14 shows precipitation transects for the last quarter of each year 1998–2015, averaged over the 180°–150°W sector. Rainfall within 5 degrees of the equator during 2015 was well above that for any other year in the relatively short CMORPH record (starting January 1998). It is likely, however, that October–December 1997 was similar, given the high rainfall along the equator in January–March 1998 under the very intense 1997/98 El Niño.

The CMORPH analysis matches reasonably well with observed rainfall in the Pacific Islands, although there is much more variability at the island scale. For Hawaii, at the northern edge of the 180°–150°W sector, the third quarter rainfall varied from about twice the average at Hilo, to ten times the average in Honolulu (www.weather.gov/media/peac/PEU/PEU_v21_n4.pdf).

Christmas Island (or Kiritimati) in eastern Kiribati lies on the equator in the same sector as Hawaii; rainfall was above normal for each of the last nine months of 2015 (www.niwa.co.nz/climate/icu), and Kiritimati received about ten times its normal December rainfall (667 mm). In contrast, islands along the southern edge of the SPCZ experienced well-below-normal rainfall from about April 2015 onward (www.niwa.co.nz/climate/icu). For example, the islands of New Caledonia, Fiji, Niue, and Tahiti were generally drier than normal for 8 or 9 of the last nine months of 2015.

## 2) ATLANTIC—A. B. Pezza and C. A. S. Coelho

The Atlantic ITCZ is a well-organized convective band that oscillates approximately between 5° and 12°N during July–November and 5°N and 5°S during January–May (Waliser and Gautier 1993; Nobre and Shukla 1996). Equatorial Kelvin waves can modulate the ITCZ intraseasonal variability (Guo et al. 2014). ENSO is also known to influence the ITCZ on the
<figure><img src="image_154.png" /><figcaption>FIG. 4.15. Spatial distribution of average global SST anomalies (°C, Reynolds et al. 2002) during 2015.</figcaption></figure>

<figure><img src="image_155.png" /><figcaption>FIG. 4.16. (a) Atlantic ITCZ position inferred from outgoing longwave radiation during May 2014. The colored thin lines indicate the approximate position for the six pentads of the month. The black thick line indicates the Atlantic ITCZ climatological position. The SST anomalies for May 2014 based on the 1982–2013 climatology are shaded (°C). The two boxes indicate the areas used for the calculation of the Atlantic index in (b). (b) Monthly SST anomaly time series averaged over the South American sector (SA region, 10°–50°W, 5°S–5°N) minus the SST anomaly time series averaged over the North Atlantic sector (NA region, 20°–50°W, 5°–25°N) for the period 2010–14, forming the Atlantic index. The positive phase of the index indicates favorable conditions for enhanced Atlantic ITCZ activity.</figcaption></figure>

interannual time scale (Münnich and Neelin 2005). In 2015, weak positive SST anomalies prevailed in the equatorial Pacific until March, followed by the development of a strong El Niño event from March onward, with a marked signature in the annual average (Fig. 4.15).

Consistent with Münnich and Neelin (2005), these conditions were associated with relatively warmer waters in the North Atlantic sector after the establishment of the El Niño, leading to a sharp negative peak in the Atlantic index (Fig 4.16a) in the second half of 2015, as measured by the north-south SST gradient (Fig. 4.16a). As a consequence, the ITCZ oscillated well north of its climatological position for most of the year, with an overall suppression of any significant activity in the Southern Hemisphere. An exception occurred in March and April (Fig. 4.16b), when the

<figure><img src="image_156.png" /><figcaption>FIG. 4.17. (a) Observed precipitation (mm day⁻¹) during 2015, (b) 1998–2014 precipitation climatology (mm day⁻¹), and (c) observed anomaly (mm day⁻¹) in 2015 derived from CPC Morphing technique (CMORPH; Joyce et al. 2004).</figcaption></figure>
ITCZ moved south of the equator during a short gap before the air-sea teleconnection effects of the strong ENSO event became fully established. This southern burst was accompanied by a brief but sharp increase of the Atlantic index.

Despite that, the effects of the southern passage of the ITCZ on potentially enhancing the convective activity over the drought-prone areas of northeastern Brazil were only minor, with an overall annual balance of well-below-average precipitation in most of the region (Fig. 4.17a-c). This “lack of convective coupling” was associated with a widespread drought within most of the Amazon as well as in central Brazil. Persistent low vegetation health indices and reduced soil moisture likely contributed to lowering the rate of evapotranspiration and relative humidity, facilitating higher temperatures as observed during heat waves in Europe (Whan et al. 2015). This large-scale drought pattern has also extended into southeastern Brazil in recent years (Coelho et al. 2015a,b) and was already established before the onset of the latest El Niño. Otto et al. (2015) explore whether droughts in different parts of Brazil could either be part of a longer-term natural oscillation or attributable to anthropogenic forcing.

### e. Tropical cyclones

#### 1) OVERVIEW—H. J. Diamond and C. J. Schreck

The IBTrACS dataset comprises historical tropical cyclone (TC) best-track data from numerous sources around the globe, including all of the WMO Regional Specialized Meteorological Centers (RSMC; Knapp et al. 2010). To date, IBTrACS represents the most complete compilation of global TC data and offers a unique opportunity to revisit the global climatology of TCs. Using IBTrACS data (Schreck et al. 2014) a 30-year average value for storms (from WMO-based RSMC numbers) is noted for each basin.

The global tallying of total TC numbers is challenging and involves more than simply adding up basin totals because some storms cross basin boundaries, some basins overlap, and multiple agencies are involved in the tracking and categorization of TCs. Compiling the activity using the IBTrACS dataset over all seven TC basins (Fig. 4.18), the 2015 season (2014/15 in the Southern Hemisphere) had 101 named storms [wind speeds ≥ 34 knots (kt; 1 kt = 0.51 m s⁻¹) or 18 m s⁻¹], which is well above the 1981–2010 average of 82 (Schreck et al. 2014) and 10 more than the 2014 total of 91 (Diamond 2015). The 2015 season also featured 62 Hurricanes/Typhoons/Cyclones (HTC; wind speeds ≥ 64 kts or 33 m s⁻¹), which is also well above the 1981–2010 average of 46 HTCs (Schreck

<figure><img src="image_157.png" /><figcaption>FIG. 4.18. Global summary of TC tracks with respect to SST anomalies for the 2015 TC season.</figcaption></figure>

et al. 2014). Of these, 36 storms reached major HTC status (wind speeds ≥ 96 kts or 49 m s⁻¹; WMO 2015), which is well above the average of 21. To assist in tallying the basin totals, this year we normalized the counts by basing them on WMO-defined basin boundaries and by using the Saffir-Simpson scale to represent intensities for all basins, realizing that the Saffir-Simpson scale is not operationally used in all basins. Therefore, Fig. 4.18 depicts as close to an overall picture of global TCs as possible, and each of the basin sections (4e2–4e8) has a graphic reflecting those normalized basin totals.

There were eight Saffir-Simpson level Category 5 systems during the year (one more than in 2014, and three more than in 2013): Patricia in the eastern North Pacific; Super Typhoons Maysak, Noul, Dolphin, Soudelor, and Atsani in the western North Pacific; Cyclone Eunice in the south Indian Ocean; and Tropical Cyclone Pam in the southwest Pacific. Patricia, with maximum sustained surface winds of 174 kt (88 m s⁻¹) and a minimum central pressure of 879 hPa, set records for these parameters for any tropical cyclone anywhere in the Western Hemisphere. Patricia was also characterized by an extraordinarily fast intensification, with a 100-hPa drop in its minimum central pressure within a 24-hour period.

There were also several Saffir-Simpson Category 3 and 4 intensity-level systems during 2015 that had major impacts: 1) Joaquin in the North Atlantic; 2) Hilda, Ignacio, and Kilo in the eastern North Pacific; 3) Koppu, Chan-hom, and Melor in the western North Pacific; 4) Chapala and Megh in the north Indian Ocean; 5) Chedza, Fundi, and Haliba in the south Indian Ocean; and 6) Marcia in the southwest Pacific. It should be noted that although TCs in the south Indian Ocean impacted life and property, the greatest impacts were caused by those storms that did not even become cyclones. This observation speaks to the damage that tropical cyclones can sometimes inflict
while not at the named storm level of intensity. The North Atlantic hurricane season was below normal (section 4e2), and both the central and eastern Pacific hurricane seasons were well above normal (section 4e3), consistent with the El Niño conditions in place (section 4b). Sidebar 4.1 also provides analysis and a summary of the overall Northern Hemisphere TC seasons and highlights the special role that El Niño plays with respect to TCs. Sidebar 4.2 describes a rare and interesting subtropical cyclone that developed over the southeast Pacific, a region usually not conducive to such development.

2) ATLANTIC BASIN—G. D. Bell, C. W. Landsea, E. S. Blake, J. Schemm, S. B. Goldenberg, T. B. Kimberlain, and R. J. Pasch

(i) 2015 seasonal activity

The 2015 Atlantic hurricane season produced 11 named storms, of which four became hurricanes and two became major hurricanes. These values are not far below the HURDAT2 30-year (1981–2010) seasonal averages of 11.8 tropical storms, 6.4 hurricanes, and 2.7 major hurricanes (Landsea and Franklin 2013). Many of the storms during 2015 were weak and short-lived, and the seasonal accumulated cyclone energy (ACE) value (Bell et al. 2000) was 67.8% of the 1981–2010 median (92.4 × 10⁴ kt²; Fig. 4.19). This value is below NOAA's upper threshold (71.4% of the median) for a below-normal season (see www.cpc.ncep.noaa.gov/products/outlooks/background_information.shtml), and consequently the season is classified as below-normal.

A single storm, Major Hurricane Joaquin, produced nearly one-half of the season's total ACE value; the remaining ten storms produced an ACE value of

only 36.1% of the median. This result highlights the large number of weak and short-lived storms during the season. Combined with a near-normal hurricane season in 2014 and a below-normal season in 2013 (Bell et al. 2015), 2013–15 marks the first time since 1992–94 in which three consecutive seasons were not above normal.

Since the current high-activity era for Atlantic hurricanes began in 1995, 13 of 21 seasons (62%) have been above normal, and four seasons (19%) have been near normal. The 2015 season marks only the fourth below-normal season since 1995. The 2015 activity was well below the averages during the recent active period (1995–2014) of 15 named storms, 7.6 hurricanes, 3.5 major hurricanes, and 141.6% of the 1981–2010 median ACE. A yearly archive of conditions during these seasons can be found in previous *State of the Climate* reports.

A main delineator between more- and less-active Atlantic hurricane seasons is the number of hurricanes and major hurricanes that originate as named storms within the Main Development Region (MDR; green boxed region in Fig. 4.20a) which spans the tropical Atlantic Ocean and Caribbean Sea between 9.5° and 21.5°N (Goldenberg and Shapiro 1996; Goldenberg et al. 2001; Bell and Chelliah 2006). Only five named storms formed in the MDR during 2015, with two becoming hurricanes and one of those being a short-lived major hurricane. The resulting ACE value from these five storms was only about 27% of the median, which is comparable to the 1981–2010 below-normal season average for the MDR of 18.1%. These values are well below the above-normal and near-normal season ACE averages for the MDR of 151.1% and 57.9% of the median, respectively.

(ii) *Storm tracks*

Two tropical storms made landfall in the United States during 2015: Tropical Storm Ana which made landfall in South Carolina in May, and Tropical Storm Bill which made landfall in Texas in June. No hurricanes made landfall in the United States this season.

No hurricanes tracked through the Caribbean Sea during 2015. This region has seen only one hurricane in the last three seasons: Gonzalo in 2014. As discussed below, and also by Bell et al. (2014, 2015), this dearth of hurricane activity over the Caribbean Sea has reflected a lack of storms forming in the region due to strong vertical wind shear and anomalous sinking motion, and also a lack of storms propagating westward into the region.

<figure><img src="image_158.png" /><figcaption>FIG. 4.19. NOAA's Accumulated Cyclone Energy (ACE) index expressed as percent of the 1981–2010 median value. ACE is calculated by summing the squares of the 6-hourly maximum sustained surface wind speed (knots) for all periods while the storm is at least tropical storm strength. Red, yellow, and blue shadings correspond to NOAA's classifications for above-, near- and below-normal seasons, respectively. The 165% threshold for a hyperactive season is indicated. Vertical brown lines separate high- and low-activity eras.</figcaption></figure>
<figure><img src="image_159.png" /></figure>

<figure><img src="image_160.png" /></figure>

<figure><img src="image_161.png" /><figcaption>**FIG. 4.20.** (a) ASO 2015 SST anomalies (°C), with the MDR indicated by the green box. (b) Time series for 1950–2015 of ASO area-averaged SST anomalies in the MDR. (c) Time series showing the difference between ASO area-averaged SST anomalies in the MDR and those for the entire global tropics (20°N–20°S). Red lines show a 5-pt. running mean of each time series. Anomalies are departures from the ERSST.v3b (Smith et al. 2008) 1981–2010 period monthly means.</figcaption></figure>

<figure><img src="image_162.png" /><figcaption>**FIG. 4.21.** Unfiltered index of the Atlantic multidecadal oscillation (AMO) during 1950–2015 averaged over ASO (red line) and JFM (blue line). Based on the Kaplan SST dataset (Enfield et al. 2001; www.esrl.noaa.gov/psd/data/timeseries/AMO).</figcaption></figure>

### (iii) Atlantic sea surface temperatures

SST anomalies warmed across the MDR as the summer progressed, with below-average SSTs during June–July and above-average SSTs during August–November. For the MDR as a whole, the area-averaged SST anomaly for October (+0.64°C) was the warmest in the 1950–2015 record, and the area-averaged anomaly for November (+0.48°C) tied for the warmest on record.

For the peak months (August–October, ASO) of the Atlantic hurricane season the mean SST departure in the MDR was +0.43°C (Fig. 4.20b), which ties for fifth warmest in the record (Fig. 4.20b). Consistent with the ongoing warmth in the MDR since 1995, objective measures of the Atlantic multidecadal oscillation (AMO; Enfield and Mestas-Nuñez 1999), such as NOAA's operational Kaplan AMO index, indicate a continuance of the AMO warm phase during ASO 2015 (Fig. 4.21). In contrast, the AMO index for January–March has been near zero for the past two years.

The warm AMO phase and the associated positive phase of the Atlantic Meridional Mode (Vimont and Kossin 2007; Kossin and Vimont 2007) are the primary climate factors associated with high-activity eras for Atlantic hurricanes (Goldenberg et al. 2001; Bell and Chelliah 2006; Bell et al. 2011, 2012). This warm phase features anomalously warm SSTs in the MDR compared to the remainder of the global tropics (Fig. 4.20c). However, the mean SST anomaly within the MDR during ASO 2015 was less than the mean anomaly for the entire global tropics, due partly to the intensifying El Niño (see section 4b).

### (iv) Atmospheric conditions

#### a. Atlantic basin

The below-normal 2015 Atlantic hurricane season resulted mainly from a set of atmospheric conditions during ASO that made the central and western MDR extremely unfavorable for TC activity. These conditions included: 1) anomalously strong vertical wind shear extending from the Caribbean Sea northeastward to the central Atlantic (Fig. 4.22), 2) anomalous upper-level (200-hPa) convergence and lower-level (850-hPa) divergence (Fig. 4.23a), 3) anomalous sinking motion throughout the troposphere (Fig. 4.23b) and, 4) midlevel drier air (Fig. 4.23c).

The vertical wind shear averaged across the Caribbean Sea during ASO was the third strongest (12.4 m s⁻¹) in the ASO 1970–2015 record (Fig. 4.22b). The two ASO seasons with larger shear values in this region were the El Niño years of 1972 and 1986. For the June–November hurricane season as a whole, the vertical wind shear over the Caribbean Sea was
<figure><img src="image_163.png" /></figure>

**FIG. 4.22.** 200–850 hPa vertical wind shear during ASO 2015: (a) magnitude (m s⁻¹) and (b) anomalous magnitude and vector. In (a), orange-red shading indicates areas where the vertical wind shear magnitude is ≤10 m s⁻¹. In (b), vector scale is below right of plot. Green box denotes the MDR. Anomalies are departures from the 1981–2010 means.

the strongest in the record (17.3 m s⁻¹), exceeding the previous largest value of 15.4 m s⁻¹ recorded in 1972. On monthly time scales, shear values greater than 8–10 m s⁻¹ are generally considered nonconducive to hurricane formation.

The main activity during the 2015 hurricane season reflected more conducive conditions over the eastern MDR and also over the western subtropical North Atlantic north of the MDR. In portions of the eastern MDR the combination of weak vertical

wind shear (Fig. 4.22a), anomalous rising motion (Fig. 4.23b), and increased midlevel moisture (Fig. 4.23c) contributed to the development of five named storms, including two hurricanes. Over the western subtropical North Atlantic, a similar combination of conditions contributed to the development of five named storms north of the MDR. Two of these storms became hurricanes, with one

becoming the only long-lived major hurricane of the season (Joaquin). Together, these five storms produced about 60% of the total seasonal ACE value.

### b. El Niño impacts

The 200-hPa circulation patterns during ASO 2015 (Fig. 4.24) show that El Niño impacted atmospheric conditions across the tropical Pacific and Atlantic Oceans in both hemispheres, so as to weaken the Atlantic hurricane season and simultaneously strengthen both the central and eastern Pacific hurricane seasons (see section 4e3).

The velocity potential, which is related to the divergent component of the wind, showed an anomaly pattern during ASO that is typical of El Niño (Fig. 4.24a). This pattern featured a core of negative anomalies across the eastern half of the Pacific basin, along with cores of positive anomalies over the western Pacific/Australasia and also over the Amazon basin and MDR. The associated pattern of divergent wind vectors shows a suppressive pattern for Atlantic hurricanes of anomalous upper-level convergence over the Caribbean Sea and central MDR.

The 200-hPa streamfunction pattern also showed a typical El Niño signal, with anticyclonic anomalies across the subtropical Pacific Ocean in both hemispheres flanking the region of enhanced El Niño-related convection (see Fig. 4.5c), along with cyclonic anomalies extending downstream from the Americas (Fig. 4.24b).

Regionally, the streamfunction pattern included an anomalous upper-level subtropical trough that extended across the entire MDR. This feature reflected an amplification of the mean tropical upper tropospheric trough (TUTT; white dashed line) in

<figure><img src="image_164.png" /></figure>

**FIG. 4.23.** ASO 2015: Atmospheric height–longitude sections averaged for 9.5°–21.5°N, of (a) anomalous divergence (× 10⁻⁶ s⁻¹), (b) anomalous vertical velocity (× 10⁻² Pa s⁻¹), and (c) percent of normal specific humidity. Green shading indicates anomalous divergence, anomalous rising motion, and increased moisture, respectively. Brown shading indicates anomalous convergence, anomalous sinking motion, and decreased moisture. Zero lines are drawn on each panel. Anomalies are departures from the 1981–2010 means.
<figure><img src="image_165.png" /></figure>

FIG. 4.24. 200-hPa circulation during ASO 2015: (a) anomalous velocity potential (× 10⁶ m² s⁻¹) and anomalous divergent wind vector (m s⁻¹), and (b) total (contours) and anomalous (shaded) streamfunction (× 10⁶ m² s⁻¹). Divergent wind vector scale in (a) is below right of plot. In (b), white dashed line indicates amplified tropical upper tropospheric trough (TUTT). Anticyclonic anomalies are indicated by positive values (orange/red) in the NH and negative values (blue) in the SH. Cyclonic anomalies are indicated by negative values in the NH and positive values in the SH. Green boxes indicate the Atlantic hurricane MDR. Anomalies are based on the 1981–2010 climatology.

the western MDR and a disappearance of the mean upper-level subtropical ridge normally located over the central and eastern MDR. These conditions contributed anomalous upper-level westerly winds, increased vertical wind shear, and anomalous sinking motion across the MDR (Figs. 4.22, 4.23), the combination of which suppressed the 2015 Atlantic hurricane season.

3) EASTERN NORTH PACIFIC AND CENTRAL NORTH PACIFIC BASINS—M. C. Kruk, C. J. Schreck, and T. Evans

(i) Seasonal activity

The eastern North Pacific (ENP) basin is officially split into two separate regions for the issuance of warnings and advisories by NOAA's National Weather Service. NOAA's National Hurricane Center in Miami, Florida, is responsible for issuing warnings in the eastern part of the basin (ENP) that extends from the Pacific Coast of North America to 140°W, while NOAA's Central Pacific Hurricane Center in Honolulu, Hawaii, is responsible for issuing warnings in the central North Pacific (CNP) region between 140°W and the date line. This section summarizes the TC activity in both warning areas using com-

bined statistics, along with information specifically addressing the observed activity and impacts in the CNP region.

The ENP/CNP hurricane season officially spans from 15 May to 30 November. Hurricane and tropical storm activity in the eastern area of the basin typically peaks in September, while in the central Pacific, TC activity normally reaches its seasonal peak in August (Blake et al. 2009). During the 2015 season, a total of 26 named storms formed in the combined ENP/CNP basin. This total included 16 hurricanes, 11 of which were major hurricanes. The 1981–2010 IBTrACS seasonal averages for the basin are 16.5 named storms, 8.5 hurricanes, and 4.0 major hurricanes (Schreck et al. 2014). The 2015 season's 26 named storms is the highest storm count since the 1992 season. In late August, Hurricanes Kilo, Ignacio, and Jimena reached Category 4 status at the same time (Fig. SB4.1a). This was the first time on record that three Category 4 or stronger TCs were present at the same time in any global TC basin.

Given that 68% of the ENP/CP hurricanes in 2015 reached major hurricane status, it is no surprise that

<figure><img src="image_166.png" /></figure>

FIG. 4.25. Seasonal TC statistics for the full ENP/CNP basin over the period 1970–2015: (a) number of named storms, hurricanes, and major hurricanes, and (b) the ACE index (× 10⁴ kt²) with the 2015 seasonal total highlighted in red. The time series shown includes the corresponding 1981–2010 base period means for each parameter.
the ACE index for 2015 was high as well, with a seasonal value of $251.6 \times 10^4$ kt$^2$ (Fig. 4.25), which is nearly double the 1981–2010 mean of $132.0 \times 10^4$ kt$^2$ (Bell et al. 2000; Bell and Chelliah 2006; Schreck et al. 2014). A record-shattering 16 tropical cyclones developed in, or entered into, the CNP basin during 2015, with a distribution of eight hurricanes (five major), six tropical storms, and two depressions (Fig. 4.25); the previous record season was 1992 with a total of 12 TCs. The long-term 1981–2010 IBTrACS mean is 4.7 storms passing through the CNP per season.

### (ii) Environmental influences on the 2015 season

Figure 4.26 illustrates the background conditions for TC activity in the ENP and CNP during 2015. Consistent with the strong El Niño conditions, the equatorial Pacific was dominated by anomalously warm SST anomalies (Fig. 4.26a). As in 2014, these warm SSTs extended throughout most of the subtropical ENP, which would be exceptionally favorable for TC activity. The ITCZ was also strongly enhanced in association with the warm SSTs, but the strongest enhancement of convection was southward of where TCs form (Fig. 4.26b). Vertical wind shear magnitudes were slightly below their climatological values

(Fig. 4.26c). The vertical wind shear anomalies were generally easterly from 120°E to the date line, which likely contributed to the record season in the CNP. The broad area of warm SSTs, enhanced convection, and moderate shear in 2015 all contributed to favorable conditions that resulted in above-normal hurricane activity.

Figure 4.26d shows a broad area of 850-hPa westerly anomalies near the equator. Similar patterns were seen in 2012–14 (Diamond 2013, 2014, 2015), although these years also featured stronger easterly anomalies to the north. Even on their own, the westerly anomalies produced the region of enhanced cyclonic vorticity within which most of the ENP storms developed. Many of these storms developed where the enhanced vorticity intersected the westerly anomalies. The westerlies could have strengthened easterly wave activity in this region through barotropic energy conversion and wave accumulation (Maloney and Hartmann 2001; Aiyyer and Molinari 2008; Rydbeck and Maloney 2014).

ENP TC activity is strongly influenced by the MJO (Maloney and Hartmann 2001; Aiyyer and Molinari 2008; Slade and Maloney 2013), and recent studies have found a greater role for convectively coupled

<figure><img src="image_167.png" /></figure>

**FIG. 4.26.** May–Nov 2015 anomaly maps of (a) SST (°C, Banzon and Reynolds 2013), (b) OLR (W m⁻², Lee 2014), (c) 200–850-hPa vertical wind shear (m s⁻¹) vector (arrows) and scalar (shading) anomalies, and (d) 850-hPa winds (m s⁻¹, arrows) and zonal wind (shading) anomalies. Anomalies are relative to the annual cycle from 1981–2010, except for SST which is relative to 1982–2010 due to data availability. Hurricane symbols with letters denote where each ENP TC attained tropical storm intensity. Wind data obtained from NCEP–NCAR reanalysis I (Kalnay et al. 1996).

<figure><img src="image_168.png" /></figure>

**FIG. 4.27.** Longitude–time Hovmoller of OLR (W m⁻², Lee 2014) averaged 5°–15°N. Unfiltered anomalies from a daily climatology are shaded. Negative anomalies (green) indicate enhanced convection. Anomalies filtered for Kelvin waves are contoured in blue at –10 W m⁻². Hurricane symbols and letters indicate genesis of ENP TCs.
Kelvin waves in modulating tropical cyclogenesis (Schreck and Molinari 2011; Ventrice et al. 2012a,b; Schreck 2015). Figure 4.27 uses OLR to examine the evolution of convection during the 2015 ENP hurricane season. Following Kiladis et al. (2009), the blue contours identify the Kelvin-filtered anomalies. Easterly waves are also apparent in the unfiltered anomalies (shading) as westward moving features, such as the ones leading up to Hurricanes Norbert and Simon.

During the 2015 ENP hurricane season, intraseasonal variability was dominated by eastward moving signals that straddled the boundaries between Kelvin waves and the MJO (Roundy 2012a,b). Three events are particularly noteworthy: early July, late August, and October. These events were all prolific TC producers, spawning strings of TC genesis from the north Indian Ocean to the North Atlantic. In the ENP/CNP alone, 5-7 TCs developed in association with each of these events, accounting for 18 of the 26 ENP/CNP TCs in 2015.

### (iii) TC impacts

During the 2015 season, only 2 of the season's 26 combined ENP/CNP tropical storms made landfall along the western coast of Mexico or Baja California, while remarkably no storms in the CNP region made landfall in Hawaii. The long-term annual average number of landfalling storms on the western coast of Mexico is 1.8 (Raga et al. 2013).

The first storm to make landfall along the Mexican coastline was Hurricane Blanca (31 May to 9 June), which had maximum sustained winds of 120 kt (61 m s⁻¹) and a minimum central pressure of 936 hPa. Blanca weakened to a tropical storm before making landfall in Baja California and made the earliest landfall in that region on record. Even as the storm was weakening, strong rip currents associated with the storm claimed four lives off the coast of Mexico.

The second landfalling storm of 2015 was Major Hurricane Patricia from 20–24 October, with maximum sustained winds of 174 kt (88 m s⁻¹) and a minimum central pressure of 879 hPa. The barometric pressure and maximum sustained winds, both as measured by hurricane reconnaissance aircraft, are now the lowest on record for pressure and highest on record for winds anywhere in the Western Hemisphere. The hurricane also intensified extraordinarily quickly, dropping 100 hPa in just 24 hours. Fortunately for the major cities and towns in coastal Mexico, Patricia made landfall as a Category 5 storm near Jalisco, Mexico, a relatively rural area, though it still caused a range of impacts. Many trees were

completely defoliated, power outages were common, and torrential rains flooded roads and resulted in landslides. In the town of Tamaulipas, 193 mm of rain was recorded from the storm. Roughly 9000 homes were damaged or destroyed and many agricultural croplands, in particular banana crops, were wiped out by the wind and rain from Patricia.

Despite no direct landfalls, high surf, coastal flooding, flooding rains, and oppressive heat impacted the Hawaiian Islands throughout the 2015 season. The largest surf came from Hurricane Ignacio as it passed to the east and northeast of the main Hawaiian Islands. Ignacio produced large waves and a small storm surge resulting in water and debris on roadways along the Big Island's (also known as Hawaii Island) and Oahu's eastern coastline, causing road and beach park closures. Heavy rain associated with Ignacio fell across the main Hawaiian Islands causing widespread flooding, including in portions of Honolulu. Hurricanes Hilda and Kilo forced deep tropical moisture over the main Hawaiian Islands, which led to significant flooding rains. Impacts of this flooding included a massive sewage spill when the Honolulu drainage system was overwhelmed, flooded homes and businesses, and one flash flood fatality. Hurricane Guillermo had the closest approach to the main Hawaiian Islands and produced coastal flooding as large waves closed roads and beach parks. Portions of the northwest Hawaiian Islands, which are not populated but host research teams, were evacuated due to large waves associated with Hurricane Kilo and Tropical Storm Malia.

### 4) WESTERN NORTH PACIFIC BASIN—S. J. Camargo

#### (i) Introduction

The WNP is unique in that TCs are tracked simultaneously by several agencies in that region. Among these are the United States military's Joint Typhoon Warning Center (JTWC) and the WMO-sanctioned RSMC-Tokyo, Japan Meteorological Agency (JMA). Data from JTWC are used here; best-track dataset for the period 1945–2014 and from the JTWC's preliminary operational data for 2015. The best-track data from the RSMC-Tokyo, Japan Meteorological Agency (JMA), was used in Fig. 4.28b. All other figures were produced using JTWC TC data. Climatology is defined using the period 1981–2010, with the exception of landfall statistics, where 1951–2010 is used.

#### (ii) Seasonal activity

The TC season in the western North Pacific (WNP) in 2015 was above normal by most measures of TC activity considered. According to the JTWC, the 2015
<figure><img src="image_169.png" /><figcaption>FIG. 4.28. (a) Number of tropical storms, typhoons, and super typhoons per year in the western North Pacific for the period 1945–2015 based on the JWTC best-track dataset. (b) Number of TCs (all storms which reach tropical storm intensity or higher) for 1951–76; number of tropical storms, severe tropical storms, and typhoons for 1977–2015 based on the JMA best-track dataset. (c), (d) The number of TCs with tropical storm intensity or higher [named storms (c) and typhoons (d)] per month in 2015 (black line) and the climatological means (blue line). The blue plus signs denote the maximum and minimum monthly historical records, and the red error bars show the climatological interquartile range for each month (in the case of no error bars, the upper and/or lower percentiles coincide with the median). (e), (f) The cumulative number of named storms (e) and super typhoons (f) per month in the WNP in 2015 (black line) and climatology (1971–2010) as box plots [interquartile range: box; median: red line; mean: blue asterisk; values in the top or bottom quartile: blue crosses; high (low) records in the 1945–2015 period: red diamonds (circles)]. [Sources: 1945–2014 JTWC best-track dataset, 2015 JTWC preliminary operational track data for (a), (c), (d), (e), and (f); 1951–2015 RSMC-Tokyo, JMA best-track dataset for panel (b).]</figcaption></figure>

season had 29 TCs form in the basin, with two additional TCs (Halola and Kilo) that formed in the central North Pacific (CNP) then crossed into the WNP. This total of 31 storms active in the basin is above the median of the climatological distribution (the climatological median is 28.5, the 75th percentile is 33). Of these, 28 TCs reached tropical storm intensity or higher (the climatological median is 26, the 75th percentile is 29.5) and 27 of them were named (only one, 12W, was not formally named). There were 3 tropical depressions (TDs; slightly below the climatological median of 3.5), 7 tropical storms (below the 25th percentile

of 8), 21 typhoons (above the 75th percentile of 20), 8 of which became super typhoons (winds ≥ 137 kt; 71 m s⁻<fnref n="1" />; in the top 5th percentile, the 75th percentile is 5). In Fig. 4.28a, the number of tropical storms, typhoons, and super typhoons per year is shown for the period 1945–2015. The number of super typhoons is one of the measures for the intensity of the 2015 season that was well above normal. A high number of super typhoons is a typical feature of El Niño events (Camaro and Sobel 2005). The percentage of typhoons that reached super typhoon status in 2015 (38%) was in the top 10%. Climatologically, only 23% of typhoons reach super typhoon intensity each season.

The JMA total for 2015 was 27 TCs (above JMA's climatological median of 26), including Hurricanes/Typhoons Halola and Kilo. Tropical Storms 12W and Vamco were only considered to be tropical depressions by JMA, and TDs are not included in

the JMA database. Of those 27, nine were greater than tropical storm strength (equal to the 25th percentile for JMA), and 18 were typhoons (top quartile for JMA). The number of TCs (1951–76), or tropical storms, severe tropical storms, and typhoons (1977–2015) according to the JMA are shown in Fig. 4.28b.<fnref n="1" />

<footnote n="1">¹ It is well known that there are systematic differences between the JMA and the JTWC and the datasets, which have been extensively documented in the literature (e.g., Wu et al. 2006; Nakazawa and Hoshino 2009; Song et al. 2010; Ying et al. 2011; Yu et al. 2012; Knapp et al. 2013; Schreck et al. 2014).</footnote>
The number of named storms and typhoons per month in 2015, compared with the climatological distribution, is shown in Figs. 4.28c,d. Super Typhoon Maysak was one of the strongest March storms in the historical record, reaching the same record intensity of Super Typhoon Mitag (February–March 2002) for that month. In May, two super typhoons formed in the WNP, Noul and Dolphin, while only Tropical Storm Kujira was active in June.<fnref n="2" /> July was an active month with six storms present in the WNP, including Super Typhoons Nangka and Soudelor. On 9 July, three storms (Chan-hom, Nangka, and Linfa) were active simultaneously on the WNP, a rare event for July. September and October had five active storms each, including Super Typhoons Champi and Lando in October.

Considering the number of TCs and named storms, the 2015 typhoon season had an active, early season (January–June), with 8 TCs (top quartile), an average peak season (July–October) with 20 TCs (median is 19), and a quiet late season with 3 TCs (bottom quartile), as can be seen in the cumulative number of named of storms per month in 2015 and the climatological distribution (Fig. 4.28e). The occurrence of a high number of super typhoons, a typical feature of El Niño years, was clear in 2015, with 8 super typhoons, 3 of which formed in the early season and 5 during the peak season. The occurrence of three super typhoons in the early season is quite unusual, having only occurred twice previously in the historical record, 2002 and 2004; these were also El Niño years. The cumulative number of super typhoons in 2015 compared with the climatological baseline is shown in Fig. 4.28f. Previously, only one super typhoon had formed in March, in 1961 (while STY Mitag reached is lifetime maximum intensity in March, it formed in February). The 2015 season is the first time in the historical record that two super typhoons formed in May; the previous historical maximum for that month was one. An active July, with two tropical storms, two typhoons, and two super typhoons was followed by a relatively quiet August. The two typhoons (one of them a super typhoon) in August is in the bottom quartile for that month. Two more super typhoons occurred in October, in the top 10% for that month.

Typical of El Niño years, the total ACE in 2015 was high (Camargo and Sobel 2005), reaching the

<figure><img src="image_170.png" /><figcaption>FIG. 4.29. (a) ACE index per year in the western North Pacific for 1945–2015. The solid green line indicates the median for the climatology years 1971–2010, and the dashed lines show the climatological 25th and 75th percentiles. (b) ACE index per month in 2015 (red line) and the median during 1971–2010 (blue line), where the green error bars indicate the 25th and 75th percentiles. In case of no error bars, the upper and/or lower percentiles coincide with the median. The blue “+” signs denote the maximum and minimum values during the period 1945–2014. (Source: 1945–2014 JTWC best-track dataset, 2015 JTWC preliminary operational track data.)</figcaption></figure>

third highest value in the historical record, just below the values in 2004 and 1997, both El Niño years (Fig. 4.29a). The bulk of the seasonal ACE occurred during July and August (Fig. 4.29b), contributing to 21% and 24% of the total ACE, respectively. The ACE for May was the largest in the historical record for that month. Other high monthly values of ACE reached the third (February and August), fourth (July), and fifth (March) highest values in the historical record for those months. In contrast, the June ACE was in the bottom quartile. Eight TCs in 2015 were in the top 10% of the ACE per storm, together contributing a total of 58.5% of the total ACE for the season. With the exception of Typhoon Goni, the other seven TCs with highest ACE in 2015 reached super typhoon status. The top ACE values in 2015 are from TCs Noul, Champ, Dolphin, Maysak, Soudelor, Atsani, Goni, and Nangka, in that order. Additionally, JTWC tracked the peak wind speed for Goni at 115 kt (59 m s⁻¹), but it is noteworthy that 5.5 of its 11 days had winds ≥ 100 kts. The ACEs of each of the top three named storms (Noul, Champ, Dolphin) reached the top 5% and contributed 25.7% of the total ACE in the season. Other storms in the top quartile of ACE per storm in 2015 were Koppu, Dujuan, Halola, In-fa, Kilo, and Chan-hom.

<footnote n="2">² Here, if a storm forms in the two last days of a month, it is counted for the following month if it lasts more than two days in the next month. This was the case in 2015 of typhoons Chan-hom (formed 29 June) and Mujigae (formed 30 September).</footnote>
<figure><img src="image_171.png" /><figcaption>**FIG. 4.30.** (a) SST anomalies, (b) potential intensity anomalies, (c) relative humidity 600-hPa anomalies, (d) genesis potential index anomalies in JASO 2015, and (e) zonal winds in Jul-Oct 2015 (positive contours are shown in solid lines, negative contours in dash dotted lines and the zero contour in a dotted line). [Source: atmospheric variables: NCEP-NCAR reanalysis data (Kalnay et al. 1996); sea surface temperature (Smith et al. 2008).]</figcaption></figure>

There were 174.75 days with TCs in 2015, near the 75th percentile (176.75 days), and 148.75 days with storms that reached tropical storm or higher, in the top 5% (median 111.75 days). From those active days, 90.75 days had typhoons, the third highest value in the historical record, less than only 1997 and 2004. There were 36.75 days with intense typhoons (Categories 3–5), in the top 10% (median 20 days). In 2015, the percentage of days with typhoons and intense typhoons were 51.9% and 21.0%, in the top 1% and 10%, respectively (median 37.9% and 12.2%, respectively). The median lifetime of named storms in 2015 was 8.75 days, slightly above the median of 8 days. The two longest-living storms were Kilo and Halola, which lasted 22 days (20 August to 11 September) and 17.75 days (10–26 July), while crossing both

the central and western North Pacific basins. The longest-living storm that formed in the WNP was Super Typhoon Nangka, which lasted a total of 15.75 days from 3–19 July.

The mean genesis location for storms with genesis in the WNP in 2015 (13.4°N, 147.3°E) was slightly eastward from the climatological mean of WNP storms (13.2°N, 141.6°E, with standard deviations of 1.9° and 5.6°). The mean track position (16.7°N, 144.5°E) was also southeastward relative to the WNP climatological mean (17.3°N, 136.6°E, with standard deviations of 1.4° and 4.7°). Although a southeastward shift is typical of El Niño years (e.g., Chia and Ropelewski 2002; Camargo et al. 2007), this 2015 shift was mostly eastward, with almost no change (mean first position) or a small southward shift (mean track) in the meridional direction.

Figure 4.30 shows the environmental conditions associated with the typhoon activity in 2015. The warm SST anomalies during July–October (JASO; Fig. 4.30a) were large in the eastern and central Pacific, but small in the WNP. These large SST anomalies led to high values of potential intensity (Emanuel

1988 and 1995; Fig. 4.30b) and 600-hPa relative humidity (Fig. 4.30c) anomalies on the eastern and central Pacific in two bands, the first in the equatorial region, the second near Hawaii. The genesis potential index (GPI; Emanuel and Nolan 2004; Camargo et al. 2007) had positive anomalies on the eastern part of the basin and negative on the western side (Fig. 4.30d), typical of El Niño years. The maximum extent of the monsoon reached the date line, as documented via the zonal winds depicted in Fig. 4.30e; this monsoonal extent helps explain the eastward shift of the location of cyclogenesis in the basin for the season.
### (iii) TC impacts

There were 18 storms that made landfall in 2015,<fnref n="3" /> slightly above the 1951–2010 climatological median (17). Of these, three systems made landfall as a TD (median is three), seven storms made landfall as tropical storms (median is six), two struck as Category 1–2 typhoons (median is five). Five storms made landfall as intense typhoons, among the top 10% of the 1951–2010 climatological distribution (the median is two): Dujuan, Goni, Koppu, Melor, and Mujigae.

Many storms led to social and economic impacts in 2015. Typhoon Maysak made landfall in both Chuuk and Yap States of the Federated States of Micronesia in March and was responsible for four deaths and led to significant damage to homes and crops in both states. Typhoon Koppu (known as Lando in the Philippines) caused at least 58 deaths and flooding in the northern Philippines, as well as heavy agricultural and economical damage across the country. The double hit of Typhoon Melor and a tropical depression in December in the Philippines led to floods and at least 45 deaths. The storms with the largest economic impacts in 2015 were Typhoons Soudelor (3.2 billion U.S. dollars) and Chan-hom (1.5 billion U.S. dollars). Soudelor caused severe impacts in the Commonwealth of the Northern Mariana Islands, Taiwan, and eastern China (at least 38 deaths), as well as some lesser impacts in Japan, the Republic of Korea, and the Philippines. Chan-hom also affected many countries in the WNP basin, particularly Japan (Okinawa), Taiwan, China, the Republic of Korea (Jeju Island), and North Korea.

### 5) NORTH INDIAN OCEAN—M. C. Kruk

The north Indian Ocean (NIO) TC season typically extends from April to December, with two peaks in activity: during May–June and again in November, when the monsoon trough is positioned over tropical waters in the basin. TCs in the NIO basin normally develop over the Arabian Sea and Bay of Bengal between 8° and 15°N. These systems are usually short-lived and relatively weak and often quickly move into the Indian subcontinent.

According to the JTWC, the 2015 TC season produced five tropical storms, two of which were major cyclones (Fig. 4.31a). The 1981–2010 IBTrACS

seasonal averages for the basin are 3.9 tropical storms, 1.4 cyclones, and 0.6 major cyclones. The season produced its highest ACE index since 1972 with a value of $30.4 \times 10^4$ kt$^2$, well above the 1981–2010 mean of $12.5 \times 10^4$ kt$^2$ (Fig. 4.31b). Typically, there is enhanced TC activity, especially in the Bay of Bengal, during the cool phase of ENSO (Singh et al. 2000); however, most of this season was characterized by a strong developing El Niño. Four of the five storms developed in the Arabian Sea, and only tropical storm Two (29–30 July) developed in the Bay of Bengal.

There were two noteworthy storms during the season: Cyclones Chapala and Megh. Chapala (28 October–4 November) formed in the Arabian Sea and became a “severe cyclonic storm” (wind $\ge$ 96 kts) on 29 October with maximum sustained winds near 114 kt (58 m s$^{-1}$) and a minimum central pressure of 940 hPa. What made these storms unique was how they tracked westward over the island of Socotra and into the Gulf of Aden—a very unusual track compared to historical records. This resulted in extreme damage across Socotra and the country of Yemen, which rarely experiences tropical cyclone landfalls, much less the tremendous rains associated with them. In fact, these were not only the first tropical cyclones to strike Socotra since 1922, but most interesting was that they did so during the same week. Rainfall data are spotty for the region, but satellite estimates suggest 610 mm of rainfall along the Yemeni coastline, which is 700% of the annual average for the region. Eight people died in Yemen, most by drowning, and several hundred homes and businesses were damaged by flooding. A storm surge of nearly 10 m was observed in the coastal town of Al Mukalla, destroying the city’s seafront and inundating many coastal structures with saltwater.

The second major storm of the season was Very Severe Tropical Cyclone Megh, which occurred from 5 to 10 November in the Arabian Sea, about a week after Cyclone Chapala. The track of Megh was similar to that of Chapala, moving over the island of Socotra and into the Gulf of Aden. Maximum sustained wind speeds reached 95 kt (48 m s$^{-1}$) with a minimum central pressure of 964 hPa. Megh made landfall in Socotra as a Category 3 equivalent storm, causing extensive devastation, resulting in nearly 20 deaths. Additionally, upwards of 3000 homes were either completely destroyed or damaged by the cyclone, which also caused havoc with local fishing operations.

<footnote n="3">³ Landfall is defined when the storm track is over land and the previous location was over ocean. In order not to miss landfall over small islands, first the tracks were interpolated from 6-hourly to 15-minute intervals, before determining if the storm track was over land or ocean using a high-resolution land mask.</footnote>
<figure><img src="image_172.png" /></figure>

FIG. 4.31. Annual TC statistics for the NIO for 1970–2015: (a) number of tropical storms, cyclones, and major cyclones and (b) the estimated annual ACE index (in kt² × 10⁴) for all TCs during which they were at least tropical storm strength or greater intensity (Bell et al. 2000). The 1981–2000 means (green lines) are included in both (a) and (b).

6) SOUTH INDIAN OCEAN—M. C. Kruk and C. Schreck

The south Indian Ocean (SIO) basin extends south of the equator from the African coastline to 90°E, with most cyclones developing south of 10°S. The SIO TC season extends from July to June encompassing equal portions of two calendar years (the 2015 season is comprised of storms from July to December 2014 and from January to June 2015). Peak activity typically occurs during December–April when the ITCZ is located in the Southern Hemisphere and migrating toward the equator. Historically, the vast majority of landfalling cyclones in the SIO affect Madagascar, Mozambique, and the Mascarene Islands, including Mauritius and Réunion Island. The RSMC on La Réunion serves as the official monitoring agency for TC activity within the basin.

The 2014/15 SIO storm season was much above average, with 14 tropical storms, of which 6 were cyclones and 4 were major cyclones (Fig. 4.32a). The 1981–2010 IBTrACS seasonal median averages are eight tropical storms, four cyclones, and one major cyclone. The active season is also reflected in the 2014/15

ACE index of 114.7 × 10⁴ kt², which was above the 1981–2010 average of 91.5 × 10⁴ kt² (Fig. 4.32b). This is the second consecutive year with above-average ACE values for the SIO. As a result of warmer-than-normal SSTs, coupled with generally below-average wind shear (Fig. 4.32), the overall season was above average. Figure 4.33a indicates that the seasonally averaged SST anomalies were above normal, stretching between 10° and 30°S across the width of the southern Indian Ocean. Moreover, Fig. 4.33c demonstrates that deep-layer vertical wind shear was also anomalously low across the same latitude belt, on the order of 1–3 m s⁻¹, below normal for the season. It appears likely that the combination of warm waters and a favorable low-shear environment helped to sustain not only the number of storms this season but also their above-average intensities, as reflected by the ACE index.

During the 2014/15 season, the strongest storm was Cyclone Eunice (27 January–2 February), which reached Category 5 equivalency with peak maximum

<figure><img src="image_173.png" /></figure>

FIG. 4.32. Annual TC statistics for the SIO for 1980–2015: (a) number of tropical storms, cyclones, and major cyclones and (b) the estimated annual ACE index (in kt² × 10⁴) for all TCs during which they were at least tropical storm strength or greater intensity (Bell et al. 2000). The 1981–2000 means (green lines) are included in both (a) and (b). Note that ACE index is estimated due to lack of consistent 6-h sustained winds for each storm.
<figure><img src="image_174.png" /></figure>

**FIG. 4.33.** Jul-Jun 2014/15 anomaly maps of (a) SST (°C, Banzon and Reynolds 2013), (b) OLR (W m⁻², Lee 2014), (c) 200-850-hPa vertical wind shear (m s⁻¹) vector (arrows) and scalar (shading) anomalies, and (d) 850-hPa winds (m s⁻¹ arrows) and zonal wind (shading) anomalies. Anomalies are relative to the annual cycle from 1981-2010, except for SST which is relative to 1982-2010 due to data availability. Letter symbols denote where each SIO TC attained tropical storm intensity. Wind data obtained from NCEP-DOE Reanalysis 2 (Kanamitsu et al. 2002).

sustained winds of 139 kt (70 m s⁻¹) and an estimated minimum central pressure of 900 hPa. The storm formed in the middle of the south Indian Ocean and remained there throughout its lifecycle, generally moving southeast before weakening over cooler waters.

Severe Tropical Storm Chedza (14–22 January 2015) was the deadliest storm of the season. Chedza formed off the southeast coast of Africa and intensified over the Mozambique Channel where it attained maximum sustained winds of 57 kt (29 m s⁻¹) and a minimum central pressure of 975 hPa. On 16 January, Chedza made landfall in western Madagascar,

resulting in extensive flooding following weeks of extreme wet weather across the island. This resulted in widespread mudslides across the region, damaging roads and homes. Nearly 4400 homes were destroyed by the floods and unfortunately the storm resulted in 80 fatalities, most of which were from landslides. The flooding rains inundated over 9000 ha (24 000 acres) of rice fields and displaced 1200 cattle.

In early February, Severe Tropical Storm Fundi developed over the southwestern shores of Madagascar, and by 6 February, the storm had reached maximum sustained winds of 55 kt (28 m s⁻¹) and a minimum central pressure of 985 hPa. Fundi brought 109 mm of rainfall to the southwestern Madagascar town of Tulear and as far inland as Toliara where five people died due to floods. While the storm never made landfall, the damage to water and sewer infrastructure caused by weeks of antecedent heavy rains, including those from Chedza, hindered ongoing relief efforts and increased the number of personal health and hygiene risks.

The final notable storm of the season was Moderate Tropical Storm Haliba (7–10 March), which was a tropical disturbance that formed east of Madagascar and tracked southeast near Réunion Island. During its development stages, Haliba produced heavy rains across eastern Madagascar, affecting over 95 000 people and killing 26. The storm intensified on 8 March with maximum sustained winds of 43 kt (22 m s⁻¹) and a minimum central pressure of 993 hPa. As it moved southeast, exceptional rain was recorded at Ganga Talao, with 135.6 mm falling in just 24 hours. The storm went on to produce 796 mm of rainfall over northern Réunion Island, and while that is a large amount of precipitation, it is not a particularly unusual amount for a tropical system at this latitude.

### 7) AUSTRALIAN BASIN—B. C. Trewin

#### (i) Seasonal activity

The 2014/15 TC season was near normal in the broader Australian basin (areas south of the equator and between 90° and 160°E,<fnref n="4" /> which includes Australian, Papua New Guinea, and Indonesian areas of responsibility), with a slightly below-normal number of cyclones but an above-normal number of severe cyclones. The season produced 9 TCs, near the 1983/84–2010/11 average<fnref n="5" /> of 10.8 and consistent with neutral to warm ENSO conditions. The 1981–2010

<footnote n="4">⁴ The Australian Bureau of Meteorology's warning area overlaps both the southern Indian Ocean and southwest Pacific.</footnote>

<footnote n="5">⁵ Averages are taken from 1983/84 onwards as that is the start of consistent satellite coverage of the region.</footnote>
<figure><img src="image_175.png" /><figcaption>FIG. 4.34. Annual TC statistics for the Australian basin for 1980–2015: (a) number of tropical storms, cyclones, and major cyclones and (b) the estimated annual ACE index (in kt² × 10⁴) for all TCs during which they were at least tropical storm strength or greater intensity (Bell et al. 2000). The 1981–2000 means (green lines) are included in both (a) and (b). Note that ACE index is estimated due to lack of consistent 6-h sustained winds for each storm.</figcaption></figure>

IBTrACS seasonal averages for the basin are 9.9 NSs, 7.5 TCs, and 4.0 major TCs, which compares with the 2014/15 counts of 9, 7, and 5 respectively.

There were four TCs in the eastern sector<fnref n="6" /> of the Australian region during 2014/15, two in the northern sector, and five in the western sector.<fnref n="7" /> Four systems made landfall in Australia as tropical cyclones, one in Western Australia, two in the Northern Territory (one after an initial landfall in Queensland), and a fourth in Queensland (Fig. 4.34). Fig. 4.34 (as noted in section 4e1) is standardized on the Saffir–Simpson scale.

### (ii) Landfalling and other significant TCs

The most intense cyclone of the season was Marcia. TC Marcia formed in the monsoon trough to the northeast of Cairns on 15 February and moved slowly east, reaching tropical cyclone intensity on 18 February. It then intensified rapidly on 19 February, intensifying from Category 1 to Category 5 on the Australian scale (see www.bom.gov.au/cyclone/about /intensity.shtml for details) in the space of 15 hours on 19 February, with maximum 10-minute sustained winds of 110 kt (57 m s⁻¹), as it moved southwest towards the central Queensland coast. Marcia made landfall at near peak intensity in Shoalwater Bay at 2200 hours UTC on 19 February (0800 20 February local time), weakening rapidly as it tracked southward over land and falling below tropical cyclone intensity by 1500 hours UTC on 20 February near Monto. The remnant tropical low moved back out over water off southeast Queensland on 21 February and drifted in the Coral Sea for several days, but did not regain cyclone intensity.

Marcia caused significant wind damage near the landfall point, especially in and around the towns of Yeppoon and Byfield, and less intense but more widespread damage in the major regional centre of Rockhampton, where it was the most significant cyclone impact since at least 1949. Some flooding also occurred in regions south of Rockhampton. Marcia is the southernmost known Category 5 landfall on the east coast of Australia.

Cyclone Lam formed in the monsoon trough south of Papua New Guinea on 12 February. It moved westward as a tropical low, crossing Cape York Peninsula, and then intensified over the northern Gulf of Carpentaria, where it reached cyclone intensity on 16 February. The system intensified steadily as it passed near the Wessel Islands, then turned southwest and reached Category 4 intensity west of Elcho Island early on 19 February, with maximum 10-minute sustained winds of 100 kt (51 m s⁻¹). Lam crossed the coast between Milingimbi and Elcho Island at peak intensity later that day (early on 20 February local time). Lam caused significant wind damage to a number of Aboriginal communities along eastern parts of the northern Arnhem Land coast and nearby islands, with Ramingining on the mainland coast and Galiwin'ku on Elcho Island the most severely impacted. This was the first known instance of two tropical cyclones of Category 4 or greater intensity making landfall in Australia on the same day.

Cyclone Olwyn formed as a tropical low approximately 900 km north of Exmouth on 8 March, moving southward and slowly strengthening. It reached

<footnote n="6">⁶ The western sector covers areas between 90° and 125°E. The eastern sector covers areas east of the eastern Australian coast to 160°E, as well as the eastern half of the Gulf of Carpentaria. The northern sector covers areas from 125°E east to the western half of the Gulf of Carpentaria.</footnote>

<footnote n="7">⁷ Lam and Nathan each passed through both the eastern and northern sectors.</footnote>
tropical cyclone intensity at 0600 hours UTC on 11 March, and continued to intensify as it approached the coast at North West Cape. It reached its peak intensity of Category 3, with 10-minute sustained winds of 75 kt (39 m s⁻¹), while it was located near North West Cape, just west of Exmouth, at 1800 hours UTC on 12 March. Olwyn then moved southward along the west coast with only minimal weakening, passing just to the west of Carnarvon at 0600 hours UTC on 13 March and crossing the coast in the Shark Bay area a few hours later. Reported wind gusts included 97 kt (50 m s⁻¹) at Learmonth and 79 kt (41 m s⁻¹) at Carnarvon. It was the most significant cyclone impact in the Carnarvon area for many years, with major crop losses (including the near-total loss of the banana crop), substantial wind damage to buildings in the town, and power and water outages that lasted for several days. Damage in Exmouth, where cyclones are a more common occurrence, was much less severe.

The fourth landfalling cyclone of the season was Nathan. Nathan formed on 10 March in the Coral Sea, south of the eastern tip of Papua New Guinea. It made an initial approach towards the east coast of Cape York Peninsula as a Category 1 system on 13 March before turning east again but then turned toward the coast again on 18 March and intensified, reaching its peak intensity of Category 4 on 19 March with maximum 10-minute sustained winds of 90 kt (46 m s⁻¹). Nathan made landfall at near peak intensity near Cape Melville around 1800 hours UTC on 19 March and weakened to a tropical low as it crossed Cape York Peninsula. It reintensified as it reached the Gulf of Carpentaria, making a second landfall as a Category 2 system near Nhulunbuy on 22 March. Nathan continued to track along the north coast before turning southwest and weakening. Minor to moderate damage was reported, principally to communities in northeast Arnhem Land and Elcho Island, and on Lizard Island off the Cape York Peninsula coast.

Three other cyclones reached Category 4 intensity during the season, all in the Indian Ocean: Kate in December, Ikola in April, and Quang in late April and early May. Neither Kate nor Ikola approached the mainland coast, although Kate passed near the Cocos (Keeling) Islands on 25 December with some minor flooding reported. Quang weakened as it neared the coast, causing a brief period of storm-force winds and associated minor damage in the Exmouth area before weakening to a tropical low at landfall on 1 May.

A noteworthy out-of-season cyclone was Raquel (a twin of Typhoon Chan-hom in the western North Pacific), which reached Category 1 intensity briefly

from 1800 hours UTC on 30 June<fnref n="8" /> in the western South Pacific northeast of the Solomon Islands. It is the first instance in the satellite era of a July tropical cyclone in the Australian sector of the South Pacific, and the first instance since 1972 in the Southern Hemisphere winter months (June, July, or August).

8) **SOUTHWEST PACIFIC BASIN**—P. R. Pearce, A. M. Lorrey, and H. J. Diamond

#### (i) Seasonal activity

The 2014/15 TC season in the southwest Pacific began in late November. The first storm developed as a tropical depression near Wallis and Futuna, and the season concluded very late with TC Raquel affecting the Solomon Islands in late June-early July. Storm-track data for November 2014–July 2015 was gathered by the Fiji Meteorological Service, Australian Bureau of Meteorology, and New Zealand MetService, Ltd. The southwest Pacific basin as defined by Diamond et al. (2012) (135°E–120°W) had nine tropical cyclones, including five severe tropical cyclones (based on the Australian TC intensity scale). As noted in section 4e1, Fig. 4.35 shows the standardized TC distribution based on the basin spanning the area from 160°E–120°W to avoid overlaps with the Australian basin that could result in double counting of storms. However, it is important to use the above definition of the southwest Pacific basin as that is how annual TC outlooks are produced and disseminated.

The 1981–2010 South Pacific Enhanced Archive of Tropical Cyclones (SPEARTC) indicates a seasonal average of 10.4 named tropical cyclones and 4.3 major tropical cyclones. The ratio of severe TCs relative to the total number of named TCs in 2014/15 was 56%, up from 36% during the previous season. Severe Tropical Cyclones Pam, Lam, and Marcia caused considerable damage and loss of life across the basin. Severe TC Pam, which devastated Vanuatu in March, was the most intense TC in the basin since Zoe in 2002.

#### (ii) Landfall and other significant TCs

The first named TC of the 2014/15 season was reported as a tropical disturbance on January 19 to the northeast of the island of Tahiti in French Polynesia. On 20 January, the disturbance was upgraded to a Category 1 storm and named TC Niko. Over the next two days the system gradually intensified further and

<footnote n="8">⁸ By definition, the formal TC year in the Southern Hemisphere goes from July to June, and any storm forming in June would be considered to be in the previous TC season (in this case the 2014/15 season).</footnote>
<figure><img src="image_176.png" /></figure>

**FIG. 4.35.** Annual TC statistics for the southwest Pacific for 1980–2015: (a) number of tropical storms, cyclones, and major cyclones and (b) the estimated annual ACE index (in kt² × 10⁴) for all TCs during which they were at least tropical storm strength or greater intensity (Bell et al. 2000). The 1981–2000 means (green lines) are included in both (a) and (b). Note that ACE index is estimated due to lack of consistent 6-h sustained winds for each storm.

became a Category 2 TC early on 22 January. On 25 January, Niko completed its extratropical transition. Severe Tropical Cyclone Ola was named on 30 January as a Category 1 storm. Over the next two days, the system intensified and became a Category 3 TC early on 1 February. Ola's peak 10-minute sustained wind speed was 81 kt (42 m s⁻¹) and central pressure was 955 hPa at its lowest.

The third TC of the season was Severe Tropical Cyclone Lam, which began as a tropical disturbance over the Gulf of Carpentaria on 13 February. See section 4e7 for a detailed timeline of Lam's development, landfall, decay, and impacts. Lam was the strongest storm to strike Australia's Northern Territory since TC Monica in 2006. In its formative stages, Lam produced heavy rainfall and flooding in Far North Queensland, and later set daily precipitation records in the Northern Territory. Total damage in the Northern Territory reached at least 64 million U.S. dollars.

The first Category 5 TC of the season was Severe Tropical Cyclone Marcia, which developed in the Coral Sea on 16 February. See section 4e7 for a detailed timeline of Marcia's development, landfall, decay, and impacts. Due to explosive intensification, Marcia became a Category 5 TC early on 20 February, with a peak 10-minute sustained wind speed of 110 kt (57 m s⁻¹) and a minimum central pressure of 930 hPa. The storm wrought extensive damage in Queensland, with losses amounting to 590 million U.S. dollars.

The most significant TC of the 2014/15 season was Severe Tropical Cyclone Pam, which developed on 6 March east of the Solomon Islands. On 9 March, Pam was named as a Category 1 storm. Located in an area of favorable conditions, Pam gradually intensified into a powerful Category 5 severe TC by 12 March. Pam's 10-minute maximum sustained wind speed peaked at 135 kt (69 m s⁻¹), along with a minimum central pressure of 896 hPa, making Pam the most intense TC of the southwest Pacific basin since Zoe in 2002, and the third-most intense storm in the Southern Hemisphere, after Zoe in 2002 and Gafilo in 2004. In addition, Pam had the highest 10-minute sustained wind speed (135 kt; 69 m s⁻¹) recorded of any South Pacific TC, and it is tied with Orson in 1989 and Monica in 2006 for having the strongest winds of any cyclone in the Southern Hemisphere.

Early in Pam's history, a damaging storm surge was felt in Tuvalu, forcing a state of emergency declaration after 45% of the nation's residents were displaced. Torrential rainfall occurred in the south-east Solomon Islands, with trees and crops flattened. In the Santa Cruz Islands, a 24-hour rainfall total of 495 mm was recorded. The storm also struck the remote islands of Anuta and Tikopia on 12 March, causing extensive damage. Approximately 1500 homes were damaged or destroyed, and Tikopia lost 90% of its food crop and fruit trees. Several hours after being named a Category 5 TC on 12 March, the TC began to curve towards the south-southeast, passing by some islands in Vanuatu but making a direct hit on others. Pam caused catastrophic damage to Efate, the main island of Vanuatu where the capital, Port Vila, is located. The islands of Erromango and Tanna were also devastated.

Pam became the single worst natural disaster in the history of Vanuatu, crippling its infrastructure. An estimated 90% of the nation's buildings were impacted by the storm's effects, telecommunications were paralyzed, and water shortages occurred. Communications with many islands were completely
severed during the storm, and four days after the
storm nearly 60% of the nation's inhabited islands
remained cut off from the outside world. According
to UNESCO, 268 million U.S. dollars was required for
total recovery and rehabilitation of Vanuatu.

The storm's winds gradually slowed afterwards as
Pam tracked west of the Tafea Islands. However, the
Fiji Meteorological Service indicated that the TC's
pressure dropped farther to 896 hPa on 14 March.
As Pam travelled farther south, the storm's eye faded
away and Pam's low-level circulation became dis-
placed from its associated thunderstorms, indicating
a rapid weakening phase. Later on 15 March, Pam en-
tered a phase of extratropical transition and affected
northeast New Zealand and the Chatham Islands
with high winds, heavy rain, and rough seas. A state
of emergency was declared in the Chatham Islands.
At least 15 people lost their lives either directly or in-
directly as a result of Pam, with many others injured.

Shortly after Pam was classified, its outer rain-
bands led to the formation of a tropical low east of
Cape York Peninsula, Australia, on 9 March. The
Category 1 TC Nathan was named later that day.
It slowly executed a cyclonic loop over the next few
days, moving across Arnhem Land, Northern Ter-
ritory, and into Western Australia. See section 4e7
for a detailed timeline of Nathan's development,
landfall, decay, and impacts. On 19 March, a tropical
disturbance developed about 375 km to the southwest
of Apia, Samoa. From 20 to 22 March, the resulting
tropical depression produced heavy rain and strong
winds over Fiji's Lau Islands. The system moved
southward as it was classified as a tropical depression.
Early on 22 March, Tropical Cyclone Reuben was
named as a Category 1 storm, located about 220 km
to the south of Nuku'alofa, Tonga. On 23 March, TC
Reuben began extratropical transition.

Tropical Cyclone Solo developed within the mon-
soon trough on 9 April, about 465 km to the south of
Honiara, Solomon Islands. Due to ideal conditions,
the system rapidly developed as it moved southward
and was named a Category 1 storm. Solo peaked
with winds of 54 kt (28 m s⁻¹), making it a Category
2 storm. As Solo turned to the south-southeast from
11 to 12 April, it moved between mainland New Cale-
donia and the Loyalty Islands. Rainfall totals up to
222 mm were recorded in New Caledonia. Significant
damage was reported there, with roads impassable in
places and contaminated drinking water in the mu-
nicipality of Pouébo. Finally, and as noted in section
4e7, Tropical Cyclone Raquel, the last storm of the
2014/15 season, developed as a tropical disturbance
about 718 km to the northeast of Honiara, Solomon

Islands, on 28 June. Over the next couple of days, the
system moved westward into the Australian region,
where it was named a TC. Raquel then moved east-
ward into the South Pacific basin, where it weakened
into a tropical depression. On 4 July, the system
moved south-westward and impacted the Solomon
Islands with high wind gusts and heavy rain.

f. *Tropical cyclone heat potential*—G. J. Goni, J. A. Knaff, and I.-L. Lin

This section summarizes the previously described
tropical cyclone (TC) basins from the standpoint of
tropical cyclone heat potential (TCHP) by focusing on
vertically integrated upper ocean temperature condi-
tions during the season for each basin with respect to
their average values. The TCHP (Goni and Trinanes
2003), defined as the excess heat content contained
in the water column between the sea surface and the
depth of the 26°C isotherm, has been linked to TC
intensity changes (Shay et al. 2000; Goni and Trinanes
2003; Lin et al. 2014). The magnitude of the in situ
TCHP was also identified as impacting the maxi-
mum potential intensity (MPI) through modulating
near-eyewall SSTs (and heat fluxes) occurring when
TC winds mechanically mix the underlying ocean
(Mainelli et al. 2008; Lin et al. 2013). In general, fields
of TCHP show high spatial and temporal variability
associated mainly with oceanic mesoscale features,
interannual variability (e.g., ENSO), or long-term
decadal variability. This variability can be assessed
using satellite altimetry observations (Goni et al.
1996; Lin et al. 2008; Goni and Knaff 2009; Pun et
al. 2013) or using a combination of altimetry and
hydrographic data (Domingues et al. 2015), and has
been used to assess meridional heat transport and
the overturning circulation in the Atlantic Ocean
(Dong et al. 2015).

Globally, the number of tropical cyclones was
10% higher than the previous season; however, in the
eastern North Pacific (ENP), the number increased
significantly from an already high number in 2014.
The 2014 and 2015 ENP hurricane seasons were the
most active in recorded history. In the western North
Pacific (WNP) basin, the 2015 number was similar to
the long-term climatological average. Nevertheless,
it is a ~40% increase as compared to the very low
occurrence in 2014.

The two following factors best illustrate the over-
all global TCHP interannual variability within and
among the basins: 1) the TCHP anomalies (departures
from the 1993–2014 mean values) during the TC sea-
sons in each hemisphere; and 2) differences in TCHP
between the 2015 and 2014 seasons.
Most basins exhibited positive TCHP anomalies (Fig. 4.36), except for the WNP and the western portion of the South Pacific basin. The WNP basin experienced a significant reduction in TCHP of ~20%, which is typical of El Niño years (Zheng et al. 2015). The TCHP in the Gulf of Mexico exhibited large positive anomalies due to the intrusion of the Loop Current and a long residence time of Loop Current rings. Despite these positive anomalies, there were no hurricanes in the Gulf of Mexico (just one tropical storm—Bill).

In the ENP basin, the positive TCHP anomalies were consistent with strong El Niño conditions and a continued positive phase of the Pacific decadal oscillation. The combination of these two phenomena is manifest in positive SST anomalies in that region and extending to the date line. Consequently, the TCHP values in this region during the season were even higher than in previous years (Fig. 4.37). As in 2014, positive TCHP and SST anomalies contributed to elevated tropical cyclone activity, with 16 hurricanes in the ENP during 2015 (Fig. 4.36).

The WNP basin also usually exhibits anomalies related to ENSO variability (Lin et al. 2014; Zheng et al. 2015). From the 1990s to 2013, it experienced a long-term decadal surface and subsurface warming associated with prevalent La Niña-like conditions

(Pun et al. 2013; England et al. 2014). However, with the developing El Niño, the warming had stopped. With 2015 being the strongest El Niño event since 1997, the TCHP over the WNP MDR (4°–19°N, 122°E–180°) fell considerably, as characterized by evident negative anomalies (Figs. 4.36, 4.37; Zheng et al. 2015). With the relaxation of the trade winds during El Niño, warm water returning from the western to the eastern Pacific produced a positive anomaly in the ENP while the WNP exhibited a negative anomaly (Figs. 4.36, 4.37; Zheng et al. 2015).

For each basin, the differences in the TCHP values between the most recent cyclone season and the previous season (Fig. 4.37) indicate that the southwest Indian Ocean, the northwest Indian Ocean, and the western portion of the ENP continued to exhibit an increase in TCHP values. TC activity in terms of Category 4 and 5 storms was correspondingly elevated in these basins. The largest changes with respect to the previous seasons occurred in the ENP and WNP basins, with differences greater in magnitude than 20 kJ cm⁻². Compared to 2014, the percentage of Category 5 TCs in the WNP was quite low, with only two of 15 TCs (13%) attaining Category 5. In contrast, in 2014, though there were only eight TCs during the TC season, there were three Category 5 TCs or 38%. The evident reduction in TCHP over the WNP may have acted as a damper by increasing the ocean cooling effect on restraining TC intensification (Zheng et al. 2015).

The 2015 season was noteworthy for several reasons with respect to intensification of TCs, including Hurricane Patricia, the strongest Western Hemisphere hurricane ever recorded and Hurricane Joaquin, the most intense TC on record to strike the Bahamas. A summary of the ocean conditions for these and some other selected TCs are as follows.

* Typhoon Koppu (Lando; Fig. 4.38a) was a Category 4 TC that formed east of the Commonwealth

<figure><img src="image_177.png" /><figcaption>FIG. 4.37. Differences between the TCHP fields in 2015 and 2014 (kJ cm⁻²).</figcaption></figure>

<figure><img src="image_178.png" /><figcaption>FIG. 4.36. Global anomalies of TCHP corresponding to 2015 computed as described in the text. The boxes indicate the seven regions where TCs occur, from left to right: Southwest Indian, North Indian, West Pacific, Southeast Indian, South Pacific, East Pacific, and North Atlantic (shown as Gulf of Mexico and tropical Atlantic separately). The green lines indicate the trajectories of all tropical cyclones reaching at least Category 1 status (1-min average wind ≥64 kts, 33 m s⁻¹) and above during Nov–Apr 2014/15 in the Southern Hemisphere and Jun–Nov 2015 in the Northern Hemisphere. The numbers above each box correspond to the number of Category 1 and above cyclones that travel within each box. The Gulf of Mexico conditions during Jun–Nov 2015 are shown in the inset in the lower right corner.</figcaption></figure>
of the Northern Mariana Islands (CNMI) on 10 October. This storm reached its peak intensity on 17 October, with sustained winds of over 100 kt (51 m s⁻¹), and 1-minute sustained winds of approximately 130 kt (67 m s⁻¹). Though it eventually reached Category 4, Koppu did not intensify as rapidly as most intense TCs over the WNP (e.g., Haiyan in 2013; Lin et al. 2014). The negative TCHP may have slowed down its intensification rate (Zheng et al. 2015). However, since the TCHP over the WNP is among the highest globally in a climatological sense, even with reduced TCHP, it is possible for intense TCs to develop (Zheng

et al. 2015). During El Niño years, TCs tend to form towards the southeast and closer to the date line. As a result, a TC can travel a longer distance across the ocean during intensification, through over reduced TCHP conditions (Zheng et al. 2015). Koppu made landfall in the north of the Philippines and quickly weakened due to its interaction with land. The cooling of SSTs caused by this typhoon was more evident west of 130°E, in both the surface and upper layer.

* Typhoon Chan-hom (Falcon; Fig. 4.38b) was characterized by its large size and long duration over the ocean. Chan-hom developed on 29 June from an atmospheric system that also developed TC Raquel in the southwest Pacific Ocean. Chan-hom's sustained winds reached values up to 89 kt. (46 m s⁻¹). This typhoon continuously intensified while traveling over warm waters with moderate (> 80 kJ cm⁻²) TCHP values. A cooling of the surface (–2°C) and the upper layer (40 kJ cm⁻²) under the track of this typhoon occurred when its intensity reached Category 1.

* Category 5 typhoon Soudelor (Hanna; Fig. 4.38c) was the second-strongest tropical cyclone to develop in the Northern Hemisphere in 2015. Though not as intense as Haiyan in 2013 (Lin et al. 2014), it was as intense as Vongfong in 2014 (Goni et al. 2015). This is in spite of the reduced TCHP in the WNP, associated with the 2015 El Niño year. This drop from the preexisting extremely high TCHP condition (Pun et al. 2013; Lin et al. 2014) was still able to provide favorable conditions for intensification. Soudelor intensified over a very favorable TCHP field of over 120 kJ cm⁻², which may have contributed to its ability to attain wind

<figure><img src="image_179.png" /><figcaption>FIG. 4.38. (left) Oceanic TCHP and surface cooling given by the difference between post- and pre-storm values of (center) tropical cyclone heat potential and (right) sea surface temperature, for 2015 Tropical Cyclones (a) Koppu (b) Chan-hom, (c) Soudelor, (d) Patricia, and (e) Joaquin. The TCHP values correspond to two days before each storm reached its maximum intensity value.</figcaption></figure>
speeds of 116 kt (60 m s⁻¹) on 3 August. Its high translation speed (~5–8 m s⁻¹) during intensification helped to reduce the ocean cooling during the TC life cycle, thus supplying more air–sea flux for intensification (Lin et al. 2009). This was the most intense storm to strike Saipan, CNMI, in the last 25 years. Cooling of the surface waters of over 5°C was observed under the full track of this typhoon, while cooling of the upper ocean layers (TCHP) was restricted to between 135° and 150°E.

* Hurricane Patricia (Fig. 4.38d) was the most intense tropical cyclone ever recorded in the Western Hemisphere in terms of barometric pressure, and the strongest ever recorded globally in terms of maximum sustained winds of 185 kt (95 m s⁻¹; Kimberlain et al. 2016). Patricia started as a tropical depression off the coast of Mexico on 20 October, and developed into a Category 5 storm within 66 hours. During its rapid intensification the TCHP values were higher than 80 kJ cm⁻².

* Hurricane Joaquin (Fig. 4.38e) was an intense TC that evolved near the Bahamas on 26 September and was one of the strongest storms to affect these islands. Joaquin underwent rapid intensification and became a Category 3 hurricane on 1 October, exhibiting maximum sustained winds of approximately 135 kt (69 m s⁻¹) on 3 October (Berg 2016). The upper ocean conditions were supportive of Atlantic tropical cyclone intensification (Maineli et al. 2008). This rapid intensification occurred during a short travel time over very high TCHP values (> 100 kJ cm⁻²). The cooling of the ocean waters was evident both in the upper layer and at the surface.

### g. Atlantic warm pool—C. Wang

The description and characteristics of the Atlantic warm pool (AWP), including its multidecadal variability, have been previously described (e.g., Wang 2015). Figure 4.39 shows the extension of the AWP time series through 2015 varying on different time scales.

While the AWP in 2015 showed similarities to 2014, there were some key differences. As in 2014, the AWP in 2015 was larger than its climatological mean each month, with the largest AWP occurring in September (Fig. 4.40a). However, the AWP in 2015 started in February and lasted through December, longer than its normal period of May to October, and had an anomalously larger value in November. After starting in February, the AWP appeared in the Gulf of Mexico in June (Fig. 4.40b). By July and August, the AWP was well developed in the Gulf of Mexico and Caribbean Sea and reached eastward into the western

<figure><img src="image_180.png" /><figcaption>**Fig. 4.39.** The AWP index for 1900–2015. The AWP area index (%) is calculated as the anomalies of the area of SST warmer than 28.5°C divided by the climatological Jun–Nov AWP area. Shown are the (a) total, (b) detrended (removing the linear trend), (c) multidecadal, and (d) interannual area anomalies. The multidecadal variability is obtained by performing a 7-year running mean to the detrended AWP index. The interannual variability is calculated by subtracting the multidecadal variability from the detrended AWP index. The black straight line in (a) is the linear trend that is fitted to the total area anomaly. The extended reconstructed SST dataset is used.</figcaption></figure>

tropical North Atlantic (Figs. 4.40c,d). By September, the AWP had further expanded southeastward and the 28.5°C isotherm covered nearly the entire tropical North Atlantic (Fig. 4.40e). The AWP started to decay after October when the waters in the Gulf of Mexico began cooling (Fig. 4.40f). In November, the 28.5°C isotherm still covered the Caribbean Sea and part of the western North Atlantic Ocean (Fig. 4.40g).

The effect of the AWP on TC steering flows and tracks has been previously documented (Wang 2015). The TC steering flow anomalies were consistent with those of other observed large AWP years (Wang et al. 2011). The TC steering flow anomalies during the North Atlantic TC season are depicted in Fig. 4.41. With the exception of June and November, the TC steering flow anomalies were unfavorable for TCs making landfall in the United States. From July to October, the TC steering flow anomalies were mostly southward or eastward in the western tropical North Atlantic, and northward and northeastward in the open ocean of the North Atlantic. This distribution
<figure><img src="image_181.png" /><figcaption>**FIG. 4.40.** (a) The monthly AWP area in 2015 (10¹² m²; blue) and the climatological AWP area (red) and the spatial distributions of the 2015 AWP in (b) Jun, (c) Jul, (d) Aug, (e) Sep, (f) Oct, and (g) Nov. The AWP is defined by SST larger than 28.5°C. The black thick contours in (b)–(g) are the climatological AWP based on the data from 1971 to 2000 and the white thick contours are the 2015 28.5°C SST values. The extended reconstructed SST dataset is used.</figcaption></figure>

of these anomalies was consistent with the fact that for all TCs that formed in the Atlantic MDR, none made landfall in the United States. For the two land-falling North Atlantic TCs (Ana and Bill), neither one formed in the Atlantic MDR (see section 4e2).

### h. Indian Ocean dipole—J.-J. Luo

Year-to-year climate variability in the tropical Indian Ocean (IO) is largely driven by local ocean-atmosphere interactions and ENSO (e.g., Luo et al. 2010). Among the former, the Indian Ocean dipole (IOD) represents one major internal climate mode in the IO, which may exert significant climate impacts on countries surrounding the IO. The IOD often starts to grow in boreal summer, peaks in September–November, and deteriorates rapidly in December in association with the reversal of monsoonal winds along the west coast of Sumatra. During late boreal summer to fall 2015, a positive IOD occurred for the

<figure><img src="image_182.png" /><figcaption>**FIG. 4.41.** The TC steering flow anomalies (10³ hPa m s⁻¹) in the 2015 Atlantic TC season of (a) Jun, (b) Jul, (c) Aug, (d) Sep, (e) Oct, and (f) Nov. The TC steering flow anomalies are calculated by the vertically averaged wind anomalies from 850 hPa to 200 hPa relative to the 1971–2000 climatology. The NCEP–NCAR reanalysis field (Kalnay et al. 1996) is used.</figcaption></figure>

first time since the last positive IOD event in 2012 (Luo 2013). The positive IOD in 2015 is the 10th such event since 1981.

SSTs and upper ocean (0–300 m) mean temperature in most of the tropical IO were warmer than normal throughout the year (Figs. 4.42, 4.43), in association with the influence of a strong El Niño in the Pacific and a pronounced long-term warming trend of the IO SST in recent decades (e.g., Luo et al. 2012). During December–February 2014/15, surface westerly anomalies occurred across the equatorial IO, corresponding to the dry–wet contrast between the IO and the Maritime Continent–western Pacific (Figs. 4.42a, 4.43a). This is consistent with a central Pacific–El Niño condition. The westerly anomalies across the equatorial IO shallow (deepen) the oceanic thermocline in the western (eastern) IO, which helps induce cold (warm) SST anomalies in the equatorial western (eastern) IO (Figs. 4.42a, 4.43a). From March to November, in accordance with a rapid development of a strong El Niño in the Pacific (see Fig. 4.3), rainfall over the Indonesia–western Pacific decreased due to a weakened Walker Cell. Meanwhile, SSTs in the western IO increased quickly and reached ~0.8°C greater than the climatology (1982–2014) during September–November (Figs. 4.42, 4.44). Correspondingly, easterly anomalies developed in the IO beginning in boreal spring (Figs. 4.43, 4.44). Weak anomalous southeasterlies initially appeared along the west coast
of Sumatra in May and then grew gradually with a westward expansion. This might have been largely driven by the surface divergence over the Indonesia-western Pacific due to the weakened Walker Cell. During June-August, considerable dry anomalies appeared west of Sumatra, consistent with a positive IOD index and easterly anomalies in the eastern IO (Figs. 4.42c, 4.43c, 4.44c). The positive IOD kept growing and reached a peak in September-November (Figs. 4.42d, 4.43d). In December, the eastern IO SST anomaly increased sharply, which reduced the IOD (Figs. 4.44a-d).

There is no clear evidence that supports local processes generating the positive IOD in 2015. Rather, it appears that the development of a strong El Niño in the Pacific played an important, if remote, role. The 2015 IOD shows distinct features compared to previous

events (Fig. 4.44). Compared to the 1997 IOD that occurred with a similarly strong El Niño, the 2015 IOD was much weaker. Although the western IO SST in 2015 is warmer than that in 1997, the eastern IO SST anomalies in 2015 are positive, in stark contrast to the strong cold anomalies in 1997. Indeed, both the western and eastern IO SSTs in 2015 are warmer than those in previous nine positive IOD events, in association with warmer general conditions across the tropical IO basin (Fig. 4.44f). While the 10 positive IOD occurred with either El Niño or La Niña, the probability of the occurrence of positive IOD during El Niño is about twice that during La Niña (Figs. 4.44c, e).

In summary, the positive IOD event in 2015 was likely driven by the development of a strong El Niño in the Pacific. However, the intensity of this IOD is much weaker than that in 1997, mainly because of the absence of cold SST anomalies in the eastern IO

<figure><img src="image_183.png" /></figure>

**FIG. 4.42.** SST (°C, colored scale) and precipitation (contour interval: 0, ±0.5, ±1, ±2, ±3, ±4, and ±5 mm day⁻¹; solid/dashed lines denote positive/negative values, and thick solid lines indicate zero contour anomalies during (a) Dec–Feb 2014/15, (b) Mar–May 2015, (c) Jun–Aug 2015, and (d) Sep–Nov 2015. The anomalies were calculated relative to the climatology over the period 1982–2014. These are based on the NCEP optimum interpolation SST (Reynolds et al. 2002) and monthly GPCP precipitation analysis (available at http://precip.gsfc.nasa.gov/).

<figure><img src="image_184.png" /></figure>

**FIG. 4.43.** Upper 300-m mean ocean temperature (°C, colored scale) and surface wind (m s⁻¹) anomalies during (a) Dec–Feb 2014/15, (b) Mar–May 2015, (c) Jun–Aug 2015, and (d) Sep–Nov 2015. These are based on the NCEP ocean reanalysis (available at www.cpc.ncep.noaa.gov/products/GODAS/) and JRA-55 atmospheric reanalysis (Ebita et al. 2011).
in 2015. It appears that the multidecadal basinwide warming trend of the tropical IO SST (partly due to increasing radiative forcing) might have affected and will continue to affect the evolution of IOD.

<figure><img src="image_185.png" /></figure>

FIG. 4.44. Monthly SST anomalies in the (a) western IO (IODW, 50°–70°E, 10°S–10°N) and (b) eastern IO (IODE, 90°–110°E, 10°S–0°) and (c) the IOD index (measured by the SST anomaly difference between the IODW and the IODE) during 10 positive IOD events since 1981. (d) As in (c) but for the surface zonal wind anomaly in the central equatorial IO (70°–90°E, 5°S–5°N). (e)–(f) As in (a)–(b), but for the monthly SST anomalies in the Niño-3.4 region (190°–240°E, 5°S–5°N) and the tropical IO basin (40°–120°E, 20°S–20°N).
The 2015 Northern Hemisphere tropical cyclone (TC) season was one for the record books. The Atlantic basin hurricane season recorded below-average activity with an ACE of 60 × 10⁴ kt². The 1981–2010 median ACE for the Atlantic is 92, and NOAA defines any season with less than 66 ACE units as a below-average season. The remainder of the Northern Hemisphere basins (the eastern North Pacific, the western North Pacific, and the north Indian) were conversely quite active. Some of the most notable records set during this record-breaking year for these three basins individually, then collectively, for the Northern Hemisphere are documented. Table SB4.1 summarizes the statistics by basin and denotes records achieved in 2015. All statistics described are based on operational advisories from the National Hurricane Center, Central Pacific Hurricane Center, and Joint Typhoon Warning Center, and are then compared with archived best-track data compiled by those agencies. The data in these basins date back to 1851 in the North Atlantic, 1949 in the northeast Pacific, 1945 in the northwest Pacific, and 1972 in the north Indian; however, it should be noted that the data quality among these datasets is not uniform prior to about 1985 (Chu et al. 2002).

### Eastern North Pacific

The eastern North Pacific (to 180°) season in 2015 tied its record for number of hurricanes and set a record for major hurricanes. ACE for the eastern North Pacific in 2015 was also quite high, trailing only 1992. Two of the most notable storm events of 2015 occurred in this basin. In late August, Hurricanes Kilo, Ignacio, and Jimena

<figure><img src="image_186.png" /></figure>

<figure><img src="image_187.png" /><figcaption>FIG. SB4.1. Satellite imagery showing (a) from left to right: Kilo, Ignacio, and Jimena at Category 4 intensity on 30 Aug 2015 and (b) Hurricane Patricia near time of peak intensity on 23 Oct 2015.</figcaption></figure>

<table><caption>TABLE SB4.1. Northern Hemisphere TC summary statistics by basin.</caption><thead><tr><td>Basin</td><td>Named Storms</td><td>Hurricanes</td><td>Major Hurricanes</td><td>Cat. 4-5 Hurricanes</td><td>ACE</td></tr></thead><tbody><tr><td>North Atlantic</td><td>11 (12)</td><td>4 (6.5)</td><td>2 (2)</td><td>1 (1)</td><td>60 (92)</td></tr><tr><td>Eastern North Pacific</td><td>26 (17)</td><td>16* (9)</td><td>11 (4)</td><td>10 (2)</td><td>288 (119)</td></tr><tr><td>Western North Pacific</td><td>26 (26.5)</td><td>20 (17)</td><td>16 (9)</td><td>14 (7)</td><td>479 (305)</td></tr><tr><td>North Indian</td><td>5 (5)</td><td>2 (1)</td><td>2* (1)</td><td>1* (0)</td><td>36 (16)</td></tr><tr><td>Northern Hemisphere</td><td>68 (59)</td><td>42 (33.5)</td><td>31 (16.5)</td><td>26 (11)</td><td>865 (545)</td></tr></tbody></table>

The 1981–2010 median values are in parentheses. Record high values are highlighted in bold-faced font, while second highest values are italicized. An asterisk by a record means that several years tied for that record. A TC is counted based in the basin where the storm first achieved a specific intensity. Northern Hemisphere ACE does not exactly add as sum of four individual basins due to rounding. In the case of Halola, it was counted as a named storm in the northeast Pacific and a hurricane in the northwest Pacific. Hurricanes are used colloquially to refer to all hurricane-strength TCs in the Northern Hemisphere.
CONT. SIDEBAR 4.1: **THE RECORD-SHATTERING 2015 NORTHERN HEMI-SPHERE TROPICAL CYCLONE SEASON**—P. J. KLOTZBACH AND C. T. FOGARTY

reached Category 4 status at the same time (Fig. SB4.1a). This was the first time on record that three Category 4 or stronger TCs were present at the same time in any global TC basin. On October 23, Hurricane Patricia became the strongest hurricane on record in the Western Hemisphere when an aircraft reconnaissance plane estimated 1-minute maximum sustained winds of 175 knots (Fig. SB4.1b). The central North Pacific (180°–140°W) portion of the eastern North Pacific was extraordinarily active (Collins et al. 2016, manuscript submitted to Geophys. Res. Lett.). Eight named storms formed in this portion of the basin, shattering the old record of four named storms set in 1982, and an additional eight storms spent some portion of their life in the basin. The central Pacific alone also accounted for an ACE level of $127 \times 10^4$ kt$^2$, breaking the record of $107 \times 10^4$ kt$^2$ set in 1994. The $127 \times 10^4$ kt$^2$ ACE level is especially impressive given that the 1981–2010 median for the full northeast Pacific basin was $119 \times 10^4$ kt$^2$.

**Western North Pacific**

The western North Pacific was quite active from an ACE perspective, generating the third highest ACE value of all time for the basin. In addition, 16 major (Category 3–5) typhoons occurred, breaking the record of $15 \times 10^4$ kt$^2$ set in 1958 and tied in 1965, both well before the era of reliable best track data (Chu 2002). As is typically the case in strong El Niño seasons, while ACE increases significantly, the number of storm formations changes little (Camargo and Sobel 2005). The western North Pacific was extraordinarily active during the month of May. Two typhoons, Noul and Dolphin, reached Category 5 status (> 137 knots) in May. This was the first time on record that two typhoons reached Category 5 status in May.

**North Indian**

The north Indian Ocean also experienced well above-average ACE in 2015, with $30 \times 10^4$ kt$^2$ generated, which is over twice the median value for the basin. Cyclones Chapala and Megh were significant storms that resulted in serious impacts on the island of Socotra. This was the first time in recorded history that two cyclone-strength TCs made landfall on Socotra in the same year (see section 4e5). Chapala also became the first cyclone-strength storm to make landfall in Yemen in recorded history, and just a week later Cyclone Megh also made landfall in Yemen.

**Northern Hemisphere**

The Northern Hemisphere shattered several records for intense TCs. A total of 31 major (Category 3–5) TCs occurred in 2015, breaking the old record of 23 major hurricanes set in 2004. The previous record of 18 Category 4–5 TCs, set in 1997 and tied in 2004, was also eclipsed in 2015, with 26 Category 4–5 TCs occurring. In addition, 62% of all hurricane-strength TCs that formed in 2015 reached Category 4–5 intensity, breaking the old record of 50% that happened four different times (1994, 1997, 2002, and 2011). As noted in Klotzbach and Landsea (2015), significant underestimates in Category 4–5 TCs are likely prior to ~1990. In terms of integrated metrics, Northern Hemisphere ACE was at its second highest level on record. The 2015 season generated $821 \times 10^4$ kt$^2$, trailing only the $876 \times 10^4$ kt$^2$ value generated in 1992. In summary, the Northern Hemisphere TC season was extraordinarily active, due in large part to the strong El Niño conditions that prevailed throughout the year.
TCs are formally defined by NOAA's National Hurricane Center as "a warm-core nonfrontal synoptic-scale cyclone, originating over tropical or subtropical waters, with organized deep convection and a closed surface wind circulation about a well-defined center." However, closely related to TCs are subtropical cyclones, which derive a significant proportion of their energy from baroclinic sources and are generally cold core in the upper troposphere and are often associated with an upper-level low or trough. Additionally, maximum winds and convection are often at a distance generally more than 110 km from the center (see www.nhc.noaa.gov/aboutgloss.shtml#s).

Until recently, TCs were believed not to form in the Mediterranean Sea, the Atlantic basin south of the equator, and the far eastern Pacific basin south of the equator (Gray 1968). Here we describe a subtropical storm identified in the southeastern Pacific basin off the Chilean coast farther east than any in the historic record as documented in either the IBTrACS (Knapp et al. 2010) or SPEArTC (Diamond et al. 2012) datasets and outside of the responsibility of any global RSMC.

The formation of Hurricane Catarina off the coast of Brazil in 2004 (McTaggart et al. 2006; Gozzo et al. 2014) demonstrated that TCs can occasionally form in previously unsuspected areas such as the South Atlantic. The existence of possible TCs in the Mediterranean Sea, which are overwhelmingly subtropical in nature, has also generated interest in recent years (Moscatello et al. 2008; Pantillon et al. 2013; and Cavicchia et al. 2014).

In late April, Earth Observing (EOSDIS) satellite imagery showed a cyclonic circulation in the southeastern Pacific basin that appeared to meet the definition of a subtropical cyclone. Originating from a stalled frontal zone near 25°S, 102°W the storm developed into a clearly nonfrontal system with the majority of convection initially to the southeast of low-level circulation. This cyclonic storm was approximately 30° east of any previously recorded TC. ASCAT satellite derived winds were as much as 50 kt. The system was visible on imagery during the period from 30 April to 5 May (Fig. SB4.2).

The NCEP-NCAR reanalysis data (Kalnay et al. 1996) for 29 April at 1200 hours UTC showed a broad low pressure area located near 25°S, 102°W. As the system developed, it drifted toward the southeast before stalling near 28°S, 100°W for approximately 36 hours. From 2 to 3 May, the system moved west then northwest, dissipating on 6 May near 18°S, 110°W.

<figure><img src="image_188.png" /><figcaption>FIG. SB4.2. Aqua satellite image of sub-TC Katie on 1 May 2015 at 2055 UTC.</figcaption></figure>

The location of the system was in a “no man’s land” of sorts as it is not within the forecast or warning area of responsibility of any RSMC. It was too far to the east of the Nadi RSMC’s area of responsibility, and while there is no formal RSMC covering the area east of 120°W, the system will be incorporated into the SPEArTC dataset under the informal name of Katie. Therefore, while the system will not necessarily be formally picked up by the IBTrACS dataset, which reflects RSMC tracks on behalf of the World Meteorological Organization’s TC Programme, the more research-oriented SPEArTC dataset, which focuses on the southwest Pacific, will include this storm in its listing of 2014/15 storms with appropriate notation of its unique subtropical nature. An upper air analysis using NCAR data shows a trough over the system at the 300-hPa surface and a possible warm core at the 850-hPa surface. This is consistent with the NHC definition of a subtropical system.

From 29 April to 4 May, the Chilean Navy Weather Service included the system in their high seas warnings, reporting an
estimated minimum central pressure of 993 hPa on 1 May at 0600 hours UTC. Using the method described by Knaff and Zehr (2007), this corresponds to a maximum sustained wind speed of approximately 40 kt (21 m s⁻¹). Several RapidScat passes (W. L. Poulsen 2015, personal communication) from 1 to 2 May showed winds in excess of 40 kt (21 m s⁻¹), with some returns of 50 kt (26 m s⁻¹). These peak winds were at some distance from the center of circulation, which is also consistent with a subtropical nature of the system (Fig. SB4.3). Phase diagrams (Hart 2003) using relative 900-600-hPa thickness symmetry and thermal winds for the system indicated that this system was warm core and symmetrical in early May (Fig. SB4.4), and the conditions described also support the identification of the system as either a tropical or subtropical cyclone.

Satellite imagery, phase diagrams, and surface analysis show that “Katie” was a tropical system located far from any previously identified TC listed in IBTrACS for the South Pacific basin. Although it may briefly have exhibited TC characteristics, and while the imagery is consistent with a subtropical cyclone, the system should be further examined for inclusion as a Southern Hemisphere tropical cyclone in the formal global archives.

<figure><img src="image_189.png" /><figcaption>FIG. SB4.3. RapidScat Wind retrieval for 2 May 2015 starting at 1038 UTC (m s⁻¹).</figcaption></figure>

<figure><img src="image_190.png" /><figcaption>FIG. SB4.4. System phase diagram for 3 May 2015 at 0600 UTC.</figcaption></figure>
5. THE ARCTIC—J. Richter-Menge and J. Mathis, Eds.

a. *Introduction*—J. Richter-Menge and J. Mathis

The Arctic chapter describes a range of observa-
tions of essential climate variables (ECV; Bojinski
et al. 2014) and other physical environmental pa-
rameters, encompassing the atmosphere, ocean, and
land in the Arctic and subarctic. As in previous years,
the 2015 report illustrates that although there are
regional and seasonal variations in the state of the
Arctic environmental system, it continues to respond
to long-term upward trends in air temperature. Over
Arctic landmasses, the rate of warming is more than
twice that of low and midlatitude regions.

In 2015, the average annual surface air tempera-
ture anomaly over land north of 60°N was +1.2°C,
relative to the 1981-2010 base period. This ties the
recent years of 2007 and 2011 for the highest value
in the temperature record starting in 1900 and repre-
sents a 2.8°C increase since the beginning of the 20th
century. Evidence of strong connections between the
Arctic and midlatitude regions occurred from 1) No-
vember 2014 through June 2015, when anomalously
warm conditions in the Pacific Arctic region were
associated with southerly air flow into and across
Alaska, and 2) February through April 2015, when
anomalously cold conditions from northeastern
North America to southwest Greenland were associ-
ated with northerly air flow.

There is clear evidence of linkages among the
various components of the Arctic system. Under
the influence of persistent warming temperatures,
the Arctic sea ice cover is diminishing in extent and
thickness. The lowest maximum sea ice extent in the
37-year satellite record occurred on 25 February 2015,
at 7% below the average for 1981–2010. This date of
occurrence was the second earliest in the record and
15 days earlier than the average date of 12 March.
Minimum sea ice extent in September 2015 was 29%
less than the 1981–2010 average and the fourth lowest
value in the satellite record. In February and March,
the oldest ice (>4 years) and first-year ice made up 3%
and 70%, respectively, of the pack ice compared to
values of 20% and 35%, respectively, in 1985.

As the extent of sea ice retreat in the summer
continues to increase, allowing previously ice-covered
water to be exposed to more solar radiation, sea sur-
face temperature (SST) and upper ocean temperatures
are increasing throughout much of the Arctic Ocean
and adjacent seas. The Chukchi Sea northwest of
Alaska and eastern Baffin Bay off west Greenland
have the largest warming trends: ~0.5°C per decade
since 1982. In 2015, SST was up to 4°C higher than

the 1982–2010 average in eastern Baffin Bay and the
Kara Sea north of central Eurasia.

The impact of sea ice retreat and warming ocean
temperatures on the ecosystem is well demonstrated
by changes in the behavior of walrus and fish com-
munities. In the Pacific Arctic, vast walrus herds are
now hauling out on land rather than on sea ice as
the ice retreats far to the north over the deep Arctic
Ocean, raising concern about the energetics of fe-
males and young animals. Warming trends in water
temperatures in the Barents Sea, which started in the
late 1990s, are linked to a community-wide shift in
fish populations: boreal communities are now found
farther north and the local Arctic (cold-water affinity)
community has been almost pushed out of the area.

Ice on land, including glaciers and ice caps outside
Greenland (Arctic Canada, Alaska, Northern Scan-
dinavia, Svalbard, and Iceland) and the Greenland
Ice Sheet itself, continues to lose mass. In 2015, the
Greenland Ice Sheet, with the capacity to contribute
~7 m to sea level rise, experienced melting over more
than 50% of the ice sheet for the first time since
the exceptional melting of 2012 and exceeded the
1981–2010 average on 50 of 92 days (54%). Reflecting
the pattern of ice melt, which is driven by the pattern
of surface air temperature anomalies, the average
albedo in 2015 was below the 2000–09 average in
northwest Greenland and above average in southwest
Greenland.

Despite above-average snow cover extent (SCE) in
April, Arctic SCE anomalies in May and June 2015
were below the 1981–2010 average, a continuation
of consistent early spring snowmelt during the past
decade. June SCE in both the North American and
Eurasian sectors of the Arctic was the second lowest
in the satellite record (1967–present). The rate of June
SCE reductions since 1979 (the start of the passive
microwave satellite era) is 18% per decade.

In 2014, the most recent year with complete data,
the combined discharge of the eight largest Arctic
rivers [2487 km³ from Eurasia (Pechora, S. Dvina,
Ob', Yenisey, Lena, and Kolyma) and North America
(Yukon and Mackenzie)] was 10% greater than the
average discharge during 1980–89. Since 1976, dis-
charge of the Eurasian and North American rivers
has increased 3.1% and 2.6% per decade, respectively.

Regional variability in permafrost temperature records indicates more substantial permafrost warming since 2000 in higher latitudes than in the subarctic, in agreement with the pattern of average air temperature anomalies. In 2015, record high temperatures at 20-m depth were measured at all permafrost observatories on the North Slope of Alaska, increasing between
0.21°C and 0.66°C decade⁻¹ since 2000. Permafrost warming in northernmost Alaska exemplifies what is happening to permafrost on a pan-Arctic scale.

Arctic cloud cover variability significantly influences ultraviolet index (UVI) anomaly patterns. Reflecting this influence, monthly average noontime UVIs for March 2015 were below the 2005–14 means in a belt stretching from the Greenland Sea and Iceland in the east to Hudson Bay and the Canadian Arctic Archipelago in the west. This region roughly agrees with the regions where the atmospheric total ozone columns (TOC) were abnormally high in March 2015. At the pan-Arctic scale, the minimum TOC in March was 389 Dobson Units (DU), 17 DU (5%) above the average of 372 DU for the period 1979–2014 and 23 DU (6%) above the average for the past decade (2000–14).

This overview alone refers to a number of different periods of observation for which average values and departures from average (anomalies) have been calculated. For the World Meteorological Organization, and national agencies such as NOAA, 1981–2010 is the current standard reference period for calculating climate normals (averages) and anomalies. In this report, the current standard reference period is used when possible, but it cannot be used for all the variables described; some organizations choose not to use 1981–2010 and many observational records postdate 1981. The use of different periods to describe the state of different elements of the Arctic environmental system is unavoidable, but it does not change the fact that change is occurring throughout the Arctic environmental system.

b. *Air temperature*—J. Overland, E. Hanna, I. Hanssen-Bauer, S.-J. Kim, J. Walsh, M. Wang, U. S. Bhatt, and R. L. Thoman

Arctic air temperatures are both an indicator and a driver of regional and global changes. Although there are year-to-year and regional differences in air temperatures due to natural variability, the magnitude and Arctic-wide character of the long-term temperature increase are major indicators of global warming (Overland 2009).

The mean annual surface air temperature anomaly for 2015 for land stations north of 60°N was +1.2°C, relative to the 1981–2010 mean value (Fig. 5.1). This ties the recent years of 2007 and 2011 for the highest value in the record starting in 1900. Currently, the Arctic is warming at more than twice the rate of lower latitudes (Fig. 5.1).

The greater rate of Arctic temperature increase compared to the global increase is referred to as Arctic amplification. Mechanisms for Arctic amplification

include reduced summer albedo due to sea ice and snow cover loss, the decrease of total cloudiness in summer and an increase in winter, and the additional heat generated by increased sea ice free ocean areas that are maintained later into the autumn (Serreze and Barry 2011; Makshtas et al. 2011). Arctic amplification is also enhanced because radiational loss of heat from the top of the atmosphere is less in the Arctic than in the subtropics (Pithan and Mauritsen 2014).

<figure><img src="image_191.png" /><figcaption>FIG. 5.1. Arctic (land stations north of 60°N) and global mean annual land surface air temperature (SAT) anomalies (in °C) for the period 1900–2015 relative to the 1981–2010 mean value. Note that there were few stations in the Arctic, particularly in northern Canada, before 1940. (Source: CRUTEM4.)</figcaption></figure>

Although there is an Arctic-wide long-term pattern of temperature increases, regional differences can be manifest in any given season based on natural variability of the atmospheric circulation (Overland et al. 2011; Kug et al. 2015).

Seasonal air temperature anomalies are described in Fig. 5.2 for winter [January–March (JFM)], spring [April–June (AMJ)], summer [July–September (JAS)], and autumn [October–December (OND)] of 2015. All seasons show extensive positive temperature anomalies across the central Arctic with many regional seasonal temperature anomalies greater than +3°C, relative to a 1981–2010 base period.

Warm temperature anomalies in winter 2015 extended across the Arctic, from the Pacific sector to the Atlantic sector (Fig. 5.2a). The warmest temperature anomalies were centered on Alaska and far eastern Siberia, including the Chukchi and East Siberian Seas. In Svalbard, in the Atlantic sector northeast of Greenland, winter temperatures were typically 2°C above the 1981–2010 average. In contrast, cold (negative) temperature anomalies of –2° to –3°C extended from southwest Greenland to central Canada and into the eastern United States.

A broad swath of warm temperature anomalies continued to stretch across the Arctic in spring
<figure><img src="image_192.png" /></figure>

**FIG. 5.2.** 2015 Seasonal anomaly patterns for near-surface air temperatures (°C) relative to the baseline period 1981–2010 in (a) winter, (b) spring, (c) summer, and (d) autumn. Temperatures are from somewhat above the surface layer (at 925 mb level) to emphasize large spatial patterns rather than local features. (Source: NOAA/ESRL.)

2015, with a continuing warm anomaly over Alaska (Fig. 5.2b). However, unlike the winter pattern (Fig. 5.2a), spring saw a shift to a very warm anomaly (+4°C) over central Eurasia. A significant cold anomaly (–3°C) was centered over Greenland. In contrast to Greenland, spring temperatures at the weather station in Svalbard were typically 2°C above the 1981–2010 average, as Svalbard was located on the margin of the broad swath of positive temperature anomalies that extended from Alaska to Eurasia.

A warm temperature anomaly over much of the Arctic Ocean, with the exception of a moderately cold anomaly over the Beaufort Sea north of Alaska, characterized summer 2015 (Fig. 5.2c). Particularly cold anomalies occurred over western Eurasia. As noted in section 5f, a new record August low temperature of –39.6°C occurred on 28 August at Summit (elevation 3216 m in the center of the ice sheet), while summer temperatures measured at most coastal weather stations were above average (Tedesco et al. 2015). Similar to coastal Greenland locations, at the Svalbard weather station the average temperature was 1°–2°C above the 1981–2010 average, the highest JAS average ever recorded in the composite Longyearbyen–Svalbard Airport record that dates to 1898 (Nordli et al. 2014).

In autumn, particularly warm air temperature anomalies were seen in the subarctic regions of the Barents and Bering Seas (Fig. 5.2d). While the central Arctic remained relatively warm, cold anomalies were seen in northeastern North America similar to winter 2015. A difference, however, is that central Asia was also relatively cold in autumn compared to the warmer previous winter.

Both winter and autumn 2015 illustrate extensive interaction of large-scale weather systems between the Arctic and midlatitudes. The anomalously warm temperatures across Alaska in winter and spring 2015 (Fig. 5.2a,b) extend a pattern that began during autumn 2014. The persistent positive (warm) near-surface air temperature anomalies in Alaska and extending into the Chukchi and Beaufort Seas were associated with warm sea surface temperatures in the Gulf of Alaska and a pattern of geopotential height anomalies characterized by higher values along the Pacific Northwest coast of North America and lower values farther offshore (Fig. 5.3a). Consequently, warm air over the northeast Pacific Ocean was affected by southerly winds into and across Alaska, contributing to high mass loss on glaciers (see section 5f). Associated with the southerly winds, a downslope component of the wind on the north side of the Alaska Range and into Interior Alaska caused dry conditions and reinforced high temperatures. The warm and dry conditions in Interior Alaska during May and June contributed to the second worst fire season on record for those months, eclipsed only by 2004.

<figure><img src="image_193.png" /></figure>

**FIG. 5.3.** (a) Large geopotential height anomalies over western and eastern North America and continuing into the North Atlantic sector in winter 2015. (b) Negative geopotential height anomalies over the North Atlantic and Bering Sea sectors in autumn 2015. The arrows indicate anomalous warm (red) and cold (blue) air flow generated as a result of these anomaly patterns.
In contrast to the warm temperature anomalies in winter in Alaska (Fig. 5.2a) due to warm, southerly air flow (Fig. 5.3a), the cold anomalies extending from eastern Canada to southwest Greenland (Fig. 5.2a) were associated with strong northwesterly air flow. These cold anomalies extended into early spring. The potential source of these relatively cold temperatures is illustrated by the extensive winter (JFM) negative geopotential height anomaly pattern (Fig. 5.3a) that shows high values over northwestern North America and low values over eastern North America, Greenland, and across the central Arctic Ocean to central Eurasia. Northwesterly winds on the west side of the trough between the two height centers channeled cold air southward from the source region in the central Arctic into northeastern North America. This geopotential height anomaly pattern may also explain the above-average winter air temperatures in Svalbard, which were associated with warm air advection across western Eurasia and into the central Arctic Ocean (Figs. 5.2a,b).

Autumn 2015 was noted for large active low pressure systems in the North Atlantic and Bering Sea (Fig. 5.3b). These low height anomaly patterns with southerly wind components to their east kept the Chukchi and Barents Seas relatively warm and sea ice free well into the autumn season.

c. Sea ice cover—D. Perovich, W. Meier, M. Tschudi, S. Farrell, S. Gerland, and S. Hendricks

Three key variables are used to describe the state of the ice cover: the ice extent, the age of the ice, and the ice thickness. Sea ice extent is used as the basic description of the state of Arctic sea ice cover. Satellite-based passive microwave instruments have been used to determine sea ice extent since 1979. There are two months each year that are of particular interest: September, at the end of summer, when the ice reaches its annual minimum extent, and March, at the end of winter, when the ice typically reaches its maximum extent. Maps of monthly average ice extents in March 2015 and September 2015 are shown in Fig. 5.4.

Based on estimates produced by the National Snow and Ice Data Center (NSIDC), the 2015 sea ice cover reached its maximum extent on 25 February, at a value of 14.54 million km². This was 7% below the 1981–2010 average and the lowest maximum value in the satellite record. Also notable, the maximum extent occurred 15 days earlier than the 1981–2010 average (12 March) and was the second earliest of the satellite record. The annual minimum extent of 4.41 million km² was reached on 11 September. This was substantially higher (30%) than the record minimum

of 3.39 million km² set in 2012. However, the 2015 summer minimum extent was still 1.81 million km² (29%) less than the 1981–2010 average minimum ice extent and 0.62 million km² (12%) less than the 2014 minimum.

Sea ice extent has decreasing trends in all months and nearly all regions (the exception being the Bering Sea during winter). In 2015, the largest losses were in the eastern Arctic in regions of warm air temperature anomalies in spring and summer (section 5b, Fig. 5.2). The September monthly average decline for the entire Arctic Ocean is now –13.4% decade⁻¹ relative to the 1981–2010 average (Fig. 5.5). The trend is smaller during

<figure><img src="image_194.png" /><figcaption>FIG. 5.4. Average sea ice extent in (a) Mar and (b) Sep 2015 illustrate the respective winter maximum and summer minimum extents. The magenta line indicates the median ice extents in Mar and Sep, respectively, during the period 1981–2010. (Source: NSIDC.)</figcaption></figure>

<figure><img src="image_195.png" /><figcaption>FIG. 5.5. Time series of ice extent anomalies in Mar (the month of maximum ice extent) and Sep (the month of minimum ice extent). The anomaly value for each year is the difference (in %) in ice extent relative to the mean values for the period 1981–2010. The black and red lines are least squares linear regression lines. Both trends are significant at the 99% confidence level.</figcaption></figure>
ing March (–2.6% decade⁻¹) but is still a statistically significant rate of decrease in sea ice extent.

Prior to 2007, there had not been a March to September loss of more than 10 million km² of ice in the record, but now such large losses are not unusual. More typical of recent years, 10.13 million km² of ice was lost between the March maximum and September minimum extent in 2015.

The age of sea ice serves as an indicator for ice physical properties, including surface roughness, melt pond coverage, and thickness. Older ice tends to be thicker and thus more resilient to changes in atmospheric and oceanic forcing than younger ice. The age of the ice is estimated using satellite observations and drifting buoy records to track ice parcels over several years (Tschudi et al. 2010; Maslanik et al. 2011). This method has been used to provide a record of the age of the ice since the early 1980s (Tschudi et al. 2015).

The oldest ice (>4 years old) continues to make up a small fraction of the Arctic ice pack in March, when the sea ice extent has been at its maximum in most years of the satellite record (Figs. 5.6a,b). In 1985, 20% of the ice pack was >4 years old, but in March 2015, this ice category only constituted 3% of the ice pack. Furthermore, we note that first-year ice now dominates the ice cover, comprising ~70% of the March 2015 ice pack, compared to about 50% in the 1980s. Given that older ice tends to be thicker, the sea ice cover has transformed from a strong, thick pack in the 1980s to a more fragile, thin, and younger pack in recent years. The thinner, younger ice is more vulnerable to melting out in the summer, resulting in lower minimum ice extents. The distribution of ice age in March 2015 was similar to that in March 2014 (Fig. 5.6a).

Most of the oldest ice accumulates along the coast of North Greenland and the Queen Elizabeth Islands of the Canadian Arctic Archipelago, and much of this ice has resided in this area for several years (Fig. 5.6b). In 2015, as in most years, ice transport patterns resulted in the movement of old ice from this area into the Beaufort

Sea. The lack of ice older than one year in the eastern Arctic (on the Eurasian side of the Arctic basin) foreshadows its susceptibility to melt out in summer. The ice in the southern Beaufort and Chukchi Seas has also melted completely in the past few summers, with even the oldest ice not surviving the season.

Observations of sea ice thickness and volume from multiple sources have revealed the continued decline of the Arctic sea ice pack over the last decade (Kwok and Rothrock 2009; Laxon et al. 2013; Kwok and Cunningham 2015). Figure 5.6c shows ice thicknesses derived from CryoSat-2 satellite results and IceBridge aircraft observations in March–April 2015. The oldest ice north of Greenland and the Canadian Arctic Archipelago remains thicker than 3 m. There is a strong gradient to thinner, seasonal ice in the Canada basin and the eastern Arctic Ocean, where ice is 1–2 m thick.

<figure><img src="image_196.png" /><figcaption>FIG. 5.6. (a) Time series of sea ice age in Mar for 1985–present, (b) sea ice age in Mar 2015, and (c) sea ice thickness derived from ESA CryoSat-2 (background map) and NASA Operation IceBridge measurements (color coded lines) for Mar/Apr 2015.</figcaption></figure>
Climate change-induced alterations in Arc-
tic ecosystems are having impacts at all trophic
levels, which are already being described as
“transformative” (Johannessen and Miles
2011). However, it remains a challenge to pre-
dict impacts in terms of population trends of
even highly visible, top trophic animals on mul-
tidecadal scales, based on changes occurring in
primary physical features that determine habi-
tat suitability. For example, sea ice declines are
clearly a major threat to ice-associated marine
mammals (e.g., Kovacs et al. 2012; Laidre et
al. 2015), but documented regional patterns
in sea ice losses are not necessarily reflected
in the trajectories of ice-dependent marine
mammal populations on a regional basis. In this
regard, walruses (Odobenus rosmarus) make an
interesting case study.

Walruses of both subspecies, *O. r. divergens*
in the North Pacific Arctic and *O. r. rosmarus*
in the North Atlantic Arctic, mate along ice
edges in the drifting pack ice during winter
and give birth on sea ice in the late spring.
Both subspecies use sea ice extensively as a
haul-out platform throughout much of the
year if it is available close enough to foraging
areas. This habitat also provides shelter from

storms and protection from some predators. Despite
these shared critical links to sea ice, the population
trajectories for the two subspecies do not consistently
reflect the relative patterns of sea ice losses in the two
broad regions occupied by the two subspecies.

The latest research indicates that the Pacific walrus
population in the Bering and Chukchi Seas likely declined
from about 1980 to 2000 (Taylor and Udevitz 2015). Prior
to this time, subsistence harvest restrictions had allowed
this population to recover from earlier overexploitation
(Fay et al. 1989) to a level that likely approached the car-
rying capacity of the environment (e.g., Hills and Gilbert
1994). But, population models suggest that a subsequent
decline of approximately 50% took place in the Pacific
population (Taylor and Udevitz 2015), which was likely
initially stimulated by changes in vital rates (e.g., birth
rates, calf survivorship) within the population. This de-
cline has almost certainly been exacerbated by declines
in sea ice in the region (Fig. SB5.1), associated with global
climate change (Taylor and Udevitz 2015). Hypothesized
mechanisms include: (1) the retreat of sea ice to a position
over the deep Arctic Ocean basin, forcing walruses to use

land-based haulouts where trampling increases mortality
of young animals (Fischbach et al. 2009; Udevitz et al. 2012)
and (2) the decline in sea ice reducing walruses' access to
prey, which could impact the adult female body condition,
ultimately reducing calf survival and recruitment (Jay et al.
2011; Taylor and Udevitz 2015). The use of land-based
haulout areas is not novel for Pacific walruses, but females
with dependent young typically utilize sea ice for hauling
out (Fay 1982), which allows them to avoid particularly
large land-based groups where crowding and trampling
events can result in high calf mortality. A lack of sea ice
over the shelf in summer in the Bering and Chukchi Seas
is already resulting in increased use of coastlines and is-
lands by females with calves, which has in turn resulted in
significant calf mortalities in recent years (Fishbach et al.
2009). Additionally, there is ongoing concern about the
impacts of declining sea ice on the future energetics of
females and young animals. These conditions require the
animals to take significantly longer feeding trips between
the coastal haul outs and offshore areas with high prey
abundance (180 km one-way), rather than utilizing nearby
ice edges for resting as they did in the past.

<figure><img src="image_197.png" /><figcaption>FIG. SB5.1. Regional comparison of trends in sea ice (length of the summer season – number of days less coverage decade⁻¹) and walrus stocks according to Laidre et al. (2015) and expert opinion for Pacific (purple) and Atlantic walrus (red) by region. Stocks are identified by black boundary lines.</figcaption></figure>
Sea ice losses in the North Atlantic Arctic, in particular the Barents Sea region, have been much more extreme than in the North Pacific (Fig. SB5.1). But, Atlantic walrus abundance is increasing or stable for all stocks for which the trend is known (see Laidre et al. 2015) despite reductions in carrying capacity that are almost certainly taking place due to the sea ice declines. Concern does remain regarding possible overharvesting of several stocks with currently unknown trends in Canada/Greenland. However, the positive turnarounds that have occurred are responses to protective management regimes that have been instituted in the early- and mid-1900s (1928 in Canada, 1952 in Norway, and 1956 in Russia), and, in the case of Greenland, much more recently, with quotas being established there in 2006 (see Wiig et al. 2014 for more details). Perhaps the most extreme example of walrus abundance increasing where environmental conditions are deteriorating due to climate change occurs in the Svalbard Archipelago. Svalbard is an Arctic hot spot that is experiencing dramatic sea ice declines and warming ocean and air temperatures, and yet walrus numbers in

the archipelago are increasing exponentially (Kovacs et al. 2014). Walruses in this area were hunted without restriction over several hundred years, up until the 1950s. When they finally became protected in 1952, there were at best a few hundred animals left. Now, after 60 years of complete protection from hunting, with some special no-go reserve areas, recovery is taking place, despite major reductions in sea ice. More females with calves are documented during surveys and historically used sites are being reoccupied as walruses continue to expand through the archipelago. These changes are occurring despite the fact that overall carrying capacity of the region for walruses is likely declining.

The population trajectories of many walrus stocks are currently a result of distant past, or more recent, hunting regimes. However, there is little question that sea ice declines are going to be a challenge for walruses in the future along with other climate change related factors such as increased shipping and development in the north, increased disease and contaminant risks, and ocean acidification impacts on the prey of walruses.

### d. Sea surface temperature—M.-L. Timmermans and A. Proshutinsky

Summer sea surface temperatures in the Arctic Ocean are set by absorption of solar radiation into the surface layer. In the Barents and Chukchi Seas, there is an additional contribution from advection of warm water from the North Atlantic and Pacific Oceans, respectively. Solar warming of the ocean surface layer is influenced by the distribution of sea ice (with more solar warming in ice-free regions), cloud cover, water color, and upper-ocean stratification. In turn, warmer SSTs can drive intensified cyclonic activity; cyclones propagating in marginal ice zones are associated with large ocean-to-atmosphere heat fluxes in ice-free regions (e.g., Inoue and Hori 2011). Here, August SSTs are reported, which are an appropriate representation of Arctic Ocean summer SSTs and are not affected by the cooling and subsequent sea ice growth that takes place in the latter half of September. SST data are from the NOAA Optimum Interpolation (OI) SST Version 2 product, which is a blend of in situ and satellite measurements (Reynolds et al. 2002, 2007; www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html).

Mean SSTs in August 2015 in ice-free regions ranged from ~0°C in some places to around +7°C to +8°C in the Chukchi, Barents, and Kara Seas and eastern Baffin Bay off the west coast of Greenland (Fig. 5.7a). August 2015 SSTs show the same general

spatial distribution as the August mean for the period 1982–2010 (Timmermans and Proshutinsky 2015; Fig. 5.24b). The August 2015 SST pattern is also similar to that of recent years, for example 2012 (Fig. 5.7b), which was the summer of lowest minimum sea ice extent in the satellite record (1979–present).

Most boundary regions and marginal seas of the Arctic had anomalously warm SSTs in August 2015 compared to the 1982–2010 August mean (Fig. 5.7c). SSTs in these seas, which are mostly ice free in August, are linked to the timing of local sea ice retreat; anomalously warm SSTs (up to +3°C relative to 1982–2010) in August 2015 in the Beaufort and Chukchi Seas were associated with low sea ice extents and exposure of surface waters to direct solar heating (Fig. 5.7c; see also section 5c). The relationship between warm SSTs and reduced sea ice is further apparent in a comparison between August 2015 and August 2014 SSTs: anomalously warm regions (including to the east of Svalbard, where SSTs were up to +3°C warmer in 2015) are associated with relatively lower sea ice extents in 2015 compared to 2014 (Fig. 5.7d). Although SSTs were warmer in general, August 2015 SSTs were cooler relative to average in some regions, for example, along the southern boundaries of the Beaufort and East Siberian Seas (Fig. 5.7c), where summer air temperatures were also below average (see section 5b).
<figure><img src="image_198.png" /></figure>

FIG. 5.7. (a) Mean SST (°C) in Aug 2015. White shading is the Aug 2015 mean sea ice extent. (b) Mean SST in Aug 2012. White shading is the Aug 2012 sea ice extent. Gray contours in (a) and (b) indicate the 10°C SST isotherm. (c) SST anomalies (°C) in Aug 2015 relative to the Aug mean for the period 1982–2010. White shading is the Aug 2015 mean ice extent and the black line indicates the median ice edge in Aug for the period 1982–2010. (d) SST anomalies (°C) in Aug 2015 relative to Aug 2014; white shading is the Aug 2015 mean ice extent and the black line indicates the median ice edge for Aug 2014. Sea ice extent and ice edge data are from NSIDC.

Anomalously warm August 2015 SSTs in eastern Baffin Bay were notable, with values as much as 4°C higher than the 1982–2010 August mean; SSTs over the region indicate a general warming trend of about 0.5°C decade⁻¹ since 1982 (Fig. 5.8a). Over the past two decades, the linear warming trend in the surface waters of eastern Baffin Bay has accelerated to about 1°C decade⁻¹ (+0.10°C yr⁻¹). Along the boundaries of the Arctic basin, the only marginal seas to exhibit statistically significant warming trends are the Chukchi and the Kara Seas. Chukchi Sea August SSTs are warming at a rate of about +0.5°C decade⁻¹, commensurate with declining trends in summer sea ice extent in the region. In the Kara Sea, August 2015 SSTs were also up to 4°C higher than the 1982–2010 August mean; SSTs in this sea have warmed by about +0.3°C decade⁻¹ since 1982. In other marginal seas, warm August SST anomalies observed in 2015 are of similar magnitude to warm anomalies observed in

past decades (Timmermans and Proshutinsky 2015, their Fig. 5.26a).

The seasonal evolution of SST in the marginal seas exhibited the same general trends and regional differences in 2015 (Fig. 5.8b) as for the preceding decade. Seasonal warming in the marginal seas begins as early as May, and the seasonal cooling period begins as early as mid-August, with cooling observed through December. The asymmetry in rates of seasonal warming and cooling, most notable in the Chukchi Sea and East Baffin Bay, suggests a source of heat in addition to solar radiation. Advection of warm water from the Bering Sea and North Atlantic likely inhibits SST cooling (e.g., Carton et al. 2011; Chepurin and Carton 2012).

<figure><img src="image_199.png" /></figure>

FIG. 5.8. (a) Time series of area-averaged SST anomalies (°C) for Aug of each year relative to the Aug mean for the period 1982–2010 for the Chukchi and Kara Seas and eastern Baffin Bay (see Fig. 5.7b). The dash-dotted black line shows the linear SST trend for the Chukchi Sea (the same warming trend as eastern Baffin Bay). Numbers in the legend correspond to linear trends (with 95% confidence intervals) in °C yr⁻¹. (b) SST (°C) in 2014–15 for each of the marginal seas, where the OISST V2 weekly product has been used in the calculation. For sea ice concentrations greater than 50%, the SST product uses a linear relationship with sea ice concentration to infer SST; variations in freezing temperature as a consequence of salinity variations imply that SSTs inferred from sea ice can be erroneously cool by as much as 0.2°C, with the highest errors in the Canadian sector (see Timmermans and Proshutinsky 2015).
Under climate warming, species tend to shift their distributions poleward (IPCC 2014). Some of the most rapid shifts are taking place in the Arctic, where warming is currently twice the global average (see section 5.b, Fig. 5.1; Hoegh-Guldberg and Bruno 2010; Doney et al. 2012). Poleward shifting marine species have been entering the Arctic Ocean from both the Atlantic and the Pacific (Grebmeier et al. 2010; Wassmann et al. 2011).

Boreal (warm-water affinity) species of fish have shifted extensively northward into the Arctic (Mueter and Litzow 2008; Grebmeier et al. 2006; Rand and Logerwell 2011; Christiansen et al. 2013; Fossheim et al. 2015).

As an example, we present the recent climate-induced changes in the fish communities of the Barents Sea, the entrance point to the Arctic Ocean from the Atlantic. The results are based on a large-scale annual Ecosystem Survey that monitors the whole ice-free shelf of the Barents Sea in August–September, the season with the least sea ice. This cooperative survey between Russia (Knipovich Polar Research Institute of Marine Fisheries and Oceanography) and Norway (Institute of Marine Research) was initiated in 2004. Our focus is on observations for the period 2004–12, as they have been most thoroughly assessed.

In the Barents Sea, the present warming trend in water temperatures started in the late 1990s (Boitsov et al. 2012). The late summer temperature at the seafloor has increased by almost 1°C during the last decade alone. In this region, sub-zero water masses in late summer have almost disappeared and the sea ice is retreating. In association with this warming, boreal fish species have entered the northern parts of the Barents Sea in large numbers. The expansions of these fish species have led to a community-wide shift: boreal communities are now found farther north and the local Arctic (cold-water affinity) community has been almost pushed out of the area (Fig. SB5.2).

<figure><img src="image_200.png" /><figcaption>FIG. SB5.2. Comparison of the fish communities between the beginning of the Ecosystem Survey taken in the Barents Sea in (a) 2004 and (b) 2012, indicates a significant change in distribution. The Atlantic (red) and central (yellow) communities (boreal fish species) have shifted north and east, taking over areas previously occupied by the Arctic (blue) community (arctic fish species). Data are available only for the shaded areas. (After Fig. 1 in Fossheim et al. 2015.)</figcaption></figure>

The fish species increasing in the north are large boreal fish predators, such as cod (Gadus morhua), beaked redfish (Sebastes mentella), and long rough dab (Hippoglossoides platessoides). These fish species are considered “generalists” in that they can use a wide range of habitats and feed on a diverse set of prey. As such, they are better able to thrive in a changing environment. Their northward expansion is likely related to warmer water temperatures and greater food availability for these fish species (Fossheim et al. 2015). For instance, increased primary productivity in the previously ice-covered area (Dalpadado et al. 2014) and increasing abundance and biomass of Atlantic zooplankton in the northern Barents Sea (Dalpadado et al. 2012) likely favor boreal over Arctic fish species.

Cod, the most important commercial species, has reached a record high population size due to a favorable climate and lower fishing pressure (Kjesbu et al. 2014). The cod stock in the Barents Sea has not been this high since the 1950s. High abundances have also been recorded for haddock (Melanogrammus aeglefinus), the other main commercial species, and for long rough dab, a common and widespread species in the Barents Sea. A poleward expansion of cod and haddock and a northeastward displacement of beaked redfish (Sebastes mentella) have been documented (Renaud et al. 2012; Hollowed et al. 2013; Fossheim et al. 2015).

The Arctic fish community, including various snail-
fishes, sculpins, and eel pouts, does not seem to cope well with warming water temperatures (Fossheim et al. 2015). Most of these Arctic fish species are relatively small, stationary, and feed on organisms living on the sea bottom. These species have a more specialized diet than the boreal fish species and are thus more vulnerable to climate change (Kortsch et al. 2015). In addition, they are adapted to life on the shallow shelf of the Barents Sea. Because the central Arctic Ocean is much deeper, it is unlikely that these species will move farther north. However, they can be found farther to the east on the neighboring shelf (e.g., Kara Sea; Fig. SB5.2).

Large fish and marine mammals can move quickly over large distances, while other species, such as small Arctic fish species and organisms that live on or near the seafloor, are more stationary. As a result, two previously separate communities are now mixing together (Fossheim et al. 2015). The larger fish species from the south will compete with the smaller Arctic species for food, and even prey on them directly. Thus, the Arctic community is being pressured from two sides: the marine environment

is changing due to rising water temperatures, and new competitors and predators are arriving. It is anticipated that this could result in the local extinction of some Arctic fish species, such as the gelatinous snailfish (Liparis fabricii) and even the most abundant Arctic species, the Polar cod (Boreogadus saida).

One consequence of the general nature of large boreal fish moving into the Arctic is the development of novel feeding links between incoming and resident species, ultimately changing the configuration of the Arctic marine food web (Kortsch et al. 2015). Arctic food webs contain fewer feeding links than boreal food webs. As cod and other large fish species feeding on many prey move into arctic waters, they establish many new links in the Arctic food web, which becomes more tightly connected. The ecological effects of perturbations will spread faster and more widely in a more interconnected arctic food web, making it more susceptible to environmental stress (Kortsch et al. 2015).

e. *Greenland Ice Sheet*—M. Tedesco, J. E. Box, J. Cappelen, X. Fettweis, K. Hansen, T. Mote, C. J. P. P. Smeets, D. van As, R. S. W. van de Wal, I. Velicogna, and J. Wahr

The Greenland Ice Sheet covers an area of 1.71 million km². With a volume of 2.85 million km³, it is the second largest glacial ice mass on Earth, smaller only than the Antarctic ice sheet. The amount of freshwater stored in the Greenland Ice Sheet has a sea level equivalent of ~7 m. The discharge of the ice to the ocean through runoff and iceberg calving not only increases sea level, but can also alter the ocean thermohaline circulation and global climate (Rahmstorf et al. 2015). Moreover, the high albedo (reflectivity) of the ice sheet surface (together with that of sea ice and snow on land) plays a crucial role in the regional surface energy balance (Tedesco et al. 2011) and the regulation of global air temperatures.

Estimates of the spatial extent of Greenland Ice Sheet surface melting (e.g., Mote 2007; Tedesco 2007; Tedesco et al. 2013) show that in 2015 (Fig. 5.9a) melting occurred over more than half of the ice sheet for the first time since the exceptional melt events of July 2012 (Nghiem et al. 2012). The 2015 melt extent exceeded two standard deviations above the 1981–2010 average, reaching a maximum of 52% of the ice sheet area on 4 July (Fig. 5.9d). By comparison, melt extent

in 2014 reached a maximum of 39% of the ice sheet area and ~90% in 2012. A second period of melting, which began in late August, covered between 15% and 20% of the ice sheet (a mean of ~5% over the same period) and lasted until early September. In the summer of 2015 (June–August), the number of melting days along the southwestern and southeastern margins of the ice sheet was close to or below the long-term average, with maximum negative anomalies (i.e., below the 1981–2010 average) of 5–10 days (Fig 5.9a). In contrast, the number of melt days in the northeastern, western, and northwestern regions was up to 30–40 days above the 1981–2010 average, setting new records in terms of meltwater production and runoff over the northwestern regions.

The surface mass balance measured along the southwestern portion of the ice sheet at the K-transect for September 2014 through September 2015 (van de Wal et al. 2005, 2012) was the third least negative since the beginning of the record in 1990 (Tedesco et al. 2015). This is consistent with the negative melting anomalies along the southwestern portion of the ice sheet (Fig. 5.9a). At all PROMICE network stations (www.promice.dk; Ahlstrøm et al. 2008; van As et al. 2011) summer 2015 ablation was low with respect to the 2011–15 period of record (Fig. 5.9b), except at
the most northerly latitudes (Kronprins Christian Land, KPC, 80°N, 25°W; Thule, THU, 76°N, 68°W), where melt totals were slightly above average. The highest recorded melt in 2015, 5.1 m on the Qaqsimiut lobe (QAS_L station, 61°N, 47°W), was just over half the record-setting 9.3 m at that site in 2010 (Fausto et al. 2012).

Consistent with the distribution of melt anomalies, measurements at weather stations of the Danish Meteorological Institute (DMI; Cappelen 2015) during spring 2015 indicate that summer average temperature anomalies (relative to the 1981–2010 average) were positive at several northerly stations around the Greenland coastline, with values exceeding one standard deviation at Pituffik (+1.2 °C), Upernavik (+1.2°C) and Danmarkshavn (+0.9°C). In contrast, temperatures in south and southwest Greenland (e.g., Paamiut, Narsarsuaq, Qaqortoq, and Prins Christian Sund) were 1.5 standard deviations below the 1981–2010 average, with temperature anomalies as much as –2.6°C at Narsarsuaq (Tedesco et al. 2015). These widespread low temperatures are consistent with a strong negative spring temperature anomaly centered over Greenland (see section 5b, Fig. 5.2b). Danmarkshavn also experienced its warmest January on record, with a +7.7°C anoma-

ly. A new record August low temperature of –39.6°C occurred on 28 August at Summit (3216 m a.s.l.).

The average albedo for the Greenland Ice Sheet in summer 2015, derived from data collected by the Moderate-resolution Imaging Spectroradiometer (MODIS, after Box et al. 2012), was below the 2000–09 average over the northwestern region and above the

average in the southwest (Fig. 5.9c), consistent with the negative surface mass balance and melting day anomalies measured over the same region (Fig. 5.9a). The trend of mean summer albedo over the entire ice sheet for the period 2000–15 remained negative and was estimated to be –5.5% ± 0.4%. In July 2015, when extensive melting occurred (Fig. 5.9d), albedo

<figure><img src="image_201.png" /><figcaption>**FIG. 5.9.** (a) Map of the anomaly (with respect to the 1981–2010 average) of the number of days when melting was detected in summer 2015 using spaceborne passive microwave data. The locations of the stations used for the in situ analysis of surface mass balance and temperature are reported on the map as black disks (PROMICE) and cyan triangles (K-transect). (b) Summer 2015 ablation at PROMICE stations with respect to the 2011–15 period of record. (c) Greenland Ice Sheet surface albedo anomaly for JJA 2015 relative to the average for those months between 2000 and 2009 derived from MODIS data. (d) Daily spatial extent of melting from Special Sensor Microwave Imager/Sounder (SSMIS) as a percentage of the total ice sheet area for all of 2015. The 1981–2010 average spatial extent of melting (dashed line) and ±2 std. dev. of the mean (shaded) are also plotted for reference.</figcaption></figure>