irregular decline in the SNAO index since its peak in the 1970s is striking (Fig. 2.30i). A negative state of the SNAO is consistent with generally strongly positive Atlantic multidecadal oscillation conditions over the last decade (Sutton and Dong 2012). However, evidence is strengthening that reductions in summer Arctic sea ice due to warming of the Arctic may also favor a negative SNAO (e.g., Knudsen et al. 2015, Petrie et al. 2015). The July 2015 MSLP anomaly pattern strongly resembled the negative SNAO. Although August also projected weakly onto the negative SNAO, Scandinavia had a high pressure anomaly with very warm temperatures over and to its south (Figs. 2.32a, b). Daily SNAO values reflect the somewhat different characters of July and August (Fig. 2.32c), with fewer days of negative SNAO in August. Despite this, the Central England Temperature (Parker et al. 1992) was close to its 1961–90 normal in both months. The HadCRUT4 temperature for July and August (Online Fig. S2.20) shows that central England was on the

boundary between warmer-than-normal conditions over most of Europe and distinctly cool conditions in the central extratropical North Atlantic Ocean. The latter is consistent with the July and August MSLP anomalies, but its strength may also reflect a persistent tendency to cool conditions in this region over the last few years. The pattern of rainfall anomalies varied consistently with MSLP patterns between July and August; most of northwestern Europe had above-average rainfall in July, with most of southern Europe drier than normal. In August, most of Scandinavia and eastern Europe were drier than normal, with a more restricted wet area than in July extending from Ireland through France to the Netherlands (section 7f; Online Fig. S2.21).

2) **SURFACE WINDS**—R. J. H. Dunn, C. Azorin-Molina, C. A. Mears, P. Berrisford, and T. R. McVicar

During 2015, over land, observational datasets have revealed generally higher surface wind speeds (Plate 2.1s; Fig. 2.33a) than in the last 20 years. This “recovery” continues the behavior observed since 2013 and concurs with Kim and Paik (2015), who reported a break from the decreasing trend in surface wind speed around the Republic of Korea during the most recent decade.

<figure><img src="image_71.png" /><figcaption>**FIG. 2.32.** HadSLP2r mean sea level pressure anomalies for Europe for (a) Jul and (b) Aug 2015. (c) EMULATE PMSL daily SNAO time series for Jul–Aug 2015 normalized over 1850–2015.</figcaption></figure>

<figure><img src="image_72.png" /><figcaption>**FIG. 2.33.** Global (excluding Australia) and regional annual time series of land surface wind speed for 1981–2015 using HadISD and ERA-Interim showing (a) wind speed anomaly (m s⁻¹) relative to 1981–2010, and occurrence frequencies (in %) for wind speeds (b) >3 m s⁻¹ and (c) >10 m s⁻¹. Frequencies for Australia are not shown in (b) and (c).</figcaption></figure>
The observed global (excluding Australia) average anomaly from the 1981–2010 climatology was +0.025 m s⁻¹ (Table 2.5) compared to −0.030 m s⁻¹ in 2014. As a result of unresolved differences between the two observing systems used in Australia (wind run, compared to wind speed in HadISD), and given agreement of modeling pan evaporation trends when using wind run (Roderick et al 2007), Australia is treated separately and the wind run results updated from McVicar et al. (2008) are used. In Australia, the positive anomaly made 2015 the fourth windiest year in the 1979–2015 record. There were positive anomalies in all other regions, with the exception of a noticeable negative anomaly in North America. Over this latter region, 2015 was the ninth calmest year in the observed record, with a slightly lower occurrence of both moderate (>3 m s⁻¹) and strong (>10 m s⁻¹) winds (Figs. 2.33b,c), in agreement with Iacono and Azorin-Molina (2014). Overall increases in Europe, central Asia, and East Asia reflected a higher occurrence of moderate winds, and particularly of strong winds in Europe (Fig. 2.33b,c).

Adapting the approach of Berrisford et al. (2015), two quality-controlled wind speed datasets from instrumental records are used: 1) the global HadISD (1973–2015, Dunn et al. 2012), with the highest station density in the Northern Hemisphere, and 2) an Australian database (1979–2015, McVicar et al. 2008). The 10-m wind speed fields from ERA-Interim (Dee et al. 2011) are also used to investigate the spatial and temporal variability of winds over regions that have few observations. Over land surfaces with high-density wind observations, the large-scale anomaly pat-

terns from ERA-Interim (Plate 2.1s) are relatively consistent with the instrumental records. Reanalysis products provide contiguous global information but have shortcomings in their representation of surface layer processes and hence near-surface winds speeds (see McVicar et al. 2008; Pryor et al. 2009; Vautard et al. 2010 for examples). In addition, reanalyzed winds are representative of larger spatial and temporal scales than point observations.

The percentage of stations showing positive and negative anomalies in 2015 from HadISD is split almost evenly, with a slight dominance of stations

with positive anomalies. At 15% of the stations, the wind speed was at least 0.5 m s⁻¹ above the 1981–2010 climatology while it was at least 0.5 m s⁻¹ below it at 11% of the stations. The wind speed was at least 1.0 m s⁻¹ above and below the climatology at 3.4% and 2.3% of stations, respectively.

Continently, negative long-term trends of observed land surface wind speed dominate over 1979–2015, with a terrestrial global (excluding Australia) change of −0.087 m s⁻¹ decade⁻¹, varying from −0.070 (East Asia) to −0.151 (Central Asia) m s⁻¹ decade⁻¹ (Table 2.5, Fig. 2.34), with Australia at −0.062 m s⁻¹ decade⁻¹. Although the ERA-Interim pattern of reanalyzed trends (Fig. 2.34) is consistent with the observational HadISD dataset, the magnitude of changes is underestimated, as previously noted for other reanalysis products (McVicar et al. 2008;

<figure><img src="image_73.png" /><figcaption>FIG. 2.34. Land surface wind speed trends for the observational HadISD and Australian datasets (points) and the ERA-Interim reanalysis (worldwide grids) over 1979–2015.</figcaption></figure>

<table><caption>TABLE 2.5. Global and regional statistics for land surface wind speed using observational HadISD and Australian (McVicar et al. 2008) datasets.</caption><thead><tr><th>Region</th><th>Mean 1981–2010 (m s<sup>-1</sup>)</th><th>Anomaly 2015 (m s<sup>-1</sup>)</th><th>Trend 1979–2015 (m s<sup>-1</sup> decade<sup>-1</sup>) and 5th to 95th percentile confidence range</th><th>Number of stations</th></tr></thead><tbody><tr><td>Globe (excluding Australia)</td><td>3.309</td><td>+0.025</td><td>-0.087 (-0.094)-(-0.081)</td><td>2264</td></tr><tr><td>North America</td><td>3.685</td><td>-0.130</td><td>-0.100 (-0.111)-(-0.088)</td><td>587</td></tr><tr><td>Europe</td><td>3.747</td><td>+0.063</td><td>-0.087 (-0.100)-(-0.071)</td><td>589</td></tr><tr><td>Central Asia</td><td>2.887</td><td>+0.212</td><td>-0.151 (-0.162)-(-0.133)</td><td>263</td></tr><tr><td>East Asia</td><td>2.623</td><td>+0.092</td><td>-0.070 (-0.079)-(-0.060)</td><td>399</td></tr><tr><td>Australia</td><td>2.066</td><td>+0.160</td><td>-0.062</td><td>41</td></tr></tbody></table>
Pryor et al. 2009; Vautard et al. 2010). This worldwide slowdown of land surface wind speed observed since the 1980s has been reported over many regions (see McVicar et al. 2012 for a review).

The precise causes of this weakening in wind speed remain largely uncertain and do not necessarily reflect wind tendency at higher altitudes (McVicar and Körner 2013) than the standard 10-m observations (Vautard et al. 2010; Troccoli et al. 2012). Increase of surface roughness due to forest growth, land use changes, and urbanization (Vautard et al. 2010; Bichet et al. 2012; Wever 2012; Wu et al. 2016); changes in large-scale atmospheric circulation (Azorin-Molina et al. 2014, 2016); instrumentation changes (Wan et al. 2010); and air pollution (Xu et al. 2006) are among the major identified hypothetical causes, which differ in importance regionally. Unlike the long-term declining trend over land, there is an apparent reversal of the trends since 2013, but still with overall negative anomalies.

Over oceans, estimates of globally-averaged wind obtained from satelliteborne microwave radiometers (Wentz 1997; Wentz et al. 2007) were slightly lower than average in 2015 (Fig. 2.35). Estimates from re-analysis products differ, with JRA-55 and ERA-Interim showing that 2015 was above average, and MERRA-2 showing the opposite. Reanalysis winds, which are in relatively good agreement with both the satellite data and each other from 1990 to 2009, diverge after 2010 (Figs. 2.35a-c). A comparison of annual mean anomaly global ocean average wind speed between ERA-Interim and satellite radiometers shows moderate agreement on short time scales and poorer agreement on long time scales, with the ERA-Interim results showing a larger long-term increasing trend. All products show an increasing trend from 1990 to 2007, followed by a drop-off in 2008-09, and a recovery in 2010. Since then, the winds have fallen slightly in most products.

<figure><img src="image_74.png" /><figcaption>**FIG. 2.35.** Global average surface wind anomaly over ocean relative to the 1981–2010 base period from (a) satellite radiometers, (b) ships, and (c) reanalyses (as described in Fig. 2.1).</figcaption></figure>

During 2015, ocean winds showed large negative anomalies in the central tropical Pacific associated with the ongoing El Niño event (Plate 2.1s), similar to those found above the surface at 850 hPa (Plate 2.1r). This weakening was most apparent in the latter half of 2015. Compared to the 1997/98 El Niño, the region of weakening did not extend as far east, and Indian and Atlantic Ocean patterns were much less striking (Online Fig. S2.22). Other regions with negative anomalies include much of the tropical Indian Ocean and the southern Pacific midlatitudes between New Zealand and Chile. Other regions of the Southern Ocean showed positive anomalies, which were also present in the western Pacific surrounding the Maritime Continent and in the eastern tropical Pacific south of the equator. Over land, the anomalies were less pronounced, with most land areas showing small positive anomalies.

### 3) UPPER AIR WINDS—L. Haimberger and M. Mayer

Upper air wind is measured routinely with balloons and aircraft. Today it is also inferred from satellite imagery, at least in the lower to midtroposphere. Historical upper air wind data are particularly crucial for detecting signals associated with meridionally asymmetric aerosol forcing (Allen et al. 2014), for example, or with ENSO.

The buildup of a strong El Niño event was one of the major large-scale climate anomaly signals in 2015. There are many ways to depict this event, but it is useful to see its impact on upper level divergent flow. Figure 2.36 compares divergent wind anomalies at 200 hPa in late 2015 (3-month average centered around November 2015 to maximize the signal) with those from the strongest ENSO event in recent history (also centered around November 1997). The divergent flow of the 2015 event, while having the strongest divergence maximum east of the date line, was much more confined to the tropical Pacific than was the case in 1997, where the flow patterns over the whole tropics were massively perturbed. Regions with divergent flow are associated with deep convection and strong thermodynamic coupling between sea surface and the atmosphere, which also has a strong imprint on regional-scale energy flows (Mayer et al. 2013, 2014). Regions with strong convergence at this level are associated with large-scale subsidence and suppressed convection.

As may be expected (Zhang and Zhu 2012), this pattern fits well with the activity pattern of tropical cyclones in late 2015. There was an all-time record of 13 tropical cyclones in the central Pacific and enhanced tropical cyclone activity in the east Pa-
cific (www.nhc.noaa.gov/text/MIATWSEP.shtml; see section 4e3). It is also interesting to note that the anomalous divergence pattern over the Arabian Sea coincided with the occurrence of two strong tropical cyclones in this region (on average there is less than one tropical cyclone per year), affecting Yemen and Oman (see section 4e5). Tropical cyclone activity over the western Pacific (which is the region of strongest divergence in the climatological mean) and Atlantic was normal or below normal. This can be seen in Fig. 2.36a from the locations of peak intensities of tropical cyclones that reached at least Category 1 on the Saffir-Simpson scale. The near-average cyclone activity in the western Pacific can partly be explained by cyclones originating in the central Pacific that reached peak intensity farther west.

The upper level convergence over Indonesia and particularly Australia in late 2015 is also consistent with the observed severe drought conditions over parts of these regions (see Fig. 2.29). As can be seen from Fig. 2.36, the upper level divergence pattern is generally less perturbed in 2015 than in 1997. If the RMS of the divergent wind speed in the tropics is used as a measure, it was 1.4 m s⁻¹ in late 1997 compared to 1.3 m s⁻¹ in late 2015. Nevertheless, its spread over the whole tropics is remarkable.

The imprint of the 2015 El Niño event can also be seen in the 850-hPa wind speed anomaly map in Plate 2.1r. There is a distinct weakening of the tropical easterlies just west of the upper level divergence maximum in Fig. 2.36a or the SST anomaly maximum in Plate 2.1c. Other features of the anomaly map are a slight poleward shift of Southern Hemisphere midlatitude westerlies that leads to enhanced wind speeds over the seas adjacent to Antarctica. These are likely related to the positive phase Antarctic Oscillation (AO) during 2015 (section 2e1), and possibly enhanced through the exceptionally warm troposphere farther equatorward (Plate 2.1b). The positive wind speed anomaly over the eastern North Atlantic is consistent with the positive phase of the North Atlantic Oscillation (NAO) prevailing in 2015 (section 2e1).

Radiosonde and pilot balloon are the best sources for station-based upper air wind climatologies, dating back to the early 1940s in the northern extratropics. Maps of upper air winds are best inferred from atmospheric reanalyses, which are well constrained by observations. It is difficult to get wind climatologies from satellite data because the altitude of observations (mostly cloud-based atmospheric motion vectors) is highly variable. Since last year's article, early upper air data have been assimilated in an experimental

<figure><img src="image_75.png" /><figcaption>**FIG. 2.36.** Three-month averages of velocity potential and divergent wind at 200 hPa compared to the 1979–2014 climatology. Anomalies centered around (a) Nov 2015 and (b) Nov 1997. On panel (a) crosses indicate location of peak intensities for Category 1 or higher tropical cyclones in second half of 2015. Percentage of tropical cyclone frequency compared to the National Hurricane Center's 1966–2009 climatology is also indicated.</figcaption></figure>

reanalysis run called ERA-preSAT (1939–67, H. Hersbach et al. 2016, unpublished manuscript) which helped to extend the quasi-biennial oscillation (QBO) time series from reanalyses backward in time to at least the late 1940s.

Figure 2.37 shows time series of zonal belt mean wind speeds in the tropics at 50 hPa to cover the QBO signal. The experimental ERA-preSAT shows potential to extend reanalysis time series backward in a more realistic manner than the surface data only reanalyses (Haimberger 2015). The depiction of the QBO signal back to the early 1950s is particularly encouraging. There are practically no digitized upper air data prior to the early 1950s reaching high enough altitudes in the tropical belt.

### f. Earth radiation budget

1) EARTH RADIATION BUDGET AT TOP-OF-ATMOSPHERE—
   P. W. Stackhouse, Jr., T. Wong, D. P. Kratz, P. Sawaengphokhai, A. C. Wilber, S. K. Gupta, and N. G. Loeb

The Earth's radiation budget (ERB) at the top-of-atmosphere (TOA) is the balance between the incoming total solar irradiance (TSI) and the sum of the
<figure><img src="image_76.png" /><figcaption>FIG. 2.37. Time series of zonal mean U-wind component in the (a) 20°–40°N belt at 300 hPa and (b) tropical belt 20°S–20°N at 50 hPa, calculated from ERA-Interim, MERRA, JRA-55, and ERA-preSAT re-analyses and pilot balloon/radiosonde winds (GRASP; Ramella-Pralungo et al. 2014). Note that positive (negative) changes in the zonal wind sped imply an increase in westerlies (easterlies). Data have been smoothed using a 12-point boxcar filter.</figcaption></figure>

reflected shortwave (RSW) and outgoing longwave radiation (OLR). This balance defines the energetic state of the Earth–atmosphere system that drives weather processes, climate forcing, and climate feedbacks.

The year 2015 is remarkable due to the development of an intense El Niño that reached official status in April–May according to the multivariate ENSO index

(MEI; Wolter and Timlin 1993, 1998; www.esrl.noaa.gov/psd/enso/mei/) and intensified into late 2015. Note that the MEI index is more appropriate than the SOI for comparing to radiative fluxes because it integrates information from across the Pacific. In contrast, 2013 was a neutral year while 2014 featured a slight shift from typical east–west Pacific conditions toward El Niño, becoming “marginal” by year end. Global annual mean OLR in 2015 increased ~0.15 W m⁻² since 2014, but was ~0.30 W m⁻² larger than for 2013 (Table 2.6). Meanwhile, the global-annual mean RSW decreased by ~0.45 W m⁻² from 2014 and was ~0.75 W m⁻² smaller than for 2013. In 2015, the global annually-averaged TSI was ~0.05 W m⁻² larger than that of both 2013 and 2014. The combination of these components amounts to an addition of 0.40 W m⁻² in the total net radiation into the Earth climate system relative to 2014 and corresponded to a ~0.50 W m⁻² increase relative to 2013. All the global annual mean changes appear to be amplifying relative to the neutral ENSO year of 2013, perhaps indicative of the atmospheric response due to the circulation anomalies over the last two years. Relative to the 2001–14 average, the 2015 global annual mean flux anomalies (Table 2.6) are +0.30, +0.10, -0.55, and +0.35 W m⁻² for OLR, TSI, RSW, and total net flux, respectively. These changes, except for the RSW anomaly, are within the corresponding 2-sigma interannual variability (Table 2.6) for this period and thus not viewed as particularly large anomalies. The 2015 global annual mean RSW flux anomaly greatly exceeds typical variability, implying a darkening of Earth’s TOA albedo. Attribution of this to El Niño and/or other large-scale processes requires further analysis. However, it appears that reduction of the annually averaged RSW is resulting in a relative increase to the total net absorbed flux of the Earth–atmosphere system, indicating a net heating over the last two years.

<table><caption>TABLE 2.6. Global-annual mean TOA radiative flux changes between 2013 and 2015, 2014 and 2015, the 2015 global-annual mean radiative flux anomalies relative to their corresponding 2001–14 mean climatological values, and the 2-σ interannual variabilities of the 2001–14 global-annual mean fluxes (all units in W m⁻²) for the outgoing longwave radiation (OLR), total solar irradiance (TSI), reflected shortwave (RSW), and total net fluxes. All flux values have been rounded to the nearest 0.05 W m⁻².</caption><thead><tr><th></th><th>Global-annual Mean<br>Difference<br>(2015 minus 2013)<br>(W m<sup>-2</sup>)</th><th>Global-annual Mean<br>Difference<br>(2015 minus 2014)<br>(W m<sup>-2</sup>)</th><th>2015 Anomaly<br>(relative to<br>climatology)<br>(W m<sup>-2</sup>)</th><th>Interannual variability<br>(2001 to 2014)<br>(W m<sup>-2</sup>)</th></tr></thead><tbody><tr><th>OLR</th><td>+0.30</td><td>+0.15</td><td>+0.30</td><td>±0.50</td></tr><tr><th>TSI</th><td>+0.05</td><td>+0.05</td><td>+0.10</td><td>±0.20</td></tr><tr><th>RSW</th><td>-0.75</td><td>-0.45</td><td>-0.55</td><td>±0.40</td></tr><tr><th>Net</th><td>+0.50</td><td>+0.40</td><td>+0.35</td><td>±0.65</td></tr></tbody></table>
Monthly mean anomaly TOA flux time series (Fig. 2.38) show that the OLR anomaly began 2015 with a value of 0.9 W m⁻², but then mostly oscillated between -0.2 and +0.7 W m⁻², which led to the slightly positive annual OLR anomaly (see Table 2.6) with higher values toward the end of 2015. This observed OLR variability is generally consistent with the NOAA-HIRS OLR (Lee et al. 2011). The absorbed shortwave (TSI - RSW) anomaly started the year with a value of 0.2 W m⁻², increased to just over 1.1 W m⁻² in September, but then decreased the last few months of the year. The positive values towards the latter half of the year were large enough to dominate the annual average, leading to a large absorbed shortwave anomaly for the year. The total net anomaly, which contains the combined OLR and absorbed shortwave anomalies, began 2015 with a value of -0.7 W m⁻², then jumped to positive values, peaking in September at 0.9 W m⁻² before falling below 0 W m⁻² by the end of the year. The positive absorbed shortwave anomaly dominates the net, resulting in the positive annual total net anomaly. Long-term trend analyses that include the last two months of the merged dataset are discouraged due to the natural fluctuation in ERB components, the uncertainty from the data merging process, and potential for drift in the FLASHFlux product.

<figure><img src="image_77.png" /><figcaption>FIG. 2.38. Time series of global-monthly mean deseasonalized anomalies (W m⁻²) of TOA earth Radiation Budget for OLR (upper panel), absorbed shortwave (TSI-RSW; middle panel), and total net (TSI-RSW-OLR; lower panel) from Mar 2000 to Dec 2015. Anomalies are relative to their calendar month climatology (2001–2014). The time series shows the CERES EBAF Ed2.8 1Deg data (Mar 2000 to Oct 2015) in red and the CERES FLASHFlux version 3B data (Nov–Dec 2015) in blue (Source: https://eosweb.larc.nasa.gov/project/ceres/ceres_table.)</figcaption></figure>

TSI data are from the Total Irradiance Monitor (TIM) instrument aboard the Solar Radiation and Climate Experiment (SORCE) spacecraft (Kopp and Lean 2011) and the Royal Meteorological Institute of Belgium (RMIB) composite dataset (Dewitte et al. 2004), both renormalized to the SORCE Version 15 data. RSW and OLR data were obtained from the Clouds and the Earth's Radiant Energy System (CERES) mission (Wielicki et al. 1996, 1998), deriving flux data from the *Terra* and *Aqua* spacecraft.

Time series (Fig. 2.38) were constructed from the CERES EBAF (Energy Balanced And Filled) Ed2.8 product (Loeb et al. 2009, 2012) from March 2000 to October 2015 and the CERES Fast Longwave and Shortwave Radiative Fluxes (FLASHFlux) products (Kratz et al. 2014; Stackhouse et al. 2006), for November and December 2015. The FLASHFlux data are normalized to the EBAF data using the following procedure based on overlapping data from January 2009 through December 2014. First, successive versions of globally-averaged FLASHFlux TOA components are normalized to each other relative to the current version 3B. Then, this unified 6-year FLASHFlux dataset is cross-calibrated to the corresponding EBAF Ed2.8 data using TOA fluxes from both datasets, accounting for multiyear bias, linear change, and seasonal dependent differences. Finally, these coefficients are used to cross-normalize FLASHFlux to EBAF and provide an estimate of monthly globally averaged TOA flux components. The resulting 2-sigma monthly uncertainty of the normalization procedure for the 6-year overlap period was ±0.22, ±0.07, ±0.19, and ±0.22 W m⁻² for the OLR, TSI, RSW, and total net radiation, respectively.

## 2) MAUNA LOA CLEAR-SKY “APPARENT” SOLAR TRANSMISSION—K. Lantz

NOAA's Global Monitoring Division (GMD) maintains one of the longest continuous records of solar transmission at the Mauna Loa Observatory (MLO) in Hawaii. Because of the observatory's remote Pacific location and high elevation above local influences (3400 m a.s.l.), the solar transmission represents the free troposphere and above with limited local influences. This record is often used to show the influence of large explosive volcanic eruptions and is useful as an indicator of changes in background stratospheric aerosols. The “apparent” clear-sky solar transmission (AT) is calculated from the ratio of direct-beam broadband irradiance measurements from a pyrheliometer using fixed atmospheric paths (Ellis and Pueschel 1971). This technique is advantageous because using the ratio of fixed atmospheric
paths removes influences due to extraterrestrial ir-
radiance and instrument calibrations. Past studies
of changes in clear-sky AT at MLO have looked at
the influence of volcanic aerosol, aerosol transport
from Asia, water vapor, ozone, and influences of
the quasi-biennial oscillation (QBO; Bodhaine et al.
1981; Dutton et al. 1985; Dutton 1992; Dutton and
Bodhaine 2001). Effects due to aerosol are the most
prominent in the record.

The monthly record of clear-sky apparent solar
transmission has been updated through December
2015 (Fig. 2.39). The monthly values are calculated
using morning values to remove boundary layer in-
fluences that occur predominantly in the afternoon
due to prevailing upslope wind conditions (Ryan
1997). Major eruptions from Agung, El Chichón,
and Mount Pinatubo are clearly visible in the record
in 1964, 1982, and 1991, respectively (Fig. 2.39). The
cleanest period of observations is between 1958 and
1962, except for a brief period in 1978. As such, this
period is treated as the “clean” background with
which to compare all other variations (dashed line
in Fig. 2.39). Seasonal trends are highlighted by a
6-month running smoothed fit to the monthly values
and have been attributed primarily to Asian aerosol
transport in the spring (Bodhaine et al. 1981). Long-
term changes are highlighted by a 24-month running
smoothed fit. The monthly clear-sky AT eventually
returned to near-background conditions in mid-1998
after the eruption of Mount Pinatubo in 1991. The
24-month fit shows a slow decrease in AT over the
subsequent decade (Fig. 2.39b). This slow decrease in
clear-sky AT was attributed to increased stratospheric
aerosol due to small volcanic eruptions (Solomon
et al. 2011; Vernier et al. 2011). These eruptions have
been shown to contribute aerosol to the layer between
the tropopause and 15 km in mid- to high latitudes
(Ridley et al. 2014). The last several years have not
shown a continued increase in the clear-sky AT. There
is a negligible change in the mean of the monthly
clear-sky AT in 2015 with respect to 2014 (−0.0006).
The amplitude of the seasonal changes in clear-sky
AT in 2015 is ~0.006, which is comparable to results
reported previously of ~0.007 (Bodhaine et al. 1981).

g. Atmospheric composition

1) LONG-LIVED GREENHOUSE GASES—E. J. Dlugokencky, B. D. Hall, M. J. Crotwell, S. A. Montzka, G. Dutton, J. Mühle, and J. W. Elkins

Carbon dioxide (CO₂), methane (CH₄), and ni-
trous oxide (N₂O), in decreasing order, are the most
dominant long-lived greenhouse gases (LLGHG)
contributing to climate forcing. Systematic measure-

<figure><img src="image_78.png" /><figcaption>FIG. 2.39. (a) Monthly mean of the clear-sky Appar-
ent Transmission at Mauna Loa Observatory. The
dashed line is the background level from 1958 to 1972.
(b) Enlarged plot to highlight the seasonal (red line,
6-month running smoothed fit) and long-term (blue
line, 24-month smoothed fit) changes in the clear-sky
AT record.</figcaption></figure>

ments of CO₂ began at Mauna Loa, Hawaii (MLO),
in 1958, when the atmospheric mole fraction was
~315 ppm (parts per million in dry air). In 2015 the
MLO annual average mole fraction of CO₂ exceeded
400 ppm (400.8 ± 0.1 ppm) for the first time (www.esrl
.noaa.gov/gmd/ccgg/trends/), while the global aver-
age CO₂ mole fraction at Earth's surface was 399.4
± 0.1 ppm (Fig. 2.40a, www.esrl.noaa.gov/gmd/ccgg
/trends/global.html).

Atmospheric CO₂ growth since 1958 is largely
attributable to a concurrent, fourfold increase in
anthropogenic emissions from fossil fuel combus-
tion and cement production (Boden et al. 2015).
About half of this anthropogenic CO₂ remains in
the atmosphere, while the other half is taken up by
the terrestrial biosphere and oceans, where it acidi-
fies seawater (see section 3l). The global growth rate
of CO₂ has risen from 0.6 ± 0.1 ppm yr⁻¹ in the early
1960s to an average of 2.1 ± 0.1 ppm yr⁻¹ during the
past 10 years. However, the increase at MLO during
2015 was 3.05 ± 0.11 ppm (0.76 ± 0.03%), the largest
annual increase observed in the 56-year measurement
record. The largest previous increase (2.93 ppm) oc-
curred in 1998, which was also a strong El Niño year.
ENSO plays a role in the interannual variability of the
CO₂ growth rate through its influence on terrestrial
carbon fluxes (Bastos et al. 2013).

Methane is emitted from both anthropogenic
(60%) and natural (40%) sources (Fung et al. 1991).
Anthropogenic sources include agriculture (e.g.,
ruminants and rice), fossil fuel extraction and use,
biomass burning, landfills, and waste. Natural sources
include wetlands, geological sources, oceans, and
<figure><img src="image_79.png" /><figcaption>FIG. 2.40. Global mean surface mole fractions (in dry air) of (a) CO₂ (ppm), (b) CH₄ (ppb), (c) N₂O (ppb), and (d) CFC-12 and CFC-11 (ppt) derived from the NOAA sampling network.</figcaption></figure>

termites (Dlugokencky et al. 2011). Fossil fuel exploitation (coal, oil, and natural gas) contributes ~20% of total global CH₄ emissions (Kirschke et al. 2013). Having increased 250% since pre industrial time, the atmospheric CH₄ burden currently contributes ~0.5 W m⁻² direct radiative forcing, with an additional ~0.3 W m⁻² indirect radiative forcing coming from the production of tropospheric O₃ and stratospheric H₂O from methane (Myhre et al. 2013). Total global CH₄ emissions are estimated at ~540 Tg CH₄ yr⁻¹ (1 Tg = 10¹² g), with a relatively small uncertainty of ~±10%, based on observations of globally averaged CH₄, its rate of increase, and an estimate of its lifetime (~9.1 yr). The complexity of the atmospheric CH₄ budget, with many sources that are difficult to quantify individually, makes bottom-up estimates by country and source difficult. The rate of CH₄ increase slowed from more than 10 ppb yr⁻¹ in the 1980s to

nearly zero in the early 2000s, then increased to an average of ~7 ppb yr⁻¹ since 2007 (Fig. 2.40b). Surface observations, including its rate of increase and spatial distribution, provide strong top-down constraints on the CH₄ source and sink budgets. Based on NOAA background air sampling sites, the 2015 globally averaged CH₄ mole fraction at Earth's surface was 1834.0 ± 0.8 ppb. The 11.5 ± 0.9 ppb increase in annual means from 2014 to 2015 is the largest since 1997/98.

Nitrous oxide is a powerful greenhouse gas produced by natural (~60%) and anthropogenic (~40%) sources and is also an ozone-depleting substance (Ciais et al. 2013; Ravishankara et al. 2009). The observed 21% increase in atmospheric N₂O over pre-industrial levels (270 to 328 ppb) is largely the result of nitrogen-based fertilizer use (Park et al. 2012). The mean global atmospheric N₂O mole fraction in 2015 was 328.2 ± 0.1 ppb, an increase of 1.1 ppb from the 2014 mean (Fig. 2.40c). The average N₂O growth rate since 2010 is 0.98 ± 0.02 ppb yr⁻¹, higher than the 0.75 ± 0.02 ppb yr⁻¹ average growth over the previous decade.

Halogenated gases, such as chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs), and CCl₄ also contribute to radiative forcing. Atmospheric mole fractions of some CFCs, such as CFC-12 and CFC-11, have been decreasing for a decade or more in response to production and consumption restrictions imposed by the Montreal Protocol and its Amendments (Fig. 2.40d; Table 2.7). However, as a result of the CFC phase-out, the atmospheric burdens of CFC replacement gases—HCFCs and HFCs—have increased (Fig. 2.41; Table 2.7; Carpenter et al. 2014; Montzka et al. 2014). Interestingly, of the most abundant ozone-depleting substances that were controlled initially by the Montreal Protocol, the surface mole fraction of only one chemical, halon-1301, is not yet decreasing (Table 2.7).

Trends in the combined direct radiative forcing by five major LLGHGs (CO₂, CH₄, N₂O, CFC-11, and CFC-12) and 15 minor gases are summarized by the NOAA Annual Greenhouse Gas Index (AGGI; Hofmann et al. 2006; www.esrl.noaa.gov/gmd/aggi/). This index represents their annual cumulative radiative forcing relative to the Kyoto Protocol baseline year of 1990. The AGGI does not include indirect radiative forcings (e.g., influences on ozone and water vapor). In 2015, CO₂ contributed 1.94 W m⁻² direct radiative forcing, 65% of the combined forcing of 2.98 W m⁻² by the 5 major LLGHGs and 15 minor gases (Fig. 2.42). The combined forcing in 2015 represents a 38% increase (2015 AGGI = 1.38) since 1990, and a 1.4% increase over 2014 (AGGI = 1.36).
<table><caption>TABLE 2.7. Summary table of long-lived greenhouse gases for 2015 (CO₂ mixing ratios are in ppm, N₂O and CH₄ in ppb, and all others in ppt).</caption><thead><tr><th>Industrial Designation or Common Name</th><th>Chemical Formula</th><th>AGGI</th><th>ODGI</th><th>Radiative Efficiency (W m<sup>-2</sup> ppb<sup>-1</sup>)<sup>a</sup></th><th>Mean Surface Mole Fraction, 2015 (change from prior year)<sup>b</sup></th><th>Lifetime (years)</th></tr></thead><tbody><tr><td>Carbon Dioxide</td><td>CO<sub>2</sub></td><td>Y</td><td>N</td><td>1.37×10<sup>-5</sup></td><td>399.4 (2.3) <sup>c</sup></td><td></td></tr><tr><td>Methane</td><td>CH<sub>4</sub></td><td>Y</td><td>N</td><td>3.63×10<sup>-4</sup></td><td>1834.0 (11.5) <sup>c</sup></td><td>9.1</td></tr><tr><td>Nitrous Oxide</td><td>N<sub>2</sub>O</td><td>Y</td><td>N</td><td>3.00×10<sup>-3</sup></td><td>328.2 (1.1) <sup>c,d</sup></td><td>123</td></tr><tr><td colspan="7" style="text-align: center; font-weight: bold;">Chlorofluorocarbons</td></tr><tr><td>CFC-11</td><td>CCl<sub>3</sub>F</td><td>Y</td><td>Y</td><td>0.26</td><td>232.1 (-1.4) <sup>c,d</sup></td><td>52</td></tr><tr><td>CFC-12</td><td>CCl<sub>2</sub>F<sub>2</sub></td><td>Y</td><td>Y</td><td>0.32</td><td>516.1 (-3.4) <sup>c,d</sup></td><td>102</td></tr><tr><td>CFC-113</td><td>CCl<sub>2</sub>FCCIF<sub>2</sub></td><td>Y</td><td>Y</td><td>0.30</td><td>71.9 (-0.5) <sup>c,d</sup></td><td>93</td></tr><tr><td colspan="7" style="text-align: center; font-weight: bold;">Hydrochlorofluorocarbons</td></tr><tr><td>HCFC-22</td><td>CHClF<sub>2</sub></td><td>Y</td><td>Y</td><td>0.21</td><td>233.0 (4.1) <sup>c</sup></td><td>11.9</td></tr><tr><td>HCFC-141b</td><td>CH<sub>3</sub>CCl<sub>2</sub>F</td><td>Y</td><td>Y</td><td>0.16</td><td>24.3 (0.5) <sup>c</sup></td><td>9.4</td></tr><tr><td>HCFC-142b</td><td>CH<sub>3</sub>CCIF<sub>2</sub></td><td>Y</td><td>Y</td><td>0.19</td><td>21.8 (-0.1) <sup>c</sup></td><td>18</td></tr><tr><td colspan="7" style="text-align: center; font-weight: bold;">Hydrofluorocarbons</td></tr><tr><td>HFC-134a</td><td>CH<sub>2</sub>FCF<sub>3</sub></td><td>Y</td><td>N</td><td>0.16</td><td>83.5 (5.9) <sup>c</sup></td><td>14</td></tr><tr><td>HFC-152a</td><td>CH<sub>3</sub>CHF<sub>2</sub></td><td>Y</td><td>N</td><td>0.10</td><td>6.6 (0.2) <sup>c</sup></td><td>1.6</td></tr><tr><td>HFC-143a</td><td>CH<sub>3</sub>CF<sub>3</sub></td><td>Y</td><td>N</td><td>0.16</td><td>16.1 (1.4) <sup>c</sup></td><td>51</td></tr><tr><td>HFC-125</td><td>CHF<sub>2</sub>CF<sub>3</sub></td><td>Y</td><td>N</td><td>0.23</td><td>17.0 (1.9) <sup>c</sup></td><td>31</td></tr><tr><td>HFC-32</td><td>CH<sub>2</sub>F<sub>2</sub></td><td>N</td><td>N</td><td>0.11</td><td>9.9 (1.6) <sup>c</sup></td><td>5.4</td></tr><tr><td>HFC-23</td><td>CHF<sub>3</sub></td><td>Y</td><td>N</td><td>0.18</td><td>28.1 (1.0) <sup>c</sup></td><td>228</td></tr><tr><td>HFC-365mfc</td><td>CH<sub>3</sub>CF<sub>2</sub>CH<sub>2</sub>CF<sub>3</sub></td><td>N</td><td>N</td><td>0.22</td><td>0.8 (0.08) <sup>c</sup></td><td>8.7</td></tr><tr><td>HFC-227ea</td><td>CF<sub>3</sub>CHFCF<sub>3</sub></td><td>N</td><td>N</td><td>0.26</td><td>1.1 (0.09) <sup>c</sup></td><td>36</td></tr><tr><td colspan="7" style="text-align: center; font-weight: bold;">Chlorocarbons</td></tr><tr><td>Methyl Chloroform</td><td>CH<sub>3</sub>CCl<sub>3</sub></td><td>Y</td><td>Y</td><td>0.07</td><td>3.1 (-0.6) <sup>c</sup></td><td>5.0</td></tr><tr><td>Carbon Tetrachloride</td><td>CCl<sub>4</sub></td><td>Y</td><td>Y</td><td>0.17</td><td>82.5 (-1.3) <sup>c,d</sup></td><td>26</td></tr><tr><td>Methyl Chloride</td><td>CH<sub>3</sub>Cl</td><td>N</td><td>Y</td><td>0.01</td><td>550 (6) <sup>c</sup></td><td>0.9</td></tr><tr><td colspan="7" style="text-align: center; font-weight: bold;">Bromocarbons</td></tr><tr><td>Methyl Bromide</td><td>CH<sub>3</sub>Br</td><td>N</td><td>Y</td><td>0.004</td><td>6.6 (-0.04) <sup>c</sup></td><td>0.8</td></tr><tr><td>Halon 1211</td><td>CBrClF<sub>2</sub></td><td>Y</td><td>Y</td><td>0.29</td><td>3.61 (-0.08) <sup>c</sup></td><td>16</td></tr><tr><td>Halon 1301</td><td>CBrF<sub>3</sub></td><td>Y</td><td>Y</td><td>0.30</td><td>3.27 (0.01) <sup>c</sup></td><td>72</td></tr><tr><td>Halon 2402</td><td>CBrF<sub>2</sub>CBrF<sub>2</sub></td><td>Y</td><td>Y</td><td>0.31</td><td>0.43 (-0.01) <sup>c</sup></td><td>28</td></tr><tr><td colspan="7" style="text-align: center; font-weight: bold;">Fully fluorinated species</td></tr><tr><td>Sulfur Hexafluoride</td><td>SF<sub>6</sub></td><td>Y</td><td>N</td><td>0.57</td><td>8.60 (0.33) <sup>c</sup></td><td>&gt;600</td></tr><tr><td>PFC-14</td><td>CF<sub>4</sub></td><td>N</td><td>N</td><td>0.09</td><td>81.9 (0.7) <sup>c</sup></td><td>~50000</td></tr><tr><td>PFC-116</td><td>C<sub>2</sub>F<sub>6</sub></td><td>N</td><td>N</td><td>0.25</td><td>4.49 (0.08) <sup>c</sup></td><td>~10000</td></tr></tbody></table>

<footnote><sup>a</sup> Radiative efficiencies were taken from IPCC AR5 (Myhre et al. 2013). Steady-state lifetimes were taken from Myhre et al. (2013) (CH₄), Ravishankara et al. (2009) (SF₆), Ko et al. (2013), and Carpenter et al. (2014). For CO₂, numerous removal processes complicate the derivation of a global lifetime.</footnote>

<footnote><sup>b</sup> Mole fractions are global, annual surface means for the indicated calendar year determined from the NOAA global cooperative air sampling network (Hofmann et al. 2006), except for PFC-14, PFC-116, and HFC-23, which were measured by AGAGE (Mühle et al. 2010; Miller et al. 2010). Changes indicated in brackets are the differences between the 2015 and 2014 global mean mole fractions.</footnote>

<footnote><sup>c</sup> Preliminary estimate.</footnote>

<footnote><sup>d</sup> Global means derived from multiple NOAA measurement programs ("Combined Dataset").</footnote>
2) **OZONE-DEPLETING GASES**—B. D. Hall, S. A. Montzka, G. Dutton, and J. W. Elkins

In addition to direct radiative forcing, chlorine- and bromine-containing gases contribute indirectly to radiative forcing through their destruction of stratospheric ozone. The emissions and atmospheric burdens of many of the most potent ozone-depleting gases have been decreasing in response to production and consumption restrictions imposed by the Montreal Protocol and its Amendments (Figs. 2.40d, 2.41). For example, the abundance of CH<sub>4</sub>CCl<sub>3</sub> at Earth's surface has declined 98% from its peak in 1992 (Fig. 2.41). CFC-11 and CFC-12, which have much longer atmospheric lifetimes (Table 2.7), have declined by 7.7% and 2.3%, respectively, from their peak mole fractions in 1994 and 2002.

Equivalent effective stratospheric chlorine (EESC) is a measure of the ozone-depleting potential of the halogen loading in the stratosphere at a given time and place. As EESC declines, stratospheric ozone is expected to show signs of recovery. Some recovery is indeed evident in the upper stratosphere, and is attributable, in part, to the decrease in EESC (Pawson et al. 2014; see section 2g4). EESC is calculated from surface measurements of halogenated, ozone-depleting gases and weighting factors that include surface-to-stratosphere transport times, mixing during transit, photolytic reactivity, and ozone-destruction efficiency (Daniel et al. 1995; Schauffler et al. 2003; Newman et al. 2007). Progress towards reducing the

stratospheric halogen load is evaluated by the NOAA Ozone-Depleting Gas Index (ODGI; Hofmann and Montzka 2009). The ODGI relates EESC in a given year to the EESC maximum (ODGI = 100) and 1980 value (ODGI = 0), a benchmark often used to assess progress towards reducing stratospheric halogen to pre-ozone hole levels (Fig. 2.43).

The EESC and ODGI are calculated for two representative stratospheric regions—Antarctica and the midlatitudes—that differ in total available reactive halogen (Fig. 2.43a). At the beginning of 2015, EESC values were ~3820 ppt and ~1620 ppt over Antarctica and the midlatitudes, respectively. EESC is larger in the Antarctic stratosphere than in the midlatitudes because more ozone-reactive halogen is released during transit to the Antarctic. Corresponding ODGI values at the beginning of 2015 were 82.9 and 59.5, compared to 84.3 and 61.5 at the beginning of 2014. These represent ~17% and ~40% reductions from the peak values in EESC over Antarctica and the midlatitudes, respectively, towards the 1980 benchmarks (Fig. 2.43b).

3) **AEROSOLS**—S. Rémy, A. Benedetti, and O. Boucher

Aerosol particles play an important role in the atmosphere through various mechanisms. They influence the radiation budget, directly by scattering and absorbing short- and long-wave radiation, and indirectly by affecting the concentrations, sizes, and chemical composition of cloud condensation nuclei (CCN) that impact the life cycle, optical properties, and precipitation activity of clouds. More information about the radiative forcing by aerosols is provided by Boucher et al. (2013). Aerosols also impact air quality and may cause serious public health issues,

<figure><img src="image_80.png" /><figcaption>FIG. 2.41. Global mean surface mole fractions at Earth's surface (ppt, dry air) for several halogenated long-lived greenhouse gases. See Table 2.7 for the 2015 global mean mole fractions of these gases.</figcaption></figure>

<figure><img src="image_81.png" /><figcaption>FIG. 2.42. Direct radiative forcing (W m⁻²) due to 5 major LLGHG and 15 minor gases (left axis) and the Annual Greenhouse Gas Index (right axis).</figcaption></figure>
<figure><img src="image_82.png" /><figcaption>FIG. 2.43. (a) Equivalent Effective Stratospheric Chlorine (EESC) and (b) the NOAA Ozone-Depleting Gas Index (ODGI). The ODGI represents the relative mole fractions of reactive halogen in the midlatitude and Antarctic stratosphere scaled such that ODGI=100 at maximum EESC and zero in 1980. Both EESC and ODGI are derived from NOAA surface measurements of ozone-depleting gases (symbols) or, for earlier years, WMO scenarios (dashed lines, Harris and Wuebbles 2014). The EESC and ODGI values from 1992 forward are for Jan of each year.</figcaption></figure>

as documented by the world media during strong particulate pollution outbreaks in 2015 in parts of western Europe (March), Indonesia and adjacent countries (September–October), and northern China (November).

For the first time in this section, a new interim reanalysis of global aerosols is utilized that spans 2003–15. This was developed within the framework of the Copernicus Atmosphere Monitoring Service (CAMS; J. Flemming, *personal communication*, Feb 2016). Collection 5 retrievals of aerosol optical depth (AOD) at 550 nm from the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS; Remer et al. 2005) were used as observational constraints. All relevant physical aerosol processes, such as emissions, wet/dry deposition, and sedimentation, are included and fully coupled with the meteorology. The aerosol types treated are naturally produced sea salt and desert dust, as well as black carbon, organic

matter, and sulfate aerosols produced by anthropogenic and natural sources. Biomass burning aerosol emissions are the sum of the black carbon and organic matter emitted by open fires and biofuel combustion. Open fire emissions for this new reanalysis were provided by the Global Fire Assimilation System (GFAS) inventory (Kaiser et al. 2012) that estimates emissions from MODIS observations of fire radiative power. Preliminary verification of total AOD using independent observations from the ground-based Aerosol Robotic Network (AERONET) shows that the reanalysis has a global average bias of –2.5% but is consistent over time (J. Flemming, *personal communication*, Feb 2016).

The 2015 annual average anomalies of AOD due to total aerosols, dust, and biomass burning (Plates 2.1v,w,x, respectively) depict strong regional anomalies from biomass-burning events in Alaska, Siberia, Canada, and Indonesia. Overall, the 2015 anomalies of biomass burning aerosols (Plate 2.1x) are consistent with those of tropospheric ozone (section 2g6), carbon monoxide (section 2g7), and fires (section 2h3). Besides the large events already mentioned, the anomaly map of biomass burning aerosols reveals that the 2015 seasonal burning in Africa was more severe than usual south of the equator and less severe north of it. Biomass burning in the Amazon basin in 2015 was similar to the 2003–14 average, interrupting the decreasing trend observed for several previous years. There is a positive anomaly of dust extending west from western Sahara across the tropical Atlantic to Central America (Plate 2.1w), pointing to more active transatlantic dust transport in 2015 than in previous years. On the other hand, dust episodes were less important in 2015 over the northern Sahara and the Mediterranean Sea, and less dust was transported from the Taklamakan and Gobi Deserts into China. Sea salt aerosol anomalies (not shown) were strongly negative in the equatorial Pacific Ocean and west of Indonesia, probably due to disturbances in the trade winds by the strong El Niño conditions during the second half of the year. Positive anomalies of sea salt in the North Atlantic Ocean were caused by a string of active storms there in November–December.

Global maps of the total 550-nm AOD average for 2003–14 and statistically significant (95% confidence) linear trends from 2003 through 2015 are shown in Fig. 2.44. The highly polluted areas of eastern Asia and India are prominent features in the map of long-term average total AOD (Fig. 2.44a), as are the dust-producing regions of the Sahara, the Arabian Peninsula, the Middle East, and the Taklamakan and Gobi Deserts. Large AOD values over the Amazon
<figure><img src="image_83.png" /></figure>

**FIG. 2.44.** (a) Total 550-nm AOD averages for 2003–14. (b) Linear trends from 2003 through 2015 of total AOD (AOD unit per year). Only trends that are statistically significant at the 95% level of confidence are shown.

basin, equatorial Africa, and Indonesia are caused by seasonal biomass burning. The linear trends highlight long-term decreases in anthropogenic aerosols over the eastern United States, Europe, and parts of southern China, while increases occurred over most of the Indian subcontinent. The area of decreasing trends in the southern Amazon basin is associated with reduced deforestation there (Chen et al. 2013). The decreasing trends over the northern Sahara and western Mediterranean indicate lower frequencies or intensities of dust episodes in these regions. Though many positive trends over the Southern Hemisphere oceans are not statistically significant, those that are could be an artefact of the MODIS Collection 5 observations used in the reanalysis. Time series of globally-averaged total AOD during 2003–15 (Fig. 2.45) show strong seasonality, typically with yearly maxima in March–April and August–September driven mainly by dust episodes and biomass burning in Africa and South America.

<figure><img src="image_84.png" /></figure>

**FIG. 2.45.** Global averages of total AOD at 550 nm averaged over monthly (red) and annual (blue) periods for 2003–15.

Aerosol monitoring relies on a multistream global observing system. Routine aerosol observations are mainly provided by two federated, ground-based networks: AERONET and Global Atmospheric Watch (GAW), which in 2015 operated 311 and >220 stations, respectively. MODIS satellite instruments on *Aqua* and *Terra* have continued to provide retrievals of AOD during 2015, while the Visible Infrared Imaging Radiometer Suite (VIIRS) on *Suomi NPP* has provided aerosol data products since 2013. Geostationary satellites are also increasingly being used to measure aerosols. For instance, AOD derived from Meteosat Second Generation (MSG) observations over Europe and Africa is available from 2014 (Carrer et al. 2014). AOD observations are now routinely incorporated into atmospheric models using data assimilation algorithms (e.g., Zhang et al. 2008; Benedetti et al. 2009; Inness et al. 2015b) to combine them with short-term forecasts. Such observationally constrained models can be used to build a reanalysis of atmospheric composition. Reanalyses can, to a large extent, be considered a good proxy for observed conditions. They provide whole atmosphere coverage and the ability to provide variables not routinely observed, such as the AOD of different aerosol types (e.g., dust, sea salt, and carbonaceous). However, their limitations should be kept in mind. To accommodate limited computing resources, models usually simplify aerosol processes and may not take into account all of the aerosol species and/or their interaction. This means that the atmospheric composition reanalysis aerosol products usually do not capture all of the observed variability and complexity of aerosol fields. Assessing the relative weight of observations and model values in the data assimilation scheme of such systems is not trivial; this can also lead to uncertainties (Inness et al. 2013).

4) **STRATOSPHERIC OZONE**—M. Weber, W. Steinbrecht, C. Roth, M. Coldewey-Egbers, D. Degenstein, Y. E. Fioletov, S. M. Frith, L. Froidevaux, J. de Laat, C. S. Long, D. Loyola, and J. D. Wild

Total ozone columns in 2015 were close to the 1998–2008 average for most of the globe, except in extended regions at high latitudes in both hemispheres,
where ozone columns were largely below average (Plate 2.1q). The strong negative anomalies at high Southern Hemisphere latitudes reflect the large Antarctic ozone hole observed in September–December, whose size reached maximum values that were near the all-time record high (see section 6h).

In Fig. 2.46 the total ozone annual means from different data sources are shown for 1970–2015 in various zonal bands: near-global (60°S–60°N), midlatitudes in both hemispheres (35°–60°), and the inner tropics (20°S–20°N). Also shown are the polar time series in March (Northern Hemisphere, 60°–90°N) and October (Southern Hemisphere, 60°–90°S), the months when polar ozone losses are largest in each hemisphere. Poleward of 60°S, a record low October mean was observed (Fig. 2.46e). Weaker-than-usual dynamical wave activity in the Southern Hemisphere winter diminished transport from the tropics, reducing ozone at Southern Hemisphere midlatitudes and in the collar region of the polar vortex, and permitting a very stable and cold polar vortex. The high vortex stability and low temperatures resulted in larger-than-usual polar ozone losses and a near-record ozone hole in terms of size and persistence. Ozone annual mean columns at mid- to polar latitudes (35°–90°) in each hemisphere are largely determined by winter/spring ozone levels. These vary considerably with changes in stratospheric meteorological conditions (e.g., Steinbrecht et al. 2011; Weber et al. 2011; Kuttippurath et al. 2015). The year-to-year variability seen in all ozone time series also reflects quasi-biennial oscillation (QBO)-related variations extending from the tropics into the extratropics (Randel and Wu 1996; Strahan et al. 2015).

It is clear that the Montreal Protocol and its Amendments have been successful in stopping the multidecadal decline in stratospheric ozone by the late 1990s (WMO 2011). However, at most latitudes, it has not yet been possible to determine a statistically significant increase in total column ozone or lower stratosphere ozone because the expected small increases are masked by large interannual variability (e.g., Chehade et al. 2014; Coldewey-Egbers et al. 2014; Frith et al. 2014; Kuttippurath et al. 2015; Nair

<figure><img src="image_85.png" /><figcaption>FIG. 2.46. Time series of annual mean total ozone in (a–d) four zonal bands and (e) polar (60°–90°) total ozone in Mar (Northern Hemisphere) and Oct (Southern Hemisphere). Data are from WOUDC ground-based measurements combining Brewer, Dobson, SAOZ, and filter spectrometer data (red: Fioletov et al. 2002, 2008); the BUV/SBUV/SBUV2 V8.6 merged products from NASA (MOD V8.6, dark blue, Chiou et al. 2014; Frith et al. 2014) and NOAA (light blue, Wild et al. 2012); the GOME/SCIAMACHY/ GOME-2 products GSG from University of Bremen (dark green, Weber et al. 2011) and GTO from ESA/ DLR (light green, Coldewey-Egbers et al. 2015); and the MSR V2 assimilated dataset extended with GOME-2 data (van der A et al. 2015). WOUDC values for 2015 are preliminary because not all ground station data were available in early 2016.</figcaption></figure>
et al. 2015; de Laat et al. 2015). The 2015 total ozone columns in Fig. 2.46 are consistent with this overall picture and lie within the expected usual variations.

In the tropics, no discernible long-term trends in total column ozone have been observed for the entire 1970–2015 period (see Fig. 2.46). Ozone trends in the tropical lower stratosphere are mainly determined by tropical upwelling (related to changes in sea surface temperature). In a changing climate it is expected that tropical upwelling will increase and thus ozone will continue to decline (Zubov et al. 2013; WMO 2014). However, there is some evidence of a hiatus in tropical upwelling trends and corresponding lower stratospheric ozone trends during the last decade (Aschmann et al. 2014). Because tropospheric ozone contributes to the total ozone columns, trends in total ozone, despite major contributions from the lower stratosphere, may differ from trends in lower stratospheric ozone (Shepherd et al. 2014).

The most recent ozone assessment (WMO 2014) and studies (Nair et al. 2015; Harris et al. 2015) indicate that the clearest signs of significant ozone increases should occur in the upper stratosphere (2%–4% decade⁻¹ at ~2 hPa or 40 km; see Fig. 2.47). However, there still are uncertainties associated with the various available data records and with the proper interpretation of statistical approaches used to derive and attribute trends (e.g., Nair et al. 2015; Kuttippurath et al. 2015; Harris et al. 2015). This is reflected in the updated Stratospheric Aerosol and Gas Experiment (SAGE)–Optical Spectrograph and Infrared Imager System (OSIRIS) record, which now better accounts for tangent altitude drifts, and in the updated Solar Backscatter Ultraviolet (SBUV) data from NOAA with improved inter-satellite adjustments. Overall, the 2015 annual means in Fig. 2.47 support the claim of recent increases in upper stratospheric, extra-polar ozone. These suggest the Montreal Protocol has successfully turned the previous downward trend in ozone into an ozone increase, at least in the upper stratosphere.

5) **STRATOSPHERIC WATER VAPOR**—S. M. Davis, K. H. Rosenlof, D. F. Hurst, and H. B. Selkirk

Variations in stratospheric water vapor (SWV) over interannual-to-decadal timescales have the potential to affect stratospheric ozone (Dvortsov and Solomon 2001) and surface climate (Solomon et al. 2010). Throughout the first 10 months of 2015, water vapor mixing ratios in the tropical lowermost stratosphere were within 10% (0.4 ppm, µmol mol⁻¹) of the previous decade's average. Then, starting in November and continuing through December,

tropical lowermost SWV increased to near-record levels, especially over the tropical western Pacific and Indian Ocean regions. The deep tropical-averaged (15°S–15°N) SWV anomaly at 82 hPa, based on data from the *Aura* Microwave Limb Sounder (MLS), was +0.7 ppm (+17%) in November and +0.9 ppm (+24%) in December. These values are in stark contrast to the weak negative (dry) tropical average anomalies of about –0.2 ppm (–6%) in November–December 2014 (Figs. 2.48, 2.49). Since the MLS record began in August 2004, the November–December 2015 anomalies at 82 hPa are surpassed only by +0.9 ppm (+25%) deep tropical anomalies in February–March 2011. The +0.7 ppm (+19%) average deep tropical anomaly at 100 hPa in November–December 2015 is the high-

<figure><img src="image_86.png" /></figure>

**FIG. 2.47.** Annual mean ozone anomalies at 2 hPa (~40 km, upper stratosphere) in three zonal bands. Data are from the merged SAGE II/OSIRIS (Bourassa et al. 2014) and GOZCARDS (Froidevaux et al. 2015) records and from the BUV/SBUV/SBUV2 v8.6 merged products from NASA (McPeters et al. 2013; Frith et al. 2014) and NOAA (Wild et al. 2012) (base period: 1998–2008). The orange curves represent EESC (effective equivalent stratospheric chlorine), scaled to reflect the expected ozone variation due to stratospheric halogens. Data points for 2015 are preliminary, because SAGE-OSIRIS data were not yet available after July 2015, and adjusted SBUV2 v8.0 data are used after July 2015 instead of v8.6 data, which are not available in early 2016.
<figure><img src="image_87.png" /></figure>

**FIG. 2.48.** (a) Vertical profiles of MLS tropical (15°S-15°N) water vapor anomalies (µmol mol⁻¹) and (b) latitudinal distributions of MLS water vapor anomalies (µmol mol⁻¹) at 82 hPa. Anomalies are differences from the 2004–15 mean water vapor mixing ratios for each month.

est ever observed by MLS at that pressure level. The change in tropical lower SWV from December 2014 to December 2015 was +1.1 ppm, ~50% of the typical seasonal mixing ratio amplitude at 82 hPa in the tropics. Strong water vapor increases in the tropical lower stratosphere at the end of 2015 were also observed at Hilo, Hawaii (20°N), and San José, Costa Rica (10°N), by balloonborne frost point hygrometers (Figs. 2.50b,c).

The seasonal variability of water vapor in the tropical lower stratosphere is predominantly controlled by the annual cycle of cold-point temperatures (CPTs) in the tropical tropopause layer (TTL). These minimum temperatures determine the amounts of water vapor that remain as moist tropospheric air masses are freeze-dried during their slow ascent into the stratosphere. Seasonal-to-interannual variations in tropical lower SWV are highly correlated with CPT

<figure><img src="image_88.png" /></figure>

**FIG. 2.49.** Global stratospheric water vapor anomalies (µmol mol⁻¹) centered on 82 hPa in (a) Dec 2014 and (b) Dec 2015 from the Aura Microwave Limb Sounder.

variations in the TTL. The dramatic increase in tropical lower SWV at the end of 2015 is consistent with the observed ~1°C increase in tropical CPTs over the same period (Fig. 2.50c).

Interannual variations in CPTs are potentially related to the changing phases of the El Niño–Southern Oscillation (ENSO) and the stratospheric quasi-bienial oscillation (QBO). In October, the QBO phase transitioned from easterly (cold) to westerly (warm) and persisted in the westerly phase through the end of 2015 (see sections 2b3, 2e3). The evolution towards a warmer TTL and wetter tropical lower stratosphere at the end of 2015 is consistent with this reversal of the QBO phase. Regionally, the enhancement of SWV in the tropical western Pacific and Indian Ocean regions is consistent with the adiabatic response of the TTL to reduced convection in this region as a result of the El Niño conditions present during 2015. Other factors such as variations in the strength of the Brewer–Dobson circulation can also impact SWV anomalies on an interannual timescale. However, given the potential interrelationships between ENSO, QBO, and the Brewer–Dobson circulation, a rigorous attribution of the positive SWV anomalies present at the end of 2015 is not possible.
<figure><img src="image_89.png" /></figure>

**FIG. 2.50.** Lower stratospheric water vapor anomalies (µmol mol⁻¹) at 82 hPa over four balloonborne frost point (FP) hygrometer stations. (a)–(d) show the anomalies of individual FP soundings (black) and of monthly zonal averages of MLS retrievals in the 5° latitude band containing the FP station (red). High-resolution FP vertical profile data were averaged between 70 and 100 hPa to emulate the MLS averaging kernel for 82 hPa. Each MLS monthly zonal mean was determined from 2000 to 3000 profiles. Tropical cold-point temperature anomalies based on the MERRA re-analysis [(c), blue curve] are generally well correlated with the tropical lower SWV anomalies.

Anomalies in tropical lower SWV propagate from the tropics to the midlatitudes of both hemispheres, as is visually demonstrated by the many “C”-shaped contours in Fig. 2.48b. The late 2015 wet anomaly in tropical lower SWV (Figs. 2.48b, 2.50c) was just starting to reach the midlatitudes of each hemisphere at the end of 2015.

During 2015, SWV anomalies over Lauder, New Zealand, were close to zero or slightly positive (Fig. 2.50d). These are consistent with the poleward transport of weak dry tropical SWV anomalies present at the end of 2014 and early 2015 (Fig. 2.49a), and

the 2014 Antarctic vortex being anomalously weak, warm, and less dehydrated (Davis et al. 2015; see sections 2b3 and 6h). In general, Southern Hemisphere midlatitude SWV can vary interannually with the degree of seasonal dehydration within the Antarctic vortex and the strength of the poleward transport of dehydrated air masses (Fig. 2.48b). Indeed, the 2015 Antarctic vortex was particularly strong (see section 6h), as evidenced by the appearance of a −0.5 ppm anomaly in the high southern latitudes near the end of 2015 (Fig. 2.48b).

**6) TROPOSPHERIC OZONE—**J. R. Ziemke and O. R. Cooper

Two of the most important reasons to monitor tropospheric ozone are that it is a surface pollutant with harmful biological effects and is a greenhouse gas that affects long-term climate change. Tropospheric ozone is also the primary source of the hydroxyl radical (OH), the main oxidizing agent for pollutants in the troposphere. Sources of tropospheric ozone include transport from the stratosphere, photochemical production from lightning NOₓ, and photochemical production from precursor gases emitted by the combustion of fossil fuels, biofuels, and biomass (e.g., Sauvage et al. 2007; Martin et al. 2007; Leung et al. 2007; Murray et al. 2013; Hess and Zbinden 2013; Young et al. 2013).

The variability of tropospheric ozone, from urban to hemispheric scales, is driven by a combination of photochemical ozone production and atmospheric transport. Tropospheric ozone production varies because its precursor gases and sunlight are variable. Transport phenomena that drive large-scale variability include ENSO (e.g., Chandra et al. 1998, 2009; Sudo and Takahashi 2001; Doherty et al. 2006; Koumoutsaris et al. 2008; Voulgarakis et al. 2011) and the Madden–Julian oscillation (MJO: Sun et al. 2014). Small- to large-scale tropospheric ozone variability also occurs over shorter periods, including weekly baroclinic timescales (e.g., Ziemke et al. 2015, and references therein), and finer scale airstream transport on the order of hours to days. Changes in tropospheric ozone at hemispheric and global scales include decadal trends (e.g., Hess and Zbinden 2013; Cooper et al. 2014; Lin et al. 2014; Parrish et al. 2014).

Global maps of annual means and anomalies of tropospheric column ozone from the satellite-based Ozone Monitoring Instrument (OMI) and MLS for 2015 are shown in Fig. 2.51 and Plate 2.1u, respectively. As in previous reports, OMI/MLS ozone trends are calculated only for latitudes 60°S–60°N where there is full annual coverage by OMI. In 2015, as for the last decade, annual average tropospheric column ozone
<figure><img src="image_90.png" /><figcaption>FIG. 2.51. Average OMI/MLS tropospheric ozone column ozone for 2015. Data poleward of ±60° are not shown due to the inability of OMI to measure ozone during polar night.</figcaption></figure>

amounts in the Northern Hemisphere exceeded those
in the Southern Hemisphere. Some basic features
of tropospheric column ozone include strong topo-
graphical effects, such as greatly reduced amounts
over the Tibetan Plateau and the western U.S. Rocky
Mountain region, with much larger amounts east and
west of these regions over both land and ocean. The
greatest annual mean tropospheric column values
were observed over the Mediterranean–South Asian
region and from eastern China eastward toward
North America. In the tropics, the west-to-east zonal
wave-1 pattern (Fishman et al. 1990) is evident, with
high values over the Atlantic and low values over the
Pacific. An extended band of high ozone was present
at 30°S, with the greatest amounts between southern
Africa and Australia. Zonally-averaged tropospheric
column averages and their 95% confidence intervals
for 2015 were 30.7 ± 2.2 DU for 60°S–60°N, 32.1 ±
2.6 DU for 0°–60°N, and 29.4 ± 1.9 DU for 0°–60°S.
These column averages convert to tropospheric bur-
dens of 291.2 ± 20.9, 152.1 ± 12.3, and 139.1 ± 9.0 Tg,
(Tg = 10¹² g), respectively. For comparison, the tro-
pospheric column averages for 2005–15 for the three
regions were 29.5 ± 2.1, 30.7 ± 2.5, and 28.2 ± 2.2 DU
(279.0 ± 19.9, 145.4 ± 11.8, and 133.6 ± 10.4 Tg).

The 2015 average tropospheric ozone burdens for each hemisphere and the globe were greater than those in 2014, and 12-month running averages of each show steady increases since October 2004 (Fig. 2.52). Linear trends (in Tg yr⁻¹) with their ± 2σ statistical uncertainties are also given. The increasing trends in OMI/MLS tropospheric column ozone are statistically significant for both hemispheric means and the near-global mean. Relative to the average burdens for 2005–15 the three trends all depict increases of 0.8%

<figure><img src="image_91.png" /><figcaption>FIG. 2.52. Monthly averages of OMI/MLS tropospheric ozone burdens (Tg) from Oct 2004 through Dec 2015. The top curve (black) shows 60°S–60°N monthly averages with 12-month running means. The bottom two curves show monthly averages and running means for the Northern Hemisphere (red) and Southern Hemisphere (blue). Slopes of linear fits (Tg yr⁻¹) of all three curves are also listed along with their ±2σ statistical uncertainties.</figcaption></figure>

yr⁻¹. The combined OMI/MLS record now exceeds
11 years and the measured increases are becoming
more indicative of true long-term trends, building on
similar findings from previous reports.

Cooper and Ziemke (2013) reported surface ozone
increasing since 1990 over eastern Asia and the west-
ern United States, but decreasing over the eastern
United States, using measurements by ground- and
satellite-based instruments. Cooper and Ziemke
(2014) presented a time series of near-global (60°S-
60°N) tropospheric burdens determined from satellite
measurements that indicated a statistically significant
increase over 2005–13 and Cooper and Ziemke (2015)
showed that the increase in global tropospheric ozone
continued through 2014.

For the past two years, the *State of the Climate*
tropospheric ozone summary was based upon only
the OMI/MLS satellite measurements (Ziemke et
al. 2006) due to insufficient updated analyses of
the ground-based measurement network data since
2012. Updates of the surface ozone data and trends
have continued to be infrequent during 2015, so once
again only the OMI/MLS satellite data are used. One
significant change from previous reports is the use of
new MLS version 4.2 ozone retrievals. A new activity
of the International Global Atmospheric Chemistry
(IGAC) project began in earnest in 2015 to produce
a Tropospheric Ozone Assessment Report (TOAR).
The TOAR is expected to be completed by the end
of 2016 and will summarize the global distribution
and trends of tropospheric ozone through 2014/15 (depending on the product) using a variety of satellite, surface, ozonesonde, lidar, and aircraft ozone measurements (www.igacproject.org/TOAR).

### 7) CARBON MONOXIDE—J. Flemming and A. Inness

Carbon monoxide (CO) is not a greenhouse gas, but plays a significant role in determining the abundance of climate forcing gases like methane ($CH_4$), through hydroxyl radical (OH) chemistry, and tropospheric ozone ($O_3$), as a chemical precursor (Hartmann et al. 2013). Thus, CO is regarded as an indirect climate forcing agent. Sources of CO include incomplete fossil fuel and biomass combustion and in situ production via the oxidation of $CH_4$ and other organic trace gases. Combustion and chemical in situ sources typically produce similar amounts of CO each year.

New in 2015 is a CAMS-based retrospective analysis of CO for the period 2003–15 based on total column CO retrievals from the Measurements of Pollution in the Troposphere (MOPITT) instrument (Deeter et al. 2013, Version 5). This dataset is part of the CAMS interim reanalysis of atmospheric composition, an extended and temporally more consistent dataset than the previous Monitoring Atmospheric Composition and Climate (MACC) reanalysis (Inness et al. 2013). The MACC has been used in previous *State of the Climate* assessments of CO and aerosols. MOPITT retrievals between 65°N and 65°S were assimilated into the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) that has been extended to simulate atmospheric chemistry (Flemming et al. 2015). The assimilation technique is documented in Inness et al. (2015b). The anthropogenic emissions for the assimilating model were taken from the MACCity inventory (Granier et al. 2011) that accounts for projected emission trends. Biomass burning emissions were taken from the Global Fire Assimilation System (v1.2, Kaiser et al. 2012). The global three-dimensional CO distribution from the CAMS interim reanalysis is used here to assess the anomalies in CO total columns for 2015.

The global CO burden in 2015 was significantly increased by the intensive El Niño-induced wildfires in Indonesia from mid-August to mid-November (Sidebar 2.2). Annual wildfire emissions from this region contributed 31% (140 Tg) of the global wildfire emissions in 2015, whereas for 2003–14 the contributions ranged from 5% to 20%. The highest total (biomass burning and anthropogenic) monthly CO emissions since 2003 were injected into the atmosphere during

the 2015 Indonesian fire period. This El Niño-related increase in Indonesian fires and CO emissions was already reported for 2014 (Flemming and Inness 2015) and high fire activity is anticipated for the March–April fire season in 2016. An analysis by Huijnen et al. (2016) suggests that the 2015 carbon emissions from the Indonesia fires were the second largest since the extreme El Niño year of 1997, although the 2015 emissions were only 25% of those in 1997.

Plate 2.1ac shows the relative 2015 anomalies of the total column CO (TCCO) from the CAMS interim reanalysis with respect to 2003–15. The strong positive TCCO anomalies were located predominantly over the Indonesian region and the eastern Indian Ocean, but the fire emissions increased CO over much of the tropics. Tropospheric CO mixing ratios between 50° and 100°E in the tropics in September and October were 50%–100% higher than the CO climatology. Larger-than-usual wildfire activity in North America during 2015 produced >10% anomalies in June–August and led to a positive anomaly in total column CO for the year. The CO anomaly of –10% over the Amazon basin reflects a decadal decrease in fires in that region, but the 2015 anomaly was not as strongly negative as in the two previous years.

The high global CO burden in 2015 occurred against a 12-year backdrop of a decreasing global CO burden. Figure 2.53 shows the time series of monthly mean global CO burdens since 2003. A decreasing linear trend of –0.86 ± 0.23% yr⁻¹ is evident, yet the monthly averaged global burdens for October–December 2015 are the highest values in the entire record. Worden et al. (2013) estimate trends of –1% yr⁻¹ for both the globe and Northern Hemisphere over the last decade by studying observations from

<figure><img src="image_92.png" /><figcaption>FIG. 2.53. Time series of monthly global CO burdens (Tg) from the CAMS interim reanalysis.</figcaption></figure>
One of the most extreme events of 2015 was the ex-
tensive burning of peat throughout large parts of Indonesia.
As a common practice in Indonesia, fires are set during
the dry season (July–October) to clear land and remove
agricultural residues. During intense dry seasons these
fires can penetrate into degraded subsurface peat soil with
enhanced flammability. They are extremely difficult to ex-
tinguish and can burn continuously until the return of the
monsoon rains, usually in late October or early November.
In 2015, the annual fires were more widespread and intense
than those that have typically occurred in Kalimantan since
the 1980s and in Sumatra since at least the 1960s (Field
et al. 2009). The strength and prevalence of these fires
are strongly influenced by large-scale climate patterns like
El Niño (Field et al. 2004; van der Werf 2008). Research
started after the strong 1997/98 El Niño, which induced a
severe fire/haze disaster in Indonesia, has provided a reli-
able understanding of how much fire and haze may occur
for a given drought strength (Usup et al. 2004; Field et al.
2009). Despite this predictive capability, the 2015 fires in
Indonesia still escalated to an environmental and public
health catastrophe (Thielen et al. 2015; Inness et al. 2015;
Field et al. 2015, manuscript submitted to Proc. Natl. Acad.
Sci. USA).

The 2015 Indonesia fire season began in August, and by September much of Sumatra, Kalimantan, Singapore, and parts of Malaysia and Thailand were covered in thick smoke, affecting the respiratory health of millions of people. Visibility was also reduced to less than 10% of normal over Borneo, and large parts of the region could not be seen from space, as was documented for previous fire events in that region (Marlier et al. 2013; Wang et al. 2004). Preliminary estimates suggest that greenhouse gas emissions from the burning (in CO₂ equivalent) exceeded Japan's 2013 emissions from fossil fuel combustion (Van der Werf 2015). Even after the worst of the 2015 Indonesian fires were no longer burning, the remaining pollution stretched halfway around the globe.

Ongoing research into the socioeconomic drivers of
the fires is beginning to identify the responsibilities of the
landholders and the need for political action in regulating
the agricultural practices in the region (Tacconi 2003). While
finding the socioeconomic causes of this event is beyond the
scope of this work, we can utilize analytical results from ob-
servations and reanalyses of atmospheric composition over
Indonesia to provide an assessment of the current monitoring
capabilities of observational and modeling systems.

To this end, we use the data assimilation system of
the Copernicus Atmosphere Monitoring Service (CAMS)
developed at the ECMWF since 2005. The interim CAMS
reanalysis is an improved version of the previous MACC
reanalysis (Inness et al. 2013) and is updated in quasi near-
real time. Observational datasets used, among others, are
the NASA MODIS Aerosol Optical Depth Collection 5
product (Remer et al. 2005) and the MOPITT V5 total
column carbon monoxide (CO) retrievals. A reanalysis
dataset provides a dynamically consistent 3D estimate of
the climate state at each time step and can be considered a
good proxy for atmospheric conditions, including variables
that are not directly observed. Here, 2015 anomalies of
CO and carbonaceous aerosols are determined from the
2003–15 CAMS reanalysis, while the ozone anomalies are
based on the 2005–14 ozone records from NASA’s Ozone
Monitoring Instrument (OMI) and Microwave Limb Sounder
(MLS) (Ziemke et al. 2006).

Realistic biomass burning emissions estimates, provided
by the Global Fire Assimilation System (GFAS; Kaiser et
al. 2012; Di Giuseppe et al. 2016, manuscript submitted
to J. Geophys. Res. Atmos.), are an important input to the
CAMS system. In the GFAS, the fire radiative power (FRP)
measured by the MODIS sensors on the Aqua and Terra
satellites is converted into emissions of 44 constituents
using the regression coefficients of Wooster et al. (2003).
The FRP observations accumulated over the period Au-
gust–October 2015 (Fig. SB2.3) provide an overview of
the extent and severity of the 2015 Indonesian fire season.

Fire emissions in Indonesia during August–October
were consistently and extraordinarily strong, as clearly
shown by the number of days in 2015 when daily emissions
of CO and biomass burning aerosols [black carbon (BC),

<figure><img src="image_93.png" /><figcaption>FIG. SB2.3. Fire radiative power (W m⁻²) accumulated over Indonesia during the 2015 fire season (Aug-Oct).</figcaption></figure>
<figure><img src="image_94.png" /><figcaption>FIG. SB2.4. (a) Daily Indonesian fire emissions in 2015 of CO (Mt day⁻¹) and OM+BC aerosols (kt day⁻¹). Red bars show the days in 2015 with emissions greater than the previous (2003–14) maximum emission estimate for that day. (b) Annual fire emissions of CO and OM+BC aerosols (Mt yr⁻¹) from Indonesia indicating their scale relative to the 2015 total anthropogenic CO emissions from the United States (red line) and Europe (blue line) from the MACCity emissions inventory. GFASv1.2 emissions of CO and OM+BC from biomass burning are directly proportional.</figcaption></figure>

and organic matter (OM)] exceeded the maximum daily emissions during the same days in 2003–14 (Fig. SB2.4a). Total annual fire emissions over Indonesia (10°S–5°N, 60°–180°E) computed by the GFASv1.2 system for CO and BC+OM are substantially greater for El Niño years 2006, 2009, and 2015 (Fig. SB2.4b). For perspective, CO emissions from the Indonesian fires for 2015 were approximately three times the 2015 total anthropogenic emissions from the continental United States (25°–50°N, 70°–130°W) and Europe (30°–70°N, 10°W–45°E).

Inness et al. (2015a) utilized reanalysis data to investigate connections between El Niño/La Niña and atmospheric composition fields such as ozone, CO, and aerosols. They concluded that anomalies of CO and biomass burning aerosols depend mainly on local emissions. Hence, their strong positive anomalies over Indonesia during August–October 2015 (Figs. SB2.5a,b) were a direct consequence of the widespread fires in that region. Anomalies in ozone (O₃; Fig. SB2.5c), also produced by these fires, were further affected by El Niño–induced dynamical changes that altered the downward transport of O₃ from the stratosphere and modified O₃ photolysis rates. Total column CO anomalies that reached 500% in the core of the fire region were remarkable (Fig. SB2.5a), but even more striking were the extremely large anomalies (~2000%) in total AOD at 550 nm for biomass burning (OM+BC) aerosols that covered large areas of the Indian and western Pacific Oceans (Fig. SB2.5b). For tropospheric ozone (Fig. SB2.5c), the positive anomalies over Indonesia were a more modest 30%–40%.

The CAMS reanalysis is a valid tool for monitoring the evolution of large-scale pollution events in quasi near–real time and providing useful information at the onset of a pollution-related crisis. Because El Niño is highly predictable on a seasonal timescale and Indonesian fires are known to assume catastrophic proportions during exceptionally intense El Niño years, further development of CAMS towards integrating a seasonal prediction system with fire risk and air quality forecasts would provide comprehensive information for early warnings and planning of mitigation actions.

<figure><img src="image_95.png" /><figcaption>FIG. SB2.5. Anomalies (%) averaged over the 2015 Indonesian fire season (Aug–Oct) from the CAMS reanalysis of (a) total column CO and (b) biomass burning AOD at 550 nm. (c) Mean OMI/MLS tropospheric column ozone anomalies for Aug–Oct 2015, with contours drawn every 5%.</figcaption></figure>
different satellite-based instruments. The spatial distribution of CO trends from the CAMS reanalysis (Fig. 2.54) shows significant decreasing trends of -1.0% to -1.5% year⁻¹ in most regions north of 40°N, up to -3.0% year⁻¹ over the Amazon basin and its outflow regions, -0.5% to -1.0% year⁻¹ for most of the rest of the globe, and almost no trends over India, eastern China, and a large region surrounding Indonesia. Diminished anthropogenic emissions in North America and Europe as well as strong reductions in fire emissions over South America are the main causes for the decreasing global CO burden during 2003–15.

### h. Land Surface Properties

#### 1) LAND SURFACE ALBEDO DYNAMICS—B. Pinty and N. Gobron

The land surface albedo is the fraction of solar radiation scattered backward by land surfaces. In the presence of vegetation, surface albedo results from complex nonlinear radiation transfer processes determining the amount of radiation that is scattered by the vegetation and its background, transmitted through the vegetation layer, or absorbed by the vegetation layer and its background (Pinty 2012).

The geographical distribution of normalized anomalies in visible and near-infrared surface albedo for 2015 calculated with respect to a 2003–15 base period [for which two MODIS sensors are available (Schaaf et al. 2002)] are shown in Plates 2.1z and 2.1aa, respectively. Mid- and high-latitude regions of the Northern Hemisphere are characterized by both positive and negative anomalies, mainly as a consequence of interannual variations in cover, amount, and duration of snow in winter and spring seasons. The large negative anomalies over eastern Europe, southern Sweden, western Russia, Caucasus, southwestern Siberia, and northern China are probably associated with a below-average snow cover in winter and early spring seasons, due to the occurrence of relatively high temperatures in some of these regions. Similarly, negative anomalies over Canada can be related to an unusually small snow cover extent (section 2c2). The amplitude of these negative changes can reach (or locally exceed) ±30% in relative units and is larger in the visible than the near-infrared domain, although with the same sign. By contrast, the average February snow cover extent across the eastern United States resulted in a positive annual anomaly.

A few snow-free regions show positive anomalies, especially in the visible domain. In the equatorial regions, these are well marked over Indonesia and, with more limited amplitude, over Amazonia, cen-

tral Africa and Queensland, Australia. These are generally associated with less favorable vegetation growing conditions compared with previous years (section 2h2), although contamination of the albedo retrievals by clouds and aerosol load, especially in Indonesia (Sidebar 2.2), may also have induced some artifacts. The majority of snow-free regions exhibit noticeable negative anomalies, particularly in the visible domain, across Mexico and the southern United States and over the southern regions of South America, Australia, India, and China. The unusually warm conditions over northern regions such as western Europe, Turkey, and northwestern Iran may have contributed to the observed limited negative anomalies. A significant fraction of these variations are attributable to vegetation dynamics (Pinty et al. 2011a, 2011b) over these regions, which are sensitive to stress from ambient conditions and, in particular, water availability. Although weaker in the near-infrared, these negative anomalies sometimes occur simultaneously in the visible and the near infrared. Generally, the amplitude of both positive and negative anomalies changes seasonally.

Analysis of the zonally-averaged albedo anomalies in the visible and near-infrared (Fig. 2.55) spectral domains indicates considerable interannual variations related to the occurrence of snow events in winter and spring at mid- and high latitudes but also in vegetation conditions during the spring and summer periods. Strong negative anomalies are noticeable between 20° and 45°S, featuring a deviation from average conditions mainly over the southern regions in Latin America, Africa, and Australia. Limited but consistent positive anomalies are discernible across equatorial regions in 2015.

<figure><img src="image_96.png" /><figcaption>FIG. 2.54. Linear trends (% yr⁻¹) in total column CO from the CAMS interim reanalysis for the period 2003–15. All trends are statistically significant at the 95% level of confidence except for those inside red contours.</figcaption></figure>
<figure><img src="image_97.png" /></figure>

**FIG. 2.55.** Zonal means of the MODIS White Sky broadband surface albedo (NASA) normalized anomalies in the (a) visible and (b) near-infrared domain relative to a 2003–15 base period.

The 12-month running mean globally averaged normalized anomalies (Fig. 2.56) vary within ~±5% (~±3%) in the visible (near-infrared) domain. Antarctica is excluded owing to missing data. The year began with globally averaged negative albedo anomalies and ended with slightly positive anomalies. The trend towards positive anomalies was driven by contributions from the Southern Hemisphere. Figure 2.56 also shows analogous interannual and multiannual variations in the visible and near infrared during the 2003–15 base period, with mostly positive anomalies at the beginning of this base period.

## 2) TERRESTRIAL VEGETATION DYNAMICS—N. Gobron

Analysis of the 18-year record shows that large spatiotemporal deviations in vegetation dynamics occurred at regional and continental scales during 2015 (Plate 2.1y). The state of vegetation is examined by merging 1998–2015 estimates of the Fraction of Ab-

<figure><img src="image_98.png" /></figure>

**FIG. 2.56.** Globally-averaged MODIS White Sky broadband surface albedo (NASA) normalized anomalies with a 12-month running mean in the (a) visible and (b) near-infrared domain relative to a 2003–15 base period at the global scale (black), Northern Hemisphere (blue), and Southern Hemisphere (red).

sorbed Photosynthetically Active Radiation (FAPAR) from three different sensors: SeaWiFS (NASA), MERIS (ESA), and MODIS (NASA) (Gobron et al. 2010; Pinty et al. 2011b; Gobron and Robustelli 2013).

A large number of regions experienced seasonal precipitation deficits in 2015 (sections 2d4 and 2d9), especially in the Southern Hemisphere, along with much higher-than-average annual temperatures across most of the globe (section 2b1). This translates into a large variation in vegetated surface conditions.

The largest annual negative anomalies (not favorable for vegetation) occurred over the high northern latitudes from Alaska to Sweden and Norway, and also over the equatorial belt from central and northeastern Brazil, central Africa, and Indonesia. To a lesser extent, regions near the Black and Caspian Seas were also affected.

The largest positive annual anomalies appeared over Mexico, the southern United States, Minas Gerais (Brazil), Turkey, and China. Limited positive anomalies occurred over eastern parts of Europe, India, and the Ural region of Russia.

Below-normal precipitation occurred during the second half of the year in Brazil and Indonesia, impacting the annual anomalies. The anomalies over southwestern and central Africa were mainly due to a warmer-than-normal spring together with below-normal precipitation.

Higher precipitation in spring over Mexico and the southern United States and in autumn over western China contributed to favorable conditions for vegetation health and growth, as was the case in 2014. Over Turkey, the positive anomalies were mainly correlated with a slight excess of rainfall and higher temperatures compared to previous years.

Zonally averaged monthly mean anomalies (Fig. 2.57) illustrate the differences between the two
<figure><img src="image_99.png" /><figcaption>FIG. 2.57. Time series of monthly zonal anomalies (base period: 1998–2015) of the Fraction of Absorbed Photo-synthetic Radiation (FAPAR) from SeaWiFS, MERRIS, and MODIS sensors. Gray areas indicate missing data.</figcaption></figure>

hemispheres, with persistent negative anomalies over the Southern Hemisphere during all seasons from around 2002 to 2009. A succession of positive and negative anomalies, suggesting a seasonal cycle, are depicted between 10°S and 30°S since 2010. In contrast, strong positive anomalies are observed over regions between 20° and 60°N since 2012; these persisted during 2015. Larger seasonal negative anomalies are seen over mid- and high latitudes in the Northern Hemisphere since mid-2012. A strong negative anomaly is depicted in 2015 around the equatorial regions, likely influenced by low precipitation and severe fires over Indonesia (Sidebar 2.2); it appeared to extend into the entire Southern Hemisphere during the last quarter of 2015.

The monthly mean averaged anomalies smoothed using a 12-month running average (Fig. 2.58) indicate that 2015 shows a relatively unhealthy state of the vegetation over the Southern Hemisphere compared with a more healthy state over the Northern Hemisphere.

### 3) BIOMASS BURNING—J. W. Kaiser, G. R. van der Werf, and A. Heil

Climate and weather provide boundary conditions for biomass burning or landscape fires to occur; in return these fires influence climate and weather by emitting greenhouse gases and aerosols and by modifying surface properties such as albedo and roughness. Generally, most fires occur in the tropics where they are often started by humans to manage the landscape. This includes frequent burning in many

<figure><img src="image_100.png" /><figcaption>FIG. 2.58. Average monthly FAPAR anomalies with a 12-month running mean (base period: 1998–2015) at the global scale (black), Northern Hemisphere (blue), and Southern Hemisphere (red).</figcaption></figure>

savannas and the use of fire to clear forest and make way for agricultural land. In temperate and boreal regions, fires tend to occur less frequently and can be either human or lightning ignited.

Since the late 1990s, fire occurrence and the associated burned area has been routinely detected by satellites. The Global Fire Assimilation System (GFAS) builds on active fire detections and their associated fire radiative power to estimate emissions in near-real time (Kaiser et al. 2012). GFAS is calibrated to partly match the Global Fire Emissions Database (GFED), which estimates emissions based on burned area and fuel consumption which have a much longer latency (van der Werf et al. 2010). The combined use of GFAS (2001–15) and GFED (1997–2014) indicates that fire emissions were on average 2.1 Pg C year⁻¹ (Pg = 10¹⁵ g), with substantial interannual variability, the latter mostly stemming from tropical deforestation zones and the boreal region where fire activity varies more from year to year than in most savanna areas.

In 2015, total global fire emissions were somewhat above average (+4%, see Table 2.8). By far, the largest anomaly was found in tropical Asia, where emissions were almost three times as high as the 2001–14 average (Plate 2.1ab, Fig. 2.59). As in 2014, North America also saw higher-than-average emissions (see sections 7b1 and 7b2). These positive anomalies were partially compensated for on a global scale by below-average emissions from South America and Northern Hemisphere Africa. The former is related to a downward trend in deforestation during the last decade (Chen et al. 2013), although fire emissions in 2015 were somewhat higher than in the previous two years. The latter is in line with an ongoing trend, possibly due to expansion of cropland (Andela and van der Werf 2014).

The exceptional fire season in tropical Asia is apparent in the pronounced aerosol and carbon monoxide (CO) anomalies (sections 2g3, 2g7; Sidebar 2.2). The fires were most active during September and October (see Fig. 2.60) and located predominantly in
<table><caption>TABLE 2.8. Annual continental-scale biomass burning budgets in terms of carbon emission (Tg C yr⁻¹). 2001–02 from GFASv1.0 (Remy and Kaiser 2014), 2003–15 from GFASv1.3.</caption><thead><tr><th rowspan="2">Time Period</th><th rowspan="2">Quantity<br>Tg C yr⁻¹</th><th colspan="2">2001–14</th><th colspan="2">2015</th></tr><tr><th>Mean Value<br>(Range)</th><th>Value</th><th>Value</th><th>Anomaly<br>(%)</th></tr></thead><tbody><tr><td>Global</td><td></td><td>2116 (1803–2371)</td><td>2201</td><td></td><td>86 (4%)</td></tr><tr><td>North America</td><td>30°–57°N<br>170°W–30°W</td><td>117 (50–171)</td><td>172</td><td></td><td>+55 (+47%)</td></tr><tr><td>Central America</td><td>0°–30°N<br>170°W–30°W</td><td>71 (54–102)</td><td>72</td><td></td><td>+1 (+1%)</td></tr><tr><td>S. Hem. America</td><td>0°–60°S<br>170°W–30°W</td><td>314 (170–477)</td><td>246</td><td></td><td>-68 (-22%)</td></tr><tr><td>Europe and Mediterranean</td><td>30°–75°N<br>30°W–60°E</td><td>39 (26–60)</td><td>36</td><td></td><td>-3 (-9%)</td></tr><tr><td>N. Hem. Africa</td><td>0°–30°N<br>30°W–60°E</td><td>405 (337–506)</td><td>369</td><td></td><td>-36 (-9%)</td></tr><tr><td>S. Hem. Africa</td><td>0°–35°S<br>30°W–60°E</td><td>519 (473–585)</td><td>509</td><td></td><td>-10 (-2%)</td></tr><tr><td>Northern Asia</td><td>30°–75°N<br>60°E–170°W</td><td>227 (122–449)</td><td>202</td><td></td><td>-25 (-11%)</td></tr><tr><td>Southeast Asia</td><td>10°–30°N<br>60°E–170°W</td><td>129 (83–173)</td><td>116</td><td></td><td>-13 (-10%)</td></tr><tr><td>Tropical Asia</td><td>10°N–10°S<br>60°E–170°W</td><td>123 (40–240)</td><td>340</td><td></td><td>+217 (+176%)</td></tr><tr><td>Australia</td><td>10°–50°S<br>60°E–170°W</td><td>172 (58–296)</td><td>140</td><td></td><td>-32 (-18%)</td></tr><tr><td>Sumatra</td><td></td><td>65 (17–147)</td><td>183</td><td></td><td>+118 (+182%)</td></tr><tr><td>Borneo</td><td></td><td>41 (8–93)</td><td>99</td><td></td><td>+58 (+142%)</td></tr></tbody></table>

Sumatra and Kalimantan (see Table 2.8, Plate 2.1ab, Fig. 2.59 and Fig. SB2.3). These regions are most vulnerable to ENSO because drainage and deforestation have created large areas with degraded peatlands that

can burn easily under El Niño-induced drought conditions. Such peat fires are difficult to extinguish and usually last until the onset of the wet season in late October or early November. Accordingly, increased emissions were observed during the previous El Niño years of 2004, 2006, and 2009 (Fig. 2.60a).

Smoke from open fires in Indonesia has a strong impact on residents and economy (Marlier et al. 2013; Sidebar 2.2). In addition, peat burning represents a net source of CO₂ to the atmosphere because drainage prevents regrowth of peat. During the 2015 fire season of tropical Asia, about 80% of the pyrogenic carbon flux occurred in peatlands. Both the carbon flux and its relative peatland contribution were the highest since the MODIS record started in 2001 (Fig. 2.60a).

Pinpointing the exact magnitude of emissions remains challenging. This is largely due to difficulties in estimating the burn depth of peat fires, leading to larger-than-average uncertainties in any kind

<figure><img src="image_101.png" /><figcaption>FIG. 2.59. Global map of fire activity in 2015 in terms of actual carbon consumption. (Source: GFASv1.3.)</figcaption></figure>
of emission assessment. Instead of fire
observations, Huijnen et al. (2016) used
satellite-based CO observations of the
smoke plume and in situ measurements
of the CO emission factors to estimate
a carbon flux of 227 ± 66 Tg C for the
most affected subregion of tropical
Asia during September and October.
The corresponding values for GFASv1.2
and GFASv1.3 are 320 and 250 Tg C,
respectively, while preliminary GFED4
estimates are about 400 Tg C (www.
globalfiredata.org/updates.html), but
this estimate includes the full fire season.
Compared to GFASv1.2, GFASv1.3 in-
cludes an improved representation of the
diurnal variability of cloud cover, which
prevents satellite observations of fires,
and a higher-resolution peat map based
on Wetland International (Wahyunto
et al. 2003, 2004). While 2015 was the
highest fire year in the GFAS record in
tropical Asia, scaling the 2015 GFAS re-
cord to GFED based on a common base
period in 2006 indicates that 2015 was
only about half as strong as the extreme
year 1997 (Fig. 2.60b).

<figure><img src="image_102.png" /><figcaption>FIG. 2.60. (a) Temporal evolution of fire emissions in tropical Asia during the Sep–Oct 2015 fire season, compared to the four most active fire seasons since 2003 (5-day smoothed GFASv1.3 data). The inset shows annual total emissions since 2003 and the relative contribution of fire emissions from peat fires, highlighting the increased relative importance of these fires in high fire years. (b) Monthly fire activity in tropical Asia for 1997–2015. The y-axis ranges are adjusted so that GFED4s and GFASv1.3 coincide graphically in Oct 2006.</figcaption></figure>
The significant 2015 El Niño included a reduction in Pacific trade winds with anomalous cross-equatorial southerly surface winds in the eastern Pacific and a shift in tropical precipitation eastward from the Maritime Continent to a region extending from the date line to South America, mostly slightly north of the equator, associated with an eastward shift in fresh surface salinities. Sea surface temperatures (SSTs) were anomalously warm in 2015 from the date-line all the way to South America along the equator, with anomalously low chlorophyll-a owing to suppression of nutrient-rich upwelling. Redistribution of warm ocean waters to the surface during El Niño contributed, along with a long-term upward trend, to record high global average SSTs in 2015. Anomalously eastward currents along the equator and in the North Equatorial Countercurrent continued a pattern from 2014. These anomalous currents contributed to sea level and upper ocean heat content (OHC) falling in the western tropical Pacific and rising in the east, again building on a 2014 pattern. To summarize in haiku form:

> *El Niño waxes, warm waters shoal, flow eastward, Earth's fever rises.*

In the North Pacific, anomalously warm SSTs, high OHC, high sea level, and low chlorophyll-a persisted (as did the warm offshore “Blob”) along the west coast of North America in 2015, the second year of the warm phase of the Pacific decadal oscillation. In these warm conditions, widespread harmful algal blooms developed along much of the West Coast.

North Atlantic SSTs southeast of Greenland were even colder in 2015 than the already cold previous year, with anomalously low OHC, fresh sea surface salinity (SSS) and subsurface salinity, low chlorophyll-a, and anomalous heat flux into the ocean. In contrast, western North Atlantic subtropical SSTs were anomalously warm in 2015, with high OHC and sea level along the east coast of North America. Subtropical mode water formation rates in the region were weak in 2014 and weaker in 2015, consistent with weaker-than-normal winds and anomalous heat flux into the ocean in their formation region. These signatures are consistent with a strong positive North Atlantic Oscillation index in 2014 and 2015. The SST pattern is also associated in climate models with a reduction

in the Atlantic meridional overturning circulation, as observed over the past decade.

In the Indian Ocean, anomalously northwesterly winds east of Madagascar in 2015 resulted in anomalous eastward flow (a diminished westward South Equatorial Current), consistent with slightly low sea level and OHC anomalies east of Madagascar, coupled with much higher sea level and OHC anomalies to the north. Surface currents on the equator were anomalously westward. Overall sea level and OHC remained elevated in the Indian Ocean, with a record high for SST.

In the Southern Ocean (see also section 6g), anomalously easterly winds (diminished westerlies) at about 40°S in the Indian sector and 50°S in the Pacific sector in 2015 were associated with anomalously high sea level and OHC at the northern edge of the Antarctic Circumpolar Current, consistent with a southward expansion of the subtropical gyres.

Globally, ocean heat content and sea level both continued to rise, reaching record high values in 2015. The ocean rate of uptake of carbon from the atmosphere has risen along with atmospheric CO₂ concentrations.

Sea surface temperatures play a key role in regulating climate and its variability by modulating air-sea fluxes and tropical precipitation anomalies. In particular, slow variations in SST, such as those associated with the El Niño-Southern Oscillation (ENSO), Atlantic multidecadal oscillation (AMO), Pacific decadal oscillation (PDO), Indian Ocean dipole (IOD), and Atlantic Niño, are sources of predictability for climate fluctuations on time scales of a season and longer (Deser et al. 2010). This summary of global SST variations in 2015 emphasizes the evolutions of El Niño, the record warming in the tropical Indian Ocean, and the persistent warming in the North Pacific. The 2015 SST anomalies are also placed in the context of the historical record since 1950.

To quantify uncertainties in SST estimates, four SST products are examined: 1) the weekly Optimal Interpolation SST version 2 (OISST; Reynolds et al. 2002); 2) the Extended Reconstructed SST version 3b (ERSST.v3b; Smith et al. 2008); 3) the Met Office Hadley Centre's sea ice and SST dataset (HadISST; Rayner et al. 2003); and 4) the recent update of ERSST, version 4 (ERSST.v4; Huang et al. 2015). OISST is a satellite-based analysis that uses in situ data for bias adjustments of Advanced Very High Resolution Radiometer (AVHRR) data with 1° resolution, available since
November 1981. ERSST.v3b, ERSST.v4, and HadISST are historical analyses beginning in the 19th century, and all apply statistical methods to data from the recent period to extend the SST analysis back in time when in situ observations were sparse. ERSST.v3b and ERSST.v4 include in situ data only and are produced at 2° resolution; both are presented here because v4 is new to this report. HadISST includes both in situ measurements and AVHRR SST retrievals from 1982 onward, available at 1° resolution. Here, SST variations are quantified as SST anomalies (SSTA), defined as departures from the 1981–2010 climatology (www.cpc.ncep.noaa.gov/products/people/yxue/sstclim).

Yearly mean 2015 SSTA (Fig. 3.1a) were characterized by a basinwide warming with a maximum amplitude exceeding +2°C in the equatorial eastern Pacific, reflecting the dominant influences of the 2015 El Niño. Warming was asymmetrical around the equator, with a second warming center around 15°N that extended from west of Hawaii to Baja California. In the high latitude North Pacific, strong positive SSTA in the northeast Pacific around 45°N dubbed “The Blob” emerged around the end of 2013 (Bond et al. 2015) and largely persisted in 2014/15. In 2015, the normalized monthly PDO index had an average value of +1.1 (www.cpc.ncep.noaa.gov/products/GODAS/), continuing a shift to positive

values that commenced in 2014. In the Atlantic Ocean, SST was above normal in the Gulf of Mexico and along the east coast of North America, and below normal in the subpolar region. In the tropical Indian Ocean, positive SSTA exceeding +0.6°C was observed across much of the basin.

SSTA tendencies from 2014 to 2015 (Fig. 3.1b) show a substantial warming in the central eastern Pacific and cooling in the western tropical Pacific, reflecting the transition from a weak central Pacific warming in 2014 to a strong eastern Pacific warming in 2015. Compared to the mean SSTA in 2014, the positive SSTA extending from Hawaii to Baja California was enhanced, while the negative SSTA in the southeastern subtropical Pacific diminished. There was a warming tendency across the tropical Indian Ocean and a cooling tendency in the subpolar North Atlantic.

Boreal winter 2014/15 (December–February; Fig. 3.2a) was characterized by positive SSTA exceeding +1 standard deviation (STD; Fig. 3.2a, black solid contour) in the western equatorial Pacific, positive SSTA exceeding +2.5 STD (Fig. 3.2a, white solid contour) along the west coast of North America, and negative SSTA exceeding –1 STD in the southeastern subtropical Pacific. By spring 2015 (Fig. 3.2b), the positive SSTA in the western equatorial Pacific built and shifted eastward to near the date line. Positive SSTA emerged in the far eastern equatorial Pacific, while the negative SSTA in the southeastern Pacific diminished. NOAA declared El Niño conditions by March 2015 (see section 4b1). The positive SSTA in the central eastern equatorial Pacific grew rapidly in summer/autumn 2015, and exceeded +2.5°C in September–November (Fig. 3.2c). With the rapid growth of the 2015 El Niño, the positive SSTA near Baja California extended southwestward to Hawaii and strengthened to exceed +2.5 STD over a large area in the northeastern subtropical Pacific in summer/autumn 2015. These conditions were favorable for eastern Pacific hurricane activity (section 4e3). Coinciding with the rapid growth of El Niño, positive SSTA in the tropical Indian Ocean grew to exceed +2.5 STD in autumn 2015 (Fig. 3.2d). In the high latitude North Pacific, positive SSTA exceeding +2.5 STD along the west coast of North America persisted most of the year. In the North Atlantic, positive SSTA along the east coast of North America and negative SSTA in the subpolar region also persisted.

To provide a historical perspective for regional and global yearly mean SSTA in 2015, three historical analyses (ERSST.v4, ERSST.v3b, and HadISST) are compared from 1950 to 2015 and one modern analy-

<figure><img src="image_103.png" /><figcaption>**FIG. 3.1.** (a) Yearly mean OISST anomaly in 2015 (°C, relative to the 1981–2010 average) and (b) 2015–2014 OISST difference.</figcaption></figure>
<figure><img src="image_104.png" /></figure>

FIG. 3.2. Seasonal mean SSTA from OISST (shading, °C, relative to the 1981–2010 average) for (a) Dec 2014 to Feb 2015, (b) Mar to May 2015, (c) Jun to Aug 2015, and (d) Sep to Nov 2015. Black solid contours are +1, black dashed –1, white solid +2.5, and white dashed –2.5 normalized seasonal mean SSTA, based on 1981–2010 seasonal mean standard deviations.

sis (OISST) from 1982 to 2015 (Fig. 3.3). The SSTA time series of OISST is largely consistent with those of ERSST.v3b in the common period, 1982–2015. HadISST also agrees well with OISST and ERSST. v3b except it is generally cooler in the tropical Indian

Ocean and the differences can reach 0.2°C. However, ERSST.v4 is noticeably warmer than other SST products (Karl et al. 2015), as discussed below.

The global mean SSTA is dominated by a warming trend superimposed with interannual variations largely associated with El Niño and La Niña events (Fig. 3.3a), where the peaks and valleys in the global ocean SSTA often correspond with those in the tropical Pacific SSTA (Fig. 3.3b). The mean SSTA in the tropical Pacific increased by 0.23°–0.29°C from 2014 to 2015, and 2015 surpassed 1997 as the warmest year since 1950. Partially owing to the strong warming in the tropical Pacific, the mean SSTA in the global ocean increased by 0.08°–0.11°C from 2014 to 2015, depending on the dataset examined, and 2015 surpassed 2014 as the warmest year since 1950.

For the global ocean, the surface warming trend for 2000–15 increased by 0.03°–0.04°C decade⁻¹ compared to

the 2000–14 trend. Because of this increase, the warming trend in 2000–15 (rising 0.13°C, 0.07°C, 0.08°C, and 0.08°C decade⁻¹ in ERSST.v4, ERSST.v3b, HadISST, and OISST, respectively) became comparable to the warming trend in 1950–99 (rising 0.09°C, 0.07°C, and 0.06°C decade⁻¹ in ERSST.v4, ERSST.v3b, and HadISST, respectively). Compared to ERSST.v3b and HadISST, the warming trend in ERSST.v4 was 0.05°–0.06°C decade⁻¹ higher in 2000–15. Three factors contribute to the stronger warming trend in ERSST.v4 relative to other products in the more recent period (Karl et al. 2015; Huang et al. 2015): 1) the correction of buoy data to ship data and an increase in buoy data (which were not included in ERSST.v3b and OISST); 2) more weight given to more accurate buoy data in the reconstruction of SST; and 3) a continuous correction of ship data based on night marine air temperature. Huang et al. (2015) and Kennedy (2014) discuss bias correction uncertainties of ship and buoy data and reconstruction of historical SST analyses.

The tropical Indian Ocean SSTA is dominated by an upward trend superimposed with interannual

<figure><img src="image_105.png" /></figure>

FIG. 3.3. Yearly mean SSTA (°C, relative to 1981–2010 averages) for ERSST.v4 (black), ERSST.v3b (blue), and HadISST (purple) for 1950–2015 and OISST (yellow) for 1982–2015, averaged over the (a) global, (b) tropical Pacific, (c) tropical Indian, (d) tropical Atlantic, (e) North Pacific, (f) North Atlantic, and (g) Southern Ocean.
variations (Fig. 3.3c). The interannual variations in the tropical Indian Ocean SSTA correspond well with those in the tropical Pacific SSTA due to the remote influences of ENSO (Kumar et al. 2014). The tropical Indian Ocean SSTA increased by 0.13°–0.20°C from 2014 to 2015, making 2015 the warmest year since 1950.

Tropical Atlantic SST reached a historical high in 2010, cooled down substantially in 2011/12, and rebounded gradually in 2013–15 (Fig. 3.3d). North Pacific SSTA increased by 0.10°–0.17°C from 2013 to 2014, and changed little from 2014 to 2015 (Fig. 3.3e).

North Atlantic SSTA reached a historical high in 2012, and cooled since that time (Fig. 3.3f). In the Southern Ocean, ERSST.v4 was warmer by about 0.08°C than ERSST.v3b, HadISST, and OISST, which show consistent values after 2009 (Fig. 3.3g).

c. Ocean heat content—G. C. Johnson, J. M. Lyman, T. Boyer, C. M. Domingues, M. Ishii, R. Killick, D. Monselesan, and S. E. Wijffels

Storage and transport of heat in the ocean are central to aspects of climate such as ENSO (Roemmich and Gilson 2011), tropical cyclones (Goni et al. 2009), sea level rise (e.g., Domingues et al. 2008), variations

SIDEBAR 3.1: A WIDESPREAD HARMFUL ALGAL BLOOM IN THE NORTHEAST PACIFIC—V. L. TRAINER, Q. DORTCH, N. G. ADAMS, B. D. BILL, G. DOUCETTE, AND R. KUDELA

In the late spring and summer 2015, a widespread harmful algal bloom (HAB) of the marine diatom *Pseudo-nitzschia*, stretching off the west coast of North America from central California to British Columbia, Canada, resulted in significant impacts to marine life, coastal resources, and the human communities that depend on these resources. Blooms of *Pseudo-nitzschia* produce a potent neurotoxin, domoic acid, which can accumulate in shellfish, other invertebrates, and sometimes fish, leading to illness and death in a variety of seabirds and marine mammals. Human consumption of toxin-contaminated shellfish can result in Amnesic Shellfish Poisoning (ASP), which can be life threatening. Detectable concentrations of toxin, although well below levels of concern for human consumption, have been measured in finfish like salmon, tuna, and pollock. The greatest human health risk is from recreationally harvested shellfish; commercial supplies are closely monitored and have not resulted in human illnesses. States maintain websites indicating where shellfish can be safely harvested.

Although these blooms can occur annually at “hot spots” along the U.S. West Coast, the largest impacts and most widespread closures typically occur in autumn. Samples collected on two research cruises in June and July 2015 demonstrated that domoic acid was measurable at most sites in Washington and Oregon (Fig. SB3.1).

The 2015 bloom was detected in early May, and in response, Washington State closed its scheduled razor clam digs on coastal beaches. The abundance of *Pseudo-nitzschia* and concentrations of domoic acid in razor clams on Washington State beaches in 2015 greatly exceeded values observed during springtime blooms that have only rarely occurred on the Washington coast since 1991, when domoic acid events were first recognized on the U.S. West Coast. Comparison with a

typical springtime bloom experienced in 2005 illustrates the magnitude of the 2015 domoic acid event (Fig. SB3.2).

Scientists quickly recognized that the bloom extended from California's Channel Islands to as far north as Vancouver Island. The bloom is the largest and its effects have been the

<figure><img src="image_106.png" /><figcaption>FIG. SB3.1. Cellular domoic acid (DA) in phytoplankton net tows (several liters seawater filtered; Research Vessel (R/V) *Frosti*, sampled north to south) or quantified on 0.45 mm filters (1 liter seawater filtered; R/V *Ocean Starr*, sampled south to north).</figcaption></figure>
in the global average surface warming rate (Meehl et al. 2013), and melting of ice sheet outlet glaciers around Greenland (Straneo and Heimbach 2013) and Antarctica (Rignot et al. 2013). Ocean warming accounts for about 93% of the total increase in energy storage in the climate system from 1971 to 2010 (Rhein et al. 2013).

Maps of annual (Fig. 3.4) upper (0–700 m) ocean heat content anomaly (OHCA) relative to a 1993–2015 baseline mean are generated from a combination of in situ ocean temperature data and satellite altimetry data following Johnson et al. (2015a), but using Argo

(Riser et al. 2016) data downloaded in January 2016. Near-global average seasonal temperature anomalies (Fig. 3.5) vs. pressure from Argo data (Roemmich and Gilson 2009, updated) since 2004 and in situ global estimates of OHCA (Fig. 3.6) for various pressure layers from multiple research groups are also discussed. Here, increases in OHCA are sometimes referred to as warming and OHCA decreases as cooling.

For the second consecutive year (see Johnson et al. 2015a) dramatic upper OHCA cooling east of the Philippines fed warming in the equatorial Pacific between 2014 and 2015 (Fig. 3.4b) via

longest-lasting of all U.S. West Coast *Pseudo-nitzschia* events in at least the past 15 years; concentrations of domoic acid in seawater, some forage fish, and crab samples were among the highest ever reported for this region. By mid-May, domoic acid concentrations in Monterey Bay, California, were 10 to 30 times the level that would be considered high for a normal *Pseudo-nitzschia* bloom. Other HAB toxins also have been detected on the West Coast in 2015. For example, an increase in saxitoxin-producing algae has been reported in areas of Alaska.

Impacts include shellfish and Dungeness crab harvesting closures in multiple states, targeted finfish closures, public health advisories for certain fish species in some areas of California, and sea lion strandings in California and Washington. Other marine mammal and bird mortalities have been reported in multiple states; domoic acid has not been confirmed as the primary cause of death, although the toxin has been detected in recovered birds. On 20 August 2015, NOAA declared an Unusual Mortality Event for large whales in the western Gulf of Alaska. Scientists have recorded the mortality of 30 large whales between May 2015 and February 2016. HABs are suspected of playing a role in the deaths of these whales given the noted warmer-than-average ocean temperatures in the Gulf of Alaska and the algal bloom documented in neighboring areas. However, there is as of this writing no conclusive evidence linking the whale deaths to HAB toxins.

While exact causes of the severity and early onset of the bloom are not yet known, unusually warm surface water in the Pacific is considered a factor (R. M. McCabe et al. 2016, manuscript submitted to *Nat. Commun.*). First reported along the West Coast in the 1990s, *Pseudo-nitzschia* blooms have also been observed off the U.S. East Coast and in the Gulf of Mexico.

<figure><img src="image_107.png" /><figcaption>**FIG. SB3.2.** Concentrations of *Pseudo-nitzschia* (cells liter⁻¹ from 1 Mar–1 Sep) and domoic acid in razor clams (ppm) in (a) 2005 and (b) 2015 on Long Beach, Wash. (location shown in Fig. SB3.1). Inset: Chains of overlapping *Pseudo-nitzschia* cells, the diatom that produces the toxin domoic acid. [*Pseudo-nitzschia* image courtesy of Zachary Forster, Washington Department of Fish and Wildlife.]</figcaption></figure>
stronger-than-normal eastward flow in the North Equatorial Countercurrent and along the equator (see Fig. 3.19). Hence, most of the equatorial Pacific was anomalously warm in 2015 (Fig. 3.4a), consistent with El Niño conditions (see section 4b). The cooling east of the Philippines brought upper OHCA (Fig. 3.4a) and sea level (see Fig. 3.15) in 2015 well below mean values there.

Conversely, eastern North Pacific upper OHCA warmed from 2014 to 2015 all along the west coast of North America (Fig. 3.4b), whereas the central North Pacific cooled. This pattern of change, together with the equatorial warming, reflects a transition of the Pacific decadal oscillation (PDO; Mantua et al. 1997) from negative in 2013 to positive in 2014 (http://research.jisao.washington.edu/pdo/). In 2015, North Pacific SST anomalies (see Fig. 3.1), upper OHCA anomalies (Fig. 3.4a), and sea level anomalies (see Fig. 3.15) reflect this positive PDO. This shift may result in an increased rate of global average surface warming (e.g., Meehl et al. 2013) and also affects regional rates of sea level rise (e.g., Zhang and Church 2012).

In the South Pacific, there was a large patch of cooling in the subtropics between 2014 and 2015 (Fig. 3.4b), but much of the South Pacific remained warm relative to 1993–2015 (Fig. 3.4a). In the Indian Ocean there was generally warming, with weak cooling in the far east and a zonal band of stronger cooling extending east of Madagascar, consistent with a reduction in the strength of the South Equatorial Current in 2015 relative to 2014 (an increase in eastward flow, see Fig. 3.19). The Brazil Current in the South Atlantic and Agulhas Current in the South Indian Ocean remained warm in 2015, despite some cooling of the latter from 2014 to 2015. Upper OHCA in the Indian Ocean remained mostly warm in 2015 (Fig. 3.4a), with cool patches in the far east and also east of Madagascar. In both locations there was cooling from 2014 to 2015 (Fig. 3.4b).

Much of the subpolar North Atlantic cooled from 2014 to 2015 while much of the Nordic Seas warmed. With these changes, in 2015 the subpolar region was anomalously cool (Fig. 3.4a), although warm upper OHCA persisted offshore of much of the east coast of North America, north of the Gulf Stream Extension. These changes may be related to a reduction in the strength of the Atlantic meridional overturning circulation (AMOC; see section 3h) in recent years (e.g., Saba et al. 2016).

Distinct and statistically significant (Fig. 3.4c) regional patterns stand out in the 1993–2015 local linear trends of upper OHCA. In the Indian Ocean, the warming trend is widespread and statistically

<figure><img src="image_108.png" /><figcaption>**FIG. 3.4.** (a) Combined satellite altimeter and in situ ocean temperature data estimate of upper (0–700 m) OHCA (×10⁹ J m⁻²) for 2015 analyzed following Willis et al. (2004), but using an Argo monthly climatology and displayed relative to the 1993–2015 baseline. (b) 2015 minus 2014 combined estimates of OHCA expressed as a local surface heat flux equivalent (W m⁻²). For panel (a) and (b) comparisons, note that 95 W m⁻² applied over one year results in a 3 × 10⁹ J m⁻² change of OHCA. (c) Linear trend from 1993–2015 of the combined estimates of upper (0–700 m) annual OHCA (W m⁻²). Areas with statistically insignificant trends are stippled.</figcaption></figure>

significant over much of the area north of 35°S, with almost no statistically significant cooling trends in that region.

In the Atlantic Ocean, the eastern seaboard of the North Atlantic, the Labrador Sea, and the Nordic Seas all trend warmer over 1993–2015 (Fig. 3.4c), all statistically robust over that interval. Eastern portions of the subtropical Atlantic and most of the tropics also
<figure><img src="image_109.png" /><figcaption>FIG. 3.5. (a) Near-global (60°S–60°N, excluding marginal seas and continental shelves) integrals of monthly temperature anomalies [°C; updated from Roemmich and Gilson (2009)] relative to record-length average monthly values, smoothed with a 5-month Hanning filter and contoured at odd 0.02°C intervals (see colorbar) vs. pressure and time. (b) Linear trend of temperature anomalies over time for the length of the record in (a) plotted vs. pressure in °C decade⁻¹.</figcaption></figure>

trend warmer across both hemispheres. There is also a warming trend in the western South Atlantic around the Brazil Current. Statistically significant cooling trends in the Atlantic are found east of Argentina and in the region of the Gulf Stream Extension and North Atlantic Current.

Statistically significant 1993–2015 regional trends (Fig. 3.4a) in the Pacific Ocean include warming in the western tropical Pacific and extra-equatorial cooling in the east, consistent with strengthening of the interior subtropical–tropical circulation attributed to trade-wind intensification (Merrifield et al. 2012). This pattern, linked to the surface warming hiatus (England et al. 2014), weakened in 2014 (Johnson et al. 2015a) and reversed in 2015 (Fig. 3.4a), reducing the strength of the long-term trend through 2015 compared with that through 2013 (Johnson et al. 2014).

In the Southern Ocean, a distinct trend of upper OHCA over 1993–2015 (Fig. 3.4c) emerges: a primarily zonal narrow band of warming immediately north of a band of cooling is visible from the western South Atlantic where the Brazil and Falkland/Malvinas Currents meet, extending eastward across much of the South Atlantic and Indian Oceans all the way to south of New Zealand. The geostrophic relation implies a

strengthening of eastward currents across this dipole, in the region of the Antarctic Circumpolar Current. Elsewhere in the region there is a cooling around South America. The apparent warming trends adjacent to Antarctica are located in both in situ and altimeter data-sparse regions and are not as robust as suggested by the statistics.

Near-global average seasonal temperature anomalies (Fig. 3.5a) largely reflect ENSO redistributing heat (e.g., Roemmich and Gilson 2011) in the upper 400 dbar (1 dbar ~ 1 m). During La Niña (most notably around 2008 in the Argo era), temperatures in the upper 100 dbar tend to be colder than average and those from around 100–300 dbar warmer because cold water is brought to the surface in the eastern equatorial Pacific as the thermocline shoals, and warm water is sequestered below the surface in the western equatorial Pacific as the thermocline deepens there. During El Niño years (most notably around the end of 2015), the sign of this pattern flips, resulting in very warm SSTs (section 3b) that, along with global warming, contributed to record high global average surface temperatures in 2015. In addition to the ENSO signature, there is an overall warming trend (Fig. 3.5b) from 2004 to 2015 that approaches 0.15°C decade⁻¹ near the surface, declines to around 0.02°C decade⁻¹ by 400 dbar, and remains near that rate down to 2000 dbar. This warming trend is found mostly south of the equator since 2006 (Roemmich et al. 2015; Wijffels et al. 2016).

A decade is short for defining long-term trends with statistical confidence, especially in the upper ocean where ENSO causes large interannual perturbations, so the analysis is extended further back in time and deeper using historical data collected mostly from ships. Five different estimates of globally integrated in situ upper (0–700 m) OHCA (Fig. 3.6a) all reveal a large increase since 1993 and indicate a record high OHCA value in 2015. Causes of the differences among estimates are discussed in previous reports (e.g., Johnson et al. 2015a). OHCA variability and net increases are also found from 700 to 2000 m (Fig. 3.6b) and even deeper in the ocean from 2000 to 6000 m (Fig. 3.6b), though for the latter, trends can only be estimated from differences between decadal surveys (Purkey and Johnson 2013).

The rate of heat gain from linear trends fit to each of the global integral estimates of 0–700 m OHCA from 1993 through 2015 (Fig. 3.6a) are 0.26 (±0.05), 0.31 (±0.12), 0.43 (±0.11), 0.35 (±0.07), and 0.41 (±0.22) W m⁻² applied over the surface area of the Earth (5.1 × 10¹⁴ m²) for the MRI/JMA, CSIRO/ACE CRC/IMAS-UTAS, PMEL/JPL/JIMAR, NCEI, and Met Office Hadley Centre estimates, respectively. Linear trends
for 1993–2015 are 0.19 (±0.09) W m⁻² from 700 to 2000 m, 0.24 (±0.04) W m⁻² from 700 to 1800 m, and 0.19 (±0.08) W m⁻² from 700 to 2000 m for the MRI/JMA, PMEL/JPL/JIMAR, and NCEI estimates, respectively. Here, 5%–95% uncertainty estimates for the trends are based on the residuals, taking their temporal correlation into account when estimating degrees of freedom (Von Storch and Zwiers 1999). For 2000–6000 m, the linear trends are about 0.07 (±0.04) W m⁻² (again at 5%–95% uncertainty) from 1992 to 2009 (update of Purkey and Johnson 2010; D. Desbruyères and S. G. Purkey, 2016, personal communication).

<figure><img src="image_110.png" /></figure>

**FIG. 3.6.** (a) Time series of annual average global integrals of in situ estimates of upper (0–700 m) OHCA ($I_{ZJ} = 10^{21} J$) for 1993–2015 with standard errors of the mean. The MRI/JMA estimate is an update of Ishii and Kimoto (2009). The CSIRO/ACE CRC/IMAS-UTAS estimate is an update of Domingues et al. (2008). The PMEL/JPL/JIMAR estimate is an update of Lyman and Johnson (2014). The NCEI estimate follows Levitus et al. (2012). The Met Office Hadley Centre estimate is computed from gridded monthly temperature anomalies (relative to 1950–2015) following Palmer et al. (2007). See Johnson et al. (2014) for more details on uncertainties, methods, and datasets. For comparison, all estimates have been individually offset (vertically on the plot), first to their individual 2005–15 means (the best sampled time period), and then to their collective 1993 mean. (b) Time series of annual average global integrals of in situ estimates of intermediate (700–2000 m for MRI/JMA and NCEI, 700–1800 m for PMEL/JPL/JIMAR) OHCA for 1993–2015 with standard errors of the mean, and a long-term trend with one standard error uncertainty shown from 1992–2009 for deep and abyssal ($z > 2000$ m) OHCA updated (D. Desbruyères and S. G. Purkey, 2016, personal communication) following Purkey and Johnson (2010).

Summing the three layers, the full-depth ocean heat gain rate ranges from 0.52 to 0.74 W m⁻².

d. Salinity—G. C. Johnson, J. Reagan, J. M. Lyman, T. Boyer, C. Schmid, and R. Locarnini

1) INTRODUCTION—G. C. Johnson and J. Reagan

Salinity patterns, both long-term means and their variations, reflect ocean storage and transport of freshwater, a key aspect of global climate (e.g., Rhein et al. 2013). Ocean salinity distributions are largely determined by patterns of evaporation, precipitation, and river runoff (e.g., Schanze et al. 2010), and in some high-latitude regions, sea ice formation, advection, and melt (e.g., Petty et al. 2014). The result is relatively salty sea surface salinity (SSS) values in the subtropics, where evaporation dominates, and fresher SSS values under the intertropical convergence zones (ITCZs) and in the subpolar regions, where precipitation dominates. These fields are further modified by ocean advection (e.g., Yu 2011). In the subsurface, fresher subpolar waters slide along isopycnals to intermediate depths, underneath saltier subtropical waters, which are in turn capped at low latitudes by fresher tropical waters (e.g., Skliris et al. 2014). Salinity changes in these layers quantify the increase of the hydrological cycle with global warming over the recent decades, likely more accurately and directly than evaporation and precipitation estimates (Skliris et al. 2014). Below that, the salty North Atlantic Deep Waters formed mostly by open ocean convection are found, with salinities that vary over decades (e.g., van Aken et al. 2011). Fresher and colder Antarctic Bottom Waters, formed mostly in proximity to ice shelves, fill the abyss of much of the ocean (Johnson 2008), freshening in recent decades (e.g., Purkey and Johnson 2013). Salinity changes also have an effect on sea level (e.g., Durack et al. 2014) and the thermohaline circulation (e.g., Kuhlbrodt et al. 2007).

To investigate interannual changes of subsurface salinity, all available subsurface salinity profile data are quality controlled following Boyer et al. (2013) and then used to derive 1° monthly mean gridded salinity anomalies relative to a long-term monthly mean for years 1955–2006 (World Ocean Atlas 2009; Antonov et al. 2010) at standard depths from the surface to 2000 m (Boyer et al. 2012). In recent years, the single largest source of salinity profiles for the world's ocean is the Argo program with its fleet of profiling floats (Riser et al. 2016). These data are a mix of real-time (preliminary) and delayed-mode (scientific quality controlled). Hence, the estimates presented here could change after all data have been subjected to scientific quality control. The SSS analysis relies on Argo in situ
data downloaded in January 2016, with annual maps generated following Johnson and Lyman (2012) as well as monthly maps from BASS (Xie et al. 2014), a bulk (as opposed to skin) SSS data product that blends in situ SSS data with data from the Aquarius (Le Vine et al. 2014) and SMOS (Soil Moisture and Ocean Salinity; Font et al. 2013) satellite missions. The Aquarius mission ended in June 2015, leaving SMOS as the sole source for satellite SSS for the rest of 2015. BASS maps can be biased fresh around land (including islands) and should be compared carefully with in situ data-based maps at high latitudes before trusting features there. Salinity is measured as a dimensionless quantity and reported on the 1978 Practical Salinity Scale, or PSS-78 (Fofonoff and Lewis 1979). Surface salinity values in the open ocean range from about 32 to 37.5, with seasonal variations exceeding 1 in a few locations (Johnson et al. 2012).

## 2) SEA SURFACE SALINITY (SSS) — G. C. Johnson and J. M. Lyman

The 2015 SSS anomalies (Fig. 3.7a, colors) reveal some large-scale patterns that largely held from 2004 to 2014 (e.g., Johnson et al. 2015b, and previous *State of the Climate* reports.). Regions around the subtropical salinity maxima are generally salty with respect to *World Ocean Atlas* (WOA) 2009 (Antonov et al. 2010). Most of the high latitude, low-salinity regions appear fresher overall than WOA 2009, both in the vicinity of much of the Antarctic Circumpolar Current near 50°S and in portions of the subpolar gyres of the North Pacific and North Atlantic. These multiyear patterns are consistent with an increase in the hydrological cycle (that is, more evaporation in drier locations and more precipitation in rainy areas) over the ocean expected in a warming climate (Rhein et al. 2013). The large, relatively fresh patch in 2015 west of Australia and the Indonesian Throughflow was more prominent in previous years back to 2011 (Johnson and Lyman 2012). Its origin is associated with the strong 2010–12 La Niña and other climate indices (Fasullo et al. 2013; Johnson et al. 2015b).

Sea surface salinity changes from 2014 to 2015 (Fig. 3.7b, colors) strongly reflect 2014 anomalies in evaporation minus precipitation (see Fig. 3.12). Advection by anomalous ocean currents (see Fig. 3.19) also plays a role in SSS changes. The most prominent large-scale SSS changes from 2014 to 2015 were freshening under the Pacific ITCZ and salinification in the tropical warm pool around the Maritime Continent (Fig. 3.7b). The freshening is associated with stronger-than-usual freshwater fluxes into the ocean under the ITCZ (see Fig. 3.12) and anomalous eastward flow

<figure><img src="image_111.png" /><figcaption>**Fig 3.7.** (a) Map of the 2015 annual surface salinity anomaly (colors in PSS-78) with respect to monthly climatological salinity fields from WOA 2009 (yearly average—gray contours at 0.5 PSS-78 intervals). (b) Difference of 2015 and 2014 surface salinity maps [colors in PSS-78 yr⁻¹ to allow direct comparison with (a)]. White ocean areas are too data-poor (retaining < 80% of a large-scale signal) to map. (c) Map of local linear trends estimated from annual surface salinity anomalies for 2005–15 (colors in PSS-78 yr⁻¹). Areas with statistically insignificant trends are stippled. All maps are made using Argo data.</figcaption></figure>

(see Fig. 3.19) of relatively fresh water in the tropical Pacific. The salinification over the tropical warm pool is associated with reduction in freshwater flux anomalies there. These changes are related to the strong El Niño event of 2015 (section 4b). In the subpolar North Atlantic, there was widespread freshening, strongest south of Iceland, but north of Iceland SSS becomes saltier. In the Indian Ocean, SSS decreased south of India from 2014 to 2015, consistent with the
westward spreading and weakening of the prominent fresh anomaly generated west of Australia circa 2011.

Seasonal variations of SSS anomalies in 2015 (Fig. 3.8) from BASS (Xie et al. 2014) show the buildup of anomalously fresh water associated with the tropical Pacific and western tropical Atlantic ITCZs (including just offshore of the Orinoco and Amazon Rivers), the increase in SSS in the tropical warm pool, and the decrease in fresh anomalies under the South Pacific convergence zone (SPCZ). Despite the lower accuracies of the satellite data relative to that of the Argo data, their higher spatial and temporal sampling allows higher spatial and temporal resolution maps than are possible using in situ data alone.

Sea surface salinity trends for 2005–15 exhibit striking patterns in all three oceans (Fig. 3.7c). These trends are estimated by local linear fits to annual average SSS maps from Argo data with a starting year of 2005, because that is when Argo coverage became near-global. Near the salinity maxima in each basin (mostly in the subtropics but closer to 30°S in the Indian Ocean), there are regions of increasing salinity, especially in the North Pacific to the west of Hawaii. In contrast, there are regions in the Southern Ocean where the trend is toward freshening. Again, these patterns are reminiscent of the multidecadal changes discussed above and suggest an intensification of the hydrological cycle over the ocean, even over the last 11 years. There is a strong freshening trend in much of the subpolar North Atlantic, roughly coincident with anomalously low upper ocean heat content there (see Fig. 3.4) suggesting an eastward expansion of the subpolar gyre that may be linked to reductions in the AMOC over the past decade (section 3h). In addition to these patterns there is a freshening trend in the eastern Indian Ocean, probably owing to a lingering signature of the strong 2010–12 La Niña, as discussed above. Freshening trends are also apparent in the eastern tropical Pacific and the South China Sea. The region to the northwest of the Gulf Stream is trending saltier, as well as warmer (section 3c).

3) **SUBSURFACE SALINITY**—J. Reagan, T. Boyer, C. Schmid, and R. Locarnini

Atlantic Ocean basin-average monthly salinity anomalies for 0–1500 m depth displayed a pattern

during 2015 similar to the previous 10 years, with salty anomalies above 700 m and fresh anomalies below (Fig. 3.9a). From 2014 to 2015 salinity increased in the upper 300 m of the Atlantic, reaching a maximum increase of ~0.01 near the surface (Fig. 3.9b). The Pacific Ocean has exhibited fresh anomalies of about –0.02 from 200 to 500 m over the last five years (Fig. 3.9c). However, the upper 75 m was about –0.04 fresher in 2015, in contrast to salty conditions there from mid-2008 to mid-2014. This change reflects the enhanced precipitation along the ITCZ (see Fig. 3.12d) and anomalous eastward equatorial currents (see Fig. 3.19) during the 2015 El Niño (see section 4b). Salty anomalies from 100 to 200 m have been present since 2011. From 2014 to 2015 the Pacific (Fig. 3.9d) freshened in the upper 75 m, approaching about –0.03 at 30 m, and became saltier from 100 to 200 m, approaching ~0.01 at 125 m. The Indian Ocean continued to show similar salinity anomaly structure to that of the previous two years, with a fresh surface anomaly from 0 to 75 m, salty subsurface anomaly from 100 to 300 m, a slightly fresh anomaly (maximum of about –0.01) from 400 to 600 m, and a slightly salty anomaly (maximum of ~0.01) from 600 to 800 m (Fig. 3.9e). From 2014 to 2015 there was weak freshening (maximum of about –0.01 at 50 m) near the surface and salinification from 100 to 200 m, with a maximum of ~0.014 at 150 m (Fig. 3.9f).

North Atlantic 2015 volume-weighted salinity anomalies from 0 to 1500 m (Fig. 3.10a) were mostly positive, with values >0.10 along the Gulf Stream. The eastern portion of the subpolar gyre in the North

<figure><img src="image_112.png" /><figcaption>FIG. 3.8. Seasonal maps of SSS anomalies (colors) from monthly blended maps of satellite and in situ salinity data (BASS; Xie et al. 2014) relative to monthly climatological salinity fields from WOA 2009 for (a) Dec–Feb 2014/15, (b) Mar–May 2015, (c) Jun–Aug 2015, and (d) Sep–Nov 2015. Arrows with maximum monthly errors exceeding 10 PSS-78 are left white.</figcaption></figure>
Atlantic exhibited a large (about -0.10) fresh anomaly. This fresh feature coincided with anomalously cool upper ocean heat content (see Fig. 3.4). The South Atlantic was dominated by positive salinity anomalies in 2015, with fresh anomalies south of 40°S, perhaps reflecting an anomalously northward position of the low salinity subantarctic front. From 2014 to 2015, positive salinity anomalies in the subtropics persisted with little change in strength, while the freshening north of the Azores Islands continued to strengthen (Fig. 3.10b).

The Indian Ocean displayed a dipole of salinity anomalies north of the equator during 2015, with salty anomalies in the Arabian Sea and fresh anomalies in the Bay of Bengal (Fig. 3.10a). Salty anomalies along the equator transitioned to fresh anomalies across the entire basin south of 15°S to 30°S. These fresh anomalies strengthened east of Madagascar from 2014 to 2015 but weakened west of Australia (Fig. 3.10b) as discussed in section 3d2. From 35°S to 50°S there was a transition from salty to fresh salinity anomalies, likely due to the position of the subantarctic front in 2015 (Fig. 3.10a).

The North Pacific, north of 20°N, was dominated by fresh anomalies in 2015; however, in the northeast

<figure><img src="image_113.png" /></figure>

**FIG. 3.9.** Average monthly salinity anomalies from 0–1500 m for the (a) Atlantic from 2005–15 and (b) the change from 2014 to 2015; (c) Pacific from 2005–15 and (d) the change from 2014 to 2015; and (e) Indian from 2005–15 and (f) the change from 2014 to 2015. Data were smoothed using a 3-month running mean. Anomalies are relative to the long-term WOA 2009 monthly salinity climatology (Antonov et al. 2010).

Pacific there was a salty anomaly (Fig. 3.10a) in close proximity to a region of anomalously warm SSTs (see Fig. 3.1). The warm SSTs were at least partly due to a persistent atmospheric ridge in the region (Bond et al. 2015). With ridging, less precipitation and more evaporation are expected. This expectation was partially met (see Fig. 3.12) and likely to have been partially responsible for the observed salty anomaly strengthening from 2014 to 2015 (Fig. 3.10b). The subtropical North Pacific was anomalously salty in 2015, contrasting with fresh anomalies along the ITCZ, consistent with the 2015 P-E anomalies (see Fig. 3.12). Salty anomalies were present in the subtropical South Pacific in 2015, with fresh anomalies along the SPCZ. These tropical and subtropical salinity anomaly features were mostly enhanced when compared to 2014, with the exception of a weakening

<figure><img src="image_114.png" /></figure>

<figure><img src="image_115.png" /></figure>

<figure><img src="image_116.png" /></figure>

**FIG. 3.10.** Near-global 0–1500 m volume-weighted salinity anomalies (a) for 2015, (b) change from 2014 to 2015, and (c) linear trend from 2005 to 2015 (yr⁻¹). Anomalies are relative to the long-term WOA 2009 monthly salinity climatology (Antonov et al. 2010). Annual figures were computed by averaging the 12 monthly salinity anomalies over calendar years.
<figure><img src="image_117.png" /><figcaption>FIG. 3.11. (a) Surface heat flux ($Q_{\text{net}}$) anomalies for 2015 relative to a 5-year (2010–14) mean. Positive values denote ocean heat gain. Panels (b), (c), and (d) are the 2015–2014 anomaly tendencies for $Q_{\text{net}}$, surface radiation (SW+LW), and turbulent heat fluxes (LH+SH), respectively. Positive anomalies denote that the ocean gained more heat in 2015 than in 2014. LH+SH are produced by the OAFlux high-resolution satellite-based analysis, and SW+LW by the NASA FLASHFlux project.</figcaption></figure>

positive salinity anomaly over the central subtropical North Pacific in 2015 (Fig. 3.10b). The South Pacific enhancement from 2014 to 2015 is inconsistent with 2015 P–E anomalies (see Fig. 3.12).

The 2005–15 linear trends of the 0–1500 m salinity anomalies (Fig. 3.10c) reveal strong similarities

to SSS trends over the same time period (see Fig. 3.7c and discussion above). This match is not surprising as most of the salinity variability from 0 to 1500 m over the global ocean occurs in the upper 300 m (Fig. 3.9). The large (> −0.01 $yr^{-1}$) freshening trend in the North Atlantic subpolar gyre could be partially responsible for the observed decline in the strength of the AMOC (Smeed et al. 2014).

e. Ocean surface heat, freshwater, and momentum fluxes—
L. Yu, R. F. Adler, G. J. Huffman, X. Jin, S. Kato, N. G. Loeb, P. W. Stackhouse, R. A. Weller, and A. C. Wilber

The ocean and atmosphere communicate via interfacial exchanges of heat, freshwater, and momentum. These air-sea

fluxes are the primary mechanisms for keeping the global climate system in balance with the incoming insolation at Earth's surface. Most of the shortwave radiation (SW) absorbed by the ocean's surface is vented into the atmosphere by three processes: longwave radiation (LW), turbulent heat loss by evaporation (latent heat flux, or LH), and turbulent heat loss by conduction (sensible heat flux, or SH). The residual heat is stored in the ocean and transported away by the ocean's surface circulation, forced primarily by the momentum transferred to the ocean by wind stress. Evaporation connects heat and moisture transfers, and the latter, together with pre-

cipitation, determines the local surface freshwater flux. Identifying changes in the air-sea fluxes is essential in deciphering observed changes in ocean circulation and its transport of heat and salt from the tropics to the poles. In particular, 2015 witnessed the interplay of three different warmings: the warm “Blob” in the

<figure><img src="image_118.png" /><figcaption>FIG. 3.12. (a) Surface freshwater (P–E) flux anomalies for 2015 relative to a 27-year (1988–2014) climatology. 2015–2014 anomaly tendencies for (b) P–E, (c) evaporation (E), and (d) precipitation (P), respectively. Green colors denote anomalous ocean moisture gain, and browns denote loss, consistent with the reversal of the color scheme in (c). P is computed from the GPCP version 2.2 product, and E from OAFlux high-resolution satellite-based analysis.</figcaption></figure>
North Pacific (Bond et al. 2015), a strong El Niño in the tropical Pacific, and a warm PDO phase. Large-scale anomalies appear in observations of SST (see Fig. 3.1), heat content (see Fig. 3.4), sea surface salinity (see Fig. 3.7), sea level (see Fig. 3.15), ocean surface currents (see Fig. 3.19), and chlorophyll-a (see Fig. 3.26a). All of these anomalies are related to changes in ocean surface forcing functions in 2015 and how the ocean and atmosphere generated anomalous conditions with unusual magnitudes and spatial dimensions.

Air-sea heat flux, freshwater flux, and wind stress in 2015 and their relationships with the ocean are assessed. The net surface heat flux, Qnet, is the sum of four terms: SW + LW + LH + SH. The net surface freshwater flux into the ocean (neglecting riverine and glacial fluxes from land) is simply Precipitation (P) minus Evaporation (E), or the P-E flux. Wind stress is computed from satellite wind retrievals using the bulk parameterization of Edson et al. (2013). The production of the global maps of Qnet, P-E, and wind stress in 2015 (Figs. 3.11–3.13) and the long-term perspective of the change of the forcing functions (Fig. 3.14) are made possible by integrating the efforts of four world-class flux analysis groups. The Objectively Analyzed air-sea Fluxes (OAFlux; http://oaflux.whoi.edu/) project at the Woods Hole Oceanographic Institution (Yu and Weller 2007) provides the satellite-based high-resolution version of the turbulent ocean flux components, including LH, SH, E, and wind stress (Yu and Jin 2012; 2014a,b; Jin and Yu 2013; Jin et al. 2015). The Clouds and the Earth's Radiant Energy Systems (CERES) Fast Longwave and Shortwave Radiative Fluxes (FLASHFlux; https://eosweb.larc.nasa.gov/project/ceres/ceres_table) project at the NASA Langley Research Center (Stackhouse et al. 2006) provides the surface SW and LW products. The Global Precipitation Climatology Project (GPCP; http://precip.gsfc.nasa.gov) at the NASA Goddard Space Flight Center (Adler et al. 2003) provides the precipitation products. The CERES Energy Balanced and Filled (EBAF) surface SW and LW products (http://ceres.larc.nasa.gov; Kato et al. 2013) are used in the time series analysis.

### I) SURFACE HEAT FLUXES

The global Qnet anomaly pattern in 2015 overall showed a remarkable hemispheric asymmetry (Fig. 3.11a), with net ocean heat loss (negative) anomalies dominating the Northern Hemisphere and net heat gain (positive) anomalies commanding the Southern Hemisphere. The 2015 minus 2014 Qnet tendency map (Fig. 3.11b) had a similar pattern, except for the northeast Pacific, where the net heat loss associated with the warm “Blob” was more

intense and widespread compared to the long-term mean background. The Qnet anomaly pattern was determined primarily by the LH+SH anomaly pattern (Fig. 3.11d), with the SW+LW anomalies contributing mostly in the tropical ocean.

The 2015 Qnet anomalies in the tropical Pacific are associated with El Niño, with mean 2015 SST in the eastern equatorial Pacific more than 2°C above normal (see Fig. 3.1). The eastward displacement of convection typically found over the west tropical Pacific is identified in the SW+LW anomaly field, featuring a striking band of negative SW+LW anomalies of magnitude exceeding 20 W m⁻² in the central and eastern equatorial Pacific (Fig. 3.11c). The band's core was centered near the international date line and zonally elongated, extending eastward along 2°–3°N. The precipitation anomaly field (Fig. 3.12d) reveals an almost identical band structure of enhanced tropical rainfall. During an El Niño, the eastward movement of the ITCZ leads to the suppression of deep convective clouds over the Indo-Pacific warm pool and the Maritime Continent, and consequently, an increase in the net downward radiation. These typical ENSO composite features (Rasmusson and Wallace 1983), that is, negative SW+LW anomalies in the central and eastern equatorial basin and positive SW+LW anomalies in the equatorial Indo-Pacific, were clear in 2015.

The ENSO LH+SH anomalies (Fig. 3.11d) were dominated by the LH anomalies and produced largely by SST and wind anomalies. As the warmer sea surface tends to evaporate more quickly, latent heat loss increased along the equatorial warm tongue in the eastern Pacific. In the central and western equatorial basin, however, the LH anomalies were not a response to the SST condition; instead they were the source of heating contributing to the ocean warming there: trade winds weakened considerably within the deep convection center near the date line (Fig. 3.13a). This weakening, in turn, subdued the latent heat loss by more than 20 W m⁻², creating a warming effect at the ocean surface. The warming effect of the LH+SH anomalies exceeded the cooling effect of the SW+LW anomalies, leading to a marginally positive net heat input to the ocean area that hosted the center of deep convection.

The changing SST-Qnet relationship from the eastern to the central equatorial Pacific demonstrates that Qnet has a dual role in the dynamics of large-scale SST anomalies. On one hand, Qnet contributes to the generation of SST anomalies. On the other hand, Qnet acts as a damping mechanism to suppress the SST anomalies once they are generated, thereby providing a feedback to control the persistence
and the amplitude of the SST anomalies (Frankignoul and Hasselmann 1977). Outside the equatorial ocean, the heat flux feedback offers an explanation for the large heat loss over the exceptionally warm waters off the North American coast. The warm “Blob” has persisted since 2013, and the enormous latent heat loss that it produced in 2015 exceeds the climatological background by a large amount (Fig. 3.11a).

The $Q_{\text{net}}$ forcing is evident in basinwide warming in the subtropical Indian and Atlantic sectors, as well as in the south-east Pacific. The southeast trade winds are usually weaker during an El Niño (Rasmusson and Carpenter 1982). Similar to the wind-evaporation-SST mechanism that operated in the central equatorial Pacific, the subdued LH+SH loss due to the weakened

winds appears to be a source of surface heating for the region.

The 2015 $Q_{\text{net}}$ anomalies in the North Atlantic exhibited a tripole-like pattern, with strong net heat loss (negative) anomalies ($<$ -20 W m⁻²) centered in the Labrador Sea and extending across the subpolar gyre north of 50°N, net heat gain (positive) anomalies (10–15 W m⁻²) in the eastern region between 30° and 50°N, and strong net heat loss anomalies ($<$ -20 W m⁻²) in the northwest Atlantic, including the Gulf Stream region. The $Q_{\text{net}}$ anomalies in this tripole-like pattern were generally negative in regions of positive SST anomalies and positive in regions of negative SST anomalies (Fig. 3.1), indicating that the atmospheric thermal forcing in the North Atlantic was a response to the SST variability.

## 2) SURFACE FRESHWATER FLUXES

The 2014 to 2015 P-E anomaly tendency (Fig. 3.12a,b) is dominated by precipitation (Fig. 3.12d) over the globe except for the extratropical North Pacific, where evaporation (Fig. 3.12c) is stronger in 2015 than 2014. The hemispheric asymmetry, which is featured in the 2015 $Q_{\text{net}}$ anomaly map, is not as obvious in the 2015 P-E anomalies (Fig. 3.12a) but is visible in the E tendencies (Fig. 3.12c). In the latter, the asymmetry is more about the contrast in the signs of the E

tendencies in the subtropical sectors, with the northern basin evaporating more and the southern basin less.

In the North Pacific, one interesting anomaly feature in 2015 is the concurrence of increased evaporation with reduced precipitation along a band that extended northeastward across the central Pacific, from the western tropical Pacific toward the Gulf of Alaska. Both effects resulted in an enhanced net moisture release to the atmosphere. This band of P-E anomalies is not a standalone feature. Along the location of the P-E anomaly band, downward SW + LW increased (Fig. 3.11a), and wind-induced Ekman upwelling anomalies were observed (Fig. 3.13c). More importantly, ocean surface warming occurred. The net downward heating, net moisture loss from the ocean, and the surface warming imply a close coupling between the atmosphere and the ocean in the extratropical central Pacific.

Above-normal freshwater input was observed in the tropical Pacific, associated with the development of the strong El Niño. Consistent with the eastward displacement of the ITCZ, the core of the precipitation band was centered near the date line and elongated eastward along the El Niño region. Along the band, the maximum increase of rainfall exceeded 1.5 m during 2015, and its impact on the tropical sea surface salinity (see Fig. 3.7) is evident. The entire

<figure><img src="image_119.png" /><figcaption>FIG. 3.13. (a) Wind stress magnitude (N m⁻²; colored background) and vector anomalies for 2015 relative to a 27-yr (1988–2014) climatology, (b) 2015–2014 anomaly tendencies in wind stress, (c) Ekman vertical velocity ($W_{EK}$) anomalies (cm day⁻¹) for 2015 relative to a 27-year (1988–2014) climatology, and (d) 2015–2014 anomaly tendencies in $W_{EK}$. In (c) and (d), positive values denote upwelling anomalies and negative downwelling. Winds are computed from the OAFlux high-resolution satellite-based vector wind analysis.</figcaption></figure>
equatorial Pacific experienced a surface freshening by as much as 0.1 PSS-78. As a consequence of the ITCZ migration, the rainfall deficit over the Indo-Pacific warm pool and the Maritime Continent was as large as 80 cm.

Interestingly, the changing P-E forcing in the Indian and Atlantic basins is only loosely linked to observed SSS anomalies (see Fig. 3.7). For instance, the tropical Indian Ocean was mostly in a wetter condition, whereas the regional sea surface became saltier over a wide area. The southern Atlantic was mostly in a drier condition, whereas the regional sea surface was fresher. The low correlation between salinity and P-E, in sharp contrast to the close correlation between SST and Qnet discussed above, reflects that P-E variations can create or modify existing salinity anomalies but are not a damping mechanism for existing anomalies. The lack of a negative feedback of SSS to the P-E flux suggests that SSS anomalies have a longer persistence than SST, and are more strongly influenced by horizontal processes anomalies such as wind-driven Ekman advection (Yu 2015).

### 3) WIND STRESS

The 2015 wind stress anomalies were largely aligned zonally, reflecting the fluctuations of the major wind belts (Fig. 3.13a,b). The vector wind anomaly directions indicate that there was a coherent weakening of the midlatitude westerlies in the Northern and Southern Hemispheres (30°-60°N and 30°-60°S). In response to the strong El Niño in the Pacific, the tropical trade winds were also weaker (Rasmusson and Carpenter 1982). The reduction in the magnitude of the trades is most evident in the center of the southeast trades (e.g., ~15°S in the Indian and Pacific basins). In the equatorial region, the shift of the deep convection to the date-line moved the Walker Circulation eastward, resulting in the equatorial westerly anomalies in the west and equatorial easterly anomalies in the east.

The spatial variations of wind stress ($\tau$) cause divergence and convergence of the Ekman transport, leading to a vertical velocity, denoted by Ekman pumping (downward) or suction (upward) velocity, $W_{EK}$, at the base of the Ekman layer. Computation of $W_{EK}$ follows the equation: $W_{EK} = 1/\rho\nabla \times (\tau/f)$, where $\rho$ is density, and $f$ the Coriolis force. The 2015 $W_{EK}$ pattern (Fig. 3.13c,d) shows strong upwelling (positive) anomalies in the western and central equatorial Pacific. The pattern corresponds well with the cooling of the upper ocean in the observed region in OHC (see Fig. 3.4) and SSH (see Fig. 3.15). In the North Atlantic, a $W_{EK}$ tripole anomaly pattern is present:

positive upwelling anomalies poleward of 60°N, negative downwelling anomalies between 40° and 60°N, and positive downwelling anomalies in the northwest subtropical Atlantic. The region of the warm "Blob" in the northeast Pacific experienced an enhanced downwelling motion.

### 4) LONG-TERM PERSPECTIVE

The time series of yearly variations of $Q_{net}$, P-E, and wind stress averaged over the global oceans (Fig. 3.14) provide decadal perspective on the ocean-surface forcing functions in 2015. The $Q_{net}$ time series (Fig. 3.14a) indicates that, despite interannual variability, net heat gain by the ocean shifted from nearly steady to higher variability around 2007, after which $Q_{net}$ shows a slight upward tendency. Whether the change is associated with the phase transition of the PDO is yet to be determined. The global average does not represent the change on the regional scale: for instance, decadal decrease of net downward heat flux is observed at a buoy off northern Chile (Weller 2015).

The P-E time series (Fig. 3.14b) is up slightly in 2015, perhaps reflecting the 2015 El Niño's influence on tropical oceanic precipitation. The GPCP precipitation dataset shows that changes over land and ocean during El Niño or La Niña years balance to first

<figure><img src="image_120.png" /><figcaption>**Fig. 3.14.** Annual-mean time series of global averages of (a) net surface heat flux ($Q_{net}$) from the combination of CERES EBAF SW+LW and OAFlux LH+SH, (b) net freshwater flux (P-E) from the combination of GPCP P and OAFlux E, and (c) wind stress magnitude from OAFlux high-resolution vector wind analysis. Shaded areas indicate one standard deviation of annual-mean variability.</figcaption></figure>
SIDEBAR 3.2: **EXTRAORDINARILY WEAK EIGHTEEN DEGREE WATER PRODUCTION CONCURS WITH STRONGLY POSITIVE NORTH ATLANTIC OSCILLATION IN LATE WINTER 2014/15**—S. BILLHEIMER AND L. D. TALLEY

The Gulf Stream and Kuroshio Extension are the largest oceanic heat loss regions in the Northern Hemisphere. Associated with that enormous winter cooling are deep winter mixed layers, which become what is referred to as subtropical mode water.

Mode waters constitute a large portion of the upper ocean volume and are composed of nearly uniform temperature and salinity. These water masses outcrop at the surface during winter, at which point they are stamped with the current atmospheric conditions. Vigorous convection drives the creation of deep mixed layers that entrain heat, freshwater, and anthropogenic CO₂ into the upper ocean. When air-sea heat flux changes sign, generally in the spring, the upper ocean begins to restratify, and the thick subtropical mode water layer (Fig. SB3.3a) is isolated from the atmosphere by the development of a seasonal pycnocline (a strong vertical density gradient). In subsequent winters, when the seasonal pycnocline breaks down, it exposes a thick layer of nearly uniform temperature set by previous winters' heat loss to the atmosphere, which renews the mode water. During normal seasonal cycles, mode water acts both to integrate several years of likely variable atmospheric conditions and to modify wintertime air-sea exchange. With several years of abnormally limited mode water renewal, the anomalous heat, freshwater, and anthropogenic CO₂ associated with the mode water reservoir would diffuse into the permanent thermocline below.

Eighteen Degree Water (EDW) is the subtropical mode water associated with the Gulf Stream extension in the western North Atlantic (Worthington 1959). EDW volume and properties are affected both regionally by the Gulf Stream and by large-scale atmospheric conditions.

The strength of EDW formation during winter is strongly associated with the North Atlantic Oscillation (NAO; Talley 1996; Joyce et al. 2000; Fig. SB3.4). During strongly negative NAO index winters, including 2014/15, the ocean-to-atmosphere heat flux that produces deep mixed layers occurs primarily in the subtropical regions (Dickson et al. 1996), resulting in vigorous EDW formation. During strongly positive NAO winters, vigorous buoyancy forcing occurs in the subpolar regions and the subtropical EDW region is deprived of strong winter atmospheric forcing, resulting in weak to near cessation of EDW formation, as demonstrated for winter 2011/12

<figure><img src="image_121.png" /><figcaption>FIG. SB3.3. Eighteen Degree Water (EDW) potential vorticity (log scale color) and EDW thickness (black contours). “EDW PV” is taken as potential vorticity on the $σ_θ = 26.5 \text{ kg m}^{-3}$ potential density surface. “EDW thickness” is the thickness of the $σ_θ = 26.2–26.7 \text{ kg m}^{-3}$ potential density layer. Contours are at 50 m intervals. High PV (orange/brown) corresponds with very weak (thin) EDW. (a) Climatology of Mar/Apr EDW PV and thickness during the Argo era. (b) 2015 Mar/Apr EDW PV anomaly and Mar/Apr 2015 EDW thickness.</figcaption></figure>

by Billheimer and Talley (2013). In late winter 2013/14, extraordinary ocean cooling in the subpolar North Atlantic that created an extreme type of Labrador Sea Water (Josey et al. 2015) coincided with a strongly positive NAO index and weak EDW formation, illustrating the spatially bimodal nature of NAO-related surface buoyancy forcing.

The Gulf Stream also plays a role in EDW formation. Strong lateral and vertical shears within the Gulf Stream
jet modify convective processes, driving cross-frontal mixing (Joyce et al. 2009, 2013; Thomas et al. 2013). The entrainment of fresh slope water originating north of the Gulf Stream, which occurs approximately between 65°W and 55°W, produces a colder, fresher variety of EDW. This mechanism of EDW formation is apparently much less affected by the intensity of winter subtropical surface heat flux (Billheimer and Talley 2013).

One measure of EDW formation strength is its winter Potential Vorticity (PV), defined by the planetary component only, neglecting relative vorticity:

$$PV = \frac{f}{\rho} \frac{\partial p}{\partial z'}$$

where f is the Coriolis parameter and ρ is density. High PV is associated with strong stratification. Hence, high EDW PV during the EDW formation season indicates strong stratification and abnormally weak EDW formation.

Here, EDW PV is calculated using the Roemmich-Gilson climatology of Argo profiling floats (Roemmich and Gilson 2009). EDW PV is taken as the PV along the potential density contour σ₀ = 26.5 kg m⁻³, considered the “EDW core” of low PV EDW (Talley and Raymer 1982; Talley 1996).

The map of climatological EDW PV for March/April, when EDW PV is lowest, shows the thickest, low PV EDW concentrated in two pools (Fig. SB3.3a). We hypothesize that the pool centered at 52°W near the Gulf Stream extension in the northeastern Sargasso Sea is largely produced within the Gulf Stream via cross-frontal mixing (Joyce et al. 2009, 2013; Joyce 2012), whereas the pool centered at roughly 66°W within the tight recirculation gyre of the Sargasso Sea is largely formed and renewed by simple one-dimensional convection—driven by surface buoyancy flux (Worthington 1959).

In March/April 2015, EDW PV was substantially higher, hence EDW was abnormally weak throughout the entire EDW region, particularly in the northern Sargasso Sea (Fig. SB3.3b). A thick (~450–500 m) EDW density layer persisted in the northeastern Sargasso Sea near the Gulf Stream, where one would expect to find EDW formed via cross-frontal mixing, though in 2015 its PV was anomalously high.

Atmospheric conditions of late winter 2014/15 appeared to be a continuation of conditions of the previous winter (Josey et al. 2015; section 3e). A strong correla-

<figure><img src="image_122.png" /><figcaption>FIG. SB3.4. (a) Jan–Mar 2015 NAO index (red) and annual minimum EDW PV (blue), which tends to be in Mar or Apr. EDW PV is the regional average of the domain mapped in Fig. SB3.3. (b) EDW volume, taken as the volume of the σ₀ = 26.2–26.7 kg m⁻³ potential density layer integrated over the domain mapped in Fig. SB3.3. The Gulf Stream north wall is the EDW northern boundary.</figcaption></figure>

tion exists between EDW PV and the NAO, where high NAO winters, particularly the late winter of 2014/15, are associated with abnormally weak EDW formation (Fig. SB3.4a). EDW volume in 2014/15 was consistent with the lack of EDW formation indicated by high winter EDW PV (Fig. SB3.4b). Peak EDW volume in late winter 2014/15 was at a minimum over the past 10 years, indicating an extraordinary lack of EDW production for a second consecutive year.

Lack of EDW renewal prior to 2011/12 was very unusual. Since 1954, when the time series at Bermuda station “S” was established, only one instance of near EDW depletion occurred, coinciding with the strongly positive NAO during the mid-1970s (Talley and Raymer 1982; Talley 1996; Joyce et al. 2000).

In summary, winter 2014/15 was the weakest EDW formation year on record during the Argo era and was associated with an extreme, strongly positive winter NAO. Three of the past four winters have had below average EDW renewal, with the most recent being the most extreme.
order, giving a global time series that is much more nearly constant than the land-only or ocean-only time series. Over the 27-year period, the *P*-*E* time series shows a slight decrease during the 1990s, then no obvious subsequent trend. A strengthening of the global winds in the 1990s, also indicated in the global wind time series (Fig. 3.14c), leveled off since the late 1990s. Global average winds were slightly reduced after the dip in 2009.

f. *Sea level variability and change*—M. A. Merrifield, E. Leuliette, P. Thompson, D. Chambers, B. D. Hamlington, S. Jevrejeva, J. J. Marra, M. Menéndez, G. T. Mitchum, R. S. Nerem, and W. Sweet

Sea level variations and trends during 2015 feature the largest El Niño event since 1997/98, the highest annual average global mean sea level (GMSL) recorded since the altimeter era began in 1993, and reversals in short-term regional trends reflecting the changing state of the PDO and other climate modes. Regional and global sea level patterns are described here using satellite altimeter data, and extreme coastal sea levels are described using tide gauge data.

The signature of El Niño is clear in the 2015 annual mean sea level anomaly (Fig. 3.15a), the change in sea level from 2014 to 2015 (Fig. 3.15b), and the 2015 deviation of sea level (minus GMSL) (Fig. 3.15c). All show the characteristic El Niño sea level pattern resulting from weakened trades and westerly wind anomalies in the tropical Pacific (see Fig. 3.13a), i.e., low sea levels in the western equatorial Pacific and high sea levels in the cold tongue region of the eastern equatorial Pacific. During the growth phase of El Niño in 2015, low sea levels in the western equatorial Pacific were more prominent north of the equator than south, with weak negative anomalies in the southern convergence zone region (Fig. 3.15c). Peak low sea levels south of the equator are expected during the decline phase of El Niño in 2016 (Widlansky et al. 2014). The poleward extension of high sea levels to mid- and high latitudes along the Pacific coasts of North and South America signifies coastal-trapped wave propagation of the high anomaly from the tropics, as well as local wind forcing at midlatitudes that drives positive anomalies along the eastern boundaries, particularly off North America (Enfield and Allen 1980; Chelton and Davis 1982). The El Niño-related negative sea level anomaly in the western equatorial Pacific is associated with reduced transport and a shallower thermocline in the Indonesian Throughflow (Sprintall et al. 2014). The thermocline depth anomalies propagate into the Indian Ocean along the coastal waveguide, reducing sea level along the west coast of Australia (Fig. 3.15c). High sea levels in the western equatorial Indian

<figure><img src="image_123.png" /><figcaption>**FIG. 3.15.** (a) Mean 2015 sea level anomaly (cm) relative to a 1993–2014 baseline; (b) 2015 minus 2014 annual means; and (c) 2015 annual mean sea level anomaly with the GMSL trend removed and normalized by the standard deviation of annual mean anomalies from 1993–2014. Data from the multimission gridded sea surface height altimeter product produced by Ssalto/Duacs, distributed by AVISO, with support from CNES (www.aviso.altimetry.fr).</figcaption></figure>

Ocean are likely associated with anomalous easterlies linked to El Niño.

Other prominent patterns in the annual mean anomalies, particularly in the standard deviations (Fig. 3.15c), include: 1) anomalously low sea levels around Antarctica and an associated meridional sea level gradient, indicative of an intensified Antarctic
Circumpolar Current (ACC); 2) generally high sea levels between 30° and 60°S related to the subtropical gyre circulations (see Fig. 3.4a; Roemmich et al. 2007); 3) high sea levels in the North Atlantic subtropical gyre region suggesting an intensified anticyclonic gyre circulation; and 4) low sea levels in the North Atlantic subpolar region corresponding to a strengthened cyclonic gyre circulation.

The seasonal progression of the 2015 El Niño (Fig. 3.16) was marked by a steady autumn of western Pacific sea levels throughout the year and the eastward propagating equatorial Kelvin waves that strengthened in boreal spring (March-May) and fully impacted the South American coast by autumn (September-November). High sea levels along North and South America preceded the arrival of the propagating high anomaly from the equatorial Pacific, indicating that these high anomalies were partly remnants from the previous year, which featured a weak Modoki-like (Ashok et al. 2007) near-El Niño, as well as locally generated wind-forced anomalies.

Other features in the annual mean sea level (Fig. 3.15) that exhibited a marked variation over the year (Fig. 3.16) include the Indian Ocean high sea levels that developed over the second half of 2015. In addition, the low sea levels along Antarctica and the heightened ACC sea level gradient were more prominent in the first half of 2015 than the second half. Sea level changes over the course of the year were also evident in the marginal seas of the North Atlantic, with high sea levels in the North Sea peaking in summer and dropping in autumn, low sea levels in the Mediterranean over most of the year switching to high levels during autumn, and high sea levels in Hudson Bay peaking in spring that fell below climatology levels over the remainder of the year.

GMSL has risen over the satellite altimeter record (1993-present) at an average rate of 3.3 ± 0.4 mm yr⁻¹ (Fig. 3.17a). GMSL in 2015 was the highest yearly average over the record and ~70 mm greater than the 1993 average, due in part to the linear trend but also to the 2015 El Niño, which caused sea level to rise an additional ~10 mm above the long-term trend (Fig. 3.17b). The high GMSL anomaly during 2015 exceeded the anomaly during the previous large El Niño in 1997/98 (Fig. 3.17b).

The rise in sea level over the altimeter period has not been uniform, with regions of high rates located in the western Pacific and Indian Oceans (Fig. 3.17c). Sea level rates have been near zero or falling over areas of the eastern Pacific, Southern, and North Atlantic Oceans. These regional patterns are largely linked to trending wind patterns over the past 20 years associated with climate modes, such as the PDO (e.g., Merrifield et al. 2012). These are not long-term trends and have reversed in many regions over the past five years (Fig. 3.17c compared to 3.17d). In particular, sea level has been falling in the western tropical Pacific and rising in the eastern Pacific due to a change from negative to positive PDO phase, and with that a shift in basin-scale wind patterns, as well as the 2014 weak Modoki-like near-El Niño and the strong 2015 El Niño.

Extreme sea level events, measured as the average of the 2% highest daily values above the annual mean from tide gauges, showed a characteristic dependence on latitude, reflecting stronger atmospheric forcings outside the tropics (Fig. 3.18a). The 2015 extreme levels were above climatology at the eastern equatorial Pacific and along the Pacific coast of North America (Fig. 3.18b), reflecting the contribution of positive El Niño anomalies and possibly some shifts in storm forcing. The high pattern did not extend to midlatitudes along the South American coast. Extremes were below climatology along the east coast of North America, due in part to slightly below average Atlantic hurricane activity (section 4e2).

<figure><img src="image_124.png" /><figcaption>FIG. 3.16. Seasonal sea level anomalies (cm, relative to the 1993-2014 average) for (a) Dec–Feb 2014/15, (b) Mar–May 2015, (c) Jun–Aug 2015, and (d) Sep–Nov 2015.</figcaption></figure>
<figure><img src="image_125.png" /><figcaption>FIG. 3.17. (a) Global mean sea level (mm) obtained from consecutive satellite altimeter missions, with 60-day smoothing and seasonal signals removed (black line indicates a rise rate of 3.3 mm yr⁻¹); (b) Detrended GMSL compared with the multivariate ENSO index (MEI; obtained from http://sealevel.colorado.edu); (c) Sea level trends (cm decade⁻¹) 1993–2015; and (d) Sea level trends (cm decade⁻¹) 2011–15. Scales differ in (c) and (d).</figcaption></figure>

g. Surface currents—R. Lumpkin, G. Goni, and K. Dohan

Ocean surface current changes, transports derived from ocean surface currents, and features such as rings inferred from surface currents, all play a role in ocean climate variations. Surface currents described here are obtained from in situ (global array of drogued drifters and moorings) and satellite (altimetry, wind stress, and SST) observations. Transports are derived from a combination of sea level anomalies (from altimetry) and climatological hydrography. For details of these calculations, see Lumpkin et al. (2011). Anomalies are calculated with respect to 1992–2007. Annually averaged zonal current anomalies for 2015, changes in anomalies from 2014 to 2015 (Fig. 3.19), and seasonal average 2015 anomalies (Fig. 3.20) are discussed below by individual ocean basin.

The dramatic El Niño of 2015/16 dominated annual mean current anomalies in the Pacific basin (Fig. 3.19a), with annually averaged eastward anomalies >20 cm s⁻¹ between 1.5° and 6°N and weaker eastward anomalies in the rest of the latitude band between 10°S and 10°N. Because 2014 was characterized by westward anomalies on the equator and eastward anomalies in a strengthened North Equatorial Countercurrent (NECC) at 5°–6°N, the 2015 minus 2014 map (Fig. 3.19b) has larger eastward anomaly tendencies on the equator and weaker

tendencies along the NECC than the 2015 anomaly map (Fig. 3.20).

In contrast to the annual mean picture, 2015 began with westward anomalies between 5°S and 5°N across the eastern half of the basin (Fig. 3.20a), with peak westward anomalies of ~25 cm s⁻¹ at 1°N. These anomalies were an enhancement of the northern branch of the westward South Equatorial Current (SEC) as seen in December 2014 (Dohan et al. 2015). Immediately north of 5°N, the eastward NECC was only marginally faster than its climatological January strength. In February, intense El Niño-related eastward anomalies first appeared in the western tropical Pacific as anomalies of 20–40 cm s⁻¹ at 145°–175°E, 2.5°S–4°N.

Throughout boreal spring, the El Niño-related anomaly pattern propagated eastward (Fig. 3.20b), reaching 160°W by March and 90°W by April. During these months, warm water advected by these current anomalies caused the NINO3 and NINO3.4 SST indices to increase rapidly (see section 4b). In April, eastward anomalies of 40–50 cm s⁻¹ were present at 95°–130°W, 2.5°S–2.5°N. Throughout March and April, equatorial zonal currents in the band 120°W–180° were close to their climatological average, straddled by the eastward anomalies centered at 5°–6°N (the latitude of the NECC) and 1°–2°S (Fig. 3.20b). In May, the anomalies south of the equator diminished to <20 cm s⁻¹, while anomalies of 35–40 cm s⁻¹ persisted in the NECC band. The eastward advection of relatively fresh water, combined with an El Niño-driven shift in the ITCZ (section 3e), likely accounts for the development of fresh SSS anomalies (section 3d).

Throughout boreal summer (June–August; Fig. 3.20c), eastward anomalies persisted across the basin, with equatorial eastward anomalies returning across the western half of the basin in July and across the entire basin in August. Averaged over these three months (Fig. 3.20c), eastward anomalies exceeded 5 cm s⁻¹ from 6°S to 9°N, with peak anomalies of 30–35 cm s⁻¹ at 4°–6°N. This pattern persisted in boreal
<figure><img src="image_126.png" /></figure>

FIG. 3.18. (a) Annual maximum sea level (cm) during 2015 computed from the mean of the 2% highest daily values relative to the 2015 annual mean, measured at tide gauges (http://uhslc.soest.hawaii.edu); (b) The 2015 annual maximum from (a) divided by the time-averaged annual maximum measured at each station with at least 20 years of data.

autumn (Fig. 3.20d), with another pulse of extremely strong (>50 cm s⁻¹) eastward anomalies appearing at 170°E–150°W, 3°–5°N in August and peaking at >60 cm s⁻¹ in October; these were the strongest monthly averaged broad-scale current anomalies seen in 2015. This pattern propagated eastward in November and weakened significantly through December. The year concluded with eastward anomalies of ~20 cm s⁻¹ across the basin from 3°N to 6°N, consistent with a stronger and wider NECC than in the December climatology. The northern edge of this NECC was close to its climatological northern extent but extended south to 2°N, compared to 3.5°N in climatology.

The Kuroshio was shifted anomalously northward in 2010–14, although this shift diminished during 2014 (Dohan et al. 2015). During 2015, the Kuroshio was close to its climatological latitude, with a maximum annually averaged speed of 35 cm s⁻¹ at 35°N between 140° and 170°E (Fig. 3.19).

Surface current anomalies in the equatorial Pacific typically lead SST anomalies by several months, with a magnitude that scales with the SST anomaly magnitude. A return to normal current conditions is also typically seen before SST returns to normal. Thus, current anomalies in this region are a valuable predictor of the evolution of SST anomalies and their related climate impacts. This leading nature can be seen in the first principal empirical orthogonal function (EOF) of surface current (SC) anomaly and separately of SST anomaly in the tropical Pacific basin (Fig. 3.21). For 1993–2015, the maximum correlation between SC and SST is 0.70 with SC leading SST by 71 days. Both SC and SST EOF amplitudes were positive throughout 2015, with the cumulative effect of positive SC EOF amplitude resulting in a steadily increasing SST EOF amplitude to almost 3 standard deviations in November 2015, nearing the November 1997 value of 3.2.

Throughout most of 2015, Indian Ocean monsoon-driven currents were much closer to climatology than the dramatic anomalies seen in the Pacific (Fig. 3.19a). This normality is in contrast to the strong eastward anomalies seen across the basin in 2013–14 (Lumpkin et al. 2014; Dohan et al. 2015). Those eastward anomalies dominate the 2015 minus 2014

<figure><img src="image_127.png" /></figure>

FIG. 3.19. Annually averaged geostrophic zonal current anomalies (cm s⁻¹) for (a) 2015 and (b) 2015 minus 2014 derived from a synthesis of drifters, altimetry, and winds. Positive (red) values denote anomalously eastward velocity.
<figure><img src="image_128.png" /><figcaption>**FIG. 3.20.** Seasonally averaged zonal geostrophic anomalies (cm s⁻¹) with respect to seasonal climatology, for (a) Dec 2014–Feb 2015, (b) Mar–May 2015, (c) Jun–Aug 2015, and (d) Sep–Nov 2015.</figcaption></figure>

zonal current tendencies in the Indian Ocean basin (Fig. 3.19b). In 2015, the strongest anomalies with respect to monthly climatology were seen in October and November, with an unusually early development of the North Monsoon Current (e.g., Beal et al. 2013) associated with westward anomalies of ~30 cm s⁻¹ at 3°S–2°N, 60°–80°E during these months (Fig. 3.20d). Large-scale current anomalies returned to near-climatological December values by the end of 2015.

The Agulhas Current transport is a key indicator of Indian–Atlantic Ocean interbasin water exchanges. The annual mean transport of the Agulhas Current has been decreasing from a high set in 2013, with values of 56 Sv in 2013 (1 Sv ≡ 10⁶ m³ s⁻¹), 53 Sv in 2014, and 50 Sv in 2015. The 2015 transport of 50 Sv is equal to the Agulhas' long-term (1993–2015) mean.

Annual mean anomalies in the Atlantic Ocean (Fig. 3.19a) indicate a 5–7 cm s⁻¹ strengthening of the eastward NECC at 4.5°–6.5°N, 30°–50°W, and conditions close to climatology along the equator. However, the annual average hides a pattern of reversing equatorial anomalies between boreal winter and spring (Fig. 3.20). The year began with eastward anomalies of 20 cm s⁻¹ from 3°S to 2°N across much of the basin, which weakened through February and were present only at 25°–35°W in March/April. In May, westward anomalies of 10–15 cm s⁻¹ developed across the basin from 2°S to 2°N. These anomalies weakened considerably through June and were no longer present in July. No significant basinwide equatorial anomalies were seen in the remainder of 2015.

The Gulf Stream in 2015 remained close to its climatological position with little change from 2014 (Fig. 3.19).

The North Brazil Current, which sheds rings that carry waters from the Southern Hemisphere into the North Atlantic and has important ecosystem impacts downstream (Kelly et al. 2000), exhibited an annual transport smaller than its long-term (1993–2015) value. As in 2014, it shed eight rings in 2015, a larger-than-average value. Sea level anomalies in the region, which have generally increased since 2001

(apart from lows in 2003 and 2008), remained higher than average in 2015.

In the southwest Atlantic Ocean, the Brazil Current carries waters from subtropical to subpolar regions, mainly in the form of large anticyclonic rings (Lentini et al. 2006). The separation of the Brazil Current front from the continental shelf break continued to exhibit a seasonal cycle, which is mainly driven by wind stress curl variations and the transport of this current. During 1993–98, the annual mean separation of the front shifted southward in response to a long-term warming in South Atlantic temperatures (cf. Lumpkin and Garzoli 2010; Goni et al. 2011). In 2015, the Brazil Current front and its separation from the continental shelf break persisted south of its mean position, unchanged from 2014.

h. Meridional overturning circulation observations in the North Atlantic Ocean—M. O. Baringer, M. Lankhorst, D. Volkov, S. Garzoli, S. Dong, U. Send, and C. S. Meinen

This section describes the Atlantic meridional overturning circulation (AMOC) and the Atlantic meridional heat transport (AMHT), determined by the large-scale ocean circulation wherein northward moving upper layer waters are transformed into deep waters that return southward, redistributing heat, freshwater, carbon, and nutrients. Previous *State of the Climate* reports (e.g., Baringer et al. 2013) and reviews (e.g., Srokosz and Bryden 2015; Perez et al. 2015; Carton et al. 2014; Srokosz et al. 2012) discuss the AMOC's impact on climate variability and ecosystems. The AMOC is computed as the maximum of the
vertical accumulation of the horizontally integrated velocity across a section (i.e., the maximum transport that occurs in either the upper or lower layer before the circulation starts to change direction again). The AMHT involves the co-variability of temperature and velocity and is only meaningful as a flux (and hence, independent of the absolute temperature scale used) when the total mass transport can be accounted for (i.e., sums to zero). Observing systems can measure both temperature and velocity, usually with tradeoffs in system design that favor the computation of one quantity over the other. Here we describe the AMOC from observing systems at 41°N, 26°N, and 16°N, and AMHT at 41°N, 26°N, and 35°S. In the future, AMOC observing systems in the South Atlantic and subpolar North Atlantic should provide additional time series (e.g., Srokosz et al. 2012).

The longest time series of ocean transport to serve as an index of the AMOC's strength in the North Atlantic (e.g., Frajka-Williams 2015; Duchez et al. 2014) is from the Florida Current (FC, as the Gulf Stream is called at 26°N), measured since 1982 (Fig. 3.22). FC and AMOC transport variations at all time scales also are inversely linked to sea level variations along the east coast (Goddard et al. 2015; McCarthy et al. 2015). The median 1982–2015 transport of the FC is 31.9 ± 0.25 Sv (one standard error of the mean assuming a 20-day integral time scale) with a small downward trend of −0.31 ± 0.26 Sv decade⁻¹ (errors estimating 95% significance as above). The 2015 median FC transport was 31.7 ± 1.7 Sv, only slightly below the long-term average. Daily FC transports compared to those of all previous years (Fig. 3.22) indicate that

<figure><img src="image_129.png" /><figcaption>FIG. 3.22. (a) Daily estimates of Florida Current transport (10⁶ m³ s⁻¹) during 2015 (orange solid line), 2014 (dashed purple line), and 1982–2012 (light gray lines) with 95% confidence interval of daily transport values computed from all years (black solid line) and the long-term mean (dashed black). (b) Daily estimates of Florida Current transport (10⁶ m³ s⁻¹) for the full time series record (light gray), smoothed using a 12-month second-order Butterworth filter (heavy black line), mean transport for the full record (dashed black line), and linear trend from 1982 through 2015 (dashed blue line). Two-year low-passed Atlantic Multidecadal (AMO, yellow line) and North Atlantic Oscillation (NAO, red line) indices are also shown.</figcaption></figure>

<figure><img src="image_130.png" /><figcaption>FIG. 3.21. EOF of surface current (SC) and SST anomaly variations in the tropical Pacific from the OSCAR model (Bonjean and Lagerloef 2002; www.esr.org/enso_index.html). (a) EOF Amplitude time series normalized by their respective standard deviations. (b) EOF Spatial structures.</figcaption></figure>

2015, like 2014, had several unusually low transport anomalies (extremes defined as outside the 95% confidence limits for daily values). These occurred during 8–9 May, 24–29 September, and 5–9 October 2015. The lowest daily 2015 FC transport was 22.2 Sv on 8 October, with transports < 23 Sv for five days around this date. During 2015 there was only one high transport event, with an average transport of > 39 Sv from 8 to 13 July.

At 41°N, a combination of profiling Argo floats (that measure ocean temperature and salinity for the upper 2000 m on broad spatial scales, as well as velocity at 1000 m) and altimetry-derived surface velocity (Willis and Fu 2008) are used to estimate the AMOC (Fig. 3.23) and AMHT (Fig. 3.24). This time series has not been updated since last year's report (Baringer et al. 2015a,b), extending from January 2002 to December 2014. At 26°N, the AMOC/AMHT (Figs. 3.23 and 3.24) is measured with full-water column moorings
<figure><img src="image_131.png" /></figure>

**FIG. 3.23.** Estimates of Atlantic meridional overturning circulation ($1 \text{ Sv} \equiv 10^6 \text{ m}^3 \text{ s}^{-1}$; AMOC) from the Argo/Altimetry estimate at 41°N (black; Willis 2010), the RAPID-MOC/MOCHA/WBTS 26°N array (red; McCarthy et al. 2015), and the German/NOAA MOVE array at 16°N (blue; Send et al. 2011) shown versus year. All time series have a three-month second-order Butterworth low-pass filter applied. Horizontal lines are mean transports during similar time periods as listed in the corresponding text. Dashed lines are trends for each series over the same time period. For MOVE data, the net zonal and vertical integral of the deep circulation represents the lower limb of the AMOC (with a negative sign indicating southward flow), and hence a stronger negative (southward) flow represents an increase in the AMOC amplitude. Light gray lines show ECCO2-derived transports: (top) thin gray is the 41°N transport, thick gray is the 26°N transport, (bottom) shows the negative meridional overturning circulation in the model for ease of comparison with the 16°N data.

that span the full basin and include direct transport measurements in the boundary currents as part of the large RAPID-MOC/MOCHA/WBTS 26°N mooring array (Smeed et al. 2015). The data from these moorings are collected every 18 months, most recently in December 2015; too late to be calibrated and finalized for this report. The 26°N data shown here extend from April 2004 to March 2014 (see last year's report for full details). At 16°N, a mooring array of inverted echo sounders, current meters, and dynamic height moorings (Send et al. 2011) measures the flow below 1000 m (the southward flowing part of the AMOC "conveyor belt") that sends North Atlantic Deep Water toward the equator; hence, the AMOC estimate at this latitude (Fig. 3.23) is a negative number (southward deep flow) to distinguish these observations from the full water column systems. Since this array only measures the deep circulation, an estimate of the AMHT is impossible at 16°N because of the

missed large signals and high correlations in the surface waters. These data have been updated since last year's report and now extend from February 2000 to February 2016. At 35°S in the South Atlantic, the AMHT (Fig. 3.24) is estimated using a combination of high-density (closely spaced) expendable bathythermograph (XBT) and broader-scale Argo profiling float data (Garzoli et al. 2012). While the AMOC has also been estimated at 35°S (e.g., Dong et al. 2009), those estimates (not shown) are rough because the XBTs only extend to 750 m. These data are collected and analyzed in near-real time, with values spanning July 2002 to October 2015.

Some guidance on 2015 AMOC and AMHT variability can be gained from state estimation model output, constrained by observations. February 1992–November 2015 monthly estimates of AMOC and AMHT from the global MITgcm in ECCO2 (cube-sphere) configuration (e.g., Menemenlis et al. 2008), forced with the new JRA-55 atmospheric fields (Kobayashi et al. 2016) and GPCP precipitation

<figure><img src="image_132.png" /></figure>

**FIG. 3.24.** Observed time series of Atlantic meridional heat transport (PW; AMHT) at (a) 41°N (from profiling floats following Hobbs and Willis 2012; blue lines), with uncertainties (light blue lines) and the trend (dashed blue line), at (b) 26°N (from mooring/hydrography data) 12-hourly values (gray line), filtered with a 3-month low-pass filter (black line), and the trend (black dashed line), and at (c) 30°–35°S (from XBTs) quarterly values (light green), filtered with yearly boxcar (dark green line), and the trend (dashed green line). Heat transports simulated by ECCO2 (orange lines) are shown at all latitudes.
(Huffman et al. 2012), are analyzed here. The ECCO2 model output is well correlated with the instrument-based measurement of the AMOC (Fig. 3.23) and AMHT (Fig. 3.24) at 26°N and 41°N, with correlations of 0.58/0.59 and 0.57/0.38, respectively, all significantly above the 95% confidence level. ECCO2 model output is not statistically significantly correlated with the 16°N AMOC or 35°S AMHT transports (correlation values of 0.12 and 0.13, respectively). At 26°N and 41°N the AMOC and AMHT in the ECCO2 output show a slight increase from 17.6 Sv and 1.02 PW (1 PW = $10^{15}$ W) in 2014 to 18.3 Sv and 1.09 PW in 2015. Preliminary analysis of the new data from 26°N (not shown) indicates that the transport has continued fairly unchanged since 2011 (through December 2015), with values lower than the earlier part of the record (D. A. Smeed, 2016, personal communication). Additionally, there is no unusual "event" in the assimilation time series, as has been clearly seen in other time periods (e.g., Fig. 3.24). This finding is supported by the FC time series and the ECCO2 state estimation (Fig. 3.22).

At 16°N, the time series of the AMOC estimate decreased from 29.0 Sv in 2013, to 28.4 Sv in 2014, and to 27.2 Sv in 2015 (as stated earlier the decrease in southward flow implies an increase in the AMOC at this latitude; Fig. 3.23). This reduction has led to a reduced estimate of the long-term trend of the AMOC from February 2000 to February 2016 at 16°N to be +3.6 Sv ± 2.5 Sv decade⁻¹. This trend is of the opposite sign from the trends at 26°N and 41°N (−4.1 ± 3.2 Sv decade⁻¹ and −1.3 ± 4.9 Sv decade⁻¹). A similar situation exists with the 35°S AMHT transport estimate. In the south, the AMHT has remained essentially constant for the last three years (mean value 0.6 PW northward). This implies a virtually steady AMOC as well (the AMOC and AMHT being highly correlated). This recently constant AMHT has reduced the long-term trend of an increasing AMHT to +0.11 ± 0.10 PW decade⁻¹. From these data it is clear that the variability at all latitudes in the Atlantic is not well correlated and, therefore, data from more than one latitude are needed to describe the state of the ocean.

i. *Global ocean phytoplankton*—B. A. Franz, M. J. Behrenfeld, D. A. Siegel, and S. R. Signorini

Marine phytoplankton represent roughly half the net primary production (NPP) on Earth, fixing atmospheric CO₂ into food that fuels global ocean ecosystems and drives biogeochemical cycles (e.g., Field et al. 1998; Falkowski et al. 1998). Satellite ocean color sensors, such as SeaWiFS (McClain 2009), MODIS (Esaias et al. 1998), and VIIRS (Oudrari et al. 2014),

provide observations of sufficient frequency and geographic coverage to globally monitor changes in the near-surface concentrations of the phytoplankton pigment chlorophyll-a (Chla; mg m⁻³). Chla provides a first-order index of phytoplankton abundance and is proportional to the maximum sunlight energy absorbed for photosynthesis (Behrenfeld et al. 2006). Here, global Chla distributions for 2015 are evaluated within the context of the 18-year continuous record provided through the combined observations of SeaWiFS (1997–2010), MODIS on Aqua (MODISA, 2002–present), and VIIRS on Suomi-NPP (2011–present). All Chla data used in this analysis correspond to version R2014.0 (http://oceancolor.gsfc.nasa.gov/cms/reprocessing/), which uses common algorithms and calibration methods to maximize consistency in the multimission satellite record.

The spatial distribution of VIIRS annual mean Chla for 2015 (Fig. 3.25) is generally consistent with the well-established, physically driven distribution of nutrients (e.g., Siegel et al. 2013). To assess changes in Chla for 2015, mean values for VIIRS Chla in each month of 2015 were subtracted from monthly climatological means for MODISA (2003–2011) within globally distributed geographic bins, and then those monthly anomaly fields were averaged (Fig. 3.26a). Identical calculations were performed on MODISA SST (°C) data to produce a companion SST annual mean anomaly (Fig. 3.26b).

In 2015, the phytoplankton Chla concentrations across much of the equatorial Pacific were strongly depressed, with concentrations 20%–50% below the climatological norm. This response is generally corre-

<figure><img src="image_133.png" /><figcaption>**Fig. 3.25.** Mean 2015 Chla distribution (mg m⁻³) derived from VIIRS with the location of the mean 15°C SST isotherm (black lines) delineating boundaries of the permanently stratified ocean (PSO). Chla data are from NASA Reprocessing version 2014.0. Data are averaged into geo-referenced equal area bins of approximately 4.6 km × 4.6 km and mapped to an equi-rectangular projection centered at 150°W.</figcaption></figure>
<figure><img src="image_134.png" /><figcaption>FIG. 3.26. Spatial distribution of summed monthly 2015 (a) VIIRS Chla anomalies expressed as % difference from climatology and (b) MODISA SST anomalies shown as absolute differences. (c) Relationships between the signs of SST and Chla anomalies from (a) and (b), with colors differentiating sign pairs and absolute changes of less than 3% in Chla or 0.1°C in SST masked in black. Monthly differences are derived relative to a MODISA 9-year climatological record (2003–11). Location of the mean 15°C SST isotherm (black lines) delineates the PSO.</figcaption></figure>

lated with elevated surface temperatures (Fig. 3.26c), consistent with a well-developed El Niño. Depressed Chla was also observed in climatologically warmer waters of the northern Indian Ocean, northeastern Pacific, and Sargasso Sea, while elevated Chla was observed in the cooler waters of the western North Pacific, much of the South Pacific, and throughout the tropical Atlantic. These regions fall within the permanently stratified ocean (PSO; Figs. 3.25 and 3.26, black lines at approximately 40°N and 40°S), defined here as the region where annual average surface temperatures are >15°C (Behrenfeld et al. 2006). The PSO is characterized by nutrient-depleted surface mixed layers shallower than the nutricline.

Typically, chlorophyll anomalies in the PSO exhibit an inverse relationship with SST anomalies (Behrenfeld et al. 2006), as annual mean SST anomalies largely coincide with surface mixed layer depth (MLD) changes in the PSO. A shallower MLD means that phytoplankton spend more time near the ocean's surface and thus have higher daily sunlight exposures than deeper mixing populations. Phytoplankton respond to this increased light by decreasing their cellular chlorophyll levels in a response called photoacclimation (thus, increased SST leads to decreased MLD, which leads to decreased Chla). A potential second consequence of a decrease in MLD is a decrease in the vertical transport of nutrients to the surface layer, but coupling between the MLD and nutricline depths throughout much of the PSO is known to be weak (Lozier et al. 2011). In the equatorial Pacific, however, the anomalously low Chla and high SST in 2015 were primarily driven by nutrient availability changes due to the El Niño event, wherein the westerly winds weaken along the equator allowing warm water, normally confined to the western Pacific, to migrate eastward. Wind-driven upwelling, a process that brings cold, nutrient-rich water to the surface along the equator, was also greatly reduced,

causing SST to rise and significantly lowering biological productivity. At higher latitudes, outside the PSO, the relationship between SST changes and light and nutrient conditions is more complex, resulting in a wide diversity of responses between anomalies in SST and Chla, (Fig. 3.26c).

Spatially integrated monthly mean Chla concentrations in the PSO (Fig. 3.27a) vary by ~20% (±0.03 mg m⁻³) around a long-term mean of approximately 0.15 mg m⁻³ over the 18-year time series. This variability includes seasonal cycles and larger departures from the climatological mean associated with climatic events. The long-term mean is approximately 0.01 mg m⁻³ higher than previous reports (Franz et al.