3214 MONTHLY WEATHER REVIEW VOLUME 143

Using Microwave Satellite Data to Assess Changes in Storminess over the Pacific Ocean

MICHAEL C. KRUK ERT Inc., Asheville, North Carolina

KYLE HILBURN Remote Sensing Systems, Santa Rosa, California

JOHN J. MARRA NOAA/National Climatic Data Center, ,

(Manuscript received 28 August 2014, in final form 20 February 2015)

ABSTRACT

This study analyzes 25 years of Special Sensor Microwave Imager (SSM/I) retrievals of rain rate and wind speed to assess changes in storminess over the open water of the Pacific Ocean. Changes in storminess are characterized by combining trends in both the statistically derived 95th percentile exceedance frequencies of rain rate and wind speed (i.e., extremes). Storminess is computed annually and seasonally, with further partitioning done by phase of the El Niño–Southern Oscillation (ENSO) index and the Pacific decadal os- 2 cillation (PDO) index. Overall, rain-rate exceedance frequencies of 6–8 mm h 1 cover most of the western and central tropical Pacific, with higher values present around the Philippines, Japan, Mexico, and the northwest coast of . Wind speed exceedance frequencies are a strong function of latitude, with values 2 less (greater) than 12 m s 1 equatorward (poleward) of 308N/S. Statistically significant increasing trends in rain rate were found in the western tropical Pacific near the Caroline Islands and the , and in the extratropics from the Aleutian Islands down the coast along British Columbia and Washington State. Statistically significant increasing trends in wind speed are present in the equatorial central Pacific near and the Republic of the (RMI), and in the extratropics along the west coast of the United States and Canada. Thus, while extreme rain and winds are both increasing across large areas of the Pacific, these areas are modulated according to the phase of ENSO and the PDO, and their intersection takes aim at specific locations.

1. Introduction Report of the Intergovernmental Panel on Climate Change (AR4; Parry et al. 2007, 7–22) small islands will The Pacific Ocean is vast, covering more than be especially susceptible to accelerated beach erosion, 165 million km2, encompasses many nations consisting increased damage to infrastructure from overtopping of small island atolls, and is host to a myriad of plant waves and storms, and changes in rainfall patterns af- and animal life. By comparison, the combined land- fecting freshwater availability. Furthermore, sea sur- mass of the small islands makes up roughly 527 000 km2, face heights and sea surface temperatures are expected or much less than 1% of the total area of the Pacific to continue to increase, drawing elevated concerns over Ocean. The weather and climate of the Pacific region coral bleaching and sea level rise (Parry et al. 2007, has a direct impact on these islands, impacts that are 7–22; Keener et al. 2012). Recently, a U.S. Geological expected to gradually worsen with time as a result of Survey report found that the Hawaiian atolls of Mid- climate change. As stated in the Fourth Assessment way and Laysan may become inundated and unin- habitable during this century (Pyper 2013). The report Corresponding author address: Michael Kruk, NOAA/National also found that the Republic of the Marshall Islands Climatic Data Center, 151 Patton Ave., Asheville, NC 28801. (RMI) and the Federated States of (FSM) E-mail: [email protected] face similar threats.

DOI: 10.1175/MWR-D-14-00280.1

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The concept of ‘‘storminess’’ is often used as a proxy satellite launch scheduled in 2016. Microwave bright- for the patterns and trends of storm frequency, intensity, ness temperatures at 19, 22, and 37 GHz are used to and impacts from high seas, strong winds, and heavy retrieve wind, vapor, cloud, and rain over the ocean. rains (Marra 2008; Marra et al. 2008; Marra et al. 2010; These frequencies are able to penetrate liquid and ice Kruk et al. 2013). Each one of these elements can occur clouds, as well as atmospheric dust and smoke, to see the either independently, such as long-period swell from an ocean surface. Dual-polarization (vertical and horizon- extratropical cyclone, or together, such as from a ty- tal) measurements at 19 and 37 GHz allow accurate re- phoon passing overhead. In this manuscript, the ex- trieval of wind speed in rain-free conditions. In rain, tremes of wind and rain are assessed both as individual they are used to separate the emission and scattering and grouped impact-based metrics—storminess. The signals of precipitation. Since wind speed estimates are distributions of extremes in rainfall and wind speed are not available in rain, nearby rain-free observations are modulated by the phases of El Niño–Southern Oscilla- used as a local proxy. For the time period of this study, tion (ENSO) and the Pacific decadal oscillation (PDO). SSM/I and SSMIS data come from nine sensors, each For example, the location of the South Pacific conver- operating on its own spacecraft. These sensors have gence zone (SPCZ) shifts northeast or southwest of its been intercalibrated and are suitable for development climatological position depending on El NiñoorLaNiña of a long time series of these variables (Wentz and [Kruk et al. (2014) and references therein]. Given the lack Schabel 2000; Wentz et al. 2007; Wentz 2013). of land-based data across the open waters of the Pacific Many studies have used SSM/I to develop climatolog- Ocean, can we assess changes in storminess using re- ical means of the parameters made available by the sat- motely sensed data, and if so, how are these changes ellite (e.g., Ferraro et al. 1996; Chang et al. 1995, Wentz influenced by the background states of ENSO and PDO? and Spencer 1998). Some of these past studies have been Using land-based station data from 1981 to 2011, applied on a global scale (e.g., Young et al. 2012); though McGree et al. (2013) substantiated a strong relationship others like Allan et al. (2010) have focused only on the between the phase of ENSO and both total and extreme tropical regions of the globe. Young (1994) examined rainfall. McGree et al. (2013) found spatially heteroge- oceanic-scale wave statistics using remote sensing satel- neous and largely not statistically significant trends in lite sensors. Young (1999) constructed a global clima- rainfall for 1961–2011. They infer that a shift in the tology of ocean wave and wind conditions finding that the phase of the interdecadal Pacific oscillation around 1999 Southern Ocean was the ‘‘roughest ocean on Earth.’’ largely reversed earlier trends. Gu and Adler (2013) also Young and Holland (1996) created an atlas of ocean wave find evidence for a climate regime shift around 1998/99. height conditions using the Geosat satellite and focused ENSO and PDO are different ocean–atmosphere tele- globally on international trade routes. Allan et al. (2010) connections that are not completely independent of one found that the interannual mean variability in the SSM/I another. This manuscript will show that wind and rain precipitation data over the tropical ocean can be ex- extremes encompassing storminess depend on the pha- plained by daily rainfall events above the 94th percentile. ses of both phenomena. Similar strong proportional linkage between extreme Assessing storminess over the ocean requires obser- rainfall and total rainfall is seen in Greene et al. (2007). vations of rain and wind, which are traditionally mea- Extreme value return periods using altimeter data were sured by buoys or other similar floating platforms. also developed (Alves and Young 2003; Chen et al. 2012; However, there remain an inadequate number of buoys/ Panchang et al. 1998; Vinoth and Young 2011). Young platforms to perform a detailed climate analysis over the et al. (2012) investigated trends in extreme values of wave vast Pacific Ocean. Moreover, while there are many heights and wind speeds over the entire globe. They island-based data networks for rainfall and wind, they found that 100-yr return intervals for the 99th percentile are typically spaced hundreds of kilometers apart, extreme wind event across the Pacific varied consider- 2 offering a relatively incomplete assessment of stormi- ably, from 50 m s 1 near the Aleutian Islands, to only 2 ness over the region. Fortunately, passive microwave 12–15 m s 1 in the equatorial Pacific, and increasing again 2 remote sensing provides a physically based technique to 44–48 m s 1 south of (Young et al. 2012). for retrieving wind and rain over the ocean. The Special In addition, Kruk et al. (2013), in concert with the Pacific Sensor Microwave Imager (SSM/I) and Special Sensor Storms Climatology Products (PSCP) suite, also defined Microwave Imager Sounder (SSMIS) operated by the extreme events as those values falling above the 95th Defense Meteorological Satellite Program (DMSP) percentile, but whose analysis was extended to encom- provide the longest continuous record of passive mi- pass wind speed and direction, sea height, and waves. The crowave measurements of the earth. The measurements primary goal of the PSCP project was to identify changes began in 1987 and continue to this day, with one more in storminess across the Pacific, where the essence of

Unauthenticated | Downloaded 09/25/21 03:31 PM UTC 3216 MONTHLY WEATHER REVIEW VOLUME 143 storminess was defined by patterns and trends of storm TABLE 1. List of all SSM/I satellites and the start date for frequency and intensity from heavy rainfall, strong winds, data usage. and high seas. Each of these variables was obtained from Satellite Start date in situ sites—both from land surface stations and buoy F08 1 Jan 1988 platforms. F11 1 Dec 1991 A recent study by Kruk et al. (2014) focused on one F13 1 Jan 1996 element of storminess—–heavy rains—and documented F17 1 Jan 2007 both the primary atmospheric drivers of and the his- torical trends in rainfall extremes and related indices in the western and northern Pacific. While that study found parameter changes has been thoroughly studied and the mostly increasing trends in extreme rainfall between the retrieval algorithm has been formulated to minimize subbasins of the Pacific Ocean, it also omitted the vast potential cross-talk between parameters (Wentz 1997; expanse of open water due to a lack of surface-based Wentz and Meissner 2000). observing systems. However, given the widespread The number of available SSM/I satellites changes over availability of the SSM/I data, its unique ability to collect time, but for this study the dataset is restricted to the subcloud layer data, and the supporting evidence lend- ‘‘core’’ satellites listed in Table 1. This provides the most ing confidence in the ability for the SSM/I to accurately consistent sampling over time for the analysis of the detect extremes, this study aims to complement Kruk temporal variability in extremes. These particular in- et al. (2014) by expanding the analysis to the overwater struments were chosen over others based on their local portion of the Pacific basin. The spatial size and geo- time of day. They all take measurements near 0600 and graphic location of storminess and how it is modulated 1800 local time and have the least drift in local time of by the phase of the ENSO and the PDO are also day among the SSM/I data. This is especially important discussed. for rain rate, which has a diurnal cycle in the tropics of 16% of the mean (Hilburn and Wentz 2008). The accuracy of SSM/I wind retrievals degrades in the 2. Methodology presence of rain because it lacks the lower-frequency Surface wind speed and rain-rate retrievals were (10 GHz and below) channels needed to see through the gathered over the Pacific Ocean from 508Sto608N and rain to the surface (Meissner and Wentz 2009). So, to from 1108E to 1008W from Remote Sensing Systems study both wind and rain trends at a particular location, (RSS) version 7 SSM/I intercalibrated ocean retrievals. the SSM/I data must be spatially aggregated to capture The version-7 SSM/I data include improvements to the neighboring rain-free pixels. The data are provided on a radiative transfer model (Meissner and Wentz 2012) and 0.25830.258 latitude–longitude earth grid. A sensitivity the antenna temperature calibration (Wentz 2013). study (not shown) indicated that the minimum grid size Previous versions of SSM/I have established its usage for needed to reliably capture both wind and rain mea- studying climate trends and variability (Wentz et al. surements is 1.58. A slightly larger grid size of 2.58 was 2007; Trenberth et al. 2005; Wentz and Schabel 2000). used for the benefit of additional noise suppression in The algorithm for retrieving wind speed is described in spatial plots. One SSM/I sensor makes between 0 and 2 Wentz (1997) with an update by Wentz and Meissner observations per day at a particular location, with a (2000). The algorithm for retrieving rain is described in mean of 1.2 observations per day. Satellite swaths pre- Wentz and Spencer (1998) with an update by Hilburn cess in longitude, so there is no east–west geographic and Wentz (2008). SSM/I rain rates (Bowman et al. pattern to the sampling. The sampling does have a 2009) and wind speeds (Mears et al. 2001) have been north–south pattern, caused by overlapping swaths at shown to be in excellent agreement with buoys. latitudes poleward of 608 latitude, which is outside the The physical basis for wind speed retrievals relies region of study. primarily upon the polarization signature. The hori- This study uses SSM/I-derived wind speeds; how- zontal polarization brightness temperature increases ever, other wind speed data are available. For exam- 2 approximately 1 K for every 1 m s 1, while the change in ple, the Blended Sea Winds (Zhang et al. 2006a,b)and vertical polarization is much smaller. Rain rate is also the Cross-Calibrated MultiPlatform (CCMP) Ocean retrieved by its polarization signature, which reduces the Surface Wind Vector (Atlas et al. 2011)bothprovidea contrast between vertically and horizontally polarized long-term record of wind speed over the ocean. Both brightness temperatures on the order of 1 K per products incorporate information from reanalysis data- 2 0.2 mm h 1 at 19 GHz. The orthogonality of these sets: Blended Sea Winds includes NCEP Reanalysis 2 brightness temperature changes with environmental and CCMP includes ERA-40. One large advantage of

Unauthenticated | Downloaded 09/25/21 03:31 PM UTC AUGUST 2015 K R U K E T A L . 3217 usingRSSversion7dataovertheBlendedSeaWinds is that by using only certain satellites from the SSM/I and SSMIS series, the highest possible level of con- sistency is achieved by local time of day of the obser- vation and spatiotemporal sampling patterns over the time record. One concern about using satellite data to study ex- treme rainfall is that it may significantly underestimate extreme rainfall rates. The satellite rain rates in this study represent an average over a 625-km2 footprint, compared with an R. M. Young tipping-bucket rain gauge with a 200-cm2 catchment area. To evaluate these concerns, 10-min rain gauge data were obtained from Pacific Marine Environmental Laboratory (PMEL). The buoy data were then collocated with DMSP F13 satellite SSM/I and F17 SSMIS data allowing a 60-min maximum time difference between satellite and buoy. Cumulative distribution functions were produced for the collocated buoy and satellite rainfall data when both were nonzero (not shown). This comparison demon- strated that the buoy rain rates have greater fractions of both light and heavy rain compared with the satellite, consistent with the vastly different sample volumes. However, while the satellite data do show a loss of FIG. 1. The 95th percentile thresholds for (a) rain rate and sensitivity at very low rain rates, this study is concerned (b) wind speed. with the 95th percentile, for which the buoy and satellite data are in excellent agreement. A separate sensitivity analysis using PMEL hourly rain gauge data showed gathering information about the sea surface height very similar results. To that end, zero rain rates were (SSH), where the sea height is computed as the differ- excluded from the percentiles because the focus of this ence between the satellite height and the altimetric study is on the most extreme values (95th percentile), range (http://www.aviso.altimetry.fr/en/home.html). AVISO which aligns well with previous studies documented in has been successfully used in the past to estimate sea 2 section 1. For wind, a bin size of 0.2 m s 1 was assigned surface heights (e.g., Merrifield et al. 2009; Young et al. 2 and for rain a bin size of 0.1 mm h 1 was used. From 2012). In these studies, the western equatorial Pacific there, the cumulative distribution functions were used to showed the largest increases in SSH, with positive 2 determine the 95th percentiles. Additionally, the anal- trends of 5–10 mm yr 1. While not the focus of this ysis of extreme values at the 95th percentile was further manuscript, we did obtain and analyze the AVISO data subdivided into seasons, where boreal winter is from to independently verify the trends in SSH over the 1 October to 30 April, and boreal summer is from 1 May Pacific Ocean. The results (not shown) perfectly mir- to 30 September, consistent with Kruk et al. (2013, 2014). rored those that were found by Merrifield et al. (2009). The percentiles were then used to calculate an exceed- Since increasing trends in SSH across the western and ance frequency, defined as the number of observations northern Pacific have been previously documented in a 2.5832.58 grid cell exceeding the percentile using satellite data, all grid cells were assigned a binary threshold value (Fig. 1) divided by the total number of ‘‘1’’ to denote increasing trends, which will be used observations. The exceedance frequencies were multi- later in conjunction with rain and wind event trends to plied by the number of days per time period to express determine changes in storminess across the domain them in units of days. (section 5). Another altimeter, called the Archiving, Validation, and Interpretation of Satellite Oceanographic (AVISO), 3. Trend results has been in operation since October 1992, but it is not a passive microwave satellite. Rather, the AVISO mea- The first step toward assessing changes in storminess is sures the satellite-to-surface, round-trip time of a ra- to identify trends in the extremes of rain rate and wind dar pulse. The AVISO satellite is particularly useful at speeds, following the definition of extremes from section 2.

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Trend results are presented by annual, boreal summer, and boreal winter. The resulting trends have spatial patterns that are regional in scale and often vary sub- stantially over small distances. Thus, geographic capital cities are marked in the figures to help identify island- level impacts. Figure 1a shows the 95th percentile threshold for rain rate and Fig. 1b shows the 95th percentile threshold for wind speed. The heaviest rain-rate thresholds are evi- dent in the darker green shading from Japan and Korea southwest along the Asian continent, and through the west-central Pacific, including many islands nations and atolls such as the RMI, FSM, , and . Mean- while, low thresholds, indicated by the brown shading, are evident across the far southeastern Pacific and along 458S. There is also an area of reduced values from the west coast of North America westward through the Hawaiian Islands. For wind speed (Fig. 1b), the highest 95th percentile values are located closer to the poles, generally along and north of 308N and south of 308S. The swath of lowest thresholds is aligned between 158S and 158N, with more moderate values elsewhere. The absolute lowest wind speed thresholds reside in the far west-central Pacific, including areas such as Koror, (PNG), and the eastern Philippines. a. Annual Annually, the trend in 95th percentile extreme rain- rate occurrences (Fig. 2a) was increasing across the far northern Pacific, decreasing along the 10th parallel, and increasing again diagonally from PNG to Fiji. Areas with statistically significant decreasing trends, enclosed by solid black lines, were noted across the far eastern Pacific to the southeast of the Hawaiian Islands FIG. 2. Trend in rain-rate 95th percentile exceedance frequency and off the coast of Baja California. Meanwhile, sta- by season for (a) annual, (b) summer, and (c) winter. Black con- tistically significant increasing trends are located along tours enclose regions of trends that are statistically significant at the the SPCZ through the Solomon Islands, , and 0.95 level. Tonga, with a secondary area of increasing trends found from Midway Island northeastward to the coast of British Columbia, Canada. The increasing trends . Other prominent increases were found along across Micronesia are consistent with the findings from the west coast of North America and the east coast of Kruk et al. (2014), which analyzed rainfall extremes China. The Hawaiian Islands were another area where from land-based rain gauges across the Pacific and high-wind events appear to be increasing on the annual found increasing trends over FSM and the Common- time scale; however, these increases are not statistically wealth of the (CNMI). significant. Negative trends stretch from Hawaii, There is a large region of annually increasing high-wind through the northern parts of the RMI, across CNMI, days (Fig. 3a) located along the equator from about , FSM, PNG, Solomon Islands, Fiji, Tonga, , 1658Eto1358W, with the area extending farther east in and American . The strongest declining trends in the Southern Hemisphere to about 1058W. The areas 95th percentile events are located in RMI, the Solomon with positive high-wind event trends include the southern Islands, and PNG. The strongest positive trends are in part of the RMI, , Kiribati, FSM, and the northern Kiribati with high values extending west into Nauru and parts of , , the , and French parts of the FSM.

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increasing rain-rate trends were found from PNG into the southern parts of FSM as well. Most other areas were neutral in trends or the trends were small and statistically insignificant. During the boreal summer (Fig. 3b), the trend in wind speeds at or above the 95th percentile changes only slightly from the annual map. Statistically significant in- creases on the order of 4–6 days of more extreme wind speeds per decade were found near the equator just east of the date line. This is the same region where Gastineau and Soden (2011) found a bull’s-eye of negative SST correla- tion with wind speed in the middle of the equatorial Pa- cific. Statistically significant increases were also found over the waters between southern Australia and western New Zealand, where trends here are two to four windier days per decade. Meanwhile, farther north, statistically 2 significant decreasing trends (2–4 days decade 1)were centered on 408N and 1658E. Smaller decreases were lo- cated southwest of the U.S. coastline and over parts of Palau and Yap in the far western Pacific, but these de- creases were not statistically significant at the 95% con- fidence interval. c. Winter The boreal winter trends (Fig. 2c) closely resemble those from the boreal summer season. Statistically significant 2 decreasing rain rates of 20.4 to 20.6 days decade 1 were found over the eastern Pacific, with another stripe of de- creasing trends from the RMI southeastward to French 2 Polynesia. Large increasing rain rates of 0.4 days decade 1 were located across much of the Alaskan peninsula, and again between Midway Island and the coast of British Columbia. The overall trend pattern changes a little more in the boreal winter (Fig. 3c) when the map is dominated by a larger area of decreasing trends in extreme wind 2 FIG. 3. Trend in wind speed 95th percentile exceedance fre- speeds. The largest decreases of 4–8 days decade 1, quency by season for (a) annual, (b) summer, and (c) winter. Black which were also statistically significant, are located contours enclose regions of trends that are statistically significant at the 0.95 level. over the CNMI, including , , and Rota. Smaller decreases extend outward from there to in- clude Midway Island, and diagonally from northern PNG through the Solomon Islands and down toward b. Summer Fiji. Many of these decreasing trends in this region of During the boreal summer (Fig. 2b), extreme rain the Pacific are also statistically significant. Increasing rates show a more muted response across the basin, but trends were located off the northern coast of western also with a few noteworthy areas. Once again the cen- North America, extending westward through the Gulf tral and eastern Pacific had decreasing trends, with an of Alaska all the way to Japan and Korea. The positive 2 area of statistically significant decreases in between trends of 2–6 days decade 1 off the coast of North Hawaii and the western United States. A ribbon of America are statistically significant. Another area of decreasing rain-rate trends was also found along 108S, increasing trends, though not statistically significant, from 1208W all the way to 1658E.TheCoralSeaalso was found from near the equator and date line south- had minor decreasing trends. Meanwhile, near the east through the into northern French equator in the western Pacific, statistically significant Polynesia.

Unauthenticated | Downloaded 09/25/21 03:31 PM UTC 3220 MONTHLY WEATHER REVIEW VOLUME 143 d. Generalized extreme value theory The analysis of extreme events is often linked to the anticipation of return intervals, that is, how likely an event is to happen again based on history. The gener- alized extreme value (GEV) approach is often used to assess return intervals in extreme events, and was done so in this study. For each grid box, the annual maximum value in rain rate and wind speed was obtained. The data were then fit to the Gumbel distribution following the statistical guidance in Coles (2001). The resulting 100-yr return values for rain rates and wind speeds are shown in Figs. 4a and 4b, respectively. For rain rates (Fig. 4a), the 100-yr values exceeding 2 30 mm h 1 are confined to the more northern latitudes, near 458N, between 1508Eand1508W. The lowest rain- 2 rate values of 0–5 mm h 1 for the 100-yr period are found off the coasts of South America and the western United States. Much of the Pacific domain is domi- 2 nated by the 10–20 mm h 1 signal. A ribbon of higher 2 return values (20–25 mm h 1) spans the southern lati- tudes, from 158 to 458S between 1508Eand1108W, inclusive of the Tasman Sea, New Zealand, and the Cook Islands. Wind speed return values at the 100-yr interval FIG. 4. GEV 100-yr return values for (a) rain rate and (Fig. 4b) were coincidentally highest in the same areas as (b) wind speed. those for rain rates, namely the far northern portion of the Pacific basin and again across the southernmost lati- exceedance frequencies for the boreal summer and tudes. The Southern Hemispheric maxima were more winter seasons were produced over the period 1988– constrained to areas due south of Australia and into the 2012 (not shown). These seasonal climatology maps Tasman Sea. Not surprisingly, the lowest wind speed 2 were subtracted from the exceedance frequency results values were found along the equator (10–20 m s 1)and for both ENSO and PDO teleconnections. For wind especially in the far eastern Pacific, were local minima of 2 speeds, this removed the strong seasonal cycle associ- 5–10 m s 1 were found. The highest values in the basin ated with the greatest number of strong wind events were located along 458N, between 1508E and 1508W, in occurring in each respective hemispheric winter. For the same location as that for the highest return values for rain rates, this removed a strong seasonal cycle in ex- rain rates in the basin. tratropical storm tracks and tropical convection. The following subsections document the relationship be- tween trends and the larger-scale teleconnections (with 4. Teleconnections and trends seasonality now being removed). An underlying factor in rain event and wind event a. El Niño–Southern Oscillation trends is that they are modulated by seasonal variability and ENSO and PDO teleconnections. Understanding Figure 5 shows the distribution of the frequency of how the trends noted in the previous section are influ- extremes in wind and rain events for each phase of enced by the background climatology requires doc- ENSO after subtracting out the seasonal normals. The umenting the relationship between these trends and the ENSO data were obtained from the National Oceanic larger-scale teleconnections. Importantly, to avoid ali- and Atmospheric Administration/Climate Prediction asing the seasonal variability into estimates of ENSO Center (NOAA/CPC) for the years 1988–2012. Not and PDO variability, the seasonal cycle had to be re- surprisingly, the results obtained herein line up well with moved before calculating the exceedance frequency by previous studies on the linkages between seasonal ENSO and/or PDO phase, where the boreal winter rainfall and the phase of ENSO. Allan and Soden (2008) spans from 1 October to 30 April, and boreal summer is examined time series of the 90th percentile rain rate from 1 May to 30 September. To do this, maps of the over the ocean and found that El Niño events coincided

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FIG. 5. The 95th percentile exceedance frequency by ENSO phase and deseasonalized for (a) rain rate, cool phase; (b) rain rate, neutral phase; (c) rain rate, warm phase; (d) wind speed, cool phase; (e) wind speed, neutral phase; and (f) wind speed, warm phase. with increased frequency of heaviest precipitation. change in rainfall rate and are discussed in the follow- Curtis et al. (2007) showed that over the tropical oceans, ing subsections. El Niño–Southern Oscillation events result in a spatial 1) COOL PHASE (LA NIÑA) redistribution and overall increase in extremes. During La Niña, an increase in the frequency of dry extremes During the cold phase of ENSO (i.e., La Niña; and no change in wet extremes were observed. Because Fig. 5a), sea surface temperature anomalies within the of the juxtaposition of tropical land areas with the as- Niño-3.4 region are defined to be less than 20.58C ac- cending branches of the global Walker circulation, El cording to the CPC. In this phase, a reduction in the Niño(LaNiña) contributes to generally dry (wet) frequency of extreme rainfall days was evident from conditions in these land areas. Karnauskas et al. (2008) along the equator eastward to the coast of South found that ENSO forces variability in the Papagayo America. The largest decreases were found across the gap winds over the east Pacific warm pool. The con- central and western Pacific, including Kiribati and most nection between convection to winds over the east of Micronesia. Increases in extreme rainfall days were Pacific warm pool has also been studied with satellite noted north of 308N, extending from the coastal waters data by Maloney and Esbensen (2007). It is encourag- of British Columbia southwest to the international date ing to see that the SSM/I data have captured this signal line. A narrow ribbon of higher frequencies of extremes

Unauthenticated | Downloaded 09/25/21 03:31 PM UTC 3222 MONTHLY WEATHER REVIEW VOLUME 143 during La Niña was also noted from the Cook Islands 3) WARM PHASE (EL NIÑO) westward through Vanuatu and into PNG. The warm phase of ENSO is characterized by sea sur- Much of the Pacific experiences stronger than aver- face temperatures in the Niño-3.4 region above 10.58C. age wind speeds during La Niña(Fig. 5d). Across a The magnitude of the distribution of extreme rainfall days large area of the central Pacific, the frequency of ex- appears to be more emphasized or exaggerated than the treme winds is nearly twice what would be expected other phases (Fig. 5c). Specifically, a dry tongue became in a typical year. This area is centered across the most pronounced from western PNG southeastward equator and the international date line and includes the through northern Australia, Vanuatu, and the southern islands of Kiribati, Tuvalu, and much of the RMI. Cook Islands. Additional drier conditions were noted Additional but smaller increases were found over the over the Gulf of Alaska southward through the Hawaiian Hawaiian Islands, most of the South China Sea, and Islands. Much of northern Australia, southeastward into northward toward Japan and the Gulf of Alaska. the Tasman Sea, has historically seen many fewer heavy Meanwhile, less windy days were found mainly in the rain days during El Niño. Meanwhile, an incredible fre- Southern Hemisphere and the far eastern equatorial 2 quency of extreme rain events (greater than 1 day yr 1) Pacific, from New Zealand east to the coast of South was located along the equator, from west to east, and America. Along the equator east of 1208Wthefre- southward into Tuvalu, Kiribati, and the northern Cook quency of extreme winds is about half what it would be Islands. Other areas of higher exceedances were found in a typical year. along the west coast of the United States and over far 2) NEUTRAL PHASE western Micronesia and CNMI. Unlike the neutral phase of ENSO, the warm phase During the neutral phase of ENSO, which is defined as features an enhancement of the frequencies in extreme sea surface temperature anomalies within the Niño-3.4 wind events (Fig. 5f), with most of the windier days oc- region falling between 20.58 and 10.58C, the distribu- curring north of 23.58N or south of 23.58S. The most ex- tion of extreme rainfall event counts shows a more treme wind speed days during El Niñowerefound muted response than either phase of ENSO. The axes of between 308 and 458N and 1658Eand1208W, just north of extreme rainfall days (Fig. 5b) extend diagonally from the Hawaiian Islands. A secondary maximum was also Honolulu southwest through the RMI and into the 2 found over the far western Pacific, including FSM, CNMI, northern Solomon Islands (10.1 to 10.3 days yr 1). A PNG, and the Solomon Islands. A third area of increasing second area of increasing heavy rain events is located wind speed days was located between 308 and 458S, in- along the alignment of the SPCZ from the equator di- cluding areas around Tasmania, New Zealand, Tonga, agonally to far southern South America at 458S. Fewer 21 and the southern Cook Islands. Minima were found over extreme rainfall days (20.3 to 20.5 days yr ) were lo- 2 the Hawaiian Islands (3–5 fewer days yr 1) and along and cated along the equator, with another area of drier 21 just south of the equator and 1658W, inclusive of parts of conditions (20.1 to 20.3 days yr ) from the coast of 2 Kiribati (7–11 fewer windy days yr 1). British Columbia southwest through Guam and into the South China Sea. b. Pacific decadal oscillation The distribution of extreme wind events shows a re- markably different pattern than either the warm or cold The Pacific decadal oscillation is characterized using phases of ENSO (Fig. 5e). During the neutral phase, the PDO index produced by the Joint Institute for the wind speed extremes are most likely to occur along and Study of the Atmosphere and Ocean (JISAO). The south of 308S and between 158 and 308N, with reduced index is derived as the leading principle component of frequencies north of 308N in the Northern Hemisphere. monthly SST anomalies in the North Pacific Ocean Specifically, much of the Solomon Islands sees more (Zhang et al. 1997). The monthly values are used occurrences of high-wind events during neutral ENSO without additional smoothing, and are separated into 2 phases (11to15daysyr 1), along with parts of Tuvalu two phases: positive or negative. The positive phase is and in the Tasman Sea. To the north, fewer extremes associated with warm SST anomalies across the equa- were noted in the Gulf of Alaska, the Philippine Sea, torial Pacific and northward along the west coast of 2 and over the islands of Kiribati (21to25daysyr 1). North America, while the negative phase is the inverse Over the Hawaiian Islands, parts of CNMI, and the far pattern (warm western Pacific waters, cold eastern northern RMI, these areas experience one to three Pacific waters). more windy days, while Samoa and the Cook Islands When the phase of the PDO becomes negative experience one to three fewer windy days during ENSO (Fig. 6a), trends are highest generally north of the neutral conditions. equator across much of the central and western Pacific

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FIG. 6. The 95th percentile exceedance frequency by PDO phase and deseasonalized for (a) rain rate, negative phase; (b) rain rate, positive phase; (c) wind speed, negative phase; and (d) wind speed, positive phase. up to and including the Gulf of Alaska. In these regions, inverse of its counterpart during a positive PDO heavy rain extremes are increasing from 10.3 (Fig. 6d). During a positive PDO phase, occurrences of 2 to 10.5 days yr 1. Meanwhile, off the coast of California extreme wind events are highest along and south of the and east of Hawaii, extreme rain events are decreasing equator to roughly 158S. A secondary area of increased 2 by 20.1 to 20.3 days yr 1. The largest area of lower frequency of windy days is found from around the Ha- rainfall rates (dry) runs across the date line, and along waiian Islands west toward Guam and CNMI. Reduced the equator, with a secondary area of lower rainfall rates frequencies of windy days are found just north of the from Samoa southeast through the northern Cook Is- equator inclusive of Kiribati and Micronesia southward lands and into . Most of these areas are through the Solomon Islands. Meanwhile, during a shaded with decreasing trends on the order of 20.3 negative PDO (Fig. 6c), increasing trends in extreme 2 to 20.5 days yr 1. wind events (five to seven more days per year) are noted Heavy rain event trends during a positive PDO from just south of the equator extending southeast to- (Fig. 6b) are significantly different compared to those ward South America, with another area of increasing of a negative PDO. During a positive PDO, dry anom- trends in the Gulf of Alaska. At the same time, a neg- alies stretch from the Gulf of Alaska southward through ative PDO phase decreases the trends in windy days the northern RMI and westward to CNMI. A secondary across all of Micronesia, much of PNG, the Solomon area of decreasing rainfall extreme events is exhibited Islands, and Fiji, with another belt of decreasing fre- from PNG southeast through Port Villa and the far quencies between 308 and 458N. southern Cook Islands. Meanwhile, increases in rainfall extreme event days are found along the west coast of the 5. Discussion United States and along the equator from the Solomon Islands eastward through the Gilbert Islands and the To assess changes in storminess across the domain, a northern Cook Islands. simple storminess index was developed. Using traffic However, the distribution of trends in wind speed light colors of red, yellow, and green, storminess maps extremes in a negative PDO (Fig. 6c) is nearly the were determined as follows:

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Green 5 (rain trend , 0) AND (wind trend , 0). Yellow 5 (rain trend . 0) OR (wind trend . 0). Red 5 (rain trend . 0) AND (wind trend . 0).

Figure 7a shows the annual changes in storminess across the Pacific, while Figs. 7b and 7c show the changes in boreal winter and summer, respectively. Later in this section, the analysis is further subdivided by phase of ENSO and PDO. Recall from section 2 that the trends in sea surface heights are increasing in each of the cities listed in Table 2. As such, it is the lowest common denominator in each of the stoplight categories; the inclusion of sea surface height trends does not significantly affect the overall result of the storminess index. The benefit of choosing a tricolor map is that it gives a quick ‘‘first look’’ at those areas that are essentially most ‘‘at risk’’ because of the recent trends in extremes of heavy rains and strong winds. Areas that are shaded in red have both increasing trends in the extremes of rain and wind, indicating that storminess is also increasing, while green-shaded areas could be considered relatively ‘‘low risk,’’ since both trends in heavy rains and strong winds are decreasing (only sea surface heights are increasing). For the annual series (Fig. 7a) the red shading includes the grid boxes representing the RMI, parts of the Gulf of Alaska, the waters near Japan, and southwestern PNG to the northern coast of Australia. The area near New Zealand, especially the southern portions, is also high- lighted in red. During the boreal summer months, the map looks similar, with the red-shaded storminess index con- tinuing in the Gulf of Alaska, the waters of far northern Japan, and much of PNG and Malaysia. The RMI capital of also remains in the red during boreal summer, as does Nuku’alofa in Tonga (cf. Table 2). There was some relaxation of the red shading at 608Nand1808,aswellas across the coast of British Columbia. When the season shifts to the boreal winter (Fig. 7b), the region with the most storminess shifts to the polar latitudes above 458N and below 458S. The exception is the northern coast of Australia, through the grid box inclusive of , and along 58–108N from RMI east to 1408W. Yellow areas have at least one increasing trend, either in heavy rains or strong winds, indicating a ‘‘moderate FIG. 7. Storminess index by season for (a) annual, (b) summer, risk’’ of increasing trends in storminess. The yellow area and (c) winter. Red indicates ‘‘high risk,’’ yellow indicates ‘‘me- encompasses the majority of the Pacific basin on the dium risk,’’ and green indicates ‘‘low risk.’’ annual time scale and during the boreal summer season. Yellow grid boxes include parts of FSM, Hawaii, the While the red areas in Fig. 7 highlight the overlap of Samoas, Fiji, and along the southwest coast of the increasing trends in both extremes of winds and rains, United States. Table 2 shows specifically how much of an increase can It is worth noting that Table 2 provides a component- be attributed to each component. For example, for based assessment of the contributions from extreme Majuro, Marshall Islands, it is clear that its ‘‘red’’ winds and rains toward the overall storminess index. storminess index assessment largely stems from the

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TABLE 2. Trend summary by capital and island/island nation.

Wind speed trends Rain-rate trends Storminess index Island/island nation Summer Winter Annual Summer Winter Annual Summer Winter Annual Alofi Niue 21.04 21.69 22.12 20.02 20.1 20.14 Green Green Green Samoa 0.53 22.93 22.07 0.03 20.19 20.26 Red Green Green Cook Islands 0.61 0.09 0.54 20.03 20.12 20.17 Yellow Yellow Yellow Tuvalu 20.71 21.94 22.4 20.18 0.01 20.03 Green Yellow Green Hagåtña Guam 22.43 20.62 22.77 0.16 20.16 20.07 Yellow Green Green Solomon Islands 20.15 21.82 20.96 0.35 0.25 0.59 Yellow Yellow Yellow Honolulu Hawaii 1.08 20.31 0.15 20.11 20.01 0 Yellow Green Yellow Koror Palau 0.67 21.97 21.57 0.38 0 0.34 Red Green Yellow Majuro Marshall Islands 2.47 0.78 1.42 0.04 0 0.03 Red Red Red Nuku’alofa Tonga 0.29 20.72 20.89 0.29 0.04 0.26 Red Yellow Yellow Federated States of 20.64 20.1 21.31 20.24 0.1 20.04 Green Yellow Green Micronesia Port Moresby Papua New Guinea 0.07 0.25 0.5 0.13 0.05 0.13 Red Red Red Port Villa Vanuatu 20.02 21.91 22.11 20.02 20.13 20.26 Green Green Green South Kiribati 4.48 0.65 3.48 20.01 20.04 20.04 Yellow Yellow Yellow Fiji 21.03 21.47 21.96 0.28 20.09 0.04 Yellow Green Yellow Yaren Nauru 20.06 20.8 20.61 20.19 20.1 20.25 Green Green Green contribution due to extreme winds, rather than extreme negative PDO, the pattern resembles cold ENSO rain events, on the summer, winter, and annual scales. (Fig. 5d). For cold ENSO and positive PDO, there is a However, such storminess assessments are subject to the strong positive wind anomaly in the western Pacific current state of ENSO or the PDO, which can act to Northern Hemisphere extratropics. enhance or weaken extremes in wind and rain. The en- Keeping in mind the question posed in the in- suing discussion examines the effect of both tele- troduction of this manuscript (Can we assess changes in connections on the storminess index across the region. storminess using remotely sensed data, and if so, how are these changes influenced by the background states of a. Storminess and teleconnections ENSO and PDO?), a storminess index map was pro- Since the PDO is based on the first principle compo- duced for the overlap of each phase of ENSO and the nent of an empirical orthogonal function, it is not sur- PDO (Fig. 8). The left column in Fig. 8 represents pos- prising that ENSO and PDO were found to be positively itive PDO and the right column corresponds to negative correlated, with a correlation coefficient r 5 0.4456. PDO. The top row is cool ENSO, followed by neutral However there are periods where the indices differ. For ENSO with the last row associated with warm ENSO. example, in 1991, 1995, and 2012, positive ENSO oc- Figure 8 shows several key changes in the placement of curred with negative PDO. In 1988, 1996, and 2006, the storminess axis (red areas) depending upon the negative ENSO occurred with positive PDO. overlapping phase of ENSO and the PDO, though it Exceedance frequencies for heavy rain and wind seems more dependent upon the state of ENSO than it events were broken down by ENSO and PDO phase does PDO. For example, the areas under the ‘‘worst’’ (not shown). For heavy rain events during warm ENSO storminess in a cool ENSO (Figs. 8a,d) appear to be and positive PDO, the pattern resembles warm ENSO, heavily dependent on the phase of the PDO. During a with a strong signal in the central Pacific. However, for cool ENSO and positive PDO (Fig. 8a), higher risk areas rain events under a warm ENSO and negative PDO include the north-central Pacific, parts of far western regime, the wet anomaly is located farther west in the Micronesia, and much of PNG, Fiji, Samoa, and Tonga. Pacific, while during both cold and neutral ENSO pha- Yet, during a cool ENSO with a negative PDO (Fig. 8d), ses, the exceedance frequencies during negative PDO much of those same areas are now shaded green, with are not significantly different than those during a posi- the higher risk locations shifting to north of 458N, west of tive PDO. For wind events during the warm phase of 1208E, and south of 158S, essentially the perimeter of the ENSO and positive PDO, the signal resembles a warm Pacific. Meanwhile, during ENSO neutral conditions ENSO phase (Fig. 5f). For the case of a warm ENSO and (Figs. 8b,e), the storminess index is less dependent on negative PDO, there is a large negative wind anomaly in the crossover with the phase of the PDO. Many areas the central Pacific and a positive wind anomaly along shaded in red in Fig. 8b, including Honolulu, Hawaii, are the Pacific Northwest coastline. For cold ENSO and also shaded in red in Fig. 8e, though the negative PDO

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FIG. 8. Storminess index by combined phases of ENSO and PDO for (a) cool ENSO, positive PDO; (b) neutral ENSO, positive PDO; (c) warm ENSO, positive PDO; (d) cool ENSO, negative PDO; (e) neutral ENSO, negative PDO; and (f) warm ENSO, negative PDO. Red is ‘‘high risk,’’ yellow ‘‘medium risk,’’ and green ‘‘low risk.’’

seems to temper some of the extremes across the basin areas dissipated somewhat as well. Following Fig. 8, during a neutral ENSO phase. Finally, during warm periods when cool ENSO overlaps with positive PDO ENSO and positive PDO (Fig. 8c), the overlap of high (Fig. 8a), and when warm ENSO overlaps with negative extremes in wind and rain are found along the west coast PDO (Fig. 8f), give rise to the most elevated risk of of the United States, along the equator through Kiribati, storminess across the Pacific. Thus, the storminess maps through parts of CNMI, and southeastward along the are a reflection of the interactions between ENSO and SPCZ from the Solomon Islands, through the Samoas, PDO teleconnection states. and into the Cook Islands. At the same time, areas near b. Tropical cyclones Alaska, New Zealand, PNG, and Hawaii are shaded green during warm ENSO and positive PDO. Yet, when The linear trend in the number of tropical storms per the phase of the PDO shifts to negative during a warm year over 1988–2012 was computed based on data from ENSO, much of the green areas faded into yellow the International Best Track Archive for Climate (‘‘moderate risk’’), but also the higher-risk red-shaded Stewardship (IBTrACS) dataset (Knapp et al. 2010)to

Unauthenticated | Downloaded 09/25/21 03:31 PM UTC AUGUST 2015 K R U K E T A L . 3227 determine if the storminess results were influenced by La Niña. However, during El Niño, an increase in fre- historical tropical cyclone activity. In the east Pacific, quency of extreme rain events was found along around Guam, and the Coral Sea, the tropical storm the equator, from west to east, and southward into the frequency over the past 25 yr is decreasing in concert Solomon Islands, Tuvalu, Kiribati, and the Cook Islands. with decreasing rain-rate and wind speed trends. Over Additionally, the warm phase of ENSO featured a nearly the Timor Sea, Arafura Sea, and Gulf of Carpentaria, exact opposite of the frequencies in extreme high-wind the tropical storm frequency is increasing, and the rain events, with most of the windier days occurring in the and wind trends are both increasing. Over the Philippine Northern Hemisphere, with many fewer windy days in Sea and around Niue, the tropical storm frequency is the Southern Hemisphere (Fig. 5). increasing, and while the rain trends are increasing, the Though we recognize the period of record is only 25 wind trends are mixed. Increasing rain over FSM does years (due to the SSM/I launching in 1987), a statistical not correspond to changes in tropical storm frequency. generalized extreme value (GEV) analysis was com- The increasing trend in wind speed in the equatorial pleted to derive 100-yr return period values for rain rate central Pacific does not correspond to changes in tropi- and wind speed (Fig. 4). Much of the Pacific domain is 2 cal storm frequency. Thus, changes in tropical cyclone dominated by the 10–20 mm h 1 rain-rate distribution, frequency do manifest themselves in storminess while the highest values for wind speed return values are changes, especially rain, on basin-wide scales. However, located along 458N, between 1508E and 1508W, gener- the large-scale pattern in storminess cannot be ex- ally in the same location as that for the highest return plained by the frequency and intensity of tropical cy- values for rain rates in the basin. clones alone. The discussion of changes to tropical Finally, a storminess index was created and mapped cyclone behavior is best dealt with from other sources in (Figs. 7 and 8). The storminess map gives a quick ‘‘first the literature (e.g., Knutson et al. 2010). look’’ at those areas that are essentially most ‘‘at risk’’ as a result of the recent trends in heavy rains, high seas, and strong winds. The areas where the overlap of in- 6. Summary creasing trends in both wind speed and rain rates were In this manuscript, 25 yr (1988–2012) of Special Sensor shaded as red, indicating the ‘‘higher’’ risk potential. In Microwave Imager (SSM/I) data were analyzed to assess all seasons, both PNG and RMI were highlighted in red trends in rain and wind events over the Pacific Ocean. (Fig. 7). Meanwhile, yellow areas were designated when SSM/I rain-rate and wind speed retrievals were analyzed at least one trend was increasing and are here labeled as on an annual and seasonal basis, with the boreal summer ‘‘moderate’’ risk locations. The yellow area encom- season extending from May to September, and the boreal passes the majority of the Pacific basin on the annual winter season from October to April. The concept of time scale and during the boreal summer season. Green ‘‘storminess’’ was used to capture the unique combina- areas, or ‘‘low’’ risk locations, were highlighted when tion of impacts caused by high seas, strong winds, and both trends in heavy rain and high-wind events were heavy rains. Many of these factors are expected to worsen decreasing. The most green shading occurs during bo- in a changing climate, that is, even higher seas, stronger real winter, with a large area of lower risk depicted in winds, and heavier extreme rain events (Parry et al. 2007, Hawaii, CNMI, northern FSM, the Samoas, Fiji, and 7–22; Keener et al. 2012). To that end, this study answers Vanuatu. For interested user groups, such as the the question, given the lack of land-based data across the decision-maker, water manager, or agroforestry plan- open waters of the Pacific Ocean, can we assess changes ner, given a predicted state of ENSO and PDO, Fig. 8 in storminess using remotely sensed data, and if so, how can be used as a tool to assess seasonal risk and inform are these changes influenced by the background states of their resilience and adaptation planning strategies. ENSO and PDO? There are several areas of future work related to The influence of ENSO on the frequency and trend in assessing trends in rain and wind events across the Pa- rain and wind events over the ocean was examined by cific. A logical next step would be to examine climate binning the data according to ENSO phase (cool, warm, model projections, such as those from the fifth phase of and neutral). For high-wind events, results indicated the Coupled Model Intercomparison Project (CMIP5) that much of the Pacific sees stronger than average wind and determine what a storminess index and trends in speeds during La Niña. In addition, the results also rain and wind events may look like in 2050 or 2100. In showed a strong drying signal across the eastern North addition, an explorative study into the impact of a Pacific, from California south toward Mexico and downscaling technique on the SSM/I data would be westward through the Hawaiian Islands and into parts of prudent to determine the feasibility of obtaining more the Northern Mariana Islands including Guam during specific-island-based results.

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