ENSO cycle modulation of grass pollen season in Auckland New Zealand with implications for allergy management

ENSO cycle modulation of grass pollen season in Auckland New Zealand with implications for allergy management | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article ENSO cycle modulation of grass pollen season in Auckland New Zealand with implications for allergy management Rewi Munro Newnham, Laura McDonald, Kat Holt, Stuti Misra, Natasha Ngadi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4598891/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In many regions, the ENSO cycle climate is a key factor in modulating climate processes that can influence seasonal variability in the production and dispersal of allergy-triggering pollen. However, the impacts on allergy health are not well known. We compare grass pollen seasons between the major modes of the ENSO cycle in Auckland, New Zealand. We find no clear difference in the timing of onset of the pollen seasons, but season length was longer, by > 30 days, during both El Niño phases than during the La Niña phase. Severity of the La Niña pollen season was also lower, although we have less confidence in this comparison due to differences in the sampling site locations. The difference in pollen season length is explained by the greater summer rainfall typically experienced in Auckland and elsewhere in northern New Zealand during La Niña phases, which tends to suppress grass pollen production and dispersal. As grass pollen is the principal source of allergenic pollen in New Zealand and in many other countries, these results have wider implications for allergy management. With ENSO forecasting often reliable with several months of lead time, there is potential for improving community preparedness and resilience to inter-annual dynamics of the grass pollen season. However, the strong geographical heterogeneity in ENSO cycle climate impacts necessitates a region-specific approach. This work further underscores the need for local-regional pollen monitoring in NZ and the risk of relying upon static, nationwide pollen calendars for informing allergy treatment. Pollen ENSO Climate Aeroallergens Precipitation Meteorology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Pollen is recognised as both a major trigger for and cause of chronic allergic respiratory diseases, with increasing medical, economic, and societal burdens (Beggs et al., 2015). Climate change is one of the key factors thought to contribute to the growing prevalence of allergic respiratory disease (ARD) in many regions (Beggs, 2004; 2016; Haberle et al., 2014; Ziska et al . , 2019; d’Amato et al., 2020; Anderegg et al., 2021). This is because climate parameters fundamentally underpin the production, release and dispersal of allergenic pollen. Much effort therefore is being made to consider the impacts of future projected climate change on these key allergy triggers (e.g., Newnham, 1999; Lake et al., 2016; Zhang & Steiner, 2022) as well as to understand how decadal-scale climate variability influences allergy response (Bonomo et al., 2019). These efforts, while important, are typically framed at future and/or decadal-centennial timescales, where long-term trends in pollen levels are projected to smooth out the seasonal to inter-annual variability that characterises currently observed pollen season dynamics. In contrast, previous studies investigating the influence of the North Atlantic Oscillation (NAO) on pollen season dynamics in Europe (e.g. D’Orico et al., 2002; Smith & Emberlin, 2006; Stach et al., 2008; Avolio et al., 2008; Smith, 2009; Galán et al., 2017) indicate the importance of understanding shorter-term pollen season dynamics and forecasting, especially because they are likely to be governed by the same climatic processes that underpin long-term climate change impacts. This research highlights the importance of understanding the climate processes that determine inter-annual variability in the pollen season dynamics of temperate grasses, probably the most important allergenic pollen source at the global scale (Garcia-Mozo, 2017). We focus on the El Niño Southern Oscillation (ENSO) cycle, one of the key modes of short-term climate variability in the wider Pacific region, and globally. We hypothesise that the ENSO cycle plays a profound role in influencing inter annual variability in grass pollen season dynamics in mid-latitude regions and test this idea with a case study in Auckland, New Zealand’s largest city. The ENSO cycle is the focus of major research investment across many platforms, ranging from understanding its mechanistic underpinning (McPhaden et al., 2006) to evolving models for developing valuable forecasts with lead times (months to a few years) that can enable community preparedness both to save lives and mitigate potentially major economic losses (L'Heureux et al., 2020). There are also efforts to evaluate the current and future societal impacts of ENSO cycle variability, including public health impacts (McGregor, 2018), although we are not aware of any research to date that specifically targets allergies. Our research takes this initiative with application at a major population centre where the ENSO cycle strongly influences inter-annual climate variability and where – unusually for New Zealand - sufficient pollen monitoring data are available to test this hypothesis. Although we focus here on ENSO, the allergy impacts of other modes of climate variability such as the North Atlantic Oscillation and Southern Annular Mode should also be considered for those regions where they exert strong short-term influences. This work is limited by the comparatively sparse pollen monitoring data available in New Zealand, with the only systematic nationwide survey undertaken >35 years ago, spanning only a single grass pollen season (Newnham, 2021). Nevertheless, the recorded summer of 1988/89 was characterised by strong La Niña conditions, providing a suitable contrasting comparison for the strong El Niño summer of 2023/24 for which we present new grass pollen data from Auckland (Fig. 1). We also present previously unpublished Auckland grass pollen season data for 1989/90, when brief or moderate El Nino conditions were experienced during an otherwise neutral phase of the ENSO cycle (Fig 2a). This third pollen season data provides a more immediate point of comparison with the strong La Niña phase of the previous summer. Background Trends in allergenic disease in New Zealand Asthma affects nearly 300 million people globally and is projected to increase in prevalence as the population grows (Viegi et al., 2020 ). In New Zealand, asthma exacerbations have increased by one third over the last 10 years (Chan et al., 2023 ), mirroring similar reported trends overseas that have seen a doubling of asthma attacks (Viegi et al., 2020 ). At the global scale, respiratory allergies are increasing not only in developed countries but also in low and middle-income countries (Biagioni et al., 2020 ). Although epidemiologic data on other allergic diseases, such as allergic rhinitis and conjunctivitis, in New Zealand are more scarce, similar trends have been reported with Māori and Pasifika groups having a greater prevalence of disease than Europeans (Moyes et al., 2012 ). Although we do not have conclusive evidence that these increases are due to climate change or variability, there are data illustrating significant relationships between environmental, meteorological and pollen variables and asthma (Tobias et al., 2003 ; Osborne et al., 2017 ; Witonsky et al., 2019 ; D’Amato et al., 2020 , Chan et al., in press; allergic rhinitis (An et al., 2023 ) and conjunctivitis (Motreff et al., 2014 ; Lu et al., 2021 ; Sheng et al., 2022), and international evidence that the allergenic pollen season is becoming longer (Zhang and Steiner, 2022 ). With the exception of a recent study linking asthma mortality rates to the Atlantic Multidecadal Oscillation (Bonomo et al., 2019 ), we are not aware of any previous work investigating the role of short-term modes of climate variability on allergenic diseases. However, inter-annual fluctuations in the timing and magnitude of pollen seasons are important for allergy sufferers who seek to plan and manage their medication, health professionals who plan treatment and clinical management, and pharmaceutical suppliers who manage the production and distribution of health care products (Emberlin et al. 1999 ). What climate factors determine grass pollen season dynamics? This study focuses on the most important source of allergenic pollen in New Zealand, the grasses (family Poaceae). These allergen sources were introduced from Britain and Europe in the 19th and 20th centuries to develop extensive pastoral agriculture, which underpins the New Zealand economy to this day. A similar suite of pastoral grasses constitute the principal source of allergenic pollen in many temperate mid-latitude regions of the Northern Hemisphere (Newnham et al, 1995 ). Therefore, we are able to draw from international research into the climate controls on variability in the grass pollen season. Most studies of pollen-meteorological relationships have focussed on temperature controls on the seasonal dynamics of allergenic tree pollen taxa in the context of observed and projected climate change (Schramm et al., 2021 ). For grasses, temperature and to a lesser extent precipitation are the most important climate variables influencing the main features of grass pollen seasons (Emberlin 1994 ; Kwharam et al., 2014). Other factors such as sunshine hours, relative humidity, atmospheric stability and wind speed are important for daily variations in pollen counts, but they are autocorrelated with the two main variables and are less significant on an annual basis. The influence of temperature and precipitation develops from mid-winter onwards but is especially important in spring and early summer when temperature influences net productivity and pollen production, ultimately determining the timing of flowering, pollen release and dispersal. The role of precipitation in grass pollen season dynamics is more complex than that of temperature. Sanchez-Mesa et al. (2003) reported a negative relationship between rainfall and pollen counts at six localities in Spain and the UK, which was consistently observed for the period 1995–2000. However, rainfall is also necessary for plant growth including pollen production. A recent review of the relevant literature globally revealed that precipitation had varying effects on pollen concentration and pollen season timing indicators (Schramm et al., 2021 ). Although increased precipitation may lower pollen concentrations in the short-term, potentially due to the "wash out" effect, the long-term effects of precipitation were positively correlated with grass pollen levels. Thunderstorm asthma poses a further complication (Kevat, 2020 ). During thunderstorms, which are typically accompanied by high rainfall, warm updrafts can sweep pollen up into high concentrations in the cloud base. Storm dynamics may also fracture pollen into smaller fragments which, upon release to ground level, are able to penetrate airways further than intact pollen. ENSO climate variability in Auckland ENSO is a recurring irregular cycle of climate resulting from changing water temperatures across the tropical Pacific Ocean (Fig. 2 ). One of the most important climate phenomena on Earth, ENSO can generate changes in atmospheric circulation, which in turn influences temperature and precipitation on a global scale (McPhaden et al., 2006 ). The ENSO cycle is measured by the Southern Oscillation Index (SOI) denoting the difference in observed surface air pressure between the tropical central and western Pacific Ocean (Fig. 2 A). El Niño (SOI 1) are terms given to the extreme phases of the ENSO cycle. Analysis of historical movements in the SOI has supported successful long-range (by several months) forecasting of significant ENSO events and their likely regional impacts that are widely used in agricultural planning (L'Heureux et al., 2020 ). In New Zealand, although ENSO accounts for less than 25% of the year-to-year variance in seasonal rainfall and temperatures at most locations, its effects can nevertheless be significant, especially in certain regions and seasons (Fig. 2 ). During El Niño phases (SOI <-1), New Zealand tends to experience stronger or more frequent winds from the west in summer, which can promote drier conditions in eastern areas and more rainfall in the west. During La Niña phases (SOI > 1), northeasterly winds tend to become more common, bringing moist, rainy conditions to northern and northeastern areas of the North Island and reduced rainfall to the southern and western South Island. Warmer than average air and sea temperatures can occur around New Zealand during La Niña. Despite the broad consistency of these patterns, each phase of El Niño and La Niña is distinctive and can result in different climate outcomes depending upon the strength of the phase as well as the interplay of other climatic modes such as the Indian Ocean Dipole and Southern Annular Mode. The Auckland-Northland region is particularly sensitive to fluctuations in the ENSO cycle (Fig. 2 B) especially in summer. La Niña phases are characterised by humid summers, often with heavy or sustained rainfall. Rainfall anomalies over the past few decades indicate that rainfall during a La Niña summer is typically 10–30% greater than normal (Fig. 2 B). Warmer sea surface temperatures may also increase the impacts of ex-tropical cyclones, as observed during early 2023. Methods 1988/90 pollen monitoring Atmospheric pollen/spore samples were collected daily using the Intermittent Cycling Rotorod sampler, an impaction collector with a retracting collector rod sampling head, routinely deployed in North America (Chapman, 1982). Particles were collected on the leading, greased, edge of two clear polystyrene collector rods spun intermittently at a fixed rate, enabling the calculation of pollen concentrations. Sampling rods were collected daily, stained with Calberla's solution, and examined under a transmitted light microscope. For 1988/89, the sampler was installed on the flat roof of the Auckland War Memorial Museum ~ 20 metres above ground level in central Auckland (Fig. 1 ) and deployed from November 14th 1988 to February 2nd 1989. For 1989/90, the sampler was installed on a flat roof ~ 2 metres above ground level at a residential property in the Auckland suburb of Onehunga, ~ 8.6 km south of the Auckland Museum (Fig. 1 ) and deployed from October 28th 1988 to April 4th 1989. A total of 10 successive days were not monitored in November 1988 due to instrument error. This period occurred during the early part of the grass pollen season. 2023/24 pollen monitoring Daily average atmospheric grass and total pollen concentrations were generated using a volumetric impaction sampler (Hirst, 1952 ) installed on the flat roof of the Auckland War Memorial Museum, the same location as for the 1988/89 monitoring. This sampler draws air in at a fixed rate enabling the calculation of concentrations of pollen and other airborne particles that are impacted onto a rotating tape. The tapes were collected on a weekly basis from July 3rd 2023 for 12 months and manually analysed for pollen using light microscopy. Analysis and comparability of pollen monitoring data For this study, we adapted the Australian Interim Pollen and Spore Monitoring Standard and Protocols of Davies et al. ( 2022 ). All daily pollen data are reported as the sum of pollen for the 24-hour period commencing at 9am and ending at 9am on the following day. The grass pollen season was defined as the period from the first day for the monitoring interval in which the grass pollen concentration exceeded 10 pollen grains/m 3 of air and the grass Seasonal Pollen Integral (SPI) was determined as the cumulative sum of daily pollen concentrations during the season (see also Galan et al., 2017). Comparison of the three pollen monitoring datasets was constrained by differences in site location and sampling instrument. The latter were assumed to be negligible as Peel et al ( 2014 ) reported comparable measurements for grass pollen concentrations between the Hirst and Rotorod samplers. The sampling locations were identical for the 1988/89 and 2023/24 monitoring periods, but the different location for 1989/90 with a sampling point much closer to ground level must be taken into account for any comparison of those data. Although comparisons of the length of grass pollen season are unlikely to be compromised by these different locations within the same city, the severity measures (daily pollen concentrations and SPI) are likely to be greater for the 1988/89 data due to closer proximity to ground level for that study as well as being a much less open, exposed setting. We substituted the 10 days of missing data during the early part of the 1988/89 grass pollen season with average values from the 5 preceding and 5 subsequent days, an adjustment that also compromises comparisons of severity measures. The Auckland pollen data from 1988/89 were reported by Newnham et al ( 1995 ) as part of their nationwide pollen survey. The 1989/90 and 2023/24 Auckland pollen data are presented here for the first time. ENSO and Auckland climate data Historical SOI data were obtained from the open access NOAA website ( https://www.ncei.noaa.gov/access/monitoring/enso/ ). Historical Auckland climate data were obtained from the NIWA Cliflo open access website ( https://cliflo.niwa.co.nz/ ) based on the Auckland airport climate station (Fig. 1 ). The classifications of El Niño and La Niña spring and summer phases were obtained from National Institute of Water and Atmosphere (NIWA) website ( https://cliflo.niwa.co.nz/ ). Results Table 1 Summary of pollen monitoring results for the three seasons and details of site locations and ENSO cycle phases. Summer 1988/89 1989/90 2023/24 ENSO Cycle La Niña El Niño (minor) El Niño Site Location War Memorial Museum Alfred St, Onehunga War Memorial Museum Pollen Sampling Method Rotorod Cyclone Rotorod Cyclone Burkhard Hirst Height of sampler 30 m 2 m 30 m Pollen Unit of Measurement Grains/m 3 air Grains/m 3 air Grains/m 3 air Sampling interval 14/11/88–17/2/89 28/10/89 − 8/4/90 1/7/23–30/6/2024 (ongoing) Missing data in grass pollen season 10 days 1 0 0 Grass Season Parameters Onset (date) 17/11/88 8/11/89 16/11/23 End (date) 27/12/89 1/2/90 31/1/24 Length (no days) 41 86 77 Severity (SPI) 685* 1169 862 Climate December total rainfall (mm) 201.8 58.0 146.8 January total rainfall (mm) 172.8 66.5 28.4 November mean temperature (℃) 16.9 17.3 16.7 December mean temperature (℃) 18.9 18.2 19.6 January mean temperature (℃) 20.2 20.1 21.6 Notes: * Includes estimated concentrations for the 10 days missing data, determined as the average of the 5 days preceding and 5 days following the gap days. Auckland grass pollen season 2023/24 The 2023/24 pollen monitoring season commenced on July 3rd (Table 1 ). Grass pollen levels remained low from this period until mid-November when they rose sharply to > 10 grains/m 3 , with moderate to high levels maintained intermittently until the end of January 2024 (Fig. 3 ). Low levels during this grass pollen season, when the daily grass pollen count falls below 10 grains/m 3 , invariably occurred on days with rainfall > 1 mm. Auckland grass pollen season 1988/89 The 1988/89 pollen data monitored at the Auckland War Memorial Museum (Newnham et al., 1995 ) are included here for comparison along with daily rainfall data (Fig. 3 ). Monitoring commenced November 14th 1988 and ended February 17th 1989. The first occurrence of daily pollen > 10 grains/m 3 , denoting the start of the grass pollen season, was November 17th. No data are available for the period 27th November to December 6th. For comparison purposes (below) we substituted the 10 days of missing data with the average values for the pre- and post-gap days. Grass pollen levels remained at moderate to high levels until December 27th when they decreased to very low levels for the remainder of the monitoring period. Late December to early January was marked by unusually high and persistent rainfall, including > 140 mm recorded for December 31st . Auckland grass pollen season 1989/90 The 1989/90 pollen data were collected in the Auckland suburb of Onehunga between 28th October 1989 and 8th April 1990 by Newnham and are presented here for the first time (Fig. 4 ). Grass pollen levels remained low until 8th November, with moderate to high levels, and then were maintained intermittently until 1st February 1990. As for the other monitoring periods, low levels during this grass pollen season, when the daily grass pollen count fell below 10 grains/m 3 , invariably occurred on days with rainfall > 1 mm. Discussion How does rainfall influence short-term grass pollen levels in Auckland? It is difficult to make precise comparisons across these datasets and direct quantitative comparisons would likely be spurious, given the high degree of day-to-day variability in Auckland rainfall and small pollen season sample size (Fig. 5 ). Another problem with such comparisons is that summer rainfall in Auckland is often intermittent and interrupted by dry, sunny intervals. Thus a 24-hour pollen count might encapsulate both high rainfall and lengthy dry periods, resulting in both negative and positive influences on pollen release and dispersal. To give illustrative contrasting examples, a day with sunny dry conditions in the morning followed by a wet afternoon would be expected to generate both high rainfall and high pollen levels reported for that day, whereas persistent rainfall during the day would likely be accompanied by low pollen levels. All three datasets show a broadly consistent correspondence in day-to-day variability between grass pollen levels and precipitation (Figs. 3 – 4 ). The intervals of rainfall, especially when heavy, tend to coincide with lower pollen levels, while drier periods are generally associated with higher pollen levels. Similar relationships have been observed in other regions for grass pollen (e.g. Sanchez-Mesa et al., 2003) and more generally across the wider spectrum of allergenic pollen taxa (Schramm et al., 2021 ). A negative rainfall influence was also noted in the only nationwide pollen survey to be undertaken in New Zealand (Newnham et al., 1995 ). Grass pollen variation across the ENSO cycle The absence of routine pollen monitoring in New Zealand has restricted our consideration of the influence of the ENSO cycle on Auckland grass pollen variability to a comparison of three seasons representing one La Niña phase, one strong El Niño phase and one weak El Niño or ENSO-neutral phase. For these three monitoring seasons, further limitations are imposed by the short monitoring period, the significant data gap for the single La Niña phase and the > 30-year timespan between the earlier two datasets and the most recent one. Despite these limitations, it is clear that summer rainfall variability is a key factor causing the differences in pollen season dynamics across these three datasets (Table. 1). As Auckland summer rainfall in turn, varies consistently with the ENSO cycle (Fig. 6 ), we contend that ENSO plays an important role in influencing the inter annual variability in grass pollen season dynamics in Auckland. The most striking example of this influence is the abrupt truncation of the 1988/89 (La Niña) pollen season that resulted in a much shorter season (41 days) than that for the two El Niño (or non- La Niña summers) analysed (86 and 77 days). The termination of the 1988/89 grass pollen season coincided with an extensive period of sustained heavy rainfall commencing in late December (Fig. 3 ). Strong rainfall persisted for much of January to the extent that grass pollen levels remained low throughout that month. The severe truncation of the 1988/89 grass season may indicate that pollen production and release were suppressed by persistent rainfall, in addition to the mechanism of ‘washing’ pollen out of the atmosphere (Schramm et al., 2021 ). Overall pollen severity (SPI) was 20% lower in the La Niña 1988/89 summer than in the El Niño summer in 2023/24, as measured at the same station (Table. 1). This difference in pollen severity may be underestimated, however, as the estimates used for the 10 days (25% of the season) with 1988/89 missing data are based on the pre- and post-gap days and these were comparatively dry periods relative to the missing data interval (Fig. 3 ). Implications These results, suggesting ENSO-modulated inter-annual variation in grass pollen season dynamics, have important implications for allergy prevalence and management. The differences in grass pollen season length and severity reported here are profound, abrupt and occur in the present, compared with long-term more gradual projections of climate change impacts on pollen season and severity. These seasonal differences are likely to be strongest for oceanic climates such as those in New Zealand, where temperature changes from year to year are typically small compared to precipitation changes (Figs. 5 , 7 ). Strong inter-annual variation in pollen seasons is especially problematic in countries such as New Zealand, which does not undertake routine pollen monitoring and is therefore reliant on other methods such as flowering observations and pollen calendars. The latter depict static pollen seasons that may be based on atypical years, at odds with our results. This work further underpins the call to develop routine pollen monitoring, so that these seasonal dynamics and their impacts on allergy response can be managed in real time. In light of these implications, it is interesting to consider how rainfall variability linked to the ENSO cycle varies during the Auckland pollen season (Fig. 5 ). Since 1988, strong inter-annual rainfall variability has occurred for the months of December and January, during the height of the pollen season. Some of this variability is consistent with ENSO cycle variability, particularly in wetter La Niña phases. For example, three of the four wettest Decembers were during La Niña phases and five of the six wettest Januarys (Fig. 5 ). The distinction between wet La Niña and dry El Niño events is apparent for all three months of the pollen season, but is most strongly observed in January. As a consequence, we suggest that January is the most sensitive month in the grass pollen season to ENSO cycle variability, a conclusion drawn from our pollen season comparisons. For the period of 1988–2023, January rainfall in Auckland was on average 94% greater during La Niña summers than during El Niño summers, compared with 25% and 27% for December and November, respectively. Although based on a limited pollen dataset comprising only three seasons, the consistency of these results with our hypothesised modulating mechanism gives some confidence that the improving seasonal forecasting of the ENSO cycle could ultimately bring a new dimension to pollen forecasting. A lead time of several months could offer a key window of opportunity for allergy patients, people living with allergenic disease, and health practitioners to better manage the treatment of allergy symptoms in more pre-emptive ways. Further benefits would follow as the potential of satellite sensing to augment longer range forecast models of grass pollen aerobiology is realised (Kwharam et al., 2017; Devadas et al., 2018 ). These observations also have implications for projecting climate change impacts on grass pollen levels in Auckland, despite the paucity of monitoring data. From the combination of pollen and climatological analyses presented here, we suggest that long term summer rainfall trends need to be considered in any projections of climate change impacts on pollen levels for Auckland and may be just as important as temperature trends. The latest IPCC projections, downscaled to the New Zealand region (Bodecker et al., 2022) suggest that the Auckland region will experience progressively drier and warmer summers during the remainder of this century, superposed by short-term variability arising from the ENSO cycle. Precipitation trends are much more difficult to project with confidence and show strong regional variability. Nevertheless, these projections are consistent with the warmer temperatures and lower rainfall observed for Auckland over the past three decades for the three pollen months, particularly during the critical month of January (Figs. 5 , 7 ; Table 2 ). Table 2 Comparison of Auckland five-year moving average of mean monthly temperatures and range ( o C) for November to January 1988–2023. The range in monthly temperature represents the difference between the highest and lowest mean temperature for that month during the period 1988–2023. 1988 2023 Range November 16.7 17.6 3.2 December 18.5 19.8 5.1 January 19.8 20.7 3.9 Regional heterogeneity of ENSO and other modes of climate variability In this section, we consider the relevance of this Auckland study to other regions where grass pollen seasons may be strongly influenced by ENSO or other modes of short-term climate variability. In doing so, we contend that while this influence may be widespread and globally relevant, the impacts are likely to be highly distinctive between regions for two reasons. First, as is evident in Fig. 1 B, the climate impacts and in particular precipitation variability can manifest very differently even for adjacent regions. The conclusions drawn from our Auckland study, for example, may be applicable to varying extents for other parts of northern and eastern New Zealand, but would be erroneous if they were applied to southwestern New Zealand. Second, the extent and even direction of ENSO influence will depend upon the distinctive bioclimatic envelope for the suite of grasses that occur in a particular region. We have shown for example that Auckland’s particular sensitivity to ENSO cycle modulation arises from a maritime climate, typically with ample rainfall for grass growth throughout the year and humid summers. A more arid climate setting, even at a similar latitude, would be expected to experience a very different response, with wetter conditions accompanying La Niña phases perhaps stimulating grass pollen production overall, rather than suppressing it as in Auckland. These distinctions are even stronger for large, diverse regions such as Australia, where marked spatial and temporal variability in grass pollen seasons is observed (Beggs et al., 2015 ; Davies et al., 2015 ). In particular, tropical grasses in the north are adapted to heavy rainfall concentrated in the summer and have different pollen seasons than the temperate grasses in the south (Medek et al., 2015). Monitoring these pollen season dynamics and the spread of new allergens is a pre-requisite to understanding the impact of both short-term climate variability and longer-term climate change on allergy burden (Haberle et al., 2014 ). These observations mirror previous studies of NAO moderation of pollen season dynamics in Europe. The NAO is a mode of interannual variability in atmospheric circulation associated with changes in the surface westerlies across the North Atlantic and into Europe (Hurrell 1995 ). The role of NAO climate dynamics, cyclicity and periodicity in moderating grass pollen seasons can be compared with that of the ENSO cycle in the Pacific. The influence exerted by the NAO on grass pollen seasons varies spatially across western Europe and even the direction of the relationship between the NAO and precipitation can change between geographical areas (Smith et al., 2009 ). These authors and others (e.g. Galan et al., 2017) emphasise a need for more regional scale studies into the influence of the NAO on grass pollen counts and other allergenic pollen types. Our Auckland results demonstrate a comparable role for the ENSO cycle that also is likely to manifest in distinctive ways across its geographical sphere of influence that will need to be independently determined. Conclusions and further work The paucity of airborne pollen monitoring data available for New Zealand generally is a major impediment to understanding the role of meteorology and climate change in both inter-annual variability and long-term trends and dynamics of allergenic pollen seasons. Here, the serendipitous alignment of three Auckland grass pollen datasets with the principal phases of the ENSO cycle has enabled some key insights into its role in modulating the grass pollen season in Auckland. Marked differences in grass pollen seasons between the summers of 1988/89 (La Niña), 1989/90 (weak El Niño/neutral) and 2023/24 (El Niño) are attributable to contrasting rainfall patterns, which is consistent with longer-term observations of the ENSO cycle in this region. From these results, we suggest that La Niña summers are likely to result in less severe pollen seasons in Auckland than El Niño summers, with January rainfall as a critical variable. These results have important implications for pollen allergy management in Auckland, especially as the ability to predict the ENSO cycle now extends to several months with increasing confidence. The ENSO cycle and other short-term modes of climate variability such as the North Atlantic Oscillation have drawn comparatively little consideration in the context of climate and meteorological factors governing pollen season variability, with far more attention given to longer-term projection of climate change impacts. Nevertheless, the insights drawn from the ENSO influence in this Auckland study may also be relevant in the longer-term, because of the critical role played by summer precipitation, which is projected to decline further in the Auckland region. Our results point to increased severity and possibly length of the grass pollen seasons accompanying the projected drier, warmer summers for northern New Zealand. We suggest that in oceanic mid-latitude regions such as Auckland, summer precipitation trends may be as important as temperature trends in influencing pollen season variability in the future. Finally, these results demonstrate that currently available static pollen calendars are of limited utility and may even be misleading. Future work to model the effects of ENSO cycles on health outcomes and healthcare utilisation could provide additional insights. We acknowledge that these conclusions are preliminary and are drawn from a limited dataset that requires substantiation by further work and in other regions where the ENSO cycle or other modes of climate variability may be postulated to play a similar role in pollen season modulation. Declarations Acknowledgements This work would not have been possible without the support of management and staff of Auckland War Memorial Museum and in particular their Botany and Facilities/Assets teams. We are also grateful to co-author Kat Holt for providing a Burkhard pollen trap to be deployed at Auckland Museum. The work was part-funded by Auckland Medical Research Foundation (Senior Research Fellowship 3725270), Life AI Corp (6001213) and New Zealand Health Research Council (HRC 22/540). Thanks to James Renwick and Ciaran Doolin for their insightful comments on the manuscript. References An Y, Ouyang Y, Zhang L (2023) Impact of airborne pollen concentration and meteorological factors on the number of outpatients with allergic rhinitis. World Allergy Organ J 16(4):100762 Anderegg WRL, Abatzoglou JT, Anderegg LDL, Bielory L, Kinney PL, Ziska L (2021) Anthropogenic climate change is worsening North American pollen seasons. Proc. Natl. Acad. Sci. U. S. 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Sci Total Environ 633:441–451 D’Amato G, Chong-Neto HJ, Monge Ortega OP, Vitale C, Ansotegui I, Rosario N, Haahtela T, Galan C, Pawankar R, Murrieta‐Aguttes M, Cecchi L (2020) The effects of climate change on respiratory allergy and asthma induced by pollen and mold allergens. Allergy 75(9):2219–2228 D’Odorico P, Yoo J, Jaeger S (2002) Changing seasons: An effect of the North Atlantic Oscillation? J Clim 15:435–445 Emberlin J (1994) The effects of patterns in climate and pollen abundance on allergy. Allergy 49:15–20 Emberlin J, Mullins J, Cordon J, Jones S, Millington W, Brooke M et al (1999) Regional variations in grass pollen seasons in the UK, long term trends and forecast models. Clin Exp Allergy 29:347–356 Galán C, Alcázar P, Oteros J, García-Mozo H, Aira MJ, Belmonte J, de la Guardia CD, Fernández-González D, Gutierrez-Bustillo M, Moreno-Grau S, Pérez-Badía R (2016) Airborne pollen trends in the Iberian Peninsula. Sci Total Environ 550:53–59 Galán C, Ariatti A, Bonini M, Clot B, Crouzy B, Dahl A, Fernandez-González D, Frenguelli G, Gehrig R, Isard S, Levetin E (2017) Recommended terminology for aerobiological studies. Aerobiologia 33:293–295 Garcia-Mozo H (2017) Poaceae pollen as the leading aeroallergen worldwide: A review. Allergy 72:1849–1858 Haberle SG, Bowman DMJS, Newnham RM, Johnston FH, Beggs PJ, Buters J et al (2014) The macroecology of airborne pollen in Australian and New Zealand urban areas. PLoS ONE 9(5):e97925. 10.1371/journal.pone.0097925 Hirst JM (1952) An automatic volumetric spore trap. Ann Appl Biol 39:257–265 Hurrell JW (1995) Decadal trends in the North Atlantic Oscillation: Regional temperatures and precipitation. Science 269:676–679 Kevat A (2020) Thunderstorm asthma: looking back and looking forward. 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Environ health Perspect Mar 125(3):385–391 L'Heureux ML, Levine AF, Newman M, Ganter C, Luo JJ, Tippett MK, Stockdale TN (2020) ENSO prediction.El Niño Southern Oscillation in a changing climate 4:227–246 Lu C-W, Fu J, Liu X-F, Chen W-W, Hao J-L, Li X-L et al (2021) Air pollution and meteorological conditions significantly contribute to the worsening of allergic conjunctivitis: a regional 20-city, 5-year study in Northeast China. Light: Science & Applications 10(1):190 McGregor GR, Ebi K (2018) El Niño Southern Oscillation (ENSO) and health: an overview for climate and health researchers. Atmosphere. 2018 9(7):282 McPhaden MJ, Zebiak SE, Glantz MH (2006) ENSO as integrating concept earth Sci Sci 314:1740–1745 Medek DE, Beggs PJ, Erbas B et al (2016) Regional and seasonal variation in airborne grass pollen levels between cities of Australia and New Zealand. Aerobiologia 32:289–302. https://doi.org/10.1007/s10453-015-9399-x Motreff Y, Golliot F, Calleja M, Le Pape A, Fuhrman C, Farrera I et al (2014) Short-term effect of pollen exposure on drug consumption for allergic rhinitis and conjunctivitis. Aerobiologia 30:35–44 Moyes CD, Clayton T, Pearce N, Asher MI, Ellwood P, Mackay R et al (2012) Time trends and risk factors for rhinoconjunctivitis in New Zealand children: an International Study of Asthma and Allergies in Childhood (ISAAC) survey. J Paediatr Child Health 48(10):913–920 Newnham RM (1999) Monitoring biogeographical response to climate change: the potential role of aeropalynology. Aerobiologia 15(2):87–94 Newnham RM (2022) Monitoring airborne pollen in New Zealand. J Royal Soc New Z 52(2):192–211 Newnham RM, Fountain DW, Cornford C, Forde MB (1995) Airborne pollen and grass flowering in New Zealand with implications for respiratory disorders. Aerobiologia 11:239–252 Osborne NJ, Alcock I, Wheeler BW, Hajat S, Sarran C, Clewlow Y et al (2017) Pollen exposure and hospitalization due to asthma exacerbations: daily time series in a European city. Int J Biometeorol 61:1837–1848 Peel R, Kennedy R, Smith M, Hertel O (2014) Relative efficiencies of the Burkard 7-Day, Rotorod and Burkard Personal samplers for Poaceae and Urticaceae pollen under field conditions. Ann Agric Environ Med 21(4):745–752. https://doi.org/10.5604/12321966.1129927 Sánchez Mesa JA, Smith M, Emberlin J, Allitt U, Caulton E, Galan C (2003) Characteristics of grass pollen seasons in areas of southern Spain and the United Kingdom. Aerobiologia 19:243–250 Schramm PJ, Brown CL, Saha S, Conlon KC, Manangan AP, Bell JE, Hess JJ (2021) A systematic review of the effects of temperature and precipitation on pollen concentrations and season timing, and implications for human health. Int J Biometeorol 65:1615–1628 Sheng W, Liu A, Peng H, Wang J, Guan L (2020) A time-series analysis on generalized additive model for atmospheric pollen concentration and the number of visits of allergic conjunctivitis, Beijing, China. Environ Sci Pollut Res 29(40):61522–61533 Smith M, Emberlin J, Stach A, Rantio-Lehtimäki A, Caulton E, Thibaudon M, Sindt C, Jäger S, Gehrig R, Frenguelli G, Jato V (2009) Influence of the North Atlantic Oscillation on grass pollen counts in Europe. Aerobiologia 25:321–332 Smith M, Emberlin J (2006) A 30-day-ahead forecast model for grass pollen in north London, United Kingdom. Int J Biometeorol 50:233–242 Stach A, Emberlin J, Smith M, Adams-Groom B, Myszkowska D (2008) Factors that determine the severity of Betula spp. pollen seasons in Poland (Poznań and Krakow) and the United Kingdom (Worcester and London). Int J Biometeorol 52:311–321 Tobias A, Galan I, Banegas J, Aranguez E (2003) Short term effects of airborne pollen concentrations on asthma epidemic. Thorax 58(8):708 Viegi G, Maio S, Fasola S, Baldacci S (2020) Global Burden of Chronic Respiratory Diseases. J Aerosol Med Pulm Drug Deliv 33(4):171–177 Witonsky J, Abraham R, Toh J, Desai T, Shum M, Rosenstreich D et al (2019) The association of environmental, meteorological, and pollen count variables with asthma-related emergency department visits and hospitalizations in the Bronx. J Asthma 56(9):927–937 Zhang Y, Steiner AL (2022) Projected climate-driven changes in pollen emission season length and magnitude over the continental United States. Nat Commun 13(1):1234 Ziska LH, Makra L, Harry SK, Bruffaerts N, Hendrickx M, Coates F et al (2019) Temperature-related changes in airborne allergenic pollen abundance and seasonality across the northern hemisphere: a retrospective data analysis. Lancet Planet Health 3:e124–e131. https://doi.org/10.1016/S2542-5196(19)30015-4 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4598891","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":319423312,"identity":"53794878-a3e5-4a68-ad6d-6869db18793f","order_by":0,"name":"Rewi Munro Newnham","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYJCCA2BSAog/kKyFcQZpdgG1MPMQo9C8vcfwAOMOm8Tt0s1PN9u2HU5sYD/8gLngD24tMmfOGBxgPJOWuHPOMbPbuSAtPGkGzDPw2CchkbvhAGPb4dwNNxLMbuecOZzbwJADdKEEHi3yb0Fa/gO1pH+7bQHSwv8GqMUAny28IC0HgFpyzG4zVAC1SIBsScCjhSf/w4HEtuR6oJaymz0V6fVtEs8MDvMcwKOF/Vjyh49tdsYGN9K33fhhYG3Mz5/88DEPnhADAxRXsDHAIncUjIJRMApGAdkAABpbVDNXdHEvAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Rewi","middleName":"Munro","lastName":"Newnham","suffix":""},{"id":319423313,"identity":"2d265bbf-0a8b-43c0-b31c-ec4866aead08","order_by":1,"name":"Laura McDonald","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"McDonald","suffix":""},{"id":319423314,"identity":"df8fe419-391f-4365-b76a-06cfe93162ad","order_by":2,"name":"Kat Holt","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Kat","middleName":"","lastName":"Holt","suffix":""},{"id":319423315,"identity":"e38dcc4a-2554-4311-ada3-a27670351cfb","order_by":3,"name":"Stuti Misra","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Stuti","middleName":"","lastName":"Misra","suffix":""},{"id":319423316,"identity":"ff7dab5b-57dc-45f8-b3de-a29164967794","order_by":4,"name":"Natasha Ngadi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Natasha","middleName":"","lastName":"Ngadi","suffix":""},{"id":319423317,"identity":"63733079-b702-424a-b336-206a5b22137b","order_by":5,"name":"Calista Ngadi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Calista","middleName":"","lastName":"Ngadi","suffix":""},{"id":319423318,"identity":"ff88a23d-ddbb-49fe-bd1d-837d4bdaf9fa","order_by":6,"name":"Amy Chan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Amy","middleName":"","lastName":"Chan","suffix":""}],"badges":[],"createdAt":"2024-06-18 09:25:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4598891/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4598891/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60578169,"identity":"b7b5cb2a-c663-4954-8e95-4b3c3107cb60","added_by":"auto","created_at":"2024-07-18 11:08:13","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":319736,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAuckland showing location of the two pollen monitoring sites and Auckland Airport climate station.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4598891/v1/a30ca8cbed1c2648a0c1537e.jpeg"},{"id":60578167,"identity":"fc14aa49-6bb2-4035-8fd6-761dd5d43ef6","added_by":"auto","created_at":"2024-07-18 11:08:13","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":204321,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eA. The Southern Oscillation Index showing the ENSO cycle since the early 1980s. Source: NOAA: https://www.ncei.noaa.gov/access/monitoring/enso/soi. The three intervals for pollen monitoring in Auckland are shown; B. Summer (December- February) rainfall anomalies across New Zealand during El Niño and La Niña phases since 1972. Anomalies are calculated with reference to 1991-2020. Source: NIWA Virtual Climate Station Network.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4598891/v1/892350d6d384674a05b68e76.jpeg"},{"id":60578601,"identity":"d1567c31-5f75-4bc5-9097-8d88fd167c4e","added_by":"auto","created_at":"2024-07-18 11:16:13","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":280429,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAuckland daily grass pollen levels from July 3\u003c/em\u003e\u003csup\u003e\u003cem\u003erd\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e 2023 to March 2024 (top panel) with the period from mid-November onwards enlarged (middle panel) to show the grass pollen season and daily rainfall. Lower panel shows Auckland daily grass pollen and rainfall levels for from 14\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e November 1988 to 17\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e February 1989.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4598891/v1/2587975161730a066dc531ef.jpeg"},{"id":60579328,"identity":"317eab8f-cd78-48ee-8f1e-6e6671c46ea5","added_by":"auto","created_at":"2024-07-18 11:24:13","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":127681,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAuckland daily grass pollen levels (Onehunga) and daily rainfall from October 28\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e 1989 to 4\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e April 1990.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4598891/v1/65b93f8c2e10ccae99426195.jpeg"},{"id":60578172,"identity":"7636ceff-b80f-46f1-b5a8-3b99b376f9fc","added_by":"auto","created_at":"2024-07-18 11:08:13","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":182002,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAuckland Monthly rainfall for November – January months 1988-2023 with 5-year moving averages (blue dashed lines). La Niña and El Niño summers (Dec, Jan) and springs (Nov) are indicated by blue and red open circles, respectively\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4598891/v1/773df8e6ac953ee8828f72a0.jpeg"},{"id":60578173,"identity":"98383626-73c0-41ce-a801-2cca4478afcc","added_by":"auto","created_at":"2024-07-18 11:08:13","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":63648,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAuckland average monthly rainfall mm for November to January from 1988-2023 compared to averages for El Niño and La Niña springs (Nov) and summers (Dec, Jan). Sources NIWA Cliflo and https://niwa.co.nz/climate/information-and-resources/elnino\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4598891/v1/4a43294d69b896335fa76c25.jpeg"},{"id":60578171,"identity":"3a8a3dde-50fc-43e9-a81f-fcad8f69ca17","added_by":"auto","created_at":"2024-07-18 11:08:13","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":347170,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAuckland Monthly temperature for November – January months 1988-2023 with 5-year moving averages.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4598891/v1/d622d755266f68755edd9e2a.jpeg"},{"id":65717590,"identity":"980cc960-79e1-4716-8393-c838810bd2ed","added_by":"auto","created_at":"2024-10-01 16:06:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2109440,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4598891/v1/0ecee4af-e4cf-4ecf-b654-471067ed25e1.pdf"}],"financialInterests":"","formattedTitle":"ENSO cycle modulation of grass pollen season in Auckland New Zealand with implications for allergy management","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePollen is recognised as both a major trigger for and cause of chronic allergic respiratory diseases, with increasing medical, economic, and societal burdens (Beggs et al., 2015).\u0026nbsp;Climate change is one of the key factors thought to contribute to the growing prevalence of allergic respiratory disease (ARD) in many regions (Beggs, 2004; 2016; Haberle et al., 2014; Ziska et al\u003cem\u003e.\u003c/em\u003e, 2019; d\u0026rsquo;Amato et al., 2020; Anderegg et al., 2021). This is because climate parameters fundamentally underpin the production, release and dispersal of allergenic pollen. Much effort therefore is being made to consider the impacts of future projected climate change on these key allergy triggers (e.g., Newnham, 1999; Lake et al., 2016; Zhang \u0026amp; Steiner, 2022) as well as to understand how decadal-scale climate variability influences allergy response (Bonomo et al., 2019). These efforts, while important, are typically framed at future and/or decadal-centennial timescales, where long-term trends in pollen levels are projected to smooth out the seasonal to inter-annual variability that characterises currently observed pollen season dynamics. In contrast, previous studies investigating the influence of the North Atlantic Oscillation (NAO) on pollen season dynamics in Europe\u0026nbsp;(e.g. D\u0026rsquo;Orico et al., 2002; Smith \u0026amp; Emberlin, 2006; Stach et al., 2008; Avolio et al., 2008; Smith, 2009;\u0026nbsp;Gal\u0026aacute;n et al., 2017)\u0026nbsp;indicate the importance of understanding shorter-term pollen season dynamics and forecasting, especially because they are likely to be governed by the same climatic processes that underpin long-term climate change impacts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research highlights the importance of understanding the climate processes that determine inter-annual variability in the pollen season dynamics of temperate grasses, probably the most important allergenic pollen source at the global scale (Garcia-Mozo, 2017). We focus on the El Ni\u0026ntilde;o Southern Oscillation (ENSO) cycle, one of the key modes of short-term climate variability in the wider Pacific region, and globally. We hypothesise that the ENSO cycle plays a profound role in influencing inter annual variability in grass pollen season dynamics in mid-latitude regions and test this idea with a case study in Auckland, New Zealand\u0026rsquo;s largest city. The ENSO cycle is the focus of major research investment across many platforms, ranging from understanding its mechanistic underpinning (McPhaden et al., 2006) to evolving models for developing valuable forecasts with lead times (months to a few years) that can enable community preparedness both to save lives and mitigate potentially major economic losses (L\u0026apos;Heureux et al., 2020). There are also efforts to evaluate the current and future societal impacts of ENSO cycle variability, including public health impacts (McGregor, 2018), although we are not aware of any research to date that specifically targets allergies. Our research takes this initiative with application at a major population centre where the ENSO cycle strongly influences inter-annual climate variability and where \u0026ndash; unusually for New Zealand - sufficient pollen monitoring data are available to test this hypothesis. Although we focus here on ENSO, the allergy impacts of other modes of climate variability such as the North Atlantic Oscillation and Southern Annular Mode should also be considered for those regions where they exert strong short-term influences.\u003c/p\u003e\n\u003cp\u003eThis work is limited by the comparatively sparse pollen monitoring data available in New Zealand, with the only systematic nationwide survey undertaken \u0026gt;35 years ago, spanning only a single grass pollen season (Newnham, 2021). Nevertheless, the recorded summer of 1988/89 was characterised by strong La Ni\u0026ntilde;a conditions, providing a suitable contrasting comparison for the strong El Ni\u0026ntilde;o summer of 2023/24 for which we present new grass pollen data from Auckland (Fig. 1). We also present previously unpublished Auckland grass pollen season data for 1989/90, when brief or moderate El Nino conditions were experienced during an otherwise neutral phase of the ENSO cycle (Fig 2a). This third pollen season data provides a more immediate point of comparison with the strong La Ni\u0026ntilde;a phase of the previous summer.\u0026nbsp;\u003c/p\u003e"},{"header":"Background","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eTrends in allergenic disease in New Zealand\u003c/h2\u003e \u003cp\u003eAsthma affects nearly 300\u0026nbsp;million people globally and is projected to increase in prevalence as the population grows (Viegi et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In New Zealand, asthma exacerbations have increased by one third over the last 10 years (Chan et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), mirroring similar reported trends overseas that have seen a doubling of asthma attacks (Viegi et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). At the global scale, respiratory allergies are increasing not only in developed countries but also in low and middle-income countries (Biagioni et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although epidemiologic data on other allergic diseases, such as allergic rhinitis and conjunctivitis, in New Zealand are more scarce, similar trends have been reported with Māori and Pasifika groups having a greater prevalence of disease than Europeans (Moyes et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Although we do not have conclusive evidence that these increases are due to climate change or variability, there are data illustrating significant relationships between environmental, meteorological and pollen variables and asthma (Tobias et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Osborne et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Witonsky et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; D\u0026rsquo;Amato et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Chan et al., in press; allergic rhinitis (An et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and conjunctivitis (Motreff et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sheng et al., 2022), and international evidence that the allergenic pollen season is becoming longer (Zhang and Steiner, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). With the exception of a recent study linking asthma mortality rates to the Atlantic Multidecadal Oscillation (Bonomo et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), we are not aware of any previous work investigating the role of short-term modes of climate variability on allergenic diseases. However, inter-annual fluctuations in the timing and magnitude of pollen seasons are important for allergy sufferers who seek to plan and manage their medication, health professionals who plan treatment and clinical management, and pharmaceutical suppliers who manage the production and distribution of health care products (Emberlin et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eWhat climate factors determine grass pollen season dynamics?\u003c/h2\u003e \u003cp\u003eThis study focuses on the most important source of allergenic pollen in New Zealand, the grasses (family Poaceae). These allergen sources were introduced from Britain and Europe in the 19th and 20th centuries to develop extensive pastoral agriculture, which underpins the New Zealand economy to this day. A similar suite of pastoral grasses constitute the principal source of allergenic pollen in many temperate mid-latitude regions of the Northern Hemisphere (Newnham et al, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Therefore, we are able to draw from international research into the climate controls on variability in the grass pollen season.\u003c/p\u003e \u003cp\u003eMost studies of pollen-meteorological relationships have focussed on temperature controls on the seasonal dynamics of allergenic tree pollen taxa in the context of observed and projected climate change (Schramm et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For grasses, temperature and to a lesser extent precipitation are the most important climate variables influencing the main features of grass pollen seasons (Emberlin \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Kwharam et al., 2014). Other factors such as sunshine hours, relative humidity, atmospheric stability and wind speed are important for daily variations in pollen counts, but they are autocorrelated with the two main variables and are less significant on an annual basis. The influence of temperature and precipitation develops from mid-winter onwards but is especially important in spring and early summer when temperature influences net productivity and pollen production, ultimately determining the timing of flowering, pollen release and dispersal.\u003c/p\u003e \u003cp\u003eThe role of precipitation in grass pollen season dynamics is more complex than that of temperature. Sanchez-Mesa et al. (2003) reported a negative relationship between rainfall and pollen counts at six localities in Spain and the UK, which was consistently observed for the period 1995\u0026ndash;2000. However, rainfall is also necessary for plant growth including pollen production. A recent review of the relevant literature globally revealed that precipitation had varying effects on pollen concentration and pollen season timing indicators (Schramm et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although increased precipitation may lower pollen concentrations in the short-term, potentially due to the \"wash out\" effect, the long-term effects of precipitation were positively correlated with grass pollen levels. Thunderstorm asthma poses a further complication (Kevat, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). During thunderstorms, which are typically accompanied by high rainfall, warm updrafts can sweep pollen up into high concentrations in the cloud base. Storm dynamics may also fracture pollen into smaller fragments which, upon release to ground level, are able to penetrate airways further than intact pollen.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eENSO climate variability in Auckland\u003c/h2\u003e \u003cp\u003eENSO is a recurring irregular cycle of climate resulting from changing water temperatures across the tropical Pacific Ocean (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). One of the most important climate phenomena on Earth, ENSO can generate changes in atmospheric circulation, which in turn influences temperature and precipitation on a global scale (McPhaden et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The ENSO cycle is measured by the Southern Oscillation Index (SOI) denoting the difference in observed surface air pressure between the tropical central and western Pacific Ocean (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). El Ni\u0026ntilde;o (SOI \u0026lt;-1) and La Ni\u0026ntilde;a (SOI\u0026thinsp;\u0026gt;\u0026thinsp;1) are terms given to the extreme phases of the ENSO cycle. Analysis of historical movements in the SOI has supported successful long-range (by several months) forecasting of significant ENSO events and their likely regional impacts that are widely used in agricultural planning (L'Heureux et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn New Zealand, although ENSO accounts for less than 25% of the year-to-year variance in seasonal rainfall and temperatures at most locations, its effects can nevertheless be significant, especially in certain regions and seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). During El Ni\u0026ntilde;o phases (SOI \u0026lt;-1), New Zealand tends to experience stronger or more frequent winds from the west in summer, which can promote drier conditions in eastern areas and more rainfall in the west. During La Ni\u0026ntilde;a phases (SOI\u0026thinsp;\u0026gt;\u0026thinsp;1), northeasterly winds tend to become more common, bringing moist, rainy conditions to northern and northeastern areas of the North Island and reduced rainfall to the southern and western South Island. Warmer than average air and sea temperatures can occur around New Zealand during La Ni\u0026ntilde;a. Despite the broad consistency of these patterns, each phase of El Ni\u0026ntilde;o and La Ni\u0026ntilde;a is distinctive and can result in different climate outcomes depending upon the strength of the phase as well as the interplay of other climatic modes such as the Indian Ocean Dipole and Southern Annular Mode.\u003c/p\u003e \u003cp\u003eThe Auckland-Northland region is particularly sensitive to fluctuations in the ENSO cycle (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) especially in summer. La Ni\u0026ntilde;a phases are characterised by humid summers, often with heavy or sustained rainfall. Rainfall anomalies over the past few decades indicate that rainfall during a La Ni\u0026ntilde;a summer is typically 10\u0026ndash;30% greater than normal (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Warmer sea surface temperatures may also increase the impacts of ex-tropical cyclones, as observed during early 2023.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e1988/90 pollen monitoring\u003c/h2\u003e \u003cp\u003eAtmospheric pollen/spore samples were collected daily using the Intermittent Cycling Rotorod sampler, an impaction collector with a retracting collector rod sampling head, routinely deployed in North America (Chapman, 1982). Particles were collected on the leading, greased, edge of two clear polystyrene collector rods spun intermittently at a fixed rate, enabling the calculation of pollen concentrations. Sampling rods were collected daily, stained with Calberla's solution, and examined under a transmitted light microscope.\u003c/p\u003e \u003cp\u003eFor 1988/89, the sampler was installed on the flat roof of the Auckland War Memorial Museum\u0026thinsp;~\u0026thinsp;20 metres above ground level in central Auckland (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and deployed from November 14th 1988 to February 2nd 1989. For 1989/90, the sampler was installed on a flat roof\u0026thinsp;~\u0026thinsp;2 metres above ground level at a residential property in the Auckland suburb of Onehunga, ~\u0026thinsp;8.6 km south of the Auckland Museum (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and deployed from October 28th 1988 to April 4th 1989. A total of 10 successive days were not monitored in November 1988 due to instrument error. This period occurred during the early part of the grass pollen season.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2023/24 pollen monitoring\u003c/h2\u003e \u003cp\u003eDaily average atmospheric grass and total pollen concentrations were generated using a volumetric impaction sampler (Hirst, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1952\u003c/span\u003e) installed on the flat roof of the Auckland War Memorial Museum, the same location as for the 1988/89 monitoring. This sampler draws air in at a fixed rate enabling the calculation of concentrations of pollen and other airborne particles that are impacted onto a rotating tape. The tapes were collected on a weekly basis from July 3rd 2023 for 12 months and manually analysed for pollen using light microscopy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis and comparability of pollen monitoring data\u003c/h2\u003e \u003cp\u003eFor this study, we adapted the Australian Interim Pollen and Spore Monitoring Standard and Protocols of Davies et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). All daily pollen data are reported as the sum of pollen for the 24-hour period commencing at 9am and ending at 9am on the following day. The grass pollen season was defined as the period from the first day for the monitoring interval in which the grass pollen concentration exceeded 10 pollen grains/m\u003csup\u003e3\u003c/sup\u003e of air and the grass Seasonal Pollen Integral (SPI) was determined as the cumulative sum of daily pollen concentrations during the season (see also Galan et al., 2017).\u003c/p\u003e \u003cp\u003eComparison of the three pollen monitoring datasets was constrained by differences in site location and sampling instrument. The latter were assumed to be negligible as Peel et al (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) reported comparable measurements for grass pollen concentrations between the Hirst and Rotorod samplers. The sampling locations were identical for the 1988/89 and 2023/24 monitoring periods, but the different location for 1989/90 with a sampling point much closer to ground level must be taken into account for any comparison of those data. Although comparisons of the length of grass pollen season are unlikely to be compromised by these different locations within the same city, the severity measures (daily pollen concentrations and SPI) are likely to be greater for the 1988/89 data due to closer proximity to ground level for that study as well as being a much less open, exposed setting. We substituted the 10 days of missing data during the early part of the 1988/89 grass pollen season with average values from the 5 preceding and 5 subsequent days, an adjustment that also compromises comparisons of severity measures.\u003c/p\u003e \u003cp\u003eThe Auckland pollen data from 1988/89 were reported by Newnham et al (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) as part of their nationwide pollen survey. The 1989/90 and 2023/24 Auckland pollen data are presented here for the first time.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eENSO and Auckland climate data\u003c/h3\u003e\n\u003cp\u003eHistorical SOI data were obtained from the open access NOAA website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncei.noaa.gov/access/monitoring/enso/\u003c/span\u003e\u003cspan address=\"https://www.ncei.noaa.gov/access/monitoring/enso/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Historical Auckland climate data were obtained from the NIWA Cliflo open access website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cliflo.niwa.co.nz/\u003c/span\u003e\u003cspan address=\"https://cliflo.niwa.co.nz/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) based on the Auckland airport climate station (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The classifications of El Ni\u0026ntilde;o and La Ni\u0026ntilde;a spring and summer phases were obtained from National Institute of Water and Atmosphere (NIWA) website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cliflo.niwa.co.nz/\u003c/span\u003e\u003cspan address=\"https://cliflo.niwa.co.nz/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of pollen monitoring results for the three seasons and details of site locations and ENSO cycle phases.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1988/89\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1989/90\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2023/24\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSO Cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLa Ni\u0026ntilde;a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEl Ni\u0026ntilde;o (minor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEl Ni\u0026ntilde;o\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite Location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWar Memorial Museum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlfred St, Onehunga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWar Memorial Museum\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePollen Sampling Method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRotorod Cyclone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRotorod Cyclone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBurkhard Hirst\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight of sampler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePollen Unit of Measurement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrains/m\u003csup\u003e3\u003c/sup\u003e air\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrains/m\u003csup\u003e3\u003c/sup\u003e air\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrains/m\u003csup\u003e3\u003c/sup\u003e air\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSampling interval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14/11/88\u0026ndash;17/2/89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28/10/89\u0026thinsp;\u0026minus;\u0026thinsp;8/4/90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/7/23\u0026ndash;30/6/2024 (ongoing)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing data in grass pollen season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 days\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrass Season Parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnset (date)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17/11/88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8/11/89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16/11/23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnd (date)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27/12/89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/2/90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31/1/24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength (no days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeverity (SPI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e685*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClimate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecember total rainfall (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e201.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e146.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJanuary total rainfall (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNovember mean temperature (℃)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecember mean temperature (℃)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJanuary mean temperature (℃)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes:\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e* Includes estimated concentrations for the 10 days missing data, determined as the average of the 5 days preceding and 5 days following the gap days.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAuckland grass pollen season 2023/24\u003c/h2\u003e \u003cp\u003eThe 2023/24 pollen monitoring season commenced on July 3rd (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Grass pollen levels remained low from this period until mid-November when they rose sharply to \u0026gt;\u0026thinsp;10 grains/m\u003csup\u003e3\u003c/sup\u003e, with moderate to high levels maintained intermittently until the end of January 2024 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Low levels during this grass pollen season, when the daily grass pollen count falls below 10 grains/m\u003csup\u003e3\u003c/sup\u003e, invariably occurred on days with rainfall\u0026thinsp;\u0026gt;\u0026thinsp;1 mm.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAuckland grass pollen season 1988/89\u003c/h2\u003e \u003cp\u003eThe 1988/89 pollen data monitored at the Auckland War Memorial Museum (Newnham et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) are included here for comparison along with daily rainfall data (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Monitoring commenced November 14th 1988 and ended February 17th 1989. The first occurrence of daily pollen\u0026thinsp;\u0026gt;\u0026thinsp;10 grains/m\u003csup\u003e3\u003c/sup\u003e, denoting the start of the grass pollen season, was November 17th. No data are available for the period 27th November to December 6th. For comparison purposes (below) we substituted the 10 days of missing data with the average values for the pre- and post-gap days.\u003c/p\u003e \u003cp\u003eGrass pollen levels remained at moderate to high levels until December 27th when they decreased to very low levels for the remainder of the monitoring period. Late December to early January was marked by unusually high and persistent rainfall, including\u0026thinsp;\u0026gt;\u0026thinsp;140 mm recorded for December 31st .\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAuckland grass pollen season 1989/90\u003c/h2\u003e \u003cp\u003eThe 1989/90 pollen data were collected in the Auckland suburb of Onehunga between 28th October 1989 and 8th April 1990 by Newnham and are presented here for the first time (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Grass pollen levels remained low until 8th November, with moderate to high levels, and then were maintained intermittently until 1st February 1990. As for the other monitoring periods, low levels during this grass pollen season, when the daily grass pollen count fell below 10 grains/m\u003csup\u003e3\u003c/sup\u003e, invariably occurred on days with rainfall\u0026thinsp;\u0026gt;\u0026thinsp;1 mm.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eHow does rainfall influence short-term grass pollen levels in Auckland?\u003c/h2\u003e \u003cp\u003eIt is difficult to make precise comparisons across these datasets and direct quantitative comparisons would likely be spurious, given the high degree of day-to-day variability in Auckland rainfall and small pollen season sample size (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Another problem with such comparisons is that summer rainfall in Auckland is often intermittent and interrupted by dry, sunny intervals. Thus a 24-hour pollen count might encapsulate both high rainfall and lengthy dry periods, resulting in both negative and positive influences on pollen release and dispersal. To give illustrative contrasting examples, a day with sunny dry conditions in the morning followed by a wet afternoon would be expected to generate both high rainfall and high pollen levels reported for that day, whereas persistent rainfall during the day would likely be accompanied by low pollen levels.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAll three datasets show a broadly consistent correspondence in day-to-day variability between grass pollen levels and precipitation (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The intervals of rainfall, especially when heavy, tend to coincide with lower pollen levels, while drier periods are generally associated with higher pollen levels. Similar relationships have been observed in other regions for grass pollen (e.g. Sanchez-Mesa et al., 2003) and more generally across the wider spectrum of allergenic pollen taxa (Schramm et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A negative rainfall influence was also noted in the only nationwide pollen survey to be undertaken in New Zealand (Newnham et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGrass pollen variation across the ENSO cycle\u003c/h2\u003e \u003cp\u003eThe absence of routine pollen monitoring in New Zealand has restricted our consideration of the influence of the ENSO cycle on Auckland grass pollen variability to a comparison of three seasons representing one La Ni\u0026ntilde;a phase, one strong El Ni\u0026ntilde;o phase and one weak El Ni\u0026ntilde;o or ENSO-neutral phase. For these three monitoring seasons, further limitations are imposed by the short monitoring period, the significant data gap for the single La Ni\u0026ntilde;a phase and the \u0026gt;\u0026thinsp;30-year timespan between the earlier two datasets and the most recent one.\u003c/p\u003e \u003cp\u003eDespite these limitations, it is clear that summer rainfall variability is a key factor causing the differences in pollen season dynamics across these three datasets (Table. 1). As Auckland summer rainfall in turn, varies consistently with the ENSO cycle (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), we contend that ENSO plays an important role in influencing the inter annual variability in grass pollen season dynamics in Auckland. The most striking example of this influence is the abrupt truncation of the 1988/89 (La Ni\u0026ntilde;a) pollen season that resulted in a much shorter season (41 days) than that for the two El Ni\u0026ntilde;o (or non- La Ni\u0026ntilde;a summers) analysed (86 and 77 days). The termination of the 1988/89 grass pollen season coincided with an extensive period of sustained heavy rainfall commencing in late December (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Strong rainfall persisted for much of January to the extent that grass pollen levels remained low throughout that month. The severe truncation of the 1988/89 grass season may indicate that pollen production and release were suppressed by persistent rainfall, in addition to the mechanism of \u0026lsquo;washing\u0026rsquo; pollen out of the atmosphere (Schramm et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOverall pollen severity (SPI) was 20% lower in the La Ni\u0026ntilde;a 1988/89 summer than in the El Ni\u0026ntilde;o summer in 2023/24, as measured at the same station (Table. 1). This difference in pollen severity may be underestimated, however, as the estimates used for the 10 days (25% of the season) with 1988/89 missing data are based on the pre- and post-gap days and these were comparatively dry periods relative to the missing data interval (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eImplications\u003c/h2\u003e \u003cp\u003eThese results, suggesting ENSO-modulated inter-annual variation in grass pollen season dynamics, have important implications for allergy prevalence and management. The differences in grass pollen season length and severity reported here are profound, abrupt and occur in the present, compared with long-term more gradual projections of climate change impacts on pollen season and severity. These seasonal differences are likely to be strongest for oceanic climates such as those in New Zealand, where temperature changes from year to year are typically small compared to precipitation changes (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Strong inter-annual variation in pollen seasons is especially problematic in countries such as New Zealand, which does not undertake routine pollen monitoring and is therefore reliant on other methods such as flowering observations and pollen calendars. The latter depict static pollen seasons that may be based on atypical years, at odds with our results. This work further underpins the call to develop routine pollen monitoring, so that these seasonal dynamics and their impacts on allergy response can be managed in real time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn light of these implications, it is interesting to consider how rainfall variability linked to the ENSO cycle varies during the Auckland pollen season (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Since 1988, strong inter-annual rainfall variability has occurred for the months of December and January, during the height of the pollen season. Some of this variability is consistent with ENSO cycle variability, particularly in wetter La Ni\u0026ntilde;a phases. For example, three of the four wettest Decembers were during La Ni\u0026ntilde;a phases and five of the six wettest Januarys (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The distinction between wet La Ni\u0026ntilde;a and dry El Ni\u0026ntilde;o events is apparent for all three months of the pollen season, but is most strongly observed in January. As a consequence, we suggest that January is the most sensitive month in the grass pollen season to ENSO cycle variability, a conclusion drawn from our pollen season comparisons. For the period of 1988\u0026ndash;2023, January rainfall in Auckland was on average 94% greater during La Ni\u0026ntilde;a summers than during El Ni\u0026ntilde;o summers, compared with 25% and 27% for December and November, respectively.\u003c/p\u003e \u003cp\u003eAlthough based on a limited pollen dataset comprising only three seasons, the consistency of these results with our hypothesised modulating mechanism gives some confidence that the improving seasonal forecasting of the ENSO cycle could ultimately bring a new dimension to pollen forecasting. A lead time of several months could offer a key window of opportunity for allergy patients, people living with allergenic disease, and health practitioners to better manage the treatment of allergy symptoms in more pre-emptive ways. Further benefits would follow as the potential of satellite sensing to augment longer range forecast models of grass pollen aerobiology is realised (Kwharam et al., 2017; Devadas et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese observations also have implications for projecting climate change impacts on grass pollen levels in Auckland, despite the paucity of monitoring data. From the combination of pollen and climatological analyses presented here, we suggest that long term summer rainfall trends need to be considered in any projections of climate change impacts on pollen levels for Auckland and may be just as important as temperature trends. The latest IPCC projections, downscaled to the New Zealand region (Bodecker et al., 2022) suggest that the Auckland region will experience progressively drier and warmer summers during the remainder of this century, superposed by short-term variability arising from the ENSO cycle. Precipitation trends are much more difficult to project with confidence and show strong regional variability. Nevertheless, these projections are consistent with the warmer temperatures and lower rainfall observed for Auckland over the past three decades for the three pollen months, particularly during the critical month of January (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e,\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Auckland five-year moving average of mean monthly temperatures and range (\u003csup\u003eo\u003c/sup\u003eC) for November to January 1988\u0026ndash;2023. The range in monthly temperature represents the difference between the highest and lowest mean temperature for that month during the period 1988\u0026ndash;2023.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1988\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNovember\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDecember\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eJanuary\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eRegional heterogeneity of ENSO and other modes of climate variability\u003c/h2\u003e \u003cp\u003eIn this section, we consider the relevance of this Auckland study to other regions where grass pollen seasons may be strongly influenced by ENSO or other modes of short-term climate variability. In doing so, we contend that while this influence may be widespread and globally relevant, the impacts are likely to be highly distinctive between regions for two reasons. First, as is evident in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, the climate impacts and in particular precipitation variability can manifest very differently even for adjacent regions. The conclusions drawn from our Auckland study, for example, may be applicable to varying extents for other parts of northern and eastern New Zealand, but would be erroneous if they were applied to southwestern New Zealand. Second, the extent and even direction of ENSO influence will depend upon the distinctive bioclimatic envelope for the suite of grasses that occur in a particular region. We have shown for example that Auckland\u0026rsquo;s particular sensitivity to ENSO cycle modulation arises from a maritime climate, typically with ample rainfall for grass growth throughout the year and humid summers. A more arid climate setting, even at a similar latitude, would be expected to experience a very different response, with wetter conditions accompanying La Ni\u0026ntilde;a phases perhaps stimulating grass pollen production overall, rather than suppressing it as in Auckland. These distinctions are even stronger for large, diverse regions such as Australia, where marked spatial and temporal variability in grass pollen seasons is observed (Beggs et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Davies et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In particular, tropical grasses in the north are adapted to heavy rainfall concentrated in the summer and have different pollen seasons than the temperate grasses in the south (Medek et al., 2015). Monitoring these pollen season dynamics and the spread of new allergens is a pre-requisite to understanding the impact of both short-term climate variability and longer-term climate change on allergy burden (Haberle et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese observations mirror previous studies of NAO moderation of pollen season dynamics in Europe. The NAO is a mode of interannual variability in atmospheric circulation associated with changes in the surface westerlies across the North Atlantic and into Europe (Hurrell \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). The role of NAO climate dynamics, cyclicity and periodicity in moderating grass pollen seasons can be compared with that of the ENSO cycle in the Pacific. The influence exerted by the NAO on grass pollen seasons varies spatially across western Europe and even the direction of the relationship between the NAO and precipitation can change between geographical areas (Smith et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). These authors and others (e.g. Galan et al., 2017) emphasise a need for more regional scale studies into the influence of the NAO on grass pollen counts and other allergenic pollen types. Our Auckland results demonstrate a comparable role for the ENSO cycle that also is likely to manifest in distinctive ways across its geographical sphere of influence that will need to be independently determined.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eConclusions and further work\u003c/h2\u003e \u003cp\u003eThe paucity of airborne pollen monitoring data available for New Zealand generally is a major impediment to understanding the role of meteorology and climate change in both inter-annual variability and long-term trends and dynamics of allergenic pollen seasons. Here, the serendipitous alignment of three Auckland grass pollen datasets with the principal phases of the ENSO cycle has enabled some key insights into its role in modulating the grass pollen season in Auckland. Marked differences in grass pollen seasons between the summers of 1988/89 (La Ni\u0026ntilde;a), 1989/90 (weak El Ni\u0026ntilde;o/neutral) and 2023/24 (El Ni\u0026ntilde;o) are attributable to contrasting rainfall patterns, which is consistent with longer-term observations of the ENSO cycle in this region. From these results, we suggest that La Ni\u0026ntilde;a summers are likely to result in less severe pollen seasons in Auckland than El Ni\u0026ntilde;o summers, with January rainfall as a critical variable.\u003c/p\u003e \u003cp\u003eThese results have important implications for pollen allergy management in Auckland, especially as the ability to predict the ENSO cycle now extends to several months with increasing confidence. The ENSO cycle and other short-term modes of climate variability such as the North Atlantic Oscillation have drawn comparatively little consideration in the context of climate and meteorological factors governing pollen season variability, with far more attention given to longer-term projection of climate change impacts. Nevertheless, the insights drawn from the ENSO influence in this Auckland study may also be relevant in the longer-term, because of the critical role played by summer precipitation, which is projected to decline further in the Auckland region. Our results point to increased severity and possibly length of the grass pollen seasons accompanying the projected drier, warmer summers for northern New Zealand. We suggest that in oceanic mid-latitude regions such as Auckland, summer precipitation trends may be as important as temperature trends in influencing pollen season variability in the future. Finally, these results demonstrate that currently available static pollen calendars are of limited utility and may even be misleading. Future work to model the effects of ENSO cycles on health outcomes and healthcare utilisation could provide additional insights.\u003c/p\u003e \u003cp\u003eWe acknowledge that these conclusions are preliminary and are drawn from a limited dataset that requires substantiation by further work and in other regions where the ENSO cycle or other modes of climate variability may be postulated to play a similar role in pollen season modulation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis work would not have been possible without the support of management and staff of Auckland War Memorial Museum and in particular their Botany and Facilities/Assets teams. We are also grateful to co-author Kat Holt for providing a Burkhard pollen trap to be deployed at Auckland Museum. The work was part-funded by Auckland Medical Research Foundation (Senior Research Fellowship 3725270), Life AI Corp (6001213) and New Zealand Health Research Council (HRC 22/540). Thanks to James Renwick and Ciaran Doolin for their insightful comments on the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAn Y, Ouyang Y, Zhang L (2023) Impact of airborne pollen concentration and meteorological factors on the number of outpatients with allergic rhinitis. World Allergy Organ J 16(4):100762\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnderegg WRL, Abatzoglou JT, Anderegg LDL, Bielory L, Kinney PL, Ziska L (2021) Anthropogenic climate change is worsening North American pollen seasons. Proc. Natl. Acad. Sci. U. S. 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Lancet Planet Health 3:e124\u0026ndash;e131. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2542-5196(19)30015-4\u003c/span\u003e\u003cspan address=\"10.1016/S2542-5196(19)30015-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pollen, ENSO, Climate, Aeroallergens, Precipitation, Meteorology","lastPublishedDoi":"10.21203/rs.3.rs-4598891/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4598891/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn many regions, the ENSO cycle climate is a key factor in modulating climate processes that can influence seasonal variability in the production and dispersal of allergy-triggering pollen. However, the impacts on allergy health are not well known. We compare grass pollen seasons between the major modes of the ENSO cycle in Auckland, New Zealand. We find no clear difference in the timing of onset of the pollen seasons, but season length was longer, by \u0026gt;\u0026thinsp;30 days, during both El Ni\u0026ntilde;o phases than during the La Ni\u0026ntilde;a phase. Severity of the La Ni\u0026ntilde;a pollen season was also lower, although we have less confidence in this comparison due to differences in the sampling site locations. The difference in pollen season length is explained by the greater summer rainfall typically experienced in Auckland and elsewhere in northern New Zealand during La Ni\u0026ntilde;a phases, which tends to suppress grass pollen production and dispersal. As grass pollen is the principal source of allergenic pollen in New Zealand and in many other countries, these results have wider implications for allergy management. With ENSO forecasting often reliable with several months of lead time, there is potential for improving community preparedness and resilience to inter-annual dynamics of the grass pollen season. However, the strong geographical heterogeneity in ENSO cycle climate impacts necessitates a region-specific approach. This work further underscores the need for local-regional pollen monitoring in NZ and the risk of relying upon static, nationwide pollen calendars for informing allergy treatment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"ENSO cycle modulation of grass pollen season in Auckland New Zealand with implications for allergy management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-18 11:08:08","doi":"10.21203/rs.3.rs-4598891/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"672ae0cb-2ce9-4fb8-9072-8ee717f8c9af","owner":[],"postedDate":"July 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-01T15:58:43+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-18 11:08:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4598891","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4598891","identity":"rs-4598891","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}