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Article

Does the ENSO Cycle Impact the Grass Pollen Season in Auckland New Zealand, with Implications for Allergy Management?

by
Rewi M. Newnham
1,*,
Laura McDonald
2,
Katherine Holt
3,
Stuti L. Misra
4,
Natasha Ngadi
2,
Calista Liviana Ngadi
2 and
Amy H. Y. Chan
5
1
School of Science in Society, Victoria University of Wellington, Wellington 6012, New Zealand
2
School of Environment, University of Auckland, Auckland 1142, New Zealand
3
School of Agriculture and Environment, Massey University, Palmerston North 4442, New Zealand
4
Department of Ophthalmology, School of Medicine, University of Auckland, Auckland 1142, New Zealand
5
School of Pharmacy, University of Auckland, Auckland 1142, New Zealand
*
Author to whom correspondence should be addressed.
Aerobiology 2025, 3(3), 8; https://doi.org/10.3390/aerobiology3030008
Submission received: 18 December 2024 / Revised: 8 September 2025 / Accepted: 10 September 2025 / Published: 15 September 2025
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Abstract

In many regions, the El Niño Southern Oscillation (ENSO) cycle 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’s largest city, within a region that is highly sensitive to quasi-predictable meteorological oscillations of the ENSO cycle. We find no clear difference in the timing of onset of the pollen seasons, but season length was shorter, by >30 days, and less severe during the La Niña phase than for the other phases. The difference in pollen season length may be explained by the greater summer rainfall typically experienced in Auckland and elsewhere in northern New Zealand during La Niña phases, which tend to suppress grass pollen abundance when excessive. 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 offering the prospect of several month’s lead time, there is potential for improving community preparedness and resilience to inter-annual dynamics of the grass pollen season. This work points to the need to better understand the influence of short-term climate cycles on seasonal variability in pollen allergy, while we also emphasise that the strong geographical heterogeneity in ENSO cycle climate impacts necessitates a region-specific approach. This work also further underscores the need for standardised, local–regional pollen monitoring in NZ and the risk of relying upon static, nationwide pollen calendars for informing allergy treatment.

1. Introduction

Pollen is recognised as both a major trigger for and cause of chronic allergic respiratory diseases, with increasing medical, economic, and societal burdens [1]. Climate change is one of the key factors thought to contribute to the growing prevalence of allergic respiratory disease in many regions [2,3,4,5,6,7]. 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., [8,9,10]) as well as to understand how decadal-scale climate variability influences allergy response [11]. 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., [12,13,14,15,16]) 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, the most important allergenic pollen source at the global scale [17]. 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 [18] 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 [19]. There are also efforts to evaluate the current and future societal impacts of ENSO cycle variability, including public health impacts [20], 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 just a single grass pollen season [21]. 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 (Figure 1). We also present previously unpublished Auckland grass pollen season data for 1989/90, representing a neutral phase of the ENSO cycle (Figure 2A) that provides a more immediate point of comparison with the strong La Niña phase of the previous summer.
An obvious limitation is that climate and other changes across the 35 years between these two pollen seasons may well have impacted pollen season dynamics independently of the ENSO cycle (see Newnham [21] for a discussion of these possible impacts for New Zealand). Whilst we cannot account for all these changes, we assess the changes that have occurred in temperature and rainfall—the most likely climate confounding factors—to assess whether they could have masked any discernible impact of the ENSO cycle.

2. Background

2.1. Trends in Allergic Disease in New Zealand

Asthma affects nearly 300 million people globally and is projected to increase in prevalence as the population grows [24]. In New Zealand, asthma exacerbations have increased by one third over the last 10 years [25], mirroring similar reported trends overseas that have seen a doubling of asthma attacks [24]. At the global scale, respiratory allergies are increasing not only in developed countries but also in low and middle-income countries [26]. 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 [27]. 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 [5,28,29,30,31], allergic rhinitis [32] and conjunctivitis [33,34,35], and international evidence that the allergenic pollen season is becoming longer [10]. With the exception of a recent study linking asthma mortality rates to the Atlantic Multidecadal Oscillation [11], 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 [36].

2.2. 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 [37]. 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 [38]. For grasses, some studies show that temperature and to a lesser extent precipitation are the most important climate variables influencing the main features of grass pollen seasons [39,40]. Other factors such as sunshine hours, relative humidity, atmospheric stability and wind speed are important for daily variations in pollen concentrations, 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. Sánchez Mesa et al. [41] reported a negative relationship between rainfall and pollen concentrations 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 [38]. 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. These time-dependent dynamics make it difficult to determine any simple statistical relationship between precipitation and pollen concentrations. Thunderstorm asthma poses a further complication [42]. 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.

2.3. ENSO Climate Variability in Auckland

ENSO is a recurring irregular cycle of climate resulting from changing water temperatures across the tropical Pacific Ocean (Figure 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 [18]. The ENSO cycle is measured by various indexes, often based on the Southern Oscillation Index (SOI) denoting the difference in observed surface air pressure between the tropical central and western Pacific Ocean (Figure 2A).
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 [19].
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 (Figure 2). During El Niño phases, 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, 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 (Figure 2B) 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 (Figure 2B). Warmer sea surface temperatures may also increase the impacts of ex-tropical cyclones, as observed during early 2023.

3. Materials and Methods

3.1. 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 [43]. 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 m above ground level in central Auckland (Figure 1) and deployed from 14 November 1988 to 17 February 1989. A total of 10 successive days were not monitored for the period 27 November to 6 December 1988 due to instrument error. For 1989/90, the sampler was installed on a flat roof ~2 m above ground level at a residential property in the Auckland suburb of Onehunga, ~8.6 km south of the Auckland Museum (Figure 1) and deployed from 28 October 1989 to 4 April 1990.

3.2. 2023/24 Pollen Monitoring

Daily average atmospheric grass and total pollen concentrations were generated using a volumetric impaction (Hirst) sampler [44] 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 3 July 2023 for 12 months and manually analysed for pollen using light microscopy.

3.3. Analysis and Comparability of Pollen Monitoring Data

For this study, we adapted the Australian Interim Pollen and Spore Monitoring Standard and Protocols of Beggs et al. [45] (see also Davies et al. [46]). All daily pollen data are reported as the sum of pollen for the 24 h 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 after mid-winter (July 1) to the last day before June 30 that the grass pollen concentration exceeded 10 pollen/m3 of air [46]. The grass Seasonal Pollen Integral (SPIn) was determined as the cumulative sum of daily pollen concentrations during the season (see also Galán et al. [47]). We distinguish days with low, moderate and high grass pollen levels as <10, 10–30, >30 grains/m3, respectively, following previous New Zealand pollen monitoring programmes [37]. We applied the Shapiro-Wilks test to precipitation and pollen datasets for 1988/89 and 2023/24 which indicated that all data show significant departure from normality (p < 0.001; W = 0.72–0.79 for pollen; 0.30–0.39 for precipitation). Accordingly, we applied Pearson Product Moment Correlation to evaluate the relationships between daily pollen and daily rainfall.
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. [48] 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. Vegetation differences between these two locations were also substantial. The Museum site (used for 1988/89 and 2023/24) is situated in an expansive urban parkland comprising a wide range of exotic and native trees and shrubs as well as managed grassland leisure areas. The Onehunga site (1989/90) is situated in a suburban residential garden in closer proximity to a smaller range of exotic and native trees, shrubs and grasses.
Although comparisons of the length of grass pollen season are unlikely to be compromised by these different locations and local vegetation settings within the same city, the severity measures (daily pollen concentrations and SPIn) are likely to be greater for the 1989/90 data due to closer proximity to ground level and pollen sources for that study [49]. We acknowledge the sub-optimal location for 1989/90 monitoring exercise but report the data nevertheless for inclusivity and in the context of New Zealand’s limited pool of reported pollen data. We emphasise that any treatment of these particular data should be exercised with caution and in context of this suboptimal monitoring setting.
The Auckland pollen data from 1988/89 were reported by Newnham et al. [37] as part of their nationwide pollen survey. The 1989/90 and 2023/24 Auckland pollen data are presented here for the first time.

3.4. ENSO Characterisation of the Pollen Seasons

To determine discrete phases of the ENSO cycle here, we apply the ENSO3.4 Index, defined as the area averaged sea-surface temperature anomalies over the Niño 3.4 region (5N–5S) (170–120W) using a climatology of 1981–2010. Using this index, onset of El Niño (La Niña) is declared when Niño 3.4 sea surface temperature anomaly exceeds +0.5 °C (−0.5 °C) for a 3-month period [22,50]. On this basis, the spring-summer months of 1988/89 was a strong La Niña phase, the equivalent months for 2023–2024 was a strong El Niño phase (Table 1) and the spring-summer of 1989–1990 was a neutral phase.

3.5. ENSO and Auckland Climate Data

Historical SOI and ENSO3.4 data were obtained from the open access NOAA WGCP website [22]. Historical Auckland climate data were obtained from the National Institute of Water and Atmosphere (NIWA) Cliflo open access website [51] based on the Auckland airport climate station (Figure 1). The classifications of El Niño and La Niña spring and summer phases were obtained from the NIWA website.

4. Results

4.1. Auckland Grass Pollen Season 2023/24

The 2023/24 pollen monitoring season commenced on July 3rd (Table 2). Grass pollen levels remained low from this period until mid-November when they rose sharply to >10 grains/m3, with moderate to high levels maintained intermittently until the end of January 2024 (Figure 3). The moderating role of rainfall during the grass pollen season is evident in two contrasting ways. First, low pollen levels, when the daily grass pollen count falls below 10 grains/m3, invariably occurred on days with rainfall >10–20 mm. Second, peaks in grass pollen often follow those higher rainfall days, with a lag time of several days.

4.2. Auckland Grass Pollen Season 1988/89

Monitoring commenced 14 November 1988 and ended 17 February 1989 (Figure 3). The first occurrence of daily pollen >10 grains/m3, 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 applied two alternative methods for estimating values for these missing data: (1) the average values for the entire pollen season and (2) the average values for the 5 days before and 5 days after the missing data days. We express the severity of the 1988/89 season as the range between these two determinations (Table 2).
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.

4.3. Auckland Grass Pollen Season 1989/90

Grass pollen levels remained low until 8th November, with moderate to high levels, and then were maintained intermittently until 1 February 1990 (Figure 4). As for the other monitoring periods, low levels during this grass pollen season, when the daily grass pollen count fell below 10 grains/m3, invariably occurred on days with rainfall >10–20 mm. Similarly, peaks in grass pollen often follow those higher rainfall days, with a lag time of several days.

5. Discussion

5.1. How Does Rainfall Influence Short-Term Grass Pollen Levels in Auckland?

All three datasets show a broadly consistent correspondence in day-to-day variability between grass pollen levels and precipitation (Figure 3 and Figure 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 [41] and more generally across the wider spectrum of allergenic pollen taxa [38,52]. In particular, Kluska et al. [52] suggested that precipitation intensity was the dominant control on pollen concentrations but that concentrations only decreased when rainfall intensities exceeded 5 mm/h. A negative rainfall influence was also noted in the only nationwide pollen survey to be undertaken in New Zealand [37].
We investigated these relationships further by correlating pollen days and rainfall with a varying lag of 0–5 days (Figure 5).
For zero lag, the 2023/24 (El Niño) and 1989/90 (ENSO neutral) datasets show essentially zero correlation, illustrating that rainfall can have both directly negative and directly positive impact on pollen concentrations, with the two effectively cancelling one another out for these seasons. In contrast, the 1988/89 (La Nina) season shows a comparatively negative direct (zero lag) concentration which is sustained for lag = 1 and remains negative for lags = 2 and 3. This sustained negative impact for 1988/89 is consistent with our contention that during the La Niña season, the negative rainfall influence on pollen levels is stronger overall than for the other seasons.
Whilst the absolute value of all correlation coefficients is low and statistically insignificant, this is likely to be a consequence of the bi-directional nature of these relationships in combination with the daily interval of measurement we employed. Thus a 24 h pollen count might encapsulate both high-intensity 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 during the daylight hours, when most grass pollen is released, followed by a wet evening 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 (Figure 6). In this scenario the same daily rainfall total could lead to both high and low (or zero) pollen concentrations. These complications may explain the inconsistent relationship we observe between daily rainfall and grass pollen concentration in our data as well as the low overall correlation coefficients.

5.2. 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 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, summer rainfall variability emerges as a key factor in the differences in pollen season dynamics across these three datasets (Table 2). As Auckland summer rainfall, in turn, varies consistently with the ENSO cycle (Figure 7 and Figure 8), we contend that ENSO may play 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 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 (Figure 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, high-intensity rainfall, in addition to the mechanism of ‘washing’ pollen out of the atmosphere [39].
Overall pollen severity (SPIn) was 21−33% 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), with this range reflecting the two different methods used to account for missing data in 1988/89 (see Table 2).

5.3. Implications for Allergy Prevalence and Management

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 (c.f., Figure 7 and Figure 9). 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 (Figure 7 and Figure 8). 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 (Figure 7). 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 (Figure 8). 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 (Figure 8).
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. With a lead time of several months, allergy patients, people living with allergic disease, and health practitioners would be able to manage the treatment of allergy symptoms pre-emptively Further benefits would follow as the potential of satellite sensing to augment longer range forecast models of grass pollen aerobiology is realised [54,55].

5.4. Climate Change Implications

A potential confounding factor for our comparison of ENSO events over the past ~35 years is that significant changes have occurred over this period that are likely to have had an impact on airborne pollen. These include increases in carbon dioxide and temperature, changes in rainfall, and changes in land use in the source area(s) [21]. A valid comparison of the influence of the ENSO cycle on pollen across these very distant time periods requires consideration and control for these other factors.
During the typical grass pollen season months of November to January, Auckland temperatures have increased by ~1 °C between 1988 and 2024 and rainfall has increased by ~9 mm (Table 3), albeit with much variability between the three months. There is also much inter-annual variability superposed upon these longer-term trends (Figure 7 and Figure 9), particularly as discussed between La Niña and El Niño periods. As the overall change in precipitation is minor, we suggest that a long-term precipitation change is unlikely to have confounded any ENSO cycle impact.
In contrast, the temperature change is stronger, with less inter-annual variability, and is observed across all 3 months, suggesting that it may have had a long-term impact on pollen season dynamics. However, observations of the role of temperature on grass pollen season dynamics have reported mixed results. A recent comprehensive review of literature reporting on relationships between local temperature and precipitation and measured airborne pollen showed that a majority of studies found no significant correlation between temperature and pollen season start date, end date or overall length [38]. The majority of the studies (63%) that considered the APIn (Annual Pollen Integral), representing the cumulative daily pollen concentrations for that year, also showed no correlation with temperature.
One study of the response of two allergenic pollen species (Phleum pratense and Alopercus pratensis) responses to temperature and elevated CO2 is insightful in this context. Tossaveinen et al. [56] found that reduced pollen integrals, and fewer inflorescences suggest that a warmer climate may negatively impact the allergenic burden of these grasses. For P. pratense, elevated CO2 increased net photosynthesis, but this effect was reduced under elevated temperature, suggesting an antagonistic interaction.
Whist we acknowledge that some uncertainty remains as to the precise impact of climate change in the differences in grass pollen dynamics we report, these previous studies do not present a convincing case for a primary role in the striking differences we observed between the La Niña and non-La Niña pollen seasons.
For the longer-term future however, our findings may 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 [57] 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.

5.5. 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 Figure 2B, 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 [1,58]. 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 [59]. 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 [6].
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 [60]. 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, although the forecasting timeframe is much shorter, typically 1–2 weeks. 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 [15]. These authors and others (e.g., Galán et al. [47]) emphasise a need for more regional-scale studies into the influence of the NAO on grass pollen concentrations 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.

5.6. Limitations

In addition to the possible confounding effect of climate change discussed earlier, we acknowledge several other limitations to this study.

5.6.1. Sparsity of New Zealand Pollen Data

This study has proceeded in the context of comparatively little historical effort made to understanding pollen season variability in New Zealand. Although the new data we present for 2023/24 is drawn from the longest continuous pollen monitoring exercise yet undertaken in New Zealand, comparable data previously reported are extremely limited. As a consequence, we are restricted to being able to make just the one paired comparison of the two ENSO phases, separated by >30 years and using different sampling methods. In this limited data context, whilst useful statistical analyses are essentially precluded, we nevertheless observe patterns consistent with expectations. We can only describe these observations qualitatively here, but we acknowledge the importance of further investigating the phenomenon of ENSO modulation of the grass pollen season in New Zealand and elsewhere, underpinned by larger datasets.

5.6.2. Land Use Change

Whilst changes in land use and, in particular, agriculture between the two sampling periods are likely to have profound impacts on grass pollen levels in the rural New Zealand environment, this is unlikely to be an important factor in the Auckland urban environment.

5.6.3. Treatment of the Missing Days for 1988/89

Accounting for the 10 days of missing pollen data during the 1988/89 study period is challenging, as is often the case with missing data. We note, however, that as the 10 missing days represent a comparatively wet period, both methods applied to account for these data, involving averaging across the entire pollen season and alternatively the 5 days before and after the missing days, may have given overestimates of the pollen levels for those days. If so, this possible overestimation for 1988/89 gives further weight to our conclusion as to why this La Niña pollen season was less severe than the El Niño comparison season.

5.6.4. Determination of 1988/89 Pollen Season Onset

The onset of the grass pollen season, determined for 1988/89, needs to be treated with caution as the pollen monitoring period commences just a few days after this date.

6. 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 (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 and neutral 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 health care 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.

Author Contributions

Software, R.M.N. and L.M.; Validation, R.M.N., L.M., K.H., S.L.M., N.N., C.L.N. and A.H.Y.C.; Formal analysis, R.M.N. Investigation, R.M.N., L.M., K.H., S.L.M., N.N., C.L.N. and A.H.Y.C.; Resources, R.M.N., L.M., K.H., S.L.M., N.N., C.L.N. and A.H.Y.C.; Data curation, R.M.N. and L.M.; Writing—original draft preparation, R.M.N. and A.H.Y.C.; Writing—review and editing, R.M.N.; Visualization, R.M.N. and L.M.; Supervision, K.H. and R.M.N.; Project administration, A.H.Y.C., K.H. and R.M.N.; Funding acquisition, A.H.Y.C., R.M.N. and K.H. All authors have read and agreed to the published version of the manuscript.

Funding

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/Hirst 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.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Auckland showing location of the two pollen monitoring sites and Auckland Airport climate station.
Figure 1. Auckland showing location of the two pollen monitoring sites and Auckland Airport climate station.
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Figure 2. (A) The Southern Oscillation Index showing the ENSO cycle since the early 1980s. Source: NOAA [22]. 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 [23].
Figure 2. (A) The Southern Oscillation Index showing the ENSO cycle since the early 1980s. Source: NOAA [22]. 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 [23].
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Figure 3. Auckland daily grass pollen levels from 3 July 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 from 14 November 1988 to 17 February 1989.
Figure 3. Auckland daily grass pollen levels from 3 July 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 from 14 November 1988 to 17 February 1989.
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Figure 4. Auckland daily grass pollen levels (Onehunga) and daily rainfall from 28 October 1989 to 4 April 1990.
Figure 4. Auckland daily grass pollen levels (Onehunga) and daily rainfall from 28 October 1989 to 4 April 1990.
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Figure 5. Pearson correlation (R2) for pollen and rainfall with pollen days lagged by 0–5 days.
Figure 5. Pearson correlation (R2) for pollen and rainfall with pollen days lagged by 0–5 days.
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Figure 6. Schematic. Hypothetical rainfall scenarios on two constrasting days (A,B) when the same total daily rainfall could result in highly contrasting pollen concentrations.
Figure 6. Schematic. Hypothetical rainfall scenarios on two constrasting days (A,B) when the same total daily rainfall could result in highly contrasting pollen concentrations.
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Figure 7. Auckland Monthly rainfall for November–January months 1988–2023 with 5-year moving averages (ashed lines). La Niña and El Niño summers (Dec, Jan) and springs (Nov) are indicated by blue and red open circles, respectively.
Figure 7. Auckland Monthly rainfall for November–January months 1988–2023 with 5-year moving averages (ashed lines). La Niña and El Niño summers (Dec, Jan) and springs (Nov) are indicated by blue and red open circles, respectively.
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Figure 8. Auckland 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: [51,53].
Figure 8. Auckland 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: [51,53].
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Figure 9. Auckland Monthly temperature for November–January months 1988–2023 with 5-year moving averages.
Figure 9. Auckland Monthly temperature for November–January months 1988–2023 with 5-year moving averages.
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Table 1. Sea surface temperature anomaly index for Niño 3.4 (5° N to 5° S, 170° W to 120° W) reproduced with permission from NOAA [22], 2025. The Auckland pollen seasons presented here are highlighted in blue (representing La Niña conditions), grey (neutral) and red (El Niño). Note that for these El Niño and La Niña pollen seasons, those phase conditions had persisted for at least 5 months prior to the pollen season commencing.
Table 1. Sea surface temperature anomaly index for Niño 3.4 (5° N to 5° S, 170° W to 120° W) reproduced with permission from NOAA [22], 2025. The Auckland pollen seasons presented here are highlighted in blue (representing La Niña conditions), grey (neutral) and red (El Niño). Note that for these El Niño and La Niña pollen seasons, those phase conditions had persisted for at least 5 months prior to the pollen season commencing.
JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember
19880.690.350.29−0.49−1.05−1.46−1.54−1.44−1.33−2.09−2.18−1.98
1989−1.95−1.37−1.33−1.11−0.8−0.64−0.47−0.58−0.38−0.4−0.3−0.13
19900.020.360.20.260.3200.150.170.120.130.080.32
2023−0.78−0.62−0.130.240.470.951.21.561.651.592.011.81
20241.711.471.10.930.410.250.20.02−0.11 --
Table 2. Summary of pollen monitoring results for the three seasons and details of site locations and ENSO cycle phases. Note that the 1989/90 data are included here for completeness and should not be used for direct comparisons of pollen severity with the other datasets.
Table 2. Summary of pollen monitoring results for the three seasons and details of site locations and ENSO cycle phases. Note that the 1989/90 data are included here for completeness and should not be used for direct comparisons of pollen severity with the other datasets.
Summer1988/891989/902023/24
ENSO CycleLa Niña El Niño (minor)El Niño
Site Location, latitude and longitudeWar Memorial Museum
−36.86201, 174.77793		Alfred St, Onehunga
−36.91755, 174.793635		War Memorial Museum
−36.86201, 174.77793
Pollen Sampling MethodRotorod CycloneRotorod CycloneBurkhard Hirst
Height of sampler20 m2 m20 m
Pollen Unit of MeasurementGrains/m3 airGrains/m3 airGrains/m3 air
Sampling interval14/11/88–17/2/8928/10/89–4/4/903/7/23–30/6/2024
Missing data in grass pollen season10 days *00
Grass Season Parameters
Onset (date)17/11/888/11/8916/11/23
End (date)27/12/881/2/9031/1/24
Length (no days)428677
Severity (SPIn)576 *–685 **1169862
Climate
December total rainfall (mm)201.858.0146.8
January total rainfall (mm)172.866.528.4
November mean temperature (℃)16.917.316.7
December mean temperature (℃)18.918.219.6
January mean temperature (℃)20.220.121.6
Notes: * Includes estimated concentrations for the 10 days missing data, determined as the average of all days across the pollen season. ** 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.
Table 3. Comparison of Auckland five-year moving average of mean monthly temperatures (°C) and rainfall (mm) for November to January 1988–2023. The range in monthly values represents the difference between the highest and lowest mean value for that month during the period 1988–2023.
Table 3. Comparison of Auckland five-year moving average of mean monthly temperatures (°C) and rainfall (mm) for November to January 1988–2023. The range in monthly values represents the difference between the highest and lowest mean value for that month during the period 1988–2023.
Temperature (°C)19882023ChangeRange
November16.717.6+0.93.2
December18.519.8+1.35.1
January19.820.7+0.93.9
November–January total18.319.41.1
Rainfall (mm)19882023ChangeRange
November100.485.8−14.663.2
December97.8108.4+10.661.5
January69.8101.0+31.271.6
November–January total89.398.49.1
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Newnham, R.M.; McDonald, L.; Holt, K.; Misra, S.L.; Ngadi, N.; Ngadi, C.L.; Chan, A.H.Y. Does the ENSO Cycle Impact the Grass Pollen Season in Auckland New Zealand, with Implications for Allergy Management? Aerobiology 2025, 3, 8. https://doi.org/10.3390/aerobiology3030008

AMA Style

Newnham RM, McDonald L, Holt K, Misra SL, Ngadi N, Ngadi CL, Chan AHY. Does the ENSO Cycle Impact the Grass Pollen Season in Auckland New Zealand, with Implications for Allergy Management? Aerobiology. 2025; 3(3):8. https://doi.org/10.3390/aerobiology3030008

Chicago/Turabian Style

Newnham, Rewi M., Laura McDonald, Katherine Holt, Stuti L. Misra, Natasha Ngadi, Calista Liviana Ngadi, and Amy H. Y. Chan. 2025. "Does the ENSO Cycle Impact the Grass Pollen Season in Auckland New Zealand, with Implications for Allergy Management?" Aerobiology 3, no. 3: 8. https://doi.org/10.3390/aerobiology3030008

APA Style

Newnham, R. M., McDonald, L., Holt, K., Misra, S. L., Ngadi, N., Ngadi, C. L., & Chan, A. H. Y. (2025). Does the ENSO Cycle Impact the Grass Pollen Season in Auckland New Zealand, with Implications for Allergy Management? Aerobiology, 3(3), 8. https://doi.org/10.3390/aerobiology3030008

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