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Wintertime outbreaks in the northern hemisphere
In Fig. 1a we use case data (see “Methods”) to estimate the effective reproductive number of infection for New York City from the start of 2020 to the present (July 2020)14. Estimated values of Reffective peak early in the outbreak and then settle close to 1 in the summer months as NPIs act to lower transmission. We assume the Reffective values approximate R0 and compare them to the predicted seasonal R0, derived from our climate-driven SIRS model. The model assumes the climate sensitivity of betacoronavirus HKU1 and that seasonal variations in transmission are driven by specific humidity. Current rates (average over second and third weeks of July) of Reffective in New York city are found to be approximately 35% below the R0 levels predicted by our climate-driven model. We assume this 35% decline is due to the efficacy of NPIs. To project future scenarios we assume that R0 remains at either the current levels (constant) or a relative 35% decrease in our climate-driven R0, which means R0 oscillates with specific humidity (Fig. 1a, top plot).
Fig. 1: Wintertime outbreaks in New York City.

Estimated and projected R0 values (top plot) assuming a 35% and b 15% reduction in R0 due to NPIs. Corresponding time series show the simulated outbreaks in the climate (blue) or constant (black/dashed) scenarios, with middle row plots assuming a 10% reporting rate and bottom row plots assuming a 3% reporting rate. Corresponding susceptible time series are shown in orange (susceptibles = S/population = N). Case data from New York City are shown in gray. Surface plots (top) show the peak wintertime proportion infected (infected = I/population = N) in the scenarios with c the constant R0 and d the climate-driven R0. e shows the difference between the climate and constant R0 scenario. The timing of peak incidence in years from July is shown for the f constant and g climate scenarios. The difference between climate and constant scenario is shown in h. Points in c–h show the scenarios is a, b. Dashed line shows estimated susceptibility in New York based on ref. 24.

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In Fig. 1a (lower plots) we show the proportion infected over time using the climate-driven and constant R0 values. We also vary the reporting rate of observed cases relative to modeled cases; while this accounts for under-reporting it also allows us to vary the proportion susceptible over a feasible range (see “Methods”). In the middle figure, the reporting rate is 10% (estimates for US reporting rates are  1 for both the climate and constant scenario and case numbers begin to grow exponentially. With a 10% reporting rate a large secondary outbreak is observed in both the constant and climate scenarios (Fig. 1b, middle plot). With a 3% reporting rate, meaning a larger depletion of susceptibles, the secondary outbreak appears much larger in the climate scenario: this supports the hypothesis that the disease will become more sensitive to climate as the susceptible proportion declines, much like the seasonal endemic diseases.
In Fig. 1c–h we simulate model outcomes across a broad range of parameter space varying the proportion susceptible (in July) and the reduction in R0 due to NPIs. The proportion susceptible is varied by initializing the epidemic with different sizes of the infected population (initializing with a large number results in a relatively larger outbreak and initializing with a small number results in a smaller outbreak). We vary this starting number over a feasible range given the case data, i.e., such that observed cases never exceed modeled cases or that the reporting rate never drops below 1%. Over this range, the model plausibly tracks the observed case data.
Figure 1e shows the change in winter peak size (max proportion infected between September–March) due to climate. Peak size results for the constant and climate scenarios are shown in Fig. 1c and d, respectively. When the susceptible proportion is high and the effect of NPIs are minimal (relative R0 given NPI = 1), large outbreaks are possible in both the climate and constant R0 scenarios meaning the relative effect of climate on peak size and timing is close to 0 (top right Fig. 1e). As the proportion susceptible declines (moving left along the x-axis of Fig. 1e), case trajectories become more sensitive to the wintertime weather resulting in larger peaks in the climate scenario. However, sufficiently strong NPIs, in combination with low susceptibility, reduce incidence to zero in both the climate and control scenarios (bottom left Fig. 1e). NPIs are not as effective at reducing cases when susceptibility is higher (bottom right Fig. 1e).
We also consider the effect of climate on secondary peak timing. Figure 1f, g shows the peak timing in years (relative to July 2020) in the constant and climate scenarios, respectively. In the climate scenario, peak timing for New York is clustered in the winter months (Fig. 1a, b). In the constant R0 scenario, secondary peaks can occur at a wide range of times over the next 1.5 years. As in the peak size results, high susceptibility and limited NPIs reduce the effect of climate and peak timing is matched for both the climate and control scenarios (top right Fig. 1h). Gray areas represent regions where there is no secondary peak in either the climate or control scenario.
Climate effects on global risk
We next consider the relative effect of climate on peak size for nine global locations (Fig. 2b). In this case, as opposed to using estimated Reffective values (given case data are not available for several of the global cities), we simulate the epidemic from July 2020 using a fixed number of infecteds and vary the starting proportion of susceptibles (example results from select global locations, using estimated Reffective, are shown in Supplementary Figs. 1–3). Results from the New York surface in Fig. 2b qualitatively match our tailored simulation in Fig. 1. Locations in the southern hemisphere are expected to be close to their maximum wintertime R0 values in mid-2020 (Fig. 2a), meaning that secondary peaks in the climate scenario are lower than the constant R0 scenario for these locations (Fig. 2b). Tropical locations experience minimal difference in the climate versus constant R0 scenario given the relatively mild seasonal variations in specific humidity in the tropics. Broadly, the results across hemisphere track the earlier results from New York: high susceptibility and a lack of NPIs lead to a limited role of climate, but an increase in NPI efficacy or a reduction in susceptibility may increase climate effects. This result is more striking in regions with a large seasonality in specific humidity (e.g. New York, Delhi and Johannesburg).
Fig. 2: Climate sensitivity of outbreaks across global locations.

a The climate effect on R0 assuming a 35% reduction due to NPIs shown for August and December. b The effect of climate, changing susceptibility, and NPIs on peak proportion infected (infected = I/population = N), post July 2020, for nine global locations.

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Drivers of variability in secondary outbreak size
Our results suggest that climate may play an increasing role in determining the future course of the SARS-CoV-2 pandemic, depending on levels of susceptibility and NPIs. We next evaluate the extent to which interannual variability in specific humidity could influence peak size. We simulate separate New York pandemic trajectories using 11 years (2008–2018) of specific humidity data. Figure 3a shows the variability in R0 and secondary peak size based on these runs (with 35% reduction in R0 due to NPIs and 10% reporting rate—the same as Fig. 1a). While a relatively large peak occurs in all years, the largest peak (0.038 proportion infected) is almost double the smallest peak year (0.020 proportion infected). In Fig. 3b we calculate the coefficient of variation of the peak size for different susceptible proportions and NPI intensities. These results qualitatively track Fig. 1e. Sensitivity to interannual variation appears most important when the susceptible population has been reduced by at least 20% and minimal controls are in place.
Fig. 3: Climate variability and wintertime cases in New York.

a Climate-driven R0 and corresponding infected time series (infected = I/population = N) based on the last 10 years of specific humidity data for New York, assuming a 35% reduction due to NPIs. b The effect of changing susceptibility and NPIs on the coefficient of variation of peak incidence for simulations using specific humidity data from 2008 to 2018. Dashed line shows estimated susceptibility in New York based on ref. 24.

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Many factors, including weather variability, determine the size of a possible secondary outbreak. Another factor that may play an important role is the length of immunity to the disease. While the length of immunity may not affect the dynamics in the early stage of the pandemic, it could have complex and uncertain outcomes for future trajectories16. In our main results, we assume a length of immunity equal to betacoronvirus HKU1, based on prior estimates1. We also assume a climate sensitivity based on estimates for HKU1. However parameters for SARS-CoV-2, such as immunity length and climate sensitivity, are still fundamentally uncertain.
We consider the possible contribution of uncertainty in parameters to the variance in the wintertime peak size following the method developed by Yip et al.17 (see “Methods”). We run our simulation for New York while varying parameter values for the efficacy of NPIs, the length of immunity to the disease, the reporting rate of prior cases (which defines susceptibility in July), the climate sensitivity of the pathogen (in terms of the strength of the relationship with specific humidity), and the weather variability (interannual variability determined by historic weather observations from a particular year, 2009–2018). We then perform an analysis of variance (ANOVA) on the determinants of wintertime peak size.
Figure 4 shows contribution to variance in wintertime peak size of these five parameters: NPIs efficacy, immunity length, reporting rate, climate sensitivity of the virus, and interannual weather variability. We find that climate sensitivity is an important factor but secondary to the efficacy of NPIs and immunity length in determining peak transmission. Uncertainty in immunity length and reporting together influence susceptibility and collectively account for the second largest portion of total uncertainty. Uncertainty in interannual variability, i.e. weather, has a smaller impact on peak size. NPIs contribute the largest proportion to total variance in peak size. It is important to note that while other parameters are external features of either the virus, climate, or disease trajectories to date, the efficacy of NPIs is determined directly by policy interventions and therefore the size of future outbreaks is largely under human control.
Fig. 4: Contribution to uncertainty in New York wintertime 20/21 peak size.

The relative importance of NPI efficacy [0–35%], immunity length (10–60 weeks), reporting (1–100%), climate sensitivity of the virus [−32.5 to −227.5], and interannual weather variability [10 years] in determining wintertime peak size. Immunity length and reporting rate collectively determine susceptibility, S.

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Increasingly madcap measures are being tried to control the invasive Asian carp in the US midwest.Credit: USACE/Alamy

Under a White Sky: The Nature of the Future Elizabeth Kolbert Crown (2021)
Humans are brilliant at coming up with solutions. But often these bring new problems that require their own solutions — that bring their own problems. It’s like the old lady who swallowed a fly in the children’s rhyme.
Civilization, essentially, has been a project to control natural systems: a river that is in the wrong place for us; earth that is too wet, or not wet enough; forests that we replace with monocultures of food, and so on. But natural systems are not compliant, and the unintended consequences of our changes require further fixes. The result is a world dominated by human influence, the Anthropocene epoch. Our problems are global and so, too, are our fixes.
These cascades of geoengineering are the subject of the latest book from Elizabeth Kolbert, Pulitzer-prizewinning environment reporter at The New Yorker. Under The White Sky looks at what people are doing to address the catastrophes that Kolbert described in two previous books — climate change (Field Notes From A Catastrophe, 2006) and biodiversity loss (The Sixth Extinction, 2014). She tours a range of cutting-edge experiments, from the restorative to the radical, across the United States, Europe and Australia. The result is an arresting montage of just how hard it is to return balance to our exquisitely interconnected biosphere, and the extraordinary efforts people go to in the attempt.

Kolbert visits the mighty Chicago River system in Illinois, which was re-plumbed to discharge the city’s sewage, with major tributaries rerouted and even reversed. Here, she documents efforts to prevent an invasive fish species — deliberately introduced into the Mississippi River basin — from causing havoc to the newly connected Great Lakes ecosystem. These include electrifying sections of river, fish-hunting carnivals and a range of madcap inventions, such as a “disco” noise-and-jet water barrier and sweet treats used as bait.
Further south, in coastal Louisiana, she finds engineers planning a multibillion-dollar artificial river system to replicate the former flows of the powerful Mississippi. Excessive tinkering with the river, straightening it and creating flood defences, have caused the land to sink and disappear, because alluvial soils are no longer replenished by regular sediment dumps. New Orleans is rapidly shrinking; smaller settlements have already been abandoned. As in her New Yorker essays, this is Kolbert at her most compelling — producing visceral, engrossing journalism with clear explanations of both science and social context.

Inside an aircraft attempting to seed clouds in Thailand in 2019.Credit: Athit Perawongmetha/Reuters

An element of the ridiculous is ever-present in the dance between human hubris and desperation. Kolbert orchestrates this comic strand with aplomb, never sacrificing empathy or the humanity of her characters. It is only a shame that the focus is entirely on problems and solutions in rich countries, given the global nature of the Anthropocene and the inequity of its burdens.
Artificial ecosystems
In the Mojave Desert, Nevada, she visits an expensively created, fully staffed, artificial pond cave, built to try to conserve a minuscule fish that humans have made critically endangered in the wild. In an aquatic laboratory in Australia, she observes coral spawning, cued by a simulated romantic sunset. This is the prelude to an in vitro fertilization programme that researchers hope will help to save the Great Barrier Reef from its calamitous decline in the wake of global heating. At one point, Kolbert wryly notes “how much easier it is to ruin an ecosystem than to run one”.

Kolbert meets genetic engineers hoping to replace struggling species such as endangered corals with ones modified to tolerate our environmental changes. Of this dramatic, ecosystem-altering step, one of the researchers points out: “We’re constantly moving genes around the world, usually in the form of entire genomes.” Consider the Peruvian potatoes planted in Europe’s fields or the domestic cats introduced by Europeans to New Zealand, where they have contributed to the extinction of at least nine native bird species.
Saving a fish species is hard, a coral-reef ecosystem immeasurably harder, but the ultimate challenge is fixing the global climate. Kolbert looks at geoengineering techniques to suck carbon dioxide from the air and store it, visiting facilities in the United States and Iceland. Options for ‘negative emissions’ were what finally got the 2015 Paris climate treaty over the line. The agreement to limit greenhouse-gas emissions factors in solutions such as planting forests to take up CO2 as they grow, and capturing industrial emissions at their source, then burying them.
The agreement does not mention more radical ‘hard geoengineering’ techniques to cool the climate, although research has been under way for decades. Kolbert talks to those studying methods to reflect the Sun’s heat, including spraying light-scattering calcite into the stratosphere, which would produce the white sky of the book’s title. This would be a drastic step. Yet, the extent of global heating brings its own terrible risks, which geoengineering could alleviate. “Doesn’t it have to be considered?” she asks, but can’t bring herself to answer.
There’s a grim fatalism to all this. We are so far down this path of global change that to turn back now is unthinkable, even impossible — like the old lady of the rhyme, who inevitably swallows the horse. Kolbert lays out this paradox perfectly. But she does so in the detached manner of an observer: always the reporter, documenting events but never asserting her own opinion. The book ends abruptly when the coronavirus ruins her plans for further research trips, leaving as much unresolved within its pages as outside them. It is, then, a superb and honest reflection of our extraordinary time. More

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Samples
Cores were collected during the RV Investigator voyage IN2018_T02 (19 and 20 May 2018, respectively, Fig. 2) to Tasmania, from sites in the Mercury Passage and Maria Island (Fig. 2). We collected one KC Denmark Multi-Core (MCS3, inner core diameter 10 cm, 36 cm long, estimated to cover the last ~ 145 years based on 210Pb dating at the Australian Nuclear Science and Technology Organisation (ANSTO, Lucas Heights, Sydney) in the Mercury Passage (MP, 42.550 S, 148.014 E; 68 m water depth), and one gravity core (GC2; inner core diameter 10 cm, 3 m long) offshore from Maria Island (42.845 S, 148.240 E; 104 m) composed of 2 sections; GC2A (bottom) and GC2B (top) estimated to cover the last ~ 8950 years based on 210Pb and 14C dating, ANSTO). The untreated cores were immediately sealed with plastic caps and sealed with duct-tape, stored initially on-board at 10 °C, followed by transport to and storage at 4 °C at ANSTO. To minimise contamination during core splitting and subsampling (October, 2018, ANSTO), we wiped working benches, sampling and cutting tools with bleach and 80% EtOH, changed gloves immediately when contaminated with sediment, and wore appropriate PPE at all times (gloves, facemask, hairnet, disposable lab gown). We removed the outer ~ 1 cm of the working core-half (working from bottom to the top of the core), then collected plunge samples by pressing sterile 15 mL centrifuge tubes (Falcon) ~ 2 cm deep into the sediment core centre at 5 cm depth intervals. All sedaDNA samples were immediately frozen at − 20 °C and transported to the Australian Centre for Ancient DNA (ACAD), Adelaide. For this study, a total of 30 samples were selected from both cores, representing ~ 2 cm depth intervals within the upper 36 cm of MCS3 and GC2, and ~ 20 cm depth intervals in GC2 downcore from 36 cm below seafloor (cmbsf).
Figure 2

Map of coring sites, inshore (MCS3) and offshore (GC2) of Maria Island, Tasmania, South-East Australian Coast. Map created in ODV (Schlitzer, R., Ocean Data View, https://odv.awi.de, 2018).

Full size image

SedaDNA extractions
We prepared sedaDNA extracts and sequencing libraries at ACAD’s ultra-clean ancient (GC2) and forensic (MCS3) facilities following ancient DNA decontamination standards24. All sample tubes were wiped with bleach on the outside prior to entering the laboratory for subsampling. Our extraction method followed the optimised (“combined”) approach outlined in detail previously7, with a minor modification in that we stored the final purified DNA in TLE buffer (50 μL Tris HCL (1 M), 10 μL EDTA (0.5 M), 5 mL nuclease-free water) instead of customary Elution Buffer (Qiagen) (see Supplementary Material Methods). To monitor laboratory contamination, we used extraction blank controls (EBCs) by processing 1–2 (depending on the extraction-batch size) empty bead-tubes through the extraction protocol. A total of 30 extracts were generated from sediment samples and 7 extracts from EBCs.
RNA-baits design
We designed two RNA hybridisation bait-sets, one targeting phyto- and zooplankton for a more detailed overview of plankton diversity (hereafter ‘Planktonbaits1’), and one targeting specific plankton organisms and their predators to enable detailed investigation of HABs, especially those caused by dinoflagellates, in coastal marine ecosystems (hereafter, ‘HABbaits1’). Planktonbaits1 was based on 18S-V9 and 16S-V4 sequences of major phyto- and zooplankton groups, whereas we designed HABbaits1 from a collection of LSU, SSU, D1-D2-LSU, COI, rbcL and ITS sequences for specific marine target organisms often associated with HABs in our study region (Table 1).
Table 1 Planktonbaits1 and HABbaits1.
Full size table

Planktonbaits1
To design Planktonbaits1 we downloaded the W2_V9_PR2 database25 (containing 18S-V9 rDNA and rRNA sequences of marine protists and their predators, downloaded on 30 July 2018), deduplicated using Geneious software (Geneious NZ), and filtered the remaining sequences to keep only those from major phyto- and zooplankton groups (Table 1). In collaboration with Arbor Biosciences, USA, we designed RNA baits based on these 15,035 target sequences by masking any repeating Ns (i.e., any consecutive Ns that were  83% overlap, and  > 95% identity). We added five 16S-V4 rRNA sequences (the prokaryotic equivalent of the small subunit ribosomal rRNA gene) of common marine cyanobacteria (one Trichodesmium erythraeum sequence, and two Prochlorococcus marinus and Synechococcus sp. sequences each), acquired from the SILVA database26; Table 1). To check and ensure target-taxon specificity, these five cyanobacterial sequences were mapped against a non-target sequence (Escherichia coli 16S RefSeq sequence NR_114042.1), then reverse-transcribed to DNA, and BLASTed to the same NCBI RefSeq database described above. BLAST hits of  More

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